{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e526d23a",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/razsahil/cardiovascular_risk-analysis/blob/main/EDA_PROJECT_.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb053e18",
   "metadata": {
    "id": "bb053e18"
   },
   "source": [
    "\n",
    "• Sex: male or female(\"M\" or \"F\")\n",
    "\n",
    "• Age: Age of the patient;(Continuous - Although the recorded ages have been truncated to whole numbers, the concept of age is continuous)\n",
    "\n",
    "Behavioral\n",
    "\n",
    "• is_smoking: whether or not the patient is a current smoker (\"YES\" or \"NO\")\n",
    "\n",
    "• Cigs Per Day: the number of cigarettes that the person smoked on average in one day.(can be considered continuous as one can have any number of cigarettes, even half a cigarette.)\n",
    "\n",
    "Medical( history)\n",
    "\n",
    "• BP Meds: whether or not the patient was on blood pressure medication (Nominal)\n",
    "\n",
    "• Prevalent Stroke: whether or not the patient had previously had a stroke (Nominal)\n",
    "\n",
    "• Prevalent Hyp: whether or not the patient was hypertensive (Nominal)\n",
    "\n",
    "• Diabetes: whether or not the patient had diabetes (Nominal)\n",
    "\n",
    "Medical(current)\n",
    "\n",
    "• Tot Chol: total cholesterol level (Continuous)\n",
    "\n",
    "• Sys BP: systolic blood pressure (Continuous)\n",
    "\n",
    "• Dia BP: diastolic blood pressure (Continuous)\n",
    "\n",
    "• BMI: Body Mass Index (Continuous)\n",
    "\n",
    "• Heart Rate: heart rate (Continuous - In medical research, variables such as heart rate though in fact discrete, yet are considered continuous because of large number of possible values.)\n",
    "\n",
    "• Glucose: glucose level (Continuous)\n",
    "\n",
    "Predict variable (desired target)\n",
    "\n",
    "• 10 year risk of coronary heart disease CHD(binary: “1”, means “Yes”, “0” means “No”)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "SQ113R1DUibN",
   "metadata": {
    "id": "SQ113R1DUibN"
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "59808f11",
   "metadata": {
    "id": "59808f11"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "import seaborn as sn\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "343f9770",
   "metadata": {
    "id": "343f9770"
   },
   "outputs": [],
   "source": [
    "data=pd.read_csv(\"data_cardiovascular_risk.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1616f32e",
   "metadata": {
    "id": "1616f32e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>sex</th>\n",
       "      <th>is_smoking</th>\n",
       "      <th>cigsPerDay</th>\n",
       "      <th>BPMeds</th>\n",
       "      <th>prevalentStroke</th>\n",
       "      <th>prevalentHyp</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>totChol</th>\n",
       "      <th>sysBP</th>\n",
       "      <th>diaBP</th>\n",
       "      <th>BMI</th>\n",
       "      <th>heartRate</th>\n",
       "      <th>glucose</th>\n",
       "      <th>TenYearCHD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>2.0</td>\n",
       "      <td>F</td>\n",
       "      <td>YES</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>221.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>4.0</td>\n",
       "      <td>M</td>\n",
       "      <td>NO</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>29.77</td>\n",
       "      <td>72.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>46</td>\n",
       "      <td>1.0</td>\n",
       "      <td>F</td>\n",
       "      <td>YES</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>20.35</td>\n",
       "      <td>88.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>M</td>\n",
       "      <td>YES</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>28.26</td>\n",
       "      <td>68.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>64</td>\n",
       "      <td>1.0</td>\n",
       "      <td>F</td>\n",
       "      <td>YES</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>136.5</td>\n",
       "      <td>85.0</td>\n",
       "      <td>26.42</td>\n",
       "      <td>70.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  age  education sex is_smoking  cigsPerDay  BPMeds  prevalentStroke   \n",
       "0   0   64        2.0   F        YES         3.0     0.0                0  \\\n",
       "1   1   36        4.0   M         NO         0.0     0.0                0   \n",
       "2   2   46        1.0   F        YES        10.0     0.0                0   \n",
       "3   3   50        1.0   M        YES        20.0     0.0                0   \n",
       "4   4   64        1.0   F        YES        30.0     0.0                0   \n",
       "\n",
       "   prevalentHyp  diabetes  totChol  sysBP  diaBP    BMI  heartRate  glucose   \n",
       "0             0         0    221.0  148.0   85.0    NaN       90.0     80.0  \\\n",
       "1             1         0    212.0  168.0   98.0  29.77       72.0     75.0   \n",
       "2             0         0    250.0  116.0   71.0  20.35       88.0     94.0   \n",
       "3             1         0    233.0  158.0   88.0  28.26       68.0     94.0   \n",
       "4             0         0    241.0  136.5   85.0  26.42       70.0     77.0   \n",
       "\n",
       "   TenYearCHD  \n",
       "0           1  \n",
       "1           0  \n",
       "2           0  \n",
       "3           1  \n",
       "4           0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3787172c",
   "metadata": {
    "id": "3787172c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3390 entries, 0 to 3389\n",
      "Data columns (total 17 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   id               3390 non-null   int64  \n",
      " 1   age              3390 non-null   int64  \n",
      " 2   education        3303 non-null   float64\n",
      " 3   sex              3390 non-null   object \n",
      " 4   is_smoking       3390 non-null   object \n",
      " 5   cigsPerDay       3368 non-null   float64\n",
      " 6   BPMeds           3346 non-null   float64\n",
      " 7   prevalentStroke  3390 non-null   int64  \n",
      " 8   prevalentHyp     3390 non-null   int64  \n",
      " 9   diabetes         3390 non-null   int64  \n",
      " 10  totChol          3352 non-null   float64\n",
      " 11  sysBP            3390 non-null   float64\n",
      " 12  diaBP            3390 non-null   float64\n",
      " 13  BMI              3376 non-null   float64\n",
      " 14  heartRate        3389 non-null   float64\n",
      " 15  glucose          3086 non-null   float64\n",
      " 16  TenYearCHD       3390 non-null   int64  \n",
      "dtypes: float64(9), int64(6), object(2)\n",
      "memory usage: 450.4+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c7343dcf",
   "metadata": {
    "id": "c7343dcf"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>cigsPerDay</th>\n",
       "      <th>BPMeds</th>\n",
       "      <th>prevalentStroke</th>\n",
       "      <th>prevalentHyp</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>totChol</th>\n",
       "      <th>sysBP</th>\n",
       "      <th>diaBP</th>\n",
       "      <th>BMI</th>\n",
       "      <th>heartRate</th>\n",
       "      <th>glucose</th>\n",
       "      <th>TenYearCHD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3390.000000</td>\n",
       "      <td>3390.000000</td>\n",
       "      <td>3303.000000</td>\n",
       "      <td>3368.000000</td>\n",
       "      <td>3346.000000</td>\n",
       "      <td>3390.000000</td>\n",
       "      <td>3390.000000</td>\n",
       "      <td>3390.000000</td>\n",
       "      <td>3352.000000</td>\n",
       "      <td>3390.00000</td>\n",
       "      <td>3390.000000</td>\n",
       "      <td>3376.000000</td>\n",
       "      <td>3389.000000</td>\n",
       "      <td>3086.000000</td>\n",
       "      <td>3390.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1694.500000</td>\n",
       "      <td>49.542183</td>\n",
       "      <td>1.970936</td>\n",
       "      <td>9.069477</td>\n",
       "      <td>0.029886</td>\n",
       "      <td>0.006490</td>\n",
       "      <td>0.315339</td>\n",
       "      <td>0.025664</td>\n",
       "      <td>237.074284</td>\n",
       "      <td>132.60118</td>\n",
       "      <td>82.883038</td>\n",
       "      <td>25.794964</td>\n",
       "      <td>75.977279</td>\n",
       "      <td>82.086520</td>\n",
       "      <td>0.150737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>978.753033</td>\n",
       "      <td>8.592878</td>\n",
       "      <td>1.019081</td>\n",
       "      <td>11.879078</td>\n",
       "      <td>0.170299</td>\n",
       "      <td>0.080309</td>\n",
       "      <td>0.464719</td>\n",
       "      <td>0.158153</td>\n",
       "      <td>45.247430</td>\n",
       "      <td>22.29203</td>\n",
       "      <td>12.023581</td>\n",
       "      <td>4.115449</td>\n",
       "      <td>11.971868</td>\n",
       "      <td>24.244753</td>\n",
       "      <td>0.357846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>107.000000</td>\n",
       "      <td>83.50000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>15.960000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>847.250000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>206.000000</td>\n",
       "      <td>117.00000</td>\n",
       "      <td>74.500000</td>\n",
       "      <td>23.020000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1694.500000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>234.000000</td>\n",
       "      <td>128.50000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>25.380000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2541.750000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>264.000000</td>\n",
       "      <td>144.00000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>28.040000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3389.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>696.000000</td>\n",
       "      <td>295.00000</td>\n",
       "      <td>142.500000</td>\n",
       "      <td>56.800000</td>\n",
       "      <td>143.000000</td>\n",
       "      <td>394.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                id          age    education   cigsPerDay       BPMeds   \n",
       "count  3390.000000  3390.000000  3303.000000  3368.000000  3346.000000  \\\n",
       "mean   1694.500000    49.542183     1.970936     9.069477     0.029886   \n",
       "std     978.753033     8.592878     1.019081    11.879078     0.170299   \n",
       "min       0.000000    32.000000     1.000000     0.000000     0.000000   \n",
       "25%     847.250000    42.000000     1.000000     0.000000     0.000000   \n",
       "50%    1694.500000    49.000000     2.000000     0.000000     0.000000   \n",
       "75%    2541.750000    56.000000     3.000000    20.000000     0.000000   \n",
       "max    3389.000000    70.000000     4.000000    70.000000     1.000000   \n",
       "\n",
       "       prevalentStroke  prevalentHyp     diabetes      totChol       sysBP   \n",
       "count      3390.000000   3390.000000  3390.000000  3352.000000  3390.00000  \\\n",
       "mean          0.006490      0.315339     0.025664   237.074284   132.60118   \n",
       "std           0.080309      0.464719     0.158153    45.247430    22.29203   \n",
       "min           0.000000      0.000000     0.000000   107.000000    83.50000   \n",
       "25%           0.000000      0.000000     0.000000   206.000000   117.00000   \n",
       "50%           0.000000      0.000000     0.000000   234.000000   128.50000   \n",
       "75%           0.000000      1.000000     0.000000   264.000000   144.00000   \n",
       "max           1.000000      1.000000     1.000000   696.000000   295.00000   \n",
       "\n",
       "             diaBP          BMI    heartRate      glucose   TenYearCHD  \n",
       "count  3390.000000  3376.000000  3389.000000  3086.000000  3390.000000  \n",
       "mean     82.883038    25.794964    75.977279    82.086520     0.150737  \n",
       "std      12.023581     4.115449    11.971868    24.244753     0.357846  \n",
       "min      48.000000    15.960000    45.000000    40.000000     0.000000  \n",
       "25%      74.500000    23.020000    68.000000    71.000000     0.000000  \n",
       "50%      82.000000    25.380000    75.000000    78.000000     0.000000  \n",
       "75%      90.000000    28.040000    83.000000    87.000000     0.000000  \n",
       "max     142.500000    56.800000   143.000000   394.000000     1.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0b325770",
   "metadata": {
    "id": "0b325770"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                   0\n",
       "age                  0\n",
       "education           87\n",
       "sex                  0\n",
       "is_smoking           0\n",
       "cigsPerDay          22\n",
       "BPMeds              44\n",
       "prevalentStroke      0\n",
       "prevalentHyp         0\n",
       "diabetes             0\n",
       "totChol             38\n",
       "sysBP                0\n",
       "diaBP                0\n",
       "BMI                 14\n",
       "heartRate            1\n",
       "glucose            304\n",
       "TenYearCHD           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "df86aa76",
   "metadata": {
    "id": "df86aa76"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 2.  4.  1.  3. nan]\n"
     ]
    }
   ],
   "source": [
    "print(data.education.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d9a3d18e",
   "metadata": {
    "id": "d9a3d18e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['F' 'M']\n"
     ]
    }
   ],
   "source": [
    "print(data.sex.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3e0150ca",
   "metadata": {
    "id": "3e0150ca"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['YES' 'NO']\n"
     ]
    }
   ],
   "source": [
    "print(data.is_smoking.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fc5c82eb",
   "metadata": {
    "id": "fc5c82eb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0]\n"
     ]
    }
   ],
   "source": [
    "print(data.TenYearCHD.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "Lf642Oowzpsx",
   "metadata": {
    "id": "Lf642Oowzpsx"
   },
   "outputs": [],
   "source": [
    "education = data.education.mode()[0]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ZLwwfvZAz1mw",
   "metadata": {
    "id": "ZLwwfvZAz1mw"
   },
   "outputs": [],
   "source": [
    "data.education.fillna(education,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "i8wVy5gWz7Fk",
   "metadata": {
    "id": "i8wVy5gWz7Fk"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       61-70\n",
       "1       31-40\n",
       "2       41-50\n",
       "3       41-50\n",
       "4       61-70\n",
       "        ...  \n",
       "3385    51-60\n",
       "3386    41-50\n",
       "3387    41-50\n",
       "3388    51-60\n",
       "3389    51-60\n",
       "Name: group_age, Length: 3390, dtype: category\n",
       "Categories (4, object): ['31-40' < '41-50' < '51-60' < '61-70']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"group_age\"]=pd.cut(data.age,[30,40,50,60,70],\n",
    "                         labels=[\"31-40\",\"41-50\",\"51-60\",\"61-70\"])\n",
    "data.group_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "MFH8dBUt0ARV",
   "metadata": {
    "id": "MFH8dBUt0ARV"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['61-70', '31-40', '41-50', '51-60']\n",
       "Categories (4, object): ['31-40' < '41-50' < '51-60' < '61-70']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['group_age'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "Wx1da5f00FIw",
   "metadata": {
    "id": "Wx1da5f00FIw"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "is_smoking    0\n",
       "cigsPerDay    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[data.is_smoking == \"NO\", ['is_smoking','cigsPerDay']].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "KsXgRAOB0JQR",
   "metadata": {
    "id": "KsXgRAOB0JQR"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>is_smoking</th>\n",
       "      <th>cigsPerDay</th>\n",
       "      <th>group_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>YES</td>\n",
       "      <td>3.0</td>\n",
       "      <td>61-70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>YES</td>\n",
       "      <td>10.0</td>\n",
       "      <td>41-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>YES</td>\n",
       "      <td>20.0</td>\n",
       "      <td>41-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>YES</td>\n",
       "      <td>30.0</td>\n",
       "      <td>61-70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>YES</td>\n",
       "      <td>35.0</td>\n",
       "      <td>31-40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3377</th>\n",
       "      <td>YES</td>\n",
       "      <td>25.0</td>\n",
       "      <td>31-40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3378</th>\n",
       "      <td>YES</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31-40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3379</th>\n",
       "      <td>YES</td>\n",
       "      <td>30.0</td>\n",
       "      <td>31-40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3380</th>\n",
       "      <td>YES</td>\n",
       "      <td>20.0</td>\n",
       "      <td>51-60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3387</th>\n",
       "      <td>YES</td>\n",
       "      <td>3.0</td>\n",
       "      <td>41-50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1687 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     is_smoking  cigsPerDay group_age\n",
       "0           YES         3.0     61-70\n",
       "2           YES        10.0     41-50\n",
       "3           YES        20.0     41-50\n",
       "4           YES        30.0     61-70\n",
       "7           YES        35.0     31-40\n",
       "...         ...         ...       ...\n",
       "3377        YES        25.0     31-40\n",
       "3378        YES         NaN     31-40\n",
       "3379        YES        30.0     31-40\n",
       "3380        YES        20.0     51-60\n",
       "3387        YES         3.0     41-50\n",
       "\n",
       "[1687 rows x 3 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1 = data.loc[data.is_smoking == \"YES\", ['is_smoking','cigsPerDay','group_age']]\n",
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "T9K73uWz0N5t",
   "metadata": {
    "id": "T9K73uWz0N5t"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "group_age\n",
       "31-40    20.0\n",
       "41-50    20.0\n",
       "51-60    20.0\n",
       "61-70    20.0\n",
       "Name: cigsPerDay, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_age_mode = data1.groupby(['group_age'])['cigsPerDay'].agg(pd.Series.mode)\n",
    "dict_age_mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4zeqEeAZ0TPK",
   "metadata": {
    "id": "4zeqEeAZ0TPK"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>is_smoking</th>\n",
       "      <th>cigsPerDay</th>\n",
       "      <th>group_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>YES</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51-60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>YES</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>YES</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>YES</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61-70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>538</th>\n",
       "      <td>YES</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>YES</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51-60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1461</th>\n",
       "      <td>YES</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1467</th>\n",
       "      <td>YES</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1597</th>\n",
       "      <td>YES</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51-60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>YES</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2031</th>\n",
       "      <td>YES</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51-60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2087</th>\n",
       "      <td>YES</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2240</th>\n",
       "      <td>YES</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2284</th>\n",
       "      <td>YES</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2527</th>\n",
       "      <td>YES</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51-60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2638</th>\n",
       "      <td>YES</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51-60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2648</th>\n",
       "      <td>YES</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31-40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2723</th>\n",
       "      <td>YES</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2879</th>\n",
       "      <td>YES</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2945</th>\n",
       "      <td>YES</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51-60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3301</th>\n",
       "      <td>YES</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51-60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3378</th>\n",
       "      <td>YES</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31-40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     is_smoking  cigsPerDay group_age\n",
       "422         YES         NaN     51-60\n",
       "466         YES         NaN     41-50\n",
       "469         YES         NaN     41-50\n",
       "491         YES         NaN     61-70\n",
       "538         YES         NaN     41-50\n",
       "767         YES         NaN     51-60\n",
       "1461        YES         NaN     41-50\n",
       "1467        YES         NaN     41-50\n",
       "1597        YES         NaN     51-60\n",
       "1986        YES         NaN     41-50\n",
       "2031        YES         NaN     51-60\n",
       "2087        YES         NaN     41-50\n",
       "2240        YES         NaN     41-50\n",
       "2284        YES         NaN     41-50\n",
       "2527        YES         NaN     51-60\n",
       "2638        YES         NaN     51-60\n",
       "2648        YES         NaN     31-40\n",
       "2723        YES         NaN     41-50\n",
       "2879        YES         NaN     41-50\n",
       "2945        YES         NaN     51-60\n",
       "3301        YES         NaN     51-60\n",
       "3378        YES         NaN     31-40"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2 = data1.loc[data.cigsPerDay.isnull(), ['is_smoking','cigsPerDay','group_age']]\n",
    "data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "YBHmzhfb0Yir",
   "metadata": {
    "id": "YBHmzhfb0Yir"
   },
   "outputs": [],
   "source": [
    "def categorise(row):  \n",
    "    if row['group_age'] == '31-40':\n",
    "        return  dict_age_mode['31-40']\n",
    "    elif row['group_age'] == '41-50':\n",
    "        return dict_age_mode['41-50']\n",
    "    elif row['group_age'] == '51-60':\n",
    "        return dict_age_mode['51-60']\n",
    "    elif row['group_age'] == '61-70':\n",
    "        return dict_age_mode['61-70']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "CdcY7jAo0eK4",
   "metadata": {
    "id": "CdcY7jAo0eK4"
   },
   "outputs": [],
   "source": [
    "data['group_age_mean'] = data.apply(lambda row: categorise(row), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "SNeDyXE30t5O",
   "metadata": {
    "id": "SNeDyXE30t5O"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['cigsPerDay'].fillna(data['group_age_mean'],inplace = True)\n",
    "data['cigsPerDay'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "yil351mD0y2s",
   "metadata": {
    "id": "yil351mD0y2s"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                   0\n",
       "age                  0\n",
       "education            0\n",
       "sex                  0\n",
       "is_smoking           0\n",
       "cigsPerDay           0\n",
       "BPMeds              44\n",
       "prevalentStroke      0\n",
       "prevalentHyp         0\n",
       "diabetes             0\n",
       "totChol             38\n",
       "sysBP                0\n",
       "diaBP                0\n",
       "BMI                 14\n",
       "heartRate            1\n",
       "glucose            304\n",
       "TenYearCHD           0\n",
       "group_age            0\n",
       "group_age_mean       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3f7fbef",
   "metadata": {
    "id": "c3f7fbef"
   },
   "source": [
    "### I want to know who smokes more male or female ?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "fb7b6c0c",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "fb7b6c0c",
    "outputId": "0cfb4d70-047b-4204-92a1-2f1ae44108b4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='sex', ylabel='count'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkQAAAG1CAYAAAAYxut7AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAwPElEQVR4nO3de1xVZb7H8e8G5CIKKMrtiEhlKg6jpo2R5ZiSeKnxXnrIKE2bRixjjpqOUlpJUpl5yUvlbUa7TKNOehpHA5VSQkVNUzQ9MWrFxVIgcbjJPn902McdWorA3vp83q/Xer3cz/OsvX7Pfr12fHvWWntZrFarVQAAAAZzcXQBAAAAjkYgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGc2ggSktL0/3336+QkBBZLBatX7/e1ldeXq7JkycrMjJS3t7eCgkJ0cMPP6xvv/3W7j3OnDmj2NhY+fj4yM/PT6NHj9a5c+fsxhw4cEB33323PD09FRoaquTk5PqYHgAAuE44NBAVFxerQ4cOWrhwYbW+8+fPa+/evZo+fbr27t2rtWvX6ujRo/rd735nNy42NlaHDh3Sli1btHHjRqWlpWns2LG2/qKiIvXu3VthYWHKzMzUyy+/rOeee05Lly6t8/kBAIDrg8VZHu5qsVi0bt06DRw48LJjdu/erd/85jc6ceKEWrZsqaysLEVERGj37t3q0qWLJGnTpk3q16+fvv76a4WEhGjRokX605/+pNzcXLm7u0uSnnnmGa1fv15Hjhy5otoqKyv17bffqnHjxrJYLNc8VwAAUPesVqt++OEHhYSEyMXl59eA3OqpplpRWFgoi8UiPz8/SVJ6err8/PxsYUiSoqOj5eLiooyMDA0aNEjp6enq3r27LQxJUkxMjGbPnq2zZ8+qSZMm1Y5TWlqq0tJS2+tvvvlGERERdTcxAABQZ06dOqUWLVr87JjrJhCVlJRo8uTJGjFihHx8fCRJubm5CggIsBvn5uampk2bKjc31zYmPDzcbkxgYKCt71KBKCkpSTNmzKjWfurUKduxAQCAcysqKlJoaKgaN278i2Ovi0BUXl6uBx54QFarVYsWLarz402ZMkUJCQm211UfqI+PD4EIAIDrzJVc7uL0gagqDJ04cUKpqal2gSQoKEj5+fl24ysqKnTmzBkFBQXZxuTl5dmNqXpdNeanPDw85OHhUZvTAAAATsypf4eoKgwdO3ZMH3/8sfz9/e36o6KiVFBQoMzMTFtbamqqKisr1bVrV9uYtLQ0lZeX28Zs2bJFbdq0ueTpMgAAYB6HBqJz585p//792r9/vyQpOztb+/fv18mTJ1VeXq6hQ4dqz549Wr16tS5cuKDc3Fzl5uaqrKxMktSuXTv16dNHY8aM0a5du7Rjxw7Fx8dr+PDhCgkJkST953/+p9zd3TV69GgdOnRI7733nl5//XW7U2IAAMBsDr3tftu2bbrnnnuqtcfFxem5556rdjF0la1bt6pHjx6Sfvxhxvj4eG3YsEEuLi4aMmSI5s2bp0aNGtnGHzhwQOPGjdPu3bvVrFkzjR8/XpMnT77iOouKiuTr66vCwkKuIQIA1FhlZaXtf+pRO9zd3S97S/3V/P12mt8hcmYEIgDAtSorK1N2drYqKysdXcoNxcXFReHh4XY/r1Plav5+O/1F1QAAXO+sVqtycnLk6uqq0NDQX/yRQFyZqh9OzsnJUcuWLa/px5MJRAAA1LGKigqdP39eISEhatiwoaPLuaE0b95c3377rSoqKtSgQYMavw8RFQCAOnbhwgVJuuRpHVybqs+06jOuKQIRAAD1hOdh1r7a+kwJRAAAwHgEIgAAHKhHjx6aMGGCo8u4ao888ogGDhx42f7nnntOHTt2rLd6rhUXVQMA4EBr1669pouBndV//dd/afz48Y4u44oRiAAAcKCmTZs6uoQ60ahRI7sfSXZ2nDIDAMCBLj5l9sYbb6h169by9PRUYGCghg4dekXv8cEHHygyMlJeXl7y9/dXdHS0iouLJf3/qa1Zs2YpMDBQfn5+mjlzpioqKjRx4kQ1bdpULVq00PLly+3e8+DBg+rZs6ftPceOHatz585dtobdu3erefPmmj17tqTqp8yq6njllVcUHBwsf39/jRs3zu5Zozk5Oerfv7+8vLwUHh6uNWvWqFWrVpo7d+4VfQ7XghUiAACcwJ49e/Tkk0/qz3/+s+68806dOXNGn3zyyS/ul5OToxEjRig5OVmDBg3SDz/8oE8++UQXP4giNTVVLVq0UFpamnbs2KHRo0dr586d6t69uzIyMvTee+/p8ccf17333qsWLVqouLhYMTExioqK0u7du5Wfn6/HHntM8fHxWrFiRbUaUlNTNXjwYCUnJ2vs2LGXrXXr1q0KDg7W1q1bdfz4cT344IPq2LGjxowZI0l6+OGH9d1332nbtm1q0KCBEhISlJ+ff/UfZg0QiIBLODkz0tEl4CItEw86ugSgzp08eVLe3t6677771LhxY4WFhalTp06/uF9OTo4qKio0ePBghYWFSZIiI+3/G9a0aVPNmzdPLi4uatOmjZKTk3X+/HlNnTpVkjRlyhS99NJL+vTTTzV8+HCtWbNGJSUlWrVqlby9vSVJCxYs0P3336/Zs2crMDDQ9t7r1q3Tww8/rLfeeksPPvjgz9bapEkTLViwQK6urmrbtq369++vlJQUjRkzRkeOHNHHH3+s3bt3q0uXLpKkt956S61bt77yD/EacMoMAAAncO+99yosLEw33XSTRo4cqdWrV+v8+fO/uF+HDh3Uq1cvRUZGatiwYXrzzTd19uxZuzHt27e3e1xIYGCgXWhydXWVv7+/bTUmKytLHTp0sIUhSerWrZsqKyt19OhRW1tGRoaGDRumP//5z78YhqrqcHV1tb0ODg62HfPo0aNyc3PTbbfdZuu/5ZZb1KRJk19839pAIAIAwAk0btxYe/fu1TvvvKPg4GAlJiaqQ4cOKigo+Nn9XF1dtWXLFv3jH/9QRESE5s+frzZt2ig7O9s25qd3sVkslku2Xe2DZ2+++Wa1bdtWy5Yts7sW6HJq45h1hUAEAICTcHNzU3R0tJKTk3XgwAH961//Umpq6i/uZ7FY1K1bN82YMUP79u2Tu7u71q1bV+M62rVrp88//9x2YbYk7dixw3bKrUqzZs2Umpqq48eP64EHHriiUHQ5bdq0UUVFhfbt22drO378eLXVrrpCIAIAwAls3LhR8+bN0/79+3XixAmtWrVKlZWVdgHkUjIyMjRr1izt2bNHJ0+e1Nq1a3X69Gm1a9euxrXExsbK09NTcXFx+uKLL7R161aNHz9eI0eOtLt+SJICAgKUmpqqI0eOaMSIEaqoqKjRMdu2bavo6GiNHTtWu3bt0r59+zR27Fh5eXnVyyNPCEQAADgBPz8/rV27Vj179lS7du20ePFivfPOO2rfvv3P7ufj46O0tDT169dPt956q6ZNm6ZXX31Vffv2rXEtDRs21D//+U+dOXNGt99+u4YOHapevXppwYIFlxwfFBSk1NRUHTx4ULGxsTV+0OqqVasUGBio7t27a9CgQRozZowaN24sT0/PGs/lSlmsF9+Xh0sqKiqSr6+vCgsL5ePj4+hyUA+4y8y5cJcZrnclJSXKzs5WeHh4vfxxv1F8/fXXCg0N1ccff6xevXpdcszPfbZX8/eb2+4BAIBTSE1N1blz5xQZGamcnBxNmjRJrVq1Uvfu3ev82AQiAACc2MmTJxUREXHZ/sOHD6tly5b1WFHdKS8v19SpU/XVV1+pcePGuvPOO7V69ep6edYbgQgAACcWEhKi/fv3/2z/jSImJkYxMTEOOTaBCAAAJ+bm5qZbbrnF0WXc8LjLDAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeNx2DwCAk+k8cVW9Hi/z5YeveKzVatW9994rV1dX/fOf/7Tre+ONNzR16lQtWLBAI0eOvOT+OTk5CgoK0vnz5/X888/r/fff1zfffKPGjRsrIiJCCQkJGjBgwDXNpyYIRAAA4IpZLBYtX75ckZGRWrJkiR5//HFJUnZ2tiZNmqRFixapRYsWkqSjR49We4ZYQECAJOn3v/+9MjIyNH/+fEVEROj777/Xzp079f3339fvhP4PgQgAAFyV0NBQvf7664qPj1fv3r3VqlUrjR49Wr1799bIkSO1bds2ST+GHz8/v0u+x4cffqjXX39d/fr1kyS1atVKnTt3rqcZVMc1RAAA4KrFxcWpV69eGjVqlBYsWKAvvvhCS5YsueL9g4KC9NFHH+mHH36owyqvHCtEAACgRpYuXar27dsrLS1Nf/vb39S8eXO7/qpTZ1XCwsJ06NAh276xsbHy9/dXhw4ddNddd2no0KHq1q1bvdV/MVaIAABAjQQEBOjxxx9Xu3btNHDgwGr9n3zyifbv32/bPvroI1tf9+7d9dVXXyklJUVDhw7VoUOHdPfdd+v555+vxxn8P1aIAABAjbm5ucnN7dJxIjw8/LLXEElSgwYNdPfdd+vuu+/W5MmT9cILL2jmzJmaPHmy3N3d66jiS2OFCAAAOIWIiAhVVFSopKSk3o/NChEAAKgT+fn51cKNv7+/GjRooB49emjEiBHq0qWL/P39dfjwYU2dOlX33HNPtVv16wOBCAAA1Ik2bdpUa0tPT9cdd9yhmJgYrVy5UlOnTtX58+cVEhKi++67T4mJiQ6oVLJYrVarQ458HSkqKpKvr68KCwsdklpR/07OjHR0CbhIy8SDji4BuCYlJSXKzs5WeHi4PD09HV3ODeXnPtur+fvNNUQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDyeZQYAgJOp78cHXe3jcR555BGtXLlSSUlJeuaZZ2zt69ev16BBg1T1VLALFy5o3rx5WrZsmY4dOyYvLy/dcccdmjZtmrp161arc7hWrBABAICr5unpqdmzZ+vs2bOX7LdarRo+fLhmzpypp556SllZWdq2bZtCQ0PVo0cPrV+/vn4L/gUODURpaWm6//77FRISIovFUu3DsVqtSkxMVHBwsLy8vBQdHa1jx47ZjTlz5oxiY2Pl4+MjPz8/jR49WufOnbMbc+DAAd19993y9PRUaGiokpOT63pqAADc0KKjoxUUFKSkpKRL9r///vv64IMPtGrVKj322GMKDw9Xhw4dtHTpUv3ud7/TY489puLi4nqu+vIcGoiKi4vVoUMHLVy48JL9ycnJmjdvnhYvXqyMjAx5e3srJiZGJSUltjGxsbE6dOiQtmzZoo0bNyotLU1jx4619RcVFal3794KCwtTZmamXn75ZT333HNaunRpnc8PAIAblaurq2bNmqX58+fr66+/rta/Zs0a3Xrrrbr//vur9f3xj3/U999/ry1bttRHqVfEodcQ9e3bV3379r1kn9Vq1dy5czVt2jQNGDBAkrRq1SoFBgZq/fr1Gj58uLKysrRp0ybt3r1bXbp0kSTNnz9f/fr10yuvvKKQkBCtXr1aZWVlWrZsmdzd3dW+fXvt379fc+bMsQtOAADg6gwaNEgdO3bUs88+q7ffftuu78svv1S7du0uuV9V+5dfflnnNV4pp72GKDs7W7m5uYqOjra1+fr6qmvXrkpPT5ckpaeny8/PzxaGpB+X8FxcXJSRkWEb0717d7m7u9vGxMTE6OjRo5c97wkAAK7M7NmztXLlSmVlZVXrq7q4+nrgtIEoNzdXkhQYGGjXHhgYaOvLzc1VQECAXb+bm5uaNm1qN+ZS73HxMX6qtLRURUVFdhsAAKiue/fuiomJ0ZQpU+zab7311kuGJEm29ltvvbXO67tSThuIHCkpKUm+vr62LTQ01NElAQDgtF566SVt2LDBdgZHkoYPH65jx45pw4YN1ca/+uqr8vf317333lufZf4spw1EQUFBkqS8vDy79ry8PFtfUFCQ8vPz7forKip05swZuzGXeo+Lj/FTU6ZMUWFhoW07derUtU8IAIAbVGRkpGJjYzVv3jxb2/DhwzVo0CDFxcXp7bff1r/+9S8dOHBAjz/+uD788EO99dZb8vb2dmDV9pw2EIWHhysoKEgpKSm2tqKiImVkZCgqKkqSFBUVpYKCAmVmZtrGpKamqrKyUl27drWNSUtLU3l5uW3Mli1b1KZNGzVp0uSSx/bw8JCPj4/dBgAALm/mzJmqrKy0vbZYLHr//fc1depUvfbaa2rTpo3uvvtunThxQtu2bdPAgQMdV+wlWKwOvOLp3LlzOn78uCSpU6dOmjNnju655x41bdpULVu21OzZs/XSSy9p5cqVCg8P1/Tp03XgwAEdPnxYnp6ekn68Uy0vL0+LFy9WeXm5Hn30UXXp0kVr1qyRJBUWFqpNmzbq3bu3Jk+erC+++EKjRo3Sa6+9dsV3mRUVFcnX11eFhYWEI0PU96/E4udd7a/oAs6mpKRE2dnZCg8Pt/39Qu34uc/2av5+O/S2+z179uiee+6xvU5ISJAkxcXFacWKFZo0aZKKi4s1duxYFRQU6K677tKmTZvsJrx69WrFx8erV69ecnFx0ZAhQ+yW7Hx9fbV582aNGzdOnTt3VrNmzZSYmMgt9wAAwMahK0TXC1aIzMMKkXNhhQjXO1aI6k5trRA57TVEAAAA9YVABAAAjEcgAgCgnnCVSu2rrc+UQAQAQB1zdXWVJJWVlTm4khtP1Wda9RnXlEPvMgMAwARubm5q2LChTp8+rQYNGsjFhfWI2lBZWanTp0+rYcOGcnO7tkhDIAIAoI5ZLBYFBwcrOztbJ06ccHQ5NxQXFxe1bNlSFovlmt6HQAQAQD1wd3dX69atOW1Wy9zd3WtlxY1ABABAPXFxceF3iJwUJzEBAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI+n3QNAPeg8cZWjS8D/yXz5YUeXACfEChEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOM5dSC6cOGCpk+frvDwcHl5eenmm2/W888/L6vVahtjtVqVmJio4OBgeXl5KTo6WseOHbN7nzNnzig2NlY+Pj7y8/PT6NGjde7cufqeDgAAcFJOHYhmz56tRYsWacGCBcrKytLs2bOVnJys+fPn28YkJydr3rx5Wrx4sTIyMuTt7a2YmBiVlJTYxsTGxurQoUPasmWLNm7cqLS0NI0dO9YRUwIAAE7IzdEF/JydO3dqwIAB6t+/vySpVatWeuedd7Rr1y5JP64OzZ07V9OmTdOAAQMkSatWrVJgYKDWr1+v4cOHKysrS5s2bdLu3bvVpUsXSdL8+fPVr18/vfLKKwoJCXHM5AAAgNNw6hWiO++8UykpKfryyy8lSZ9//rk+/fRT9e3bV5KUnZ2t3NxcRUdH2/bx9fVV165dlZ6eLklKT0+Xn5+fLQxJUnR0tFxcXJSRkXHJ45aWlqqoqMhuAwAANy6nXiF65plnVFRUpLZt28rV1VUXLlzQiy++qNjYWElSbm6uJCkwMNBuv8DAQFtfbm6uAgIC7Prd3NzUtGlT25ifSkpK0owZM2p7OgAAwEk59QrR+++/r9WrV2vNmjXau3evVq5cqVdeeUUrV66s0+NOmTJFhYWFtu3UqVN1ejwAAOBYTr1CNHHiRD3zzDMaPny4JCkyMlInTpxQUlKS4uLiFBQUJEnKy8tTcHCwbb+8vDx17NhRkhQUFKT8/Hy7962oqNCZM2ds+/+Uh4eHPDw86mBGAADAGTn1CtH58+fl4mJfoqurqyorKyVJ4eHhCgoKUkpKiq2/qKhIGRkZioqKkiRFRUWpoKBAmZmZtjGpqamqrKxU165d62EWAADA2Tn1CtH999+vF198US1btlT79u21b98+zZkzR6NGjZIkWSwWTZgwQS+88IJat26t8PBwTZ8+XSEhIRo4cKAkqV27durTp4/GjBmjxYsXq7y8XPHx8Ro+fDh3mAEAAElOHojmz5+v6dOn6w9/+IPy8/MVEhKixx9/XImJibYxkyZNUnFxscaOHauCggLddddd2rRpkzw9PW1jVq9erfj4ePXq1UsuLi4aMmSI5s2b54gpAQAAJ2SxXvyzz7ikoqIi+fr6qrCwUD4+Po4uB/Xg5MxIR5eAi7RMPOjoEq5Z54mrHF0C/k/myw87ugTUk6v5++3U1xABAADUB6c+ZWYa/g/Seaxr7OgKAAD1iRUiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABivRoGoZ8+eKigoqNZeVFSknj17XmtNAAAA9apGgWjbtm0qKyur1l5SUqJPPvnkmosCAACoT25XM/jAgQO2fx8+fFi5ubm21xcuXNCmTZv0H//xH7VXHQAAQD24qkDUsWNHWSwWWSyWS54a8/Ly0vz582utOAAAgPpwVYEoOztbVqtVN910k3bt2qXmzZvb+tzd3RUQECBXV9daLxIAAKAuXVUgCgsLkyRVVlbWSTEAAACOcFWB6GLHjh3T1q1blZ+fXy0gJSYmXnNhAAAA9aVGgejNN9/UE088oWbNmikoKEgWi8XWZ7FYCEQAAOC6UqNA9MILL+jFF1/U5MmTa7seAACAelej3yE6e/ashg0bVtu1AAAAOESNAtGwYcO0efPm2q4FAADAIWp0yuyWW27R9OnT9dlnnykyMlINGjSw63/yySdrpTgAAID6UKNAtHTpUjVq1Ejbt2/X9u3b7fosFguBCAAAXFdqdMosOzv7sttXX31VqwV+8803euihh+Tv7y8vLy9FRkZqz549tn6r1arExEQFBwfLy8tL0dHROnbsmN17nDlzRrGxsfLx8ZGfn59Gjx6tc+fO1WqdAADg+lWjQFRfzp49q27duqlBgwb6xz/+ocOHD+vVV19VkyZNbGOSk5M1b948LV68WBkZGfL29lZMTIxKSkpsY2JjY3Xo0CFt2bJFGzduVFpamsaOHeuIKQEAACdUo1Nmo0aN+tn+ZcuW1aiYn5o9e7ZCQ0O1fPlyW1t4eLjt31arVXPnztW0adM0YMAASdKqVasUGBio9evXa/jw4crKytKmTZu0e/dudenSRZI0f/589evXT6+88opCQkJqpVYAAHD9qvFt9xdv+fn5Sk1N1dq1a1VQUFBrxX344Yfq0qWLhg0bpoCAAHXq1ElvvvmmrT87O1u5ubmKjo62tfn6+qpr165KT0+XJKWnp8vPz88WhiQpOjpaLi4uysjIqLVaAQDA9atGK0Tr1q2r1lZZWaknnnhCN9988zUXVeWrr77SokWLlJCQoKlTp2r37t168skn5e7urri4OOXm5kqSAgMD7fYLDAy09eXm5iogIMCu383NTU2bNrWN+anS0lKVlpbaXhcVFdXanAAAgPOptWuIXFxclJCQoNdee6223lKVlZW67bbbNGvWLHXq1Eljx47VmDFjtHjx4lo7xqUkJSXJ19fXtoWGhtbp8QAAgGPV6kXV//M//6OKiopae7/g4GBFRETYtbVr104nT56UJAUFBUmS8vLy7Mbk5eXZ+oKCgpSfn2/XX1FRoTNnztjG/NSUKVNUWFho206dOlUr8wEAAM6pRqfMEhIS7F5brVbl5OTov//7vxUXF1crhUlSt27ddPToUbu2L7/8UmFhYZJ+vMA6KChIKSkp6tixo6QfT29lZGToiSeekCRFRUWpoKBAmZmZ6ty5syQpNTVVlZWV6tq16yWP6+HhIQ8Pj1qbBwAAcG41CkT79u2ze+3i4qLmzZvr1Vdf/cU70K7G008/rTvvvFOzZs3SAw88oF27dmnp0qVaunSppB9/BHLChAl64YUX1Lp1a4WHh2v69OkKCQnRwIEDJf24otSnTx/bqbby8nLFx8dr+PDh3GEGAAAk1TAQbd26tbbruKTbb79d69at05QpUzRz5kyFh4dr7ty5io2NtY2ZNGmSiouLNXbsWBUUFOiuu+7Spk2b5OnpaRuzevVqxcfHq1evXnJxcdGQIUM0b968epkDAABwfjUKRFVOnz5tO6XVpk0bNW/evFaKuth9992n++6777L9FotFM2fO1MyZMy87pmnTplqzZk2t1wYAAG4MNbqouri4WKNGjVJwcLC6d++u7t27KyQkRKNHj9b58+dru0YAAIA6VaNAlJCQoO3bt2vDhg0qKChQQUGB/v73v2v79u364x//WNs1AgAA1KkanTL729/+pg8++EA9evSwtfXr109eXl564IEHtGjRotqqDwAAoM7VaIXo/Pnz1X4dWpICAgI4ZQYAAK47NQpEUVFRevbZZ+2eKP/vf/9bM2bMUFRUVK0VBwAAUB9qdMps7ty56tOnj1q0aKEOHTpIkj7//HN5eHho8+bNtVogAABAXatRIIqMjNSxY8e0evVqHTlyRJI0YsQIxcbGysvLq1YLBAAAqGs1CkRJSUkKDAzUmDFj7NqXLVum06dPa/LkybVSHAAAte3kzEhHl4CLtEw86OgSJNXwGqIlS5aobdu21drbt29f50+iBwAAqG01CkS5ubkKDg6u1t68eXPl5ORcc1EAAAD1qUaBKDQ0VDt27KjWvmPHDh6YCgAArjs1uoZozJgxmjBhgsrLy9WzZ09JUkpKiiZNmsQvVQMAgOtOjQLRxIkT9f333+sPf/iDysrKJEmenp6aPHmypkyZUqsFAgAA1LUaBSKLxaLZs2dr+vTpysrKkpeXl1q3bi0PD4/arg8AAKDO1SgQVWnUqJFuv/322qoFAADAIWp0UTUAAMCNhEAEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAONdV4HopZdeksVi0YQJE2xtJSUlGjdunPz9/dWoUSMNGTJEeXl5dvudPHlS/fv3V8OGDRUQEKCJEyeqoqKinqsHAADO6roJRLt379aSJUv061//2q796aef1oYNG/TXv/5V27dv17fffqvBgwfb+i9cuKD+/furrKxMO3fu1MqVK7VixQolJibW9xQAAICTui4C0blz5xQbG6s333xTTZo0sbUXFhbq7bff1pw5c9SzZ0917txZy5cv186dO/XZZ59JkjZv3qzDhw/rL3/5izp27Ki+ffvq+eef18KFC1VWVuaoKQEAACdyXQSicePGqX///oqOjrZrz8zMVHl5uV1727Zt1bJlS6Wnp0uS0tPTFRkZqcDAQNuYmJgYFRUV6dChQ5c8XmlpqYqKiuw2AABw43JzdAG/5N1339XevXu1e/fuan25ublyd3eXn5+fXXtgYKByc3NtYy4OQ1X9VX2XkpSUpBkzZtRC9QAA4Hrg1CtEp06d0lNPPaXVq1fL09Oz3o47ZcoUFRYW2rZTp07V27EBAED9c+pAlJmZqfz8fN12221yc3OTm5ubtm/frnnz5snNzU2BgYEqKytTQUGB3X55eXkKCgqSJAUFBVW766zqddWYn/Lw8JCPj4/dBgAAblxOHYh69eqlgwcPav/+/batS5cuio2Ntf27QYMGSklJse1z9OhRnTx5UlFRUZKkqKgoHTx4UPn5+bYxW7ZskY+PjyIiIup9TgAAwPk49TVEjRs31q9+9Su7Nm9vb/n7+9vaR48erYSEBDVt2lQ+Pj4aP368oqKidMcdd0iSevfurYiICI0cOVLJycnKzc3VtGnTNG7cOHl4eNT7nAAAgPNx6kB0JV577TW5uLhoyJAhKi0tVUxMjN544w1bv6urqzZu3KgnnnhCUVFR8vb2VlxcnGbOnOnAqgEAgDO57gLRtm3b7F57enpq4cKFWrhw4WX3CQsL00cffVTHlQEAgOuVU19DBAAAUB8IRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHhOHYiSkpJ0++23q3HjxgoICNDAgQN19OhRuzElJSUaN26c/P391ahRIw0ZMkR5eXl2Y06ePKn+/furYcOGCggI0MSJE1VRUVGfUwEAAE7MqQPR9u3bNW7cOH322WfasmWLysvL1bt3bxUXF9vGPP3009qwYYP++te/avv27fr22281ePBgW/+FCxfUv39/lZWVaefOnVq5cqVWrFihxMRER0wJAAA4ITdHF/BzNm3aZPd6xYoVCggIUGZmprp3767CwkK9/fbbWrNmjXr27ClJWr58udq1a6fPPvtMd9xxhzZv3qzDhw/r448/VmBgoDp27Kjnn39ekydP1nPPPSd3d3dHTA0AADgRp14h+qnCwkJJUtOmTSVJmZmZKi8vV3R0tG1M27Zt1bJlS6Wnp0uS0tPTFRkZqcDAQNuYmJgYFRUV6dChQ5c8TmlpqYqKiuw2AABw47puAlFlZaUmTJigbt266Ve/+pUkKTc3V+7u7vLz87MbGxgYqNzcXNuYi8NQVX9V36UkJSXJ19fXtoWGhtbybAAAgDO5bgLRuHHj9MUXX+jdd9+t82NNmTJFhYWFtu3UqVN1fkwAAOA4Tn0NUZX4+Hht3LhRaWlpatGiha09KChIZWVlKigosFslysvLU1BQkG3Mrl277N6v6i60qjE/5eHhIQ8Pj1qeBQAAcFZOvUJktVoVHx+vdevWKTU1VeHh4Xb9nTt3VoMGDZSSkmJrO3r0qE6ePKmoqChJUlRUlA4ePKj8/HzbmC1btsjHx0cRERH1MxEAAODUnHqFaNy4cVqzZo3+/ve/q3HjxrZrfnx9feXl5SVfX1+NHj1aCQkJatq0qXx8fDR+/HhFRUXpjjvukCT17t1bERERGjlypJKTk5Wbm6tp06Zp3LhxrAIBAABJTh6IFi1aJEnq0aOHXfvy5cv1yCOPSJJee+01ubi4aMiQISotLVVMTIzeeOMN21hXV1dt3LhRTzzxhKKiouTt7a24uDjNnDmzvqYBAACcnFMHIqvV+otjPD09tXDhQi1cuPCyY8LCwvTRRx/VZmkAAOAG4tTXEAEAANQHAhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADCeUYFo4cKFatWqlTw9PdW1a1ft2rXL0SUBAAAnYEwgeu+995SQkKBnn31We/fuVYcOHRQTE6P8/HxHlwYAABzMmEA0Z84cjRkzRo8++qgiIiK0ePFiNWzYUMuWLXN0aQAAwMGMCERlZWXKzMxUdHS0rc3FxUXR0dFKT093YGUAAMAZuDm6gPrw3Xff6cKFCwoMDLRrDwwM1JEjR6qNLy0tVWlpqe11YWGhJKmoqKhO67xQ+u86fX9cuR8aXHB0CbhIXX/36gPfb+fB99u51OX3u+q9rVbrL441IhBdraSkJM2YMaNae2hoqAOqgSP8ytEFwF6Sr6MrwA2E77eTqYfv9w8//CBf358/jhGBqFmzZnJ1dVVeXp5de15enoKCgqqNnzJlihISEmyvKysrdebMGfn7+8tisdR5vXCsoqIihYaG6tSpU/Lx8XF0OQBqEd9vs1itVv3www8KCQn5xbFGBCJ3d3d17txZKSkpGjhwoKQfQ05KSori4+Orjffw8JCHh4ddm5+fXz1UCmfi4+PDfzCBGxTfb3P80spQFSMCkSQlJCQoLi5OXbp00W9+8xvNnTtXxcXFevTRRx1dGgAAcDBjAtGDDz6o06dPKzExUbm5uerYsaM2bdpU7UJrAABgHmMCkSTFx8df8hQZcDEPDw89++yz1U6bArj+8f3G5VisV3IvGgAAwA3MiB9mBAAA+DkEIgAAYDwCEQAAMB6BCLjII488IovFUm07fvy4o0sDUENV3+vf//731frGjRsni8WiRx55pP4Lg1MhEAE/0adPH+Xk5Nht4eHhji4LwDUIDQ3Vu+++q3//+/+fKVdSUqI1a9aoZcuWDqwMzoJABPyEh4eHgoKC7DZXV1dHlwXgGtx2220KDQ3V2rVrbW1r165Vy5Yt1alTJwdWBmdBIAIAGGHUqFFavny57fWyZct4WgFsCETAT2zcuFGNGjWybcOGDXN0SQBqwUMPPaRPP/1UJ06c0IkTJ7Rjxw499NBDji4LTsKoX6oGrsQ999yjRYsW2V57e3s7sBoAtaV58+bq37+/VqxYIavVqv79+6tZs2aOLgtOgkAE/IS3t7duueUWR5cBoA6MGjXK9ginhQsXOrgaOBMCEQDAGH369FFZWZksFotiYmIcXQ6cCIEIAGAMV1dXZWVl2f4NVCEQAQCM4uPj4+gS4IR42j0AADAet90DAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgA3rA8++ECRkZHy8vKSv7+/oqOjVVxcLEl666231K5dO3l6eqpt27Z64403bPuNGjVKv/71r1VaWipJKisrU6dOnfTwww87ZB4A6h6BCMANKScnRyNGjNCoUaOUlZWlbdu2afDgwbJarVq9erUSExP14osvKisrS7NmzdL06dO1cuVKSdK8efNUXFysZ555RpL0pz/9SQUFBVqwYIEjpwSgDrk5ugAAqAs5OTmqqKjQ4MGDFRYWJkmKjIyUJD377LN69dVXNXjwYElSeHi4Dh8+rCVLliguLk6NGjXSX/7yF/32t79V48aNNXfuXG3dulU+Pj4Omw+AumWxWq1WRxcBALXtwoULiomJ0a5duxQTE6PevXtr6NChcnd3V6NGjeTl5SUXl/9fJK+oqJCvr6/y8vJsbVOnTlVSUpImT56sl156yRHTAFBPWCECcENydXXVli1btHPnTm3evFnz58/Xn/70J23YsEGS9Oabb6pr167V9qlSWVmpHTt2yNXVVcePH6/X2gHUP64hAnDDslgs6tatm2bMmKF9+/bJ3d1dO3bsUEhIiL766ivdcsstdlt4eLht35dffllHjhzR9u3btWnTJi1fvtyBMwFQ11ghAnBDysjIUEpKinr37q2AgABlZGTo9OnTateunWbMmKEnn3xSvr6+6tOnj0pLS7Vnzx6dPXtWCQkJ2rdvnxITE/XBBx+oW7dumjNnjp566in99re/1U033eToqQGoA1xDBOCGlJWVpaefflp79+5VUVGRwsLCNH78eMXHx0uS1qxZo5dfflmHDx+Wt7e3IiMjNWHCBPXt21edO3fWXXfdpSVLltjeb8CAAfruu++UlpZmd2oNwI2BQAQAAIzHNUQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGO9/ATeM57zcKb3bAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.countplot(x='sex',data = data,hue='is_smoking')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7d74bcb",
   "metadata": {
    "id": "f7d74bcb"
   },
   "source": [
    "###### from above count plot i concluded that male smoke more as compared to female "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f461418f",
   "metadata": {
    "id": "f461418f"
   },
   "source": [
    "### I want to find that how many male and female aged below 18 smoke ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b102a49d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "b102a49d",
    "outputId": "7ee0cd67-4515-49f3-f8a4-620feb302fe8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "p = (data['age'] <= 18)\n",
    "print(p.sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62908218",
   "metadata": {
    "id": "62908218"
   },
   "source": [
    "#### so from above i conclude that there is no one below age 18 , who smokes ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "27e784eb",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "27e784eb",
    "outputId": "69d888bd-476d-4d54-b0e0-345901bed1ee"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(data.cigsPerDay)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e2173842",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "e2173842",
    "outputId": "69f13bef-4637-443e-a473-b850b4eb3ad3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='prevalentStroke', ylabel='count'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.countplot( x='prevalentStroke',data = data,hue='sex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "o7j-v0_8Eabz",
   "metadata": {
    "id": "o7j-v0_8Eabz"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "462"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['test'] = (data['age'] > 60)\n",
