{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## I. Introduction\n",
    "\n",
    "Name    : Theo Nugraha\n",
    "\n",
    "Batch   : RMT-021\n",
    "\n",
    "Deployment Link : [HUGGING FACE - deployment](https://huggingface.co/spaces/nugrahatheo/deployment)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### I.I. Project Background"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I am a Data Scientist who has a client at the largest hospital in San Francisco. I was asked to create a model to predict whether a patient will die or not using the dataset provided by the hospital. The hospital asked me to create a model with the Random Forest Classifier algorithm and one of the boosting algorithms using the dataset provided by the hospital."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### I.II. About Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**QUERY**\n",
    "```\n",
    "SELECT *\n",
    "FROM `ftds-hacktiv8-project.phase1_ftds_021_rmt.heart-failure`;\n",
    "```\n",
    "\n",
    "This dataset contains features:\n",
    "- `age` = Age\n",
    "- `anaemia` = Decrease of red blood cells or hemoglobin.\n",
    "- `creatinine_phosphokinase` = Level of the CPK enzyme in the blood (mcg/L).\n",
    "- `diabetes` = If the patients has diabetes.\n",
    "- `ejection_fraction` = Percentage of blood leaving the heart at each contraction (percentage).\n",
    "- `high_blood_pressure` = If the patients has hypertention.\n",
    "- `platelets` = Platelets in the blood (kiloplatelets/mL).\n",
    "- `serum_creatinine` = Level of serum creatinine in the blood (mg/dL).\n",
    "- `serum_sodium` = Level of serum sodium in the blood (mEq/L).\n",
    "- `sex` = Woman or man.\n",
    "- `smoking` = If the patient smokes or not.\n",
    "- `time` = Follow-up period (days).\n",
    "- `DEATH_EVENT` = If the patient decreased during the follow-up period."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### I.III. Objective"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This project aims to create a model with Random Forest Classifier and GradientBoost Classifier algorithms to predict whether a patient will die or not."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## II. Import Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import Library\n",
    "\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pickle\n",
    "import json\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from sklearn.metrics import confusion_matrix, classification_report, ConfusionMatrixDisplay\n",
    "from sklearn.model_selection import GridSearchCV, cross_val_score\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## III. Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>anaemia</th>\n",
       "      <th>creatinine_phosphokinase</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>ejection_fraction</th>\n",
       "      <th>high_blood_pressure</th>\n",
       "      <th>platelets</th>\n",
       "      <th>serum_creatinine</th>\n",
       "      <th>serum_sodium</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoking</th>\n",
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       "      <td>43</td>\n",
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       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>279000.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>134</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>186</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>63.0</td>\n",
       "      <td>1</td>\n",
       "      <td>122</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>267000.00</td>\n",
       "      <td>1.2</td>\n",
       "      <td>145</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <th>295</th>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>308</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>377000.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>136</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>186</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>70.0</td>\n",
       "      <td>0</td>\n",
       "      <td>97</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>220000.00</td>\n",
       "      <td>0.9</td>\n",
       "      <td>138</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>139</td>\n",
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       "      <td>0</td>\n",
       "      <td>215</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>298</th>\n",
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       "      <td>582</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>147000.00</td>\n",
       "      <td>0.8</td>\n",
       "      <td>140</td>\n",
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       "      <td>1</td>\n",
       "      <td>192</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>299 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age  anaemia  creatinine_phosphokinase  diabetes  ejection_fraction  \\\n",
       "0    42.0        1                       250         1                 15   \n",
       "1    46.0        0                       168         1                 17   \n",
       "2    65.0        1                       160         1                 20   \n",
       "3    53.0        1                        91         0                 20   \n",
       "4    50.0        1                       582         1                 20   \n",
       "..    ...      ...                       ...       ...                ...   \n",
       "294  63.0        1                       122         1                 60   \n",
       "295  45.0        0                       308         1                 60   \n",
       "296  70.0        0                        97         0                 60   \n",
       "297  53.0        1                       446         0                 60   \n",
       "298  50.0        0                       582         0                 62   \n",
       "\n",