    "data['test'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "k2xz1t76Ea1P",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 280
    },
    "id": "k2xz1t76Ea1P",
    "outputId": "c4b39f6f-9934-4220-b239-490997b7e4ae"
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: 'YES'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[33], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m datas\u001b[38;5;241m=\u001b[39mdata[data[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mage\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m>\u001b[39m\u001b[38;5;241m60\u001b[39m]\n\u001b[1;32m----> 2\u001b[0m \u001b[43msn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcountplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdatas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mis_smoking\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      3\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n",
      "File \u001b[1;32mc:\\Users\\hp\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\seaborn\\categorical.py:2943\u001b[0m, in \u001b[0;36mcountplot\u001b[1;34m(data, x, y, hue, order, hue_order, orient, color, palette, saturation, width, dodge, ax, **kwargs)\u001b[0m\n\u001b[0;32m   2940\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m x \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m y \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   2941\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot pass values for both `x` and `y`\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m-> 2943\u001b[0m plotter \u001b[38;5;241m=\u001b[39m \u001b[43m_CountPlotter\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   2944\u001b[0m \u001b[43m    \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morder\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhue_order\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2945\u001b[0m \u001b[43m    \u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrorbar\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_boot\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43munits\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mseed\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2946\u001b[0m \u001b[43m    \u001b[49m\u001b[43morient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpalette\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msaturation\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2947\u001b[0m \u001b[43m    \u001b[49m\u001b[43mwidth\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrcolor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrwidth\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcapsize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdodge\u001b[49m\n\u001b[0;32m   2948\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   2950\u001b[0m plotter\u001b[38;5;241m.\u001b[39mvalue_label \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcount\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   2952\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ax \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[1;32mc:\\Users\\hp\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\seaborn\\categorical.py:1530\u001b[0m, in \u001b[0;36m_BarPlotter.__init__\u001b[1;34m(self, x, y, hue, data, order, hue_order, estimator, errorbar, n_boot, units, seed, orient, color, palette, saturation, width, errcolor, errwidth, capsize, dodge)\u001b[0m\n\u001b[0;32m   1525\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, x, y, hue, data, order, hue_order,\n\u001b[0;32m   1526\u001b[0m              estimator, errorbar, n_boot, units, seed,\n\u001b[0;32m   1527\u001b[0m              orient, color, palette, saturation, width,\n\u001b[0;32m   1528\u001b[0m              errcolor, errwidth, capsize, dodge):\n\u001b[0;32m   1529\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Initialize the plotter.\"\"\"\u001b[39;00m\n\u001b[1;32m-> 1530\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mestablish_variables\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morient\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1531\u001b[0m \u001b[43m                             \u001b[49m\u001b[43morder\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhue_order\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43munits\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1532\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mestablish_colors(color, palette, saturation)\n\u001b[0;32m   1533\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mestimate_statistic(estimator, errorbar, n_boot, seed)\n",
      "File \u001b[1;32mc:\\Users\\hp\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\seaborn\\categorical.py:516\u001b[0m, in \u001b[0;36m_CategoricalPlotter.establish_variables\u001b[1;34m(self, x, y, hue, data, orient, order, hue_order, units)\u001b[0m\n\u001b[0;32m    513\u001b[0m     plot_data \u001b[38;5;241m=\u001b[39m data\n\u001b[0;32m    515\u001b[0m \u001b[38;5;66;03m# Convert to a list of arrays, the common representation\u001b[39;00m\n\u001b[1;32m--> 516\u001b[0m plot_data \u001b[38;5;241m=\u001b[39m \u001b[43m[\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43masarray\u001b[49m\u001b[43m(\u001b[49m\u001b[43md\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mfloat\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43md\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mplot_data\u001b[49m\u001b[43m]\u001b[49m\n\u001b[0;32m    518\u001b[0m \u001b[38;5;66;03m# The group names will just be numeric indices\u001b[39;00m\n\u001b[0;32m    519\u001b[0m group_names \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(plot_data)))\n",
      "File \u001b[1;32mc:\\Users\\hp\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\seaborn\\categorical.py:516\u001b[0m, in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    513\u001b[0m     plot_data \u001b[38;5;241m=\u001b[39m data\n\u001b[0;32m    515\u001b[0m \u001b[38;5;66;03m# Convert to a list of arrays, the common representation\u001b[39;00m\n\u001b[1;32m--> 516\u001b[0m plot_data \u001b[38;5;241m=\u001b[39m [\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43masarray\u001b[49m\u001b[43m(\u001b[49m\u001b[43md\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mfloat\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m d \u001b[38;5;129;01min\u001b[39;00m plot_data]\n\u001b[0;32m    518\u001b[0m \u001b[38;5;66;03m# The group names will just be numeric indices\u001b[39;00m\n\u001b[0;32m    519\u001b[0m group_names \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(plot_data)))\n",
      "File \u001b[1;32mc:\\Users\\hp\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\series.py:917\u001b[0m, in \u001b[0;36mSeries.__array__\u001b[1;34m(self, dtype)\u001b[0m\n\u001b[0;32m    870\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    871\u001b[0m \u001b[38;5;124;03mReturn the values as a NumPy array.\u001b[39;00m\n\u001b[0;32m    872\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    914\u001b[0m \u001b[38;5;124;03m      dtype='datetime64[ns]')\u001b[39;00m\n\u001b[0;32m    915\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    916\u001b[0m values \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values\n\u001b[1;32m--> 917\u001b[0m arr \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39masarray(values, dtype\u001b[38;5;241m=\u001b[39mdtype)\n\u001b[0;32m    918\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m using_copy_on_write() \u001b[38;5;129;01mand\u001b[39;00m astype_is_view(values\u001b[38;5;241m.\u001b[39mdtype, arr\u001b[38;5;241m.\u001b[39mdtype):\n\u001b[0;32m    919\u001b[0m     arr \u001b[38;5;241m=\u001b[39m arr\u001b[38;5;241m.\u001b[39mview()\n",
      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: 'YES'"
     ]
    }
   ],
   "source": [
    "datas=data[data[\"age\"]>60]\n",
    "sn.countplot(datas.is_smoking)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eZzUqv_F4zyC",
   "metadata": {
    "id": "eZzUqv_F4zyC"
   },
   "source": [
    "# aged people smoke less  and most of them they din't smoke (age>=60 or 60+)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "q99saID4BYhD",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 280
    },
    "id": "q99saID4BYhD",
    "outputId": "732686a6-ea54-4c67-eb98-75bed7f15ee7"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "datas=data[data[\"age\"]>60]\n",
    "sn.countplot(data=datas , x='is_smoking',hue='prevalentStroke')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Pg5Pxm-D6EJ0",
   "metadata": {
    "id": "Pg5Pxm-D6EJ0"
   },
   "source": [
    "## And the aged people those who din't smoke they gets more prevalent stroke as compare to those who smoke ."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Ij4-P_J4VLYo",
   "metadata": {
    "id": "Ij4-P_J4VLYo"
   },
   "source": [
    "## Which age people smoke more ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "jejsFBzNPURz",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 389
    },
    "id": "jejsFBzNPURz",
    "outputId": "173b2023-c85e-4152-e5e2-0864176c5ac3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x900 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 587.861x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,9))\n",
    "sn.displot(x=\"age\",data=data,hue=\"is_smoking\",palette=['#ffff00','#ff00ff'])\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "utq-0KeVVRES",
   "metadata": {
    "id": "utq-0KeVVRES"
   },
   "source": [
    "### With the increase in age (means 40+)the percentage of smoker  decreases and from 28 to 40 it increases rapidly(means people smoke more at this stage ).\n",
    "##At the age of 40 smoking  is at its peak.\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "q9bxPcGIVVV9",
   "metadata": {
    "id": "q9bxPcGIVVV9"
   },
   "source": [
    "### Those who smoke they got stroke or not?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "BbpBl77MIMtM",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 301
    },
    "id": "BbpBl77MIMtM",
    "outputId": "13d0b45a-b9c3-42a6-b47f-30429061c0ca"
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "barplot() takes from 0 to 1 positional arguments but 2 positional arguments (and 1 keyword-only argument) were given",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[36], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43msn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mbarplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mis_smoking\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mprevalentStroke\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43mpalette\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m#ffff00\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m#ff00ff\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[1;31mTypeError\u001b[0m: barplot() takes from 0 to 1 positional arguments but 2 positional arguments (and 1 keyword-only argument) were given"
     ]
    }
   ],
   "source": [
    "sn. barplot(data[\"is_smoking\"], data[\"prevalentStroke\"],palette=['#ffff00','#ff00ff'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "UePmSDL6VqRl",
   "metadata": {
    "id": "UePmSDL6VqRl"
   },
   "source": [
    "#### Therefore from above graph i conclude that somking is not correlated  with stroke or pevelent -stroke which means that if i they  dose not smoke  there is a chance to get stroke  ."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0CJX4-9aWC8E",
   "metadata": {
    "id": "0CJX4-9aWC8E"
   },
   "source": [
    "## who are having more chances to get infected with CHD male or female ? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "xH95sdFuCGn3",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "id": "xH95sdFuCGn3",
    "outputId": "0152b3c0-1b40-4255-dae3-4c307aad190e"
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "lineplot() takes from 0 to 1 positional arguments but 2 were given",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[37], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43msn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlineplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msex\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mTenYearCHD\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43mcolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mgreen\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m \n",
      "\u001b[1;31mTypeError\u001b[0m: lineplot() takes from 0 to 1 positional arguments but 2 were given"
     ]
    }
   ],
   "source": [
    "sn.lineplot(data[\"sex\"],data['TenYearCHD'],color='green') "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0wWFqNoNWQ_l",
   "metadata": {
    "id": "0wWFqNoNWQ_l"
   },
   "source": [
    "### from above i conclude that there is more  risk to  male  to get  CHD (10 year risk of coronary heart disease CHD)as compared to female ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "gNaf88cCQW3u",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "gNaf88cCQW3u",
    "outputId": "e6bf1c37-e931-4baf-bc5a-1c1ac26781cf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='prevalentStroke', ylabel='Density'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "sn.distplot(data[\"prevalentStroke\"],color='darkBlue') "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aNgFc7NqWanD",
   "metadata": {
    "id": "aNgFc7NqWanD"
   },
   "source": [
    "## percentage of male and female ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "Fgf9SpaPRNvE",
   "metadata": {
    "id": "Fgf9SpaPRNvE"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "M\n",
      "F\n",
      "F\n",
      "F\n",
      "F\n",
      "M\n",
      "M\n",
      "F\n"
     ]
    }
   ],
   "source": [
    "for i in data['sex']:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "CUHmYLRmJr-a",
   "metadata": {
    "id": "CUHmYLRmJr-a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "for i in data['sex']:\n",
    "    if i == 'M':\n",
    "        print(0)\n",
    "    else:\n",
    "        print(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1qeYkANUJv31",
   "metadata": {
    "id": "1qeYkANUJv31"
   },
   "outputs": [],
   "source": [
    "males=0\n",
    "females=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "RWoKSIB3J97F",
   "metadata": {
    "id": "RWoKSIB3J97F"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "1\n",
      "2\n",
      "2\n",
      "3\n",
      "4\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "5\n",
      "10\n",
      "6\n",
      "11\n",
      "12\n",
      "13\n",
      "7\n",
      "14\n",
      "15\n",
      "16\n",
      "17\n",
      "18\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "19\n",
      "12\n",
      "13\n",
      "20\n",
      "14\n",
      "15\n",
      "21\n",
      "22\n",
      "23\n",
      "24\n",
      "16\n",
      "25\n",
      "17\n",
      "18\n",
      "26\n",
      "27\n",
      "28\n",
      "29\n",
      "30\n",
      "31\n",
      "32\n",
      "19\n",
      "20\n",
      "33\n",
      "21\n",
      "22\n",
      "34\n",
      "35\n",
      "36\n",
      "37\n",
      "38\n",
      "39\n",
      "23\n",
      "40\n",
      "41\n",
      "24\n",
      "42\n",
      "25\n",
      "26\n",
      "43\n",
      "27\n",
      "28\n",
      "29\n",
      "44\n",
      "45\n",
      "30\n",
      "46\n",
      "31\n",
      "47\n",
      "32\n",
      "33\n",
      "34\n",
      "35\n",
      "36\n",
      "48\n",
      "49\n",
      "37\n",
      "38\n",
      "39\n",
      "40\n",
      "41\n",
      "42\n",
      "50\n",
      "51\n",
      "43\n",
      "52\n",
      "53\n",
      "54\n",
      "44\n",
      "45\n",
      "46\n",
      "55\n",
      "47\n",
      "56\n",
      "48\n",
      "57\n",
      "58\n",
      "59\n",
      "49\n",
      "50\n",
      "60\n",
      "61\n",
      "62\n",
      "63\n",
      "51\n",
      "64\n",
      "65\n",
      "66\n",
      "52\n",
      "53\n",
      "67\n",
      "68\n",
      "54\n",
      "69\n",
      "70\n",
      "71\n",
      "72\n",
      "55\n",
      "56\n",
      "73\n",
      "57\n",
      "58\n",
      "59\n",
      "74\n",
      "60\n",
      "75\n",
      "76\n",
      "77\n",
      "61\n",
      "78\n",
      "79\n",
      "80\n",
      "62\n",
      "81\n",
      "82\n",
      "83\n",
      "63\n",
      "84\n",
      "64\n",
      "65\n",
      "66\n",
      "85\n",
      "67\n",
      "86\n",
      "87\n",
      "68\n",
      "69\n",
      "88\n",
      "70\n",
      "71\n",
      "89\n",
      "72\n",
      "90\n",
      "91\n",
      "92\n",
      "73\n",
      "93\n",
      "94\n",
      "74\n",
      "95\n",
      "96\n",
      "97\n",
      "98\n",
      "75\n",
      "99\n",
      "100\n",
      "101\n",
      "102\n",
      "76\n",
      "77\n",
      "78\n",
      "103\n",
      "104\n",
      "105\n",
      "106\n",
      "107\n",
      "79\n",
      "80\n",
      "81\n",
      "108\n",
      "109\n",
      "110\n",
      "82\n",
      "83\n",
      "111\n",
      "112\n",
      "84\n",
      "113\n",
      "114\n",
      "115\n",
      "116\n",
      "85\n",
      "117\n",
      "86\n",
      "118\n",
      "119\n",
      "120\n",
      "121\n",
      "122\n",
      "87\n",
      "123\n",
      "124\n",
      "125\n",
      "88\n",
      "126\n",
      "127\n",
      "89\n",
      "128\n",
      "90\n",
      "91\n",
      "92\n",
      "93\n",
      "129\n",
      "130\n",
      "131\n",
      "132\n",
      "94\n",
      "133\n",
      "95\n",
      "96\n",
      "134\n",
      "97\n",
      "98\n",
      "135\n",
      "136\n",
      "137\n",
      "138\n",
      "139\n",
      "99\n",
      "140\n",
      "100\n",
      "141\n",
      "101\n",
      "142\n",
      "102\n",
      "103\n",
      "143\n",
      "144\n",
      "145\n",
      "104\n",
      "146\n",
      "105\n",
      "147\n",
      "148\n",
      "106\n",
      "149\n",
      "150\n",
      "151\n",
      "107\n",
      "152\n",
      "153\n",
      "154\n",
      "108\n",
      "109\n",
      "110\n",
      "155\n",
      "156\n",
      "157\n",
      "111\n",
      "112\n",
      "113\n",
      "158\n",
      "159\n",
      "160\n",
      "161\n",
      "162\n",
      "163\n",
      "164\n",
      "114\n",
      "165\n",
      "166\n",
      "115\n",
      "116\n",
      "117\n",
      "167\n",
      "168\n",
      "118\n",
      "169\n",
      "170\n",
      "119\n",
      "171\n",
      "172\n",
      "120\n",
      "173\n",
      "174\n",
      "175\n",
      "121\n",
      "122\n",
      "176\n",
      "123\n",
      "124\n",
      "125\n",
      "177\n",
      "126\n",
      "178\n",
      "179\n",
      "127\n",
      "128\n",
      "180\n",
      "181\n",
      "182\n",
      "183\n",
      "129\n",
      "184\n",
      "185\n",
      "186\n",
      "187\n",
      "188\n",
      "130\n",
      "131\n",
      "132\n",
      "133\n",
      "134\n",
      "189\n",
      "135\n",
      "190\n",
      "191\n",
      "192\n",
      "193\n",
      "194\n",
      "195\n",
      "136\n",
      "196\n",
      "197\n",
      "198\n",
      "137\n",
      "199\n",
      "138\n",
      "200\n",
      "201\n",
      "202\n",
      "139\n",
      "203\n",
      "204\n",
      "140\n",
      "141\n",
      "142\n",
      "143\n",
      "144\n",
      "205\n",
      "206\n",
      "145\n",
      "146\n",
      "147\n",
      "207\n",
      "208\n",
      "209\n",
      "210\n",
      "211\n",
      "212\n",
      "213\n",
      "148\n",
      "149\n",
      "150\n",
      "214\n",
      "151\n",
      "215\n",
      "152\n",
      "216\n",
      "217\n",
      "218\n",
      "219\n",
      "153\n",
      "220\n",
      "154\n",
      "155\n",
      "221\n",
      "156\n",
      "157\n",
      "158\n",
      "159\n",
      "160\n",
      "222\n",
      "223\n",
      "224\n",
      "161\n",
      "225\n",
      "226\n",
      "162\n",
      "163\n",
      "227\n",
      "228\n",
      "229\n",
      "164\n",
      "230\n",
      "231\n",
      "232\n",
      "233\n",
      "234\n",
      "165\n",
      "166\n",
      "235\n",
      "167\n",
      "236\n",
      "237\n",
      "168\n",
      "169\n",
      "238\n",
      "239\n",
      "170\n",
      "171\n",
      "240\n",
      "241\n",
      "242\n",
      "172\n",
      "243\n",
      "244\n",
      "173\n",
      "245\n",
      "246\n",
      "247\n",
      "248\n",
      "249\n",
      "250\n",
      "251\n",
      "252\n",
      "174\n",
      "253\n",
      "175\n",
      "176\n",
      "177\n",
      "178\n",
      "179\n",
      "254\n",
      "180\n",
      "255\n",
      "181\n",
      "256\n",
      "257\n",
      "182\n",
      "183\n",
      "184\n",
      "185\n",
      "186\n",
      "258\n",
      "259\n",
      "260\n",
      "261\n",
      "187\n",
      "188\n",
      "262\n",
      "189\n",
      "190\n",
      "263\n",
      "191\n",
      "192\n",
      "193\n",
      "194\n",
      "264\n",
      "265\n",
      "195\n",
      "196\n",
      "266\n",
      "267\n",
      "268\n",
      "197\n",
      "269\n",
      "198\n",
      "199\n",
      "200\n",
      "201\n",
      "202\n",
      "270\n",
      "271\n",
      "272\n",
      "273\n",
      "203\n",
      "274\n",
      "204\n",
      "275\n",
      "276\n",
      "205\n",
      "277\n",
      "206\n",
      "207\n",
      "278\n",
      "279\n",
      "280\n",
      "208\n",
      "209\n",
      "281\n",
      "282\n",
      "283\n",
      "284\n",
      "210\n",
      "285\n",
      "286\n",
      "211\n",
      "287\n",
      "288\n",
      "289\n",
      "212\n",
      "290\n",
      "291\n",
      "213\n",
      "214\n",
      "292\n",
      "293\n",
      "294\n",
      "215\n",
      "216\n",
      "295\n",
      "217\n",
      "218\n",
      "296\n",
      "297\n",
      "219\n",
      "298\n",
      "299\n",
      "300\n",
      "220\n",
      "301\n",
      "221\n",
      "302\n",
      "222\n",
      "303\n",
      "223\n",
      "224\n",
      "304\n",
      "225\n",
      "305\n",
      "226\n",
      "227\n",
      "228\n",
      "229\n",
      "306\n",
      "307\n",
      "308\n",
      "309\n",
      "310\n",
      "230\n",
      "311\n",
      "312\n",
      "313\n",
      "231\n",
      "232\n",
      "314\n",
      "315\n",
      "233\n",
      "234\n",
      "316\n",
      "235\n",
      "317\n",
      "318\n",
      "319\n",
      "320\n",
      "321\n",
      "322\n",
      "323\n",
      "324\n",
      "325\n",
      "236\n",
      "326\n",
      "327\n",
      "237\n",
      "328\n",
      "329\n",
      "238\n",
      "330\n",
      "239\n",
      "240\n",
      "241\n",
      "242\n",
      "243\n",
      "244\n",
      "331\n",
      "245\n",
      "246\n",
      "247\n",
      "248\n",
      "332\n",
      "333\n",
      "249\n",
      "250\n",
      "251\n",
      "334\n",
      "252\n",
      "335\n",
      "336\n",
      "253\n",
      "337\n",
      "338\n",
      "339\n",
      "254\n",
      "340\n",
      "255\n",
      "341\n",
      "342\n",
      "256\n",
      "257\n",
      "343\n",
      "258\n",
      "259\n",
      "260\n",
      "261\n",
      "344\n",
      "345\n",
      "346\n",
      "347\n",
      "348\n",
      "349\n",
      "350\n",
      "262\n",
      "351\n",
      "352\n",
      "353\n",
      "263\n",
      "264\n",
      "265\n",
      "266\n",
      "267\n",
      "354\n",
      "355\n",
      "356\n",
      "268\n",
      "357\n",
      "358\n",
      "359\n",
      "360\n",
      "361\n",
      "362\n",
      "363\n",
      "269\n",
      "270\n",
      "364\n",
      "365\n",
      "271\n",
      "272\n",
      "273\n",
      "274\n",
      "366\n",
      "275\n",
      "276\n",
      "367\n",
      "368\n",
      "369\n",
      "277\n",
      "278\n",
      "370\n",
      "279\n",
      "280\n",
      "281\n",
      "282\n",
      "283\n",
      "371\n",
      "372\n",
      "373\n",
      "374\n",
      "284\n",
      "375\n",
      "376\n",
      "285\n",
      "377\n",
      "378\n",
      "286\n",
      "379\n",
      "380\n",
      "381\n",
      "382\n",
      "287\n",
      "383\n",
      "288\n",
      "289\n",
      "384\n",
      "290\n",
      "291\n",
      "292\n",
      "385\n",
      "386\n",
      "387\n",
      "388\n",
      "293\n",
      "389\n",
      "294\n",
      "390\n",
      "295\n",
      "296\n",
      "391\n",
      "392\n",
      "297\n",
      "393\n",
      "298\n",
      "394\n",
      "395\n",
      "396\n",
      "397\n",
      "398\n",
      "399\n",
      "400\n",
      "299\n",
      "300\n",
      "401\n",
      "402\n",
      "403\n",
      "404\n",
      "405\n",
      "406\n",
      "407\n",
      "301\n",
      "302\n",
      "303\n",
      "408\n",
      "304\n",
      "305\n",
      "306\n",
      "307\n",
      "308\n",
      "309\n",
      "409\n",
      "410\n",
      "310\n",
      "411\n",
      "311\n",
      "312\n",
      "412\n",
      "313\n",
      "413\n",
      "414\n",
      "415\n",
      "416\n",
      "314\n",
      "315\n",
      "417\n",
      "418\n",
      "419\n",
      "420\n",
      "316\n",
      "421\n",
      "422\n",
      "423\n",
      "424\n",
      "425\n",
      "317\n",
      "426\n",
      "318\n",
      "319\n",
      "320\n",
      "321\n",
      "427\n",
      "428\n",
      "429\n",
      "322\n",
      "323\n",
      "324\n",
      "430\n",
      "431\n",
      "432\n",
      "433\n",
      "325\n",
      "326\n",
      "434\n",
      "435\n",
      "436\n",
      "327\n",
      "437\n",
      "328\n",
      "438\n",
      "439\n",
      "329\n",
      "330\n",
      "440\n",
      "441\n",
      "331\n",
      "332\n",
      "333\n",
      "442\n",
      "334\n",
      "335\n",
      "443\n",
      "444\n",
      "336\n",
      "445\n",
      "446\n",
      "337\n",
      "338\n",
      "447\n",
      "448\n",
      "339\n",
      "449\n",
      "340\n",
      "341\n",
      "342\n",
      "343\n",
      "344\n",
      "450\n",
      "451\n",
      "345\n",
      "452\n",
      "346\n",
      "347\n",
      "348\n",
      "453\n",
      "349\n",
      "454\n",
      "350\n",
      "455\n",
      "351\n",
      "456\n",
      "352\n",
      "457\n",
      "458\n",
      "459\n",
      "460\n",
      "353\n",
      "461\n",
      "462\n",
      "463\n",
      "464\n",
      "354\n",
      "355\n",
      "465\n",
      "356\n",
      "466\n",
      "357\n",
      "358\n",
      "467\n",
      "359\n",
      "468\n",
      "469\n",
      "360\n",
      "470\n",
      "471\n",
      "361\n",
      "472\n",
      "362\n",
      "363\n",
      "473\n",
      "474\n",
      "475\n",
      "364\n",
      "476\n",
      "477\n",
      "365\n",
      "478\n",
      "479\n",
      "480\n",
      "481\n",
      "366\n",
      "367\n",
      "482\n",
      "483\n",
      "484\n",
      "485\n",
      "486\n",
      "368\n",
      "487\n",
      "488\n",
      "489\n",
      "490\n",
      "491\n",
      "369\n",
      "492\n",
      "493\n",
      "494\n",
      "495\n",
      "496\n",
      "497\n",
      "370\n",
      "498\n",
      "499\n",
      "371\n",
      "372\n",
      "500\n",
      "373\n",
      "501\n",
      "502\n",
      "374\n",
      "503\n",
      "504\n",
      "375\n",
      "505\n",
      "376\n",
      "506\n",
      "507\n",
      "508\n",
      "377\n",
      "378\n",
      "379\n",
      "509\n",
      "380\n",
      "381\n",
      "510\n",
      "382\n",
      "383\n",
      "511\n",
      "384\n",
      "512\n",
      "513\n",
      "514\n",
      "385\n",
      "386\n",
      "515\n",
      "516\n",
      "517\n",
      "387\n",
      "518\n",
      "388\n",
      "389\n",
      "519\n",
      "390\n",
      "520\n",
      "391\n",
      "392\n",
      "521\n",
      "393\n",
      "394\n",
      "395\n",
      "396\n",
      "397\n",
      "398\n",
      "399\n",
      "400\n",
      "522\n",
      "523\n",
      "524\n",
      "525\n",
      "401\n",
      "526\n",
      "527\n",
      "528\n",
      "402\n",
      "403\n",
      "404\n",
      "529\n",
      "405\n",
      "406\n",
      "407\n",
      "408\n",
      "530\n",
      "409\n",
      "410\n",
      "411\n",
      "412\n",
      "531\n",
      "532\n",
      "533\n",
      "534\n",
      "535\n",
      "536\n",
      "413\n",
      "414\n",
      "537\n",
      "538\n",
      "539\n",
      "415\n",
      "416\n",
      "540\n",
      "417\n",
      "418\n",
      "541\n",
      "419\n",
      "420\n",
      "542\n",
      "543\n",
      "544\n",
      "545\n",
      "546\n",
      "547\n",
      "548\n",
      "549\n",
      "421\n",
      "422\n",
      "423\n",
      "550\n",
      "551\n",
      "424\n",
      "552\n",
      "425\n",
      "426\n",
      "553\n",
      "554\n",
      "555\n",
      "427\n",
      "428\n",
      "556\n",
      "429\n",
      "557\n",
      "558\n",
      "559\n",
      "430\n",
      "560\n",
      "561\n",
      "431\n",
      "562\n",
      "432\n",
      "563\n",
      "564\n",
      "565\n",
      "566\n",
      "433\n",
      "434\n",
      "567\n",
      "568\n",
      "569\n",
      "570\n",
      "571\n",
      "435\n",
      "436\n",
      "572\n",
      "573\n",
      "437\n",
      "574\n",
      "575\n",
      "576\n",
      "438\n",
      "439\n",
      "440\n",
      "577\n",
      "441\n",
      "442\n",
      "578\n",
      "579\n",
      "580\n",
      "581\n",
      "443\n",
      "444\n",
      "445\n",
      "582\n",
      "583\n",
      "446\n",
      "584\n",
      "447\n",
      "448\n",
      "585\n",
      "449\n",
      "586\n",
      "450\n",
      "451\n",
      "587\n",
      "588\n",
      "452\n",
      "589\n",
      "453\n",
      "590\n",
      "591\n",
      "454\n",
      "455\n",
      "592\n",
      "456\n",
      "593\n",
      "457\n",
      "594\n",
      "458\n",
      "459\n",
      "595\n",
      "596\n",
      "597\n",
      "598\n",
      "460\n",
      "599\n",
      "461\n",
      "462\n",
      "463\n",
      "600\n",
      "464\n",
      "601\n",
      "602\n",
      "465\n",
      "603\n",
      "604\n",
      "466\n",
      "467\n",
      "605\n",
      "606\n",
      "468\n",
      "607\n",
      "608\n",
      "609\n",
      "469\n",
      "610\n",
      "470\n",
      "611\n",
      "471\n",
      "612\n",
      "472\n",
      "473\n",
      "474\n",
      "475\n",
      "613\n",
      "614\n",
      "615\n",
      "476\n",
      "616\n",
      "617\n",
      "477\n",
      "618\n",
      "478\n",
      "619\n",
      "620\n",
      "479\n",
      "621\n",
      "622\n",
      "480\n",
      "481\n",
      "623\n",
      "624\n",
      "482\n",
      "625\n",
      "483\n",
      "626\n",
      "627\n",
      "484\n",
      "485\n",
      "486\n",
      "487\n",
      "628\n",
      "488\n",
      "489\n",
      "629\n",
      "630\n",
      "631\n",
      "632\n",
      "633\n",
      "634\n",
      "490\n",
      "635\n",
      "636\n",
      "491\n",
      "492\n",
      "637\n",
      "493\n",
      "638\n",
      "639\n",
      "640\n",
      "494\n",
      "495\n",
      "641\n",
      "642\n",
      "496\n",
      "643\n",
      "497\n",
      "644\n",
      "498\n",
      "499\n",
      "645\n",
      "500\n",
      "501\n",
      "502\n",
      "503\n",
      "504\n",
      "505\n",
      "506\n",
      "507\n",
      "646\n",
      "647\n",
      "508\n",
      "509\n",
      "648\n",
      "649\n",
      "510\n",
      "650\n",
      "651\n",
      "511\n",
      "512\n",
      "652\n",
      "653\n",
      "654\n",
      "655\n",
      "656\n",
      "657\n",
      "658\n",
      "513\n",
      "514\n",
      "515\n",
      "659\n",
      "516\n",
      "660\n",
      "661\n",
      "517\n",
      "662\n",
      "518\n",
      "663\n",
      "519\n",
      "520\n",
      "521\n",
      "664\n",
      "665\n",
      "666\n",
      "667\n",
      "668\n",
      "522\n",
      "669\n",
      "670\n",
      "671\n",
      "523\n",
      "524\n",
      "525\n",
      "672\n",
      "526\n",
      "527\n",
      "528\n",
      "673\n",
      "529\n",
      "530\n",
      "674\n",
      "675\n",
      "531\n",
      "532\n",
      "676\n",
      "677\n",
      "678\n",
      "679\n",
      "680\n",
      "681\n",
      "682\n",
      "533\n",
      "683\n",
      "534\n",
      "535\n",
      "536\n",
      "684\n",
      "537\n",
      "685\n",
      "538\n",
      "686\n",
      "687\n",
      "539\n",
      "688\n",
      "540\n",
      "689\n",
      "690\n",
      "691\n",
      "692\n",
      "693\n",
      "541\n",
      "694\n",
      "695\n",
      "696\n",
      "697\n",
      "698\n",
      "699\n",
      "542\n",
      "700\n",
      "701\n",
      "543\n",
      "702\n",
      "544\n",
      "703\n",
      "704\n",
      "705\n",
      "706\n",
      "545\n",
      "707\n",
      "546\n",
      "708\n",
      "547\n",
      "548\n",
      "549\n",
      "709\n",
      "710\n",
      "550\n",
      "711\n",
      "712\n",
      "713\n",
      "714\n",
      "715\n",
      "716\n",
      "551\n",
      "717\n",
      "552\n",
      "718\n",
      "553\n",
      "554\n",
      "719\n",
      "720\n",
      "555\n",
      "721\n",
      "556\n",
      "557\n",
      "558\n",
      "559\n",
      "722\n",
      "723\n",
      "560\n",
      "724\n",
      "725\n",
      "561\n",
      "726\n",
      "727\n",
      "562\n",
      "563\n",
      "564\n",
      "565\n",
      "566\n",
      "567\n",
      "728\n",
      "568\n",
      "729\n",
      "730\n",
      "731\n",
      "569\n",
      "570\n",
      "732\n",
      "733\n",
      "734\n",
      "571\n",
      "735\n",
      "736\n",
      "737\n",
      "738\n",
      "572\n",
      "739\n",
      "740\n",
      "573\n",
      "741\n",
      "742\n",
      "574\n",
      "743\n",
      "744\n",
      "745\n",
      "575\n",
      "576\n",
      "577\n",
      "578\n",
      "579\n",
      "580\n",
      "581\n",
      "746\n",
      "747\n",
      "582\n",
      "583\n",
      "748\n",
      "749\n",
      "584\n",
      "585\n",
      "586\n",
      "750\n",
      "587\n",
      "751\n",
      "752\n",
      "753\n",
      "588\n",
      "754\n",
      "589\n",
      "755\n",
      "756\n",
      "590\n",
      "757\n",
      "758\n",
      "591\n",
      "759\n",
      "760\n",
      "761\n",
      "762\n",
      "763\n",
      "764\n",
      "592\n",
      "593\n",
      "765\n",
      "594\n",
      "595\n",
      "766\n",
      "767\n",
      "768\n",
      "596\n",
      "769\n",
      "597\n",
      "770\n",
      "771\n",
      "772\n",
      "598\n",
      "773\n",
      "774\n",
      "775\n",
      "599\n",
      "776\n",
      "777\n",
      "600\n",
      "778\n",
      "779\n",
      "601\n",
      "780\n",
      "781\n",
      "782\n",
      "783\n",
      "602\n",
      "784\n",
      "785\n",
      "786\n",
      "603\n",
      "604\n",
      "787\n",
      "605\n",
      "606\n",
      "788\n",
      "789\n",
      "790\n",
      "791\n",
      "792\n",
      "607\n",
      "793\n",
      "794\n",
      "608\n",
      "795\n",
      "609\n",
      "796\n",
      "610\n",
      "797\n",
      "611\n",
      "798\n",
      "612\n",
      "613\n",
      "799\n",
      "614\n",
      "800\n",
      "615\n",
      "801\n",
      "616\n",
      "802\n",
      "617\n",
      "803\n",
      "804\n",
      "805\n",
      "806\n",
      "807\n",
      "618\n",
      "808\n",
      "619\n",
      "809\n",
      "620\n",
      "810\n",
      "811\n",
      "621\n",
      "622\n",
      "812\n",
      "813\n",
      "814\n",
      "815\n",
      "816\n",
      "623\n",
      "817\n",
      "818\n",
      "819\n",
      "820\n",
      "821\n",
      "624\n",
      "822\n",
      "823\n",
      "824\n",
      "825\n",
      "826\n",
      "625\n",
      "626\n",
      "627\n",
      "628\n",
      "629\n",
      "827\n",
      "630\n",
      "828\n",
      "829\n",
      "830\n",
      "831\n",
      "832\n",
      "833\n",
      "834\n",
      "631\n",
      "835\n",
      "836\n",
      "837\n",
      "838\n",
      "839\n",
      "632\n",
      "633\n",
      "634\n",
      "635\n",
      "636\n",
      "637\n",
      "840\n",
      "841\n",
      "842\n",
      "843\n",
      "844\n",
      "638\n",
      "845\n",
      "639\n",
      "640\n",
      "641\n",
      "642\n",
      "643\n",
      "846\n",
      "644\n",
      "847\n",
      "645\n",
      "646\n",
      "848\n",
      "647\n",
      "648\n",
      "849\n",
      "850\n",
      "851\n",
      "649\n",
      "650\n",
      "651\n",
      "652\n",
      "852\n",
      "853\n",
      "653\n",
      "654\n",
      "655\n",
      "854\n",
      "656\n",
      "855\n",
      "856\n",
      "657\n",
      "658\n",
      "857\n",
      "858\n",
      "859\n",
      "860\n",
      "861\n",
      "862\n",
      "659\n",
      "863\n",
      "864\n",
      "865\n",
      "660\n",
      "661\n",
      "662\n",
      "663\n",
      "664\n",
      "866\n",
      "867\n",
      "665\n",
      "666\n",
      "868\n",
      "869\n",
      "667\n",
      "668\n",
      "870\n",
      "669\n",
      "871\n",
      "670\n",
      "671\n",
      "672\n",
      "673\n",
      "674\n",
      "872\n",
      "873\n",
      "675\n",
      "676\n",
      "677\n",
      "678\n",
      "874\n",
      "875\n",
      "876\n",
      "877\n",
      "679\n",
      "680\n",
      "878\n",
      "879\n",
      "681\n",
      "682\n",
      "880\n",
      "881\n",
      "683\n",
      "882\n",
      "684\n",
      "883\n",
      "884\n",
      "885\n",
      "886\n",
      "887\n",
      "685\n",
      "888\n",
      "686\n",
      "687\n",
      "688\n",
      "689\n",
      "889\n",
      "690\n",
      "691\n",
      "890\n",
      "692\n",
      "891\n",
      "892\n",
      "693\n",
      "893\n",
      "894\n",
      "694\n",
      "695\n",
      "895\n",
      "896\n",
      "897\n",
      "898\n",
      "696\n",
      "697\n",
      "899\n",
      "900\n",
      "901\n",
      "698\n",
      "699\n",
      "902\n",
      "903\n",
      "904\n",
      "905\n",
      "906\n",
      "700\n",
      "907\n",
      "701\n",
      "702\n",
      "908\n",
      "703\n",
      "704\n",
      "909\n",
      "910\n",
      "911\n",
      "912\n",
      "913\n",
      "914\n",
      "705\n",
      "706\n",
      "915\n",
      "916\n",
      "917\n",
      "918\n",
      "919\n",
      "920\n",
      "707\n",
      "921\n",
      "922\n",
      "923\n",
      "924\n",
      "708\n",
      "925\n",
      "926\n",
      "709\n",
      "710\n",
      "927\n",
      "928\n",
      "711\n",
      "929\n",
      "712\n",
      "930\n",
      "931\n",
      "932\n",
      "933\n",
      "934\n",
      "935\n",
      "936\n",
      "937\n",
      "938\n",
      "713\n",
      "939\n",
      "940\n",
      "714\n",
      "941\n",
      "715\n",
      "942\n",
      "716\n",
      "943\n",
      "944\n",
      "945\n",
      "946\n",
      "947\n",
      "948\n",
      "717\n",
      "949\n",
      "950\n",
      "951\n",
      "718\n",
      "952\n",
      "953\n",
      "954\n",
      "719\n",
      "720\n",
      "955\n",
      "721\n",
      "722\n",
      "723\n",
      "956\n",
      "957\n",
      "724\n",
      "958\n",
      "959\n",
      "725\n",
      "960\n",
      "726\n",
      "961\n",
      "727\n",
      "962\n",
      "728\n",
      "729\n",
      "730\n",
      "963\n",
      "731\n",
      "964\n",
      "965\n",
      "966\n",
      "732\n",
      "733\n",
      "967\n",
      "734\n",
      "735\n",
      "968\n",
      "969\n",
      "970\n",
      "971\n",
      "972\n",
      "973\n",
      "736\n",
      "974\n",
      "737\n",
      "975\n",
      "976\n",
      "738\n",
      "977\n",
      "739\n",
      "978\n",
      "979\n",
      "740\n",
      "980\n",
      "741\n",
      "742\n",
      "743\n",
      "981\n",
      "982\n",
      "983\n",
      "984\n",
      "985\n",
      "986\n",
      "987\n",
      "744\n",
      "988\n",
      "745\n",
      "746\n",
      "747\n",
      "989\n",
      "990\n",
      "991\n",
      "992\n",
      "748\n",
      "993\n",
      "994\n",
      "749\n",
      "995\n",
      "750\n",
      "996\n",
      "751\n",
      "997\n",
      "752\n",
      "753\n",
      "754\n",
      "998\n",
      "755\n",
      "999\n",
      "756\n",
      "1000\n",
      "1001\n",
      "757\n",
      "1002\n",
      "758\n",
      "1003\n",
      "759\n",
      "760\n",
      "1004\n",
      "761\n",
      "1005\n",
      "762\n",
      "1006\n",
      "1007\n",
      "1008\n",
      "763\n",
      "764\n",
      "765\n",
      "1009\n",
      "766\n",
      "1010\n",
      "1011\n",
      "767\n",
      "1012\n",
      "1013\n",
      "1014\n",
      "1015\n",
      "1016\n",
      "768\n",
      "769\n",
      "770\n",
      "1017\n",
      "771\n",
      "772\n",
      "773\n",
      "1018\n",
      "1019\n",
      "774\n",
      "1020\n",
      "1021\n",
      "775\n",
      "776\n",
      "1022\n",
      "777\n",
      "778\n",
      "779\n",
      "780\n",
      "781\n",
      "1023\n",
      "782\n",
      "1024\n",
      "1025\n",
      "1026\n",
      "1027\n",
      "783\n",
      "1028\n",
      "1029\n",
      "784\n",
      "1030\n",
      "1031\n",
      "785\n",
      "1032\n",
      "1033\n",
      "786\n",
      "1034\n",
      "1035\n",
      "1036\n",
      "1037\n",
      "1038\n",
      "1039\n",
      "1040\n",
      "1041\n",
      "1042\n",
      "1043\n",
      "787\n",
      "788\n",
      "1044\n",
      "789\n",
      "790\n",
      "1045\n",
      "791\n",
      "1046\n",
      "1047\n",
      "1048\n",
      "1049\n",
      "792\n",
      "1050\n",
      "793\n",
      "1051\n",
      "794\n",
      "795\n",
      "1052\n",
      "796\n",
      "797\n",
      "1053\n",
      "1054\n",
      "1055\n",
      "1056\n",
      "798\n",
      "799\n",
      "1057\n",
      "1058\n",
      "1059\n",
      "1060\n",
      "800\n",
      "1061\n",
      "801\n",
      "1062\n",
      "802\n",
      "803\n",
      "804\n",
      "1063\n",
      "805\n",
      "1064\n",
      "1065\n",
      "806\n",
      "1066\n",
      "807\n",
      "808\n",
      "809\n",
      "1067\n",
      "1068\n",
      "1069\n",
      "810\n",
      "1070\n",
      "811\n",
      "812\n",
      "1071\n",
      "1072\n",
      "813\n",
      "1073\n",
      "814\n",
      "815\n",
      "816\n",
      "1074\n",
      "1075\n",
      "1076\n",
      "817\n",
      "818\n",
      "1077\n",
      "819\n",
      "1078\n",
      "1079\n",
      "820\n",
      "1080\n",
      "1081\n",
      "1082\n",
      "1083\n",
      "821\n",
      "1084\n",
      "1085\n",
      "822\n",
      "823\n",
      "824\n",
      "1086\n",
      "825\n",
      "826\n",
      "827\n",
      "828\n",
      "829\n",
      "830\n",
      "1087\n",
      "831\n",
      "1088\n",
      "832\n",
      "1089\n",
      "1090\n",
      "833\n",
      "1091\n",
      "1092\n",
      "834\n",
      "1093\n",
      "835\n",
      "1094\n",
      "1095\n",
      "1096\n",
      "1097\n",
      "1098\n",
      "1099\n",
      "1100\n",
      "1101\n",
      "836\n",
      "1102\n",
      "1103\n",
      "1104\n",
      "837\n",
      "1105\n",
      "1106\n",
      "1107\n",
      "838\n",
      "1108\n",
      "1109\n",
      "839\n",
      "840\n",
      "1110\n",
      "1111\n",
      "841\n",
      "1112\n",
      "842\n",
      "1113\n",
      "1114\n",
      "1115\n",
      "843\n",
      "1116\n",
      "1117\n",
      "844\n",
      "1118\n",
      "1119\n",
      "1120\n",
      "1121\n",
      "1122\n",
      "1123\n",
      "845\n",
      "846\n",
      "1124\n",
      "847\n",
      "848\n",
      "1125\n",
      "849\n",
      "1126\n",
      "850\n",
      "1127\n",
      "1128\n",
      "851\n",
      "852\n",
      "853\n",
      "1129\n",
      "1130\n",
      "1131\n",
      "854\n",
      "1132\n",
      "1133\n",
      "1134\n",
      "1135\n",
      "1136\n",
      "855\n",
      "1137\n",
      "1138\n",
      "856\n",
      "1139\n",
      "1140\n",
      "857\n",
      "1141\n",
      "1142\n",
      "858\n",
      "859\n",
      "860\n",
      "1143\n",
      "1144\n",
      "1145\n",
      "1146\n",
      "1147\n",
      "1148\n",
      "861\n",
      "862\n",
      "863\n",
      "864\n",
      "865\n",
      "866\n",
      "867\n",
      "1149\n",
      "1150\n",
      "868\n",
      "869\n",
      "1151\n",
      "1152\n",
      "870\n",
      "871\n",
      "872\n",
      "1153\n",
      "873\n",
      "874\n",
      "1154\n",
      "1155\n",
      "875\n",
      "876\n",
      "1156\n",
      "1157\n",
      "1158\n",
      "1159\n",
      "1160\n",
      "877\n",
      "1161\n",
      "878\n",
      "1162\n",
      "1163\n",
      "1164\n",
      "879\n",
      "880\n",
      "1165\n",
      "1166\n",
      "1167\n",
      "881\n",
      "1168\n",
      "882\n",
      "883\n",
      "884\n",
      "885\n",
      "1169\n",
      "1170\n",
      "886\n",
      "1171\n",
      "1172\n",
      "1173\n",
      "1174\n",
      "887\n",
      "888\n",
      "1175\n",
      "1176\n",
      "1177\n",
      "889\n",
      "890\n",
      "891\n",
      "892\n",
      "1178\n",
      "893\n",
      "894\n",
      "1179\n",
      "1180\n",
      "1181\n",
      "895\n",
      "1182\n",
      "1183\n",
      "896\n",
      "897\n",
      "898\n",
      "1184\n",
      "899\n",
      "900\n",
      "1185\n",
      "1186\n",
      "901\n",
      "902\n",
      "1187\n",
      "1188\n",
      "903\n",
      "904\n",
      "1189\n",
      "1190\n",
      "905\n",
      "1191\n",
      "1192\n",
      "906\n",
      "907\n",
      "1193\n",
      "1194\n",
      "908\n",
      "909\n",
      "910\n",
      "911\n",
      "912\n",
      "1195\n",
      "913\n",
      "1196\n",
      "914\n",
      "1197\n",
      "915\n",
      "916\n",
      "1198\n",
      "1199\n",
      "1200\n",
      "917\n",
      "918\n",
      "1201\n",
      "1202\n",
      "919\n",
      "1203\n",
      "920\n",
      "921\n",
      "922\n",
      "923\n",
      "1204\n",
      "1205\n",
      "924\n",
      "1206\n",
      "1207\n",
      "925\n",
      "926\n",
      "1208\n",
      "1209\n",
      "927\n",
      "928\n",
      "1210\n",
      "1211\n",
      "1212\n",
      "929\n",
      "930\n",
      "1213\n",
      "931\n",
      "932\n",
      "1214\n",
      "933\n",
      "934\n",
      "935\n",
      "936\n",
      "937\n",
      "1215\n",
      "938\n",
      "939\n",
      "1216\n",
      "1217\n",
      "1218\n",
      "940\n",
      "941\n",
      "1219\n",
      "1220\n",
      "942\n",
      "943\n",
      "1221\n",
      "1222\n",
      "944\n",
      "945\n",
      "1223\n",
      "1224\n",
      "946\n",
      "1225\n",
      "1226\n",
      "1227\n",
      "1228\n",
      "1229\n",
      "947\n",
      "948\n",
      "949\n",
      "950\n",
      "951\n",
      "1230\n",
      "952\n",
      "1231\n",
      "953\n",
      "954\n",
      "1232\n",
      "1233\n",
      "955\n",
      "1234\n",
      "1235\n",
      "956\n",
      "1236\n",
      "1237\n",
      "1238\n",
      "957\n",
      "958\n",
      "959\n",
      "1239\n",
      "960\n",
      "1240\n",
      "1241\n",
      "1242\n",
      "961\n",
      "1243\n",
      "1244\n",
      "962\n",
      "963\n",
      "964\n",
      "1245\n",
      "1246\n",
      "1247\n",
      "1248\n",
      "1249\n",
      "1250\n",
      "1251\n",
      "1252\n",
      "1253\n",
      "1254\n",
      "1255\n",
      "965\n",
      "966\n",
      "967\n",
      "968\n",
      "1256\n",
      "969\n",
      "1257\n",
      "1258\n",
      "970\n",
      "1259\n",
      "1260\n",
      "971\n",
      "1261\n",
      "972\n",
      "973\n",
      "974\n",
      "1262\n",
      "1263\n",
      "975\n",
      "1264\n",
      "1265\n",
      "1266\n",
      "976\n",
      "1267\n",
      "977\n",
      "1268\n",
      "978\n",
      "1269\n",
      "979\n",
      "1270\n",
      "1271\n",
      "1272\n",
      "980\n",
      "981\n",
      "1273\n",
      "1274\n",
      "1275\n",
      "1276\n",
      "982\n",
      "983\n",
      "984\n",
      "1277\n",
      "1278\n",
      "985\n",
      "1279\n",
      "1280\n",
      "986\n",
      "1281\n",
      "987\n",
      "1282\n",
      "1283\n",
      "988\n",
      "989\n",
      "1284\n",
      "990\n",
      "991\n",
      "992\n",
      "1285\n",
      "993\n",
      "1286\n",
      "1287\n",
      "1288\n",
      "1289\n",
      "1290\n",
      "1291\n",
      "994\n",
      "995\n",
      "1292\n",
      "996\n",
      "1293\n",
      "997\n",
      "998\n",
      "1294\n",
      "1295\n",
      "1296\n",
      "1297\n",
      "1298\n",
      "1299\n",
      "1300\n",
      "1301\n",
      "999\n",
      "1000\n",
      "1001\n",
      "1302\n",
      "1303\n",
      "1304\n",
      "1002\n",
      "1003\n",
      "1305\n",
      "1306\n",
      "1307\n",
      "1004\n",
      "1005\n",
      "1006\n",
      "1007\n",
      "1308\n",
      "1309\n",
      "1008\n",
      "1009\n",
      "1010\n",
      "1310\n",
      "1311\n",
      "1011\n",
      "1312\n",
      "1313\n",
      "1314\n",
      "1315\n",
      "1316\n",
      "1012\n",
      "1317\n",
      "1318\n",
      "1319\n",
      "1013\n",
      "1014\n",
      "1320\n",
      "1015\n",
      "1016\n",
      "1017\n",
      "1321\n",
      "1322\n",
      "1018\n",
      "1323\n",
      "1019\n",
      "1020\n",
      "1324\n",
      "1021\n",
      "1022\n",
      "1023\n",
      "1024\n",
      "1025\n",
      "1026\n",
      "1325\n",
      "1326\n",
      "1327\n",
      "1328\n",
      "1027\n",
      "1028\n",
      "1329\n",
      "1330\n",
      "1029\n",
      "1331\n",
      "1030\n",
      "1332\n",
      "1333\n",
      "1334\n",
      "1335\n",
      "1336\n",
      "1337\n",
      "1031\n",
      "1338\n",
      "1032\n",
      "1339\n",
      "1033\n",
      "1340\n",
      "1341\n",
      "1034\n",
      "1342\n",
      "1343\n",
      "1344\n",
      "1345\n",
      "1346\n",
      "1035\n",
      "1347\n",
      "1348\n",
      "1349\n",
      "1350\n",
      "1351\n",
      "1352\n",
      "1036\n",
      "1037\n",
      "1038\n",
      "1353\n",
      "1354\n",
      "1355\n",
      "1039\n",
      "1356\n",
      "1040\n",
      "1041\n",
      "1042\n",
      "1357\n",
      "1358\n",
      "1359\n",
      "1043\n",
      "1360\n",
      "1361\n",
      "1362\n",
      "1044\n",
      "1045\n",
      "1363\n",
      "1046\n",
      "1047\n",
      "1364\n",
      "1048\n",
      "1049\n",
      "1365\n",
      "1050\n",
      "1051\n",
      "1366\n",
      "1052\n",
      "1367\n",
      "1368\n",
      "1369\n",
      "1370\n",
      "1053\n",
      "1371\n",
      "1054\n",
      "1372\n",
      "1373\n",
      "1374\n",
      "1055\n",
      "1056\n",
      "1057\n",
      "1375\n",
      "1376\n",
      "1058\n",
      "1059\n",
      "1377\n",
      "1060\n",
      "1061\n",
      "1378\n",
      "1062\n",
      "1063\n",
      "1064\n",
      "1065\n",
      "1066\n",
      "1379\n",
      "1380\n",
      "1067\n",
      "1068\n",
      "1381\n",
      "1069\n",
      "1070\n",
      "1382\n",
      "1071\n",
      "1383\n",
      "1384\n",
      "1385\n",
      "1386\n",
      "1072\n",
      "1073\n",
      "1387\n",
      "1388\n",
      "1389\n",
      "1390\n",
      "1391\n",
      "1392\n",
      "1074\n",
      "1075\n",
      "1393\n",
      "1076\n",
      "1077\n",
      "1394\n",
      "1395\n",
      "1396\n",
      "1078\n",
      "1397\n",
      "1398\n",
      "1399\n",
      "1400\n",
      "1401\n",
      "1402\n",
      "1079\n",
      "1403\n",
      "1404\n",
      "1080\n",
      "1081\n",
      "1405\n",
      "1406\n",
      "1407\n",
      "1082\n",
      "1408\n",
      "1409\n",
      "1083\n",
      "1410\n",
      "1084\n",
      "1411\n",
      "1085\n",
      "1086\n",
      "1412\n",
      "1413\n",
      "1087\n",
      "1088\n",
      "1414\n",
      "1415\n",
      "1416\n",
      "1417\n",
      "1418\n",
      "1419\n",
      "1420\n",
      "1421\n",
      "1422\n",
      "1423\n",
      "1424\n",
      "1089\n",
      "1090\n",
      "1091\n",
      "1425\n",
      "1426\n",
      "1092\n",
      "1427\n",
      "1428\n",
      "1093\n",
      "1429\n",
      "1430\n",
      "1431\n",
      "1432\n",
      "1094\n",
      "1433\n",
      "1095\n",
      "1434\n",
      "1435\n",
      "1436\n",
      "1437\n",
      "1096\n",
      "1438\n",
      "1439\n",
      "1440\n",
      "1097\n",
      "1098\n",
      "1099\n",
      "1441\n",
      "1442\n",
      "1443\n",
      "1444\n",
      "1445\n",
      "1100\n",
      "1446\n",
      "1447\n",
      "1448\n",
      "1101\n",
      "1449\n",
      "1450\n",
      "1102\n",
      "1103\n",
      "1451\n",
      "1452\n",
      "1453\n",
      "1104\n",
      "1454\n",
      "1455\n",
      "1105\n",
      "1456\n",
      "1106\n",
      "1107\n",
      "1108\n",
      "1457\n",
      "1109\n",
      "1110\n",
      "1111\n",
      "1112\n",
      "1458\n",
      "1113\n",
      "1459\n",
      "1460\n",
      "1461\n",
      "1462\n",
      "1463\n",
      "1464\n",
      "1465\n",
      "1114\n",
      "1466\n",
      "1467\n",
      "1468\n",
      "1115\n",
      "1116\n",
      "1469\n",
      "1117\n",
      "1118\n",
      "1119\n",
      "1120\n",
      "1121\n",
      "1470\n",
      "1471\n",
      "1122\n",
      "1123\n",
      "1472\n",
      "1124\n",
      "1473\n",
      "1474\n",
      "1475\n",
      "1125\n",
      "1126\n",
      "1127\n",
      "1476\n",
      "1128\n",
      "1129\n",
      "1477\n",
      "1130\n",
      "1131\n",
      "1478\n",
      "1132\n",
      "1133\n",
      "1479\n",
      "1480\n",
      "1134\n",
      "1481\n",
      "1135\n",
      "1136\n",
      "1482\n",
      "1483\n",
      "1137\n",
      "1138\n",
      "1484\n",
      "1139\n",
      "1140\n",
      "1485\n",
      "1486\n",
      "1487\n",
      "1488\n",
      "1489\n",
      "1141\n",
      "1142\n",
      "1143\n",
      "1490\n",
      "1491\n",
      "1144\n",
      "1145\n",
      "1492\n",
      "1493\n",
      "1146\n",
      "1494\n",
      "1147\n",
      "1495\n",
      "1496\n",
      "1497\n",
      "1498\n",
      "1148\n",
      "1149\n",
      "1499\n",
      "1150\n",
      "1500\n",
      "1151\n",
      "1501\n",
      "1502\n",
      "1152\n",
      "1503\n",
      "1504\n",
      "1153\n",
      "1505\n",
      "1506\n",
      "1154\n",
      "1507\n",
      "1508\n",
      "1155\n",
      "1509\n",
      "1510\n",
      "1511\n",
      "1512\n",
      "1513\n",
      "1514\n",
      "1515\n",
      "1516\n",
      "1517\n",
      "1156\n",
      "1518\n",
      "1519\n",
      "1520\n",
      "1157\n",
      "1158\n",
      "1521\n",
      "1522\n",
      "1523\n",
      "1159\n",
      "1524\n",
      "1160\n",
      "1161\n",
      "1525\n",
      "1526\n",
      "1527\n",
      "1162\n",
      "1528\n",
      "1163\n",
      "1164\n",
      "1529\n",
      "1530\n",
      "1165\n",
      "1531\n",
      "1532\n",
      "1533\n",
      "1534\n",
      "1535\n",
      "1536\n",
      "1537\n",
      "1538\n",
      "1166\n",
      "1167\n",
      "1539\n",
      "1540\n",
      "1168\n",
      "1541\n",
      "1169\n",
      "1170\n",
      "1171\n",
      "1542\n",
      "1172\n",
      "1543\n",
      "1173\n",
      "1174\n",
      "1544\n",
      "1545\n",
      "1546\n",
      "1547\n",
      "1548\n",
      "1549\n",
      "1175\n",
      "1550\n",
      "1176\n",
      "1177\n",
      "1551\n",
      "1178\n",
      "1179\n",
      "1180\n",
      "1552\n",
      "1181\n",
      "1553\n",
      "1182\n",
      "1183\n",
      "1554\n",
      "1555\n",
      "1556\n",
      "1557\n",
      "1558\n",
      "1559\n",
      "1560\n",
      "1184\n",
      "1185\n",
      "1186\n",
      "1187\n",
      "1561\n",
      "1188\n",
      "1562\n",
      "1563\n",
      "1189\n",
      "1190\n",
      "1564\n",
      "1191\n",
      "1565\n",
      "1566\n",
      "1567\n",
      "1568\n",
      "1569\n",
      "1192\n",
      "1193\n",
      "1570\n",
      "1571\n",
      "1194\n",
      "1572\n",
      "1573\n",
      "1195\n",
      "1196\n",
      "1574\n",
      "1197\n",
      "1575\n",
      "1576\n",
      "1577\n",
      "1578\n",
      "1579\n",
      "1198\n",
      "1199\n",
      "1200\n",
      "1201\n",
      "1202\n",
      "1580\n",
      "1203\n",
      "1581\n",
      "1204\n",
      "1582\n",
      "1205\n",
      "1583\n",
      "1584\n",
      "1206\n",
      "1585\n",
      "1207\n",
      "1208\n",
      "1209\n",
      "1210\n",
      "1211\n",
      "1212\n",
      "1586\n",
      "1587\n",
      "1588\n",
      "1589\n",
      "1213\n",
      "1214\n",
      "1590\n",
      "1215\n",
      "1216\n",
      "1591\n",
      "1592\n",
      "1593\n",
      "1594\n",
      "1217\n",
      "1595\n",
      "1596\n",
      "1597\n",
      "1218\n",
      "1598\n",
      "1219\n",
      "1220\n",
      "1599\n",
      "1600\n",
      "1601\n",
      "1602\n",
      "1603\n",
      "1221\n",
      "1222\n",
      "1223\n",
      "1604\n",
      "1605\n",
      "1606\n",
      "1607\n",
      "1224\n",
      "1608\n",
      "1609\n",
      "1225\n",
      "1610\n",
      "1611\n",
      "1612\n",
      "1613\n",
      "1614\n",
      "1615\n",
      "1226\n",
      "1616\n",
      "1227\n",
      "1617\n",
      "1618\n",
      "1619\n",
      "1228\n",
      "1229\n",
      "1620\n",
      "1621\n",
      "1230\n",
      "1231\n",
      "1232\n",
      "1622\n",
      "1233\n",
      "1623\n",
      "1624\n",
      "1625\n",
      "1626\n",
      "1234\n",
      "1627\n",
      "1628\n",
      "1629\n",
      "1630\n",
      "1631\n",
      "1235\n",
      "1632\n",
      "1236\n",
      "1633\n",
      "1634\n",
      "1635\n",
      "1237\n",
      "1238\n",
      "1636\n",
      "1637\n",
      "1638\n",
      "1239\n",
      "1240\n",
      "1639\n",
      "1241\n",
      "1640\n",
      "1641\n",
      "1642\n",
      "1643\n",
      "1644\n",
      "1645\n",
      "1242\n",
      "1646\n",
      "1647\n",
      "1648\n",
      "1243\n",
      "1244\n",
      "1649\n",
      "1245\n",
      "1246\n",
      "1650\n",
      "1247\n",
      "1248\n",
      "1249\n",
      "1651\n",
      "1652\n",
      "1250\n",
      "1251\n",
      "1252\n",
      "1253\n",
      "1653\n",
      "1254\n",
      "1654\n",
      "1655\n",
      "1255\n",
      "1656\n",
      "1657\n",
      "1658\n",
      "1256\n",
      "1659\n",
      "1257\n",
      "1258\n",
      "1259\n",
      "1660\n",
      "1661\n",
      "1260\n",
      "1662\n",
      "1663\n",
      "1664\n",
      "1665\n",
      "1261\n",
      "1262\n",
      "1666\n",
      "1263\n",
      "1667\n",
      "1264\n",
      "1265\n",
      "1668\n",
      "1266\n",
      "1267\n",
      "1268\n",
      "1269\n",
      "1669\n",
      "1270\n",
      "1271\n",
      "1272\n",
      "1670\n",
      "1671\n",
      "1672\n",
      "1273\n",
      "1274\n",
      "1673\n",
      "1275\n",
      "1674\n",
      "1675\n",
      "1276\n",
      "1277\n",
      "1676\n",
      "1677\n",
      "1678\n",
      "1679\n",
      "1680\n",
      "1278\n",
      "1681\n",
      "1279\n",
      "1280\n",
      "1682\n",
      "1281\n",
      "1282\n",
      "1683\n",
      "1684\n",
      "1685\n",
      "1686\n",
      "1687\n",
      "1688\n",
      "1689\n",
      "1690\n",
      "1691\n",
      "1283\n",
      "1692\n",
      "1693\n",
      "1694\n",
      "1695\n",
      "1284\n",
      "1696\n",
      "1697\n",
      "1698\n",
      "1699\n",
      "1700\n",
      "1285\n",
      "1286\n",
      "1701\n",
      "1287\n",
      "1702\n",
      "1703\n",
      "1704\n",
      "1705\n",
      "1288\n",
      "1706\n",
      "1707\n",
      "1708\n",
      "1289\n",
      "1709\n",
      "1290\n",
      "1710\n",
      "1291\n",
      "1711\n",
      "1292\n",
      "1712\n",
      "1293\n",
      "1713\n",
      "1294\n",
      "1295\n",
      "1714\n",
      "1296\n",
      "1715\n",
      "1716\n",
      "1717\n",
      "1718\n",
      "1719\n",
      "1297\n",
      "1298\n",
      "1720\n",
      "1721\n",
      "1299\n",
      "1722\n",
      "1300\n",
      "1723\n",
      "1301\n",
      "1302\n",
      "1724\n",
      "1725\n",
      "1303\n",
      "1304\n",
      "1305\n",
      "1306\n",
      "1307\n",
      "1308\n",
      "1309\n",
      "1726\n",
      "1310\n",
      "1311\n",
      "1727\n",
      "1312\n",
      "1728\n",
      "1313\n",
      "1729\n",
      "1730\n",
      "1314\n",
      "1315\n",
      "1316\n",
      "1317\n",
      "1731\n",
      "1318\n",
      "1319\n",
      "1320\n",
      "1732\n",
      "1733\n",
      "1734\n",
      "1735\n",
      "1321\n",
      "1322\n",
      "1323\n",
      "1736\n",
      "1324\n",
      "1325\n",
      "1737\n",
      "1738\n",
      "1326\n",
      "1327\n",
      "1739\n",
      "1740\n",
      "1741\n",
      "1742\n",
      "1328\n",
      "1329\n",
      "1743\n",
      "1744\n",
      "1745\n",
      "1746\n",
      "1330\n",
      "1747\n",
      "1748\n",
      "1331\n",
      "1749\n",
      "1750\n",
      "1332\n",
      "1751\n",
      "1752\n",
      "1333\n",
      "1753\n",
      "1754\n",
      "1755\n",
      "1756\n",
      "1757\n",
      "1758\n",
      "1334\n",
      "1335\n",
      "1759\n",
      "1336\n",
      "1760\n",
      "1337\n",
      "1338\n",
      "1761\n",
      "1762\n",
      "1763\n",
      "1339\n",
      "1340\n",
      "1764\n",
      "1341\n",
      "1765\n",
      "1766\n",
      "1342\n",
      "1767\n",
      "1343\n",
      "1768\n",
      "1344\n",
      "1769\n",
      "1345\n",
      "1346\n",
      "1347\n",
      "1348\n",
      "1349\n",
      "1770\n",
      "1350\n",
      "1771\n",
      "1351\n",
      "1772\n",
      "1773\n",
      "1774\n",
      "1352\n",
      "1775\n",
      "1353\n",
      "1776\n",
      "1777\n",
      "1354\n",
      "1778\n",
      "1779\n",
      "1780\n",
      "1781\n",
      "1782\n",
      "1355\n",
      "1356\n",
      "1357\n",
      "1783\n",
      "1784\n",
      "1358\n",
      "1785\n",
      "1359\n",
      "1786\n",
      "1360\n",
      "1361\n",
      "1362\n",
      "1363\n",
      "1787\n",
      "1364\n",
      "1365\n",
      "1788\n",
      "1789\n",
      "1790\n",
      "1791\n",
      "1792\n",
      "1793\n",
      "1366\n",
      "1367\n",
      "1794\n",
      "1795\n",
      "1368\n",
      "1796\n",
      "1369\n",
      "1370\n",
      "1371\n",
      "1797\n",
      "1798\n",
      "1372\n",
      "1373\n",
      "1374\n",
      "1375\n",
      "1376\n",
      "1799\n",
      "1800\n",
      "1801\n",
      "1377\n",
      "1802\n",
      "1378\n",
      "1803\n",
      "1379\n",
      "1804\n",
      "1805\n",
      "1806\n",
      "1380\n",
      "1807\n",
      "1381\n",
      "1382\n",
      "1383\n",
      "1384\n",
      "1385\n",
      "1808\n",
      "1386\n",
      "1387\n",
      "1388\n",
      "1389\n",
      "1390\n",
      "1809\n",
      "1810\n",
      "1811\n",
      "1812\n",
      "1391\n",
      "1813\n",
      "1392\n",
      "1814\n",
      "1815\n",
      "1816\n",
      "1817\n",
      "1818\n",
      "1819\n",
      "1820\n",
      "1821\n",
      "1822\n",
      "1823\n",
      "1393\n",
      "1394\n",
      "1824\n",
      "1395\n",
      "1396\n",
      "1825\n",
      "1826\n",
      "1397\n",
      "1827\n",
      "1828\n",
      "1398\n",
      "1399\n",
      "1829\n",
      "1830\n",
      "1400\n",
      "1831\n",
      "1832\n",
      "1833\n",
      "1401\n",
      "1834\n",
      "1402\n",
      "1835\n",
      "1836\n",
      "1837\n",
      "1838\n",
      "1403\n",
      "1404\n",
      "1839\n",
      "1405\n",
      "1840\n",
      "1406\n",
      "1841\n",
      "1842\n",
      "1843\n",
      "1407\n",
      "1844\n",
      "1845\n",
      "1408\n",
      "1846\n",
      "1409\n",
      "1847\n",
      "1848\n",
      "1849\n",
      "1850\n",
      "1410\n",
      "1851\n",
      "1852\n",
      "1853\n",
      "1854\n",
      "1855\n",
      "1411\n",
      "1412\n",
      "1413\n",
      "1414\n",
      "1856\n",
      "1415\n",
      "1857\n",
      "1858\n",
      "1859\n",
      "1860\n",
      "1861\n",
      "1862\n",
      "1416\n",
      "1863\n",
      "1864\n",
      "1865\n",
      "1417\n",
      "1418\n",
      "1866\n",
      "1419\n",
      "1867\n",
      "1868\n",
      "1869\n",
      "1870\n",
      "1420\n",
      "1871\n",
      "1421\n",
      "1422\n",
      "1872\n",
      "1873\n",
      "1874\n",
      "1875\n",
      "1876\n",
      "1877\n",
      "1878\n",
      "1879\n",
      "1423\n",
      "1880\n",
      "1881\n",
      "1882\n",
      "1424\n",
      "1883\n",
      "1425\n",
      "1884\n",
      "1885\n",
      "1886\n",
      "1426\n",
      "1887\n",
      "1427\n",
      "1428\n",
      "1888\n",
      "1889\n",
      "1429\n",
      "1430\n",
      "1890\n",
      "1891\n",
      "1892\n",
      "1893\n",
      "1431\n",
      "1894\n",
      "1432\n",
      "1433\n",
      "1434\n",
      "1435\n",
      "1436\n",
      "1437\n",
      "1438\n",
      "1895\n",
      "1896\n",
      "1439\n",
      "1897\n",
      "1440\n",
      "1441\n",
      "1898\n",
      "1442\n",
      "1899\n",
      "1443\n",
      "1900\n",
      "1901\n",
      "1444\n",
      "1445\n",
      "1446\n",
      "1447\n",
      "1448\n",
      "1902\n",
      "1449\n",
      "1450\n",
      "1451\n",
      "1903\n",
      "1452\n",
      "1904\n",
      "1905\n",
      "1453\n",
      "1454\n",
      "1455\n",
      "1456\n",
      "1906\n",
      "1457\n",
      "1458\n",
      "1907\n",
      "1908\n",
      "1909\n",
      "1459\n",
      "1460\n",
      "1910\n",
      "1911\n",
      "1912\n",
      "1913\n",
      "1914\n",
      "1461\n",
      "1462\n",
      "1915\n",
      "1463\n",
      "1916\n",
      "1464\n",
      "1917\n",
      "1918\n",
      "1465\n",
      "1919\n",
      "1920\n",
      "1921\n",
      "1922\n",
      "1466\n",
      "1467\n",
      "1923\n"
     ]
    }
   ],
   "source": [
    "for i in data['sex']:\n",
    "    if i=='M':\n",
    "        males+=1\n",
    "        print(males)\n",
    "    else:\n",
    "        females+=1\n",
    "        print(females)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "hW5PXu3SKGRf",
   "metadata": {
    "id": "hW5PXu3SKGRf"
   },
   "outputs": [],
   "source": [
    "total=males+females\n",
    "data_gender=[males/total*100,females/total*100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "JKStND2zKMx0",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 264
    },
    "id": "JKStND2zKMx0",
    "outputId": "7f462166-c5da-44e2-c1a3-d5d93be8fce4"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "label=['Males','Females']\n",
    "colors = ['#ffff00','#ff00ff']\n",
    "plt.pie(x=data_gender,labels=label,colors=colors,autopct=\"%1.1f%%\")\n",
    "plt.title(\"Males VS Females\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "l4h5bOXPWiem",
   "metadata": {
    "id": "l4h5bOXPWiem"
   },
   "source": [
    "### female percentage are more as compare to male."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "GADsLYHOKgQ7",
   "metadata": {
    "id": "GADsLYHOKgQ7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='cigsPerDay', ylabel='Density'>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiwAAAINCAYAAAAdhyR6AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABcbklEQVR4nO3de1hU1f4/8PcMMAPITUAYQBBUVLwA3iDMMpMjaqck0aPmyUsm1hFL6WJUap3qYHk5WJn+9KTmN0mzzEoLI0w9JoqCl7zhXVAYEBFGucPs3x+cGZ1A5TIzewPv1/PME+xZs/ZnmJS3a629tkwQBAFEREREEiYXuwAiIiKiB2FgISIiIsljYCEiIiLJY2AhIiIiyWNgISIiIsljYCEiIiLJY2AhIiIiyWNgISIiIsmzFLuAlkqr1SInJwf29vaQyWRil0NERNRiCIKAW7duwdPTE3J5w8ZOGFiaKCcnB97e3mKXQURE1GJlZ2ejY8eODWrLwNJE9vb2AGp/2A4ODiJXQ0RE1HJoNBp4e3vrf5c2BANLE+mmgRwcHBhYiIiImqAxSyq46JaIiIgkj4GFiIiIJI+BhYiIiCSPgYWIiIgkj4GFiIiIJI+BhYiIiCSPgYWIiIgkj4GFiIiIJI+BhYiIiCSPgYWIiIgkj4GFiIiIJI+BhYiIiCSPgYWIiIgkj4GFiIiIJI+BhYiIiCSPgYWIiIgkj4GFiIiIJE8SgWXFihXw9fWFtbU1QkNDkZaWds+2a9aswSOPPIL27dujffv2CA8Pr9NeEAQsWLAAHh4esLGxQXh4OM6dO2fQprCwEJMmTYKDgwOcnJwwffp03L592yTvj4iIiJpH9MCyefNmxMbGYuHChcjIyEBQUBAiIiKQn59fb/vdu3dj4sSJ+O2335Camgpvb28MHz4c165d07f56KOP8PHHH2PVqlU4ePAg2rVrh4iICJSXl+vbTJo0CSdPnkRycjK2b9+OvXv3Ijo62uTvl4iIiBpPJgiCIGYBoaGhGDhwID799FMAgFarhbe3N2bPno033njjga+vqalB+/bt8emnn2Ly5MkQBAGenp545ZVX8OqrrwIAiouL4e7ujvXr12PChAk4ffo0evbsiUOHDmHAgAEAgKSkJIwaNQpXr16Fp6fnA8+r0Wjg6OiI4uJiODg4NOMnIF2rVx9r9Guio4NMUAkREbUmTfkdKuoIS2VlJdLT0xEeHq4/JpfLER4ejtTU1Ab1UVpaiqqqKjg7OwMALl26BLVabdCno6MjQkND9X2mpqbCyclJH1YAIDw8HHK5HAcPHqz3PBUVFdBoNAYPIiIiMg9RA0tBQQFqamrg7u5ucNzd3R1qtbpBfcybNw+enp76gKJ73f36VKvVcHNzM3je0tISzs7O9zxvfHw8HB0d9Q9vb+8G1UdERETNJ/oaluZYtGgRNm3ahO+++w7W1tYmPVdcXByKi4v1j+zsbJOej4iIiO6wFPPkrq6usLCwQF5ensHxvLw8qFSq+752yZIlWLRoEX799VcEBgbqj+tel5eXBw8PD4M+g4OD9W3+vKi3uroahYWF9zyvUqmEUqls8HsjIiIi4xF1hEWhUKB///5ISUnRH9NqtUhJSUFYWNg9X/fRRx/hvffeQ1JSksE6FADw8/ODSqUy6FOj0eDgwYP6PsPCwlBUVIT09HR9m127dkGr1SI0NNRYb4+IiIiMRNQRFgCIjY3FlClTMGDAAISEhCAhIQElJSWYNm0aAGDy5Mnw8vJCfHw8AODDDz/EggULkJiYCF9fX/2aEzs7O9jZ2UEmk2HOnDl4//334e/vDz8/P8yfPx+enp6IjIwEAAQEBGDEiBGYMWMGVq1ahaqqKsTExGDChAkNukKIiIiIzEv0wDJ+/Hhcv34dCxYsgFqtRnBwMJKSkvSLZrOysiCX3xkIWrlyJSorKzF27FiDfhYuXIh33nkHAPD666+jpKQE0dHRKCoqwuDBg5GUlGSwzmXjxo2IiYnBsGHDIJfLERUVhY8//tj0b5iIiIgaTfR9WFoq7sNSP+7DQkRED9Li9mEhIiIiaggGFiIiIpI8BhYiIiKSPAYWIiIikjwGFiIiIpI8BhYiIiKSPAYWIiIikjwGFiIiIpI8BhYiIiKSPAYWIiIikjwGFiIiIpI8BhYiIiKSPAYWIiIikjwGFiIiIpI8BhYiIiKSPAYWIiIikjwGFiIiIpI8BhYiIiKSPAYWIiIikjwGFiIiIpI8BhYiIiKSPAYWIiIikjwGFiIiIpI8BhYiIiKSPAYWIiIikjwGFiIiIpI8BhYiIiKSPAYWIiIikjwGFiIiIpI8BhYiIiKSPAYWIiIikjwGFiIiIpI8BhYiIiKSPAYWIiIikjwGFiIiIpI8BhYiIiKSPAYWIiIikjwGFiIiIpI8BhYiIiKSPAYWIiIikjwGFiIiIpI8BhYiIiKSPAYWIiIikjwGFiIiIpI80QPLihUr4OvrC2tra4SGhiItLe2ebU+ePImoqCj4+vpCJpMhISGhThvdc39+zJo1S9/mscceq/P8Cy+8YIq3R0REREYgamDZvHkzYmNjsXDhQmRkZCAoKAgRERHIz8+vt31paSk6d+6MRYsWQaVS1dvm0KFDyM3N1T+Sk5MBAOPGjTNoN2PGDIN2H330kXHfHBERERmNqIFl2bJlmDFjBqZNm4aePXti1apVsLW1xdq1a+ttP3DgQCxevBgTJkyAUqmst02HDh2gUqn0j+3bt6NLly4YMmSIQTtbW1uDdg4ODkZ/f0RERGQcogWWyspKpKenIzw8/E4xcjnCw8ORmppqtHN8+eWXeO655yCTyQye27hxI1xdXdG7d2/ExcWhtLT0vn1VVFRAo9EYPIiIiMg8LMU6cUFBAWpqauDu7m5w3N3dHWfOnDHKObZt24aioiJMnTrV4PgzzzyDTp06wdPTE8ePH8e8efOQmZmJrVu33rOv+Ph4vPvuu0api4iIiBpHtMBiDp9//jlGjhwJT09Pg+PR0dH6r/v06QMPDw8MGzYMFy5cQJcuXertKy4uDrGxsfrvNRoNvL29TVM4ERERGRAtsLi6usLCwgJ5eXkGx/Py8u65oLYxrly5gl9//fW+oyY6oaGhAIDz58/fM7Aolcp7rpshIiIi0xJtDYtCoUD//v2RkpKiP6bVapGSkoKwsLBm979u3Tq4ubnhiSeeeGDbo0ePAgA8PDyafV4iIiIyPlGnhGJjYzFlyhQMGDAAISEhSEhIQElJCaZNmwYAmDx5Mry8vBAfHw+gdhHtqVOn9F9fu3YNR48ehZ2dHbp27arvV6vVYt26dZgyZQosLQ3f4oULF5CYmIhRo0bBxcUFx48fx9y5c/Hoo48iMDDQTO+ciIiIGkPUwDJ+/Hhcv34dCxYsgFqtRnBwMJKSkvQLcbOysiCX3xkEysnJQd++ffXfL1myBEuWLMGQIUOwe/du/fFff/0VWVlZeO655+qcU6FQ4Ndff9WHI29vb0RFReHtt9823RslIiKiZpEJgiCIXURLpNFo4OjoiOLi4la7h8vq1cca/Zro6CATVEJERK1JU36Hir41PxEREdGDMLAQERGR5DGwEBERkeQxsBAREZHkMbAQERGR5DGwEBERkeQxsBAREZHkMbAQERGR5DGwEBERkeQxsBAREZHkMbAQERGR5DGwEBERkeQxsBAREZHkMbAQERGR5DGwEBERkeQxsBAREZHkMbAQERGR5DGwEBERkeQxsBAREZHkMbAQERGR5DGwEBERkeQxsBAREZHkMbAQERGR5DGwEBERkeQxsBAREZHkMbAQERGR5DGwEBERkeQxsBAREZHkMbAQERGR5DGwEBERkeQxsBAREZHkMbAQERGR5DGwEBERkeQxsBAREZHkMbAQERGR5DGwEBERkeQxsBAREZHkMbAQERGR5DGwEBERkeQxsBAREZHkMbAQERGR5DGwEBERkeQxsBAREZHkiR5YVqxYAV9fX1hbWyM0NBRpaWn3bHvy5ElERUXB19cXMpkMCQkJddq88847kMlkBo8ePXoYtCkvL8esWbPg4uICOzs7REVFIS8vz9hvjYiIiIxE1MCyefNmxMbGYuHChcjIyEBQUBAiIiKQn59fb/vS0lJ07twZixYtgkqlume/vXr1Qm5urv6xb98+g+fnzp2LH3/8EVu2bMGePXuQk5ODMWPGGPW9ERERkfGIGliWLVuGGTNmYNq0aejZsydWrVoFW1tbrF27tt72AwcOxOLFizFhwgQolcp79mtpaQmVSqV/uLq66p8rLi7G559/jmXLluHxxx9H//79sW7dOuzfvx8HDhww+nskIiKi5hMtsFRWViI9PR3h4eF3ipHLER4ejtTU1Gb1fe7cOXh6eqJz586YNGkSsrKy9M+lp6ejqqrK4Lw9evSAj49Ps89LREREpiFaYCkoKEBNTQ3c3d0Njru7u0OtVje539DQUKxfvx5JSUlYuXIlLl26hEceeQS3bt0CAKjVaigUCjg5OTXqvBUVFdBoNAYPIiIiMg9LsQswtpEjR+q/DgwMRGhoKDp16oSvv/4a06dPb3K/8fHxePfdd41RIhERETWSaCMsrq6usLCwqHN1Tl5e3n0X1DaWk5MTunXrhvPnzwMAVCoVKisrUVRU1KjzxsXFobi4WP/Izs42Wo1ERER0f6IFFoVCgf79+yMlJUV/TKvVIiUlBWFhYUY7z+3bt3HhwgV4eHgAAPr37w8rKyuD82ZmZiIrK+u+51UqlXBwcDB4EBERkXmIOiUUGxuLKVOmYMCAAQgJCUFCQgJKSkowbdo0AMDkyZPh5eWF+Ph4ALULdU+dOqX/+tq1azh69Cjs7OzQtWtXAMCrr76KJ598Ep06dUJOTg4WLlwICwsLTJw4EQDg6OiI6dOnIzY2Fs7OznBwcMDs2bMRFhaGhx56SISfAhERET2IqIFl/PjxuH79OhYsWAC1Wo3g4GAkJSXpF+JmZWVBLr8zCJSTk4O+ffvqv1+yZAmWLFmCIUOGYPfu3QCAq1evYuLEibhx4wY6dOiAwYMH48CBA+jQoYP+df/+978hl8sRFRWFiooKRERE4LPPPjPPmyYiIqJGkwmCIIhdREuk0Wjg6OiI4uLiVjs9tHr1sUa/Jjo6yASVEBFRa9KU36Gib81PRERE9CAMLERERCR5DCxEREQkeQwsREREJHkMLERERCR5DCxEREQkeQwsREREJHkMLERERCR5DCxEREQkeQwsREREJHkMLERERCR5DCxEREQkeQwsREREJHkMLERERCR5DCxEREQkeQwsREREJHkMLERERCR5DCxEREQkeQwsREREJHkMLERERCR5DCxEREQkeQwsREREJHkMLERERCR5DCxEREQkeQwsREREJHkMLERERCR5DCxEREQkeQwsREREJHkMLERERCR5DCxEREQkeQwsREREJHkMLERERCR5DCxEREQkeQwsREREJHkMLERERCR5DCxEREQkeQwsREREJHkMLERERCR5DCxEREQkeQwsREREJHkMLERERCR5DCxEREQkeQwsREREJHkMLERERCR5ogeWFStWwNfXF9bW1ggNDUVaWto92548eRJRUVHw9fWFTCZDQkJCnTbx8fEYOHAg7O3t4ebmhsjISGRmZhq0eeyxxyCTyQweL7zwgrHfGhERERmJqIFl8+bNiI2NxcKFC5GRkYGgoCBEREQgPz+/3valpaXo3LkzFi1aBJVKVW+bPXv2YNasWThw4ACSk5NRVVWF4cOHo6SkxKDdjBkzkJubq3989NFHRn9/REREZByWYp582bJlmDFjBqZNmwYAWLVqFXbs2IG1a9fijTfeqNN+4MCBGDhwIADU+zwAJCUlGXy/fv16uLm5IT09HY8++qj+uK2t7T1DDxEREUmLaCMslZWVSE9PR3h4+J1i5HKEh4cjNTXVaOcpLi4GADg7Oxsc37hxI1xdXdG7d2/ExcWhtLT0vv1UVFRAo9EYPIiIiMg8RBthKSgoQE1NDdzd3Q2Ou7u748yZM0Y5h1arxZw5c/Dwww+jd+/e+uPPPPMMOnXqBE9PTxw/fhzz5s1DZmYmtm7des++4uPj8e677xqlLiIiImocUaeETG3WrFk4ceIE9u3bZ3A8Ojpa/3WfPn3g4eGBYcOG4cKFC+jSpUu9fcXFxSE2Nlb/vUajgbe3t2kKJyIiIgOiBRZXV1dYWFggLy/P4HheXp5R1pbExMRg+/bt2Lt3Lzp27HjftqGhoQCA8+fP3zOwKJVKKJXKZtdFREREjSfaGhaFQoH+/fsjJSVFf0yr1SIlJQVhYWFN7lcQBMTExOC7777Drl274Ofn98DXHD16FADg4eHR5PMSERGR6Yg6JRQbG4spU6ZgwIABCAkJQUJCAkpKSvRXDU2ePBleXl6Ij48HULtQ99SpU/qvr127hqNHj8LOzg5du3YFUDsNlJiYiO+//x729vZQq9UAAEdHR9jY2ODChQtITEzEqFGj4OLiguPHj2Pu3Ll49NFHERgYKMJPgYiIiB5E1MAyfvx4XL9+HQsWLIBarUZwcDCSkpL0C3GzsrIgl98ZBMrJyUHfvn313y9ZsgRLlizBkCFDsHv3bgDAypUrAdRuDne3devWYerUqVAoFPj111/14cjb2xtRUVF4++23TftmiYiIqMlkgiAIYhfREmk0Gjg6OqK4uBgODg5il2MSq1cfa/RroqODTFAJERG1Jk35HSr61vxERERED8LAQkRERJLHwEJERESSx8BCREREksfAQkRERJLHwEJERESSx8BCREREksfAQkRERJLHwEJERESSx8BCREREksfAQkRERJLHwEJERESS16TAcvHiRWPXQURERHRPTQosXbt2xdChQ/Hll1+ivLzc2DURERERGWhSYMnIyEBgYCBiY2OhUqkwc+ZMpKWlGbs2IiIiIgBNDCzBwcFYvnw5cnJysHbtWuTm5mLw4MHo3bs3li1bhuvXrxu7TiIiImrDmrXo1tLSEmPGjMGWLVvw4Ycf4vz583j11Vfh7e2NyZMnIzc311h1EhERURvWrMBy+PBh/OMf/4CHhweWLVuGV199FRcuXEBycjJycnIwevRoY9VJREREbZhlU160bNkyrFu3DpmZmRg1ahQ2bNiAUaNGQS6vzT9+fn5Yv349fH19jVkrERERtVFNCiwrV67Ec889h6lTp8LDw6PeNm5ubvj888+bVRwRERER0MTAkpycDB8fH/2Iio4gCMjOzoaPjw8UCgWmTJlilCKJiIiobWvSGpYuXbqgoKCgzvHCwkL4+fk1uygiIiKiuzUpsAiCUO/x27dvw9raulkFEREREf1Zo6aEYmNjAQAymQwLFiyAra2t/rmamhocPHgQwcHBRi2QiIiIqFGB5ciRIwBqR1j++OMPKBQK/XMKhQJBQUF49dVXjVshERERtXmNCiy//fYbAGDatGlYvnw5HBwcTFIUERER0d2adJXQunXrjF0HERER0T01OLCMGTMG69evh4ODA8aMGXPftlu3bm12YUREREQ6DQ4sjo6OkMlk+q+JiIiIzKXBgeXuaSBOCREREZE5NWkflrKyMpSWluq/v3LlChISEvDLL78YrTAiIiIinSYFltGjR2PDhg0AgKKiIoSEhGDp0qUYPXo0Vq5cadQCiYiIiJoUWDIyMvDII48AAL755huoVCpcuXIFGzZswMcff2zUAomIiIiaFFhKS0thb28PAPjll18wZswYyOVyPPTQQ7hy5YpRCyQiIiJqUmDp2rUrtm3bhuzsbOzcuRPDhw8HAOTn53MzOSIiIjK6JgWWBQsW4NVXX4Wvry9CQ0MRFhYGoHa0pW/fvkYtkIiIiKhJO92OHTsWgwcPRm5uLoKCgvTHhw0bhqefftpoxREREREBTQwsAKBSqaBSqQyOhYSENLsgIiIioj9rUmApKSnBokWLkJKSgvz8fGi1WoPnL168aJTiiIiIiIAmBpbnn38ee/bswbPPPgsPDw/9lv1EREREptCkwPLzzz9jx44dePjhh41dDxEREVEdTbpKqH379nB2djZ2LURERET1alJgee+997BgwQKD+wkRERERmUqTpoSWLl2KCxcuwN3dHb6+vrCysjJ4PiMjwyjFEREREQFNHGGJjIzEK6+8gldffRVjx47F6NGjDR6NsWLFCvj6+sLa2hqhoaFIS0u7Z9uTJ08iKioKvr6+kMlkSEhIaFKf5eXlmDVrFlxcXGBnZ4eoqCjk5eU1qm4iIiIynyaNsCxcuNAoJ9+8eTNiY2OxatUqhIaGIiEhAREREcjMzISbm1ud9qWlpejcuTPGjRuHuXPnNrnPuXPnYseOHdiyZQscHR0RExODMWPG4PfffzfK+yIiIiLjkgmCIDTlhUVFRfjmm29w4cIFvPbaa3B2dkZGRgbc3d3h5eXVoD5CQ0MxcOBAfPrppwAArVYLb29vzJ49G2+88cZ9X+vr64s5c+Zgzpw5jeqzuLgYHTp0QGJiIsaOHQsAOHPmDAICApCamoqHHnqoQbVrNBo4OjqiuLi41d4/afXqY41+TXR00IMbERFRm9aU36FNmhI6fvw4unXrhg8//BBLlixBUVERAGDr1q2Ii4trUB+VlZVIT09HeHj4nWLkcoSHhyM1NbUpZTWoz/T0dFRVVRm06dGjB3x8fO573oqKCmg0GoMHERERmUeTAktsbCymTp2Kc+fOwdraWn981KhR2Lt3b4P6KCgoQE1NDdzd3Q2Ou7u7Q61WN6WsBvWpVquhUCjg5OTUqPPGx8fD0dFR//D29m5SjURERNR4TQoshw4dwsyZM+sc9/LyanLYkLq4uDgUFxfrH9nZ2WKXRERE1GY0adGtUqmsd0rk7Nmz6NChQ4P6cHV1hYWFRZ2rc/Ly8urcVLGhGtKnSqVCZWUlioqKDEZZHnRepVIJpVLZpLqIiIioeZo0wvLUU0/hn//8J6qqqgAAMpkMWVlZmDdvHqKiohrUh0KhQP/+/ZGSkqI/ptVqkZKSgrCwsKaU1aA++/fvDysrK4M2mZmZyMrKavJ5iYiIyLSavHHc2LFj0aFDB5SVlWHIkCFQq9UICwvDBx980OB+YmNjMWXKFAwYMAAhISFISEhASUkJpk2bBgCYPHkyvLy8EB8fD6B2Ue2pU6f0X1+7dg1Hjx6FnZ0dunbt2qA+HR0dMX36dMTGxsLZ2RkODg6YPXs2wsLCGnyFEBEREZlXkwKLo6MjkpOT8fvvv+PYsWO4ffs2+vXrZ3DlTUOMHz8e169fx4IFC6BWqxEcHIykpCT9otmsrCzI5XcGgXJyctC3b1/990uWLMGSJUswZMgQ7N69u0F9AsC///1vyOVyREVFoaKiAhEREfjss8+a8qMgIiIiM2j0PixarRbr16/H1q1bcfnyZchkMvj5+WHs2LF49tlnIZPJTFWrpHAflvpxHxYiInoQk+/DIggCnnrqKTz//PO4du0a+vTpg169euHKlSuYOnUqnn766SYVTkRERHQ/jZoSWr9+Pfbu3YuUlBQMHTrU4Lldu3YhMjISGzZswOTJk41aJBEREbVtjRph+eqrr/Dmm2/WCSsA8Pjjj+ONN97Axo0bjVYcEREREdDIwHL8+HGMGDHins+PHDkSx441ft0DERER0f00KrAUFhbW2fb+bu7u7rh582aziyIiIiK6W6MCS01NDSwt773sxcLCAtXV1c0uioiIiOhujVp0KwgCpk6des8t6isqKoxSFBEREdHdGhVYpkyZ8sA2vEKIiIiIjK1RgWXdunWmqoOIiIjonpp080MiIiIic2JgISIiIsljYKF7+v33a9i796rYZRARETXtbs3U+n3++R/YsOEUAMDBQYHgYDeRKyIioraMIyxUx++/X8OLLybrv//qqzMoK+P+OkREJB4GFjJw+3YloqK+R1WVFsHBHdChgw2Kiiqwbdt5sUsjIqI2jIGFDOzenY28vFJ07GiPadN6Y9KkAADAnj3ZuH69VOTqiIiorWJgIQO7d2cDAEaM8IW1tSUCAlzg7+8EQQDOnCkUuToiImqrGFjIgC6wDB3qoz/WrVt7AMD580VilERERMTAQncUFZXjyJF8AMBjj3nrj3ftWhtYzp3jnbiJiEgcDCyk99//XoNWK6Bbt/bw9LTTH+/c2RFyuQw3bpSjsLBMxAqJiKitYmAhvd9+ywJgOLoCANbWlvDxsQcAnDtXZO6yiIiIGFjojvrWr+j4++vWsXBaiIiIzI+BhQAAN2+W4+jR2vUrQ4Z0rPN8165OADjCQkRE4mBgIQDAwYO5EITakRQPD7s6z+sCS25uCW7frjRzdURE1NYxsBAA4PTpGwCAoKAO9T5vZ6eAh0c7AMCFC8Vmq4uIiAhgYKH/0W0K16OH8z3beHvXLrzNzb1tlpqIiIh0GFgIQMMCi0pVO8KiVpeYpSYiIiIdBhYC0NjAwnsKERGReTGwEAoLy5CfXxtCune/d2DRrWHJzS2BIAhmqY2IiAhgYCEAmZm1e6t07GgPOzvFPdt16GALmQwoL6+GRsMrhYiIyHwYWAhnztReIXS/6SAAsLKSo0MHWwC1oyxERETmwsBCDVq/oqNSMbAQEZH5MbBQIwNL7aZyvFKIiIjMiYGF9IHlfgtudXQjLAwsRERkTgwsbVxlZQ0uXCgC0LARFt2VQgwsRERkTgwsbdyFC0WoqRHQrp0VvLzq3kPoz3R7sRQVVaCsrNrU5REREQFgYGnz7l6/IpPJHtje1tYKDg61lz7n5XGUhYiIzIOBpY3TTQf5+7dv8Gt0oyy8UoiIiMyFgaWNu3JFAwDo1Mmhwa9xd69deHv9OrfoJyIi82BgaeOaElhcXW0AADdulJukJiIioj9jYGnjsrIaH1hcXGoDS0FBmUlqIiIi+jMGljaueSMsDCxERGQeDCxtmEZTgaKiCgCAj0/jR1iKiipQVaU1SW1ERER3k0RgWbFiBXx9fWFtbY3Q0FCkpaXdt/2WLVvQo0cPWFtbo0+fPvjpp58MnpfJZPU+Fi9erG/j6+tb5/lFixaZ5P1JlW50pX17a9jb3/suzX9mb28FhUIOQQAKCznKQkREpid6YNm8eTNiY2OxcOFCZGRkICgoCBEREcjPz6+3/f79+zFx4kRMnz4dR44cQWRkJCIjI3HixAl9m9zcXIPH2rVrIZPJEBUVZdDXP//5T4N2s2fPNul7lZqmTAcBtYFQN8rChbdERGQOogeWZcuWYcaMGZg2bRp69uyJVatWwdbWFmvXrq23/fLlyzFixAi89tprCAgIwHvvvYd+/frh008/1bdRqVQGj++//x5Dhw5F586dDfqyt7c3aNeuXTuTvlepaWpgAbjwloiIzEvUwFJZWYn09HSEh4frj8nlcoSHhyM1NbXe16Smphq0B4CIiIh7ts/Ly8OOHTswffr0Os8tWrQILi4u6Nu3LxYvXozq6ra11XxTrhDS4cJbIiIyJ0sxT15QUICamhq4u7sbHHd3d8eZM2fqfY1ara63vVqtrrf9F198AXt7e4wZM8bg+EsvvYR+/frB2dkZ+/fvR1xcHHJzc7Fs2bJ6+6moqEBFRYX+e41G88D3J3XNGWFxdbUGwMBCRETmIWpgMYe1a9di0qRJsLa2NjgeGxur/zowMBAKhQIzZ85EfHw8lEplnX7i4+Px7rvvmrxec9IFFh8f+0a/9s6UENewEBGR6Yk6JeTq6goLCwvk5eUZHM/Ly4NKpar3NSqVqsHt//vf/yIzMxPPP//8A2sJDQ1FdXU1Ll++XO/zcXFxKC4u1j+ys7Mf2KfUNW+EhVNCRERkPqIGFoVCgf79+yMlJUV/TKvVIiUlBWFhYfW+JiwszKA9ACQnJ9fb/vPPP0f//v0RFBT0wFqOHj0KuVwONze3ep9XKpVwcHAweLRkFRXV+psXNmfRrUZTicrKGqPWRkRE9GeiTwnFxsZiypQpGDBgAEJCQpCQkICSkhJMmzYNADB58mR4eXkhPj4eAPDyyy9jyJAhWLp0KZ544gls2rQJhw8fxurVqw361Wg02LJlC5YuXVrnnKmpqTh48CCGDh0Ke3t7pKamYu7cufj73/+O9u0bftfiliw7+xYAwMbGEh062Db69ba2lrC2tkR5eTUKCsrg6Wln7BKJiIj0RA8s48ePx/Xr17FgwQKo1WoEBwcjKSlJv7A2KysLcvmdgaBBgwYhMTERb7/9Nt588034+/tj27Zt6N27t0G/mzZtgiAImDhxYp1zKpVKbNq0Ce+88w4qKirg5+eHuXPnGqxrae3urF9xgEwma/TrZTIZXF2tcfXqbdy4wcBCRESmJRMEQRC7iJZIo9HA0dERxcXFLXJ6aN26P/DcczsxfLgvdu4cW2+b1auP3bePzz47imPHrmPChB4YOtQbABAd/eDpNyIiatua8jtU9I3jSBzNuUJIhwtviYjIXESfEiJx5OTULrjt2LHpgcXZufZS8cJCXtrcXA8azboXjmgRUVvBEZY2KifnNgA0a+1J+/a1geXmTQYWIiIyLQaWNupOYGn6/ZM4wkJERObCwNJGGWOERRdYiosrUFOjNUpdRERE9WFgaYOqqmqQn18KoHmBxd5eAQsLGQShNrQQERGZCgNLG5SXVwpBACwsZE3aNE5HLpfByan2vkuFhQwsRERkOgwsbZBuOsjDww5yeeM3jbvbnXUsvLSZiIhMh4GlDTLGgludO1cKcYSFiIhMh4GlDTLGglsdXilERETmwMDSBuk2jTNmYOFeLEREZEoMLG2QMUdYdFNCHGEhIiJTYmBpg0yzhoWBhYiITIeBpQ0yxRqW27erUFlZ0+z+iIiI6sPA0gYZcw2Lra0llEoLABxlISIi02FgaWMqKqpx40btninGCCwymYzrWIiIyOQYWNqY3Nza0RWFwkI/ndNc7dvX7nbLvViIiMhUGFjamLsX3MpkzdvlVoeXNhMRkakxsLQxxlxwq8MpISIiMjUGljbGmAtudbjbLRERmRoDSxtjyhEWTgkREZGpMLC0McbcNE6Ha1iIiMjUGFjaGFOMsOgCS3l5DYqLeaUQEREZHwNLG2OKwKJQWKBdOysAQFaWxmj9EhER6TCwtDGmWHQL3NmLJTv7llH7JSIiAhhY2pSSkkr9lI2xA4tuWoiBhYiITIGBpQ3R7XJra2sJBweFUfvWXSnEKSEiIjIFBpY25O71K8ba5VaHIyxERGRKDCxtiKnWrwAMLEREZFoMLG1Ibq7xrxDS0U0JMbAQEZEpMLC0IabYNE7n7sCi1QpG75+IiNo2BpY2xJRTQu3bKyGTAZWVNbh+vdTo/RMRUdvGwNKGmGLTOB0LCzkcHbkXCxERmQYDSxtiysACcB0LERGZDgNLG2L6wFI7wsK9WIiIyNgYWNqIW7cqcft2FQDAw8P4i24BjrAQEZHpMLC0EbrRFQcHBezsjLvLrQ73YiEiIlNhYGkjTD0dBDCwEBGR6TCwtBHmCCy8nxAREZkKA0sbYc4RltzcElRXa012HiIiansYWNqIO5vGmWbBLQDY2ytgZSWHVivoAxIREZExMLC0EeYYYZHLZejY0R4A17EQEZFxMbC0EeYILADg7V0bWLiOhYiIjImBpY0wV2Dx8XEAwBEWIiIyLkkElhUrVsDX1xfW1tYIDQ1FWlrafdtv2bIFPXr0gLW1Nfr06YOffvrJ4PmpU6dCJpMZPEaMGGHQprCwEJMmTYKDgwOcnJwwffp03L7dOtddCIJgljUswJ0RFgYWIiIyJtEDy+bNmxEbG4uFCxciIyMDQUFBiIiIQH5+fr3t9+/fj4kTJ2L69Ok4cuQIIiMjERkZiRMnThi0GzFiBHJzc/WPr776yuD5SZMm4eTJk0hOTsb27duxd+9eREdHm+x9iqmoqALl5dUAAA8P80wJMbAQEZExiR5Yli1bhhkzZmDatGno2bMnVq1aBVtbW6xdu7be9suXL8eIESPw2muvISAgAO+99x769euHTz/91KCdUqmESqXSP9q3b69/7vTp00hKSsJ//vMfhIaGYvDgwfjkk0+wadMm5OTkmPT9ikE3HeTsbA1ra0uTnsvHh2tYiIjI+EQNLJWVlUhPT0d4eLj+mFwuR3h4OFJTU+t9TWpqqkF7AIiIiKjTfvfu3XBzc0P37t3x4osv4saNGwZ9ODk5YcCAAfpj4eHhkMvlOHjwYL3nraiogEajMXi0FOZavwIA3t5cw0JERMYnamApKChATU0N3N3dDY67u7tDrVbX+xq1Wv3A9iNGjMCGDRuQkpKCDz/8EHv27MHIkSNRU1Oj78PNzc2gD0tLSzg7O9/zvPHx8XB0dNQ/vL29G/1+xWLewFI7wlJQUIaysiqTn4+IiNoG084PiGTChAn6r/v06YPAwEB06dIFu3fvxrBhw5rUZ1xcHGJjY/XfazSaFhNazLXgFgCcnJRo184KJSVVyM6+hW7dnE1+TiIiav1EHWFxdXWFhYUF8vLyDI7n5eVBpVLV+xqVStWo9gDQuXNnuLq64vz58/o+/ryot7q6GoWFhffsR6lUwsHBweDRUphzhEUmk+nXsXBaiIiIjEXUwKJQKNC/f3+kpKToj2m1WqSkpCAsLKze14SFhRm0B4Dk5OR7tgeAq1ev4saNG/Dw8ND3UVRUhPT0dH2bXbt2QavVIjQ0tDlvSZLMGVgArmMhIiLjE/0qodjYWKxZswZffPEFTp8+jRdffBElJSWYNm0aAGDy5MmIi4vTt3/55ZeRlJSEpUuX4syZM3jnnXdw+PBhxMTEAABu376N1157DQcOHMDly5eRkpKC0aNHo2vXroiIiAAABAQEYMSIEZgxYwbS0tLw+++/IyYmBhMmTICnp6f5fwgmZv7AwhEWIiIyLtHXsIwfPx7Xr1/HggULoFarERwcjKSkJP3C2qysLMjld3LVoEGDkJiYiLfffhtvvvkm/P39sW3bNvTu3RsAYGFhgePHj+OLL75AUVERPD09MXz4cLz33ntQKpX6fjZu3IiYmBgMGzYMcrkcUVFR+Pjjj8375s1ErMDCS5uJiMhYRA8sABATE6MfIfmz3bt31zk2btw4jBs3rt72NjY22Llz5wPP6ezsjMTExEbV2RJptQJyc8236BYA17AQEZHRiT4lRKZ140YZqqq0AACVyjyBhWtYiIjI2BhYWjnddJCbmy2srCzMcs67d7sVBMEs5yQiotaNgaWVM/f6FeDOHZtv367CjRtlZjsvERG1XgwsrZw5N43Tsba2hIdH7fkuX+bCWyIiaj4GllZOjBEWAPDzcwQAXLpUbNbzEhFR68TA0soxsBARUWvAwNLKMbAQEVFrwMDSyjGwEBFRa8DA0srpFt3qFsGai69vbWDholsiIjIGBpZWrLpaC7W6NrB07Ghv1nP7+dVe2nz5cjG0Wu7FQkREzcPA0oqp1SXQagVYWsrh5mZr1nN7ezvAwkKGiooafWgiIiJqKgaWVuzq1dqt8T0920Eul5n13JaWcv1NELmOhYiImouBpRW7dq12wa25p4N0dOtYGFiIiKi5GFhaMd0Ii1iBRXel0OXLDCxERNQ8DCytmC6weHmZ95JmHV7aTERExsLA0oqJPSXEwEJERMbCwNKKXb1aG1g4wkJERC0dA0srJvYaFl/f2r1YsrNvobpaK0oNRETUOjCwtFKCINw1JSTOCIuHhx2srS1RUyPgyhXueEtERE3HwNJKFRSUobKyBkBtcBCDXC6Dv78TAODs2UJRaiAiotaBgaWV0k0HubvbQqGwEK2Obt3aAwDOnr0pWg1ERNTyMbC0UrrpIC8vcdav6HTr5gwAOHeOgYWIiJqOgaWVurPgVpzpIJ07U0IMLERE1HQMLK2U2Huw6OhGWBhYiIioORhYWimxd7nV0a1hycrSoKysStRaiIio5WJgaaV0m8aJPcLi6moDJyclBAG4cKFI1FqIiKjlYmBppa5dk8YIi0wm45VCRETUbAwsrZRURlgArmMhIqLmY2BphYqLK3DrViUA8a8SAu5cKcRLm5uusrIG16+XoqaGtzggorbJUuwCyPguX6692aCLiw3atVOIXA1HWJrj8uVirF17Anl5pQCA9u2t8cILgfD1dRS5MiIi8+IISyuku29Pp04OIldSi2tYmubSpWIkJGTow4pMBty8WY7Fiw/j4MFckasjIjIvjrC0QrrAortbstj8/WsDS35+KYqKyuHkZC1yRdJ39eotJCSko7y8Bl27OmHmzEBYWcnx+ecn8McfBVi37gQ8PcWf7iMiMheOsLRCUhthsbdXwMOjHQAgM5OjLA8iCAI2bjyN8vIadOvWHi+91A8ODkrY2FjhH/8IRnBwBwgCsGnTGQiCIHa5RERmwcDSCkktsABAnz4dAAB//HFd5Eqk7+BBNS5eLIZSaYHnnusNpfLOzSvlchnGj+8OhUKO8+eLkJh4WsRKiYjMh4GlFZJmYHEFABw/zsByP+Xl1di69SwAYNQoP7RvX3f6zNnZBiNHdgYAvPrqHpSUVJq1RiIiMTCwtEJSDCyBgbUjLAws9/fLL1dQXFyJDh1sMGxYp3u2+8tfOsHFxRpqdQm++uqMGSskIhIHA0srU1pahfz82qtKpBhY/vijgOsu7qGioga7d2cDACIju8LK6t5/PK2s5HjsMW8AwGefHeXPlIhaPQaWViYrq3Z0xc7Oqt7pBLEEBDjDwkKGwsJy5OTcFrscSUpNzUFJSRVcXW3Qr5/7A9sPGuQJpdICR47kIy1NbYYKiYjEw8DSyty5pNkRMplM5GruUCot0b177QZynBaqS6sVkJJyBQAwbJgP5PIHf3Z2dgqMH98dALBy5VFTlkdEJDoGllZGiutXdHQLb//4o0DkSqTn+PHryM8vg62tJQYN8mzw6158MRhA7SXON26Umag6IiLxMbC0MlIOLFx4e2+7dtWuXXn00Y6wtm74fo6hoR4IDnZDRUUNtmzJNFV5RESiY2BpZRhYWp5z524iM7MQMhkwZEjHRr1WJpPhmWd6AAC+/pqBhYhaLwaWVkbKgUU3JXTmTCEqK2tErkY6/vOf4wCAXr1c4exs0+jXjxtXu45lz56rUKtLjFobEZFUMLC0MpcvSzew+Pg4wMFBgaoqLTIzC8UuRxIqK2uwbt0JAMAjj3g1qQ9fX0eEhKig1Qr6TeeIiFobSQSWFStWwNfXF9bW1ggNDUVaWtp922/ZsgU9evSAtbU1+vTpg59++kn/XFVVFebNm4c+ffqgXbt28PT0xOTJk5GTk2PQh6+vL2QymcFj0aJFJnl/5lJVVaO/ZFiKgUUmkyEoyA0AkJ6eJ3I10vD99+dx/XoZHB0V+hGopvjb32pHWTgtREStleiBZfPmzYiNjcXChQuRkZGBoKAgREREID8/v972+/fvx8SJEzF9+nQcOXIEkZGRiIyMxIkTtf9KLS0tRUZGBubPn4+MjAxs3boVmZmZeOqpp+r09c9//hO5ubn6x+zZs036Xk3t6tVb0GoFKJUWcHdvJ3Y59QoNVQGo3XOEgDVraqeDBg3ygoVF0/84jh3bDQCwd+9V5OZynxsian1EDyzLli3DjBkzMG3aNPTs2ROrVq2Cra0t1q5dW2/75cuXY8SIEXjttdcQEBCA9957D/369cOnn34KAHB0dERycjL+9re/oXv37njooYfw6aefIj09HVlZWQZ92dvbQ6VS6R/t2knzl3xDnTtXBADo3NmxQft4iGHQoNppDwYW4OLFIiQn1+69Mnhw06aDdDp1csRDD3lAEICtW88ZozwiIkkRNbBUVlYiPT0d4eHh+mNyuRzh4eFITU2t9zWpqakG7QEgIiLinu0BoLi4GDKZDE5OTgbHFy1aBBcXF/Tt2xeLFy9GdXX1PfuoqKiARqMxeEjNuXM3AQD+/u1FruTewsJq9xg5caIAxcUVIlcjrs8//wMAMHy4L1xdG7/Y9s/GjPEHAPz444Vm90VEJDUN3/DBBAoKClBTUwN3d8NtyN3d3XHmTP03dFOr1fW2V6vr35q8vLwc8+bNw8SJE+HgcGddx0svvYR+/frB2dkZ+/fvR1xcHHJzc7Fs2bJ6+4mPj8e7777bmLdndi0hsKhU7eDn54hLl4qRlpaLv/zFt0n9rF59rNGviY4OatK5TKGqqgZr19ZOY86Y0QeFheXN7vOpp7ri9df3YteuLGg0FXBwUDa7TyIiqRB9SsiUqqqq8Le//Q2CIGDlypUGz8XGxuKxxx5DYGAgXnjhBSxduhSffPIJKirq/1d/XFwciouL9Y/s7GxzvIVGOX++CADQtauTqHU8iG6UZf/+tjsttGPHRajVJXBzs8VTT3U1Sp/duzujW7f2qKrS4pdfLhulTyIiqRA1sLi6usLCwgJ5eYZXjOTl5UGlUtX7GpVK1aD2urBy5coVJCcnG4yu1Cc0NBTV1dW4fPlyvc8rlUo4ODgYPKSmJYywANBvPd+W17GsXl272Hbq1F5QKCyM1u9TT3UBAPzwA6eFiKh1ETWwKBQK9O/fHykpKfpjWq0WKSkpCAsLq/c1YWFhBu0BIDk52aC9LqycO3cOv/76K1xcXB5Yy9GjRyGXy+Hm5tbEdyOu6motLl4sBiD9wKIbYTlwIBdarSByNeaXlaVBUtIlAMDzzwcatW/daM2OHRdRXa01at9ERGISdQ0LUDs1M2XKFAwYMAAhISFISEhASUkJpk2bBgCYPHkyvLy8EB8fDwB4+eWXMWTIECxduhRPPPEENm3ahMOHD2P16tUAasPK2LFjkZGRge3bt6Ompka/vsXZ2RkKhQKpqak4ePAghg4dCnt7e6SmpmLu3Ln4+9//jvbtpf3L/l6uXNGguloLa2tLdOxoL3Y59xUY2AG2tpYoLq7A6dM30KtX0/cfaYnWrv0DggAMHept9HAZFuYJFxcb3LhRhv37r+HRR72N2j8RkVhEX8Myfvx4LFmyBAsWLEBwcDCOHj2KpKQk/cLarKws5Obm6tsPGjQIiYmJWL16NYKCgvDNN99g27Zt6N27NwDg2rVr+OGHH3D16lUEBwfDw8ND/9i/fz+A2umdTZs2YciQIejVqxc++OADzJ07Vx96WiLddFCXLtK9pFnH0lKOkBAPAMC+fddErsa8amq0+Pzz2sW2plgEbGkpxxNP+AHgtBARtS6ij7AAQExMDGJiYup9bvfu3XWOjRs3DuPGjau3va+vLwTh/tMM/fr1w4EDBxpdp5S1lPUrOo8/7oPdu7Px88+XMHOmdK7eMbWkpEu4evUWXFxs8PTTxlls+2dPPdUVGzacwvffn8fixUMgk0k7wBIRNYToIyxkHC0tsPz1r50BAMnJl1Fefu/9b1ob3WLbKVN6Qqk0zb8Xhg/3hUJhgfPni3jPJiJqNSQxwkLNpwssYl/S3ND9UQRBgJOTEkVFFdizJxsREX4mrkx8V64UY8eOiwCMv9j2bvb2Cgwd6o2dOy/jhx8uoEePBy86JyKSOo6wtBK6PVhaygiLTCZD7961i223b78ocjXmsXx5BmpqBAwb5oOAANOGCF7eTEStDQNLK1BVVYNLl1rGJc13CwysDSw7dlx84Lqjlq64uAL/+U/tVvyvvDLA5Od78snawLJ//zVcv15q8vMREZkap4RagcuXNaipEWBjYwlPTzuxy2mwHj1cYGkpx6VLxTh9+gZ69pTe5c3GugXAmjXHcetWJXr2dMGIEaaf/vL2dkDfvm44ciQfO3ZcxNSpvU1+zj9r6bdPICJp4QhLK3DmTO3CSn//9pK/pPluSqUFunevHRFqzVMXVVU1WL48A0Dt6Iq5rtrhtBARtSYMLK3AH39cBwD9mpCWpF+/2p2F16070WqnhdatO4GrV2/B3d0WkyYFmO28ul1vf/mlbV2JRUStEwNLK/DHHwUA7qwJaUkGDFDB3l6Bs2dv4rffpHdDyeYqL6/GP/+ZCgB4882HTHYpc3369nWDl5cdSkqq8NtvWWY7LxGRKTCwtALHj9eOsPTp00HkShrP2toSzz7bEwCwatVRcYsxgZUrj+Latdvw9rbHzJmmu5S5PjKZjNNCRNRqMLC0cBUV1frNwQIDW15gAaDf6fa7784jN/e2yNUYz61blfjXvw4CABYuHGTW0RUd3bTQDz9caLVTbkTUNjCwtHCnTxeipqZ2EzYvr5ZzhdDdAgM7YNAgT1RXa7FmzXGxyzGad975HQUFZfD3b48pU3qJUsPQod6ws7NCTs5tZGTkiVIDEZExMLC0cLoFt4GBHVr0PWNmzeoLAFi69DDU6hKRq2m+I0fykJBQe2XQ8uVDYWkpzh81pdISERG+ADgtREQtGwNLC6dbv9JSp4N0JkzogYEDVdBoKvHGG3vFLqdZamq0iI7+BVqtgPHju2PkyM6i1qObFtq69ZyodRARNQcDSwunu0KoT5+Wd4XQ3eRyGT79dBgA4IsvTmL//msiV9R0ixcfwuHDeXB0VOLf/x4qdjl48skusLKS48SJApw8WSB2OURETcLA0sK1lhEWAAgJ8cD06X0AAFOm/Iy8vJY3NXT27E28/fY+AMDSpY/Bw0P8dUXt21vrd9fdvPmMyNUQETUNA0sLVlBQitzc2l/qvXq17BEWnUWLHkGnTg44f74Iw4d/g5s3y8UuqcE0mgr85z/HUVMj4Nlne+K558y/Hf69TJjQAwDw1VdneLUQEbVIDCwtmG46qHNnR9jbK0SuxjhcXW3x66/j4O5ui+PHr+PRRzchNTVH7LIeqKysGp98cgTFxZXo1csFK1eGS2oR9FNPdYGNjSXOny/i1UJE1CIxsLRg6em1v3haw3TQ3bp2bY/k5HFwcbHBiRMFGDQoEWPHfo/160/gzJkbyM8vQXl5NW7dqsSNG2VQq0uQnX0LOTm3UVBQhupqrVnrrayswWefHUVW1i3Y2Vlh69bRaNdOWgHSzk6Bv/61dvHvpk2cFiKilod3a27B9u+vHXkIC/MUuRLj69OnA06dmoq4uP9i7doT+Pbbc/j224Zd5SKTAS4uNvDzc0DPnq7o08fVZCNQpaVVWLXqOM6evQlra0u8/HI/dOvmbJJzNdfEiQHYsuUsvvrqDOLjHxXtUmsioqZgYGmhBEHA77/XXknz8MNeIldjGm5u7fD55yMQE9MX3357DsnJl3HmTCE0mkp9GysrORQKC1haylBTI6CiogZVVVoUFJShoKAMhw7lwcJChqCgDujSxQmPP+5jtKmawsIyfPLJEeTklECptMCsWcHw8XEwSt+mMGqUH1xcbHDt2m38/PMlPPlkF7FLIiJqMAaWFurChSLk55dCobBA//7uYpdjUn37uqNvX3e8//5gALX7nKxceRSWlnLI5YbhQxAEaDSVUKtLcOZMIU6cKEBW1i1kZOQjPHwL+vZ1w7x5IYiK6tasEYaMjDx8+eVplJRUwclJiZiYvvD2tm/W+zQ1pdISU6f2wtKlh/H//t8xBhYialEYWFoo3XRQ//7usLZuWx+jhUXtqEp9ZDIZHB2VcHRUont3Z4we3RVXr97C3r1XceiQGkeO5GPChO3o3NkRr7wyANOm9YaNjVWDz11YWIbvvjuPtDQ1AMDHxx4vvhgMZ2dro7w3U4uODsTSpYfx88+XkJWlkfSIEBHR3TiJ3ULdmQ5qfetXjK1jR3s880wArlyJxjvvDIKLiw0uXizGrFkp6NRpNd57LxWXLxfft4+cnNvYvDkT8+fvR1qaGjIZMHKkH+bNC2kxYQUAunVzxtCh3tBqBfznP63nvk1E1Pq1rX+atyK6EZZBg1rn+hVTcHW1xcKFg/DqqwOwbt0JLF16GJcva7Bgwe9YsOB39O3rhpAQD/To4QxrawtUVmrxzTeZuHSpGFev3rmLdLdu7REV5Q9fX0cR303TzZwZhN9+y8Z//vMH3n477J6jVUREUsLA0gIVFZXrt1gfNIgjLI3Vrp0CMTH98MILwfj660ysWXMce/dexZEj+ThyJL/e18jlMgQGuuLRRzuiZ08XSe2x0lhPP+0PD492yM0twfr1JxAdHSR2SURED8TA0gKlpuZAEICuXZ3g7t5O7HJaLEtLOZ55JgDPPBOA69dLkZx8BSdPFuDs2ZuoqRFgaSmDRlOJTp0c4O/fHg4O0tpbpakUCgvMmxeCOXN+w7/+dRBTp/bmKAsRSR4DSwu0Z89VABxdMaYOHWzxzDMBdY6vXn1MhGpMLzo6EIsWpeHKFQ3+7/9O6e/hZCqCIKCwsByXL2tQVaWFhYUM7u626NjRvs6VXkRE9WFgaYF+/PECACAiwk/kSqilsrGxwuuvD0Rs7G588MEBPPtsT5OMshQXV2DPnqvYvz+n3vtC2dtboW9fN0RE+MHV1cbo5yei1oNXCbUw58/fxKlTN2BpKceoUQws1HQzZwbB3d0Wly4VY9Gig0btW6OpwJYtmXjzzf9ix46LuHmzHHK5DJ06OSAgwBlduzpBqbTArVtV2Lv3GubP/x1ffXUGt25VPrhzImqTOMLSwvzwQ+3oypAhHeHk1HIupyXpsbW1QkLC45g4cTvef/8AoqK6Nfuu34IgYPPmTMyZswt5eaUAam/O+fjjPggK6mAwilNdrcW5czexc+dlnD5diN27sxEc/AX+7/9G8eo3IqqDIywtzPffnwcAPPVUV5ErodZg/PjuePLJLqiq0mL69J2oqWn6jSPz80swduwPmDhxO/LySuHubovZs/ti3rwQDByoqjPlZGkpR0CAC+bM6Y85c/rBxcUaFy8W45FHNmHVqqPNfGdE1NowsLQgBQWl2LevdsO40aO5rTo1n0wmw2efhcPBQYGDB3Pxj3/8CkEQGt3Pli2Z6NVrPbZuPQdLSzneeWcQFiwIQ+/eDRuxCQhwwfz5D2HixB7QagW8+OKveP31PdBqG18LEbVOnBJqQX766RK0WgFBQR3QqVPL3LSMpKdjR3usXz8SY8f+gNWrj8PV1QYffPBIg16bna3B3Lm/6e+kHRjYAV98MRLBwW6NvsLKxsYKGzc+gZ49XTB//u9YvPgQLl0qxoYNIxt1+wRTacoVY9zjhsh4OMLSgiQmngYAjB7N6SAyrqef9seqVX8BAPzrXwcxZcpPKCqqe1WPTlFROd5/PxU9eqzFt9+eg4WFDPPnP4RDh/6O4GC3Jtchk8nw9tth+PLLUVAoLPDNN2fx+ONfIz+/pMl9ElHrwBGWFiIzsxA7d16GTAZMmdJL7HKoFZoxIxAaTQVee20PNmw4hZSULPzjH8EYPborVKp2qK7W4tAhNXbuvIwvvjipv6Ln4Ye9sGLFMAQFNT2o/NmkST3RsaM9nn76exw4kIuwsET89FMUund3Nto5iKhlYWBpIT799AgA4Mknu6BzZydxi6FW65VXBuKhhzwxderPOH++CG+9tQ9vvbWv3ra9erkgLi4UzzwTYJJbFQwZ4o39+ydi1KituHixGGFhidi2bTQefdTb6OciIunjlFALUFxcgfXrTwAAZs/uJ3I11No9/LAXjh2bgtWrh2PkSD+Dq3u6dHHC88/3wU8/jcEff0zFpEk9TXpfpR49XHDgwDMIDfXAzZvl+MtfvtFPjRJR28IRlhZg/foTuH27CgEBzhg2zEfscqiFa8zi0cjIrhg9uguefz4QglB7KbK5ubm1w65df8Ozz/6ErVvPYdKkHcjMLMSCBWGwsBDn31y3b1fiwoViXLt2Czk5JSgsLIdGU4GKihoIggALCznatbPCv/+dDjc3W6hU7eDr6wAfH3tYWd1/R2Eu1CWqHwOLxBUVlWPRojQAtaMrLfkuwdQyyWQy0YKBjq2tFbZseQqvv74HS5cexj//mYq9e69i48Yn4OlpZ/LzC4KAy5eLceRIPo4fL0BOzu0HvqaoqAIAcOZMof6YhYUM3bq1R58+HRAY6IoOHWxNVjNRa8PAInFvvbUPanUJunVrj2nTeotdDpFo5HIZlix5DEFBHfDii79i9+5s9Oy5DvHxjyA6OtDooaq6Wot9+67iu+/O47vvziE7+5bB8x4e7dCpkwM8PNqhQwdbODoqoFRaQi6Xobpai9LSKhQVVSAvrxQ5Obdx8WIxbt2qxOnThTh9uhBff50Jlaod+vZ1Q0iIyizBi6glY2CRsLS0XKxceRQAsGrVX2BtzY+L6NlneyEkxAOTJu1Aenoe/vGPX7FmzXHMnx+G0aO7NuvuzxUV1UhJycLWrefw/ffnUVBQpn9OqbRA796uCA7ugIAAF9jbKxrVtyAIyMsrxYkTBTh+vADnzt2EWl2Cn3++hJ9/vgQvLzuEhKgwfLgvfH25zxLRn/E3oETduFGGKVN+hiAAzz7bE0OHcu0KkU737s44eHASVq48irfe2ocjR/IxZsz36NatPSZNCsC4cd3Ro4fzA6dQa2q0yMjIQ0pKFlJSsrBv3zWUl1frn3d2tsZTT3XBmDHdcOVKcbPuaC2TyaBStYNK1Q7h4Z1QVlaFEydu4NAhNU6cKMC1a7f/N5pzHoMGeeKZZwIwblw3uLm1a/I5iVoTBhYJunWrEiNHfoszZwrh5WWHJUuGiF2SSTVlB1EiCws5YmL6YcKEHli+PAMff5yBs2dvYuHC/Vi4cD9cXW0wcKAKvr4O8PCwg5WVHIJQ+48BtboEmZm1UzMlJVUG/Xp52SEysivGjOmGRx/tqF9obOz/T21srDBwoAoDB6pQUlKFI0fykZamxtmzhdi/Pwf79+fg5Zd3ITy8EyZO7IGnn/aHg4PSqDUQtSQMLBJz9eotTJy4HYcOqeHiYoPk5HH8FxbRfWzdeg7e3vZ4772H7/qlfxMFBWX4+edLD3y9o6MSQ4d64/HHfTBsmA8CAlzMvri9XTsrDB7shcGDvfDXv3bB119nIjHxtH6jvp07L2PmzGT89a+dMXFiAIYN8+Hd2qnNkURgWbFiBRYvXgy1Wo2goCB88sknCAkJuWf7LVu2YP78+bh8+TL8/f3x4YcfYtSoUfrnBUHAwoULsWbNGhQVFeHhhx/GypUr4e/vr29TWFiI2bNn48cff4RcLkdUVBSWL18OOztxFr4JgoAvvzyF2bN3obi4Avb2CiQlRSEgwEWUeqjpOGIkDmtrS4SFeSIszBNVVVpkZ2tw9ept3LxZDo2mEoIgQBBqw8GwYT7o2rU9evVygb9/e9Gvgrqbp6fd/+5g3R/nz9/EV1+dQWLiaZw5U4hvvz2Hb789B5ms9r5Njz7aEY8+2hHBwW7w9XUU5bJzInMRPbBs3rwZsbGxWLVqFUJDQ5GQkICIiAhkZmbCza3uVt/79+/HxIkTER8fj7/+9a9ITExEZGQkMjIy0Lt37VU0H330ET7++GN88cUX8PPzw/z58xEREYFTp07B2rr2XyWTJk1Cbm4ukpOTUVVVhWnTpiE6OhqJiYlmff86P/54AZMn/wwACAlRYcOGUdyGnKiJrKzk6NzZ6Z67QreUvU66dm2P+fPD8PbbD+HYsev46qvT2LbtPM6evYljx67j2LHr+OST2l2wrazk6NLFCd27O6NTJwe4utroH05OSiiVFlAqLWFtbfG/r2u/132tUFjAykou+a0TdFdglZVVo7S0CuXlNfowqrvTuO6G41ZWctjYWP7vYQUbG0uGuhZMJjTlXvJGFBoaioEDB+LTTz8FAGi1Wnh7e2P27Nl444036rQfP348SkpKsH37dv2xhx56CMHBwVi1ahUEQYCnpydeeeUVvPrqqwCA4uJiuLu7Y/369ZgwYQJOnz6Nnj174tChQxgwYAAAICkpCaNGjcLVq1fh6en5wLo1Gg0cHR1RXFwMBweHZv8ctFoBI0d+i0cf7Yh580Ik8YeKIwWk05Rf8Oa6u7GUz9MUDalNrS7Bf/97Ff/971Xs23cNZ84Uoqys+oGvawhLSxksLeWwsJDDykoOC4va7y0t5fDwaAeFwsIg5OiCj0IhNwhBCoUccrlMHyQEofbvOd33Wm3to6ysWv8oLa2+6/sqg2O6kFJVpW3W+5PLZVAo5LCysoCzszVsbWvDTO1/LWFra/h17X8toVBYQC6XwcJC9r//yv/0ve4Y6oS+u7+933N/fv5+zzWmX2P04+Fhh7CwB/9ubKim/A4VdYSlsrIS6enpiIuL0x+Ty+UIDw9Hampqva9JTU1FbGyswbGIiAhs27YNAHDp0iWo1WqEh4frn3d0dERoaChSU1MxYcIEpKamwsnJSR9WACA8PBxyuRwHDx7E008/Xee8FRUVqKio0H9fXFwMoPaHbiybN/8FcrkMpaUP3pTKHMrKpFEHia8p/5835f8fc51n+fLfG/0ac2lMbV26WKNLly6YMmU0rl27hXPnbv7vculS3LhRhhs3ynHjRhmKi2t34a2srEF5eTUqK2tQUlKN6uq6v/yrq2sf9bl2raCpb8skbGxqR4xkMhkqKmr+98v1zi/cmhotqqq0Bu9TqwXKy2sft24Z7+/v1u6JJzojMfGvRutP92e9MWMmogaWgoIC1NTUwN3d3eC4u7s7zpw5U+9r1Gp1ve3VarX+ed2x+7X583STpaUlnJ2d9W3+LD4+Hu+++26d497evBEbtX5z5rSu87Q2bfXnVlZW+yDT27EDcDTB9kC3bt2CYwM7Fn0NS0sRFxdnMLKj1WpRWFgIFxfzX1FgDhqNBt7e3sjOzjbKlBc1Hz8TaeHnIT38TKTnXp+JIAi4detWg5Zg6IgaWFxdXWFhYYG8vDyD43l5eVCpVPW+RqVS3be97r95eXnw8PAwaBMcHKxvk5+fb9BHdXU1CgsL73lepVIJpdJwDwQnJ6f7v8FWwMHBgX/wJYafibTw85AefibSU99n0tCRFR1RV3YqFAr0798fKSkp+mNarRYpKSkICwur9zVhYWEG7QEgOTlZ397Pzw8qlcqgjUajwcGDB/VtwsLCUFRUhPT0dH2bXbt2QavVIjQ01Gjvj4iIiIxD9Cmh2NhYTJkyBQMGDEBISAgSEhJQUlKCadOmAQAmT54MLy8vxMfHAwBefvllDBkyBEuXLsUTTzyBTZs24fDhw1i9ejWA2hXOc+bMwfvvvw9/f3/9Zc2enp6IjIwEAAQEBGDEiBGYMWMGVq1ahaqqKsTExGDChAmNGp4iIiIi8xA9sIwfPx7Xr1/HggULoFarERwcjKSkJP2i2aysLMjldwaCBg0ahMTERLz99tt488034e/vj23btun3YAGA119/HSUlJYiOjkZRUREGDx6MpKQk/R4sALBx40bExMRg2LBh+o3jPv74Y/O9cYlTKpVYuHBhnWkwEg8/E2nh5yE9/Eykx5ifiej7sBARERE9iPi7kxERERE9AAMLERERSR4DCxEREUkeAwsRERFJHgML1WvFihXw9fWFtbU1QkNDkZaWJnZJbUJ8fDwGDhwIe3t7uLm5ITIyEpmZmQZtysvLMWvWLLi4uMDOzg5RUVF1NlMk01i0aJF+6wQdfh7md+3aNfz973+Hi4sLbGxs0KdPHxw+fFj/vCAIWLBgATw8PGBjY4Pw8HCcO3dOxIpbt5qaGsyfPx9+fn6wsbFBly5d8N577xncJ8gon4lA9CebNm0SFAqFsHbtWuHkyZPCjBkzBCcnJyEvL0/s0lq9iIgIYd26dcKJEyeEo0ePCqNGjRJ8fHyE27dv69u88MILgre3t5CSkiIcPnxYeOihh4RBgwaJWHXbkJaWJvj6+gqBgYHCyy+/rD/Oz8O8CgsLhU6dOglTp04VDh48KFy8eFHYuXOncP78eX2bRYsWCY6OjsK2bduEY8eOCU899ZTg5+cnlJWViVh56/XBBx8ILi4uwvbt24VLly4JW7ZsEezs7ITly5fr2xjjM2FgoTpCQkKEWbNm6b+vqakRPD09hfj4eBGrapvy8/MFAMKePXsEQRCEoqIiwcrKStiyZYu+zenTpwUAQmpqqlhltnq3bt0S/P39heTkZGHIkCH6wMLPw/zmzZsnDB48+J7Pa7VaQaVSCYsXL9YfKyoqEpRKpfDVV1+Zo8Q254knnhCee+45g2NjxowRJk2aJAiC8T4TTgmRgcrKSqSnpyM8PFx/TC6XIzw8HKmpqSJW1jYVFxcDAJydnQEA6enpqKqqMvh8evToAR8fH34+JjRr1iw88cQTBj93gJ+HGH744QcMGDAA48aNg5ubG/r27Ys1a9bon7906RLUarXBZ+Lo6IjQ0FB+JiYyaNAgpKSk4OzZswCAY8eOYd++fRg5ciQA430mou90S9JSUFCAmpoa/U7DOu7u7jhz5oxIVbVNWq0Wc+bMwcMPP6zfyVmtVkOhUNS58aa7uzvUarUIVbZ+mzZtQkZGBg4dOlTnOX4e5nfx4kWsXLkSsbGxePPNN3Ho0CG89NJLUCgUmDJliv7nXt/fYfxMTOONN96ARqNBjx49YGFhgZqaGnzwwQeYNGkSABjtM2FgIZKoWbNm4cSJE9i3b5/YpbRZ2dnZePnll5GcnGxwaw8Sj1arxYABA/Cvf/0LANC3b1+cOHECq1atwpQpU0Surm36+uuvsXHjRiQmJqJXr144evQo5syZA09PT6N+JpwSIgOurq6wsLCoc5VDXl4eVCqVSFW1PTExMdi+fTt+++03dOzYUX9cpVKhsrISRUVFBu35+ZhGeno68vPz0a9fP1haWsLS0hJ79uzBxx9/DEtLS7i7u/PzMDMPDw/07NnT4FhAQACysrIAQP9z599h5vPaa6/hjTfewIQJE9CnTx88++yzmDt3rv6mxcb6TBhYyIBCoUD//v2RkpKiP6bVapGSkoKwsDARK2sbBEFATEwMvvvuO+zatQt+fn4Gz/fv3x9WVlYGn09mZiaysrL4+ZjAsGHD8Mcff+Do0aP6x4ABAzBp0iT91/w8zOvhhx+uc6n/2bNn0alTJwCAn58fVCqVwWei0Whw8OBBfiYmUlpaanCTYgCwsLCAVqsFYMTPxChLhKlV2bRpk6BUKoX169cLp06dEqKjowUnJydBrVaLXVqr9+KLLwqOjo7C7t27hdzcXP2jtLRU3+aFF14QfHx8hF27dgmHDx8WwsLChLCwMBGrblvuvkpIEPh5mFtaWppgaWkpfPDBB8K5c+eEjRs3Cra2tsKXX36pb7No0SLByclJ+P7774Xjx48Lo0eP5mXNJjRlyhTBy8tLf1nz1q1bBVdXV+H111/XtzHGZ8LAQvX65JNPBB8fH0GhUAghISHCgQMHxC6pTQBQ72PdunX6NmVlZcI//vEPoX379oKtra3w9NNPC7m5ueIV3cb8ObDw8zC/H3/8Uejdu7egVCqFHj16CKtXrzZ4XqvVCvPnzxfc3d0FpVIpDBs2TMjMzBSp2tZPo9EIL7/8suDj4yNYW1sLnTt3Ft566y2hoqJC38YYn4lMEO7aio6IiIhIgriGhYiIiCSPgYWIiIgkj4GFiIiIJI+BhYiIiCSPgYWIiIgkj4GFiIiIJI+BhYiIiCSPgYWITOby5cuQyWQ4evSo2KUQUQvHwEJEJuPt7Y3c3Fz07t3bKP3pApDu4eLiguHDh+PIkSPN7vvuftu1awd/f39MnToV6enpRqiciJqLgYWITMbCwgIqlQqWlpZG7ffXX39Fbm4udu7cidu3b2PkyJF17pjcUJWVlfqv161bh9zcXJw8eRIrVqzA7du3ERoaig0bNhipciJqKgYWImo2rVaLjz76CF27doVSqYSPjw8++OCDeqeEfvjhB/j7+8Pa2hpDhw7FF198AZlMpg8cV65cwZNPPon27dujXbt26NWrF3766SeD87m4uEClUmHAgAFYsmQJ8vLycPDgQQDAvn378Mgjj8DGxgbe3t546aWXUFJSon+tr68v3nvvPUyePBkODg6Ijo7WP+fk5ASVSgVfX18MHz4c33zzDSZNmoSYmBjcvHkTAHDjxg1MnDgRXl5esLW1RZ8+ffDVV1/p+9iwYQNcXFxQUVFhUHNkZCSeffZZo/y8idoiBhYiara4uDgsWrQI8+fPx6lTp5CYmAh3d/c67S5duoSxY8ciMjISx44dw8yZM/HWW28ZtJk1axYqKiqwd+9e/PHHH/jwww9hZ2d3z3Pb2NgAqB0puXDhAkaMGIGoqCgcP34cmzdvxr59+xATE2PwmiVLliAoKAhHjhzB/Pnz7/ve5s6di1u3biE5ORkAUF5ejv79+2PHjh04ceIEoqOj8eyzzyItLQ0AMG7cONTU1OCHH37Q95Gfn48dO3bgueeeu++5iOg+jHvPRiJqazQajaBUKoU1a9bUee7SpUsCAOHIkSOCIAjCvHnzhN69exu0eeuttwQAws2bNwVBEIQ+ffoI77zzTr3n+nN/N2/eFJ5++mnBzs5OUKvVwvTp04Xo6GiD1/z3v/8V5HK5/jb2nTp1EiIjI+v0DUD47rvv6hwvKysTAAgffvjhPX8GTzzxhPDKK6/ov3/xxReFkSNH6r9funSp0LlzZ0Gr1d6zDyK6P+NOLBNRm3P69GlUVFRg2LBhD2ybmZmJgQMHGhwLCQkx+P6ll17Ciy++iF9++QXh4eGIiopCYGCgQZtBgwZBLpejpKQEnTt3xubNm+Hu7o5jx47h+PHj2Lhxo76tIAjQarW4dOkSAgICAAADBgxo8PsT/ndDe5lMBgCoqanBv/71L3z99de4du0aKisrUVFRAVtbW/1rZsyYgYEDB+LatWvw8vLC+vXrMXXqVH0fRNR4DCxE1Cy6KRljef755xEREYEdO3bgl19+QXx8PJYuXYrZs2fr22zevBk9e/aEi4sLnJyc9Mdv376NmTNn4qWXXqrTr4+Pj/7rdu3aNbie06dPAwD8/PwAAIsXL8by5cuRkJCAPn36oF27dpgzZ47B4t2+ffsiKCgIGzZswPDhw3Hy5Ens2LGjweckorq4hoWImsXf3x82NjZISUl5YNvu3bvj8OHDBscOHTpUp523tzdeeOEFbN26Fa+88grWrFlT5/kuXboYhBUA6NevH06dOoWuXbvWeSgUisa/OQAJCQlwcHBAeHg4AOD333/H6NGj8fe//x1BQUHo3Lkzzp49W+d1zz//PNavX49169YhPDwc3t7eTTo/EdViYCGiZrG2tsa8efPw+uuvY8OGDbhw4QIOHDiAzz//vE7bmTNn4syZM5g3bx7Onj2Lr7/+GuvXrwdwZ8plzpw52LlzJy5duoSMjAz89ttv+qmcB5k3bx7279+PmJgYHD16FOfOncP3339fZ9HtvRQVFUGtVuPKlStITk7G2LFjkZiYiJUrV+rDkb+/P5KTk7F//36cPn0aM2fORF5eXp2+nnnmGVy9ehVr1qzhYlsiI+CUEBE12/z582FpaYkFCxYgJycHHh4eeOGFF+q08/PzwzfffINXXnkFy5cvR1hYGN566y28+OKLUCqVAGrXiMyaNQtXr16Fg4MDRowYgX//+98NqiMwMBB79uzBW2+9hUceeQSCIKBLly4YP358g14/bdo0ALUhzMvLC4MHD0ZaWhr69eunb/P222/j4sWLiIiIgK2tLaKjoxEZGYni4mKDvhwdHREVFYUdO3YgMjKyQecnonuTCboVZUREIvjggw+watUqZGdni12K0Q0bNgy9evXCxx9/LHYpRC0eR1iIyKw+++wzDBw4EC4uLvj999+xePHiBk/ZtBQ3b97E7t27sXv3bnz22Wdil0PUKjCwEJFZnTt3Du+//z4KCwvh4+ODV155BXFxcWKXZVR9+/bFzZs38eGHH6J79+5il0PUKnBKiIiIiCSPVwkRERGR5DGwEBERkeQxsBAREZHkMbAQERGR5DGwEBERkeQxsBAREZHkMbAQERGR5DGwEBERkeQxsBAREZHk/X8RQH/JeCOg1wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "sn.distplot(data[\"cigsPerDay\"],color='darkBlue') "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8M-P5kjedkWf",
   "metadata": {
    "id": "8M-P5kjedkWf"
   },
   "source": [
    "## Those who smoke they got stroke or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "B2s69zgwZ0ft",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "id": "B2s69zgwZ0ft",
    "outputId": "02ee405c-751a-403f-8b8c-d989309055bc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='prevalentStroke'>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = ['#ffff00','#ff00ff']\n",
    "data.groupby(\"is_smoking\")[\"prevalentStroke\"].sum().plot.pie()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "V-zAVqhsdy4_",
   "metadata": {
    "id": "V-zAVqhsdy4_"
   },
   "source": [
    "##### therefore from above graph i conclude that somking is not correlated  with stroke or pevelent -stroke which means that if i they  dose not smoke  there is a chance to get stroke  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "cdT65QbcK4rU",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "cdT65QbcK4rU",
    "outputId": "9c749abf-860c-461e-c547-18786653349f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='is_smoking', ylabel='count'>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.countplot(data=data,x='is_smoking',hue=\"prevalentStroke\", palette ='RdYlBu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "hmViffzGK_vg",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 287
    },
    "id": "hmViffzGK_vg",
    "outputId": "bf9610a6-9263-4dea-e681-9d0791e6fc54"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjAAAAHPCAYAAABN3eA+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA6CklEQVR4nO3de1hVZf7//9dGBBTdoKYcCgWtTMxR0yLK0pIRzUzLDiaplcmkYJpl5afyNJVlpWaa5kymfYaOVk5ZmoiijaIiDpXHnDyRBugHYXtIQFm/P/q5vrMFTWzj5sbn47rWdbHv+15rvddeG/aLddjbYVmWJQAAAIP4eLsAAACAyiLAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAVWDevHlyOBzavXu33dalSxd16dLFazV5y+7du+VwODRv3jxvl2I0h8Oh8ePHe7sMoNogwABesn//fo0fP17Z2dneLgU1GK8z1FS+3i4AuFgsXbrU7fH+/fs1YcIERUZGql27dt4p6gJo1qyZfv31V9WuXdvbpRjt119/la9v5f9kXyyvM1x8OAIDXCB+fn7y8/Or8vUcPXq0ytdRGQ6HQwEBAapVq5a3S6mU48ePq6yszNtl2AICAs4rwAA1FQEGuED++xqY9PR0XXvttZKkhx56SA6Ho9x1IuvWrVP37t0VFBSkunXrqnPnzlq9erXbMsePHy+Hw6EtW7aof//+atCggTp16iRJ+v777/Xggw+qefPmCggIUGhoqB5++GH93//9X7na9u3bp8GDBys8PFz+/v6KiorS0KFDVVJSop07d8rhcGjq1Knl5luzZo0cDoc++OCDM253RdfAPPjgg6pXr5727dunPn36qF69emrcuLGefPJJnTx58pyez7feekutW7eWv7+/wsPDlZSUpMLCQrcxkZGRevDBB8vNe/r1SOnp6XI4HPrwww/13HPP6dJLL1XdunXlcrnOuP4PP/xQHTp0UP369eV0OtWmTRu98cYbbmN27type+65Rw0bNlTdunV1/fXX66uvviq3rOPHj2v8+PG68sorFRAQoLCwMN1111366aef7DEVXQOzb98+PfzwwwoJCZG/v79at26tuXPnum3XmV5n48aNU+3atXXgwIFy9SQmJio4OFjHjx8/4/YD3kacB7ygVatWmjhxosaOHavExETddNNNkqQbbrhBkrR8+XL16NFDHTp00Lhx4+Tj46N3331Xt956q7799ltdd911bsu75557dMUVV+ill16SZVmSpNTUVO3cuVMPPfSQQkNDtXnzZs2ZM0ebN2/W2rVr5XA4JP12iuG6665TYWGhEhMTddVVV2nfvn1asGCBjh07pubNm+vGG29USkqKHn/8cbf1pqSkqH79+urdu3eln4OTJ08qPj5eMTExeu2117Rs2TK9/vrratGihYYOHXrWecePH68JEyYoLi5OQ4cO1fbt2zVr1ixlZmZq9erV53266q9//av8/Pz05JNPqri4+IxHzFJTU3X//fera9eueuWVVyRJW7du1erVqzVixAhJUl5enm644QYdO3ZMjz32mBo1aqT58+frjjvu0IIFC3TnnXfaz8Ptt9+utLQ09evXTyNGjNDhw4eVmpqqTZs2qUWLFhXWkJeXp+uvv14Oh0PJyclq3LixFi9erMGDB8vlcmnkyJFnfZ116tRJEydO1EcffaTk5GR7uSUlJVqwYIH69u2rgICA83oegQvCAuBx7777riXJ2rVrl93WuXNnq3PnzvbjzMxMS5L17rvvus1bVlZmXXHFFVZ8fLxVVlZmtx87dsyKioqy/vznP9tt48aNsyRZ999/f7kajh07Vq7tgw8+sCRZq1atstsGDhxo+fj4WJmZmeXGn1r/22+/bUmytm7daveVlJRYl1xyiTVo0KAzPg+WZVm7du0qt52DBg2yJFkTJ050G9u+fXurQ4cOZ11efn6+5efnZ3Xr1s06efKk3T5jxgxLkjV37ly7rVmzZhXWd/q+WLFihSXJat68eYXP2+lGjBhhOZ1O68SJE2ccM3LkSEuS9e2339pthw8ftqKioqzIyEi79rlz51qSrClTppRbxn/vf0nWuHHj7MeDBw+2wsLCrIMHD7rN069fPysoKMjejjO9zizLsmJjY62YmBi3ts8++8ySZK1YseKM2wZUB5xCAqqZ7Oxs7dixQ/3799f//d//6eDBgzp48KCOHj2qrl27atWqVeWuzXj00UfLLadOnTr2z8ePH9fBgwd1/fXXS5I2btwoSSorK9PChQvVq1cvdezYsdwyTh2luffeexUQEKCUlBS775tvvtHBgwf1wAMPnPe2nl73TTfdpJ07d551nmXLlqmkpEQjR46Uj8//+xM2ZMgQOZ3OCk/RnKtBgwa5PW9nEhwcrKNHjyo1NfWMY77++mtdd9119ik9SapXr54SExO1e/dubdmyRZL06aef6pJLLtHw4cPLLePU8386y7L06aefqlevXrIsy36NHDx4UPHx8SoqKrL38dkMHDhQ69atcztVlZKSooiICHXu3Pl35we8iQADVDM7duyQ9NubaePGjd2mv//97youLlZRUZHbPFFRUeWWU1BQoBEjRigkJER16tRR48aN7XGn5j9w4IBcLpeuvvrqs9YUHBysXr166f3337fbUlJSdOmll+rWW289r+0MCAhQ48aN3doaNGigQ4cOnXW+PXv2SJJatmzp1u7n56fmzZvb/eejouexIsOGDdOVV16pHj166LLLLtPDDz+sJUuWlKvz9Bql304fnuqXpJ9++kktW7as1AW6Bw4cUGFhoebMmVPuNfLQQw9JkvLz8393Offdd5/8/f3tYFpUVKRFixYpISHhjOEJqC64BgaoZk4dXXn11VfPeNtrvXr13B5XdNTg3nvv1Zo1azR69Gi1a9dO9erVU1lZmbp3735ed9cMHDhQn3zyidasWaM2bdroiy++0LBhw9yOglTGhbgr6UxvwidPnqxw/edy9EWSmjRpouzsbH3zzTdavHixFi9erHfffVcDBw7U/Pnz/1DN5+LU/nvggQc0aNCgCsf86U9/+t3lNGjQQLfffrtSUlI0duxYLViwQMXFxX/oqBpwoRBgAC8505vrqYs2nU6n4uLizmvZhw4dUlpamiZMmKCxY8fa7aeO7pzSuHFjOZ1Obdq06XeX2b17dzVu3FgpKSmKiYnRsWPHNGDAgPOq749o1qyZJGn79u1q3ry53V5SUqJdu3a5PWcNGjQod2eS9NvRj/+e93z4+fmpV69e6tWrl8rKyjRs2DC9/fbbev7553X55ZerWbNm2r59e7n5tm3b5rYdLVq00Lp161RaWnrOFx83btxY9evX18mTJ3/3NfJ7R1IGDhyo3r17KzMzUykpKWrfvr1at259TnUA3sQpJMBLAgMDJancG2yHDh3UokULvfbaazpy5Ei5+Sq67fV0p44uWP//HUmnTJs2ze2xj4+P+vTpoy+//FIbNmwot5z/nt/X11f333+/Pv74Y82bN09t2rQ5p//yPS0uLk5+fn6aPn26W33vvPOOioqK1LNnT7utRYsWWrt2rUpKSuy2RYsWKScn5w/VcPqt6D4+PvZzUVxcLEm67bbbtH79emVkZNjjjh49qjlz5igyMlLR0dGSpL59++rgwYOaMWNGufWcvv9OqVWrlvr27atPP/20wvD536+RM73OTunRo4cuueQSvfLKK1q5ciVHX2AMjsAAXtKiRQsFBwdr9uzZql+/vgIDAxUTE6OoqCj9/e9/V48ePdS6dWs99NBDuvTSS7Vv3z6tWLFCTqdTX3755VmX7XQ6dfPNN2vy5MkqLS3VpZdeqqVLl2rXrl3lxr700ktaunSpOnfurMTERLVq1Uq//PKLPvnkE/3rX/9ScHCwPXbgwIGaPn26VqxYYd8+fKE1btxYY8aM0YQJE9S9e3fdcccd2r59u9566y1de+21bm/AjzzyiBYsWKDu3bvr3nvv1U8//aR//OMfZ7w1+Vw98sgjKigo0K233qrLLrtMe/bs0Ztvvql27drZ17g888wz+uCDD9SjRw899thjatiwoebPn69du3bp008/tU+9DRw4UO+9955GjRql9evX66abbtLRo0e1bNkyDRs27Iy3qL/88stasWKFYmJiNGTIEEVHR6ugoEAbN27UsmXLVFBQIOnsrzNJql27tvr166cZM2aoVq1auv/++//QcwNcMN68BQqoqc7lNmrLsqx//vOfVnR0tOXr61vuVtd///vf1l133WU1atTI8vf3t5o1a2bde++9Vlpamj3m1G3UBw4cKFfDzz//bN15551WcHCwFRQUZN1zzz3W/v37y92Oa1mWtWfPHmvgwIFW48aNLX9/f6t58+ZWUlKSVVxcXG65rVu3tnx8fKyff/75nJ6LM91GHRgYWG7sqe05FzNmzLCuuuoqq3bt2lZISIg1dOhQ69ChQ+XGvf7669all15q+fv7WzfeeKO1YcOGM95G/cknn5zTuhcsWGB169bNatKkieXn52c1bdrU+stf/mL98ssvbuN++ukn6+6777aCg4OtgIAA67rrrrMWLVpUbnnHjh2znn32WSsqKsqqXbu2FRoaat19993WTz/9ZI+paL/l5eVZSUlJVkREhD1f165drTlz5riNO9vrzLIsa/369ZYkq1u3bue0/UB14LCsMxyjBIAKtG/fXg0bNlRaWpq3S4GHfPfdd2rXrp3ee+89r1zXBJwProEBcM42bNig7OxsDRw40NulwIP+9re/qV69errrrru8XQpwzrgGBsDv2rRpk7KysvT6668rLCxM9913n7dLggd8+eWX2rJli+bMmaPk5GT7gl/ABAQYAL9rwYIFmjhxolq2bKkPPviA78ipIYYPH668vDzddtttmjBhgrfLASqFa2AAAIBxuAYGAAAYhwADAACMU2OvgSkrK9P+/ftVv359vpQMAABDWJalw4cPKzw8/KzftVZjA8z+/fsVERHh7TIAAMB5yMnJ0WWXXXbG/hobYOrXry/ptyfA6XR6uRoAAHAuXC6XIiIi7PfxM6mxAebUaSOn00mAAQDAML93+QcX8QIAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACM4+vtAmqqdEe6t0vwii5WF2+XAAC4CHAEBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxKh1gVq1apV69eik8PFwOh0MLFy4849hHH31UDodD06ZNc2svKChQQkKCnE6ngoODNXjwYB05csRtzPfff6+bbrpJAQEBioiI0OTJkytbKgAAqKEqHWCOHj2qtm3baubMmWcd9/nnn2vt2rUKDw8v15eQkKDNmzcrNTVVixYt0qpVq5SYmGj3u1wudevWTc2aNVNWVpZeffVVjR8/XnPmzKlsuQAAoAbyrewMPXr0UI8ePc46Zt++fRo+fLi++eYb9ezZ061v69atWrJkiTIzM9WxY0dJ0ptvvqnbbrtNr732msLDw5WSkqKSkhLNnTtXfn5+at26tbKzszVlyhS3oAMAAC5OHr8GpqysTAMGDNDo0aPVunXrcv0ZGRkKDg62w4skxcXFycfHR+vWrbPH3HzzzfLz87PHxMfHa/v27Tp06FCF6y0uLpbL5XKbAABAzeTxAPPKK6/I19dXjz32WIX9ubm5atKkiVubr6+vGjZsqNzcXHtMSEiI25hTj0+NOd2kSZMUFBRkTxEREX90UwAAQDXl0QCTlZWlN954Q/PmzZPD4fDkon/XmDFjVFRUZE85OTkXdP0AAODC8WiA+fbbb5Wfn6+mTZvK19dXvr6+2rNnj5544glFRkZKkkJDQ5Wfn+8234kTJ1RQUKDQ0FB7TF5entuYU49PjTmdv7+/nE6n2wQAAGomjwaYAQMG6Pvvv1d2drY9hYeHa/To0frmm28kSbGxsSosLFRWVpY93/Lly1VWVqaYmBh7zKpVq1RaWmqPSU1NVcuWLdWgQQNPlgwAAAxU6buQjhw5ov/85z/24127dik7O1sNGzZU06ZN1ahRI7fxtWvXVmhoqFq2bClJatWqlbp3764hQ4Zo9uzZKi0tVXJysvr162ffct2/f39NmDBBgwcP1tNPP61NmzbpjTfe0NSpU//ItgIAgBqi0gFmw4YNuuWWW+zHo0aNkiQNGjRI8+bNO6dlpKSkKDk5WV27dpWPj4/69u2r6dOn2/1BQUFaunSpkpKS1KFDB11yySUaO3Yst1ADAABJksOyLMvbRVQFl8uloKAgFRUVeeV6mHRH+gVfZ3XQxeri7RIAAAY71/dvvgsJAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEqHWBWrVqlXr16KTw8XA6HQwsXLrT7SktL9fTTT6tNmzYKDAxUeHi4Bg4cqP3797sto6CgQAkJCXI6nQoODtbgwYN15MgRtzHff/+9brrpJgUEBCgiIkKTJ08+vy0EAAA1TqUDzNGjR9W2bVvNnDmzXN+xY8e0ceNGPf/889q4caM+++wzbd++XXfccYfbuISEBG3evFmpqalatGiRVq1apcTERLvf5XKpW7duatasmbKysvTqq69q/PjxmjNnznlsIgAAqGkclmVZ5z2zw6HPP/9cffr0OeOYzMxMXXfdddqzZ4+aNm2qrVu3Kjo6WpmZmerYsaMkacmSJbrtttv0888/Kzw8XLNmzdKzzz6r3Nxc+fn5SZKeeeYZLVy4UNu2bTun2lwul4KCglRUVCSn03m+m3je0h3pF3yd1UEXq4u3SwAAGOxc37+r/BqYoqIiORwOBQcHS5IyMjIUHBxshxdJiouLk4+Pj9atW2ePufnmm+3wIknx8fHavn27Dh06VOF6iouL5XK53CYAAFAzVWmAOX78uJ5++mndf//9dorKzc1VkyZN3Mb5+vqqYcOGys3NtceEhIS4jTn1+NSY002aNElBQUH2FBER4enNAQAA1USVBZjS0lLde++9sixLs2bNqqrV2MaMGaOioiJ7ysnJqfJ1AgAA7/CtioWeCi979uzR8uXL3c5hhYaGKj8/3238iRMnVFBQoNDQUHtMXl6e25hTj0+NOZ2/v7/8/f09uRkAAKCa8vgRmFPhZceOHVq2bJkaNWrk1h8bG6vCwkJlZWXZbcuXL1dZWZliYmLsMatWrVJpaak9JjU1VS1btlSDBg08XTIAADBMpQPMkSNHlJ2drezsbEnSrl27lJ2drb1796q0tFR33323NmzYoJSUFJ08eVK5ubnKzc1VSUmJJKlVq1bq3r27hgwZovXr12v16tVKTk5Wv379FB4eLknq37+//Pz8NHjwYG3evFkfffSR3njjDY0aNcpzWw4AAIxV6duo09PTdcstt5RrHzRokMaPH6+oqKgK51uxYoW6dOki6bcPsktOTtaXX34pHx8f9e3bV9OnT1e9evXs8d9//72SkpKUmZmpSy65RMOHD9fTTz99znVyG7V3cBs1AOCPONf37z/0OTDVGQHGOwgwAIA/otp8DgwAAICnEWAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYJxKB5hVq1apV69eCg8Pl8Ph0MKFC936LcvS2LFjFRYWpjp16iguLk47duxwG1NQUKCEhAQ5nU4FBwdr8ODBOnLkiNuY77//XjfddJMCAgIUERGhyZMnV37rAABAjVTpAHP06FG1bdtWM2fOrLB/8uTJmj59umbPnq1169YpMDBQ8fHxOn78uD0mISFBmzdvVmpqqhYtWqRVq1YpMTHR7ne5XOrWrZuaNWumrKwsvfrqqxo/frzmzJlzHpsIAABqGodlWdZ5z+xw6PPPP1efPn0k/Xb0JTw8XE888YSefPJJSVJRUZFCQkI0b9489evXT1u3blV0dLQyMzPVsWNHSdKSJUt022236eeff1Z4eLhmzZqlZ599Vrm5ufLz85MkPfPMM1q4cKG2bdt2TrW5XC4FBQWpqKhITqfzfDfxvKU70i/4OquDLlYXb5cAADDYub5/e/QamF27dik3N1dxcXF2W1BQkGJiYpSRkSFJysjIUHBwsB1eJCkuLk4+Pj5at26dPebmm2+2w4skxcfHa/v27Tp06JAnSwYAAAby9eTCcnNzJUkhISFu7SEhIXZfbm6umjRp4l6Er68aNmzoNiYqKqrcMk71NWjQoNy6i4uLVVxcbD92uVx/cGsAAEB1VWPuQpo0aZKCgoLsKSIiwtslAQCAKuLRABMaGipJysvLc2vPy8uz+0JDQ5Wfn+/Wf+LECRUUFLiNqWgZ/72O040ZM0ZFRUX2lJOT88c3CAAAVEsePYUUFRWl0NBQpaWlqV27dpJ+O5Wzbt06DR06VJIUGxurwsJCZWVlqUOHDpKk5cuXq6ysTDExMfaYZ599VqWlpapdu7YkKTU1VS1btqzw9JEk+fv7y9/f35ObA5yz9HSHt0vwii5dzvseAAD4Qyp9BObIkSPKzs5Wdna2pN8u3M3OztbevXvlcDg0cuRIvfDCC/riiy/0ww8/aODAgQoPD7fvVGrVqpW6d++uIUOGaP369Vq9erWSk5PVr18/hYeHS5L69+8vPz8/DR48WJs3b9ZHH32kN954Q6NGjfLYhgMAAHNV+gjMhg0bdMstt9iPT4WKQYMGad68eXrqqad09OhRJSYmqrCwUJ06ddKSJUsUEBBgz5OSkqLk5GR17dpVPj4+6tu3r6ZPn273BwUFaenSpUpKSlKHDh10ySWXaOzYsW6fFQMAAC5ef+hzYKozPgfGOy7Wz4HhFBIAeIZXPgcGAADgQiDAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcjweYkydP6vnnn1dUVJTq1KmjFi1a6K9//assy7LHWJalsWPHKiwsTHXq1FFcXJx27NjhtpyCggIlJCTI6XQqODhYgwcP1pEjRzxdLgAAMJDHA8wrr7yiWbNmacaMGdq6dateeeUVTZ48WW+++aY9ZvLkyZo+fbpmz56tdevWKTAwUPHx8Tp+/Lg9JiEhQZs3b1ZqaqoWLVqkVatWKTEx0dPlAgAAA/l6eoFr1qxR79691bNnT0lSZGSkPvjgA61fv17Sb0dfpk2bpueee069e/eWJL333nsKCQnRwoUL1a9fP23dulVLlixRZmamOnbsKEl68803ddttt+m1115TeHi4p8sGAAAG8fgRmBtuuEFpaWn68ccfJUnfffed/vWvf6lHjx6SpF27dik3N1dxcXH2PEFBQYqJiVFGRoYkKSMjQ8HBwXZ4kaS4uDj5+Pho3bp1Fa63uLhYLpfLbQIAADWTx4/APPPMM3K5XLrqqqtUq1YtnTx5Ui+++KISEhIkSbm5uZKkkJAQt/lCQkLsvtzcXDVp0sS9UF9fNWzY0B5zukmTJmnChAme3hwAAFANefwIzMcff6yUlBS9//772rhxo+bPn6/XXntN8+fP9/Sq3IwZM0ZFRUX2lJOTU6XrAwAA3uPxIzCjR4/WM888o379+kmS2rRpoz179mjSpEkaNGiQQkNDJUl5eXkKCwuz58vLy1O7du0kSaGhocrPz3db7okTJ1RQUGDPfzp/f3/5+/t7enMAAEA15PEjMMeOHZOPj/tia9WqpbKyMklSVFSUQkNDlZaWZve7XC6tW7dOsbGxkqTY2FgVFhYqKyvLHrN8+XKVlZUpJibG0yUDAADDePwITK9evfTiiy+qadOmat26tf79739rypQpevjhhyVJDodDI0eO1AsvvKArrrhCUVFRev755xUeHq4+ffpIklq1aqXu3btryJAhmj17tkpLS5WcnKx+/fpxBxIAAPB8gHnzzTf1/PPPa9iwYcrPz1d4eLj+8pe/aOzYsfaYp556SkePHlViYqIKCwvVqVMnLVmyRAEBAfaYlJQUJScnq2vXrvLx8VHfvn01ffp0T5cLAAAM5LD++yNyaxCXy6WgoCAVFRXJ6XRe8PWnO9Iv+Dqrgy5WF2+X4BXp6Q5vl+AVXbrUyD8fALzoXN+/+S4kAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMapkgCzb98+PfDAA2rUqJHq1KmjNm3aaMOGDXa/ZVkaO3aswsLCVKdOHcXFxWnHjh1uyygoKFBCQoKcTqeCg4M1ePBgHTlypCrKBQAAhvF4gDl06JBuvPFG1a5dW4sXL9aWLVv0+uuvq0GDBvaYyZMna/r06Zo9e7bWrVunwMBAxcfH6/jx4/aYhIQEbd68WampqVq0aJFWrVqlxMRET5cLAAAM5LAsy/LkAp955hmtXr1a3377bYX9lmUpPDxcTzzxhJ588klJUlFRkUJCQjRv3jz169dPW7duVXR0tDIzM9WxY0dJ0pIlS3Tbbbfp559/Vnh4+O/W4XK5FBQUpKKiIjmdTs9t4DlKd6Rf8HVWB12sLt4uwSvS0x3eLsErunTx6J8PADjn92+PH4H54osv1LFjR91zzz1q0qSJ2rdvr7/97W92/65du5Sbm6u4uDi7LSgoSDExMcrIyJAkZWRkKDg42A4vkhQXFycfHx+tW7euwvUWFxfL5XK5TQAAoGbyeIDZuXOnZs2apSuuuELffPONhg4dqscee0zz58+XJOXm5kqSQkJC3OYLCQmx+3Jzc9WkSRO3fl9fXzVs2NAec7pJkyYpKCjIniIiIjy9aQAAoJrweIApKyvTNddco5deeknt27dXYmKihgwZotmzZ3t6VW7GjBmjoqIie8rJyanS9QEAAO/xeIAJCwtTdHS0W1urVq20d+9eSVJoaKgkKS8vz21MXl6e3RcaGqr8/Hy3/hMnTqigoMAeczp/f385nU63CQAA1EweDzA33nijtm/f7tb2448/qlmzZpKkqKgohYaGKi0tze53uVxat26dYmNjJUmxsbEqLCxUVlaWPWb58uUqKytTTEyMp0sGAACG8fX0Ah9//HHdcMMNeumll3Tvvfdq/fr1mjNnjubMmSNJcjgcGjlypF544QVdccUVioqK0vPPP6/w8HD16dNH0m9HbLp3726feiotLVVycrL69et3TncgAQCAms3jAebaa6/V559/rjFjxmjixImKiorStGnTlJCQYI956qmndPToUSUmJqqwsFCdOnXSkiVLFBAQYI9JSUlRcnKyunbtKh8fH/Xt21fTp0/3dLkAAMBAHv8cmOqCz4HxDj4H5uLC58AA8DSvfQ4MAABAVSPAAAAA4xBgAACAcQgwAADAOAQYAABgHI/fRg0ANZ1jwsV515k1jrvOUH1wBAYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDhVHmBefvllORwOjRw50m47fvy4kpKS1KhRI9WrV099+/ZVXl6e23x79+5Vz549VbduXTVp0kSjR4/WiRMnqrpcAABggCoNMJmZmXr77bf1pz/9ya398ccf15dffqlPPvlEK1eu1P79+3XXXXfZ/SdPnlTPnj1VUlKiNWvWaP78+Zo3b57Gjh1bleUCAABDVFmAOXLkiBISEvS3v/1NDRo0sNuLior0zjvvaMqUKbr11lvVoUMHvfvuu1qzZo3Wrl0rSVq6dKm2bNmif/zjH2rXrp169Oihv/71r5o5c6ZKSkqqqmQAAGCIKgswSUlJ6tmzp+Li4tzas7KyVFpa6tZ+1VVXqWnTpsrIyJAkZWRkqE2bNgoJCbHHxMfHy+VyafPmzRWur7i4WC6Xy20CAAA1k29VLPTDDz/Uxo0blZmZWa4vNzdXfn5+Cg4OdmsPCQlRbm6uPea/w8up/lN9FZk0aZImTJjggeoBAEB15/EjMDk5ORoxYoRSUlIUEBDg6cWf0ZgxY1RUVGRPOTk5F2zdAADgwvJ4gMnKylJ+fr6uueYa+fr6ytfXVytXrtT06dPl6+urkJAQlZSUqLCw0G2+vLw8hYaGSpJCQ0PL3ZV06vGpMafz9/eX0+l0mwAAQM3k8QDTtWtX/fDDD8rOzranjh07KiEhwf65du3aSktLs+fZvn279u7dq9jYWElSbGysfvjhB+Xn59tjUlNT5XQ6FR0d7emSAQCAYTx+DUz9+vV19dVXu7UFBgaqUaNGdvvgwYM1atQoNWzYUE6nU8OHD1dsbKyuv/56SVK3bt0UHR2tAQMGaPLkycrNzdVzzz2npKQk+fv7e7pkAABgmCq5iPf3TJ06VT4+Purbt6+Ki4sVHx+vt956y+6vVauWFi1apKFDhyo2NlaBgYEaNGiQJk6c6I1yAQBANeOwLMvydhFVweVyKSgoSEVFRV65HibdkX7B11kddLG6eLsEr0hPd3i7BK/o0qVG/vn4XY4JF+f+tsZdnPsbF9a5vn975QgMAACmcFyceVXV/fAGX+YIAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHE8HmAmTZqka6+9VvXr11eTJk3Up08fbd++3W3M8ePHlZSUpEaNGqlevXrq27ev8vLy3Mbs3btXPXv2VN26ddWkSRONHj1aJ06c8HS5AADAQB4PMCtXrlRSUpLWrl2r1NRUlZaWqlu3bjp69Kg95vHHH9eXX36pTz75RCtXrtT+/ft111132f0nT55Uz549VVJSojVr1mj+/PmaN2+exo4d6+lyAQCAgRyWZVlVuYIDBw6oSZMmWrlypW6++WYVFRWpcePGev/993X33XdLkrZt26ZWrVopIyND119/vRYvXqzbb79d+/fvV0hIiCRp9uzZevrpp3XgwAH5+fn97npdLpeCgoJUVFQkp9NZlZtYoXRH+gVfZ3XQxeri7RK8Ij3d4e0SvKJLlyr981FtOSZcnPvbGneR7u+Lc3eratPBmZ3r+3eVXwNTVFQkSWrYsKEkKSsrS6WlpYqLi7PHXHXVVWratKkyMjIkSRkZGWrTpo0dXiQpPj5eLpdLmzdvrnA9xcXFcrlcbhMAAKiZqjTAlJWVaeTIkbrxxht19dVXS5Jyc3Pl5+en4OBgt7EhISHKzc21x/x3eDnVf6qvIpMmTVJQUJA9RUREeHhrAABAdVGlASYpKUmbNm3Shx9+WJWrkSSNGTNGRUVF9pSTk1Pl6wQAAN7hW1ULTk5O1qJFi7Rq1SpddtlldntoaKhKSkpUWFjodhQmLy9PoaGh9pj169e7Le/UXUqnxpzO399f/v7+Ht4KAABQHXn8CIxlWUpOTtbnn3+u5cuXKyoqyq2/Q4cOql27ttLS0uy27du3a+/evYqNjZUkxcbG6ocfflB+fr49JjU1VU6nU9HR0Z4uGQAAGMbjR2CSkpL0/vvv65///Kfq169vX7MSFBSkOnXqKCgoSIMHD9aoUaPUsGFDOZ1ODR8+XLGxsbr++uslSd26dVN0dLQGDBigyZMnKzc3V88995ySkpI4ygIAADwfYGbNmiVJ6tKli1v7u+++qwcffFCSNHXqVPn4+Khv374qLi5WfHy83nrrLXtsrVq1tGjRIg0dOlSxsbEKDAzUoEGDNHHiRE+XCwAADOTxAHMuHysTEBCgmTNnaubMmWcc06xZM3399deeLA0AANQQfBcSAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMap1gFm5syZioyMVEBAgGJiYrR+/XpvlwQAAKqBahtgPvroI40aNUrjxo3Txo0b1bZtW8XHxys/P9/bpQEAAC+rtgFmypQpGjJkiB566CFFR0dr9uzZqlu3rubOnevt0gAAgJdVywBTUlKirKwsxcXF2W0+Pj6Ki4tTRkaGFysDAADVga+3C6jIwYMHdfLkSYWEhLi1h4SEaNu2bRXOU1xcrOLiYvtxUVGRJMnlclVdoWdxVEe9sl5v89bz7W1HL87dfdHubx33dgHecdHu74uUt3b3qdeZZVlnHVctA8z5mDRpkiZMmFCuPSIiwgvVXMSCvF0ALix2+MUk6GX298UkyMu7+/Dhwwo6SxHVMsBccsklqlWrlvLy8tza8/LyFBoaWuE8Y8aM0ahRo+zHZWVlKigoUKNGjeRwOKq03urE5XIpIiJCOTk5cjqd3i4HVYz9fXFhf19cLtb9bVmWDh8+rPDw8LOOq5YBxs/PTx06dFBaWpr69Okj6bdAkpaWpuTk5Arn8ff3l7+/v1tbcHBwFVdafTmdzovqBX+xY39fXNjfF5eLcX+f7cjLKdUywEjSqFGjNGjQIHXs2FHXXXedpk2bpqNHj+qhhx7ydmkAAMDLqm2Aue+++3TgwAGNHTtWubm5ateunZYsWVLuwl4AAHDxqbYBRpKSk5PPeMoIFfP399e4cePKnU5DzcT+vriwvy8u7O+zc1i/d58SAABANVMtP8gOAADgbAgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4CpQU7/Rm7UPFu2bNGwYcPUvn17hYWFKSwsTO3bt9ewYcO0ZcsWb5cH4A/g97ty+BwYw6Wmpmrq1KnKyMiwv4Lc6XQqNjZWo0aNUlxcnJcrhKcsXrxYffr00TXXXKP4+Hj7U6nz8vKUmpqqrKws/fOf/1R8fLyXK4WnbNmyRTNmzFBGRoZyc3MlSaGhoYqNjVVycrKio6O9XCE8hd/vyiPAGGz+/Pl65JFHdPfdd5d7wS9dulQLFizQO++8owEDBni5UnhC27Zt1bt3b02cOLHC/vHjx+uzzz7T999/f4ErQ1XgDe3iwu935RFgDHbllVdqxIgRSkpKqrD/rbfe0tSpU7Vjx44LXBmqQp06dZSdna2WLVtW2L99+3a1a9dOv/766wWuDFWBN7SLC7/flcc1MAbbu3fvWU8Rde3aVT///PMFrAhVKTIyUl999dUZ+7/66is1a9bsAlaEqvTjjz8qISHhjP33338//5zUIPx+V161/jJHnF3r1q31zjvvaPLkyRX2z507l3PkNcjEiRPVv39/paenKy4uzu2UQlpampYsWaL333/fy1XCU069oZ3pP3Le0GoWfr8rj1NIBktPT9ftt9+u5s2bV/iC37lzp7766ivdfPPNXq4UnrJmzRpNnz69wos6R4wYodjYWC9XCE/55JNP1L9/f/Xo0eOsb2h9+/b1cqXwFH6/K4cAY7jdu3dr1qxZWrt2bbkX/KOPPqrIyEjvFgjgvPGGBpwZAQYAABiHi3iBGuJ//ud/9PDDD3u7DABVgN/v8ggwNdigQYN06623ersMXCD79u3T7t27vV0GLhDe0C4u/H6Xx11INVh4eLh8fMioF4v58+d7uwRcQD///DMfk3ARsCxLDoeD3+8KcA0MAADVlJ+fn7777ju1atXK26VUOxyBqcFycnI0btw4zZ0719ulwEN+/fVXZWVlqWHDhuU+4+f48eP6+OOPNXDgQC9VB0/bunWr1q5dq9jYWF111VXatm2b3njjDRUXF+uBBx7gFHENMmrUqArbT548qZdfflmNGjWSJE2ZMuVCllWtcQSmBvvuu+90zTXX6OTJk94uBR7w448/qlu3btq7d68cDoc6deqkDz/8UGFhYZJ++3yQ8PBw9ncNsWTJEvXu3Vv16tXTsWPH9Pnnn2vgwIFq27atysrKtHLlSi1dupQQU0P4+Piobdu2Cg4OdmtfuXKlOnbsqMDAQDkcDi1fvtw7BVZDBBiDffHFF2ft37lzp5544gne0GqIO++8U6WlpZo3b54KCws1cuRIbdmyRenp6WratCkBpoa54YYbdOutt+qFF17Qhx9+qGHDhmno0KF68cUXJUljxoxRVlaWli5d6uVK4Qkvv/yy5syZo7///e9uobR27dr67rvv+FT1ChBgDObj4yOHw6Gz7UKHw8EbWg0REhKiZcuWqU2bNpJ+u7hv2LBh+vrrr7VixQoFBgYSYGqQoKAgZWVl6fLLL1dZWZn8/f21fv16tW/fXpK0adMmxcXF2R9wB/NlZmbqgQceUK9evTRp0iTVrl2bAHMW3KJisLCwMH322WcqKyurcNq4caO3S4QH/frrr/L1/X+XrTkcDs2aNUu9evVS586d9eOPP3qxOlQFh8Mh6bd/VgICAhQUFGT31a9fX0VFRd4qDVXg2muvVVZWlg4cOKCOHTtq06ZN9msA5RFgDNahQwdlZWWdsf/3js7ALFdddZU2bNhQrn3GjBnq3bu37rjjDi9UhaoSGRnp9m3TGRkZatq0qf1479699vVPqDnq1aun+fPna8yYMYqLi+OI6lkQYAw2evRo3XDDDWfsv/zyy7VixYoLWBGq0p133qkPPvigwr4ZM2bo/vvvJ7DWIEOHDnV787r66qvdjsAtXryYC3hrsH79+mnDhg367LPP+NbxM+AaGAAAYByOwAAAAOMQYAAAgHEIMAAAwDgEGABVLj09XQ6HQ4WFhd4uRZGRkZo2bZq3ywDwBxFgANRI8+bNK/ex7NJvHxaWmJh44QsC4FF8mSOAi0rjxo29XQIAD+AIDIBKKysr06RJkxQVFaU6deqobdu2WrBggd3/9ddf68orr1SdOnV0yy23aPfu3W7zjx8/Xu3atXNrmzZtmiIjI93a5s6dq9atW8vf319hYWFKTk62+6ZMmaI2bdooMDBQERERGjZsmI4cOSLpt1NWDz30kIqKiuRwOORwODR+/HhJ5U8h7d271/7SRKfTqXvvvVd5eXnlav3f//1fRUZGKigoSP369dPhw4fP/wkE8IcRYABU2qRJk/Tee+9p9uzZ2rx5sx5//HE98MADWrlypXJycnTXXXepV69eys7O1iOPPKJnnnmm0uuYNWuWkpKSlJiYqB9++EFffPGFLr/8crvfx8dH06dP1+bNmzV//nwtX75cTz31lKTfvghx2rRpcjqd+uWXX/TLL7/oySefLLeOsrIy9e7dWwUFBVq5cqVSU1O1c+dO3XfffW7jfvrpJy1cuFCLFi3SokWLtHLlSr388suV3iYAnsMpJACVUlxcrJdeeknLli1TbGysJKl58+b617/+pbfffluRkZFq0aKFXn/9dUlSy5Yt9cMPP+iVV16p1HpeeOEFPfHEExoxYoTddu2119o/jxw50v45MjJSL7zwgh599FG99dZb8vPzU1BQkBwOh0JDQ8+4jrS0NP3www/atWuXIiIiJEnvvfeeWrdurczMTHt9ZWVlmjdvnurXry9JGjBggNLS0uxvhgZw4RFgAFTKf/7zHx07dkx//vOf3dpLSkrUvn17/frrr4qJiXHrOxV0zlV+fr7279+vrl27nnHMsmXLNGnSJG3btk0ul0snTpzQ8ePHdezYMdWtW/ec1rN161ZFRETY4UWSoqOjFRwcrK1bt9oBJjIy0g4v0m9fpJqfn1+pbQLgWZxCAlApp64z+eqrr5SdnW1PW7ZscbsO5mx8fHzKfW9TaWmp/XOdOnXOOv/u3bt1++23609/+pM+/fRTZWVlaebMmZJ+C1KeVrt2bbfHDodDZWVlHl8PgHPHERgAlRIdHS1/f3/t3btXnTt3LtffqlUrffHFF25ta9eudXvcuHFj5ebmyrIsORwOSVJ2drbdX79+fUVGRiotLU233HJLuXVkZWWprKxMr7/+unx8fvs/7OOPP3Yb4+fn97vf5NuqVSvl5OQoJyfHPgqzZcsWFRYWKjo6+qzzAvAuAgyASqlfv76efPJJPf744yorK1OnTp1UVFSk1atXy+l06tFHH9Xrr7+u0aNH65FHHlFWVpbmzZvntowuXbrowIEDmjx5su6++24tWbJEixcvltPptMeMHz9ejz76qJo0aaIePXro8OHDWr16tYYPH67LL79cpaWlevPNN9WrVy+tXr1as2fPdltHZGSkjhw5orS0NLVt21Z169Ytd2opLi5Obdq0UUJCgqZNm6YTJ05o2LBh6ty5szp27FhlzyEAD7AAoJLKysqsadOmWS1btrRq165tNW7c2IqPj7dWrlxpWZZlffnll9bll19u+fv7WzfddJM1d+5cS5J16NAhexmzZs2yIiIirMDAQGvgwIHWiy++aDVr1sxtPbNnz7bXERYWZg0fPtzumzJlihUWFmbVqVPHio+Pt957771y63j00UetRo0aWZKscePGWZZlWc2aNbOmTp1qj9mzZ491xx13WIGBgVb9+vWte+65x8rNzbX7x40bZ7Vt29atrqlTp5arFcCF5bCs005EAwAAVHNcxAsAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcf4/Jb/Ws0HC+XoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.education.value_counts().plot.bar(color=[\"m\",\"y\",\"g\",\"b\"])\n",
    "plt.title(\"literacy in our society\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a8wi1jwf0RG",
   "metadata": {
    "id": "2a8wi1jwf0RG"
   },
   "source": [
    "#### from above i can conclude that very less people are educated "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ZMWO7iMRLDK-",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "ZMWO7iMRLDK-",
    "outputId": "9ea51b76-9860-40d8-ef5f-28b1f2921442"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='is_smoking', ylabel='count'>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.countplot(data=data,x=\"is_smoking\",hue=\"education\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "qHJUMrzrzi1d",
   "metadata": {
    "id": "qHJUMrzrzi1d"
   },
   "source": [
    "## i can conclude that the people who are not educates(illetrate ) they smoke more as compare to educated peploe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "3ZsoCsr1Dqgn",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "id": "3ZsoCsr1Dqgn",
    "outputId": "60321c90-8de6-432a-af69-5f3e095b473d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='prevalentStroke', ylabel='count'>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.countplot(data=data,x=\"prevalentStroke\",hue=\"education\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "Z7n64WtfLQ3r",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "id": "Z7n64WtfLQ3r",
    "outputId": "0049dd43-55d0-430e-9e18-775169e0413e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='count'>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.diabetes.value_counts().plot.pie(autopct=\"%1.1f%%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "jwkyQtd12hBr",
   "metadata": {
    "id": "jwkyQtd12hBr"
   },
   "source": [
    "from pie chart we can conclude that very less people have diabeetes \n",
    "\n",
    "### The good news is, people with any type of diabetes, who maintain good control over their blood sugar, cholesterol, and blood pressure, have a lower risk of kidney disease, eye disease, nervous system problems, heart attack, and stroke, and can live, a long and healthy life."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "Nr9gQup0LWZd",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "id": "Nr9gQup0LWZd",
    "outputId": "3fe6de1b-3384-415a-a379-1342e85d0960",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='age', ylabel='Density'>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.distplot(data.age)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "N38tnr4f1AvH",
   "metadata": {
    "id": "N38tnr4f1AvH"
   },
   "source": [
    "## The graph is not normally distributed  which means (40 to 50 age people are more)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "WZ60SI1ALbNy",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 248
    },
    "id": "WZ60SI1ALbNy",
    "outputId": "6f1d2b40-fea4-4474-80fc-acbcf50d8f8b"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.groupby(\"sex\")[\"prevalentStroke\"].count().plot.pie(colors=[\"g\",\"m\"])\n",
    "plt.xticks(rotation=45,ha=\"right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eNBuUmW2xWAd",
   "metadata": {
    "id": "eNBuUmW2xWAd"
   },
   "source": [
    "## from here we can conclude fmale gets more prevalentstroke as compare to male \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "L081UNzYL0TS",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 592
    },
    "id": "L081UNzYL0TS",
    "outputId": "246b6b85-2dc9-4c2c-fdcb-d7d40e082b71"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "y1= data.sysBP\n",
    "y2= data.diaBP\n",
    "plt.plot(y1)\n",
    "plt.plot(y2)\n",
    "plt.show()    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "vXRF9Vz8ykT_",
   "metadata": {
    "id": "vXRF9Vz8ykT_"
   },
   "source": [
    "## as from above stats i can conclude that very less people have normal blood pressure\n",
    "\n",
    "### and most people high blood presure or low blood presure "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "ljD0kxwxL5mh",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 311
    },
    "id": "ljD0kxwxL5mh",
    "outputId": "cbc926d3-c05e-41e2-8b90-0424f2f7dcb7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 600x500 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,5))\n",
    "cross_tab_prop = pd.crosstab(index = data['sex'], columns = data['is_smoking'], normalize = \"index\")\n",
    "cross_tab_prop.plot(kind='bar', stacked=True, colormap='gnuplot_r')\n",
    "plt.title(\"Sex vs is_smoking\")\n",
    "plt.xlabel(\"Sex\")\n",
    "plt.ylabel(\"Is_smoking\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "IqDZ4uxWycWo",
   "metadata": {
    "id": "IqDZ4uxWycWo"
   },
   "source": [
    "## we found that male smoke more as compare to female"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "kGPAF-ZNL9OO",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 299
    },
    "id": "kGPAF-ZNL9OO",
    "outputId": "d1931e46-67ad-4e13-ea54-af5f0998c244"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'heartRate')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.kdeplot(data=data,x='glucose', color='r', shade=True)\n",
    "sn.kdeplot(data=data, x='heartRate', color='g', shade=True) \n",
    "plt.ylabel('glucose')\n",
    "plt.xlabel('heartRate')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "onMk8MPGyKJF",
   "metadata": {
    "id": "onMk8MPGyKJF"
   },
   "source": [
    "## We found that resting heart rate was significantly higher in patients with higher fasting blood glucose levels compared with patients with normal glucose levels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "vfGzLGxWOqml",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 369
    },
    "id": "vfGzLGxWOqml",
    "outputId": "47446d40-d260-4428-b591-d96598a37828"
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'seaborn' has no attribute 'factorplot'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[57], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43msn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfactorplot\u001b[49m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBPMeds\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdiabetes\u001b[39m\u001b[38;5;124m\"\u001b[39m,hue\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124meducation\u001b[39m\u001b[38;5;124m\"\u001b[39m,col\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprevalentStroke\u001b[39m\u001b[38;5;124m\"\u001b[39m,data\u001b[38;5;241m=\u001b[39mdata)\n\u001b[0;32m      2\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n",
      "\u001b[1;31mAttributeError\u001b[0m: module 'seaborn' has no attribute 'factorplot'"
     ]
    }
   ],
   "source": [
    "sn.factorplot(\"BPMeds\",\"diabetes\",hue=\"education\",col=\"prevalentStroke\",data=data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "aZ4ySJN_ydup",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 394
    },
    "id": "aZ4ySJN_ydup",
    "outputId": "c3c403bb-5f02-4426-de36-8ebd44a2f345"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cross_tab_prop = pd.crosstab(index = data['education'], columns = data['diabetes'], normalize = \"index\")\n",
    "cross_tab_prop.plot(kind='bar', stacked=True, colormap='viridis', figsize=(10, 6))\n",
    "plt.legend(loc=\"upper right\", ncol=2)\n",
    "plt.xlabel(\"education\")\n",
    "plt.ylabel(\"diabetes\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "K0YGqs07yhmA",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 326
    },
    "id": "K0YGqs07yhmA",
    "outputId": "6be09b4e-21c4-4a4a-c2b7-c73f6264ab79"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.heatmap(data[[\"BPMeds\",\"prevalentHyp\",\"TenYearCHD\",\"totChol\"]].corr(), annot=True, cmap=\"CMRmap_r\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "ljopiFsPym0B",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "ljopiFsPym0B",
    "outputId": "6c4f96ba-e74a-47f1-b0a5-eebe9b3712a0"
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: 'F'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[60], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m10\u001b[39m,\u001b[38;5;241m20\u001b[39m))\n\u001b[1;32m----> 2\u001b[0m sn\u001b[38;5;241m.\u001b[39mheatmap(\u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcorr\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m,annot\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,cmap\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mviridis\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "File \u001b[1;32mc:\\Users\\hp\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\frame.py:10059\u001b[0m, in \u001b[0;36mDataFrame.corr\u001b[1;34m(self, method, min_periods, numeric_only)\u001b[0m\n\u001b[0;32m  10057\u001b[0m cols \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mcolumns\n\u001b[0;32m  10058\u001b[0m idx \u001b[38;5;241m=\u001b[39m cols\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[1;32m> 10059\u001b[0m mat \u001b[38;5;241m=\u001b[39m \u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_numpy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mfloat\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mna_value\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnan\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m  10061\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpearson\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m  10062\u001b[0m     correl \u001b[38;5;241m=\u001b[39m libalgos\u001b[38;5;241m.\u001b[39mnancorr(mat, minp\u001b[38;5;241m=\u001b[39mmin_periods)\n",
      "File \u001b[1;32mc:\\Users\\hp\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\frame.py:1838\u001b[0m, in \u001b[0;36mDataFrame.to_numpy\u001b[1;34m(self, dtype, copy, na_value)\u001b[0m\n\u001b[0;32m   1836\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dtype \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   1837\u001b[0m     dtype \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mdtype(dtype)\n\u001b[1;32m-> 1838\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mgr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mas_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mna_value\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mna_value\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1839\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m dtype:\n\u001b[0;32m   1840\u001b[0m     result \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(result, dtype\u001b[38;5;241m=\u001b[39mdtype, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
      "File \u001b[1;32mc:\\Users\\hp\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:1732\u001b[0m, in \u001b[0;36mBlockManager.as_array\u001b[1;34m(self, dtype, copy, na_value)\u001b[0m\n\u001b[0;32m   1730\u001b[0m         arr\u001b[38;5;241m.\u001b[39mflags\u001b[38;5;241m.\u001b[39mwriteable \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m   1731\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1732\u001b[0m     arr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_interleave\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mna_value\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mna_value\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1733\u001b[0m     \u001b[38;5;66;03m# The underlying data was copied within _interleave, so no need\u001b[39;00m\n\u001b[0;32m   1734\u001b[0m     \u001b[38;5;66;03m# to further copy if copy=True or setting na_value\u001b[39;00m\n\u001b[0;32m   1736\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m na_value \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m lib\u001b[38;5;241m.\u001b[39mno_default:\n",
      "File \u001b[1;32mc:\\Users\\hp\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:1794\u001b[0m, in \u001b[0;36mBlockManager._interleave\u001b[1;34m(self, dtype, na_value)\u001b[0m\n\u001b[0;32m   1792\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1793\u001b[0m         arr \u001b[38;5;241m=\u001b[39m blk\u001b[38;5;241m.\u001b[39mget_values(dtype)\n\u001b[1;32m-> 1794\u001b[0m     \u001b[43mresult\u001b[49m\u001b[43m[\u001b[49m\u001b[43mrl\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindexer\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;241m=\u001b[39m arr\n\u001b[0;32m   1795\u001b[0m     itemmask[rl\u001b[38;5;241m.\u001b[39mindexer] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m   1797\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m itemmask\u001b[38;5;241m.\u001b[39mall():\n",
      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: 'F'"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x2000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,20))\n",
    "sn.heatmap(data.corr(),annot=True,cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "roaHsP6vB2f7",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 389
    },
    "id": "roaHsP6vB2f7",
    "outputId": "205f5b1e-90f6-4b02-e9b7-8efa279936bb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x900 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 621.861x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,9))\n",
    "sn.displot(x=\"age\",data=data,hue=\"prevalentStroke\",palette=['#FF0000','#000000'])\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3RcvqJRiBIfx",
   "metadata": {
    "id": "3RcvqJRiBIfx"
   },
   "source": [
    "# **People** **ages** **between** **53** **to** **65** **they** **got** **more** **number** **of** **prevalent** **stroke** **bold text**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "289c64f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  0.,  20.,  40.,  60.,  80., 100.]),\n",
       " [Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, '')])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAACWkAAAT5CAYAAACr5gHvAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAADXgElEQVR4nOzdd5gV1f0/8M/S67J0EBBEQEBBFFEQRBCxohElirHGWGI0xsRYosaWaGJMscUkGoMldn7YSKwoUbAjTUQQaVIUpHdYuL8/fLhf7ta7wLCAr9fz7PPszJ4zc+bO3HNnd957Tk4qlUoFAAAAAAAAAAAAiahQ3g0AAAAAAAAAAADYnQlpAQAAAAAAAAAAJEhICwAAAAAAAAAAIEFCWgAAAAAAAAAAAAkS0gIAAAAAAAAAAEiQkBYAAAAAAAAAAECChLQAAAAAAAAAAAASJKQFAAAAAAAAAACQICEtAAAAAAAAAACABAlpAQDwnTd37tx4+eWX44EHHog77rgjfvvb38add94ZQ4YMiRdffDFmzpxZ3k3crbRq1SpycnIiJycnWrVqVd7N2SFuuumm9DHn5OTEyJEjy7tJuwWv665ty3PXp0+fxPazs1wnI0eOzGjHTTfdVC7tAHZOffr0yegjAHYG7l8AAGD7qlTeDQAAgPIwbty4ePDBB+OFF16I2bNnl1q+Tp060bNnzzjllFPie9/7XtSvX38HtBIAAAAAAIDdgZG0AAD4Tpk8eXIcd9xxccABB8S9996bVUArImLZsmXx3//+N370ox9F06ZN48ILL4wZM2Yk3FoAAAAAAAB2B0bSAgDgO+P++++Pn/3sZ7F27dpCP6tcuXLsu+++0aBBg2jQoEGsW7cuFixYEPPmzSsUxtqwYUM88MADMWTIkFi4cGHk5eXtoCMAAAAAAABgVySkBQDAd8Itt9wSN954Y8a6nJycOOWUU+L888+PXr16Rc2aNYusO3fu3Hj11Vdj6NCh8d///je9Pj8/P/Lz8xNtNwAAAAAAALs+0x0CALDbe+aZZwoFtDp27Bjjxo2LZ555Jo4++uhiA1oREc2aNYsf/vCH8Z///CfGjx8fp512WtJNBrJw0003RSqVSn/16dOnvJvETsh1AgAAAADsDIS0AADYrU2ZMiXOO++8jHW9e/eO0aNHR+fOncu8vc6dO8eTTz4ZL7zwQjRq1Gh7NRMAAAAAAIDdmJAWAAC7tWuuuSZWrlyZXm7UqFEMHTo08vLytmm7J5xwQkycODFq1669jS0EAAAAAABgd1epvBsAAABJmTx5cjz//PMZ6+65555o2LDhdtn+9hhJa9asWTF27NiYP39+LF68OPLy8mLgwIGxxx57FFvn66+/jk8//TS++OKLWLp0aaxfvz7y8vKiQYMG0bVr19h77723uV0FbdiwIUaPHh2zZs2K+fPnR25ubjRv3jz69OkTubm523Vf33zzTbzzzjsxZ86cWLZsWdSvXz/at28fPXr0iMqVK2/XfZVmyZIlMW7cuPj8889j2bJlsW7duqhevXrk5eVFy5Yto0OHDtGsWbNt3s/GjRtj9OjRMX369Pjqq6+iRo0a0axZs+jdu/d2uV5XrFgRo0ePjrlz58bChQujatWq0ahRo+jQoUMccMABkZOTs8372B1MnTo1xo8fH3PmzIn8/Pxo2LBhdO3aNTp16rTN2541a1a8++67MX/+/Ni4cWM0a9Ys2rVrF127dt0OLd89ffnll/Hhhx/GnDlzYs2aNdGgQYPo1KlTHHTQQVGhwo7/n7N58+bFBx98EAsXLoxFixZFhQoVIi8vL9q1axddunTZ5vBvRMSECRNi0qRJMXfu3MjJyYlGjRpFjx49ok2bNtt+AGW0YsWK+PTTT2Pq1KnxzTffxKpVq6J27dpRr1692G+//aJz585RsWLFbd7Ppk2b4r333ospU6bE119/Hbm5udGsWbM4+OCDo2nTptvhSDKtXLkyRo8eHfPmzYuFCxdGxYoV0/3hgQceWC7XVkTEZ599FmPGjIn58+dHpUqVolmzZrHvvvtGx44dE9vnhg0b4p133onZs2fHV199FZs2bYp99903BgwYUGK9xYsXx6hRo+Krr76KRYsWRc2aNaNhw4ax//77J9repK1ZsybefvvtmDNnTixYsCDq1q0bLVu2jD59+kS1atXKu3mxYsWKePvtt+PLL7+MxYsXR926dWPvvfeOXr16RfXq1bd6uxs2bIjJkyfHlClTYv78+bFixYqoVq1a1K1bN1q3bh0HH3xw1KhRY5vanp+fH5988kl88skn8c0338TKlSujcuXKUatWrWjWrFnsvffe0bFjx+3Sp2yNL7/8Mj766KOYO3durFixIt33ZnM9jxs3Lj7++ONYsGBBVKlSJfbYY4/o27dvNG7ceJvbNW/evHjvvffi66+/jiVLlkSdOnWiYcOG0a1bt9hrr722efsFbc3vJRE7X7+6I6+38ePHx+TJk2Pu3LmxcePG2HPPPePwww8v9fNr/fr18e6778akSZNiyZIlkZubG61bt46+fftu8/stImLSpEkxYcKEWLhwYaxatSrq168fTZs2jZ49e0a9evW2eftJW7t2bbz77rsxe/bsWLhwYaRSqWjYsGG0adMmunfvHpUqbd/HWmW99nf2Pg0AgJ1cCgAAdlMXXnhhKiLSXy1atEjl5+fv0DZsuf/DDz88vf7ZZ59N9ejRI5WTk5NRJiJSzz77bMY28vPzUyNGjEhdfPHFqbZt2xYqX/CrefPmqdtuuy21dOnSrNs5ZMiQjG0MGTIklUqlUkuWLEldccUVqYYNGxa5r6pVq6YGDx6cmj17dtb7atmyZbp+y5Yt0+s//fTT1EknnZSqXLlykfvKzc1N3XzzzanVq1dnva+t9cYbb6SOOuqoVMWKFUt9vZs1a5Y6//zzU+PGjSt2ezfeeGNGnTfffDOVSqVSa9asSV1//fWpJk2aFLntnJyc1IknnpiaPHnyVh3H6NGjU0cffXSxr2lEpBo1apS64oorUt98802p2/vhD3+YUfeLL74otU6XLl0KvQ9Ls3jx4lSFChXSdb73ve8VWa6417UoM2bMyCh7zjnnpH/24osvprp3717sa9S6devUv//971LbXZQ33ngjdfDBBxe77Xbt2qXuu+++dPlzzjkn4+czZszYqv0WtHbt2lT16tXT2z3ssMNKrTNu3LhC7f31r39dar0777yzxD5ts+L6x9GjR6eOPPLIjGtgy6/GjRun7r777tTGjRuzOvayXCcFrVy5MvWHP/whtc8++5TYD1SoUCF18MEHp+68885i+94333wzo86NN96Y/tlDDz2U2m+//Yrd/v7775966aWXsm731po4cWLq+uuvT3Xr1q3U/q927dqpiy++OKt+oCjr1q1L3XrrranGjRsXuf2KFSumjj322NTHH3+crlPcNZONl19+OdW3b98S+8MGDRqkrr766tTixYu36pi2xjPPPJPq2LFjsW068MADU0899VS6/OGHH57x85IUd+0vWrQo9ZOf/CRVr169Iq+14vznP/9J9ezZs9j3ZkSk9txzz9RvfvOb1MqVK7M6/pL65mxsj9dj7ty5qQsuuCBVu3btIo+pVq1aqR//+MepRYsWlaltZVXcscyZMyd19tlnp2rUqFFk+6pVq5b62c9+Vqbrdt68eam777471b9//1TNmjVLfK9XqlQpNWDAgNRbb71V5mP66quvUj/96U9T9evXL3EfEZGqUaNG6qijjkrfg25Pxd17jhgxItWvX79ir+lDDz00ow/a0pAhQ1J77713sZ8Jp556amru3LllbuvGjRtT//73v1P7779/ia9Xu3btUvfee29q/fr1WW+7uD60LL+XbGln61e39/VWXP+0adOm1D/+8Y/UvvvuW+zn1xlnnJH6+uuvC21z1apVqeuuuy6Vl5dXZN3q1aunrr/++tSaNWvKfPwrVqxI3XzzzakWLVoUe9wVKlRI9erVq0z3FCXdv5Rkzpw5qQMOOCCjbvfu3Yt8XTZ77733UieccELGPWvBr9zc3NRFF11UpvfX9rr2d5Y+DQCAXZuQFgAAu61WrVpl/KH0hhtu2OFtKPgH4Q0bNqR+9KMflfgH3YJ/EP7Tn/5U6h+Bi/pq1apVasyYMVm1s6iQ1meffZbxUKukr1q1aqWef/75rPZV1IOyf/3rX8U+fCz41bNnz9SSJUvKcBayt2nTptRll122Va/3ddddV+x2i3owPHXq1FTnzp2z2nbt2rVTI0aMyPo41q9fnzrvvPPK1P46deqkXnjhhRK3+8QTT2TU+dvf/lZi+QULFhT50KO00NkzzzyTUf7ee+8tsty2hrTy8/NTl156adav0SWXXJLatGlTiW3fbNOmTanLL788622feOKJqZUrVyYW0kqlUqmjjz46vd3KlSunli9fXmL5O+64o1A7DznkkFL3c/zxx6fLV6xYsdjQ0pbb3fzA7NZbb80qHBkRqYEDB6bWrVtXanu2NqQ1fPjwVIMGDcrcFxT3MK6oh5wrV65MDRo0KOtt33777Vm1fWuMGTNmq/q+6tWrpx599NEy7WvOnDnFPtwu+FW1atXUAw88kEqlti6ktXDhwtSRRx5ZpmOqX79+6n//+19ZX8IyWb9+ferUU0/Nuk0XXnhhKj8/f5tDSaNGjUo1a9as2P0UFdJavnx56rjjjivTa9i0adPUe++9V+rrUN4hrdGjRxcZVivqq2HDhlkd09Yq6lj+85//ZBUGiIhU+/btsw7NFxdIK+3r5z//eWrDhg1Z7WPEiBGpOnXqlHkfzZo12+rXsDhF3Xv+5je/ybqP2zLQsmbNmtRJJ52UVd0WLVqkpk2blnU7582bV2Kwu6ivffbZJzV16tSstr9lva39vSSV2jn71SSut6L6p9WrV6cGDBiQ1bZbtmyZmjlzZnp7M2fOTLVv3z6ruv369SvTP6aMHj262NBzcV8nnnhiasWKFaVue2tCWmPGjEntscceGfVOO+20YsNnq1atSp1++ullan+NGjVSzzzzTFavz/a49nemPg0AgF2b6Q4BANgtzZ49O2bOnJmx7vDDDy+fxmzhl7/8ZTz44IPp5X322SfatWsXNWvWjPnz58eHH35YqM6mTZsylqtUqRLt27eP5s2bR506dWLjxo2xcOHCGD9+fCxevDhdbubMmXHEEUfEmDFjyjwF4qJFi+Koo46K2bNnR0RE5cqV45BDDolmzZrFypUrY+LEiemfRXw7zcmgQYNi2LBhpU6TVNAzzzwTP/rRjyKVSkVERMuWLWO//faLOnXqxMKFC+O9996LFStWpMuPHj06LrroonjqqafKtJ9s3HbbbXH33XdnrKtUqVJ07tw5WrZsGTVr1ow1a9bEkiVL4rPPPot58+Zt1X4WLlwY5513XsyYMSMiImrWrBkHH3xwNG7cONatWxfjx4+P6dOnp8uvWLEiBg0aFJ988kmpU85s2LAhjj/++HjttdcKHUe3bt2iRYsWsWbNmvSUmZstW7YsBg4cGP/617/i7LPPLnLbRx55ZOTk5KTP1WuvvRY//vGPi23La6+9li5bcH379u2Lrffqq69mLPfv37/YstviZz/7Wfz1r3+NiIicnJzo3LlztG7dOqpWrRqzZs2KDz/8MPLz89Pl//rXv8a+++4bF198canbvuyyy+Lee+/NWJeTkxNdunSJvfbaK3JycmL69Okxbty4SKVS8cILL8SFF16Y6JSe/fv3j1deeSUivr1ORo4cGSeccEKx5Queh4iIjz76KJYuXVrs1H4bNmyI//3vf+nlgw8+OOrUqZNV+/74xz/Gddddl17eZ599Yp999kn3j++9916sXbs2/fNnn302fv3rX8ftt9+e1fbL4s9//nNceeWVhfrfWrVqxYEHHhiNGzeOnJycWLRoUXzyySfx9ddfl3kfmzZtih/84AfxwgsvRERExYoVo2vXrtGiRYuoWLFiTJs2LcaOHZvxHrr66qujU6dOceyxx27bARbTni3l5OTEXnvtFW3bto3c3NyoXLlyLFmyJCZNmpTR/69ZsybOOuusqFatWgwaNKjU/SxYsCAOP/zwjP4n4tvXdnM/uHTp0hg3blzMnz8/1q1bFxdeeGG0aNGizMc0bdq0OProozP604iI2rVrR9euXaNx48axcePGmDlzZnz88cfp12DRokXRv3//eOGFF+Loo48u835Lk0qlYtCgQelzv1nFihWjW7duseeee8a6deti6tSpMXny5IiIuP/++6NBgwbbtN/p06fHlVdemb5XqFmzZnTr1i2aNGkSK1eujM8//7xQnaVLl0bfvn1j3LhxGeurVq0a3bt3j6ZNm8by5ctj/PjxMXfu3PTP58+fH3379o3nn38+sT58W02bNi2uvPLKWLp0aUREVK9ePbp3756+BjdPYbfZwoUL46ijjooRI0bEQQcdlHj7Ro8eHSeffHKsW7cuIiIaN24cBx54YNStWzeWLl0a77//fixatChd/rPPPovTTjstRo0aVer0cgXf740bN44OHTpEvXr1okaNGrFixYqYNm1afPbZZ7Fx48Z0ub/85S+xfv36Qp9vBX3++ecxYMCAWLNmTcb6zVNF161bNyIili9fHjNmzIjPP/88NmzYUPqLsp3cfffd8etf/zq9vN9++0Xbtm2jSpUq8fnnn2f0vWvWrIlTTz01Jk2aFM2bN49TTz01XnzxxYjIvLfKz8+PCRMmxLRp09Lb/fLLL+O0006L999/v9Rpz2bNmhWHH354zJo1K2N97dq14+CDD45GjRrF4sWL46OPPso471OmTIlDDz00Xn/99dh///3L9Dpsze8lO2O/uiOvtzPPPDOGDx8eEd/+frT5/K9fv77Q/fusWbPilFNOiffffz+WLFkSffr0Sf9+Wrt27TjkkEOiYcOGsWLFinj33XczzuuIESPiV7/6Vdx5552ltunll1+OgQMHZtwjRUQ0a9Ys9t9//8jNzU3fR23uTyIiXnjhhTj88MPjzTff3K7T1z/33HNxxhlnxOrVq9PrrrvuuvjNb35T5BTrCxcujGOOOSY+/vjjjPXVq1ePAw44IPbYY4+oWLFiehrszedu9erVceqpp8Y///nPOO+888rUxrJe+zt7nwYAwC6mHANiAACQmIIj/uTk5KSWLVu2w9uxZRu2HLXg+OOPT3366aeFyi9fvjy1cOHCjHV33HFHqkmTJqmrr7469dZbbxU7gsHGjRtTr7zySqFpJbIZ+abgSFp169ZNv26XXXZZkVP8vPrqq4Wmealfv37qq6++KnFfW45mULNmzfR0Fr1790598MEHhcqvXr06dc011xT6r+StmXanJEuXLk1Vq1Ytvf2KFSumbrrpphJH7Zo3b17qX//6V6pPnz6p66+/vthyBUfv2Dw6T/369VP3339/kaMBvfzyy4WmmTzvvPNKPY4rr7yy0LX/k5/8JLVgwYJCZUeNGpXq1KlTRvlq1aqlxo8fX+z2DzzwwHTZvLy8EqcQPffcc4v8j/IBAwaUeAxbjoK35557FltuW0bS2nKEpPPPPz/15ZdfFqozZ86cQqPH5ObmljqN1/PPP1/omAcNGpQxmsJmM2fOTJ188slFtiti+46kNWHChIxtX3rppcWWXbNmTbFTzQwdOrTYeiNHjswoW9IIhluWq1evXnq6qZNOOqnI0dYWL15c6JqqWLFiqa9RWUfSGjZsWKER4Dp27JgaNmxYsdNKTZkyJXXrrbem9txzz6xH0tp8ritWrJi65ppripxy9LPPPis0qkqbNm2yHtGtLD788MNU9erVUz/84Q9Tzz33XImja0ycODH1/e9/v9BnXElTGG02cODAjHo1a9ZM3XnnnYVG19i0aVNq+PDh6f6g4HujtJG0Vq1aVWgKyX322Sc1dOjQIvutefPmpS644IJC52jOnDmlHlNZFZwSNCJSF198cZH99MSJE9MjLOXk5BQa9akkBa/9zfchdevWTf3tb39LrV27tlCd6dOnZywXPM+VK1dO/frXvy40Et/m81VwBM4GDRqUOCVVeY6ktflep0qVKkVO0Zifn5968sknC41O07Zt20SmXi54LJuv+c6dO6dee+21QuU3bNiQ+stf/lJo9MFHHnmk1H3Vrl07dcwxx6QefPDB1Lx584otN2/evNS1116bqlKlSsY+SpsqbfDgwRnljzjiiBKnhV67dm3qlVdeSf34xz9OtW/fvtT2l1XBe8/NxzNgwIDUlClTCpWfMGFCoRGPzj333NQf/vCHVMS3U8ZdeeWVRU7h99xzz6Vq1aqVUbe06c42bNiQ6tGjR0adWrVqpe66665CfeOGDRtSjz76aKE+sV27dqWOilRUfxCR/e8lO2u/mtT1VrB/2vL3o+LO/7Bhwwqd/4ceeig9ymheXl7qH//4R6H7ifXr16duvvnmjHoVKlQodSS2L7/8stDnwl577ZX6z3/+U+heYdmyZanrrrsuValSpYzyP/jBD0rcR1lG0rrjjjsypg+tUqVKiX3Sxo0bU/369cvY/h577JH617/+VeRn1JIlS1K/+tWvMvZRrVq1Es93KrXt1/7O1qcBALBrE9ICAGC3VHCKwKZNm5ZLOwo+hI2I1EUXXVSmB+xz584tNhhQlLVr12ZMaxZReiihYEhr89c999xTYr0FCxYUeoh15plnllinqCkUf/CDH5Q6fc7PfvazMu2nrJ566qmsH0AUpaQHtgUfDG++Jkt78PLhhx9mPHytUaNGiVPUjRs3rlC45O677y5xHytWrEh17949o063bt2KLX/11VdnlH3nnXeKLbvllFpbhp1q1apV7DU9derUjO2ff/75xW5/W0Jam7/uu+++YuukUt8+DO3atWtGnX/+858llm/evHlG+csuu6zEfaRSqdRPfvKTItu3PUNaqVQq1bRp04wHusV59dVX0+UqV66cMa3RhRdeWGy9a6+9NqP9o0aNKrZsUcd71VVXlXoM3/ve9zLqlBSQTKXKdp0sXLgwlZubm1H+5JNPzjqQsWHDhkJB280KPuSM+Dag9eKLL5a4zWXLlqVatGiRUe/111/Pqj1lsWzZsiIfOJek4GtbWr/50ksvZZSvXr16iddIKvXtQ/6CgeCI0kNaBd9Txx57bFbnseD9ww9/+MNS65TFN998U2h63z//+c8l1tmwYUPqxBNPLPI9U5KiPnvq1auXmjhxYlZtLRg4rVixYmrYsGEl1pk3b16qTZs2GfUGDRpUbPnyDGltPqaipnPb0tSpUwsFtUrrd7ZGwWOJiFTfvn1LDQb/5S9/yajTq1evUvdV1s+W1157LSPYUdL7b9OmTamaNWtmfNYUFbYoThIBuKLuPS+44IIS78dnzJiREVauWrVq+r372GOPlbi/gveUpZ2Tu+66K6N8zZo1S7y/SqVSqU8//bRQUOuXv/xliXWK6kPK8nvJztivJnm9FXfvWFoQsuD53xzuqlu3bmrSpEkl1r344ovL1NcUDD63b9++1MD0k08+mRFyiojU8OHDiy2fTUhr/fr1qfPPPz+jXDZTXG4OPm7+OvDAA4sMrRf0zDPPZPzO07dv3xLLb8u1vzP2aQAA7NqEtAAA2C1dd911GX+E7dChQ7m0o+Afgzt06FDkqEnb27x581KVK1dO77ekoEsqVXRI66STTspqXx999FHGH/orV66cmj9/frHlCz4oa9OmTVZ/vF60aFHGSA577LFHVu3LVsGHBNk+xM5GUQ+GX3311azqDho0KOt6Z599dkbZU045Jat9zJo1K+PhQ0QU+1BlxIgRGeVuvvnmIstNmjQpo9z48eMzHvAWt/2//vWvGfWeeuqpYtu9rSGt0kYO2Gz48OFZ13v66aczynbu3LnUAGIq9e3DrX333bdQG7d3SOuss87K2P6sWbOKLLfliGyHH354xnnZa6+9it1+t27d0uVq165d4rEXPNZevXqlNm7cWOoxfPLJJxn1Dj300BLLl+U6KRgy23///cv0IK4kRYW0rr322qzq3nvvvVtVL2kbN25Mde7cOaM/L0nBkeluv/32rPYzatSoQgHUkkIic+fOzfi8aNWqVWrVqlVZH9eW7axSpUqJn2llVfCz5thjj82q3uLFiwsFhSLKHkrKZpSlzXr37p1R94orrsiq3gcffJBxX1ChQoVCI3RtVt4hrcsvvzyr/QwbNiyjXoMGDbZb37BZwWPJy8vLanS6DRs2pJo0aZKuV6lSpURCAZdeemlG+4obDWnBggVbdd0kqeC95z777JPV+TvvvPMKXTM/+tGPstpn69at03UqVqxY7DnZuHFjaq+99srYR2n/JLHZ0KFDM+rl5uaWGOYveCxl+b1kZ+1Xk7zeirp3PPfcc7Oqu+X53/z16KOPllpv1qxZGZ93PXr0KLbstGnTMvraihUrpsaOHZtV+wq+n0sKOZUW0lqyZEnqiCOOyCjTrl271Oeff15iG1avXp0xanCdOnVKHNmvoIKhwTFjxhRbdluu/Z2xTwMAYNdWIQAAYDe0aNGijOU6deqUqX6DBg0iJyen1K9WrVqVabu/+MUvokqVKmWqszWaNm0ahx56aHr5nXfeKfM2brnllqzKde3aNU466aT08oYNG+Lxxx/Pej9XXHFFVK9evdRy9erVyzimefPmxYIFC7LeT1klue1DDjkk+vfvn1XZAQMGZCyPHTu2yHLr1q2Lp556KmPdbbfdltU+9txzz7j44osz1j300ENFlu3Zs2fUqFEjvfzqq68WWe61115Lf9+mTZvo3LlzHHLIIWWqV6FChejXr1+p7d9av/71r7Mqd9RRR2W8b4s7BxERTzzxRMby1VdfHZUqVSp1H5UrV45rrrkmq/Zsi4LXXTbnoX///nHUUUell2fMmBHTpk0rVGfJkiUxZsyY9HLfvn2zOvbNrrvuuqhQofQ/U+y7774Zfe+4ceOy3kdJNm3aFP/4xz8y1t15551RtWrV7bL9gqpXrx5XXHFFVmWz7Qd2tAoVKsTAgQPTy9OmTYuFCxcWWXbp0qXx8ssvp5fr1KkTP/3pT7PaT8+ePeOII47Iul1///vfY/369enlG2+8MaPfKs2W52X9+vUZ7d5WBfuI66+/Pqt6devWjUsvvXSb9t2iRYs444wzsio7c+bMeOutt9LL1atXz7rP7NatW5x88snp5U2bNsWjjz5atsbuAFWqVMn69R84cGAceOCB6eVvvvkm/vOf/yTVtIiIuOiii6JRo0allqtUqVIcc8wx6eX8/PyYOHHidm/PKaeckrGc7b1lkvdTW+uKK67Iqm8v6l4t28/qLetu3Lix2HPy1ltvxYwZM9LLzZs3j5/85CdZ7eOUU06Jgw46KL28fPnyePbZZ7OqG1G230t25n51S0lfb9dee21W5QpeOy1btowf/OAHpdbbc889o127dunl8ePHRyqVKrLso48+Gps2bUovn3rqqdGlS5es2nfzzTdnvAfefPPNmDVrVlZ1t/TFF19E9+7d44033kiv69OnT7z33nvRpk2bEus+8cQTGfcMl19+eTRt2jTrfRe8h3rhhReyrrstv5PvjH0aAAC7FiEtAADYgb73ve9t1+2tXr06vv7665g1a1bMnDkz46tu3brpcp999lmxf+AvSqdOnaJTp05Zly/40GHUqFFZ1z3++OOzLtuhQ4eM5e35R/L27dtnLF933XWxcuXK7bb9LSVxzB9++GGsW7cuvdytW7eMhzylOfvsszOWizuHVatWjd69e6eX33///Vi+fHmhcluGfzYHfLYM+hQVDsrPz48333wzvXzAAQdE/fr1szyCsmndunWhc16cypUrx957751eLum6e/fddzPqbRlgKc3JJ59cplDT1sgmpLVgwYIYP358evmoo46KNm3axF577VVivddffz3jYWG2QcSIbwMgZQnhbPm+WL169XZ5r44fPz4j4LvPPvtEnz59tnm7xenRo0fUq1cvq7ItW7bMeBi+ox8Qrlu3Lr755psiP2sKhmwnT55c5Dbee++9jOtjwIABWQV0Nzv11FOzLrtlyLBixYoxaNCgrOtGRPTq1Svjvfj222+XqX5xVq1aFRMmTEgvN2vWLCN8XJqyvAZFOfHEE7MKQkYU/gwYMGBAmQLv2X6mlKejjz66TJ8x23KvszXK4/4oPz8/li5dGl9++WWh93pOTk5G2eLe6w0aNIgGDRqkl4cOHbrd3kPby7HHHptVubZt22Yst2nTptTgSXF1v/766yLLFbyOTj/99KzfpxHb9l4ry+8lO2u/uiOvt7333rvQeS1OwXL9+/fP+rxuWbeke5yC5/rMM8/MavsR3/7zS8E+ZvTo0VnXj/j2HB5yyCExZcqU9Lpzzz03Xn311YzfQ4uz5TUVEXHaaaeVaf+tW7eOPffcM6M92SrLtb8r9GkAAOxakv3rLwAAlJOCD76XLVtWTi35P82bN4+GDRtudf1NmzbFyJEjY+jQofHhhx/Gp59+GqtXr8667vLly7N+wLrliEdbU/7DDz/Mql6tWrWiRYsWWe+n4B/8t+d57devXzRq1Cj9YPO9996Ltm3bxo9+9KMYOHBgHHDAAWV6aFaSjh07Zl0222P+6KOPMpbL8uA/ImK//faL3NzcdODq888/j2XLlhV5zfTv3z89AsLmYNWWDzs2bNgQ//vf/9LLm8NZ/fv3jxtvvDEiIsaMGROLFy/OeK9+8MEHGce3ZahreyvLOYjIPA/FnYOvv/46vvrqq4x9lCWEUqNGjejYsWNGiGN7a9KkSXTq1Ck9oseIESNi06ZNGdf266+/ng511q9fP7p27RoR356/+++/PyK+fbBWcKSPgg/bynL+9t577zKNaFDU+6JWrVpZ1y9KwVFhkgxoRZT9GszLy0v3+Ul/pn3yySfx1FNPxdtvvx2TJk2Kb775Juu6S5YsKXL9lsG/iMgY/SUb3bp1y6rc2rVrM0Z0a9GiRXzzzTdlOoaIb1/vzXW++OKLMtUtzieffBIbN25ML5f1NWjbtm3UqVNnq8//AQcckHXZbf1MKVg+2/uCHWlH3etsrSTuFQr66quv4plnnolXXnklJk6cGLNnz856n8W913NycmLw4MFx7733RkTEmjVrom/fvjFo0KAYPHhwHHnkkdvcX2+LWrVqRfPmzbMqW/AeqGAYrix1iwq0R5Tfe60sv5fszP3qjrzetuX8b+u1U7t27ULlCl47PXr0yHofEd9eO8OGDUsvf/jhh1mN9hXx7She559/fnp0tZycnPjtb3+b9UhjEZkhsypVqkTVqlVj5syZWdeP+PZ3/s39VrbXVFl/J9/Z+zQAAHY9QloAAOyWCo6MUNYHmmPHjs14kLrZe++9F6effvpWtSmbKWuK8/bbb8ell166TeGN4gI3RSnLCEwR3/6xu1q1arF27dqIyH4Eh2z+y3pLlStXzljesGFDmeqXpEaNGvH3v/89Bg0alB7t5auvvopbb701br311sjLy4sePXpEjx494rDDDovu3btHtWrVtmpfZTnubI+54Gte1nOYk5MT7dq1y3jgs2DBgiKvmYLhm1dffTUjpDV69OhYtWpVRHw7DVPfvn0jIuLggw+OvLy8WLp0aWzatClGjBgR3//+9zO2s6WyjMRUVtty7eXn5xdZpuDDypYtW5a5XXvuuWeiIa2Ib8/f5pDW4sWLY8yYMRkBmC3PQ79+/dIBrqOOOiod0nrjjTciPz8/Y1SMLUNaBafrKc3O0BfMnz8/Y3nffffd5m2WZFuOeXv2fVuaNWtWXH755fHcc89t9TaK+7zd1vfHlqNllOSrr77KeH1mzpyZMQrc1li8ePE21d9sW1+DnJycaNGixVaHtMpyH7Ktnyn169ePBg0apI956dKlsWHDhkLv3fJU1mMqODJO0iPaJXGvsNnq1avjpptuirvuuitjCruyKOk6vOmmm+Kll15KhyY2btwYTz31VDz11FNRsWLF6NKlS/To0SN69eoVvXv3LtMUZ9uqLK9rwdEt8/LytrpuUvdvBUcFzfa6LEt/sDP3qxE77nrbma6d9evXZ7wHGzZsWOb7iq29dh599NGYPn16erl69erxyCOPlGl0tU2bNsW8efPSy+vXr88YtXZrZHtNbc3v5DtznwYAwK7HdIcAAOyWCv5h9Kuvvir2P9iL0qJFi2jVqlWhryZNmmx1m4r6D+hsDB06NPr167fNwY0tp5kqTW5ubpm3v2WYZ/369emQTkm218hU28vAgQPj5ZdfLnIqm6VLl8ZLL70UN9xwQ/Tt2zcaNmwYZ5xxxlaNpJHEcRcc0aIs01IVV6e4hx377bdfxnusYLhqy7BO9+7d09dTxYoVM6a0K6lejRo1yjyaRFkkcQ6WLl2asbw17/mtee+VVWlTHr7++uvp77cM5PXr1y8qVqwYEd+O6vD++++nf/b5559njH5Q1oDdztAXbDnVYUTZQ1RltTMc85YmT54cPXr02KaAVkTxnzXb+v7I9r1R8DxuDytWrNgu2ynvPqIs+9uRnynlpayv5Y4+nqT6iJUrV8YxxxwTd9xxx1YHtCJKvq+sX79+vPvuu3HKKacU+tnGjRtjzJgxce+998bgwYPT034++OCDiQVQt7Qtr+vOeP9Ws2bNjFBPttdlWfqDnblfjdhx19vOdO2UZx+9ZUAr4tupWYt67UuyZMmSMv1umo1sr6mt+ezdmfs0AAB2PTvXXwQBAGA76dmzZ8bypk2bMqbp2FXMmjUrzjnnnIw/8DZq1Ch+/vOfx7Bhw2LChAnxzTffxOrVq2PTpk2RSqXSX+ecc85W7zcnJ2eb2749tlEe+vfvH5MnT45hw4bF4MGDo3HjxkWWW7lyZTz++ONx8MEHx3nnnRdr1qzZwS3NtHl6us2SPodHHnlk+vtp06ZlBHS2DFsVHHVry/DOluWWL18eH3zwQXr58MMPj6pVq25Vu8tLwfZuzcPvbXlgnq3evXtntHXL8zBp0qSYO3duennL85eXl5cxPduW9QoGvZKcqnJH2VX7sK2Rn58fgwcPzhhNrFq1anHWWWfFQw89FB999FHMnz8/Vq5cGfn5+RmfNUOGDMlqH9v6/si2fBLvoYL969baVfqIiB3/mVIeytqeguV3tuPJ1tVXXx1vv/12ejknJyf69+8fd911V4waNSpmzpwZK1asiPXr12e812fMmFGm/TRs2DCGDh0aEydOjKuuuio6depU5GuWSqXi3XffjfPPPz86depUaGrU3d32fq8lcV3uzP3qZt+16608++gjjjgiatasmV4eOnRonHPOOcWONFuUHfVZtj19164xAACSI6QFAMBuac8994xWrVplrPvf//5XPo3ZBrfffnusXr06vXzCCSfE9OnT489//nMMHDgwOnXqFPXr14/q1asX+iNxWUYOK2hrplLask6VKlWiRo0aW73/8lapUqUYOHBgPPHEE/HVV1/F559/Ho888khcdNFF0bp160LlhwwZEmeccUY5tPT/1KtXL2N5W89hRMkjCRU15WHE/02fV1y5LZdnzZoVU6dOjYj/mz5vsySnOkxKwder4CgH2dgRI81Ur149evXqlV5+5513YuXKlRGRGbxq3759tGjRIqPuludvy2DWlvUqVKgQ/fr12+7tTlqDBg0ylne2UX+StDn0u1mbNm1i0qRJ8cgjj8Q555wTXbt2jSZNmkTNmjXTo6ltlu1nzba+P7I9HwXP41FHHZURNNmary1DqNtiV+kjInb8Z8rWKmpq6myV9ZgKjoSW9Gh7SZg/f378/e9/Ty9Xq1YtXn755Xj11Vfjsssui549e0bLli2jVq1ahaZQ3Nr7yv322y9uv/32mDBhQixevDhefvnl+PWvfx2HHXZYoWndpkyZEv369StzIGxXtq3vtVWrVmXcPyVxXe7M/WpB35XrrTz76MMOOyxee+21jPKPPvponHLKKbF27dqstlG/fv2M5Xbt2m3zNbW9g3/F+a5cYwAAJEdICwCA3VbBcMiQIUO26WFeeXj++efT39eqVSv+/e9/Z/zncknmzZu31fvdHJzJ1pw5czL+KN+oUaOt3vfOqE2bNnHWWWfF3//+9/jiiy9i7Nixcfrpp2eUefbZZzOmidvRCr7mZT2HqVQqPv/884x1DRs2LLZ8//79M4KBmwM7I0aMSE9fUrdu3YyRlyIiWrduHXvvvXeheluGfDZvf1ezxx57ZDycmThxYpm38cknn2zPJhVry9d3w4YNMXLkyIgoeRS0gus++OCDWLZsWeTn58ebb76ZXn/AAQcUevi2Kyg4Te6nn35aTi3Z8bb8rImIuP/++4sMpBYl28+ali1bZiyX9f2RbfmCox+WtS9M0ra+BqtWrUos2FDQtn6mLF68OL755pv0cl5eXqHQT0QUeqBdlpFYIrYu6LZZWY+p4GfkrnivM3z48Iwpxq666qqsRz7clvvKzfLy8uLoo4+OW265Jd566634+uuv44477siYdm3RokXx61//epv3tavY1vfalClTStze9rAz96sl2Z2vtypVqmQcx4IFCwoFSUuzLddOjx494n//+1/GvdMLL7wQxx57bFbTDlapUiUj5DVjxoxdcmrA3fkaAwAgOUJaAADsti6//PKMEMns2bNj2LBh5diislm9enXGA7HevXtHbm5uVnXXrl0bY8eO3ep9v/fee2Uq//7772csd+vWbav3vSvo0qVLPP744/GTn/wkY/2zzz5bTi2KQmGod955p0z1J02alPEf9W3bto28vLxiyzdu3Dg6deqUXn7jjTdi06ZNGaMr9evXr9CoOxFFT3m4Zb2mTZvGfvvtV6b27wyqV68e+++/f3r566+/jsmTJ2ddf9KkSbFgwYIkmlZIwYfyr732Wqxfvz5jxMGignLdu3dP90MbN26MN954I95///2MEVZ2xYBdROFpcnfF0Re31pbhk5o1a0afPn2yrpttX9O9e/eM5S2DfdnItnxubm7su+++6eWZM2cWCteUlzZt2mSMSDNu3LgyPVQfOXJkRsAmSdv6mVKwfHH3BQXva8ryeqxbty6++OKLMrVrS9/Fe52C74UBAwZkXbes10A26tWrF7/85S/jlVdeybhnf+GFF3bYtV7edtR7bVvszP1qWexu11vBa+fdd98tU/1tvXY6deoUo0aNygh1jxw5Mvr27ZsR0i3OoYcemv5+y38Y2JXtbtcYAADJENICAGC31aFDhzjppJMy1v30pz+NhQsXlk+Dyqjgg8ot/yO3NI8//nisX79+q/f9ySeflGmEj8cffzxjecup1HZnP/rRjzKWy3Mqi4MOOiiqVq2aXv7ggw/K9ADt0UcfzVjO5hxuGcZZsmRJfPjhhxkjMRUX1tkyIPTmm2/GtGnTYtq0aaXW2xUceeSRGcsPPPBA1nXvv//+7d2cYnXp0iVjpLRXX3013nnnnVi1alVERFSuXLnIoE6lSpUy1r/66quFRkHLdlSWnU3nzp0zXpPJkyfHW2+9VY4t2nG2/LzJzc0tNH1ucaZNmxajR4/OqmzHjh0zRtz44IMPMqZYLMmaNWsK9VElOfroozOWy/I+TNqWU4GuW7cuHnnkkazr7sg+ouBnwPDhw8s03V22nym1a9eO6tWrp5fLMoLdiBEjsp5aqyivvvpqLFq0KOvyu8O9ztbeW27cuDH+/e9/J9Cibx1yyCEZ4ewVK1ZkFfLYHRS8jp544okyhTm25v5ta+zM/WpZ7S7XW8Fz/dhjj2Vdd8mSJTF8+PCMdQXD6tlo3bp1jBo1KuP1HDNmTBx22GHx5Zdfllh3d7qmCtpdrjEAAJIhpAUAwG7t97//fdSuXTu9/PXXX8egQYMyRgzaWW05BURExGeffZZVvWXLlsVvfvObbd7/DTfckFW5MWPGxHPPPZderly5cvzgBz/Y5v3vCgpO07RlSGpHq1atWpx66qkZ666//vqs6s6ZMyfuu+++jHXnnHNOqfUKhnHuvffemDVrVrE/3+yII45Ij7C1YsWKuPnmmzN+viuHtC688MKoUOH/ftX+61//mtUUhuPHj4+//e1vSTYtQ05OTkag7LPPPosHH3wwvXzooYdGrVq1iqy75Xl99dVXM0ZBq1GjRsbICLuSnJycQqPjXX755dsUeN1VbPl5s2DBgqynkLvqqquyDhNUqFAhLrzwwox1l1xySVbTEN9www3x9ddfZ7WfiIiLL744o3++5557YtKkSVnXT9KPf/zjjOWbb745vvrqq1Lr/fe//40XXnghqWYV0qpVqzjssMPSy6tXr45bb701q7pjxoyJ//f//l96uUKFCnHmmWcWWTYnJye6dOmSXp4xY0ZW52rTpk1Zt6c469evj9/+9rdZlX322Wfj448/Ti83aNAgjj/++G3af3nY2nvLe++9N/Eg+s50T7Uj9e7dO/baa6/08pdffhn/+Mc/sqr77LPPxgcffJBezs3NLfQPKtvLztyvbo3d4Xo788wzM+45n3rqqaz/yeamm26KdevWpZf79OlTaErebDVt2jTeeuutjBEzP/vss+jVq1eJU2OeddZZGaP2PvPMMzFixIitasPOaHe4xgAASIaQFgAAu7V27drFQw89lLHurbfeil69emUVnCgo2wfX20P16tWjbdu26eWxY8dmhCGKsnr16hg8eHDMnDlzm/f/3HPPxV//+tcSyyxcuDDOPPPMjIf0p556ajRp0mSb97+jPfbYY2Wami4iCo2A0qFDh+3ZpDL7+c9/njECztNPP11q8GfVqlVx2mmnxcqVK9PrunbtGocffnip+zvssMOiWrVq6eUt/4O/bdu20apVqyLr1alTJw4++OAi60UUHo1qV9K6deuMkOL69evj6KOPLnHEoPHjx8cxxxwTGzZs2BFNTCsYhtvyPJQ0GtaW9aZPn54xZVjv3r136YdQl112WcYDw7Fjx8aZZ56Z8SCzJPn5+bvkSAlbTtO5cePGuOOOO0qtc+ONN5Z5itef/OQnUa9evfTyqFGj4owzzojVq1cXWT6VSsUdd9wRf/zjH8u0nzZt2sQPf/jD9PLatWvjuOOOK9MoTRHfjnRV8B5iW/Xp0ycj/LR48eI48sgjY/bs2cXWGTFiRJx22mnbtR3ZuOKKKzKW//znP8eLL75YYp2vv/46fvCDH2SE7wYOHJgxHVZBW44uFpFdwPiqq67aLtPv3XPPPfH888+XWGbatGmFApwXXXTRLtnXbflej4j44x//WGpQ8qWXXoqrr746632MGzcunn322awCmJtNmDAhxo8fn15u2rRpmUaQ3ZVVqFAhfvazn2Wsu/rqqzPCV0WZMmVKodDnBRdckPXU6GW1s/ar3+XrrU2bNnHiiSeml/Pz82Pw4MGl3ocMHTo07r333ox1Bfv7sqpbt268/vrrGfePs2fPjl69esXYsWOLrJOXlxdXXnllxrpBgwbFqFGjyrTvjRs3xrBhw2Lx4sVlb3gWkr7GHnroocjJyUl/Fff7EwAAuw8hLQAAdnsnn3xyoZESPvnkk9h///3j9NNPj9deey3WrFlTbP0NGzbEq6++GmeddVZ8//vfT7q5GQo+lP3+978fjz32WKGRS1KpVLz++uvRo0ePePnllyMiMqbtKqvNIy389Kc/jcsvv7zIcNrrr78ehx56aMYoDPXq1Svzw/SdxTPPPBP77rtv9O3bN/7617+WGHRbuHBh/PKXv4w///nP6XUljRSyoxxwwAHxi1/8ImPdJZdcEpdddlmRUzq9++670atXr4wH3VWrVo1//vOfWe2vevXqGVOtpFKp9PelTXm35c+3rNepU6ddMuS3pbvuuitjWrd58+ZF165d4/zzz4/hw4fHpEmTYtKkSTF8+PA4//zz46CDDoqvvvoqGjRoUCiskKSC5yjb89euXbuMB0hb1tuVR0GL+LYPe+SRRzLCjs8880x069YtXnjhhcjPzy+y3ueffx6/+93vok2bNoWmD9oVFPys+d3vfhfXX399RnhzsylTpsTJJ58ct9xyS0SU7bOmUaNGhcK/Tz31VHTo0CFuu+22GD16dHz++efx0Ucfxf333x89evSIq666KiIiBg8eXKZj+vOf/xydO3dOL8+ePTsOOuiguO6660qcgmnNmjXx+uuvx2WXXRYtWrTICCVsL0OGDImaNWumlydNmhQdO3aMyy+/PF599dX47LPPYsKECTF06NA49dRTo3///rFy5cpo27ZtHHjggdu9PcX53ve+F6ecckp6OT8/PwYNGhS33HJLoWsjlUrFSy+9FD169MgYOaVevXpx1113lbif8847L2M0mOeeey7OO++8Iu89Jk+eHN/73vfiT3/6U0RERqiyrOrWrRsbN26MU089NW699db0dK+bbdy4MZ5++uno1atXxmhnbdq0iWuvvXar91uejj322IwRZt9+++0YOHBgke+JRYsWxTXXXBMnnnhirFu3Luv3+syZM+Pkk0+OvffeOx2mKy6EnJ+fH0OHDo2jjz464942m9E8dyeXXHJJHHLIIenlFStWRP/+/eO+++4rFBLOz8+Pxx57LA477LBYsGBBen2bNm3ixhtvTLSdO2O/+l2/3u6+++6MEfI+/fTT6NGjR7zyyisZ92cR315XN954Y/zgBz/IOP7TTjstBgwYsM1tqVmzZrz44osxaNCg9LqFCxdGnz59ip0++qqrrsq451y6dGn06dMnLrnkkpgyZUqx+9qwYUO88847cfXVV8fee+8dp5xySpmm5C2L7/o1BgDA9lep9CIAALDru+6666Jx48Zx2WWXpQNZmzZtiieffDKefPLJqFq1auy7777RsGHDqFevXqRSqVixYkXMnj07pk6dWuQoKtWqVUvk4e2WrrjiivjXv/4V8+bNi4iI5cuXx5lnnhm//OUv46CDDoo6derE4sWLY9y4cTF//vx0vTPOOCMqVaoUDz/88Fbt99prr4277747vvzyy7jrrrvivvvui+7du0ezZs1i1apVMWHChIxp7SK+nebw4Ycf3qUDNqlUKkaOHBkjR46MSy+9NOrXrx/77rtv1K9fP2rWrBmrV6+O6dOnx8SJEwv9N/W1114bHTt2LKeW/5/bbrstxo4dG2+88UZEfHtM99xzT/ztb3+LQw45JJo3bx5r166NSZMmxbRp0zLqVqhQIe67776MqadK079//3j99dcLrc8mpFVwmsNs6u0K6tWrFy+//HIceeSRsXDhwoj49qHNgw8+mDGl4JYqV64cjz32WDz++OMZ67cMC21vzZo1iw4dOhQaQa5+/fqlBkH69+8fDzzwQKH1u8P5O+GEE+LPf/5zXHHFFekHbBMnTozvfe97Ubt27ejatWs0atQocnJyYtGiRTFx4sQyTcW3M+rXr18ce+yx8dJLL6XX3XrrrXHXXXfFwQcfHE2bNo2VK1fG1KlTM66XDh06xI9//ONCo8CUZPDgwTFjxoyMkMvs2bPjuuuuK7bOvvvuGw888EA8+eST6XWlvTdq1aoVw4cPj6OOOiodJl6zZk3cdtttcdttt0Xr1q2jffv2kZeXF/n5+bFs2bKYOXNmTJs2rUyjZWyNvffeO55//vk48cQT06OIrVq1Ku66665iA021atWKoUOHxmWXXZZo2wp64IEHYurUqekptNavXx833nhj/O53v4vu3btHkyZNYsWKFTF+/PiYM2dORt1q1arFY489Fs2aNStxH3vttVdcccUVGSO4DRkyJJ544ono0aNHNG3aNFatWhVTpkzJCIZfe+21MXr06Pjf//63Vcf2+9//Pq666qpYtmxZXH/99XHbbbdF9+7do3HjxrFs2bIYM2ZMofd2bm5uPP7441GjRo2t2md5q1u3blx//fUZI2O9+OKL8d///je6du0arVu3jvz8/Pjyyy/jo48+Sr8XatWqFffff38MHDgw633NmjUr7rjjjrjjjjuiSpUq0bFjx2jWrFnk5eXFxo0b46uvvoqxY8cWmoK8ffv2WU/XvLuoVKlSPPHEE3H44Yenw07Lly+PSy65JH71q1/FIYccEg0aNIglS5bERx99VGikpHr16sXTTz+dEcBLws7cr35Xr7cWLVrEo48+Gqecckr699Vp06bFMcccE82bN48uXbpE7dq1Y/78+fHee+/F2rVrM+p36dIl6+k1s1GlSpV46qmn4qKLLkr/08fy5cvjmGOOiaeffrpQGKxSpUrx9NNPx3HHHZf+p5GNGzfGfffdF/fdd180a9Ys9ttvv6hXr15s2rQpli9fHnPmzInPPvtsh49A+129xgAA2P6EtAAA+M44//zzo1evXvGLX/wi40F0xLdTb3z88cdZbad69epx2mmnxc033xx77rlnEk1Ny8vLi+HDh8cxxxyT8d/yX331VbGjtZxxxhkxZMiQuOCCC7Z6vw0aNIhXXnkljj766Pjyyy9jw4YN8fbbbxdbvmbNmvHYY49tl//C3pksWrSo2P/83qxSpUrx61//Om644YYd1KqSValSJV566aW44IILMqZjzM/Pj9GjRxdbLzc3Nx5++OE46aSTyrS/o446qtA0SJUrV46+ffuWWO/ggw+OOnXqFHqQsauPxLRZ586dY/To0XH++eeXeg21bNkynnrqqTjkkEMKhbiSfuB61FFHFQppHXnkkRmj2hSlqJBW06ZNY7/99tvubSwPl19+ebRu3Tp++MMfZkyfs2LFihg5cmT5NSxBjz/+ePTv3z8++uij9LqVK1emA58FdenSJYYPHx6vvfZamff1q1/9Klq0aBG/+MUv0kHG4nz/+9+Pf/7zn1GxYsWM9dm8N1q0aBEffvhh/PjHPy40rer06dNj+vTppW5jW0ZqKkm/fv1i5MiRcf7555c4HWrEt/3J008/Hfvss0/GyJ9J9w8R34Z6Ro0aFaeeemq88sor6fVr164t8b3QpEmT+H//7//FoYcemtV+fvvb38a0adMyptBcu3ZtvPnmm0WWv/rqq+PWW2+NPn36ZLX9orRr1y6GDx8eJ5xwQixdujRWr15d7PUe8e2ocS+88EJ069Ztq/e5M7jqqqti2rRpGX34xo0b44MPPihyir369evHsGHDtumed/369TFu3LgYN25cieV69uwZw4YNyxhp7rtir732ivfeey9OPPHEGDNmTHr98uXLS+xn27ZtGy+++GLss88+O6KZO3W/utl37Xo7/vjj47XXXotBgwZl/L44Z86cQuHZgvWeeOKJ7f5ZUqFChXjggQeiXr168Yc//CEivg3zDRw4MB566KE444wzMsrXqVMnRo4cGddcc03cddddGYG+uXPnxty5c0vdZ40aNXboFLTftWsMAIDty3SHAAB8p7Rv3z7++9//xtixY+PSSy/N+oFTvXr14phjjol//vOf8dVXX8WQIUMSD2htdsABB8S4cePi7LPPjsqVKxdZJicnJ3r16hXPPPNM/Pvf/y62XFl06NAhxo0bFz/72c+ifv36RZapWrVqnHbaaenph3Zld911V9x9991x3HHHZUwbUpzc3Nw455xzYsKECTtNQGuzKlWqxMMPPxxvv/129O/fv8TroWHDhvHzn/88vvjiizIHtCIi9t9//2jUqFHGuu7du5f6wKdSpUqFglxVq1aN3r17l7kNO6u2bdvG//73v/jPf/4T55xzTrRr1y5q164dNWvWjLZt28aAAQPisccei8mTJ6enOVq6dGnGNurUqZNoG4sKxWUzGlZRQa4jjzxyu7VrZ3DiiSfGzJkz4ze/+U3stddeJZatVKlS9OzZM/72t79lTA+3K8nLy4tRo0bFDTfcEPXq1Su23D777BO33357vPfee6WOkFSSM888M6ZOnRr33HNPHHnkkdG8efOoWrVqNGjQIPbff//4yU9+Eu+99148/fTTkZubu9XvjVq1asW///3vGD9+fJx55plZ9e977LFHnHHGGfHMM89kTHO3vXXr1i0+/vjjeOqpp+LUU0+N1q1bR40aNSI3Nzc6dOgQ3//+9+P555+PMWPGpAMYW74OSfcPm+Xm5sbLL78cL7zwQvTo0aPEEGfz5s3j5ptvjs8//zzrgFbEt59bQ4cOjXvuuafYETlzcnLi8MMPj9deey1+//vfl/k4itKrV6+YMGFCnHvuuVGrVq0iy9SqVSsuuuii+Oyzz6J79+7bZb/l7f77749///vf0bZt22LLNGzYMC699NKYNGlSmT6bjzrqqBg2bFicf/750aZNm1LL5+TkxGGHHRaPPfZYvP3224XuKb5L9thjj/jggw/i4YcfzphWsCht27aNu+++Oz755JMdFtDabGfqV11v3zrssMNi2rRpceONN5b42VyhQoU49NBDY/jw4TF8+PBEw7633357Rl+dn58fZ511VqFpjyO+/QePP/3pTzF16tS4+OKLo3HjxqVuv379+nHyySfHQw89FF9//XXGVOPbk2sMAIDtLSdVcHJyAAD4jpkzZ05MnDgxZs+eHUuXLo3169dH7dq1Iy8vLz3dXevWrcu7mRHx7cPZUaNGxfTp02PlypVRr169aNKkSRx00EHRvHnzrd7uQw89lDF145AhQ+Lcc89NL69fvz5GjRoVs2bNiq+//jpq1aoVLVq0iL59+0Zubu62HNJOKZVKxbRp0+Lzzz+P2bNnx7Jly2LDhg1Rq1at9DWx3377RZUqVcq7qVlZsWJFvP322zF37tz45ptvomrVqtGwYcPo0KFDdO3aNdEp9SibFi1apEc9aNCgQamjDLHjfPHFFzFmzJhYuHBhLF26NKpUqRL16tWLtm3bRpcuXXarvnDdunXx/vvvx6RJk2LJkiVRq1ataNKkSXTo0CE6depULm0aMWJERhDwl7/8Zcb0eNnatGlTTJgwIT799NNYvHhxLF26NKpVqxa5ubnRqlWr6NChQ7Ro0WJ7Nn27Wbt2beTm5qaneDrooIPiww8/3OHt+Oabb2L06NExf/78WLx4cdSsWTMaNmwYnTt33i6j6W3atCk++uijmDBhQnzzzTdRvXr1aNasWXTr1i1atmy51du96aabMqbZffPNNzNG4lq1alWMGjUqvvzyy1i4cGHUrVs3WrZsGX369Inq1atvyyHttFKpVEycODE++uijWLhwYVSsWDGaNGkSLVu2jB49ekSlSts+CcOiRYti0qRJMWPGjFi0aFGsXr06qlatGrm5udGmTZvo0qVLsf8M8F03d+7ceO+99+Lrr7+OpUuXRu3ataNRo0bRrVu3neZ3k4idq191vX3rk08+ifHjx8c333wTq1ativr160fTpk2jZ8+eu8zxT548OSZMmBCLFi2KpUuXRqVKlSI3NzdatGgR7du3j9atW5fL7zCuMQAAtpWQFgAAUGpIC0jejBkzMh66Hn300fHyyy+XY4tg53HjjTfGLbfckl5+4oknYvDgweXYoh3vzTffjCOOOCK9fNFFF8Xf//73cmzRrqW0kBYAAABA0kx3CAAAADuBv/3tbxnLPXr0KKeWwM5lw4YN8eCDD2as212mnSsLfQQAAADArk1ICwAAAMrZuHHj4q677kov5+TkxJlnnlmOLYKdx3XXXRdz585NL/fu3TtatWpVfg0qB//973/jmWeeSS/XqlUrTj755HJsEQAAAABlJaQFAAAA29lf//rX+MMf/hArVqwoteyIESOif//+sX79+vS6E044Ifbee+8kmwjl5vTTT49XX301UqlUieXWr18f11xzTdxxxx0Z6y+//PIEW7dj3HjjjXH//ffHunXrSi37xBNPxPe///2Mdeedd17Url07qeYBAAAAkIBK5d0AAAAA2N0sXLgwbr755rj55pvjxBNPjN69e0enTp2iQYMGERGxaNGi+Pjjj+PZZ5+NN998M6NuvXr14r777iuPZsMO8e6778aTTz4ZrVq1ilNOOSUOOeSQaN26ddSpUydWrVoV8+bNi7fffjseeeSRjBG0IiJOO+20GDhwYDm1fPuZNWtW3HLLLXH11VfHwIEDo1evXtGxY8eoV69e5Ofnx8KFC+ODDz6Ip556KsaMGZNRd++9945bb721nFoOAAAAwNYS0gIAAICErF69Op588sl48sknsypfr169eO6556JZs2YJtwzK38yZM+NPf/pT1uX79esX999/f4It2vGWLl0aQ4YMiSFDhmRVvmXLlvHCCy9ErVq1Em4ZAAAAANub6Q4BAABgO6tXr17k5OSUqc5xxx0X77//fhx22GEJtQp2DptHlMtWbm5u3HDDDfHyyy9Hbm5uQq3ascr6GlSsWDF+8IMfxAcffBAdO3ZMqFUAAAAAJMlIWlnatGlTzJs3L2rXrl3mP7QDAMDObs2aNYWWly9fXk6tgV3fueeeG0cddVS8/vrr8f7778eUKVNizpw5sWzZsli7dm3UqlUr8vLyonXr1tGzZ8847rjjYr/99ouI8N5jtzdixIj46KOP4q233oqPP/44pk+fHvPnz49Vq1ZFKpWKOnXqRL169aJLly7Rs2fPOOmkkyIvLy9Wr15d3k3fbm644YY444wz4o033ogPP/wwpk6dGnPnzo3ly5fH+vXro3bt2lGvXr1o165d9OzZM0444YRo3bp1ROgjtta6desylletWuW1BAAAALaLVCoVK1asiD322CMqVCh+vKycVCqV2oHt2mXNmTMnWrRoUd7NAAAAAAAAAAAAdjJffvllNG/evNifG0krS7Vr146Ib1/Q3WVofQAAAAAAAAAAYOstX748WrRokc4WFUdIK0ubpzjMzc0V0gIAAAAAAAAAANI2Z4uKU/xEiAAAAAAAAAAAAGwzIS0AAAAAAAAAAIAECWkBAAAAAAAAAAAkqFJ5NwAAAAAAIiJSqVTk5+fHxo0by7spu42KFStGpUqVIicnp7ybAgAAAPCdJqQFAAAAQLlbv359zJ8/P1avXl3eTdnt1KhRI5o2bRpVqlQp76YAAAAAfGcJaQEAAABQrjZt2hQzZsyIihUrxh577BFVqlQx8tN2kEqlYv369bFw4cKYMWNGtG3bNipUqFDezQIAAAD4ThLSAgAAAKBcrV+/PjZt2hQtWrSIGjVqlHdzdivVq1ePypUrx6xZs2L9+vVRrVq18m4SAAAAwHeSf50DAAAAYKdglKdkeF0BAAAAyp+/0AAAAAAAAAAAACRISAsAAAAAAAAAACBBQloAAAAA7NT69OkTl19+eXk3o8zOPffcOOmkk4r9+U033RRdunTZYe0BAAAAoPxUKu8GAAAAAEBJhg0bFpUrVy7vZmx3v/zlL+OnP/1peTcDAAAAgB1ASAsAAACAnVq9evXKuwmJqFWrVtSqVau8mwEAAADADmC6QwAAAAB2altOd3jfffdF27Zto1q1atG4ceMYNGhQVtsYOnRodOrUKapXrx7169ePI488MlatWhUR/zct4W233RaNGzeOvLy8uPnmmyM/Pz+uvPLKqFevXjRv3jz+9a9/ZWxz4sSJccQRR6S3eeGFF8bKlSuLbcOYMWOiUaNGceutt0ZE4ekON7fjj3/8YzRt2jTq168fl1xySWzYsCFdZv78+XH88cdH9erVY6+99orHH388WrVqFXfeeWdWrwMAAAAA5cNIWgAAAADsEj766KO47LLL4tFHH41DDz00Fi9eHG+//Xap9ebPnx+nn356/OEPf4iBAwfGihUr4u23345UKpUu88Ybb0Tz5s3jrbfeitGjR8ePfvSjePfdd6N3797x/vvvx1NPPRU//vGPo3///tGiRYtYvXp1HHPMMdG9e/f48MMPY8GCBXH++efHpZdeGg899FChNowcOTJOOumk+N3vfhcXX3xxsW198803o2nTpvHmm2/GtGnT4rTTTosuXbrEBRdcEBERZ599dnzzzTcxcuTIqFy5cvziF7+IBQsWlP3FBAAAAGCHEtICAAAAYJcwe/bsqFmzZgwYMCBq164dLVu2jAMOOKDUevPnz4/8/Pw4+eSTo2XLlhER0alTp4wy9erVi7vvvjsqVKgQ++yzT/zhD3+I1atXx7XXXhsREb/61a/i97//fYwePToGDx4cjz32WKxZsyYeeeSRqFmzZkRE3HvvvXHCCSfE7bffHo0bN05v+/nnn4+zzjor/vGPf8Tpp59eYlvr1q0b9957b1SsWDHat28fxx9/fIwYMSIuuOCC+Oyzz+L111+PDz/8MA466KCIiPjnP/8Zbdu2zf5FBAAAAKBcmO4QAAAAgF1C//79o2XLltG6des466yz4rHHHovVq1eXWm///fePfv36RadOneL73/9+PPDAA7FkyZKMMvvuu29UqPB/fypr3LhxRpCrYsWKUb9+/fSoVZMnT479998/HdCKiOjZs2ds2rQppkyZkl73/vvvxymnnBIPP/xwqQGtze2oWLFierlp06bpfU6ZMiUqVaoUBx54YPrnbdq0ibp165a6XQAAAADKl5AWAAAAALuE2rVrx8cffxxPPPFENG3aNG644YbYf//9Y+nSpSXWq1ixYrz22mvx0ksvRceOHeOee+6JffbZJ2bMmJEuU7ly5Yw6OTk5Ra7btGlTRESkUqnIyckpcn9brt97772jffv28a9//SvWr19f6jGWts+iFLceAAAAgJ2HkBYAAAAAu4xKlSrFkUceGX/4wx9iwoQJMXPmzHjjjTdKrZeTkxM9e/aMm2++OcaOHRtVqlSJZ599dqvb0bFjxxg3blysWrUqvW706NFRoUKFaNeuXXpdgwYN4o033ogvvvgiTjvttNiwYcNW77N9+/aRn58fY8eOTa+bNm1aqSE1AAAAAMqfkBYAAAAAu4Thw4fH3XffHePGjYtZs2bFI488Eps2bYp99tmnxHrvv/9+3HbbbfHRRx/F7NmzY9iwYbFw4cLo0KHDVrfljDPOiGrVqsU555wTn3zySbz55pvx05/+NM4666xo3LhxRtlGjRrFG2+8EZ999lmcfvrpkZ+fv1X7bN++fRx55JFx4YUXxgcffBBjx46NCy+8MKpXr17sqF4AAAAA7ByEtAAAAADYJeTl5cWwYcPiiCOOiA4dOsTf//73eOKJJ2LfffctsV5ubm689dZbcdxxx0W7du3i+uuvjz/96U9x7LHHbnVbatSoEa+88kosXrw4unXrFoMGDYp+/frFvffeW2T5Jk2axBtvvBETJ06MM844IzZu3LhV+33kkUeicePG0bt37xg4cGBccMEFUbt27ahWrdpWHwsAAAAAyctJpVKp8m7ErmD58uVRp06dWLZsWeTm5pZ3cwAAAAB2G2vXro0ZM2bEXnvtJWxURnPmzIkWLVrE66+/Hv369SuyjNcXAAAAIDnZZooq7cA2AQAAAADb4I033oiVK1dGp06dYv78+XHVVVdFq1atonfv3uXdNAAAAABKIKQFAAAAwC5t9uzZ0bFjx2J//umnn8aee+65A1uUnA0bNsS1114b06dPj9q1a8ehhx4ajz32WFSuXLm8mwYAAABACYS0AAAAANil7bHHHjFu3LgSf767OProo+Poo48u72YAAAAAUEZCWgAAAADs0ipVqhRt2rQp72YAAAAAQLEqlHcDAAAAAAAAAAAAdmdCWgAAAAAAAAAAAAkS0gIAAAAAAAAAAEiQkBYAAAAAAAAAAECChLQAAAAAAAAAAAASJKQFAAAAAAAAAACQoErl3QAAAAAAKKuuVz6yw/Y15o6zsy6bSqWif//+UbFixXjllVcyfnbffffFr371q7jnnnvinHPOKbL+/Pnzo0mTJrFq1aq45ZZb4plnnol58+ZF7dq1Y999941f/vKXMWDAgG06HgAAAAB2PCEtAAAAANhOcnJyYsiQIdGpU6f4xz/+ERdddFFERMyYMSOuvvrquOeee2LPPfeMiIgpU6ZEbm5uRv1GjRpFRMSPf/zj+OCDD+Lee++Njh07xqJFi+Kdd96JRYsW7dgDAgAAAGC7ENICAAAAgO2oRYsWcdddd8Wll14aRx11VLRq1Sp+9KMfRb9+/eLcc8+NkSNHRsS3gay8vLwit/Hiiy/GXXfdFccdd1xERLRq1Sq6du26g44AAAAAgO2tQnk3AAAAAAB2N+ecc07069cvfvjDH8a9994bn3zySdx///1Z12/SpEn897//jRUrViTYSgAAAAB2FCNpAQAAAEAC7r///thvv/3i7bffjqFDh6anMtysefPmGcvNmjWLKVOmpOueccYZUb9+/dh///2jV69eMWjQoOjZs+cOaz8AAAAA24+RtAAAAAAgAY0aNYoLL7wwOnToEAMHDiz087fffjvGjRuX/nrllVfSP+vdu3dMnz49RowYEaecckpMmjQpDjvssPjNb36zIw8BAAAAgO3ESFoAAAAAkJBKlSpFpUpF/wlur732iry8vGLrVq5cOQ477LA47LDD4pprronf/va3ccstt8TVV18dVapUSajFAAAAACTBSFoAAAAAsAvo2LFj5Ofnx9q1a8u7KQAAAACUkZG0AAAAAKAcLFiwoFDgqn79+lG5cuXo06dPnH766XHQQQdF/fr149NPP41rr702+vbtG7m5ueXUYgAAAAC2lpAWAAAAALucMXecXd5N2Gb77LNPoXXvvvtudO/ePY4++uh4+OGH49prr43Vq1fHHnvsEQMGDIgbbrihHFoKAAAAwLbKSaVSqfJuxK5g+fLlUadOnVi2bJn/VgQAAADYjtauXRszZsyIvfbaK6pVq1bezdnteH0BAAAAkpNtpqjCDmwTAAAAAAAAAADAd46QFgAAAAAAAAAAQIKEtAAAAAAAAAAAABIkpAUAAAAAAAAAAJAgIS0AAAAAAAAAAIAECWkBAAAAAAAAAAAkSEgLAAAAAAAAAAAgQUJaAAAAAAAAAAAACRLSAgAAAAAAAAAASFCl8m4AAEA2ul75SKLbH3PH2YluHwAAAAAAAPjuEtICAAAAYJcz+5ZOO2xfe94wscx1zj333Hj44Yfjd7/7XVxzzTXp9c8991wMHDgwUqlURERs3Lgx7r777hgyZEhMnTo1qlWrFj169Ijrr78+evbsud2OAQAAAIDyZbpDAAAAAEhAtWrV4vbbb48lS5YU+fNUKhWDBw+OW265JS677LKYPHly/O9//4sWLVpEnz594rnnntuxDQYAAAAgMUJaAAAAAJCAI488Mpo0aRK/+93vivz5008/HUOHDo1HHnkkzj///Nhrr71i//33j/vvvz9OPPHEOP/882PVqlU7uNUAAAAAJEFICwAAAAASULFixbjtttvinnvuiTlz5hT6+eOPPx7t2rWLE044odDPrrjiili0aFG89tprO6KpAAAAACRMSAsAAAAAEjJw4MDo0qVL3HjjjYV+NnXq1OjQoUOR9Tavnzp1aqLtAwAAAGDHENICAAAAgATdfvvt8fDDD8enn35a5ro5OTkJtAgAAACAHU1ICwAAAAAS1Lt37zj66KPj2muvzVjfrl27YoNbkydPjoiItm3bJt4+AAAAAJInpAUAAAAACfvd734XL774YrzzzjvpdYMHD47PP/88XnzxxULl//SnP0X9+vWjf//+O7KZAAAAACRESAsAAAAAEta5c+c444wz4p577kmvGzx4cAwcODDOOeecePDBB2PmzJkxYcKEuOiii+KFF16If/7zn1GzZs1ybDUAAAAA20ul8m4AAAAAAJTVnjdMLO8mlNlvfvObePrpp9PLOTk58fTTT8ddd90Vf/nLX+KSSy6JqlWrRo8ePeLNN9+MXr16lWNrAQAAANiehLQAAAAAYDt76KGHCq1r2bJlrF27NmNdpUqV4oorrogrrrhiB7UMAAAAgPJgukMAAAAAAAAAAIAECWkBAAAAAAAAAAAkSEgLAAAAAAAAAAAgQUJaAAAAAAAAAAAACRLSAgAAAGCnkEqlyrsJuyWvKwAAAED5E9ICAAAAoFxVrlw5IiJWr15dzi3ZPW1+XTe/zgAAAADseJXKuwEAAAAAfLdVrFgx8vLyYsGCBRERUaNGjcjJySnnVu36UqlUrF69OhYsWBB5eXlRsWLF8m4SAAAAwHeWkBYAAAAA5a5JkyYREemgFttPXl5e+vUFAAAAoHwIaQEAAABQ7nJycqJp06bRqFGj2LBhQ3k3Z7dRuXJlI2gBAAAA7ASEtAAAAADYaVSsWFGoCAAAAIDdToXybgAAAAAAAAAAAMDuTEgLAAAAAAAAAAAgQUJaAAAAAAAAAAAACRLSAgAAAAAAAAAASJCQFgAAAAAAAAAAQIKEtAAAAAAAAAAAABIkpAUAAAAAAAAAAJAgIS0AAAAAAAAAAIAECWkBAAAAAAAAAAAkSEgLAAAAAAAAAAAgQUJaAAAAAAAAAAAACRLSAgAAAAAAAAAASJCQFgAAAAAAAAAAQIKEtAAAAAAAAAAAABIkpAUAAAAAAAAAAJAgIS0AAAAAAAAAAIAECWkBAAAAAAAAAAAkSEgLAAAAAAAAAAAgQUJaAAAAAAAAAAAACRLSAgAAAAAAAAAASJCQFgAAAAAAAAAAQIKEtAAAAAAAAAAAABIkpAUAAAAAAAAAAJAgIS0AAAAAAAAAAIAECWkBAAAAAAAAAAAkSEgLAAAAAAAAAAAgQUJaAAAAAAAAAAAACRLSAgAAAAAAAAAASJCQFgAAAAAAAAAAQIKEtAAAAAAAAAAAABIkpAUAAAAAAAAAAJAgIS0AAAAAAAAAAIAECWkBAAAAAAAAAAAkSEgLAAAAAAAAAAAgQUJaAAAAAAAAAAAACRLSAgAAAAAAAAAASJCQFgAAAAAAAAAAQIKEtAAAAAAAAAAAABIkpAUAAAAAAAAAAJAgIS0AAAAAAAAAAIAECWkBAAAAAAAAAAAkSEgLAAAAAAAAAAAgQUJaAAAAAAAAAAAACRLSAgAAAAAAAAAASJCQFgAAAAAAAAAAQIKEtAAAAAAAAAAAABIkpAUAAAAAAAAAAJAgIS0AAAAAAAAAAIAEVSrvBgAAALuvrlc+kuj2x9xxdqLbBwAAAAAA2B6MpAUAAAAAAAAAAJAgIS0AAAAAAAAAAIAECWkBAAAAAAAAAAAkSEgLAAAAAAAAAAAgQUJaAAAAAAAAAAAACRLSAgAAAAAAAAAASJCQFgAAAAAAAAAAQIKEtAAAAAAAAAAAABIkpAUAAAAAAAAAAJAgIS0AAAAAAAAAAIAECWkBAAAAAAAAAAAkSEgLAAAAAAAAAAAgQUJaAAAAAAAAAAAACRLSAgAAAAAAAAAASJCQFgAAAAAAAAAAQIKEtAAAAAAAAAAAABIkpAUAAAAAAAAAAJAgIS0AAAAAAAAAAIAECWkBAAAAAAAAAAAkSEgLAAAAAAAAAAAgQUJaAAAAAAAAAAAACRLSAgAAAAAAAAAASFCl8m4AAAB0vfKRxLY95o6zE9s2AAAAAAAAZMNIWgAAAAAAAAAAAAkS0gIAAAAAAAAAAEiQkBYAAAAAAAAAAECChLQAAAAAAAAAAAASJKQFAAAAAAAAAACQICEtAAAAAAAAAACABAlpAQAAAAAAAAAAJEhICwAAAAAAAAAAIEFCWgAAAAAAAAAAAAkS0gIAAAAAAAAAAEiQkBYAAAAAAAAAAECChLQAAAAAAAAAAAASJKQFAAAAAAAAAACQICEtAAAAAAAAAACABAlpAQAAAAAAAAAAJEhICwAAAAAAAAAAIEFCWgAAAAAAAAAAAAkS0gIAAAAAAAAAAEiQkBYAAAAAAAAAAECChLQAAAAAAAAAAAASJKQFAAAAAAAAAACQICEtAAAAAAAAAACABAlpAQAAAAAAAAAAJKhSeTcAANh6Xa98JNHtj7nj7ES3DwAAAAAAAPBdYCQtAAAAAAAAAACABAlpAQAAAAAAAAAAJEhICwAAAAAAAAAAIEFCWgAAAAAAAAAAAAkS0gIAAAAAAAAAAEiQkBYAAAAAAAAAAECChLQAAAAAAAAAAAASJKQFAAAAAAAAAACQICEtAAAAAAAAAACABAlpAQAAAAAAAAAAJEhICwAAAAAAAAAAIEFCWgAAAAAAAAAAAAkS0gIAAAAAAAAAAEiQkBYAAAAAAAAAAECChLQAAAAAAAAAAAASJKQFAAAAAAAAAACQICEtAAAAAAAAAACABAlpAQAAAAAAAAAAJEhICwAAAAAAAAAAIEFCWgAAAAAAAAAAAAkS0gIAAAAAAAAAAEiQkBYAAAAAAAAAAECChLQAAAAAAAAAAAASJKQFAAAAAAAAAACQICEtAAAAAAAAAACABAlpAQAAAAAAAAAAJEhICwAAAAAAAAAAIEFCWgAAAAAAAAAAAAkS0gIAAAAAAAAAAEiQkBYAAAAAAAAAAECChLQAAAAAAAAAAAASJKQFAAAAAAAAAACQICEtAAAAAAAAAACABAlpAQAAAAAAAAAAJKhSeTcAAACAXU/XKx9JdPtj7jg70e0DAAAAAMCOZCQtAAAAAAAAAACABAlpAQAAAAAAAAAAJEhICwAAAAAAAAAAIEFCWgAAAAAAAAAAAAkS0gIAAAAAAAAAAEjQLhvSGjt2bJx00kmxxx57RI0aNaJ9+/Zxyy23xOrVqzPKffzxx3HkkUdGrVq1Ii8vL04++eSYPn16ObUaAAAAAAAAAAD4rtklQ1qffvppHHrooTFz5sy48847Y/jw4TF48OC45ZZb4vTTT0+X++yzz6JPnz6xfv36ePrpp+Nf//pXTJ06NQ477LBYuHBhOR4BAAAAAAAAAADwXVGpvBuwNR5//PFYu3Zt/L//9/9i7733joiII444IubPnx/3339/LFmyJOrWrRs33HBDVK1aNYYPHx65ubkREdG1a9do27Zt/PGPf4zbb7+9PA8DAAAAAAAAAAD4DtglR9KqXLlyRETUqVMnY31eXl5UqFAhqlSpEvn5+TF8+PA45ZRT0gGtiIiWLVtG375949lnn92hbQYAAAAAAAAAAL6bdsmQ1jnnnBN5eXlx8cUXx/Tp02PFihUxfPjw+Mc//hGXXHJJ1KxZM7744otYs2ZNdO7cuVD9zp07x7Rp02Lt2rXl0HoAAAAAAAAAAOC7ZJec7rBVq1bx7rvvxsCBA9PTHUZEXHbZZXHnnXdGRMSiRYsiIqJevXqF6terVy9SqVQsWbIkmjZtWuQ+1q1bF+vWrUsvL1++fDseAQAAAAAAAAAA8F2xS4a0Zs6cGSeccEI0btw4hg4dGg0bNoz3338/fvvb38bKlSvjwQcfTJfNyckpdjsl/ex3v/td3Hzzzdu13QAA2ep65SOJbn/MHWcnun0AAAAAAADg/+ySIa1rrrkmli9fHuPGjYuaNWtGRETv3r2jQYMGcd5558XZZ58dTZo0iYj/G1FrS4sXL46cnJzIy8srdh+/+tWv4he/+EV6efny5dGiRYvteyAAAAAAAAAAAMBub5cMaY0bNy46duyYDmht1q1bt4iI+OSTT6Jnz55RvXr1mDhxYqH6EydOjDZt2kS1atWK3UfVqlWjatWq27fhAAAAAAAAAADAd06F8m7A1thjjz1i0qRJsXLlyoz17777bkRENG/ePCpVqhQnnHBCDBs2LFasWJEuM3v27HjzzTfj5JNP3qFtBgAAAAAAAAAAvpt2yZDW5ZdfHt988030798/nn766XjjjTfitttui1/84hfRsWPHOPbYYyMi4uabb47Vq1fHgAED4qWXXopnn302jj/++GjQoEFcccUV5XwUAAAAAAAAAADAd8EuGdI68cQTY8SIEZGbmxs/+9nPYsCAAfHwww/HRRddFG+99VZUqVIlIiLat28fI0eOjMqVK8egQYPi3HPPjTZt2sRbb70VDRs2LOejAAAAAAAAAAAAvgsqlXcDtlbfvn2jb9++pZbr2rVrvP766zugRQAAAAAAAAAAAIXtkiNpAQAAAAAAAAAA7CqEtAAAAAAAAAAAABIkpAUAAAAAAAAAAJAgIS0AAAAAAAAAAIAECWkBAAAAAAAAAAAkSEgLAAAAAAAAAAAgQUJaAAAAAAAAAAAACRLSAgAAAAAAAAAASJCQFgAAAAAAAAAAQIKEtAAAAAAAAAAAABIkpAUAAAAAAAAAAJAgIS0AAAAAAAAAAIAECWkBAAAAAAAAAAAkSEgLAAAAAAAAAAAgQUJaAAAAAAAAAAAACRLSAgAAAAAAAAAASFCl8m4AAADA7qDrlY8kuv0xd5yd6PYBAAAAAIDkGEkLAAAAAAAAAAAgQUJaAAAAAAAAAAAACRLSAgAAAAAAAAAASJCQFgAAAAAAAAAAQIKEtAAAAAAAAAAAABIkpAUAAAAAAAAAAJAgIS0AAAAAAAAAAIAECWkBAAAAAAAAAAAkSEgLAAAAAAAAAAAgQUJaAAAAAAAAAAAACRLSAgAAAAAAAAAASJCQFgAAAAAAAAAAQIKEtAAAAAAAAAAAABIkpAUAAAAAAAAAAJAgIS0AAAAAAAAAAIAECWkBAAAAAAAAAAAkSEgLAAAAAAAAAAAgQUJaAAAAAAAAAAAACRLSAgAAAAAAAAAASJCQFgAAAAAAAAAAQIKEtAAAAAAAAAAAABIkpAUAAAAAAAAAAJAgIS0AAAAAAAAAAIAECWkBAAAAAAAAAAAkSEgLAAAAAAAAAAAgQUJaAAAAAAAAAAAACRLSAgAAAAAAAAAASJCQFgAAAAAAAAAAQIKEtAAAAAAAAAAAABIkpAUAAAAAAAAAAJAgIS0AAAAAAAAAAIAECWkBAAAAAAAAAAAkSEgLAAAAAAAAAAAgQUJaAAAAAAAAAAAACapU3g0AAIDvkq5XPpLo9sfccXai2wcAgF3V7Fs6Jbr9PW+YmOj2AQAA2LUZSQsAAAAAAAAAACBBQloAAAAAAAAAAAAJEtICAAAAAAAAAABIkJAWAAAAAAAAAABAgoS0AAAAAAAAAAAAEiSkBQAAAAAAAAAAkCAhLQAAAAAAAAAAgAQJaQEAAAAAAAAAACRISAsAAAAAAAAAACBBQloAAAAAAAAAAAAJEtICAAAAAAAAAABIUKXybgAAAADs7Lpe+Uii2x9zx9mJbh8AAAAAgPJlJC0AAAAAAAAAAIAECWkBAAAAAAAAAAAkSEgLAAAAAAAAAAAgQUJaAAAAAAAAAAAACRLSAgAAAAAAAAAASJCQFgAAAAAAAAAAQIKEtAAAAAAAAAAAABIkpAUAAAAAAAAAAJAgIS0AAAAAAAAAAIAECWkBAAAAAAAAAAAkSEgLAAAAAAAAAAAgQUJaAAAAAAAAAAAACRLSAgAAAAAAAAAASJCQFgAAAAAAAAAAQIKEtAAAAAAAAAAAABIkpAUAAAAAAAAAAJAgIS0AAAAAAAAAAIAECWkBAAAAAAAAAAAkSEgLAAAAAAAAAAAgQUJaAAAAAAAAAAAACRLSAgAAAAAAAAAASJCQFgAAAAAAAAAAQIIqlXcDAGB76nrlI4luf8wdZye6fQAAgJ3R7Fs6JbbtPW+YmNi2AQAAAHYWRtICAAAAAAAAAABIkJAWAAAAAAAAAABAgoS0AAAAAAAAAAAAEiSkBQAAAAAAAAAAkCAhLQAAAAAAAAAAgAQJaQEAAAAAAAAAACRISAsAAAAAAAAAACBBQloAAAAAAAAAAAAJEtICAAAAAAAAAABIkJAWAAAAAAAAAABAgoS0AAAAAAAAAAAAEiSkBQAAAAAAAAAAkCAhLQAAAAAAAAAAgAQJaQEAAAAAAAAAACRISAsAAAAAAAAAACBBQloAAAAAAAAAAAAJEtICAAAAAAAAAABIkJAWAAAAAAAAAABAgoS0AAAAAAAAAAAAEiSkBQAAAAAAAAAAkCAhLQAAAAAAAAAAgAQJaQEAAAAAAAAAACRISAsAAAAAAAAAACBBQloAAAAAAAAAAAAJEtICAAAAAAAAAABIkJAWAAAAAAAAAABAgoS0AAAAAAAAAAAAEiSkBQAAAAAAAAAAkCAhLQAAAAAAAAAAgAQJaQEAAAAAAAAAACRISAsAAAAAAAAAACBBQloAAAAAAAAAAAAJEtICAAAAAAAAAABIkJAWAAAAAAAAAABAgoS0AAAAAAAAAAAAEiSkBQAAAAAAAAAAkCAhLQAAAAAAAAAAgAQJaQEAAAAAAAAAACRISAsAAAAAAAAAACBBQloAAAAAAAAAAAAJEtICAAAAAAAAAABIkJDW/2fv3qO1rOv8/79u3JyUg25ETQXGgmJUdhYesDTQ8XzCActxZmksZ1mamTZEac13KzQllda4Ki2n8YBgpQVT4KEJxdNUljLWViNTKhzHEMQDIeAQ1++PftAwgIL3/rD3zX481rrXyuu+rs/nff+ReXj2uQAAAAAAAAAAAAoSaQEAAAAAAAAAABQk0gIAAAAAAAAAAChIpAUAAAAAAAAAAFCQSAsAAAAAAAAAAKCgpo4eAAAAAOg6Fk0ZUXT9wa1tRdcHAAAAAHgjnKQFAAAAAAAAAABQkEgLAAAAAAAAAACgIJEWAAAAAAAAAABAQSItAAAAAAAAAACAgkRaAAAAAAAAAAAABYm0AAAAAAAAAAAAChJpAQAAAAAAAAAAFCTSAgAAAAAAAAAAKEikBQAAAAAAAAAAUJBICwAAAAAAAAAAoCCRFgAAAAAAAAAAQEEiLQAAAAAAAAAAgIJEWgAAAAAAAAAAAAWJtAAAAAAAAAAAAAoSaQEAAAAAAAAAABQk0gIAAAAAAAAAAChIpAUAAAAAAAAAAFCQSAsAAAAAAAAAAKAgkRYAAAAAAAAAAEBBIi0AAAAAAAAAAICCRFoAAAAAAAAAAAAFibQAAAAAAAAAAAAKauroAQAAAIDOY+SkaUXXn9W36PIAAAAAAJ2Sk7QAAAAAAAAAAAAKEmkBAAAAAAAAAAAUJNICAAAAAAAAAAAoSKQFAAAAAAAAAABQkEgLAAAAAAAAAACgIJEWAAAAAAAAAABAQSItAAAAAAAAAACAgkRaAAAAAAAAAAAABYm0AAAAAAAAAAAAChJpAQAAAAAAAAAAFCTSAgAAAAAAAAAAKKipowcAAAAAAOgsFk0ZUXT9wa1tRdffWl3t9wIA5fnrCwDYNCdpAQAAAAAAAAAAFCTSAgAAAAAAAAAAKEikBQAAAAAAAAAAUJBICwAAAAAAAAAAoCCRFgAAAAAAAAAAQEEiLQAAAAAAAAAAgIJEWgAAAAAAAAAAAAWJtAAAAAAAAAAAAAoSaQEAAAAAAAAAABQk0gIAAAAAAAAAAChIpAUAAAAAAAAAAFBQU0cPAADAtrdoyoii6w9ubSu6PgAAAAAAADQSJ2kBAAAAAAAAAAAUJNICAAAAAAAAAAAoSKQFAAAAAAAAAABQkEgLAAAAAAAAAACgIJEWAAAAAAAAAABAQSItAAAAAAAAAACAgkRaAAAAAAAAAAAABYm0AAAAAAAAAAAAChJpAQAAAAAAAAAAFCTSAgAAAAAAAAAAKEikBQAAAAAAAAAAUJBICwAAAAAAAAAAoCCRFgAAAAAAAAAAQEEiLQAAAAAAAAAAgIJEWgAAAAAAAAAAAAWJtAAAAAAAAAAAAAoSaQEAAAAAAAAAABQk0gIAAAAAAAAAAChIpAUAAAAAAAAAAFCQSAsAAAAAAAAAAKAgkRYAAAAAAAAAAEBBIi0AAAAAAAAAAICCRFoAAAAAAAAAAAAFibQAAAAAAAAAAAAKEmkBAAAAAAAAAAAUJNICAAAAAAAAAAAoSKQFAAAAAAAAAABQkEgLAAAAAAAAAACgIJEWAAAAAAAAAABAQSItAAAAAAAAAACAgkRaAAAAAAAAAAAABYm0AAAAAAAAAAAAChJpAQAAAAAAAAAAFCTSAgAAAAAAAAAAKEikBQAAAAAAAAAAUJBICwAAAAAAAAAAoCCRFgAAAAAAAAAAQEEiLQAAAAAAAAAAgIJEWgAAAAAAAAAAAAWJtAAAAAAAAAAAAApq6EjrgQceyAknnJBddtklvXv3zrBhw/LpT396g3vmz5+fo446Kn369MnOO++ccePGZeHChR00MQAAAAAAAAAA0NU0bKR18803Z/To0enfv3+mTZuW22+/PZ/4xCdSVdX6exYsWJAxY8bk1VdfzS233JLrrrsuTzzxRA4//PAsWbKkA6cHAAAAAAAAAAC6iqaOHuCNeOaZZ/KBD3wgH/zgB3P11Vevv37EEUdscF9ra2t69uyZOXPmpF+/fkmSkSNHZtiwYbniiivyuc99bpvODQAAAAAAAAAAdD0NeZLWN77xjaxYsSKf+MQnNnvPmjVrMmfOnIwfP359oJUkQ4YMyRFHHJFZs2Zti1EBAAAAAAAAAIAuriEjrfvuuy/Nzc1ZsGBBDjjggDQ1NWW33XbLueeem5dffjlJ8tRTT2XlypVpaWnZ6PmWlpY8+eSTWbVq1bYeHQAAAAAAAAAA6GIaMtJ65pln8sorr+S9731vTj/99MydOzeTJk3KtGnTcsIJJ6Sqqjz//PNJkubm5o2eb25uTlVVeeGFFza7x+rVq/Pyyy9v8AEAAAAAAAAAANhaTR09wBuxdu3arFq1KpdeemkuvvjiJMmYMWPSo0ePXHTRRbnrrruy4447Jklqtdpm13mt7y6//PJMnjy5fQcHAAAAAAAAAAC6nIY8SWvAgAFJkmOPPXaD68cff3ySZP78+evvWXei1v+2bNmy1Gq17Lzzzpvd45JLLslLL720/vP000+30/QAAAAAAAAAAEBX0pCRVktLyyavV1WVJOnWrVve8pa3pHfv3mlra9vovra2tgwdOjS9evXa7B49e/ZMv379NvgAAAAAAAAAAABsrYaMtMaPH58kueOOOza4fvvttydJRo0alaamppx88smZOXNmli9fvv6eRYsWZd68eRk3bty2GxgAAAAAAAAAAOiymjp6gDfimGOOycknn5wpU6Zk7dq1GTVqVB566KFMnjw5J510Ug477LAkyeTJk3PQQQflpJNOysUXX5xVq1altbU1u+66ayZOnNjBvwIAAAAAAAAAAOgKGvIkrST59re/nYsuuijXXnttjj/++FxzzTX56Ec/mu985zvr7xk+fHjuueeedO/ePaeddlomTJiQoUOH5r777svAgQM7cHoAAAAAAAAAAKCraMiTtJKkd+/emTp1aqZOnfqa940cOTJz587dRlMBAAAAAAAAAABsqGFP0gIAAAAAAAAAAGgEIi0AAAAAAAAAAICCRFoAAAAAAAAAAAAFibQAAAAAAAAAAAAKauroAQAAoKRFU0YUXX9wa1vR9aGr8t9dAACgKyr590L+PggAoGM5SQsAAAAAAAAAAKAgkRYAAAAAAAAAAEBBIi0AAAAAAAAAAICCRFoAAAAAAAAAAAAFibQAAAAAAAAAAAAKEmkBAAAAAAAAAAAUJNICAAAAAAAAAAAoSKQFAAAAAAAAAABQkEgLAAAAAAAAAACgIJEWAAAAAAAAAABAQSItAAAAAAAAAACAgpo6egAAAAAAto2Rk6YVW/vhL5xVbO03ouRvTTrf7wXo6hZNGVF0/cGtbUXXB7Z//jwFADhJCwAAAAAAAAAAoCCRFgAAAAAAAAAAQEEiLQAAAAAAAAAAgIJEWgAAAAAAAAAAAAWJtAAAAAAAAAAAAAoSaQEAAAAAAAAAABQk0gIAAAAAAAAAAChIpAUAAAAAAAAAAFCQSAsAAAAAAAAAAKAgkRYAAAAAAAAAAEBBIi0AAAAAAAAAAICCmjp6AAAAAAAa36IpI4quP7i1rej6AEDn5K8xAADYXjhJCwAAAAAAAAAAoCCRFgAAAAAAAAAAQEEiLQAAAAAAAAAAgIJEWgAAAAAAAAAAAAWJtAAAAAAAAAAAAAoSaQEAAAAAAAAAABQk0gIAAAAAAAAAAChIpAUAAAAAAAAAAFCQSAsAAAAAAAAAAKAgkRYAAAAAAAAAAEBBTR09AAAAAAA0mkVTRhRdf3BrW9H1AQAAANi2nKQFAAAAAAAAAABQkEgLAAAAAAAAAACgIJEWAAAAAAAAAABAQSItAAAAAAAAAACAgkRaAAAAAAAAAAAABYm0AAAAAAAAAAAAChJpAQAAAAAAAAAAFNRUz8PTpk1Lkpx66qnp16/fFj3zhz/8ITNnzkySnHXWWfVsDwAAAAAAAAAA0OnVFWlNmDAhtVotBx54YPbdd98tembx4sWZMGFCunXrJtICAAAAAAAAAAC2ex32usOqqjpqawAAAAAAAAAAgG1mm0daa9asSZI0NdV1iBcAAAAAAAAAAEBD2Oal1K9+9askSXNz87beGgDYSoumjCi29uDWtmJrAwAAAAAAAHQmWxVp3XfffZu8/rOf/SxLly59zWdXr16dp556KldccUVqtVoOOOCArdkaAAAAAAAAAACgIW1VpDVmzJjUarUNrlVVlbPPPnuL16iqKrVaLR/84Ae3ZmsAAAAAAAAAAICG1G1rH6iqav1nU9de77P33nvnq1/9ak499dT2/B0AAAAAAAAAAACd0ladpDVv3rz1/7mqqhx55JGp1Wr513/91+yzzz6bfa5Wq6VXr15505velEGDBr3xaQEAAAAAAAAAABrMVkVao0eP3uT1gw8+OPvuu2+7DAQAAAAAAAAAALA92apI6//6zW9+kyTZa6+92mUYAAAAAAAAAACA7U1dkdaQIUPaaw4AAAAAAAAAAIDtUreOHgAAAAAAAAAAAGB7VtdJWuusWbMmt912W+6///4sXLgwy5cvzx//+MfXfKZWq+Wuu+5qj+0BAAAAAAAAAAA6rbojrQceeCBnnnlmFi1atP5aVVWbvb9Wq6WqqtRqtXq3BgAAAAAAAAAA6PTqirQWLFiQ4447LitXrkxVVenRo0eGDRuW5ubmdOvmTYoAAAAAAAAAAAB1RVqf/exn88orr2SHHXbI5MmT85GPfCR9+vRpr9kAALaZRVNGFF1/cGtb0fUBgDdm5KRpRdd/+AtnFV0fAAAalX8eBwB0NXVFWnfffXdqtVouvPDCfPKTn2yvmQAAAAAAAAAAALYbdb2TcOnSpUmSv/7rv26XYQAAAAAAAAAAALY3dUVaAwcOTJL07t27XYYBAAAAAAAAAADY3tQVaR122GFJkkcffbRdhgEAAAAAAAAAANje1BVp/cM//EN22GGHXHXVVVmzZk17zQQAAAAAAAAAALDdqCvSOuigg/LFL34xjzzySMaNG5elS5e211wAAAAAAAAAAADbhaZ6Hp4yZUqS5JBDDsmcOXMyZMiQHH300Rk+fHh23HHH132+tbW1nu0BAAAAAAAAAAA6vboircsuuyy1Wi1JUqvVsnLlysyePTuzZ8/eoudFWgAAAAAAAAAAwPaurkgrSaqqes0/BgAAAAAAAAAA6MrqirTWrl3bXnMAAAAAAAAAAABsl7p19AAAAAAAAAAAAADbs7pfdwgAAEB5i6aMKLr+4Na2ousDAAAAAEBX5iQtAAAAAAAAAACAgkRaAAAAAAAAAAAABdX1usMddtjhDT9bq9WyZs2aerYHAAAAAAAAAADo9OqKtKqqaq85AAAAAAAAAAAAtkt1RVqXXnrp696zYsWK/OpXv8rcuXOzatWqHHLIITn22GPr2RYAAAAAAAAAAKBhFI+01lm6dGnOPvvs3H777TnjjDNywQUX1LM1AAAAAAAAAABAQ+i2rTbaddddM3PmzLS0tGTixIl58MEHt9XWAAAAAAAAAAAAHWabRVpJ0tTUlI985CNZs2ZNvvjFL27LrQEAAAAAAAAAADrENo20kmTEiBFJkv/4j//Y1lsDAAAAAAAAAABsc9s80lq1alWSZOnSpdt6awAAAAAAAAAAgG2uaVtvOHPmzCTJwIEDt/XWAACw3Vs0ZUTR9Qe3thVdHwAAAAAAYHu0zSKtFStW5Mtf/nKuuuqq1Gq1/NVf/dW22hoAAAAAAAAAAKDD1BVpHXnkka97z9q1a/PCCy/kiSeeyKuvvpqqqtKnT5986lOfqmdrAAAAAAAAAACAhlBXpHXPPfekVqulqqotfmbIkCGZPn16hg0bVs/WAAAAAAAAAAAADaGuSOs973lParXaa97TrVu39O3bN/vss09Gjx6dE088Md27d69nWwAAAAAAAAAAgIZR90laAAAAAAAAAAAAbF63jh4AAAAAAAAAAABgeybSAgAAAAAAAAAAKKiu1x1uyuLFi/Poo49m2bJlSZLm5ubsv//+2X333dt7KwAAAAAAAAAAgE6vXSKtqqpy7bXX5itf+Uoef/zxTd6z77775oILLsg555yTWq3WHtsCAAAAAAAAAAB0enW/7vCFF17I4Ycfng996EN5/PHHU1XVJj+PP/54zjvvvLznPe/Jiy++2A6jAwAAAAAAAAAAdH51naRVVVXGjh2bH/3oR0mSAQMG5H3ve18OOeSQ7LHHHqmqKosXL85Pf/rT3HLLLVm6dGl+9KMfZezYsbn33nvb5QcAAAAAAAAAAAB0ZnVFWjfffHMeeOCB1Gq1/O3f/m2uvvrq9O3bd6P7zjrrrEydOjXnn39+brrppjzwwAP55je/mTPOOKOe7QEAAAAAAAAAADq9ul53ePPNNydJRo8enZtuummTgdY6ffr0yY033pjRo0enqqpMnz69nq0BAAAAAAAAAAAaQl2R1vz581Or1fLhD394i5+54IILkiT/+Z//Wc/WAAAAAAAAAAAADaGuSGvZsmVJkn322WeLn1l377pnAQAAAAAAAAAAtmd1RVr9+/dPkvz3f//3Fj+z7t5+/frVszUAAAAAAAAAAEBDqCvS2n///ZMk119//RY/c911123wLAAAAAAAAAAAwPasrkjrtNNOS1VVmTVrVi677LJUVbXZe6uqymWXXZZZs2alVqvlve99bz1bAwAAAAAAAAAANISmeh4+55xz8pWvfCULFizIpz/96Xz3u9/NhAkTcsghh2T33XdPrVbL73//+zz44IO58cYb89hjjyVJhg8fnnPOOaddfgAAAAAAAAAAAEBnVlek1b1799xxxx058sgj85vf/CaPP/54Pv7xj2/2/qqq8uY3vzl33HFHmprq2hoAAAAAAAAAAKAh1PW6wyQZMmRIfvGLX2TixInp379/qqra5Kd///752Mc+lkceeSSDBw9uj9kBAAAAAAAAAAA6vXY5zmqnnXbKF77whXzmM5/Jww8/nEcffTTLli1LkjQ3N2f//ffPyJEj06NHj/bYDgAAAAAAAAAAoGG06zsHe/TokUMPPTSHHnpoey4LAAAAAAAAAADQsNo10gIAAADgzxZNGVF0/cGtbUXXBwAA4LX5+z4AttRWRVrPPPNMvvvd7yZJWlpaMmbMmC1+dt68eWlr+9P/gLzvfe/LHnvssTVbAwAAAAAAAAAANKStirQmTpyYW2+9NbvttlsefvjhrdrorW99a84444wsWbIk8+fPzw033LBVzwMAAAAAAAAAADSiblt6429/+9vceuutSZLPf/7z2XPPPbdqo7322itXXnllqqrK9OnT8/TTT2/dpAAAAAAAAAAAAA1oiyOtGTNmpKqqDBs2LGeeeeYb2uzv/u7vMnz48FRVlRkzZryhNQAAAAAAAAAAABrJFkda999/f2q1WsaNG1fXhuPHj09VVbn33nvrWgcAAAAAAAAAAKARbHGk9eijjyZJ3v3ud9e14ahRozZYDwAAAAAAAAAAYHu2xZHWsmXLkiR77LFHXRuue37degAAAAAAAAAAANuzLY60dthhhyTJ//zP/9S14brna7VaXesAAAAAAAAAAAA0gi2OtAYOHJgk+a//+q+6Nlz3/Lr1AAAAAAAAAAAAtmdbHGkNGzYsSXL33XfXteG659/61rfWtQ4AAAAAAAAAAEAjaNrSG48++uj88Ic/zM0335wpU6Zk11133erNli5dmhkzZqRWq+Woo47a6ucBeGNGTppWbO2Hv3BWsbUBAADg/yr597hJMqtv0eUBAACALmqLT9L6m7/5m/Ts2TPLly/P2WefnbVr127VRlVV5e///u+zfPny9OjRI2ecccZWDwsAAAAAAAAAANBotjjS2nvvvfOhD30oVVXltttuywknnJBnn312i5599tlnc+KJJ2b27Nmp1Wo577zzsvfee7/hoQEAAAAAAAAAABrFFr/uMEkuv/zy/OQnP8mPf/zj/PCHP8zQoUNz2mmn5aSTTso73/nO7L777tlpp52yYsWKLF68OPPnz89tt92WW2+9NatWrUqSHHLIIZk6dWqRHwMAAAAAAAAAANDZbFWk1aNHj8yePTunn3567rrrrqxcuTLTp0/P9OnTX/O5qqqSJEcccURuueWW9OjR441PDAAAAAAAAAAA0EC2+HWH6zQ3N+ff//3f86UvfSl77bVXqqp63c+ee+6ZL37xi5k7d24GDBhQ4ncAAAAAAAAAAAB0Slt1ktY6tVotF154Yc4///z84Ac/yL333puf//znWbp0aZYvX56+fftm1113zdvf/vaMHj06xxxzTLp3797eswMAAAAAAAAAAHR6byjSWv9wU1NOPPHEnHjiie01DwAAAAAAAAAAwHZlq193CAAAAAAAAAAAwJYTaQEAAAAAAAAAABQk0gIAAAAAAAAAAChIpAUAAAAAAAAAAFBQU0cPAAAAAAAAwJZZNGVE0fUHt7YVXR8AALoqJ2kBAAAAAAAAAAAUJNICAAAAAAAAAAAoSKQFAAAAAAAAAABQkEgLAAAAAAAAAACgIJEWAAAAAAAAAABAQU3tveAzzzyT3//+93nllVdy4IEHpnfv3u29BQAAAAAAAAAAQMNol5O0li9fnksvvTSDBg3K4MGDc/DBB2fMmDH5zW9+s8F93/rWt/K+970v55xzTntsCwAAAAAAAAAA0OnVfZLWk08+meOPPz4LFy5MVVXrr9dqtY3uPfTQQ3PmmWdm7dq1ef/735/DDjus3u0BAAAAAAAAAAA6tbpO0lq9enVOPPHEPPXUU9lxxx3z8Y9/PHPmzNns/UOGDMkRRxyRJPn+979fz9YAAAAAAAAAAAANoa6TtL72ta/l17/+dXbaaafcf//9OeCAA173meOPPz5z587Nj3/843q2BgAAAAAAAAAAaAh1naQ1c+bM1Gq1XHjhhVsUaCVJS0tLkuTXv/51PVsDAAAAAAAAAAA0hLpO0nr88ceTJMccc8wWPzNgwIAkyYsvvljP1gDQIRZNGVF0/cGtbUXXB9je+PMyAABsmr9W7jgjJ00ruv6svkWXBwAACqnrJK3ly5cnSfr377/Fz6xatSpJ0r1793q2BgAAAAAAAAAAaAh1RVrrTsVavHjxFj/T1van/3fN7rvvXs/WAAAAAAAAAAAADaGuSOuAAw5Iktx1111b/Mx1112XWq2WQw45pJ6tAQAAAAAAAAAAGkJdkda4ceNSVVW+/vWv53e/+93r3j958uQ8+OCDSZLTTz+9nq0BAAAAAAAAAAAaQl2R1oQJE/KXf/mXWb58eUaPHp3bbrstVVWt/75Wq2Xt2rW5//77c/LJJ2fKlCmp1Wo56KCDcsopp9Q9PAAAAAAAAAAAQGfXVM/DO+ywQ77//e/n3e9+dxYtWpRTTjklO+644/rvTz755CxevDivvPJKkqSqquy555659dZb65saAAAAAAAAAACgQdR1klaSvOUtb8kjjzySE088MVVVZcWKFUn+FGQtXLgwK1asSFVVqaoqxxxzTH72s59l0KBBdQ8OAAAAAAAAAADQCOo6SWudPfbYI7Nnz85jjz2W733ve3nooYfy3HPP5Y9//GMGDBiQd7zjHRk7dmwOPPDA9tgOAAAAAAAAAACgYbRLpLXOfvvtl/322689lwQAAAAAAAAAAGhodb/uEAAAAAAAAAAAgM1r15O0AAAAAIBtb+SkaUXXn9W36PIAAAAA2726Iq1FixZt9TO1Wi29evVK//7906NHj3q2BwAAAAAAAAAA6PTqirT22Wefujbfe++9M2rUqEyYMCHHH398XWsBAAAAAAAAAAB0Rt3qebiqqro+Tz/9dL7zne/kpJNOyrHHHpsXXnihvX4XAAAAAAAAAABAp1DXSVrXX399kuRrX/taHnzwwfTq1SvHHntsDjzwwAwcODBJsmTJkjz00EP5wQ9+kNWrV+fggw/OBz7wgbz88st59NFH873vfS9Lly7N3Llzc+qpp+bee++t/1cBAAAAAAAAAAB0EnVFWu9///tz7rnn5qc//WnGjh2br3/969ltt902ee9zzz2XD3zgA5k9e3b222+/fOMb30iSfPnLX865556badOm5YEHHsi3v/3tnH766fWMBQAAAAAAAAAA0GnU9brDf/u3f8u1116bQw89NDNnztxsoJUku+22W2bNmpVRo0bl+uuvzy233JIk6dWrV6677rq8853vTJJ861vfqmckAAAAAAAAAACATqWuSOvqq69OrVbLhRdemFqt9rr312q1XHTRRamqKtdee+2fh+jWLeecc06qqspDDz1Uz0gAAAAAAAAAAACdSl2R1i9+8YskydChQ7f4mXX3trW1bXC9paUlSfL888/XMxIAAAAAAAAAAECnUlek9fLLLydJlixZssXPrLt3+fLlG1zv3bt3kqR79+71jAQAAAAAAAAAANCp1BVpDRo0KEkyY8aMLX7mpptuSpIMHjx4g+vPPfdckmTgwIH1jAQAAAAAAAAAANCp1BVpnXLKKamqKtOnT88///M/v+79X/rSlzJjxozUarWccsopG3z34IMPJkmGDBlSz0gAAAAAAAAAAACdSlM9D1988cW58cYb8/zzz2fixImZMWNGzjrrrIwcOTK77bZbkj+dkPXQQw/lpptuyvz585P86bSsiy++eIO1vvWtb6VWq+Xoo4+uZyQAAAAAAOj0Rk6aVnT9WX2LLg8AAMBWqivSGjBgQH74wx/muOOOy+LFizN//vz1IdamVFWVPfbYI3feeWeam5vXX1+4cGEOPvjgHHzwwRk/fnw9IwEAAAAAAAAAAHQqdb3uMEne/va355e//GUuuOCC9OvXL1VVbfLTr1+/XHDBBXnsscfS0tKywRpvfvObc/311+f666/PsGHD6h0JAAAAAAAAAACg06jrJK11dt5551x11VX5/Oc/n4cffjiPPvpoXnjhhSTJLrvskv322y8HHnhgevbs2R7bAQAAAAAAAAAANIx2ibTW6dmzZ971rnflXe96V3suCwAAAAAAAAAA0LDqft0hAAAAAAAAAAAAmyfSAgAAAAAAAAAAKKjdXndYVVUeeeSR/PznP8/SpUuzcuXKVFX1ms+0tra21/YAAAAAAAAAAACdUrtEWjfeeGMmT56c3/3ud1v1nEgLAAAAAAAAAADY3tUdaX3qU5/K1KlTX/fUrCSp1WpbdB8AAAAAAAAAAMD2ols9Dz/44IO5/PLLkyRHH310HnnkkcyfPz/Jn4KsP/7xj1m6dGnuvPPOjB07NlVV5bDDDsuzzz6btWvX1j89AAAAAAAAAABAJ1dXpHXNNdckSYYMGZLbbrstLS0t6d69+/rva7Vampubc8wxx2TWrFn56le/mgceeCDHHXdcXn311fomBwAAAAAAAAAAaAB1RVo/+tGPUqvV8pGPfCRNTa//5sTzzjsv48ePzy9+8YtcffXV9WwNAAAAAAAAAADQEOqKtJ599tkkyX777ffnBbv9ecn/+Z//2eiZM888M1VV5dvf/nY9WwMAAAAAAAAAADSEuiKtdRHWbrvttv5anz591v/nJUuWbPTMoEGDkiRPPvlkPVsDAAAAAAAAAAA0hLoirYEDByZJXn755fXXdt999+ywww5Jkl/+8pcbPbPu9K3ly5fXszUAAAAAAAAAAEBDqCvSWveawwULFqy/1qNHj/XXN/VKwxkzZiRJ9txzz3q2BgAAAAAAAAAAaAh1RVqHH354qqrKvHnzNrh++umnp6qqXHfddWltbc1jjz2Wn/3sZ/nwhz+cb37zm6nVajn++OPrGhwAAAAAAAAAAKAR1BVpnXrqqUmSOXPmbPDKwwsvvDB/8Rd/kbVr1+Yzn/lMWlpaMmrUqFxzzTVJkl122SWXXHJJPVsDAAAAAAAAAAA0hLpfdzhv3rzMmjUra9asWX99xx13zLx58/Lud787VVVt8Nl///1z1113Ze+99657eAAAAAAAAAAAgM6uqd4FRo8evcnrQ4YMyf33359f/epXeeyxx7JmzZoMGzYs73jHO+rdEgAAAAAAAAAAoGHUHWm9nre97W1529veVnobAAAAAAAAAACATqmu1x0CAAAAAAAAAADw2kRaAAAAAAAAAAAABYm0AAAAAAAAAAAAChJpAQAAAAAAAAAAFCTSAgAAAAAAAAAAKEikBQAAAAAAAAAAUJBICwAAAAAAAAAAoCCRFgAAAAAAAAAAQEEiLQAAAAAAAAAAgIJEWgAAAAAAAAAAAAWJtAAAAAAAAAAAAAoSaQEAAAAAAAAAABQk0gIAAAAAAAAAACioqaMHAAAAAABg00ZOmlZ0/Vl9iy4PAAAA/P+cpAUAAAAAAAAAAFCQSAsAAAAAAAAAAKAgkRYAAAAAAAAAAEBBIi0AAAAAAAAAAICCRFoAAAAAAAAAAAAFibQAAAAAAAAAAAAKEmkBAAAAAAAAAAAUJNICAAAAAAAAAAAoSKQFAAAAAAAAAABQkEgLAAAAAAAAAACgIJEWAAAAAAAAAABAQU0dPQAAAAAAACTJyEnTiq09q2+xpQEAAOB1OUkLAAAAAAAAAACgIJEWAAAAAAAAAABAQSItAAAAAAAAAACAgkRaAAAAAAAAAAAABYm0AAAAAAAAAAAACtouIq1vfOMbqdVq6dOnz0bfzZ8/P0cddVT69OmTnXfeOePGjcvChQs7YEoAAAAAAAAAAKAravhI65lnnsnHPvax7Lnnnht9t2DBgowZMyavvvpqbrnlllx33XV54okncvjhh2fJkiUdMC0AAAAAAAAAANDVNHykde655+Y973lPjj766I2+a21tTc+ePTNnzpyccMIJGTduXG677bYsWbIkV1xxRQdMCwAAAAAAAAAAdDUNHWlNnz499957b66++uqNvluzZk3mzJmT8ePHp1+/fuuvDxkyJEcccURmzZq1LUcFAAAAAAAAAAC6qIaNtJ577rlcdNFFmTp1avbee++Nvn/qqaeycuXKtLS0bPRdS0tLnnzyyaxatWpbjAoAAAAAAAAAAHRhTR09wBv1oQ99KG9729ty3nnnbfL7559/PknS3Ny80XfNzc2pqiovvPBC3vSmN23y+dWrV2f16tXr//jll19uh6kBAAAAAAAAAICupiFP0vrud7+b2bNn51/+5V9Sq9Ve897X+v61vrv88svTv3//9Z9Bgwa94XkBAAAAAAAAAICuq+EirT/84Q85//zzc8EFF2TPPffMiy++mBdffDGvvvpqkuTFF1/MihUrMmDAgCR/PlHrf1u2bFlqtVp23nnnze5zySWX5KWXXlr/efrpp4v8HgAAAAAAAAAAYPvWcK87XLp0aRYvXpwrr7wyV1555Ubf77LLLhk7dmy+853vpHfv3mlra9vonra2tgwdOjS9evXa7D49e/ZMz54923V2AAAAAAAAAACg62m4SGuPPfbIvHnzNro+derU3Hvvvbnjjjuy6667pqmpKSeffHJmzpyZz3/+8+nbt2+SZNGiRZk3b14++tGPbuvRAQAAAAAAAACALqjhIq1evXplzJgxG12/4YYbssMOO2zw3eTJk3PQQQflpJNOysUXX5xVq1altbU1u+66ayZOnLjthgYAAAAAAAAAALqsbh09QEnDhw/PPffck+7du+e0007LhAkTMnTo0Nx3330ZOHBgR48HAAAAAAAAAAB0AQ13ktbm3HDDDbnhhhs2uj5y5MjMnTt32w8EAAAAAAAAAACQ7fwkLQAAAAAAAAAAgI4m0gIAAAAAAAAAAChIpAUAAAAAAAAAAFCQSAsAAAAAAAAAAKCgpo4eAKAjjJw0rej6D3/hrKLrAwAAAEAjKf3P42b1Lbo8AABA3ZykBQAAAAAAAAAAUJBICwAAAAAAAAAAoCCRFgAAAAAAAAAAQEEiLQAAAAAAAAAAgIJEWgAAAAAAAAAAAAWJtAAAAAAAAAAAAAoSaQEAAAAAAAAAABQk0gIAAAAAAAAAAChIpAUAAAAAAAAAAFCQSAsAAAAAAAAAAKAgkRYAAAAAAAAAAEBBIi0AAAAAAAAAAICCRFoAAAAAAAAAAAAFibQAAAAAAAAAAAAKEmkBAAAAAAAAAAAUJNICAAAAAAAAAAAoSKQFAAAAAAAAAABQkEgLAAAAAAAAAACgIJEWAAAAAAAAAABAQSItAAAAAAAAAACAgkRaAAAAAAAAAAAABYm0AAAAAAAAAAAAChJpAQAAAAAAAAAAFCTSAgAAAAAAAAAAKEikBQAAAAAAAAAAUJBICwAAAAAAAAAAoCCRFgAAAAAAAAAAQEEiLQAAAAAAAAAAgIJEWgAAAAAAAAAAAAWJtAAAAAAAAAAAAAoSaQEAAAAAAAAAABQk0gIAAAAAAAAAAChIpAUAAAAAAAAAAFCQSAsAAAAAAAAAAKCgpo4eAGB7tGjKiKLrD25tK7o+AAAAAMCWGDlpWtH1Z/UtuvxW62q/F+D/KvnvwPz7L2B75yQtAAAAAAAAAACAgkRaAAAAAAAAAAAABYm0AAAAAAAAAAAAChJpAQAAAAAAAAAAFCTSAgAAAAAAAAAAKEikBQAAAAAAAAAAUJBICwAAAAAAAAAAoCCRFgAAAAAAAAAAQEEiLQAAAAAAAAAAgIJEWgAAAAAAAAAAAAWJtAAAAAAAAAAAAAoSaQEAAAAAAAAAABQk0gIAAAAAAAAAAChIpAUAAAAAAAAAAFCQSAsAAAAAAAAAAKAgkRYAAAAAAAAAAEBBIi0AAAAAAAAAAICCRFoAAAAAAAAAAAAFibQAAAAAAAAAAAAKEmkBAAAAAAAAAAAUJNICAAAAAAAAAAAoSKQFAAAAAAAAAABQkEgLAAAAAAAAAACgoKaOHgAAAAC6ukVTRhRbe3BrW7G1AQAAAADYMk7SAgAAAAAAAAAAKEikBQAAAAAAAAAAUJBICwAAAAAAAAAAoCCRFgAAAAAAAAAAQEEiLQAAAAAAAAAAgIJEWgAAAAAAAAAAAAWJtAAAAAAAAAAAAAoSaQEAAAAAAAAAABQk0gIAAAAAAAAAAChIpAUAAAAAAAAAAFCQSAsAAAAAAAAAAKCgpo4eAAAAAAAAAOhcRk6aVnT9WX2LLg8A0Ok4SQsAAAAAAAAAAKAgkRYAAAAAAAAAAEBBIi0AAAAAAAAAAICCRFoAAAAAAAAAAAAFibQAAAAAAAAAAAAKEmkBAAAAAAAAAAAUJNICAAAAAAAAAAAoSKQFAAAAAAAAAABQkEgLAAAAAAAAAACgIJEWAAAAAAAAAABAQSItAAAAAAAAAACAgkRaAAAAAAAAAAAABYm0AAAAAAAAAAAAChJpAQAAAAAAAAAAFCTSAgAAAAAAAAAAKEikBQAAAAAAAAAAUJBICwAAAAAAAAAAoCCRFgAAAAAAAAAAQEEiLQAAAAAAAAAAgIJEWgAAAAAAAAAAAAWJtAAAAAAAAAAAAAoSaQEAAAAAAAAAABQk0gIAAAAAAAAAAChIpAUAAAAAAAAAAFCQSAsAAAAAAAAAAKAgkRYAAAAAAAAAAEBBIi0AAAAAAAAAAICCRFoAAAAAAAAAAAAFibQAAAAAAAAAAAAKEmkBAAAAAAAAAAAUJNICAAAAAAAAAAAoSKQFAAAAAAAAAABQkEgLAAAAAAAAAACgIJEWAAAAAAAAAABAQSItAAAAAAAAAACAgkRaAAAAAAAAAAAABYm0AAAAAAAAAAAAChJpAQAAAAAAAAAAFCTSAgAAAAAAAAAAKEikBQAAAAAAAAAAUJBICwAAAAAAAAAAoCCRFgAAAAAAAAAAQEEiLQAAAAAAAAAAgIJEWgAAAAAAAAAAAAWJtAAAAAAAAAAAAAoSaQEAAAAAAAAAABQk0gIAAAAAAAAAAChIpAUAAAAAAAAAAFCQSAsAAAAAAAAAAKAgkRYAAAAAAAAAAEBBIi0AAAAAAAAAAICCRFoAAAAAAAAAAAAFibQAAAAAAAAAAAAKEmkBAAAAAAAAAAAUJNICAAAAAAAAAAAoSKQFAAAAAAAAAABQkEgLAAAAAAAAAACgoKaOHgAAAAAAAACAbWfkpGnF1p7Vt9jSANDQnKQFAAAAAAAAAABQkEgLAAAAAAAAAACgIJEWAAAAAAAAAABAQSItAAAAAAAAAACAgkRaAAAAAAAAAAAABYm0AAAAAAAAAAAAChJpAQAAAAAAAAAAFCTSAgAAAAAAAAAAKEikBQAAAAAAAAAAUJBICwAAAAAAAAAAoCCRFgAAAAAAAAAAQEEiLQAAAAAAAAAAgIJEWgAAAAAAAAAAAAWJtAAAAAAAAAAAAAoSaQEAAAAAAAAAABQk0gIAAAAAAAAAAChIpAUAAAAAAAAAAFCQSAsAAAAAAAAAAKAgkRYAAAAAAAAAAEBBIi0AAAAAAAAAAICCRFoAAAAAAAAAAAAFibQAAAAAAAAAAAAKEmkBAAAAAAAAAAAUJNICAAAAAAAAAAAoSKQFAAAAAAAAAABQkEgLAAAAAAAAAACgIJEWAAAAAAAAAABAQSItAAAAAAAAAACAgkRaAAAAAAAAAAAABYm0AAAAAAAAAAAAChJpAQAAAAAAAAAAFCTSAgAAAAAAAAAAKEikBQAAAAAAAAAAUJBICwAAAAAAAAAAoKCmjh4AAAAAAAAAAEoYOWla0fVn9S26PADbESdpAQAAAAAAAAAAFCTSAgAAAAAAAAAAKEikBQAAAAAAAAAAUJBICwAAAAAAAAAAoCCRFgAAAAAAAAAAQEEiLQAAAAAAAAAAgIJEWgAAAAAAAAAAAAWJtAAAAAAAAAAAAAoSaQEAAAAAAAAAABQk0gIAAAAAAAAAAChIpAUAAAAAAAAAAFBQU0cPAEBjWzRlRNH1B7e2FV0fAAAAAAAA/i//Dgxob07SAgAAAAAAAAAAKEikBQAAAAAAAAAAUJBICwAAAAAAAAAAoCCRFgAAAAAAAAAAQEEiLQAAAAAAAAAAgIJEWgAAAAAAAAAAAAWJtAAAAAAAAAAAAAoSaQEAAAAAAAAAABQk0gIAAAAAAAAAAChIpAUAAAAAAAAAAFCQSAsAAAAAAAAAAKAgkRYAAAAAAAAAAEBBIi0AAAAAAAAAAICCRFoAAAAAAAAAAAAFibQAAAAAAAAAAAAKEmkBAAAAAAAAAAAUJNICAAAAAAAAAAAoSKQFAAAAAAAAAABQkEgLAAAAAAAAAACgIJEWAAAAAAAAAABAQSItAAAAAAAAAACAgkRaAAAAAAAAAAAABTV19AAAAAAAAAAAHWnkpGlF15/Vt+jyAHVbNGVE0fUHt7YVXR8agZO0AAAAAAAAAAAAChJpAQAAAAAAAAAAFCTSAgAAAAAAAAAAKEikBQAAAAAAAAAAUJBICwAAAAAAAAAAoCCRFgAAAAAAAAAAQEEiLQAAAAAAAAAAgIJEWgAAAAAAAAAAAAWJtAAAAAAAAAAAAAoSaQEAAAAAAAAAABQk0gIAAAAAAAAAAChIpAUAAAAAAAAAAFCQSAsAAAAAAAAAAKAgkRYAAAAAAAAAAEBBIi0AAAAAAAAAAICCRFoAAAAAAAAAAAAFibQAAAAAAAAAAAAKashI6+67787ZZ5+d4cOHZ6eddspee+2VsWPH5uGHH97o3vnz5+eoo45Knz59svPOO2fcuHFZuHBhB0wNAAAAAAAAAAB0RQ0ZaV1zzTX57W9/mwsvvDC33357rrrqqjz33HMZNWpU7r777vX3LViwIGPGjMmrr76aW265Jdddd12eeOKJHH744VmyZEkH/gIAAAAAAAAAAKCraOroAd6Ir371q9ltt902uHbcccdl6NCh+exnP5sjjzwySdLa2pqePXtmzpw56devX5Jk5MiRGTZsWK644op87nOf2+azAwAAAAAAAAAAXUtDnqT1fwOtJOnTp0/23XffPP3000mSNWvWZM6cORk/fvz6QCtJhgwZkiOOOCKzZs3aZvMCAAAAAAAAAABdV0NGWpvy0ksvZf78+dlvv/2SJE899VRWrlyZlpaWje5taWnJk08+mVWrVm3rMQEAAAAAAAAAgC6mIV93uCnnn39+VqxYkU996lNJkueffz5J0tzcvNG9zc3NqaoqL7zwQt70pjdtcr3Vq1dn9erV6//45ZdfLjA1AAAAAAAAAACwvdsuTtL6f//v/2XGjBn50pe+lJEjR27wXa1W2+xzr/Xd5Zdfnv79+6//DBo0qN3mBQAAAAAAAAAAuo6Gj7QmT56cf/qnf8pnPvOZfPjDH15/fcCAAUn+fKLW/7Zs2bLUarXsvPPOm133kksuyUsvvbT+8/TTT7f77AAAAAAAAAAAwPavoV93OHny5Fx22WW57LLL8slPfnKD797ylrekd+/eaWtr2+i5tra2DB06NL169drs2j179kzPnj3bfWYAAAAAAAAAAKBradiTtD796U/nsssuyz/+4z/m0ksv3ej7pqamnHzyyZk5c2aWL1++/vqiRYsyb968jBs3bluOCwAAAAAAAAAAdFENeZLWlVdemdbW1hx33HE58cQT85Of/GSD70eNGpXkTydtHXTQQTnppJNy8cUXZ9WqVWltbc2uu+6aiRMndsToAAAAAAAAAABAF9OQkdbs2bOTJHfeeWfuvPPOjb6vqipJMnz48Nxzzz35xCc+kdNOOy1NTU058sgjc8UVV2TgwIHbdGYAAAAAAAAAAKBrashI65577tnie0eOHJm5c+eWGwYAAAAAAAAAAOA1dOvoAQAAAAAAAAAAALZnIi0AAAAAAAAAAICCRFoAAAAAAAAAAAAFibQAAAAAAAAAAAAKauroAYDOYeSkaUXXf/gLZxVdHwAAAAAAAACgs3KSFgAAAAAAAAAAQEEiLQAAAAAAAAAAgIJEWgAAAAAAAAAAAAWJtAAAAAAAAAAAAAoSaQEAAAAAAAAAABQk0gIAAAAAAAAAAChIpAUAAAAAAAAAAFCQSAsAAAAAAAAAAKAgkRYAAAAAAAAAAEBBIi0AAAAAAAAAAICCRFoAAAAAAAAAAAAFibQAAAAAAAAAAAAKEmkBAAAAAAAAAAAUJNICAAAAAAAAAAAoSKQFAAAAAAAAAABQkEgLAAAAAAAAAACgIJEWAAAAAAAAAABAQSItAAAAAAAAAACAgkRaAAAAAAAAAAAABYm0AAAAAAAAAAAAChJpAQAAAAAAAAAAFCTSAgAAAAAAAAAAKEikBQAAAAAAAAAAUJBICwAAAAAAAAAAoCCRFgAAAAAAAAAAQEEiLQAAAAAAAAAAgIJEWgAAAAAAAAAAAAWJtAAAAAAAAAAAAAoSaQEAAAAAAAAAABQk0gIAAAAAAAAAAChIpAUAAAAAAAAAAFCQSAsAAAAAAAAAAKAgkRYAAAAAAAAAAEBBIi0AAAAAAAAAAICCRFoAAAAAAAAAAAAFibQAAAAAAAAAAAAKEmkBAAAAAAAAAAAUJNICAAAAAAAAAAAoSKQFAAAAAAAAAABQkEgLAAAAAAAAAACgIJEWAAAAAAAAAABAQSItAAAAAAAAAACAgkRaAAAAAAAAAAAABYm0AAAAAAAAAAAAChJpAQAAAAAAAAAAFCTSAgAAAAAAAAAAKEikBQAAAAAAAAAAUJBICwAAAAAAAAAAoCCRFgAAAAAAAAAAQEEiLQAAAAAAAAAAgIJEWgAAAAAAAAAAAAWJtAAAAAAAAAAAAAoSaQEAAAAAAAAAABQk0gIAAAAAAAAAAChIpAUAAAAAAAAAAFCQSAsAAAAAAAAAAKAgkRYAAAAAAAAAAEBBIi0AAAAAAAAAAICCRFoAAAAAAAAAAAAFibQAAAAAAAAAAAAKEmkBAAAAAAAAAAAUJNICAAAAAAAAAAAoSKQFAAAAAAAAAABQkEgLAAAAAAAAAACgIJEWAAAAAAAAAABAQSItAAAAAAAAAACAgkRaAAAAAAAAAAAABYm0AAAAAAAAAAAAChJpAQAAAAAAAAAAFCTSAgAAAAAAAAAAKEikBQAAAAAAAAAAUJBICwAAAAAAAAAAoCCRFgAAAAAAAAAAQEEiLQAAAAAAAAAAgIJEWgAAAAAAAAAAAAWJtAAAAAAAAAAAAAoSaQEAAAAAAAAAABQk0gIAAAAAAAAAAChIpAUAAAAAAAAAAFCQSAsAAAAAAAAAAKAgkRYAAAAAAAAAAEBBIi0AAAAAAAAAAICCRFoAAAAAAAAAAAAFibQAAAAAAAAAAAAKEmkBAAAAAAAAAAAUJNICAAAAAAAAAAAoSKQFAAAAAAAAAABQkEgLAAAAAAAAAACgIJEWAAAAAAAAAABAQSItAAAAAAAAAACAgkRaAAAAAAAAAAAABYm0AAAAAAAAAAAAChJpAQAAAAAAAAAAFNTU0QMAAAAAAAAAALB5IydNK7r+rL5FlwfiJC0AAAAAAAAAAICiRFoAAAAAAAAAAAAFibQAAAAAAAAAAAAKEmkBAAAAAAAAAAAUJNICAAAAAAAAAAAoSKQFAAAAAAAAAABQkEgLAAAAAAAAAACgIJEWAAAAAAAAAABAQSItAAAAAAAAAACAgkRaAAAAAAAAAAAABYm0AAAAAAAAAAAACmrq6AGArmHRlBFF1x/c2lZ0fQAAAAAAAACAN8pJWgAAAAAAAAAAAAWJtAAAAAAAAAAAAAoSaQEAAAAAAAAAABQk0gIAAAAAAAAAAChIpAUAAAAAAAAAAFCQSAsAAAAAAAAAAKAgkRYAAAAAAAAAAEBBIi0AAAAAAAAAAICCRFoAAAAAAAAAAAAFibQAAAAAAAAAAAAKauroAaAzGzlpWtH1H/7CWUXXBwAAAAAAAAD+bNGUEUXXH9zaVnR9GpeTtAAAAAAAAAAAAAoSaQEAAAAAAAAAABQk0gIAAAAAAAAAAChIpAUAAAAAAAAAAFCQSAsAAAAAAAAAAKAgkRYAAAAAAAAAAEBBIi0AAAAAAAAAAICCRFoAAAAAAAAAAAAFibQAAAAAAAAAAAAKEmkBAAAAAAAAAAAUJNICAAAAAAAAAAAo6P9r797jrK7r/IG/BwaH6yByK8K7GK5CJKikiXkhb2hetkLNW8saPmrXyvtmXlt1tVx1NysVV5NVUwwtr1GpPTRJkSTRtVJBMQ1FEBQHDPj8/tjfzA7DXL4zzHcO53uez8eDx2M65zuH98vPzJlX83hzjiUtAAAAAAAAAACAHFnSAgAAAAAAAAAAyJElLQAAAAAAAAAAgBxZ0gIAAAAAAAAAAMiRJS0AAAAAAAAAAIAcWdICAAAAAAAAAADIkSUtAAAAAAAAAACAHFnSAgAAAAAAAAAAyJElLQAAAAAAAAAAgBxZ0gIAAAAAAAAAAMiRJS0AAAAAAAAAAIAcWdICAAAAAAAAAADIUXWpB4BK9trFo3J77K3Ofy63xwYAAAAAAAA2PWPP/HGujz+zX64PD1BoXkkLAAAAAAAAAAAgR5a0AAAAAAAAAAAAcmRJCwAAAAAAAAAAIEeWtAAAAAAAAAAAAHJkSQsAAAAAAAAAACBHlrQAAAAAAAAAAAByZEkLAAAAAAAAAAAgR5a0AAAAAAAAAAAAcmRJCwAAAAAAAAAAIEeWtAAAAAAAAAAAAHJkSQsAAAAAAAAAACBH1aUeAAAAAAAAAACgPcae+eNcH39mv1wfHqhAXkkLAAAAAAAAAAAgR5a0AAAAAAAAAAAAcmRJCwAAAAAAAAAAIEeWtAAAAAAAAAAAAHJkSQsAAAAAAAAAACBHlrQAAAAAAAAAAAByZEkLAAAAAAAAAAAgR5a0AAAAAAAAAAAAcmRJCwAAAAAAAAAAIEeWtAAAAAAAAAAAAHJUXeoBAAAAAAAAAACg3tgzf5zbY8/sl9tDQ6u8khYAAAAAAAAAAECOLGkBAAAAAAAAAADkyJIWAAAAAAAAAABAjixpAQAAAAAAAAAA5MiSFgAAAAAAAAAAQI4saQEAAAAAAAAAAOTIkhYAAAAAAAAAAECOLGkBAAAAAAAAAADkyJIWAAAAAAAAAABAjixpAQAAAAAAAAAA5MiSFgAAAAAAAAAAQI4saQEAAAAAAAAAAOTIkhYAAAAAAAAAAECOLGkBAAAAAAAAAADkyJIWAAAAAAAAAABAjixpAQAAAAAAAAAA5MiSFgAAAAAAAAAAQI4saQEAAAAAAAAAAOTIkhYAAAAAAAAAAECOLGkBAAAAAAAAAADkyJIWAAAAAAAAAABAjixpAQAAAAAAAAAA5MiSFgAAAAAAAAAAQI6qSz0AAAAAAAAAAADQ+V67eFSuj7/V+c/l+vhF4pW0AAAAAAAAAAAAcmRJCwAAAAAAAAAAIEeWtAAAAAAAAAAAAHJkSQsAAAAAAAAAACBHlrQAAAAAAAAAAAByZEkLAAAAAAAAAAAgR5a0AAAAAAAAAAAAcmRJCwAAAAAAAAAAIEeWtAAAAAAAAAAAAHJkSQsAAAAAAAAAACBH1aUegPIy9swf5/r4M/tdmevjb3X+c7k+PgAAAAAAAABAVvnvYeT68Juc1y4elevjb8zeiVfSAgAAAAAAAAAAyJElLQAAAAAAAAAAgBxZ0gIAAAAAAAAAAMiRJS0AAAAAAAAAAIAcWdICAAAAAAAAAADIkSUtAAAAAAAAAACAHFnSAgAAAAAAAAAAyJElLQAAAAAAAAAAgBxZ0gIAAAAAAAAAAMiRJS0AAAAAAAAAAIAcWdICAAAAAAAAAADIUXWpByh3Y8/8ca6P/8yVJ+T6+AAAAAAAAAAAQL68khYAAAAAAAAAAECOLGkBAAAAAAAAAADkyJIWAAAAAAAAAABAjixpAQAAAAAAAAAA5MiSFgAAAAAAAAAAQI4Kv6T1/vvvx9e//vUYNmxY9OzZM8aMGRN33HFHqccCAAAAAAAAAAAqRHWpB8jbUUcdFU8//XRcfvnlseOOO8Ztt90WxxxzTKxbty6OPfbYUo8HAAAAAAAAAAAUXKGXtB544IGYNWtWw2JWRMS+++4br776apx55pnxxS9+Mbp3717iKQEAAAAAAAAAgCIr9Nsdzpw5M/r27Ruf//zn17v95JNPjjfeeCN+97vflWgyAAAAAAAAAACgUhR6SWv+/Pmx0047RXX1+i8YNnr06Ib7AQAAAAAAAAAA8lTotzt85513Yrvtttvg9i222KLh/pasXr06Vq9e3fC/ly9fHhERK1asWO+6tavrOmPUFjX9+0ot77zv9Vib6+O3979nOeetpKwR8rYlz7yVlDVC3s5USVkj5G2L56nOI2/nak/eSsoaIW9n8zO388jbOj9zO4+8naeSskbI2xbPU51H3s7l/xu0TN7O5Wdu55G3dX7mdh55O08lZY2Qty2epzrPppY3b++t6vq89bellFr93KrU1hVlbMcdd4ztt98+HnzwwfVuf/PNN2PYsGFx2WWXxTnnnNPs51544YVx0UUXdcWYAAAAAAAAAABAGVu0aFEMHz68xfsL/UpaAwcObPbVspYuXRoR//eKWs0599xz45vf/GbD/163bl0sXbo0Bg4cGFVVVZ0/bDNWrFgRW265ZSxatChqa2u75O8sJXmLq5KyRlRW3krKGiFvkVVS1ojKyltJWSPkLbJKyhohb5FVUtaIyspbSVkj5C2ySsoaUVl5KylrhLxFVklZI+QtskrKGlFZeSspa4S8RVZJWSMqK28lZY2Qt6uklOK9996LYcOGtXpdoZe0Ro0aFbfffnusWbMmqqv/L+pzzz0XERG77LJLi59bU1MTNTU16922+eab5zJnW2prayvim6WevMVVSVkjKitvJWWNkLfIKilrRGXlraSsEfIWWSVljZC3yCopa0Rl5a2krBHyFlklZY2orLyVlDVC3iKrpKwR8hZZJWWNqKy8lZQ1Qt4iq6SsEZWVt5KyRsjbFfr379/mNd26YI6SOfLII+P999+Pu+++e73bb7nllhg2bFjsscceJZoMAAAAAAAAAACoFIV+Ja2DDz44Jk6cGKeeemqsWLEidthhh7j99tvjoYceiunTp0f37t1LPSIAAAAAAAAAAFBwhV7Sioj46U9/Gt/61rfi/PPPj6VLl8bIkSPj9ttvj8mTJ5d6tDbV1NTEBRdcsMHbLhaVvMVVSVkjKitvJWWNkLfIKilrRGXlraSsEfIWWSVljZC3yCopa0Rl5a2krBHyFlklZY2orLyVlDVC3iKrpKwR8hZZJWWNqKy8lZQ1Qt4iq6SsEZWVt5KyRsi7qalKKaVSDwEAAAAAAAAAAFBU3Uo9AAAAAAAAAAAAQJFZ0gIAAAAAAAAAAMiRJS0AAAAAAAAAAIAcWdIqoWeffTYOPfTQ2GqrraJXr16xxRZbxKc+9amYPn16wzVr166Nq666Kg466KAYPnx49O7dO3baaac455xz4t133y3d8B2QJW9ExLXXXhvjx4+PQYMGRU1NTWy11VYxefLkeP7550s0eftlzdpYSikmTJgQVVVV8bWvfa0Lp914WfOedNJJUVVVtcGfkSNHlmjy9mvP2f7tb3+Lq666KkaNGhW9evWKzTffPPbcc8/47W9/W4LJOyZr3ubOtcjnm1KKG264IcaOHRu1tbUxcODA2GeffeL+++8v0eTt156s1157bYwcOTJqamriox/9aJx66qmxbNmyEk3eeW688caoqqqKvn37bnDf3Llz44ADDoi+ffvG5ptvHkcddVS88sorJZiyc7SU9fHHH48pU6bE2LFjo6amJqqqqmLhwoWlGbITNZe3SJ2qsZbOtgh9qjmtfd/WK+dO1VRLeYvQqZpq7WyL0KmaailvUTpVYy1lLUKfak5recu9Uz366KMtfn3Onj17vWuL0KWy5i1Cn8qStUhdKuvZFqFPtef7tl45d6mseYvSpdpzvuXep7JmLUqXypq3KH2qPXnLvU/Ve/zxx+OQQw6JAQMGRK9evWLEiBFxySWXrHdNEfpUvbbyFqFP1Wsta5H6VL22zrYIfaqxLN+79cq5U0W0nbUofapelrMt9z5Vr62sRelT9drKW5Q+VS9L3nLvUy09/zTXHzfVPlVd6gEq2bvvvhtbbrllHHPMMfGxj30sVq5cGf/93/8dxx9/fCxcuDDOO++8qKuriwsvvDCOOeaYmDJlSgwaNCjmzp0b3/nOd+LnP/95zJkzJ3r16lXqKJlkyRsR8c4778TBBx8cn/jEJ2LAgAHxyiuvxOWXXx577LFHPPPMM/Hxj3+8xEnaljVrY9///vfjpZdeKsG0G689eXv16hW//vWv1/v8cvkajsiede3atXHkkUfG448/HmeddVbsueeesXLlynjmmWdi5cqVJU6RXda8Tz755Aaf+7vf/S6+/vWvx5FHHtnVY3dY1rwXXHBBXHLJJTF16tS4/PLLY9WqVfEf//EfMWnSpLj77rvjqKOOKnGStmXNesYZZ8TVV18dZ5xxRhxwwAHxwgsvxPnnnx9PP/10PPnkk9GjR48SJ+mYv/zlL3HGGWfEsGHDYvny5evd9+KLL8ZnPvOZGDNmTNx5552xatWqOP/882PvvfeOZ599NgYPHlyiqTumtay/+tWv4pe//GV88pOfjNra2nj00UdLM2QnailvkTpVvdbOtgh9qqnW8jZWzp2qsbbylnunaqy1rEXpVI21lrconapea1mL0Keaai1vkTrVpZdeGvvuu+96t+2yyy4NHxetS7WVt0h9qrWsRexSbZ1tkfpUW1kbK0KXypK3SF2qrbxF6lNtZS1al2orb9H6VFt5i9Knbrvttjj++OPjC1/4Qvz4xz+Ovn37xssvvxxvvPFGwzVF6lNZ8halT7WVtWh9KsvZFqlPZcnbWDl3qqxZi9KnsuQtSp/KkrVIfSpL3iL1qSx5i9Cnvv3tb8fUqVM3uP2www6Lmpqa2G233SJiE+9TiU3OHnvskbbccsuUUkpr1qxJS5Ys2eCau+66K0VEuvXWW7t6vE7XOG9LXnjhhRQR6dvf/nYXTZWPlrIuWLAg9e3bN/30pz9NEZG++tWvlmC6ztc074knnpj69OlTwony0zTrv//7v6du3bqlJ598soRT5SfL9+1JJ52Uqqqq0p///Ocumio/TfN+7GMfS5/+9KfXu6auri71798/HX744V09XqdqnPX1119P3bt3T//0T/+03jW33XZbioh0/fXXl2LETjFp0qR02GGHNfu89PnPfz4NGjQoLV++vOG2hQsXph49eqSzzjqrq0fdaK1lXbt2bcPHV155ZYqItGDBgi6esHO1lLeInaq1s21OufepLHmL1Klay1u0TtVa1iJ2qvZ+75Zzp2otaxH7VEt5i9KpHnnkkRQR6a677mr1uqJ0qax5i9CnsmQtUpfKerbNKbc+1d6s5d6lsuYtSpfKmrcIfWpjvm/LsUtlzVuUPpUlb1H61Ouvv5769OmTTj311FavK0qfypq3CH0qS9Yi9amsZ9uccutTKbU/bzl3qqxZi9KnsuYtQp/amO/bcuxTWfMWpU9lyVuUPtWcRx99NEVEOu+88xpu25T7lLc73AQNGjQoqqv/90XOunfvHgMHDtzgmt133z0iIhYtWtSls+Whcd6W1G8ytnXdpq6lrKecckpMnDix7DaQ25LlbIuiadZrrrkmJkyYEOPHjy/hVPlp62zfe++9uOuuu2KfffaJHXbYoQsny0fTvD169Ij+/fuvd03Pnj0b/pSzxllnz54da9eujUMOOWS9ayZNmhQREXfffXeXz9cZpk+fHo899lhcd911G9y3Zs2auO++++Loo4+O2trahtu33nrr2HfffWPmzJldOepGay1rRES3bsWqgq3lLVqnautsm1POfSpr3qJ0qo6cb7lqK2vROlV7z7acO1VbWYvWp1rLW9RO1ZyidaksitanWlK0LtVR5dynsihKl2J9RetT7VHOXSqLovWp1hSlT914442xcuXKOPvss1u8pkh9KkveiGL0qSxZi9Snsp5tc8qxT7U3bzl3qo0523KUNW8R+lRHz7Zc+1TWvEXpU1nyFqVPNWfatGlRVVUVX/7ylyNi0+9T5d98CmDdunWxZs2aePvtt+O6666Lhx9+uM0njPqXj9x55527YsROlTXv2rVrY/Xq1fHiiy/GlClTYsiQIXHyySeXYOKOy5L1xhtvjKeeeir+8z//s0RTdp4seevq6uIjH/lIdO/ePYYPHx5f+9rXYunSpSWauONay7po0aJYuHBhjBo1Kv7lX/4lhg4dGtXV1bHzzjvHLbfcUuLJO6a9z1N33HFHrFy5MqZMmdKFU3aetvKedtpp8dBDD8W0adNi2bJl8eabb8Y3v/nNWL58efzzP/9zCSdvv9ayfvjhhxERUVNTs97n9OjRI6qqquIPf/hDl8+7sd566634+te/HpdffnkMHz58g/tffvnlqKuri9GjR29w3+jRo+Oll16KVatWdcWoG62trEXT0bzl2Knak7UIfSpr3qJ0qqx5i9Cp2spatE7Vkeepcu1UWbIWqU+1lbdoneqrX/1qVFdXR21tbRx44IHx+OOPN9xXpC5Vr7W8RdORrOXYpeplzVuEPpUla1G6VES2vEXoUvVay1u0PtXe56ly7VL12spbpD4V0XreovSp3/zmN7HFFlvEiy++GGPGjInq6uoYMmRITJ06NVasWBERxepTWfIWxcZkLcc+1d685d6n2pO33DtVe7IWoU9lyVuUPtXR56ly7VNZ8xalT2XJW5Q+1dTy5ctjxowZsf/++8e2224bEWXQp0r6Ol6klFL6yle+kiIiRUTabLPN0nXXXdfq9a+//noaOnRoGjdu3HovA1susuatqalpuG7HHXdML7zwQhdPuvHayvr666+n/v37px/96EcNt0WZvfRpY23lveqqq9JVV12VfvGLX6Rf/OIX6Vvf+lbq3bt3GjlyZHrvvfdKNHXHtJb1ySefTBGRamtr09/93d+lO++8Mz388MPp7//+78v25SLb+zy1xx57pM033zzV1dV10YSdK0veH/7wh+s9T22xxRZp1qxZJZh247SW9dlnn00RkS655JL1PudXv/pVw/Xl5uijj0577rlnWrduXUppw5dlfuKJJ1JEpNtvv32Dz7300ktTRKQ33nijy+bdGG1lbapcX06+XnvzplS+nao9WYvQp7LkLVKnypK3KJ2qraxF61QdeZ4q106VNWtR+lRbeYvSqebOnZtOO+20NHPmzPSb3/wm3XTTTWmnnXZK3bt3Tw899FBKqVhdKkvepsq1T3Uka0rl26Xam7ec+1TWrEXpUlnzFqVLZclblD7V0eepcu1S7clbhD6VJW9R+tTHP/7x1LNnz9SvX7906aWXpkceeSRdccUVqVevXmmvvfZK69atK1SfypK3qXLtUx3JmlL59qn25i3nPpVS9rxF6FRZsxalT2XJW5Q+1dHnqXLtU+3JW4Q+lSVvUfpUUz/4wQ826E6bep+ypLUJePXVV9PTTz+d7r///jR16tTUrVu3dOWVVzZ77TvvvJNGjx6dhgwZkl5++eUunrRzZM37zDPPpCeffDJNnz49jR07Ng0dOjTNnz+/BBN3XFtZJ02alCZMmLDeD4JyK2yNtedrud6MGTNSRKSrrrqqi6bsHK1lrX/i32yzzdLChQsbPmfdunVp1113TcOHDy/V2B3WnrOdP39+WX8dp9R23ptuuinV1NSk008/Pf3yl79MDzzwQJo8eXLq3bt3q78c3BS1lXXChAmptrY23XnnnWnZsmXpiSeeSCNGjEjdu3dPPXv2LOHk7Tdjxoy02Wabpeeff77htpaWtO64444NPr++uL355ptdMu/GyJK1qXL9JVhKHctbrp2qvVnLvU9lzVuUTtWRr+XGn1tOnao9z8lF6FQdOdty7VRZsxalT2XNW6RO1diyZcvS8OHD0+jRo1NKxelSLWmat6ly7lNNtZW1XLtUS1rLW+59qqnmshalSzWnra/leuXWpVrS0vNyEfpUU22dbbl2qZY0l7cofao5zeUtQp8aMWJEioh02WWXrXf71VdfnSIizZo1q1B9Kkvepsq1T3Ukazn3qfbmLfc+lTVvETpVR76W65Vjn2rP83K596mOnG0596mseYvSp7LmLUKfamrcuHFp4MCBadWqVQ23bep9ypLWJmjq1Kmpuro6vfXWW+vdvnTp0rTrrrumgQMHpnnz5pVous7XUt7GVqxYkYYMGZIOP/zwLpys8zXOetddd6Xq6uo0e/bstGzZsoY/EZH+8R//MS1btix9+OGHpR55o2Q527Vr16Y+ffqkL3zhC104WedrnPXFF19MEdHsL4nOPffcFBFp8eLFJZiy87R2tt/4xjdSRKTf//73XT9YThrnXbp0aerVq1ezpXSfffZJ22yzTQkm7DxNz3bx4sXp4IMPXu/Vts4+++w0duzYtP3225d42uzee++9NHTo0HT66aev95x7zDHHpD59+qRly5al999/v+H79/vf//4Gj3HGGWekqqqqTf5fjGTN2lS5/hKsI3nLtVN19GzrlVufypq3KJ1qY8+3nDpVe5+Ty71TdfRsy7FTZc1alD7VnrMtSqdqztSpU1NEpA8++KAQXaotjfM2Va59qiUtZS3XLtWW1s62Xrn1qZY0zlqULtWaLGdbTl2qLc09L5d7n2pJa2dbjl2qLY3zFqVPtabp+RahT40fPz5FRJo7d+56t//xj39MEZH+7d/+rVB9Kkvepsq1T7U3a7n3qY6cbb1y7FNZ8halU23M2ZZjn2rP83K596mOnG0596kseYvUp7KebxH6VGPz5s1LEZFOO+209W7f1PtUt2CTs/vuu8eaNWvilVdeabht2bJlccABB8SCBQti1qxZzb5/ZrlqLm9T/fr1i5EjR8af/vSnLpys8zXOOn/+/FizZk2MHz8+BgwY0PAnIuKGG26IAQMGxP3331/iiTdOlrONiEgpRbdu5f101Djr9ttvH7179272upRSRESh8jb24Ycfxq233hpjx46NMWPGlGa4HDTO+8c//jHq6upit9122+C6cePGxcKFC+P9998vwZSdo+nZDhkyJB544IFYvHhxzJs3L9566624+OKL409/+lNMmDChxNNmt2TJkli8eHF873vfW+859/bbb4+VK1fGgAED4rjjjovtt98+evXqFc8999wGj/Hcc8/FDjvsED179ixBguyyZi2K9uYt5061sWdbbn0qa96idKrO+N4tl07VnufkInSqjpxtuXaqrFmL0qfac7ZF6VTNqf9+rKqqKkSXakvjvEXXXNZy7lJtyXK25danWtI4a1G6VGuyft+WS5dqS9Pn5SL0qZa0dLbl2qXa0jhvUfpUa5qebxH6VEs/Nxt/PxapT2XJWxTtyVqEPrUxZ1uOfSpL3qJ0qo39vi23PpX1ebkIfaq9Z1vufSpL3iL1qaznW4Q+1di0adMiImLKlCnr3b6p96nqkv3NtOiRRx6Jbt26xXbbbRcR/1fYXnnllZg1a1Z88pOfLPGEnatp3uYsWbIknnvuudhrr726cLLO1zjrSSedFJ/5zGc2uGbfffeNI444Ik477bTYZZddun7ITpTlbGfMmBEffPBBjB8/vgsn63yNs1ZXV8fnPve5mDFjRixcuDC22WabiPjfH4QPPfRQbL/99jFo0KDSDryRWjrbn/3sZ7FkyZK4+OKLSzRZPhrnrauri4iI2bNnx4knnthwTUopZs+eHQMGDIg+ffqUatSN1tLZDhkyJIYMGRIREddee22sXLkyvva1r5VixA75yEc+Eo888sgGt19++eXx2GOPxYMPPhiDBg2K6urqOOyww+KnP/1pXHHFFdGvX7+IiHjttdfikUceiW984xtdPXq7Zc1aFO3JW+6damPPttz6VNa8ffv2LUSn2tjzLadO1Z7n5CJ0qo6cbbl2qqxZa2trI6L8+1RHzrbcO1VTy5Yti/vuuy/GjBnT8Mutcu9SrWkub1E1l7Xcu1Rrsp5tufWp5jTNWvTfT2U923LqUq1pLm8R+lRzWjvbcu1SrWmad9iwYRFR/n2qJa2dbzn3qaOPPjquv/76ePDBB9f7OfrAAw9ERMT48eML8bupelnyFkXWrEXpUxtztuXYp7LkHT58eCE61cacbTn2qazPy0XoU+0923LvU1nyFqlPtfd8y7lP1Vu9enVMnz49dt999w2eXzf1PmVJq4ROOeWUqK2tjd133z2GDh0aS5Ysibvuuit+8pOfxJlnnhmDBw+Ourq6OPDAA+P3v/99XH311bFmzZqYPXt2w2MMHjw4tt9++xKmyC5L3uXLl8fEiRPj2GOPjREjRkSvXr3iT3/6U1xzzTWxevXquOCCC0odI5MsWQcPHtzwg7ypj33sY82WuU1VlryvvvpqHHvssTF58uTYYYcdoqqqKh577LG4+uqrY+edd95gw3VTlSVrRMQll1wSDz74YBx00EFx4YUXRm1tbdx4440xb968uPPOO0ucIruseetNmzYtevXqFccee2yJJt44WfMeddRRcf3110dNTU0ccsghsXr16rjlllviiSeeiEsuuaQs/lV91qw33HBDRPzv1vm7774bDz74YEybNi0uvfTS2HXXXUsZoV169uzZ7PPqzTffHN27d1/vvosuuih22223mDRpUpxzzjmxatWqOP/882PQoEFx+umnd93QHdSerG+//XY89thjEREN/6LgwQcfbPg5tc8++3TFyBsla94idKqsWYvSp9rztVyETpU1bxE6VXvOtgidqj1565Vrp2pP1iL0qfbkLUKnOvbYY2OrrbaKcePGxaBBg+LPf/5zfO9734vFixfHzTff3HBduXepelnzFqFPZclahC5VL0veovSpLFm32WabQnSpiGx5i9Cl6mV9nipCn8qatV65dql6WfJutdVWhehTEdnPtwh96rOf/WwcdthhcfHFF8e6deti/PjxMWfOnLjoooti0qRJ8elPfzoiitOnsuYtQp/KkrVIfSpL3qL0qYjsX8tF6FRZshapT2U92yL0qaxZ65V7n8qatyh9KmveIvSpevfcc08sXbq0xeecTbpP5ftuirTmpptuSnvvvXcaNGhQqq6uTptvvnnaZ5990q233tpwzYIFCxreE7S5PyeeeGLpArRTlryrVq1KU6ZMSTvttFPq27dvqq6uTsOHD09f+tKX0vPPP1/C6dsnS9aWRESz7327KcuSd+nSpenII49M22yzTerVq1fabLPN0ogRI9JZZ52V3n333RJO3z7tOdvnnnsuHXrooalfv36pZ8+eafz48ennP/95CabuuPbkfe2111K3bt3SCSecUIJJO0fWvHV1denKK69Mo0ePTv369UtbbLFFGj9+fJo+fXpat25diaZvn6xZf/SjH6Wddtop9e7dO/Xt2zftvffe6Z577inR1J3vxBNPTH369Nng9jlz5qT9998/9e7dO9XW1qYjjjgivfTSSyWYsPM0l/WRRx5psWPss88+pRm0kzTNW6RO1VTTrEXpUy1p6fu2qXLsVM1pmrconao5LZ1tETpVc1rKW4RO1VRzWYvQp1rSXN4idKrLLrssjRkzJvXv3z917949DR48OB155JHpqaee2uDaInSprHmL0KeyZC1Sl8qStyh9qj3ft02VY5fKkrdIXao951vufao9WYvQpbLmLUqfypq3CH0qpZQ++OCDdPbZZ6ctt9wyVVdXp6222iqde+65adWqVetdV4Q+lVK2vEXoUym1nbVIfSqltvMWpU/Vy/q921Q5dqq2shapT6WU/WzLvU+llD1rEfpUStnyFqVPpZQtb1H6VEopTZw4MfXp0yetWLGixWs21T5VldL/fyNKAAAAAAAAAAAAOl23Ug8AAAAAAAAAAABQZJa0AAAAAAAAAAAAcmRJCwAAAAAAAAAAIEeWtAAAAAAAAAAAAHJkSQsAAAAAAAAAACBHlrQAAAAAAAAAAAByZEkLAAAAAAAAAAAgR5a0AAAAAAAAAAAAcmRJCwAAAAAAAAAAIEeWtAAAAAAAAAAAAHJkSQsAAAAAAAAAACBHlrQAAAAAAAAAAAByZEkLAAAAAAAAAAAgR5a0AAAAACis+fPnx3e+85048MADY/jw4VFTUxN9+/aNESNGxIknnhizZ89u8zGWLFkSZ555Zuy4447Rq1evGDp0aEycODFmzpwZERE333xzVFVVRVVVVSxcuLDFx1mzZk1MmzYtDjnkkBg2bFjU1NTEoEGDYsKECXH11VfHqlWrOis2AAAAAJuYqpRSKvUQAAAAANDZHn300dh3333bvO6cc86Jyy67rNn75s2bFxMnToy333672ftPOeWU+NSnPhUnn3xyREQsWLAgttlmmw2ue/nll+Pwww+PF154ocU5RowYEffff3+MGDGizZkBAAAAKC/VpR4AAAAAAPKwZs2a6NOnTxx66KGx3377xciRI6O2tjbeeuuteP755+Paa6+NV199NS6//PLYcccdGxat6i1btiwOOuighgWt4447Lr70pS/F4MGD46WXXoprrrkmrr/++pg3b16rc7z55pux1157xeLFi6Nfv35xyimnxAEHHBBDhw6N5cuXxy9+8Yu45ppr4s9//nMcdNBBMXfu3Ojfv39u/10AAAAA6HpeSQsAAACAQlqyZElUV1fH5ptv3uz9H374YUyaNClmzZoVW2+9dbz88svRvXv3hvtPO+20uPbaayMi4rvf/W6cfvrp633+2rVr4+ijj45777234bbmXknrsMMOi/vuuy+23HLLePTRR2O77bbbYJbf//73sffee8fKlSvjvPPOi0suuaSDqQEAAADYFFnSAgAAAKBizZs3L8aMGRMREXPmzImxY8dGRMSqVaviIx/5SCxfvjx23XXXmDNnTlRVVW3w+YsXL45tttkmVq1aFREbLmnNnz8/Ro0aFRER9957bxx++OEtznL22WfHFVdcEcOGDYu//OUvnZQQAAAAgE1Bt1IPAAAAAABdYfXq1fHaa6/FCy+8EPPnz4/58+dH43+/2PhtC5955plYvnx5RESccMIJzS5oRUQMHTo0DjzwwBb/zvpX2erdu3cceuihrc43YcKEiIh44403YtGiRdlCAQAAAFAWqks9AAAAAADkZeXKlXHttdfGHXfcEc8//3ysXbu2xWuXLFnS8PH8+fMbPq5/da2WjBs3br23PGxszpw5ERHxwQcfRHV19l/F/fWvf40tt9wy8/UAAAAAbNosaQEAAABQSAsXLoz99tsvFixYkOn6urq6ho+XLVvW8PGQIUNa/bzBgwe3eN9bb72V6e9u6oMPPujQ5wEAAACwabKkBQAAAEAhHX/88bFgwYKoqqqKk08+OSZPnhw77bRTDB48OGpqaiIiYt26ddG9e/eIiPXe+rCz1L9y17bbbhs/+9nPMn/etttu2+mzAAAAAFA6lrQAAAAAKJwXX3wxHn/88YiIOPfcc+Nf//Vfm72u8StmNTZgwICGj996663YcccdW/y73n777RbvGzhwYERELF68OEaOHNmutzwEAAAAoDi6lXoAAAAAAOhszz//fMPHkydPbvG6OXPmNHv7zjvv3OY1We7/5Cc/GRH/+/aFTzzxRKuPAwAAAEBxWdICAAAAoHDWrFnT8PEHH3zQ4nU//OEPm7193Lhx0b9//4iIuPXWW1t8K8TFixfHww8/3OLjf+5zn2v4+Iorrmh1ZgAAAACKy5IWAAAAAIUzYsSIho9vueWWZq/5wQ9+EPfcc0+z9/Xs2TNOOOGEiIiYO3duXHXVVRtcs27duvjKV74Sq1atanGO3XbbLT772c9GRMQDDzwQF1xwQatzL1y4MG6//fZWrwEAAACg/FSllv4ZIAAAAACUqZRSjB49OubPnx8REcccc0wcd9xx8dGPfjQWLVoU06dPjxkzZsRee+3V8DaEF1xwQVx44YUNj7F06dLYeeed469//WtERBx33HFx/PHHx+DBg+Oll16Ka665Jn7729/G7rvvHk899VRE/O+S1dZbb73eLG+88UaMGzcu3nzzzYiI2GOPPeLLX/5yjBo1Knr27BnvvPNO/OEPf4iHHnoofv3rX8cRRxwRM2bMyPs/EQAAAABdyJIWAAAAAIX07LPPxn777RfLli1r9v5Ro0bFww8/HMOGDYuIDZe0IiLmzZsXEydOjLfffrvZxzjppJNi7733jn/4h3+IiIi//vWvMXTo0A2ue/XVV+Pzn/98PP30023OffLJJ8dNN93U5nUAAAAAlA9vdwgAAABAIY0ZMyaeffbZmDp1amy99dbRo0eP2GKLLWL33XeP7373u/HUU0/FRz/60VYf4xOf+ES88MILcfrpp8eIESOipqYmBg0aFPvuu2/cdttt8V//9V+xYsWKhuv79+/f7ONsvfXW8bvf/S5mzpwZkydPjm233TZ69+4dPXr0iMGDB8eee+4Zp59+ejz22GMxbdq0Tv3vAAAAAEDpeSUtAAAAANgIU6ZMiWnTpsXw4cNj0aJFpR4HAAAAgE2QV9ICAAAAgA6qq6uLe++9NyIixo8fX+JpAAAAANhUWdICAAAAgBa8/PLL0dIL0a9duzZOPfXUWLJkSUREnHjiiV05GgAAAABlxNsdAgAAAEALTjrppHjqqadi8uTJsccee8SQIUOirq4u/vCHP8QNN9wQc+fOjYiI/fffP2bNmhVVVVUlnhgAAACATVF1qQcAAAAAgE3Z//zP/8QFF1zQ4v177bVX/OQnP7GgBQAAAECLvJIWAAAAALTgj3/8Y9x9990xa9asePXVV+Ptt9+Ov/3tbzFw4MAYN25cfPGLX4zJkydHt27dSj0qAAAAAJswS1oAAAAAAAAAAAA58k/8AAAAAAAAAAAAcmRJCwAAAAAAAAAAIEeWtAAAAAAAAAAAAHJkSQsAAAAAAAAAACBHlrQAAAAAAAAAAAByZEkLAAAAAAAAAAAgR5a0AAAAAAAAAAAAcmRJCwAAAAAAAAAAIEeWtAAAAAAAAAAAAHL0/wBPSM2ZjmxfWAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 3000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30,15), facecolor='w')\n",
    "sn.countplot(x=\"age\",data=data,hue=\"is_smoking\")\n",
    "plt.title(\"Graph showing which age group has more smokers.\", size=30)\n",
    "plt.xlabel(\"age\", size=20)\n",
    "plt.ylabel(\"age Count\", size=20)\n",
    "plt.xticks(size=12)\n",
    "plt.yticks(size=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "CUeaeYR4HoFo",
   "metadata": {
    "id": "CUeaeYR4HoFo"
   },
   "source": [
    "## conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b_aU84I9Dxfc",
   "metadata": {
    "id": "b_aU84I9Dxfc"
   },
   "source": [
    "## uneducated people(1) are likely to be more addicted to the ciagratte or smoking, and same for prevalent stroke and diabetes.\n",
    "## from above stats i can conclude that fEmale gets more prevalent stroke as compare to male.\n",
    "## With the increase in age (means 40+)the percentage of smoker  decreases and from 28 to 40 it increases rapidly(means people smoke more at this stage ).\n",
    "## At the age of 40 smoking  is at its peak.\n",
    "## And the aged people those who didn't smoke they get more prevalent stroke as compared to those who smokes .\n",
    "# And the most intesting fact is that smoking no where is correlated with prevent stroke \n",
    "# There is more  risk to  male  to get  CHD (10 year risk of coronary heart disease CHD)as compared to female ."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e896b05b",
   "metadata": {
    "id": "3WPsPIsnyyNP"
   },
   "source": [
    "# Data Splitting"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2c3cb01",
   "metadata": {},
   "source": [
    "dealing with the categorical variable using labelencoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "092da44d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>sex</th>\n",
       "      <th>is_smoking</th>\n",
       "      <th>cigsPerDay</th>\n",
       "      <th>BPMeds</th>\n",
       "      <th>prevalentStroke</th>\n",
       "      <th>prevalentHyp</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>totChol</th>\n",
       "      <th>sysBP</th>\n",
       "      <th>diaBP</th>\n",
       "      <th>BMI</th>\n",
       "      <th>heartRate</th>\n",
       "      <th>glucose</th>\n",
       "      <th>TenYearCHD</th>\n",
       "      <th>group_age</th>\n",
       "      <th>group_age_mean</th>\n",
       "      <th>test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>29.77</td>\n",
       "      <td>72.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0</td>\n",
       "      <td>31-40</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>46</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>20.35</td>\n",
       "      <td>88.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>0</td>\n",
       "      <td>41-50</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>28.26</td>\n",
       "      <td>68.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>1</td>\n",
       "      <td>41-50</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>64</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>136.5</td>\n",
       "      <td>85.0</td>\n",
       "      <td>26.42</td>\n",
       "      <td>70.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>0</td>\n",
       "      <td>61-70</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>61</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>272.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>32.80</td>\n",
       "      <td>85.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>1</td>\n",
       "      <td>61-70</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  age  education  sex  is_smoking  cigsPerDay  BPMeds  prevalentStroke   \n",
       "1   1   36        4.0    1           0         0.0     0.0                0  \\\n",
       "2   2   46        1.0    0           1        10.0     0.0                0   \n",
       "3   3   50        1.0    1           1        20.0     0.0                0   \n",
       "4   4   64        1.0    0           1        30.0     0.0                0   \n",
       "5   5   61        3.0    0           0         0.0     0.0                0   \n",
       "\n",
       "   prevalentHyp  diabetes  totChol  sysBP  diaBP    BMI  heartRate  glucose   \n",
       "1             1         0    212.0  168.0   98.0  29.77       72.0     75.0  \\\n",
       "2             0         0    250.0  116.0   71.0  20.35       88.0     94.0   \n",
       "3             1         0    233.0  158.0   88.0  28.26       68.0     94.0   \n",
       "4             0         0    241.0  136.5   85.0  26.42       70.0     77.0   \n",
       "5             1         0    272.0  182.0  121.0  32.80       85.0     65.0   \n",
       "\n",
       "   TenYearCHD group_age  group_age_mean  test  \n",
       "1           0     31-40            20.0     0  \n",
       "2           0     41-50            20.0     0  \n",
       "3           1     41-50            20.0     0  \n",
       "4           0     61-70            20.0     1  \n",
       "5           1     61-70            20.0     1  "
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_col=data[[\"sex\",'is_smoking','test']]\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "le=LabelEncoder()\n",
    "for col in cat_col:\n",
    "    data[col]=le.fit_transform(data[col])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "9b2eba21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>sex</th>\n",
       "      <th>is_smoking</th>\n",
       "      <th>cigsPerDay</th>\n",
       "      <th>BPMeds</th>\n",
       "      <th>prevalentStroke</th>\n",
       "      <th>prevalentHyp</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>totChol</th>\n",
       "      <th>sysBP</th>\n",
       "      <th>diaBP</th>\n",
       "      <th>BMI</th>\n",
       "      <th>heartRate</th>\n",
       "      <th>glucose</th>\n",
       "      <th>TenYearCHD</th>\n",
       "      <th>group_age</th>\n",
       "      <th>group_age_mean</th>\n",
       "      <th>test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>29.77</td>\n",
       "      <td>72.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0</td>\n",
       "      <td>31-40</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>46</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>20.35</td>\n",
       "      <td>88.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>0</td>\n",
       "      <td>41-50</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>28.26</td>\n",
       "      <td>68.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>1</td>\n",
       "      <td>41-50</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>64</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>136.5</td>\n",
       "      <td>85.0</td>\n",
       "      <td>26.42</td>\n",
       "      <td>70.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>0</td>\n",
       "      <td>61-70</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>61</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>272.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>32.80</td>\n",
       "      <td>85.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>1</td>\n",
       "      <td>61-70</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3384</th>\n",
       "      <td>3384</td>\n",
       "      <td>61</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>217.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>26.98</td>\n",
       "      <td>105.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>0</td>\n",
       "      <td>61-70</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3385</th>\n",
       "      <td>3385</td>\n",
       "      <td>60</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>123.5</td>\n",
       "      <td>79.0</td>\n",
       "      <td>29.28</td>\n",
       "      <td>70.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>0</td>\n",
       "      <td>51-60</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3386</th>\n",
       "      <td>3386</td>\n",
       "      <td>46</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>199.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>21.96</td>\n",
       "      <td>80.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>0</td>\n",
       "      <td>41-50</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3387</th>\n",
       "      <td>3387</td>\n",
       "      <td>44</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>352.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>28.92</td>\n",
       "      <td>73.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>1</td>\n",
       "      <td>41-50</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3389</th>\n",
       "      <td>3389</td>\n",
       "      <td>54</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>288.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>29.88</td>\n",
       "      <td>79.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>0</td>\n",
       "      <td>51-60</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3023 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  age  education  sex  is_smoking  cigsPerDay  BPMeds   \n",
       "1        1   36        4.0    1           0         0.0     0.0  \\\n",
       "2        2   46        1.0    0           1        10.0     0.0   \n",
       "3        3   50        1.0    1           1        20.0     0.0   \n",
       "4        4   64        1.0    0           1        30.0     0.0   \n",
       "5        5   61        3.0    0           0         0.0     0.0   \n",
       "...    ...  ...        ...  ...         ...         ...     ...   \n",
       "3384  3384   61        1.0    0           0         0.0     0.0   \n",
       "3385  3385   60        1.0    0           0         0.0     0.0   \n",
       "3386  3386   46        1.0    0           0         0.0     0.0   \n",
       "3387  3387   44        3.0    1           1         3.0     0.0   \n",
       "3389  3389   54        3.0    0           0         0.0     0.0   \n",
       "\n",
       "      prevalentStroke  prevalentHyp  diabetes  totChol  sysBP  diaBP    BMI   \n",
       "1                   0             1         0    212.0  168.0   98.0  29.77  \\\n",
       "2                   0             0         0    250.0  116.0   71.0  20.35   \n",
       "3                   0             1         0    233.0  158.0   88.0  28.26   \n",
       "4                   0             0         0    241.0  136.5   85.0  26.42   \n",
       "5                   0             1         0    272.0  182.0  121.0  32.80   \n",
       "...               ...           ...       ...      ...    ...    ...    ...   \n",
       "3384                0             1         0    217.0  182.0   86.0  26.98   \n",
       "3385                0             0         0    261.0  123.5   79.0  29.28   \n",
       "3386                0             0         0    199.0  102.0   56.0  21.96   \n",
       "3387                0             1         0    352.0  164.0  119.0  28.92   \n",
       "3389                0             0         0    288.0  124.0   77.0  29.88   \n",
       "\n",
       "      heartRate  glucose  TenYearCHD group_age  group_age_mean  test  \n",
       "1          72.0     75.0           0     31-40            20.0     0  \n",
       "2          88.0     94.0           0     41-50            20.0     0  \n",
       "3          68.0     94.0           1     41-50            20.0     0  \n",
       "4          70.0     77.0           0     61-70            20.0     1  \n",
       "5          85.0     65.0           1     61-70            20.0     1  \n",
       "...         ...      ...         ...       ...             ...   ...  \n",
       "3384      105.0    113.0           0     61-70            20.0     1  \n",
       "3385       70.0    103.0           0     51-60            20.0     0  \n",
       "3386       80.0     84.0           0     41-50            20.0     0  \n",
       "3387       73.0     72.0           1     41-50            20.0     0  \n",
       "3389       79.0     92.0           0     51-60            20.0     0  \n",
       "\n",
       "[3023 rows x 20 columns]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data= data.dropna()\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "0190b38f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn import tree\n",
    "from IPython.display import SVG\n",
    "from IPython.display import display\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.metrics import roc_auc_score, confusion_matrix, accuracy_score, roc_curve\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "3dec7754",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=data[['age', 'education', 'sex', 'is_smoking', 'cigsPerDay', 'BPMeds',\n",
    "       'prevalentStroke', 'prevalentHyp', 'diabetes', 'totChol', 'sysBP',\n",
    "        'diaBP', 'BMI', 'heartRate', 'glucose']].copy()\n",
    "y=data['TenYearCHD'].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "19d50531",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2418, 15)\n",
      "(605, 15)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split \n",
    "X_train, X_test, y_train, y_test = train_test_split( X,y , test_size = 0.2, random_state = 0) \n",
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "76ddfb77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>sex</th>\n",
       "      <th>is_smoking</th>\n",
       "      <th>cigsPerDay</th>\n",
       "      <th>BPMeds</th>\n",
       "      <th>prevalentStroke</th>\n",
       "      <th>prevalentHyp</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>totChol</th>\n",
       "      <th>sysBP</th>\n",
       "      <th>diaBP</th>\n",
       "      <th>BMI</th>\n",
       "      <th>heartRate</th>\n",
       "      <th>glucose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>35</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>231.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>23.09</td>\n",
       "      <td>83.0</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3102</th>\n",
       "      <td>60</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>18.64</td>\n",
       "      <td>65.0</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1921</th>\n",
       "      <td>40</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>226.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>24.75</td>\n",
       "      <td>87.0</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1038</th>\n",
       "      <td>58</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>271.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>23.07</td>\n",
       "      <td>75.0</td>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2887</th>\n",
       "      <td>41</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>24.20</td>\n",
       "      <td>80.0</td>\n",
       "      <td>86.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>843</th>\n",
       "      <td>52</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>25.11</td>\n",
       "      <td>71.0</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>927</th>\n",
       "      <td>63</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>25.92</td>\n",
       "      <td>64.0</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1850</th>\n",
       "      <td>59</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>276.0</td>\n",
       "      <td>127.5</td>\n",
       "      <td>85.5</td>\n",
       "      <td>22.91</td>\n",
       "      <td>78.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2931</th>\n",
       "      <td>63</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>217.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>21.99</td>\n",
       "      <td>72.0</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3071</th>\n",
       "      <td>45</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>27.45</td>\n",
       "      <td>84.0</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2418 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age  education  sex  is_smoking  cigsPerDay  BPMeds  prevalentStroke   \n",
       "251    35        2.0    0           1        20.0     0.0                0  \\\n",
       "3102   60        1.0    1           1        20.0     0.0                0   \n",
       "1921   40        3.0    0           0         0.0     0.0                0   \n",
       "1038   58        1.0    0           1         2.0     0.0                0   \n",
       "2887   41        2.0    0           1         9.0     0.0                0   \n",
       "...   ...        ...  ...         ...         ...     ...              ...   \n",
       "843    52        3.0    0           1        20.0     0.0                0   \n",
       "927    63        3.0    0           0         0.0     0.0                0   \n",
       "1850   59        1.0    0           0         0.0     0.0                0   \n",
       "2931   63        4.0    1           1        43.0     0.0                0   \n",
       "3071   45        1.0    0           0         0.0     0.0                0   \n",
       "\n",
       "      prevalentHyp  diabetes  totChol  sysBP  diaBP    BMI  heartRate  glucose  \n",
       "251              0         0    231.0  150.0   90.0  23.09       83.0     72.0  \n",
       "3102             1         0    174.0  164.0  113.0  18.64       65.0     93.0  \n",
       "1921             0         0    226.0  118.0   72.0  24.75       87.0     79.0  \n",
       "1038             0         0    271.0  146.0   92.0  23.07       75.0     83.0  \n",
       "2887             0         0    200.0  124.0   76.0  24.20       80.0     86.0  \n",
       "...            ...       ...      ...    ...    ...    ...        ...      ...  \n",
       "843              0         0    215.0  132.0   82.0  25.11       71.0     75.0  \n",
       "927              1         0    315.0  156.0   90.0  25.92       64.0     74.0  \n",
       "1850             0         0    276.0  127.5   85.5  22.91       78.0     60.0  \n",
       "2931             0         0    217.0  110.0   68.0  21.99       72.0     68.0  \n",
       "3071             0         0    285.0  120.0   80.0  27.45       84.0     93.0  \n",
       "\n",
       "[2418 rows x 15 columns]"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "db2064ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TenYearCHD\n",
       "0    2051\n",
       "1     367\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "52ec7179",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TenYearCHD\n",
       "0    514\n",
       "1     91\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2568e58c",
   "metadata": {},
   "source": [
    "## **Logistic Regression**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "5264993c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=10000)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" checked><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=10000)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(max_iter=10000)"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "clf = LogisticRegression(fit_intercept=True, max_iter=10000)\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "e518818e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.06469229, -0.07382753,  0.54915858,  0.25554663,  0.01618691,\n",
       "         0.10989029,  0.48810453,  0.05921606, -0.20881052,  0.00319327,\n",
       "         0.0164355 , -0.00588405,  0.01478995, -0.00431834,  0.00765057]])"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the model coefficients\n",
    "clf.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "327add2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-8.71624712])"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "f28c3ab6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the predicted classes\n",
    "train_class_preds = clf.predict(X_train)\n",
    "test_class_preds = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "adcd2444",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "c85a9668",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy on train data is  0.8540115798180314\n",
      "The accuracy on test data is  0.856198347107438\n"
     ]
    }
   ],
   "source": [
    "# Get the accuracy scores\n",
    "train_accuracy = accuracy_score(train_class_preds,y_train)\n",
    "test_accuracy = accuracy_score(test_class_preds,y_test)\n",
    "\n",
    "print(\"The accuracy on train data is \", train_accuracy)\n",
    "print(\"The accuracy on test data is \", test_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "b2b7ae57",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2042    9]\n",
      " [ 344   23]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'Negative'), Text(0, 1.5, 'Positive')]"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the confusion matrix for both train and test\n",
    "plt.figure(figsize=(5,3))\n",
    "labels = ['Negative', 'Positive']\n",
    "cm = confusion_matrix(y_train, train_class_preds)\n",
    "print(cm)\n",
    "\n",
    "ax= plt.subplot()\n",
    "sns.heatmap(cm, annot=True, ax = ax) #annot=True to annotate cells\n",
    "\n",
    "# labels, title and ticks\n",
    "ax.set_xlabel('Predicted labels')\n",
    "ax.set_ylabel('True labels')\n",
    "ax.set_title('Confusion Matrix')\n",
    "ax.xaxis.set_ticklabels(labels)\n",
    "ax.yaxis.set_ticklabels(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "1e29bd34",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[508   6]\n",
      " [ 81  10]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'Retained'), Text(0, 1.5, 'Churned')]"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the confusion matrix for both train and test\n",
    "plt.figure(figsize=(5,3))\n",
    "labels = ['Retained', 'Churned']\n",
    "cm = confusion_matrix(y_test, test_class_preds)\n",
    "print(cm)\n",
    "\n",
    "ax= plt.subplot()\n",
    "sns.heatmap(cm, annot=True, ax = ax); #annot=True to annotate cells\n",
    "\n",
    "# labels, title and ticks\n",
    "ax.set_xlabel('Predicted labels')\n",
    "ax.set_ylabel('True labels')\n",
    "ax.set_title('Confusion Matrix')\n",
    "ax.xaxis.set_ticklabels(labels)\n",
    "ax.yaxis.set_ticklabels(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "161ddadd",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_lr_predict_pro=clf.predict_proba(X_test)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "9ad6f161",
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr, tpr, thresholds = roc_curve(y_test, y_lr_predict_pro)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "995b8bc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7481720614016334"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_test,y_lr_predict_pro)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "7087278e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "plt.plot([0,1],[0,1],'k--')\n",
    "plt.plot(fpr,tpr, label='Logistic Regression')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Logistic Regression ROC curve')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52acd714",
   "metadata": {},
   "source": [
    "\n",
    "## Handling Data Imbalalance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "b52df470",
   "metadata": {},
   "outputs": [],
   "source": [
    "import imblearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "a969c7ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "smote = SMOTE(sampling_strategy='minority')\n",
    "X_sm, y_sm = smote.fit_resample(X, y)\n",
    "# X_sm, y_sm = smote.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "71cb358b",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_sm=pd.DataFrame(y_sm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "0020b610",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TenYearCHD\n",
       "0             2565\n",
       "1             2565\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_sm.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "21fc5fe0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4104, 15)\n",
      "(1026, 15)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split( X_sm,y_sm , test_size = 0.2, random_state = 0) \n",
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "61023715",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TenYearCHD\n",
       "1             2060\n",
       "0             2044\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "23d5d172",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TenYearCHD\n",
       "0             521\n",
       "1             505\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "98773edf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=10000)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" checked><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=10000)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(max_iter=10000)"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = LogisticRegression(fit_intercept=True, max_iter=10000)\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "d9c9f42f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the predicted classes\n",
    "train_class_preds = clf.predict(X_train)\n",
    "test_class_preds = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "0b8b1393",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy on train data is  0.6778752436647173\n",
      "The accuracy on test data is  0.682261208576998\n"
     ]
    }
   ],
   "source": [
    "# Get the accuracy scores\n",
    "train_accuracy = accuracy_score(y_train,train_class_preds)\n",
    "test_accuracy = accuracy_score(y_test,test_class_preds)\n",
    "\n",
    "print(\"The accuracy on train data is \", train_accuracy)\n",
    "print(\"The accuracy on test data is \", test_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "4d84726d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix for training Data\n",
      "[[1357  687]\n",
      " [ 635 1425]]\n",
      "Confusion Matrix for Test Data\n",
      "[[344 177]\n",
      " [149 356]]\n"
     ]
    }
   ],
   "source": [
    "# Get the confusion matrix for both train and test\n",
    "\n",
    "cm = confusion_matrix(y_train, train_class_preds)\n",
    "print('Confusion Matrix for training Data')\n",
    "print(cm)\n",
    "cm = confusion_matrix(y_test, test_class_preds)\n",
    "print('Confusion Matrix for Test Data')\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "54d80a7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_lr_predict_pro=clf.predict_proba(X_test)[:,1]\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_lr_predict_pro)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "b57dfbae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7402481898861671"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_test,y_lr_predict_pro)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "3e925740",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "plt.plot([0,1],[0,1],'k--')\n",
    "plt.plot(fpr,tpr, label='Logistic Regression')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Logistic Regression ROC curve')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "161612a3",
   "metadata": {},
   "source": [
    "\n",
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "e71c8b47",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "d9ddb2b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-10 {color: black;background-color: white;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-10 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-10 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=RandomForestClassifier(),\n",
       "             param_grid={&#x27;max_depth&#x27;: [3, 5, 7, 9, 12],\n",
       "                         &#x27;n_estimators&#x27;: [50, 65, 80, 95, 120]},\n",
       "             scoring=&#x27;roc_auc&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=RandomForestClassifier(),\n",
       "             param_grid={&#x27;max_depth&#x27;: [3, 5, 7, 9, 12],\n",
       "                         &#x27;n_estimators&#x27;: [50, 65, 80, 95, 120]},\n",
       "             scoring=&#x27;roc_auc&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=RandomForestClassifier(),\n",
       "             param_grid={'max_depth': [3, 5, 7, 9, 12],\n",
       "                         'n_estimators': [50, 65, 80, 95, 120]},\n",
       "             scoring='roc_auc')"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = RandomForestClassifier() # For GBM, use GradientBoostingClassifier()\n",
    "grid_values = {'n_estimators':[50, 65, 80, 95,120], 'max_depth':[3, 5, 7,9,12]}\n",
    "GSclassifier = GridSearchCV(classifier, param_grid = grid_values, scoring = 'roc_auc', cv=5)\n",
    "\n",
    "# Fit the object to train dataset\n",
    "GSclassifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "9f5499f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 12, 'n_estimators': 120}"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bestvalues=GSclassifier.best_params_\n",
    "GSclassifier.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "77ffcdc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-11 {color: black;background-color: white;}#sk-container-id-11 pre{padding: 0;}#sk-container-id-11 div.sk-toggleable {background-color: white;}#sk-container-id-11 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-11 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-11 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-11 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-11 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-11 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-11 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-11 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-11 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-11 div.sk-item {position: relative;z-index: 1;}#sk-container-id-11 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-11 div.sk-item::before, #sk-container-id-11 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-11 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-11 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-11 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-11 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-11 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-11 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-11 div.sk-label-container {text-align: center;}#sk-container-id-11 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-11 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=12, n_estimators=120)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" checked><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=12, n_estimators=120)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(max_depth=12, n_estimators=120)"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = RandomForestClassifier(max_depth=bestvalues['max_depth'],n_estimators=bestvalues['n_estimators']) # For GBM, use GradientBoostingClassifier()\n",
    "\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "5acfc014",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_preds_rf =  classifier.predict(X_train)\n",
    "y_test_preds_rf= classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "f1285f47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9875730994152047"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obtain accuracy on train set\n",
    "accuracy_score(y_train,y_train_preds_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "70d15269",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8859649122807017"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obtain accuracy on test set\n",
    "accuracy_score(y_test,y_test_preds_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "8847307c",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_rf_predict_pro=classifier.predict_proba(X_test)[:,1]\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_rf_predict_pro)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "59bfe244",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9618137245586362"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_test,y_rf_predict_pro)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "975ae430",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,3))\n",
    "plt.plot([0,1],[0,1],'k--')\n",
    "plt.plot(fpr,tpr, label='Random Forest')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Random Forest ROC curve')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "1c026a32",
   "metadata": {},
   "outputs": [],
   "source": [
    "features = X.columns\n",
    "importances = classifier.feature_importances_\n",
    "indices = np.argsort(importances)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "4c7b5c99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,9))\n",
    "plt.title('Feature Importance')\n",
    "plt.barh(range(len(indices)), importances[indices], color='red', align='center')\n",
    "plt.yticks(range(len(indices)), [features[i] for i in indices])\n",
    "plt.xlabel('Relative Importance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9ca1dc0",
   "metadata": {},
   "source": [
    "## KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "dc9c2ecd",
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {'n_neighbors':np.arange(1,50)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "1fae53cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-12 {color: black;background-color: white;}#sk-container-id-12 pre{padding: 0;}#sk-container-id-12 div.sk-toggleable {background-color: white;}#sk-container-id-12 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-12 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-12 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-12 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-12 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-12 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-12 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-12 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-12 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-12 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-12 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-12 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-12 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-12 div.sk-item {position: relative;z-index: 1;}#sk-container-id-12 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-12 div.sk-item::before, #sk-container-id-12 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-12 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-12 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-12 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-12 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-12 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-12 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-12 div.sk-label-container {text-align: center;}#sk-container-id-12 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-12 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-12\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=KNeighborsClassifier(),\n",
       "             param_grid={&#x27;n_neighbors&#x27;: array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
       "       35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49])})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=KNeighborsClassifier(),\n",
       "             param_grid={&#x27;n_neighbors&#x27;: array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
       "       35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49])})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=KNeighborsClassifier(),\n",
       "             param_grid={'n_neighbors': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
       "       35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49])})"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier()\n",
    "knn_cv= GridSearchCV(knn,param_grid,cv=5)\n",
    "knn_cv.fit(X_sm,y_sm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "dcb2e176",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_neighbors': 2}"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bestPermet=knn_cv.best_params_\n",
    "knn_cv.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "d4a3f03c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Setup arrays to store training and test accuracies\n",
    "neighbors = np.arange(1,30)\n",
    "train_accuracy =np.empty(len(neighbors))\n",
    "test_accuracy = np.empty(len(neighbors))\n",
    "\n",
    "for i,k in enumerate(neighbors):\n",
    "    # Setup a knn classifier with k neighbors\n",
    "    knn = KNeighborsClassifier(n_neighbors=k)\n",
    "    \n",
    "    # Fit the model\n",
    "    knn.fit(X_train, y_train)\n",
    "    \n",
    "    # Compute accuracy on the training set\n",
    "    train_accuracy[i] = knn.score(X_train, y_train)\n",
    "    \n",
    "    # Compute accuracy on the test set\n",
    "    test_accuracy[i] = knn.score(X_test, y_test) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "c0ed1b49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('k-NN Varying number of neighbors')\n",
    "plt.plot(neighbors, test_accuracy, label='Testing Accuracy')\n",
    "plt.plot(neighbors, train_accuracy, label='Training accuracy')\n",
    "plt.legend()\n",
    "plt.xlabel('Number of neighbors')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "b5f7c06a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setup a knn classifier with k neighbors\n",
    "knn = KNeighborsClassifier(n_neighbors=bestPermet['n_neighbors'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "daba8138",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-13 {color: black;background-color: white;}#sk-container-id-13 pre{padding: 0;}#sk-container-id-13 div.sk-toggleable {background-color: white;}#sk-container-id-13 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-13 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-13 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-13 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-13 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-13 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-13 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-13 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-13 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-13 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-13 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-13 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-13 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-13 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-13 div.sk-item {position: relative;z-index: 1;}#sk-container-id-13 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-13 div.sk-item::before, #sk-container-id-13 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-13 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-13 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-13 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-13 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-13 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-13 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-13 div.sk-label-container {text-align: center;}#sk-container-id-13 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-13 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-13\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" checked><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=2)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=2)"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the model\n",
    "knn.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "0f328350",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8966861598440545"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "e48c060d",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pred_knn = knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "5661352b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[436,  85],\n",
       "       [ 21, 484]], dtype=int64)"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,y_test_pred_knn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "5f391cc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_proba = knn.predict_proba(X_test)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "16f7da1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred_proba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "ce1dad91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9104844073658807"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_test,y_pred_proba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "4ba7e1fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcoAAAHWCAYAAAD3iMk8AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABrtElEQVR4nO3dd1hT1x8G8Ddhb0RkKYp714HVuiooCq46K44qzg5na7V1z7rqqPan1rpqXXXvWQe4atW6qxZUQK0KioM9k/P7gyYaGRJMcoG8n+fJ0+Zy780318Cbc+8558qEEAJERESULbnUBRARERVkDEoiIqJcMCiJiIhywaAkIiLKBYOSiIgoFwxKIiKiXDAoiYiIcsGgJCIiygWDkoiIKBcMSpLE4MGD0bJlS6nLyLMpU6ZAJpO907YxMTFvXVcmk2Ho0KH5ep3C6ObNmzA1NcXff/8tdSlEOWJQGpk1a9ZAJpPhr7/+0lgeGxuL+vXrw9LSEocOHdJrDREREVi5ciXGjRun19chaSiVSqxZswYfffQRPD09YWNjgxo1auC7775DSkqKxrrVqlVD27ZtMWnSpDztW/X5VT1MTU1RsmRJ9O3bFw8fPsx2GyEE1q1bhw8//BCOjo6wtrZGzZo1MW3aNCQmJub4Wjt37kTr1q3h7OwMc3NzeHh4oFu3bjh+/HjeDwYVCQxKQlxcHFq1aoVr165h586dCAgI0OvrLVq0CGXLloWvr69eX0eXJkyYgOTkZKnLKBSSkpLQr18/PH36FJ9//jkWLlyI+vXrY/LkyWjdujXenF76888/x86dO3H37t08v8a0adOwbt06LFu2DK1bt8b69evRrFmzLEGsUCjQvXt39OnTB0Bm637hwoWoXbs2pk6dig8++ADR0dEa2wgh0K9fP3Tu3BnR0dEYOXIkli1bhiFDhiA8PBwtWrTAH3/8kc+jQ4WSIKPyyy+/CADiwoULQggh4uLixAcffCDMzc3Fvn379P76aWlpwtnZWUyYMEHvr1VQTJ48WQAQT58+feu6AMSQIUP0Wk9CQoJe95+amirOnDmTZfnUqVMFAHHkyBGN5WlpaaJYsWJi4sSJb933m59flW+//VYAEJs3b9ZYPnPmTAFAjBo1Ksu+9uzZI+RyuQgICNBYPnfuXAFAfPnll0KpVGbZbu3ateLcuXNvrVWf9P1vSJrYojRiCQkJCAgIwKVLl7B9+3a0bdtW4+d9+/aFra0tHj58iI4dO8LW1hYlSpTAqFGjoFAo1OtFRkZCJpNh3rx5WL58OcqXLw8LCwu8//77uHDhgsY+T58+jZiYGPj5+WksDwkJgUwmw5YtWzBjxgyUKlUKlpaWaNGiBe7cuaPV+1Kdnjtz5gxGjhyJEiVKwMbGBp06dcLTp0+zrH/w4EE0bdoUNjY2sLOzQ9u2bXHjxg2NdbK7RpmcnIzhw4fD2dkZdnZ2+Oijj/Dw4UPIZDJMmTIly+u8fPkSffv2haOjIxwcHNCvXz8kJSVl+x42bNiAypUrw9LSEt7e3jh58mSWdS5fvozWrVvD3t4etra2aNGiBf78889sj8WJEycwePBguLi4oFSpUgCA+Ph4fPnll/Dy8oKFhQVcXFzQsmVLXLp0Kdfj+zbm5uZo1KhRluWdOnUCANy6dUtjuZmZGXx8fLB79+58v2bTpk0BQKNVmpycjLlz56JSpUqYNWtWlm3at2+PoKAgHDp0SH3ckpOTMWvWLFSpUgXz5s3L9rp07969Ub9+/VzrUSqVWLRoEWrWrAlLS0uUKFECAQEB6kseqt+ZNWvWZNn2zc+P6rN38+ZN9OzZE8WKFUOTJk3U9d27dy/LPsaOHQtzc3O8ePFCvezcuXMICAiAg4MDrK2t0axZM5w5cybX90GZGJRGKjExEa1bt8aFCxewdetWtGvXLtv1FAoF/P39Ubx4ccybNw/NmjXD/PnzsXz58izrbty4EXPnzsVnn32G7777DpGRkejcuTPS09PV6/zxxx+QyWSoU6dOtq83e/Zs7Ny5E6NGjcLYsWPx559/olevXvl6j8OGDcPVq1cxefJkfPHFF9i7d2+WjjLr1q1D27ZtYWtrizlz5mDixIm4efMmmjRpgsjIyFz337dvX/zvf/9DmzZtMGfOHFhZWWX5svG6bt26IT4+HrNmzUK3bt2wZs0aTJ06Nct6J06cwJdffolPPvkE06ZNw7NnzxAQEKDR4eXGjRto2rQprl69im+++QYTJ05EREQEfHx8cO7cuSz7HDx4MG7evIlJkyZhzJgxADJPef7000/o0qULli5dilGjRsHKykojyJKSkhATE/PWx+t/kHMSFRUFAHB2ds7yM29vb/z999+Ii4t7636yo/q3KlasmHrZ6dOn8eLFC/Ts2ROmpqbZbqc6Jbtv3z71Ns+fP0fPnj1hYmKSr1oAYMCAAfjyyy/h6emJOXPmYMyYMbC0tMzyRUYbH3/8MZKSkjBz5kwMGjQI3bp1U3+5fNOWLVvQqlUr9fE4fvw4PvzwQ8TFxWHy5MmYOXMmXr58iebNm+P8+fP5rsloSN2kJcNSnboqU6aMMDMzE7t27cpx3aCgIAFATJs2TWN5nTp1hLe3t/p5RESEACCKFy8unj9/rl6+e/duAUDs3btXveyTTz4RxYsXz/JawcHBAoCoWrWqSE1NVS9ftGiRACCuX7+u9Xv08/PTOHX21VdfCRMTE/Hy5UshhBDx8fHC0dFRDBo0SGP7qKgo4eDgoLFcdfpU5eLFi+rTc6/r27evACAmT56cZdv+/ftrrNupU6csxwKAACD++usv9bJ79+4JS0tL0alTJ/Wyjh07CnNzc3H37l31skePHgk7Ozvx4YcfZjkWTZo0ERkZGRqv5eDg8NbTvKra3/YoU6ZMrvsRQgg/Pz9hb28vXrx4keVnGzduFADeekpT9X6OHj0qnj59Kh48eCC2bdsmSpQoISwsLMSDBw/U6y5cuFAAEDt37sxxf8+fPxcAROfOnYUQrz5vuW3zNsePHxcAxPDhw7P8TPV5VP3O/PLLL1nWyenz06NHjyzrNmzYUON3UQghzp8/LwCItWvXql+zYsWKwt/fX+P3ISkpSZQtW1a0bNkyP2/TqGT/NYuKvOjoaFhaWsLT0/Ot637++ecaz5s2bYp169ZlWS8wMFDjG73qdFh4eLh62bNnzzTWeVO/fv1gbm6e7T5q1Kjx1lpf9+mnn2qcOmvatCl++OEH3Lt3D++99x6OHDmCly9fokePHhpDN0xMTNCgQQMEBwfnuG9Vz+DBgwdrLB82bFi2p9OA7I/jzp07ERcXB3t7e/Xyhg0bwtvbW/28dOnS6NChA/bu3as+5f3777+jY8eOKFeunHo9d3d39OzZEytWrMiyz0GDBmVpITk6OuLcuXN49OgRPDw8sq25T58+aNKkSU6HQc3KyirXn8+cORNHjx7F0qVL4ejomOXnqs9EXobQAMhy6t7Lywvr169Xn1YGMk8tA4CdnV2O+1H9TNWSVf03t23eZvv27ZDJZJg8eXKWn+V3iBGQ9fMDZP7Offnll7h79y7Kly8PANi8eTMsLCzQoUMHAMCVK1dw+/ZtTJgwAc+ePdPYvkWLFli3bh2USiXkcp5gzAmD0kj9/PPPGDlyJAICAnDq1ClUrlw52/VU11deV6xYsWxPtZUuXTrLegCyrCve6PWYn33kxdv2dfv2bQBA8+bNs93+9aB507179yCXy1G2bFmN5RUqVMhXPa+/VsWKFbNsW6lSJSQlJamvsSYlJWX7b1a1alUolUo8ePAA1atXVy9/s04A+P777xEUFARPT094e3ujTZs26NOnj0b4litXTuN5fmzevBkTJkzAgAED8MUXX2S7juozkdcgWbJkCSpVqoTY2FisXr0aJ0+ehIWFhcY6qrBTBWZ23gxT1b9Dbtu8zd27d+Hh4QEnJ6d87yM72f0bfvzxxxg5ciQ2b96McePGQQiBrVu3qq9dA68+50FBQTnuOzY2NtcvsMaOQWmkqlWrhgMHDqBFixZo2bIlzpw5k23rUpvrNDmt+3owFi9ePNfQy8s+dFWPUqkEkHmd0s3NLct6OV3Xyi9dvjdtZdfi69atm7pV+/vvv2Pu3LmYM2cOduzYgdatWwPI7PCVkJDw1v2bmJhk+UIFAEeOHEGfPn3Qtm1bLFu2LMftVZ+J7K5fZqd+/fqoV68eAKBjx45o0qQJevbsidDQUNja2gLI/NIAANeuXUPHjh2z3c+1a9cAZP4+AECVKlUAANevX89xG13I6QvB653k3pTdv6GHhweaNm2KLVu2YNy4cfjzzz9x//59zJkzR72O6nM+d+5c1K5dO9t9q44ZZY9tbSNWv3597Nq1C0+ePEHLli2z7RGqa1WqVMGLFy8QGxur99d6G9WpKhcXF/j5+WV5+Pj45LhtmTJloFQqERERobFc2x662VG1AF4XFhYGa2trlChRAiVKlIC1tTVCQ0OzrPfPP/9ALpfn6ZQ6kHm6dvDgwdi1axciIiJQvHhxzJgxQ/3zefPmwd3d/a2P999/P8u+z507h06dOqFevXrYsmVLrl88IiIiIJfLUalSpTzV/ToTExPMmjULjx49wuLFi9XLmzRpAkdHR2zcuDHHAFq7di0AqDuzNWnSBMWKFcNvv/2Wa2jlpnz58nj06BGeP3+e4zqq1tvLly81lmfXg/VtAgMDcfXqVYSGhmLz5s2wtrZG+/btNeoBMlvL2X3O/fz8YGZmpvXrGhMGpZFr0aIFfvvtN9y5cwcBAQH57nWYVw0bNoQQAhcvXtTr6+SFv78/7O3tMXPmTI2euSq5fXHw9/cHACxdulRj+f/+9793ruvs2bMaQzQePHiA3bt3o1WrVjAxMYGJiQlatWqF3bt3a/TMjY6OxsaNG9GkSZNcTxsDmS2XN7+suLi4wMPDA6mpqeplffr0wZEjR9762LBhg8a+bt26hbZt28LLywv79u176zXMixcvonr16nBwcHjb4cmWj48P6tevj4ULF6onHbC2tsaoUaMQGhqK8ePHZ9lm//79WLNmDfz9/fHBBx+ot/n2229x69YtfPvtt9m29tevX59rT9EuXbpACJFtj2bV/uzt7eHs7Jxl2M+bn6e86NKlC0xMTPDbb7+pe7Db2Niof+7t7Y3y5ctj3rx52Z4dMMQX5MKOp14JnTp1wooVK9C/f3989NFHOHToECwtLfXyWk2aNEHx4sVx9OjRHK8NGoq9vT1++ukn9O7dG3Xr1kX37t1RokQJ3L9/H/v370fjxo01Wiiv8/b2RpcuXbBw4UI8e/YMH3zwAU6cOIGwsDAA79Zpo0aNGvD398fw4cNhYWGh/uP5+h/e7777DkeOHEGTJk0wePBgmJqa4ueff0Zqaiq+//77t75GfHw8SpUqha5du6JWrVqwtbXF0aNHceHCBcyfP1+9Xn6uUcbHx8Pf3x8vXrzA6NGjsX//fo2fly9fHg0bNlQ/T09PV4/zfBejR4/Gxx9/jDVr1qg7vowZMwaXL1/GnDlzcPbsWXTp0gVWVlY4ffo01q9fj6pVq+LXX3/Nsp8bN25g/vz5CA4ORteuXeHm5oaoqCjs2rUL58+fz3VmHl9fX/Tu3Rs//vgjbt++jYCAACiVSpw6dQq+vr7qIUoDBw7E7NmzMXDgQNSrVw8nT55Uf3604eLiAl9fXyxYsADx8fEIDAzU+LlcLsfKlSvRunVrVK9eHf369UPJkiXx8OFDBAcHw97eHnv37tX6dY2KRL1tSSI5zWwihBDz5s0TAES7du1Eenq6CAoKEjY2NlnWe3OohKqr+9y5c7Osize6ugshxPDhw0WFChU0lqmGh2zdulVjeW7d6LV9j6rXCA4OzrLc399fODg4CEtLS1G+fHnRt29fjSEab75nIYRITEwUQ4YMEU5OTsLW1lZ07NhRhIaGCgBi9uzZWbZ9c2YeVZ0RERHqZfhvZp7169eLihUrCgsLC1GnTp0sNQshxKVLl4S/v7+wtbUV1tbWwtfXV/zxxx95Ohapqali9OjRolatWsLOzk7Y2NiIWrVqiaVLl+Z4XPNK9W+W0yMoKEhj/YMHDwoA4vbt22/dd26fX4VCIcqXLy/Kly+vMRRGoVCIX375RTRu3FjY29sLS0tLUb16dTF16tRcZ7jZtm2baNWqlXBychKmpqbC3d1dBAYGipCQkLfWmZGRIebOnSuqVKkizM3NRYkSJUTr1q3FxYsX1eskJSWJAQMGCAcHB2FnZye6desmnjx5kuPwkNxmdlqxYoUAIOzs7ERycnK261y+fFl07txZFC9eXFhYWIgyZcqIbt26iWPHjr31/Rg7mRAG6ElA9Jrw8HBUqVIFBw8eRIsWLaQuR6euXLmCOnXqYP369fmeKMHYdOzYETKZDDt37pS6FKJsMShJEl988QXu3LmDI0eOSF1KviUnJ2e59ta3b1+sW7cOkZGRee5QY8xu3bqFmjVr4sqVK1qPkyUyFAYlFRrJyclv7S3r5OSkMWGBPk2dOhUXL16Er68vTE1NcfDgQRw8eBCffvopfv75Z4PUQET6x6CkQmPNmjXo169frusEBwfnOqxDl44cOYKpU6fi5s2bSEhIQOnSpdG7d2+MHz9e52MwiUg6DEoqNB4/fpzlrh5v8vb25gwjRKRTDEoiIqJccMIBIiKiXBjdhRSlUolHjx7Bzs7unQaFExFR4SaEQHx8PDw8PHK9e4rRBeWjR4/YbZ+IiNQePHigcYu2NxldUKpup/PgwYO3zodJRERFV1xcHDw9Pd96/1GjC0rV6VZ7e3sGJRERvfUyHDvzEBER5YJBSURElAsGJRERUS4YlERERLlgUBIREeWCQUlERJQLBiUREVEuGJRERES5YFASERHlgkFJRESUC0mD8uTJk2jfvj08PDwgk8mwa9eut24TEhKCunXrwsLCAhUqVMCaNWv0XicRERkvSYMyMTERtWrVwpIlS/K0fkREBNq2bQtfX19cuXIFX375JQYOHIjDhw/ruVIiIjJWkk6K3rp1a7Ru3TrP6y9btgxly5bF/PnzAQBVq1bF6dOn8cMPP8Df319fZRIRkRErVHcPOXv2LPz8/DSW+fv748svv8xxm9TUVKSmpqqfx8XF6as8MlJP4lNw41Ecbj6Kw83HcYh4mgilEFKXRVTkLepeB5Xdcr9Fli4UqqCMioqCq6urxjJXV1fExcUhOTkZVlZWWbaZNWsWpk6daqgSqQhTKAUinyWqA1EVjjEJqW/fmIh0LiVdYZDXKVRBmR9jx47FyJEj1c9VN+okyk1KugKhUfH/BWIsbj6Kwz9R8UhKy/qLKZMB5ZxtUN3DAdU87FHRxRYWpiYSVE1UdCmFEtFR0XB3d1cvK1vCxiCvXaiC0s3NDdHR0RrLoqOjYW9vn21rEgAsLCxgYWFhiPKokHqRmKYRiDcfx+Hu00QolFlPn1qayVHFzR7VPOxRzd0e1T3sUdnNDtbmhepXiahQUSqV+Oyzz7B161YcPXoU9erVM+jrF6rf7oYNG+LAgQMay44cOYKGDRtKVBEVJkII/PsiWSMQbzyKw+PYlGzXd7IxR3UPzVAs62wLE3nud0MnIt1RKpX49NNPsWrVKsjlcty5c8e4gjIhIQF37txRP4+IiMCVK1fg5OSE0qVLY+zYsXj48CHWrl0LAPj888+xePFifPPNN+jfvz+OHz+OLVu2YP/+/VK9BSqg0jKUuP0kXiMQbz2OQ3xKRrbrlylunRmK7pnBWN3DAS52FpDJGIpEUlEqlRg0aBBWr14NuVyOdevWoXv37gavQ9Kg/Ouvv+Dr66t+rrqWGBQUhDVr1uDx48e4f/+++udly5bF/v378dVXX2HRokUoVaoUVq5cyaEhRi4uJR233uhgc/tJPNIVWU+dmpvIUcnNNjMQ3e1RvaQDqrjZwc7STILKiSgnCoUCAwcOxJo1ayCXy7FhwwZJQhIAZEIYVz/2uLg4ODg4IDY2Fvb29lKXQ1oQQiAqLgU3H8VpDMe4/zwp2/XtLU3/O23qoD6FWr6ELcxNOXMjUUGmUCgwYMAA/PrrrzAxMcGGDRsQGBio89fJax4UqmuUZDwyFEpExCRmBuLjV6H4PDEt2/VLOlqh6n/XEVXXFEsVs+KpU6JCKD09HQ8fPoSJiQk2btyIbt26SVoPg5Ikl5SWgVuP418F4qNY/BMVj9QMZZZ1TeQyVChhqxGI1Tzs4WhtLkHlRKQPlpaW2L17N/788080b95c6nIYlGRYMQmp6tOmNx7FZs5kE5OI7C4A2JiboKr7671OHVDR1RaWZhyjSFTUZGRkYPv27ejWrRtkMhmsra0LREgCDErSE6VS4N7zpP9Omcaqw/FJfPaz2LjYWWgEYjUPe5RxsoacQzGIiryMjAz06dMHv/32G/7++29Mnz5d6pI0MCjpnaWkK3A7OkEjEG89jkNiDrPYlHW20QjEau72KGHHSSGIjFFGRgY++eQTbN68GaampvD29pa6pCwYlKSVl0lpr11LzOxgc+dJAjKymcXGwlSOKu6vj020RxXOYkNE/8nIyECvXr2wZcsWmJmZYevWrejQoYPUZWXBv1iUo7QMJU6EPcXfD2PV4fjwZXK26xazNlO3EFUD98s628DUhEMxiCir9PR09OrVC1u3boWZmRm2b9+O9u3bS11WthiUlKNp+25g/Z/3sywv7WStntKt2n8PN3tLDsUgojwRQqhD0tzcHNu3b0e7du2kLitHDErK0V+RLwAAflVd0LiCM6q526Oqhz3sOYsNEb0DmUwGf39/7N27F9u2bUPbtm2lLilXDErKVrpCibtPEwAAk9tXh6eTtcQVEVFRMmDAAAQEBKBkyZJSl/JWvIBE2YqMSUS6QsDG3AQlHbO/hRkRUV6lpaXh66+/xpMnT9TLCkNIAgxKykFodDwAoKKrHccyEtE7SUtLw8cff4wFCxagbdu2UCqzzrpVkDEoKVth0ZmnXSu72klcCREVZqmpqejatSv27NkDS0tLzJgxA3J54YoeXqOkbIVFZbYoK7kxKIkof1JTU9GlSxfs378flpaW2LNnD1q2bCl1WVpjUFK2wv479VrJ1VbiSoioMEpJSUGXLl1w4MABWFlZYe/evWjRooXUZeULg5KySElXIPJZIgCeeiWi/BkxYoQ6JPft21dgJjjPj8J1opgM4s6TBCgF4GhtxjlYiShfxo4di2rVqmH//v2FOiQBtigpG69Ou9pxth0iyjMhhPpvhpeXF65duwYTk8J/Wzy2KCkL1dAQnnYlorxKTk5G+/btsXPnTvWyohCSAIOSsnH7v6Eh7PFKRHmRlJSEjz76CPv370f//v0RGxsrdUk6xVOvlEVoFFuURJQ3qpA8duwYbGxssGfPHjg4OEhdlk4xKElDfEq6+lZaHBpCRLlJTExE+/btERwcDFtbWxw8eBBNmjSRuiydY1CShttPMk+7uthZwNHaXOJqiKigSkxMRLt27RASEgJbW1scOnQIjRs3lrosveA1StKgmpGnMq9PElEuli9fjpCQENjZ2eHw4cNFNiQBtijpDaGvDQ0hIsrJiBEjcO/ePQQGBqJhw4ZSl6NXDErSEMahIUSUg8TERJibm8PMzAxyuRwLFy6UuiSD4KlX0hDGoSFElI34+HgEBASgV69eSE9Pl7ocg2KLktSeJ6bhaXwqAKCiC3u8ElGm+Ph4tG7dGmfOnIGDgwPCw8NRuXJlqcsyGLYoSU112rVUMSvYWPA7FBEBcXFxCAgIwJkzZ+Do6IijR48aVUgCbFHSa3h9koheFxsbi4CAAPz5558oVqwYjhw5Am9vb6nLMjgGJamF8mbNRPSf2NhY+Pv749y5cyhWrBiOHj2KunXrSl2WJHjqldTYoiQilRs3buDq1atwcnLCsWPHjDYkAbYo6T9CiFctSgYlkdFr1KgR9u3bh+LFi6N27dpSlyMpBiUBAJ7EpyIuJQMmchnKlbCRuhwiksCLFy/w5MkTdWedFi1aSFxRwcBTrwTg1fVJr+LWsDQrGveQI6K8e/78Ofz8/NCsWTPcunVL6nIKFAYlAXh1fZKnXYmMjyokL126BKVSCYVCIXVJBQqDkgCA1yeJjNSzZ8/QokULXL58GS4uLggODkaNGjWkLqtA4TVKAvBaj1cODSEyGs+ePYOfnx+uXLmiDslq1apJXVaBwxYlQakUr+Z4ZYuSyCioWpJXrlyBq6srQzIXbFES/n2RjOR0BcxN5PAqbi11OURkAKamprCwsICbmxuCg4NRpUoVqUsqsBiUpL4HZXkXW5ia8CQDkTFwcHDA4cOH8fTpU1SsWFHqcgo0/lWk12bk4R1DiIqyJ0+e4JdfflE/d3R0ZEjmAVuU9GpoCDvyEBVZ0dHRaN68OW7evInU1FR8/vnnUpdUaLBFSa+GhrgwKImKoqioKPj6+uLmzZvw8PDgjDtaYlAauXSFEuFPEwFwaAhRUfT48WP4+vri1q1bKFmyJEJCQni6VUs89Wrk7j1LRJpCCWtzE5R0tJK6HCLSIVVIhoaGolSpUggODkaFChWkLqvQYYvSyIVGZY6frOhqB7lcJnE1RKQrSUlJ6pD09PRESEgIQzKfGJRGLpQ9XomKJGtrawwYMAClS5dGSEgIypcvL3VJhRaD0sjd5mToREXW6NGjce3aNZQrV07qUgo1BqWRC+Ucr0RFxsOHD9GrVy/ExcWplzk4OEhYUdHAzjxGLCVdgciYzB6vbFESFW4PHjyAr68v7t69C4VCgU2bNkldUpHBFqURu/s0AUoBOFiZwcXOQupyiCif7t+/Dx8fH9y9exdly5bFnDlzpC6pSGFQGrFXU9fZQSZjj1eiwujevXvw8fFBeHg4ypUrh5CQEJQpU0bqsooUBqURUw0NqeTGHq9EhVFkZCR8fHwQERGB8uXLIyQkBKVLl5a6rCKH1yiN2OstSiIqXIQQ6NGjByIjI9UhWapUKanLKpLYojRiYRwaQlRoyWQyrFq1Ck2aNMGJEycYknrEFqWRSkjNwL8vkgEwKIkKk4yMDJiaZv7prlatGk6ePMk+BnrGFqWRUk00UMLOAsVszCWuhojyIjw8HDVq1MDx48fVyxiS+segNFK8PklUuNy9exc+Pj4IDQ3F6NGjoVQqpS7JaEgelEuWLIGXlxcsLS3RoEEDnD9/Ptf1Fy5ciMqVK8PKygqenp746quvkJKSYqBqiw51j1cGJVGBd+fOHfj4+ODBgweoUqUK9u/fD7lc8j/fRkPSI71582aMHDkSkydPxqVLl1CrVi34+/vjyZMn2a6/ceNGjBkzBpMnT8atW7ewatUqbN68GePGjTNw5YWfukXJoSFEBdrt27fh4+ODf//9F1WrVkVwcDDc3NykLsuoSBqUCxYswKBBg9CvXz9Uq1YNy5Ytg7W1NVavXp3t+n/88QcaN26Mnj17wsvLC61atUKPHj3e2gqlrELZ45WowFOF5MOHD1GtWjWGpEQkC8q0tDRcvHgRfn5+r4qRy+Hn54ezZ89mu02jRo1w8eJFdTCGh4fjwIEDaNOmTY6vk5qairi4OI2HsXuRmIan8akAMu9DSUQF08KFC/Ho0SNUr14dwcHBcHV1lbokoyTZ8JCYmBgoFIos//Curq74559/st2mZ8+eiImJQZMmTSCEQEZGBj7//PNcT73OmjULU6dO1WnthZ3qtGupYlawteAIIaKCauHChbC1tcXXX38NFxcXqcsxWoXqanBISAhmzpyJpUuX4tKlS9ixYwf279+P6dOn57jN2LFjERsbq348ePDAgBUXTJxogKjgevjwobpHq5mZGebMmcOQlJhkzQlnZ2eYmJggOjpaY3l0dHSO5+AnTpyI3r17Y+DAgQCAmjVrIjExEZ9++inGjx+fbS8wCwsLWFjwzhiv4/VJooLp1q1b8PX1RYcOHfDTTz+xZ2sBIdm/grm5Oby9vXHs2DH1MqVSiWPHjqFhw4bZbpOUlJTlg2NiYgIgc95Dypuw/4aGsMcrUcFx8+ZN+Pj4IDo6GufOnUNCQoLUJdF/JL1ANXLkSAQFBaFevXqoX78+Fi5ciMTERPTr1w8A0KdPH5QsWRKzZs0CALRv3x4LFixAnTp10KBBA9y5cwcTJ05E+/bt1YFJuRNCsEVJVMDcuHEDzZs3x5MnT1C7dm0cPXoU9vb2UpdF/5E0KAMDA/H06VNMmjQJUVFRqF27Ng4dOqTu4HP//n2NFuSECRMgk8kwYcIEPHz4ECVKlED79u0xY8YMqd5CofMkPhWxyemQy4DyJdiiJJLa33//jebNm+Pp06eoU6cOjh49CicnJ6nLotfIhJGds4yLi4ODgwNiY2ON8hvbqdtP0XvVeZQrYYPjX/tIXQ6RUbt+/TqaN2+OmJgY1K1bF0eOHGFIGlBe84BXio1MaBTneCUqKMLDw/Hy5Ut4e3uzJVmAcRCdkeHQEKKCo0OHDti/fz/ef/99FCtWTOpyKAcMSiMTGs3J0ImkdPXqVTg6OqJMmTIAgFatWklcEb0NT70aEaVSqO9DyaEhRIZ36dIl+Pr6wtfXl5OfFCIMSiPy8GUyktIUMDeRo0xxG6nLITIqqrmtX7x4AVdXV6PsTFhYMSiNiKojT7kSNjAz4T89kaH89ddf6pBs2LAhDh8+DAcHB6nLojziX0sjEqo+7crrk0SGcuHCBbRs2RIvX75Eo0aNcOjQIbYmCxl25jEit9njlcigLl26hJYtWyI2NhaNGzfGwYMHYWfH37/ChkFpRFQ9XjmGksgwSpUqhZIlS6JmzZo4cOAAQ7KQYlAaiQyFEnefcGgIkSG5uLggODgY1tbWsLVlT/PCitcojUTksySkKZSwMjNBqWJWUpdDVGSdPXsWv/76q/q5i4sLQ7KQY4vSSLyakccWcrlM4mqIiqY//vgDAQEBSEhIgLOzM9q2bSt1SaQDbFEaCdXQEJ52JdKPM2fOwN/fH/Hx8fDx8YGPj4/UJZGOMCiNRBiHhhDpzenTp9UtyebNm2Pfvn2wseGkHkUFg9JIcDJ0Iv04deqUOiRbtGiBvXv3wtraWuqySIcYlEYgJV2ByGdJANiiJNKl8PBwtG7dGomJiWjZsiVDsohiZx4jEP40EQqlgL2lKVzsLKQuh6jIKFu2LIYMGYIrV65g165dsLJij/KiiEFpBF6/PimTsccrka7IZDLMnj0b6enpMDc3l7oc0hOeejUCobw+SaQzx48fR8eOHZGcnAwgMywZkkUbg9IIhEWxxyuRLhw7dgzt2rXD7t278f3330tdDhkIg9IIsEVJ9O6OHj2Kdu3aITk5GW3btsWYMWOkLokMhEFZxCWmZuDfF5mniBiURPlz5MgRtG/fHikpKWjXrh22b98OCwt2jDMWDMoi7vZ/E6GXsLOAkw2voxBp6/Dhw+qQbN++PbZt28aQNDIMyiIuLOrVHK9EpJ2kpCQEBQUhNTUVHTp0YEgaKQZlEcfrk0T5Z21tjT179qBPnz7YsmULe7caKY6jLOLUYygZlER5lpiYqJ6rtX79+qhfv77EFZGU2KIs4tR3DeHQEKI82bdvH8qVK4cLFy5IXQoVEAzKIuxlUhqexKcCACq68Bol0dvs3bsXnTt3xpMnT7Bs2TKpy6ECgkFZhIVFZ/Z4LeloBTtLM4mrISrY9uzZgy5duiA9PR0ff/wxg5LUGJRFWCjvQUmUJ7t370bXrl2Rnp6OwMBAbNy4EWZm/HJJmRiURZhqaEhFDg0hytHOnTvVIdm9e3esX78epqbs50ivMCiLsFD2eCXKlRACK1euREZGBnr06IF169YxJCkLBmURJYRQDw3hGEqi7MlkMmzbtg3ff/891q5dy5CkbDEoi6in8al4mZQOuQyowB6vRBquX78OIQQAwMrKCqNHj2ZIUo4YlEWU6rSrV3EbWJqZSFwNUcGxdetW1KlTB+PGjVOHJVFuGJRFlGpoCE+7Er2yefNm9OjRAwqFAo8fP2ZQUp4wKIuoMM7IQ6Tht99+Q8+ePaFQKNCvXz+sWrUKcjn/BNLb8VNSRLHHK9ErGzduxCeffAKlUon+/ftj5cqVMDHhJQnKm3cKypSUFF3VQTqkVArcjubttYgAYP369ejduzeUSiUGDBiAFStWsCVJWtH606JUKjF9+nSULFkStra2CA8PBwBMnDgRq1at0nmBpL2HL5ORmKaAmYkMXs42UpdDJKm0tDQolUoMHDgQy5cvZ0iS1rT+xHz33XdYs2YNvv/+e417s9WoUQMrV67UaXGUP6rxk+VL2MLMhH8UyLj1798fJ0+exM8//8yQpHzR+lOzdu1aLF++HL169dI4x1+rVi38888/Oi2O8oc3ayZjt337djx58kT9vGnTpgxJyjetPzkPHz5EhQoVsixXKpVIT0/XSVH0blQ9XjkZOhmj1atX4+OPP0bz5s0RGxsrdTlUBGgdlNWqVcOpU6eyLN+2bRvq1Kmjk6Lo3XAMJRmrVatWYeDAgRBCwMfHB/b29lKXREWA1nM2TZo0CUFBQXj48CGUSiV27NiB0NBQrF27Fvv27dNHjaSFDIUSd55mBiWHhpAxWbFiBT799FMAwLBhw7Bo0SLIZDKJq6KiQOsWZYcOHbB3714cPXoUNjY2mDRpEm7duoW9e/eiZcuW+qiRtHDveRLSMpSwMjNBqWJWUpdDZBDLly9Xh+SIESMYkqRT+ZoFuGnTpjhy5IiuayEdeP0elHI5/1BQ0bd+/Xp89tlnAIAvv/wSCxYsYEiSTmndoixXrhyePXuWZfnLly9Rrlw5nRRF+ccer2RsmjRpgjJlyuCrr75iSJJeaN2ijIyMhEKhyLI8NTUVDx8+1ElRlH9hnLqOjIyXlxcuXrwIJycnhiTpRZ6Dcs+ePer/P3z4MBwcHNTPFQoFjh07Bi8vL50WR9oL5WToZASWLFkCd3d3dO7cGQBQvHhxiSuioizPQdmxY0cAmXcEDwoK0viZmZkZvLy8MH/+fJ0WR9pJzVAg8lkSALYoqej68ccfMWLECJiamuLy5cuoUaOG1CVREZfnoFQqlQCAsmXL4sKFC3B2dtZbUZQ/4U8ToVAK2FuawtXeQupyiHRu0aJF+PLLLwEAo0aNQvXq1aUtiIyC1tcoIyIi9FEH6UDYax15eK2GipoffvgBI0eOBACMGzcO3333HT/nZBD5Gh6SmJiIEydO4P79+0hLS9P42fDhw3VSGGmP1yepqFqwYAG+/vprAMD48eMxffp0hiQZjNZBefnyZbRp0wZJSUlITEyEk5MTYmJiYG1tDRcXFwalhNjjlYqi33//XR2SEydOxNSpUxmSZFBaj6P86quv0L59e7x48QJWVlb4888/ce/ePXh7e2PevHn6qJHyiGMoqSjy8/PDwIEDMXnyZEybNo0hSQandYvyypUr6vu6mZiYIDU1FeXKlcP333+PoKAgdXdtMqzE1Aw8eJ4MAKjkaitxNUTvTqlUQi6XQy6XY/ny5QxIkozWLUozMzP1fd1cXFxw//59AICDgwMePHig2+ooz+48yZwI3dnWAsVt2eOVCrdZs2YhMDBQfes+hiRJSesWZZ06dXDhwgVUrFgRzZo1w6RJkxATE4N169ZxPJOEVKddK7uxNUmF28yZMzF+/HgAQPfu3dGlSxeJKyJjp3WLcubMmXB3dwcAzJgxA8WKFcMXX3yBp0+f4ueff9a6gCVLlsDLywuWlpZo0KABzp8/n+v6L1++xJAhQ+Du7g4LCwtUqlQJBw4c0Pp1ixr1ZOguvD5Jhdd3332nDsnp06czJKlA0LpFWa9ePfX/u7i44NChQ/l+8c2bN2PkyJFYtmwZGjRogIULF8Lf3x+hoaFwcXHJsn5aWhpatmwJFxcXbNu2DSVLlsS9e/fg6OiY7xqKilctSgYlFU7Tpk3D5MmTAWR+CR83bpzEFRFl0rpFmZNLly6hXbt2Wm2zYMECDBo0CP369UO1atWwbNkyWFtbY/Xq1dmuv3r1ajx//hy7du1C48aN4eXlhWbNmqFWrVq6eAuFWhh7vFIhNnXqVHVIzpo1iyFJBYpWQXn48GGMGjUK48aNQ3h4OADgn3/+QceOHfH++++rp7nLi7S0NFy8eBF+fn6vipHL4efnh7Nnz2a7zZ49e9CwYUMMGTIErq6uqFGjBmbOnJnt3UxUUlNTERcXp/Eoal4mpSE6LhUAe7xS4RMREYHZs2cDAGbPno0xY8ZIXBGRpjyfel21ahUGDRoEJycnvHjxAitXrsSCBQswbNgwBAYG4u+//0bVqlXz/MIxMTFQKBRwdXXVWO7q6op//vkn223Cw8Nx/Phx9OrVCwcOHMCdO3cwePBgpKenq7+NvmnWrFmYOnVqnusqjMKiM3u8lnS0gp2lmcTVEGmnbNmy2LdvH65evaqeoo6oIMlzi3LRokWYM2cOYmJisGXLFsTExGDp0qW4fv06li1bplVI5pdSqYSLiwuWL18Ob29vBAYGYvz48Vi2bFmO24wdOxaxsbHqR1EcwvLqtCtbk1Q4CCHw5MkT9fMWLVowJKnAynNQ3r17Fx9//DEAoHPnzjA1NcXcuXNRqlSpfL2ws7MzTExMEB0drbE8Ojoabm5u2W7j7u6OSpUqwcTERL2satWqiIqKyjLnrIqFhQXs7e01HkWNOijZkYcKASEEJk6ciPfeew+3bt2Suhyit8pzUCYnJ8Pa2hpA5uBfCwsL9TCR/DA3N4e3tzeOHTumXqZUKnHs2DE0bNgw220aN26MO3fuaFwLDQsLg7u7O8zNzfNdS2Gnmgydc7xSQSeEwPjx4zFjxgxER0fj5MmTUpdE9FZaDQ9ZuXIlbG0zT+9lZGRgzZo1We5Lqc2k6CNHjkRQUBDq1auH+vXrY+HChUhMTES/fv0AAH369EHJkiUxa9YsAMAXX3yBxYsXY8SIERg2bBhu376NmTNnGvVE7EII9nilQkEIgXHjxqk77ixatAifffaZxFURvV2eg7J06dJYsWKF+rmbmxvWrVunsY5MJtMqtAIDA/H06VNMmjQJUVFRqF27Ng4dOqTu4HP//n31dHkA4OnpicOHD+Orr77Ce++9h5IlS2LEiBH49ttv8/yaRc3ThFS8SEqHTAZUcOE1SiqYhBAYM2YMvv/+ewDAjz/+iGHDhklcFVHeyIQQQuoiDCkuLg4ODg6IjY0tEtcrT9+OwSerzqGssw2CR/lIXQ5RFkIIfPPNN+q7Cy1evBhDhgyRuCqivOdBvm7cTAVHKHu8UgGXkpKCU6dOAcicsnLw4MESV0SkHQZlIXebN2umAs7KygqHDx/G77//ru45T1SY6GwKO5JGKIeGUAEkhMDx48fVzx0cHBiSVGgxKAsxIYT6riFsUVJBIYTAiBEj0KJFC8yfP1/qcojeGU+9FmIPXyYjMU0BMxMZvJxtpC6HCEIIDB8+HIsXL4ZMJuOdfahIyFeL8u7du5gwYQJ69Oihnobq4MGDuHHjhk6Lo9ypxk+Wc7aFmQlPDpC0hBAYOnSoOiRXrlyJAQMGSF0W0TvT+q/riRMnULNmTZw7dw47duxAQkLmhNxXr17NcWJy0o/QqMxjz+uTJDWlUokhQ4Zg6dKlkMlkWL16Nfr37y91WUQ6oXVQjhkzBt999x2OHDmiMW1c8+bN8eeff+q0OMpdmLrHK4eGkHSEEBgyZAh++uknyGQy/PLLL+jbt6/UZRHpjNZBef36dXTq1CnLchcXF8TExOikKMob1RyvnLqOpCSTyVC+fHnI5XL8+uuvCAoKkrokIp3SOigdHR3x+PHjLMsvX76MkiVL6qQoejuFUuDO08xTr5V56pUkNmrUKFy/fh29e/eWuhQindM6KLt3745vv/0WUVFRkMlkUCqVOHPmDEaNGoU+ffroo0bKxr1niUjLUMLSTA7PYtZSl0NGRqlUYvbs2YiNjVUvq1atmoQVEemP1kE5c+ZMVKlSBZ6enkhISEC1atXw4YcfolGjRpgwYYI+aqRsqK5PVnSxg1wuk7gaMiZKpRIDBw7E2LFj0bZtW43b3hEVRVqPozQ3N8eKFSswceJE/P3330hISECdOnVQsWJFfdRHOVD3eOX1STIghUKBgQMHYs2aNZDL5Rg6dKjGHX6IiiKtg/L06dNo0qQJSpcujdKlS+ujJsoDdY9XN/Z4JcNQKBTo378/1q5dCxMTE2zYsAGBgYFSl0Wkd1p/FWzevDnKli2LcePG4ebNm/qoifIglDdrJgNSKBTo16+fOiR/++03hiQZDa2D8tGjR/j6669x4sQJ1KhRA7Vr18bcuXPx77//6qM+ykZqhgIRMYkA2OOVDOPrr7/GunXrYGJigk2bNnGCczIqWgels7Mzhg4dijNnzuDu3bv4+OOP8euvv8LLywvNmzfXR430hoiYRCiUAnaWpnCzt5S6HDICn376KUqVKoXNmzeja9euUpdDZFDvNCl62bJlMWbMGNSqVQsTJ07EiRMndFUX5SL0tTuGyGTs8Ur6V61aNYSFhcHKykrqUogMLt/d1c6cOYPBgwfD3d0dPXv2RI0aNbB//35d1kY5UA8N4fVJ0pOMjAz0798fx44dUy9jSJKx0rpFOXbsWGzatAmPHj1Cy5YtsWjRInTo0AHW1hz0biiqoSGc45X0ISMjA7169cKWLVuwY8cOREZG8nZZZNS0DsqTJ09i9OjR6NatG5ydnfVRE72FqkXJu4aQrqWnp6NXr17YunUrzMzMsHbtWoYkGT2tg/LMmTP6qIPyKCktA/efJwHIvEZJpCvp6eno0aMHtm/fDnNzc2zfvh3t2rWTuiwiyeUpKPfs2YPWrVvDzMwMe/bsyXXdjz76SCeFUfZuR2eednW2NUdxWwuJq6GiIi0tDd27d8fOnTthbm6OHTt2oG3btlKXRVQg5CkoO3bsiKioKLi4uKBjx445rieTyaBQKHRVG2UjjBMNkB4sWbJEHZI7d+5EmzZtpC6JqMDIU1C+PukxJ0CWFoOS9GHo0KG4ePEievXqhdatW0tdDlGBovXwkLVr1yI1NTXL8rS0NKxdu1YnRVHOQqM5GTrpRlpamvqLr5mZGdavX8+QJMqG1kHZr18/jXvQqcTHx6Nfv346KYpyFhbFydDp3aWmpqJr1674/PPPeZaI6C20DkohRLazwfz7779wcHDQSVGUvdikdETFpQDgZAOUf6qQ3Lt3L9atW4dbt25JXRJRgZbn4SF16tSBTCaDTCZDixYtYGr6alOFQoGIiAgEBATopUjKFPYkszXp4WAJe0sziauhwiglJQVdunTBgQMHYGlpib1796J69epSl0VUoOU5KFW9Xa9cuQJ/f3/Y2r469Wdubg4vLy906dJF5wXSK6o5XjnRAOVHSkoKOnfujIMHD8LKygp79+5FixYtpC6LqMDLc1BOnjwZAODl5YXAwEBYWvKuFYZ2O/rVZOhE2khJSUGnTp1w6NAhWFlZYf/+/fD19ZW6LKJCQeuZeYKCgvRRB+UBb9ZM+XX+/HkcPXoU1tbW2L9/P3x8fKQuiajQyFNQOjk5ISwsDM7OzihWrFiut3Z6/vy5zoqjV4QQr26vxVOvpKUPP/wQmzdvRvHixdGsWTOpyyEqVPIUlD/88APs7OzU/897IBpeTEIaXiSlQyYDypfg0BB6u6SkJDx//hylSpUCAHTu3FniiogKpzwF5eunW/v27auvWigXqhl5yjhZw8rcROJqqKBLSkrCRx99hLt37yIkJARlypSRuiSiQkvrcZSXLl3C9evX1c93796Njh07Yty4cUhLS9NpcfSKuscrr0/SWyQlJaF9+/Y4duwYYmJi8OjRI6lLIirUtA7Kzz77DGFhYQCA8PBwBAYGwtraGlu3bsU333yj8wIpk6pFyeuTlJvExES0a9cOx48fh52dHQ4fPoyGDRtKXRZRoaZ1UIaFhaF27doAgK1bt6JZs2bYuHEj1qxZg+3bt+u6PvoPJ0Ont0lMTETbtm0RHBysDslGjRpJXRZRoZevKexUc0MePXpUfTseT09PxMTE6LY6ApB5zMP+mwydLUrKTkJCAtq0aYMTJ07A3t4ev//+O1uSRDqidVDWq1cP3333HdatW4cTJ06ob+4aEREBV1dXnRdIwKPYFCSkZsDMRAav4jZSl0MFUHJyMp49e6YOyQ8++EDqkoiKDK0nHFi4cCF69eqFXbt2Yfz48ahQoQIAYNu2bTzNoyeqO4aUdbaBuanW323ICJQoUQLHjx/HgwcP4O3tLXU5REWK1kH53nvvafR6VZk7dy5MTDhsQR84Iw9lJy4uDsHBwejQoQMAwMXFBS4uLhJXRVT0aB2UKhcvXlTfnqdatWqoW7euzooiTep7UDIo6T9xcXEICAjAn3/+iTVr1qBPnz5Sl0RUZGkdlE+ePEFgYCBOnDgBR0dHAMDLly/h6+uLTZs2oUSJErqu0eipW5TsyEMAYmNj1SFZrFgx3iaLSM+0vuA1bNgwJCQk4MaNG3j+/DmeP3+Ov//+G3FxcRg+fLg+ajRqCqXAnSf/9Xhli9LoxcbGwt/fXx2SR48e5TVJIj3TukV56NAhHD16FFWrVlUvq1atGpYsWYJWrVrptDgC7j9PQmqGEpZmcng6WUtdDkno5cuX8Pf3x/nz5+Hk5ISjR4+iTp06UpdFVORpHZRKpRJmZmZZlpuZmanHV5LuqKauq+hiBxM5J6M3VsnJyWjVqhUuXLgAJycnHDt2TD3xBxHpl9anXps3b44RI0ZozB/58OFDfPXVV7xbuh6oZuSp6Mo7hhgzS0tLNG/eHMWLF8fx48cZkkQGpHVQLl68GHFxcfDy8kL58uVRvnx5lC1bFnFxcfjf//6njxqNmqojD69PGjeZTIZZs2bh2rVrqFWrltTlEBkVrU+9enp64tKlSzh27Jh6eEjVqlXh5+en8+Lo1dAQ9ng1Ps+fP8e0adMwa9YsWFlZQSaTwcPDQ+qyiIyOVkG5efNm7NmzB2lpaWjRogWGDRumr7oIQGqGAhExiQDYojQ2z549g5+fH65cuYKYmBisX79e6pKIjFaeg/Knn37CkCFDULFiRVhZWWHHjh24e/cu5s6dq8/6jFpETCIylAJ2FqZwd7CUuhwykJiYGPj5+eHq1atwcXHB2LFjpS6JyKjl+Rrl4sWLMXnyZISGhuLKlSv49ddfsXTpUn3WZvRUdwyp5GYHmYw9Xo1BTEwMWrRogatXr8LV1RXBwcGcUIBIYnkOyvDwcAQFBamf9+zZExkZGXj8+LFeCqPXrk/ytKtRePr0KZo3b45r166pQ7JatWpSl0Vk9PIclKmpqbCxeXWLJ7lcDnNzcyQnJ+ulMHp9MnQODSnqhBDo0qULrl+/Djc3N4SEhGhM6kFE0tGqM8/EiRNhbf1qdpi0tDTMmDEDDg4O6mULFizQXXVGLoxDQ4yGTCbD/PnzERQUhJ07d6Jy5cpSl0RE/8lzUH744YcIDQ3VWNaoUSOEh4ern/M6mu4kpWXg/vMkABwaUpQJIdS/N++//z6uX7/O29URFTB5DsqQkBA9lkFvuvMkAUIAxW3M4WxrIXU5pAfR0dHo3LkzFi5ciPfffx8AGJJEBZDWM/OQYYSyI0+RFhUVBV9fX/zxxx/o378/50kmKsAKRFAuWbIEXl5esLS0RIMGDXD+/Pk8bbdp0ybIZDJ07NhRvwVK4Lbq1lo87VrkPH78GL6+vrh16xZKlSqFnTt3Qi4vEL+KRJQNyX87N2/ejJEjR2Ly5Mm4dOkSatWqBX9/fzx58iTX7SIjIzFq1Cg0bdrUQJUaFluURZMqJP/55x94enoiJCQEFSpUkLosIsqF5EG5YMECDBo0CP369UO1atWwbNkyWFtbY/Xq1Tluo1Ao0KtXL0ydOhXlypUzYLWGE8ahIUXOo0eP4OPjg9DQUJQuXRohISEoX7681GUR0VtIGpRpaWm4ePGixoTqcrkcfn5+OHv2bI7bTZs2DS4uLhgwYMBbXyM1NRVxcXEaj4IuNjkdj2NTAAAV2aIsMqZNm4awsDCUKVMGISEhRfZLHlFRk6+gPHXqFD755BM0bNgQDx8+BACsW7cOp0+f1mo/MTExUCgUcHV11Vju6uqKqKiobLc5ffo0Vq1ahRUrVuTpNWbNmgUHBwf1w9PTU6sapXD7v9aku4MlHKyy3iSbCqcffvgB/fr1Q0hICMqWLSt1OUSUR1oH5fbt2+Hv7w8rKytcvnwZqampAIDY2FjMnDlT5wW+Lj4+Hr1798aKFSvg7Oycp23Gjh2L2NhY9ePBgwd6rVEXXs3Iw9ZkYffy5UsIIQAAVlZWWL16Nby8vKQtioi0onVQfvfdd1i2bBlWrFgBM7NXrZ3GjRvj0qVLWu3L2dkZJiYmiI6O1lgeHR0NNze3LOvfvXsXkZGRaN++PUxNTWFqaoq1a9diz549MDU1xd27d7NsY2FhAXt7e41HQaea45U9Xgu3+/fvw9vbG2PHjlWHJREVPloHZWhoKD788MMsyx0cHPDy5Uut9mVubg5vb28cO3ZMvUypVOLYsWNo2LBhlvWrVKmC69ev48qVK+rHRx99BF9fX1y5cqVQnFbNC/VdQ9iiLLTu378PHx8fhIeHY8uWLVr/bhBRwaHVXK8A4Obmhjt37mQ5fXT69Ol8dU4YOXIkgoKCUK9ePdSvXx8LFy5EYmIi+vXrBwDo06cPSpYsiVmzZsHS0hI1atTQ2N7R0REAsiwvzDjHa+F27949+Pr6IiIiAuXKlUNISAiKFSsmdVlElE9aB+WgQYMwYsQIrF69GjKZDI8ePcLZs2cxatQoTJw4UesCAgMD8fTpU0yaNAlRUVGoXbs2Dh06pO7gc//+faMajB2TkIpniWmQyYAKLhwaUthERkbC19cXkZGRKF++PEJCQlCqVCmpyyKidyATWl48EUJg5syZmDVrFpKSMifttrCwwKhRozB9+nS9FKlLcXFxcHBwQGxsbIG8XvnHnRj0XHkOZYpb48RoX6nLIS1ERkbCx8cH9+7dQ4UKFRASEoKSJUtKXRYR5SCveaB1i1Imk2H8+PEYPXo07ty5g4SEBFSrVg22tmz96AJ7vBZef/75J+7fv4+KFSsiODiYIUlURGgdlCrm5ua8+7oe8Ppk4dW9e3fIZDI0bdoUHh4eUpdDRDqidVD6+vrmet/J48ePv1NBxk49xyuHhhQK4eHhsLGxUV9TDwwMlLgiItI1rYOydu3aGs/T09Nx5coV/P333wgKCtJVXUZJCIHb/w0NYYuy4Ltz5w58fX3h4OCA48ePw8XFReqSiEgPtA7KH374IdvlU6ZMQUJCwjsXZMwex6YgPjUDpnIZyjrbSF0O5eL27dvw9fXFw4cPYWdnx/tJEhVhOht38cknn+R6xw96O1VHnnIlbGBuajxDYgqbsLAw+Pj44OHDh6hWrRqCg4OznUmKiIoGnf01Pnv2LCwtLXW1O6OkmrqOdwwpuEJDQ+Hj44NHjx6hevXqOH78eJZJ/YmoaNH61Gvnzp01ngsh8PjxY/z111/5mnCAXgllj9cCLTQ0FL6+vnj8+DFq1KiBY8eO8bokkRHQOigdHBw0nsvlclSuXBnTpk1Dq1atdFaYMQrjGMoCzdLSEhYWFqhZsyaOHTuGEiVKSF0SERmAVkGpUCjQr18/1KxZk3NX6phC+VqPVw4NKZBUN1y2trZmSBIZEa2uUZqYmKBVq1a8E4IePHiehNQMJSxM5SjtZC11OfSfmzdvYvfu3ernZcqUYUgSGRmtO/PUqFED4eHh+qjFqKmuT1Z0tYWJPOcJHchwbty4AR8fH3Tt2hW///671OUQkUTydePmUaNGYd++fXj8+DHi4uI0HpQ/qh6vvD5ZMPz999/w9fXF06dPUbNmTdSrV0/qkohIInm+Rjlt2jR8/fXXaNOmDQDgo48+0pjKTggBmUwGhUKh+yqNACdDLziuX7+O5s2bIyYmBnXr1sWRI0fg5OQkdVlEJJE8B+XUqVPx+eefIzg4WJ/1GC1Ohl4wXLt2Dc2bN8ezZ8/g7e2NI0eOsOMakZHLc1CqblvZrFkzvRVjrNIylAh/mgiAk6FL6d69e+qQrFevHo4cOQJHR0epyyIiiWk1PCS3u4ZQ/kXEJCJDKWBrYQoPB85uJBVPT0906tQJV69exe+//86QJCIAWgZlpUqV3hqWz58/f6eCjNGr65O2/DIiIblcjp9//hlJSUm8ETkRqWkVlFOnTs0yMw+9u9uq65M87Wpwly5dwk8//YSlS5fCzMwMcrmcIUlEGrQKyu7du3NuSz0I5dAQSVy8eBF+fn54+fIlSpYsiSlTpkhdEhEVQHkeR8lTgvrDOV4N76+//lKHZKNGjTBy5EipSyKiAirPQanq9Uq6lZymwL3nSQAYlIZy4cIFdUg2btwYhw4dgr29vdRlEVEBledTr7yDu37ceZIAIQAnG3M425pLXU6Rd+7cObRq1QpxcXFo0qQJDhw4ADs7fkEhopzp7MbNlD/s8Wo4ycnJ6NixI+Li4tC0aVMcPHiQIUlEb8WglBhn5DEcKysrrF+/HgEBAThw4AB7txJRnmh942bSLXVHHg4N0ZuMjAyYmmZ+1Fu0aIHmzZuz9U5EecYWpcRUdw1hi1I/zpw5g6pVq+LmzZvqZQxJItIGg1JCcSnpeBSbAgCoyKDUudOnTyMgIAB37tzB9OnTpS6HiAopBqWEVDPyuNlbwsHKTOJqipZTp04hICAACQkJaNGiBVatWiV1SURUSDEoJRQalQCA1yd17eTJk2jdujUSExPh5+eHvXv3wtraWuqyiKiQYlBK6FWPV/a+1JUTJ06oQ7Jly5bYs2cPrKyspC6LiAoxBqWEOMerbgkhMH36dCQlJcHf3x+7d+9mSBLRO2NQSuj2E941RJdkMhm2b9+O0aNHY9euXQxJItIJBqVEYhJSEZOQBpkMqODCU6/v4sGDB+r/d3BwwPfffw9LS94Am4h0g0EpEdX1ydJO1rA257wP+XX06FFUrlwZ8+bNk7oUIiqiGJQSUU00UNGFp13z68iRI2jfvj2Sk5Nx4sQJKBQKqUsioiKIQSmR0OjMoSGV3XjaNT8OHz6M9u3bIyUlBe3bt8e2bdtgYmIidVlEVAQxKCXCmzXn36FDh9ChQwekpqbio48+wrZt22BhYSF1WURURDEoJSCEeDXHK3u8auXgwYPo2LEjUlNT0aFDB2zduhXm5ryPJxHpD4NSAlFxKYhPzYCpXIZyzjz1qo3Q0FCkpqaiU6dO2LJlC0OSiPSO3S0loJpooKyzDcxN+V1FG19++SW8vLzQtm1bmJlxflwi0j/+lZYA70GpneDgYMTGxqqfd+zYkSFJRAbDoJSAejJ0Dg15qz179sDf3x/+/v6Ij4+XuhwiMkIMSgmoJ0Pn0JBc7d69G127dkV6ejq8vLw4JR0RSYJBaWAKpVDP8cqhITnbuXOnOiS7d++O9evXw9SUl9SJyPAYlAb24HkSUtKVMDeVo0xxG6nLKZB27NiBbt26ISMjAz179sS6desYkkQkGQalgalOu1Z0sYWJXCZxNQXP7t27ERgYiIyMDPTq1Qtr165lSBKRpPgXyMBe3ayZp12zU6lSJTg5OcHf3x+//PILp6UjIskxKA1MNccrh4Zkr2rVqrhw4QJKlizJkCSiAoGnXg1MNXVdJVf2eFXZvHkzjh07pn5eunRphiQRFRhsURpQWoYSd5/+16LkqVcAwG+//YZPPvkEFhYWOH/+PGrUqCF1SUREGtiiNKDIZ4nIUArYmJugpCPHBG7cuBGffPIJlEolevbsiWrVqkldEhFRFgxKA1LN8VrJzQ4ymXH3eF2/fj169+4NpVKJgQMHYvny5ZDL+XEkooKHf5kMiD1eM61btw59+vSBUqnEoEGD8PPPPzMkiajA4l8nA+LNmjMnOA8KCoIQAp999hmWLVvGkCSiAo2deQwo7L+hIcZ8s+YmTZqgS5cuKFGiBBYvXsyQJKICj0FpICnpCkQ+SwQAVDTioSFmZmb47bffIJfLGZJEVCjwL5WB3HmSACGAYtZmKGFrIXU5BrVq1Sp89tlnUCqVAABTU1OGJBEVGmxRGoi6x6urcfV4XbFiBT799FMAQPPmzREYGChxRURE2ikQX+uXLFkCLy8vWFpaokGDBjh//nyO665YsQJNmzZFsWLFUKxYMfj5+eW6fkHx6h6UxnN9cvny5eqQHD58OLp16yZxRURE2pM8KDdv3oyRI0di8uTJuHTpEmrVqgV/f388efIk2/VDQkLQo0cPBAcH4+zZs/D09ESrVq3w8OFDA1eunVAj6/H6888/47PPPgMAjBgxAgsXLjSqljQRFR0yIYSQsoAGDRrg/fffx+LFiwEASqUSnp6eGDZsGMaMGfPW7RUKBYoVK4bFixejT58+b10/Li4ODg4OiI2Nhb29/TvXn1eNZx/Hw5fJ2Pp5Q7zv5WSw15XCTz/9hMGDBwMAvvrqK8yfP58hSUQFTl7zQNIWZVpaGi5evAg/Pz/1MrlcDj8/P5w9ezZP+0hKSkJ6ejqcnLIPn9TUVMTFxWk8DC0+JR0PXyYDACq5FO0WZWRkJEaMGAEAGDlyJEOSiAo9SYMyJiYGCoUCrq6uGstdXV0RFRWVp318++238PDw0Ajb182aNQsODg7qh6en5zvXrS3V+Ek3e0s4WJsZ/PUNycvLC5s2bcK3336LefPmMSSJqNCT/Brlu5g9ezY2bdqEnTt3wtLSMtt1xo4di9jYWPXjwYMHBq7yVUeeojx+MjExUf3/nTt3xuzZsxmSRFQkSBqUzs7OMDExQXR0tMby6OhouLm55brtvHnzMHv2bPz+++947733clzPwsIC9vb2Gg9DUw0NKapzvP7444+oUaMG7t27J3UpREQ6J2lQmpubw9vbW+OmvUqlEseOHUPDhg1z3O7777/H9OnTcejQIdSrV88Qpb4T9RyvRXBoyMKFCzFixAhERkZi69atUpdDRKRzkk84MHLkSAQFBaFevXqoX78+Fi5ciMTERPTr1w8A0KdPH5QsWRKzZs0CAMyZMweTJk3Cxo0b4eXlpb6WaWtrC1vbgnlqs6jeNeSHH37AyJEjAQDjx4/H119/LXFFRES6J3lQBgYG4unTp5g0aRKioqJQu3ZtHDp0SN3B5/79+xrTnf30009IS0tD165dNfYzefJkTJkyxZCl58mzhFTEJKQBKFrXKOfPn49Ro0YBACZMmIBp06bxmiQRFUmSj6M0NEOPozx79xl6rPgTpZ2scfIbX72/niHMmzcPo0ePBgBMmjQJU6ZMYUgSUaFTKMZRGoOidg/K5ORkrFmzBkBmK37q1KkMSSIq0iQ/9VrUvZq6rmicdrWyssLx48exfft2fPHFF1KXQ0Skd2xR6llYVNGYDP3q1avq/3dxcWFIEpHRYFDqkRCiSEyGPmPGDNSuXRurVq2SuhQiIoNjUOpRVFwK4lMyYCKXoVwJG6nLyZfp06djwoQJAJDjHV2IiIoyBqUeqeZ4LetsAwtTE4mr0d7UqVMxadIkAJlz5o4dO1biioiIDI+defQorBBPXTdlyhRMnToVQOYkD998843EFRERSYNBqUeF8fqkEAJTpkzBtGnTAGROF6gaM0lEZIwYlHoUVkiHhqSnpwPInFiA09IRkbFjUOqJUikK5WToMpkMM2bMQJs2bdCkSROpyyEikhw78+jJgxdJSElXwtxUjjJO1lKXkyshBFavXo3k5GQAmWHJkCQiysSg1BPVPSgrlLCFqUnBPcxCCIwdOxYDBgxAx44doVAopC6JiKhA4alXPbn9JHNoSEGekUcIgW+//RZz584FALRv3x4mJoVvGAsRkT4xKPVE1aIsqD1ehRD45ptvMG/ePADA4sWLMWTIEImrIiIqeBiUeqK+WbNbwevxKoTAqFGjsGDBAgDAkiVLMHjwYImrIiIqmBiUepCuUOLu08xTrxVdCl6LctKkSeqQ/Omnn/D5559LXBERUcFVcHuZFGKRMYlIVwjYmJugpKOV1OVk8dFHH8HR0RE///wzQ5KI6C3YotQD1Yw8FV3tIJcXvJsav//++7hz5w6KFy8udSlERAUeW5R6UNDmeBVCYMyYMTh//rx6GUOSiChvGJR6EFqAZuQRQmDo0KGYM2cOAgIC8OLFC6lLIiIqVBiUenD7v9trSd2iVCqVGDJkCJYuXQqZTIb58+ejWLFiktZERFTY8BqljqWkKxD5LBEAUEnCoSFKpRKDBw/Gzz//DJlMhl9++QVBQUGS1UNEVFgxKHXszpMEKAVQzNoMJWwtJKlBqVTi888/x4oVKyCTybBmzRr06dNHklqIiAo7BqWOhb3W41Umk6bH65IlS7BixQrI5XL8+uuv+OSTTySpg4ioKGBQ6piqI4+U1ycHDhyIAwcO4JNPPkGvXr0kq4OIqChgUOqYamiIoXu8KpVKyGQyyGQyWFlZ4cCBA5K1aImIihL2etWxMAl6vCoUCgwYMABjx46FEAIAGJJERDrCFqUOxaek4+HLzJsfV3I1TI9XVUj++uuvMDExQY8ePVCrVi2DvDYRkTFgUOqQ6h6UrvYWcLQ21/vrKRQK9OvXD+vWrYOJiQk2btzIkCQi0jEGpQ6FGfAelAqFAn379sX69ethYmKCTZs2oWvXrnp/XSIiY8Og1CH11HV6DsqMjAwEBQVh48aNMDU1xaZNm9ClSxe9viYRkbFiUOpQmIGGhpw+fRq//fYbTE1NsXnzZnTu3Fmvr0dEZMwYlDoUGpV5jVLfQ0N8fHywcuVKFCtWDJ06ddLraxERGTsGpY48T0xDTEIqAKCii+57vGZkZCA2NlZ9e6z+/fvr/DWIiCgrjqPUEdVpV08nK9hY6Pb7R3p6Onr27IkPP/wQ0dHROt03ERHlji1KHdHX9cn09HT06NED27dvh7m5Oa5fvw5XV1edvgYREeWMQakjoXoYGpKeno7u3btjx44dMDc3x44dO+Dn56ez/RMR0dsxKHUkTMdDQ9LS0tC9e3fs3LkT5ubm2LlzJ9q0aaOTfRMRUd4xKHVACKHTFmVaWhq6deuG3bt3w8LCArt27UJAQMA775eIiLTHoNSB6LhUxKVkwEQuQ7kSNu+8v2fPnuHatWuwsLDA7t274e/vr4MqiYgoPxiUOqCakceruDUszUzeeX/u7u4IDg7GnTt30KJFi3feHxER5R+Hh+jAbVWP13eYaCA1NRUnT55UPy9TpgxDkoioAGBQ6sC7Xp9MSUlB586d0aJFC+zcuVOXpRER0TtiUOrAu4yhVIXkgQMHYGZmBgcHB12XR0RE74DXKN+RUikQFp05x2tFLYMyJSUFnTp1wqFDh2BlZYV9+/ahefPm+iiTiIjyiUH5jv59kYzkdAXMTeTwKm6d5+2Sk5PRsWNH/P7777C2tsb+/fvh4+Ojv0KJiChfGJTvSNXjtbyLLUxN8nYmOzU1FR06dMCRI0dgbW2NAwcOoFmzZvosk4iI8onXKN/Rq+uTeb9jiJmZGcqWLQsbGxscPHiQIUlEVIAxKN+Reuo6LYaGyOVy/PTTT/jrr7/w4Ycf6qs0IiLSAQblO1INDXlbj9fExETMmDED6enpADLDskqVKnqvj4iI3g2vUb6DdIUS4U8TAeQ+hjIxMRHt2rVDSEgI7t69i9WrVxuqRCIiekcMyndw71ki0hRKWJuboKSjVbbrJCQkoG3btjh58iTs7OwwaNAgA1dJRETvgkH5DkKjXo2flMtlWX6ekJCANm3a4NSpU7C3t8fhw4fxwQcfGLpMIiJ6BwzKdxCaS4/X+Ph4tGnTBqdPn4a9vT1+//13NGjQwNAlEhHRO2JnnncQlsMcr0IIdO3aFadPn4aDgwOOHDnCkCQiKqQYlO8g7En2dw2RyWT49ttv4e7ujiNHjqB+/fpSlEdERDrAU6/5lJKuQGRMZo/X7IaGNG/eHHfv3oWVVfadfIiIqHBgizKf7j5NgFIAjtZmKGFngdjYWHTs2BE3b95Ur8OQJCIq/NiizCf1jDyudoiLi4O/vz/OnTuHsLAwXL9+HSYmJhJXSEREulAgWpRLliyBl5cXLC0t0aBBA5w/fz7X9bdu3YoqVarA0tISNWvWxIEDBwxU6SuqoSFlHM3RqlUrnDt3Dk5OTtiwYQNDkoioCJE8KDdv3oyRI0di8uTJuHTpEmrVqgV/f388efIk2/X/+OMP9OjRAwMGDMDly5fRsWNHdOzYEX///bdB61a1KA9uWoXz58+jePHiOH78OOrUqWPQOoiISL9kQgghZQENGjTA+++/j8WLFwMAlEolPD09MWzYMIwZMybL+oGBgUhMTMS+ffvUyz744APUrl0by5Yte+vrxcXFwcHBAbGxsbC3t8933Q1nHsXjuFREbfgWtslROHbsGGrVqpXv/RERkWHlNQ8kbVGmpaXh4sWL8PPzUy+Ty+Xw8/PD2bNns93m7NmzGusDgL+/f47rp6amIi4uTuPxrhJSM/A4LhUAYCcScPz4cYYkEVERJWlQxsTEQKFQwNXVVWO5q6sroqKist0mKipKq/VnzZoFBwcH9cPT0/Od677932lX0/REHD+4F++9994775OIiAqmIt/rdezYsRg5cqT6eVxc3DuHZVV3e+wa0hgvktJQs7LLu5ZIREQFmKRB6ezsDBMTE0RHR2ssj46OhpubW7bbuLm5abW+hYUFLCwsdFPwfyzNTFDb01Gn+yQiooJJ0lOv5ubm8Pb2xrFjx9TLlEoljh07hoYNG2a7TcOGDTXWB4AjR47kuD4REdG7kPzU68iRIxEUFIR69eqhfv36WLhwIRITE9GvXz8AQJ8+fVCyZEnMmjULADBixAg0a9YM8+fPR9u2bbFp0yb89ddfWL58uZRvg4iIiijJgzIwMBBPnz7FpEmTEBUVhdq1a+PQoUPqDjv379+HXP6q4duoUSNs3LgREyZMwLhx41CxYkXs2rULNWrUkOotEBFRESb5OEpD09U4SiIiKtwKxThKIiKigo5BSURElAsGJRERUS4YlERERLlgUBIREeWCQUlERJQLBiUREVEuGJRERES5YFASERHlgkFJRESUC8nnejU01Yx9cXFxEldCRERSUuXA22ZyNbqgjI+PB4B3vnkzEREVDfHx8XBwcMjx50Y3KbpSqcSjR49gZ2cHmUyW7/3ExcXB09MTDx484OTqr+FxyRmPTfZ4XHLGY5M9XR0XIQTi4+Ph4eGhcZeqNxldi1Iul6NUqVI625+9vT0/wNngcckZj032eFxyxmOTPV0cl9xakirszENERJQLBiUREVEuGJT5ZGFhgcmTJ8PCwkLqUgoUHpec8dhkj8clZzw22TP0cTG6zjxERETaYIuSiIgoFwxKIiKiXDAoiYiIcsGgJCIiygWDMhdLliyBl5cXLC0t0aBBA5w/fz7X9bdu3YoqVarA0tISNWvWxIEDBwxUqWFpc1xWrFiBpk2bolixYihWrBj8/PzeehwLM20/MyqbNm2CTCZDx44d9VugRLQ9Li9fvsSQIUPg7u4OCwsLVKpUib9P/1m4cCEqV64MKysreHp64quvvkJKSoqBqjWMkydPon379vDw8IBMJsOuXbveuk1ISAjq1q0LCwsLVKhQAWvWrNFdQYKytWnTJmFubi5Wr14tbty4IQYNGiQcHR1FdHR0tuufOXNGmJiYiO+//17cvHlTTJgwQZiZmYnr168buHL90va49OzZUyxZskRcvnxZ3Lp1S/Tt21c4ODiIf//918CV65+2x0YlIiJClCxZUjRt2lR06NDBMMUakLbHJTU1VdSrV0+0adNGnD59WkRERIiQkBBx5coVA1euf9oemw0bNggLCwuxYcMGERERIQ4fPizc3d3FV199ZeDK9evAgQNi/PjxYseOHQKA2LlzZ67rh4eHC2trazFy5Ehx8+ZN8b///U+YmJiIQ4cO6aQeBmUO6tevL4YMGaJ+rlAohIeHh5g1a1a263fr1k20bdtWY1mDBg3EZ599ptc6DU3b4/KmjIwMYWdnJ3799Vd9lSiZ/BybjIwM0ahRI7Fy5UoRFBRUJINS2+Py008/iXLlyom0tDRDlSgZbY/NkCFDRPPmzTWWjRw5UjRu3FivdUopL0H5zTffiOrVq2ssCwwMFP7+/jqpgades5GWloaLFy/Cz89PvUwul8PPzw9nz57NdpuzZ89qrA8A/v7+Oa5fGOXnuLwpKSkJ6enpcHJy0leZksjvsZk2bRpcXFwwYMAAQ5RpcPk5Lnv27EHDhg0xZMgQuLq6okaNGpg5cyYUCoWhyjaI/BybRo0a4eLFi+rTs+Hh4Thw4ADatGljkJoLKn3//TW6SdHzIiYmBgqFAq6urhrLXV1d8c8//2S7TVRUVLbrR0VF6a1OQ8vPcXnTt99+Cw8Pjywf6sIuP8fm9OnTWLVqFa5cuWKACqWRn+MSHh6O48ePo1evXjhw4ADu3LmDwYMHIz09HZMnTzZE2QaRn2PTs2dPxMTEoEmTJhBCICMjA59//jnGjRtniJILrJz+/sbFxSE5ORlWVlbvtH+2KMlgZs+ejU2bNmHnzp2wtLSUuhxJxcfHo3fv3lixYgWcnZ2lLqdAUSqVcHFxwfLly+Ht7Y3AwECMHz8ey5Ytk7o0yYWEhGDmzJlYunQpLl26hB07dmD//v2YPn261KUVaWxRZsPZ2RkmJiaIjo7WWB4dHQ03N7dst3Fzc9Nq/cIoP8dFZd68eZg9ezaOHj2K9957T59lSkLbY3P37l1ERkaiffv26mVKpRIAYGpqitDQUJQvX16/RRtAfj4z7u7uMDMzg4mJiXpZ1apVERUVhbS0NJibm+u1ZkPJz7GZOHEievfujYEDBwIAatasicTERHz66acYP358rvdULMpy+vtrb2//zq1JgC3KbJmbm8Pb2xvHjh1TL1MqlTh27BgaNmyY7TYNGzbUWB8Ajhw5kuP6hVF+jgsAfP/995g+fToOHTqEevXqGaJUg9P22FSpUgXXr1/HlStX1I+PPvoIvr6+uHLlCjw9PQ1Zvt7k5zPTuHFj3LlzR/3FAQDCwsLg7u5eZEISyN+xSUpKyhKGqi8Uwoin7db731+ddAkqgjZt2iQsLCzEmjVrxM2bN8Wnn34qHB0dRVRUlBBCiN69e4sxY8ao1z9z5owwNTUV8+bNE7du3RKTJ08ussNDtDkus2fPFubm5mLbtm3i8ePH6kd8fLxUb0FvtD02byqqvV61PS73798XdnZ2YujQoSI0NFTs27dPuLi4iO+++06qt6A32h6byZMnCzs7O/Hbb7+J8PBw8fvvv4vy5cuLbt26SfUW9CI+Pl5cvnxZXL58WQAQCxYsEJcvXxb37t0TQggxZswY0bt3b/X6quEho0ePFrdu3RJLlizh8BBD+d///idKly4tzM3NRf369cWff/6p/lmzZs1EUFCQxvpbtmwRlSpVEubm5qJ69epi//79Bq7YMLQ5LmXKlBEAsjwmT55s+MINQNvPzOuKalAKof1x+eOPP0SDBg2EhYWFKFeunJgxY4bIyMgwcNWGoc2xSU9PF1OmTBHly5cXlpaWwtPTUwwePFi8ePHC8IXrUXBwcLZ/N1THIigoSDRr1izLNrVr1xbm5uaiXLly4pdfftFZPbzNFhERUS54jZKIiCgXDEoiIqJcMCiJiIhywaAkIiLKBYOSiIgoFwxKIiKiXDAoiYiIcsGgJCIiygWDkigf1qxZA0dHR6nLyDeZTIZdu3bluk7fvn3RsWNHg9RDVJAxKMlo9e3bFzKZLMvjzp07UpeGNWvWqOuRy+UoVaoU+vXrhydPnuhk/48fP0br1q0BAJGRkZDJZFnui7lo0SKsWbNGJ6+XkylTpqjfp4mJCTw9PfHpp5/i+fPnWu2HoU76xNtskVELCAjAL7/8orGsRIkSElWjyd7eHqGhoVAqlbh69Sr69euHR48e4fDhw++877zc/s3BweGdXycvqlevjqNHj0KhUODWrVvo378/YmNjsXnzZoO8PtHbsEVJRs3CwgJubm4aDxMTEyxYsAA1a9aEjY0NPD09MXjwYCQkJOS4n6tXr8LX1xd2dnawt7eHt7c3/vrrL/XPT58+jaZNm8LKygqenp4YPnw4EhMTc61NJpPBzc0NHh4eaN26NYYPH46jR48iOTkZSqUS06ZNQ6lSpWBhYYHatWvj0KFD6m3T0tIwdOhQuLu7w9LSEmXKlMGsWbM09q069Vq2bFkAQJ06dSCTyeDj4wNAs5W2fPlyeHh4aNz6CgA6dOiA/v37q5/v3r0bdevWhaWlJcqVK4epU6ciIyMj1/dpamoKNzc3lCxZEn5+fvj4449x5MgR9c8VCgUGDBiAsmXLwsrKCpUrV8aiRYvUP58yZQp+/fVX7N69W906DQkJAQA8ePAA3bp1g6OjI5ycnNChQwdERkbmWg/RmxiURNmQy+X48ccfcePGDfz66684fvw4vvnmmxzX79WrF0qVKoULFy7g4sWLGDNmDMzMzABk3qQ5ICAAXbp0wbVr17B582acPn0aQ4cO1aomKysrKJVKZGRkYNGiRZg/fz7mzZuHa9euwd/fHx999BFu374NAPjxxx+xZ88ebNmyBaGhodiwYQO8vLyy3e/58+cBAEePHsXjx4+xY8eOLOt8/PHHePbsGYKDg9XLnj9/jkOHDqFXr14AgFOnTqFPnz4YMWIEbt68iZ9//hlr1qzBjBkz8vweIyMjcfjwYY37TiqVSpQqVQpbt27FzZs3MWnSJIwbNw5btmwBAIwaNQrdunVDQEAAHj9+jMePH6NRo0ZIT0+Hv78/7OzscOrUKZw5cwa2trYICAhAWlpanmsi4m22yGgFBQUJExMTYWNjo3507do123W3bt0qihcvrn7+yy+/CAcHB/VzOzs7sWbNmmy3HTBggPj00081lp06dUrI5XKRnJyc7TZv7j8sLExUqlRJ1KtXTwghhIeHh5gxY4bGNu+//74YPHiwEEKIYcOGiebNmwulUpnt/gGInTt3CiGEiIiIEADE5cuXNdZ587ZfHTp0EP3791c///nnn4WHh4dQKBRCCCFatGghZs6cqbGPdevWCXd392xrECLz/opyuVzY2NgIS0tL9e2UFixYkOM2QggxZMgQ0aVLlxxrVb125cqVNY5BamqqsLKyEocPH851/0Sv4zVKMmq+vr746aef1M9tbGwAZLauZs2ahX/++QdxcXHIyMhASkoKkpKSYG1tnWU/I0eOxMCBA7Fu3Tr16cPy5csDyDwte+3aNWzYsEG9vhACSqUSERERqFq1ara1xcbGwtbWFkqlEikpKWjSpAlWrlyJuLg4PHr0CI0bN9ZYv3Hjxrh69SqAzNOmLVu2ROXKlREQEIB27dqhVatW73SsevXqhUGDBmHp0qWwsLDAhg0b0L17d8jlcvX7PHPmjEYLUqFQ5HrcAKBy5crYs2cPUlJSsH79ely5cgXDhg3TWGfJkiVYvXo17t+/j+TkZKSlpaF27dq51nv16lXcuXMHdnZ2GstTUlJw9+7dfBwBMlYMSjJqNjY2qFChgsayyMhItGvXDl988QVmzJgBJycnnD59GgMGDEBaWlq2f/CnTJmCnj17Yv/+/Th48CAmT56MTZs2oVOnTkhISMBnn32G4cOHZ9mudOnSOdZmZ2eHS5cuQS6Xw93dHVZWVgCAuLi4t76vunXrIiIiAgcPHsTRo0fRrVs3+Pn5Ydu2bW/dNift27eHEAL79+/H+++/j1OnTuGHH35Q/zwhIQFTp05F586ds2xraWmZ437Nzc3V/wazZ89G27ZtMXXqVEyfPh0AsGnTJowaNQrz589Hw4YNYWdnh7lz5+LcuXO51puQkABvb2+NLygqBaXDFhUODEqiN1y8eBFKpRLz589Xt5ZU18NyU6lSJVSqVAlfffUVevTogV9++QWdOnVC3bp1cfPmzSyB/DZyuTzbbezt7eHh4YEzZ86gWbNm6uVnzpxB/fr1NdYLDAxEYGAgunbtioCAADx//hxOTk4a+1NdD1QoFLnWY2lpic6dO2PDhg24c+cOKleujLp166p/XrduXYSGhmr9Pt80YcIENG/eHF988YX6fTZq1AiDBw9Wr/Nmi9Dc3DxL/XXr1sXmzZvh4uICe3v7d6qJjBs78xC9oUKFCkhPT8f//vc/hIeHY926dVi2bFmO6ycnJ2Po0KEICQnBvXv3cObMGVy4cEF9SvXbb7/FH3/8gaFDh+LKlSu4ffs2du/erXVnnteNHj0ac+bMwebNmxEaGooxY8bgypUrGDFiBABgwYIF+O233/DPP/8gLCwMW7duhZubW7aTJLi4uMDKygqHDh1CdHQ0YmNjc3zdXr16Yf/+/Vi9erW6E4/KpEmTsHbtWkydOhU3btzArVu3sGnTJkyYMEGr99awYUO89957mDlzJgCgYsWK+Ouvv3D48GGEhYVh4sSJuHDhgsY2Xl5euHbtGkJDQxETE4P09HT06tULzs7O6NChA06dOoWIiAiEhIRg+PDh+Pfff7WqiYyc1BdJiaSSXQcQlQULFgh3d3dhZWUl/P39xdq1awUA8eLFCyGEZmeb1NRU0b17d+Hp6SnMzc2Fh4eHGDp0qEZHnfPnz4uWLVsKW1tbYWNjI957770snXFe92ZnnjcpFAoxZcoUUbJkSWFmZiZq1aolDh48qP758uXLRe3atYWNjY2wt7cXLVq0EJcuXVL/HK915hFCiBUrVghPT08hl8tFs2bNcjw+CoVCuLu7CwDi7t27Weo6dOiQaNSokbCyshL29vaifv36Yvny5Tm+j8mTJ4tatWplWf7bb78JCwsLcf/+fZGSkiL69u0rHBwchKOjo/jiiy/EmDFjNLZ78uSJ+vgCEMHBwUIIIR4/fiz69OkjnJ2dhYWFhShXrpwYNGiQiI2NzbEmojfJhBBC2qgmIiIquHjqlYiIKBcMSiIiolwwKImIiHLBoCQiIsoFg5KIiCgXDEoiIqJcMCiJiIhywaAkIiLKBYOSiIgoFwxKIiKiXDAoiYiIcvF/HkPc0HKbvAgAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "plt.plot([0,1],[0,1],'k--')\n",
    "plt.plot(fpr,tpr, label='Knn')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Knn(n_neighbors=2) ROC curve')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "daf2d3af",
   "metadata": {},
   "source": [
    "## SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "b1070bdd",
   "metadata": {},
   "outputs": [],
   "source": [
    "svc_scores = []\n",
    "kernels = ['linear', 'poly', 'rbf', 'sigmoid']\n",
    "for i in range(len(kernels)):\n",
    "    svc_classifier = SVC(kernel = kernels[i])\n",
    "    svc_classifier.fit(X_train, y_train)\n",
    "    svc_scores.append(round(svc_classifier.score(X_test, y_test),2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "7076377f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Support Vector Classifier scores for different kernels')"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# colors = rainbow(np.linspace(0, 1, len(kernels)))\n",
    "plt.figure(figsize=(7,4))\n",
    "plt.bar(kernels, svc_scores,color=['red', 'yellow', 'blue', 'orange'])\n",
    "for i in range(len(kernels)):\n",
    "    plt.text(i, svc_scores[i], svc_scores[i])\n",
    "plt.xlabel('Kernels')\n",
    "plt.ylabel('Scores')\n",
    "plt.title('Support Vector Classifier scores for different kernels')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "8e4b002f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-14 {color: black;background-color: white;}#sk-container-id-14 pre{padding: 0;}#sk-container-id-14 div.sk-toggleable {background-color: white;}#sk-container-id-14 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-14 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-14 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-14 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-14 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-14 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-14 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-14 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-14 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-14 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-14 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-14 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-14 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-14 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-14 div.sk-item {position: relative;z-index: 1;}#sk-container-id-14 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-14 div.sk-item::before, #sk-container-id-14 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-14 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-14 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-14 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-14 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-14 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-14 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-14 div.sk-label-container {text-align: center;}#sk-container-id-14 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-14 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-14\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(probability=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" checked><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(probability=True)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(probability=True)"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm=SVC(probability=True)\n",
    "svm.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "30e08c1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.672514619883041"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "7c20defb",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_svm_predi=svm.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "eddbb97b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[320, 201],\n",
       "       [135, 370]], dtype=int64)"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,y_svm_predi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "d95f5d4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_svm_predict_pro=svm.predict_proba(X_test)[:,1]\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_svm_predict_pro)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "1073bd42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7192907774462667"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_test,y_svm_predict_pro)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "b31831a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "plt.plot([0,1],[0,1],'k--')\n",
    "plt.plot(fpr,tpr, label='SVM')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('SVM ROC curve')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "312f501a",
   "metadata": {},
   "source": [
    "# XGBOOST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "e5fdf4e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-15 {color: black;background-color: white;}#sk-container-id-15 pre{padding: 0;}#sk-container-id-15 div.sk-toggleable {background-color: white;}#sk-container-id-15 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-15 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-15 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-15 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-15 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-15 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-15 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-15 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-15 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-15 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-15 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-15 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-15 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-15 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-15 div.sk-item {position: relative;z-index: 1;}#sk-container-id-15 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-15 div.sk-item::before, #sk-container-id-15 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-15 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-15 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-15 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-15 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-15 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-15 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-15 div.sk-label-container {text-align: center;}#sk-container-id-15 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-15 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-15\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(alpha=10, base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=1.0, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=4, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=100, n_jobs=None,\n",
       "              num_parallel_tree=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" checked><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(alpha=10, base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=1.0, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=4, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=100, n_jobs=None,\n",
       "              num_parallel_tree=None, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(alpha=10, base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=1.0, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=4, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=100, n_jobs=None,\n",
       "              num_parallel_tree=None, ...)"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "\n",
    "\n",
    "# declare parameters\n",
    "params = {\n",
    "            'objective':'binary:logistic',\n",
    "            'max_depth': 4,\n",
    "            'alpha': 10,\n",
    "            'learning_rate': 1.0,\n",
    "            'n_estimators':100\n",
    "        }\n",
    "            \n",
    "            \n",
    "            \n",
    "# instantiate the classifier \n",
    "xgb_clf = XGBClassifier(**params)\n",
    "\n",
    "\n",
    "\n",
    "# fit the classifier to the training data\n",
    "xgb_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "7a200c56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8538011695906432"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_xg=xgb_clf.predict(X_test)\n",
    "accuracy_score(y_test,y_pred_xg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "498414b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Support Vector Classifier accuracy: 67.2514619883041%\n",
      "Logistic Regression accuracy: 61.40350877192983%\n",
      "K Nearest Neighbors Classifier accuracy: 81.28654970760235%\n",
      "Random Forest Classifier accuracy: 92.30019493177389%\n",
      "XGBoost Classifier accuracy: 90.05847953216374%\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([  0.,  20.,  40.,  60.,  80., 100.]),\n",
       " [Text(0, 0.0, '0'),\n",
       "  Text(0, 20.0, '20'),\n",
       "  Text(0, 40.0, '40'),\n",
       "  Text(0, 60.0, '60'),\n",
       "  Text(0, 80.0, '80'),\n",
       "  Text(0, 100.0, '100')])"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "import matplotlib.cm as cm\n",
    "accuracy_scores = np.zeros(5)\n",
    "\n",
    "# Support Vector Classifier\n",
    "clf = SVC().fit(X_train, y_train)\n",
    "prediction = clf.predict(X_test)\n",
    "accuracy_scores[0] = accuracy_score(y_test, prediction)*100\n",
    "print('Support Vector Classifier accuracy: {}%'.format(accuracy_scores[0]))\n",
    "\n",
    "# Logistic Regression\n",
    "clf = LogisticRegression().fit(X_train, y_train)\n",
    "prediction = clf.predict(X_test)\n",
    "accuracy_scores[1] = accuracy_score(y_test, prediction)*100\n",
    "print('Logistic Regression accuracy: {}%'.format(accuracy_scores[1]))\n",
    "\n",
    "# K Nearest Neighbors\n",
    "clf = KNeighborsClassifier().fit(X_train, y_train)\n",
    "prediction = clf.predict(X_test)\n",
    "accuracy_scores[2] = accuracy_score(y_test, prediction)*100\n",
    "print('K Nearest Neighbors Classifier accuracy: {}%'.format(accuracy_scores[2]))\n",
    "\n",
    "# Random Forest\n",
    "rclf = RandomForestClassifier().fit(X_train, y_train)\n",
    "prediction = rclf.predict(X_test)\n",
    "accuracy_scores[3] = accuracy_score(y_test, prediction)*100\n",
    "print('Random Forest Classifier accuracy: {}%'.format(accuracy_scores[3]))\n",
    "\n",
    "# XGBoost\n",
    "clf = XGBClassifier().fit(X_train, y_train)\n",
    "prediction = clf.predict(X_test)\n",
    "accuracy_scores[4] = accuracy_score(y_test, prediction)*100\n",
    "# Display the accuracy of the XGBoost Classifier\n",
    "print('XGBoost Classifier accuracy: {}%'.format(accuracy_scores[-1]))\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "colors = cm.rainbow(np.linspace(0, 1, 4))\n",
    "labels = ['Support Vector Classifier', 'Logsitic Regression', 'K Nearest Neighbors', 'Random Forest','XGBoost']\n",
    "plt.bar(labels,\n",
    "        accuracy_scores,\n",
    "        color = colors)\n",
    "plt.xlabel('Classifiers',fontsize=18)\n",
    "plt.ylabel('Accuracy',fontsize=18)\n",
    "plt.title('Accuracy of various algorithms',fontsize=20)\n",
    "plt.xticks(rotation=45,fontsize=12)\n",
    "plt.yticks(fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "31f75ce6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['model.joblib']"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "# Save the model to a file\n",
    "joblib.dump(rclf, 'model.joblib')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eeb086ce",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "include_colab_link": true,
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}