       "     high_blood_pressure  platelets  serum_creatinine  serum_sodium  sex  \\\n",
       "0                      0  213000.00               1.3           136    0   \n",
       "1                      1  271000.00               2.1           124    0   \n",
       "2                      0  327000.00               2.7           116    0   \n",
       "3                      1  418000.00               1.4           139    0   \n",
       "4                      1  279000.00               1.0           134    0   \n",
       "..                   ...        ...               ...           ...  ...   \n",
       "294                    0  267000.00               1.2           145    1   \n",
       "295                    1  377000.00               1.0           136    1   \n",
       "296                    1  220000.00               0.9           138    1   \n",
       "297                    1  263358.03               1.0           139    1   \n",
       "298                    1  147000.00               0.8           140    1   \n",
       "\n",
       "     smoking  time  DEATH_EVENT  \n",
       "0          0    65            1  \n",
       "1          0   100            1  \n",
       "2          0     8            1  \n",
       "3          0    43            1  \n",
       "4          0   186            0  \n",
       "..       ...   ...          ...  \n",
       "294        0   147            0  \n",
       "295        0   186            0  \n",
       "296        0   186            0  \n",
       "297        0   215            0  \n",
       "298        1   192            0  \n",
       "\n",
       "[299 rows x 13 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data Loading\n",
    "\n",
    "df = pd.read_csv('h8dsft_P1G3_theo.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset has 13 columns or features and 299 rows of data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "    age  anaemia  creatinine_phosphokinase  diabetes  ejection_fraction  \\\n",
       "0  42.0        1                       250         1                 15   \n",
       "1  46.0        0                       168         1                 17   \n",
       "2  65.0        1                       160         1                 20   \n",
       "3  53.0        1                        91         0                 20   \n",
       "4  50.0        1                       582         1                 20   \n",
       "5  70.0        1                       125         0                 25   \n",
       "6  65.0        1                        52         0                 25   \n",
       "7  70.0        0                       161         0                 25   \n",
       "8  60.0        1                        76         1                 25   \n",
       "9  59.0        1                       280         1                 25   \n",
       "\n",
       "   high_blood_pressure  platelets  serum_creatinine  serum_sodium  sex  \\\n",
       "0                    0   213000.0               1.3           136    0   \n",
       "1                    1   271000.0               2.1           124    0   \n",
       "2                    0   327000.0               2.7           116    0   \n",
       "3                    1   418000.0               1.4           139    0   \n",
       "4                    1   279000.0               1.0           134    0   \n",
       "5                    1   237000.0               1.0           140    0   \n",
       "6                    1   276000.0               1.3           137    0   \n",
       "7                    0   244000.0               1.2           142    0   \n",
       "8                    0   196000.0               2.5           132    0   \n",
       "9                    1   302000.0               1.0           141    0   \n",
       "\n",
       "   smoking  time  DEATH_EVENT  \n",
       "0        0    65            1  \n",
       "1        0   100            1  \n",
       "2        0     8            1  \n",
       "3        0    43            1  \n",
       "4        0   186            0  \n",
       "5        0    15            1  \n",
       "6        0    16            0  \n",
       "7        0    66            1  \n",
       "8        0    77            1  \n",
       "9        0    78            1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displays the top 10 data\n",
    "\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>anaemia</th>\n",
       "      <th>creatinine_phosphokinase</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>ejection_fraction</th>\n",
       "      <th>high_blood_pressure</th>\n",
       "      <th>platelets</th>\n",
       "      <th>serum_creatinine</th>\n",
       "      <th>serum_sodium</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoking</th>\n",
       "      <th>time</th>\n",
       "      <th>DEATH_EVENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>64.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1610</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>242000.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>137</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>244000.00</td>\n",
       "      <td>0.9</td>\n",
       "      <td>139</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>117</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>53.0</td>\n",
       "      <td>0</td>\n",
       "      <td>196</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>220000.00</td>\n",
       "      <td>0.7</td>\n",
       "      <td>133</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>134</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>70.0</td>\n",
       "      <td>1</td>\n",
       "      <td>171</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>176000.00</td>\n",
       "      <td>1.1</td>\n",
       "      <td>145</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>146</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>60.0</td>\n",
       "      <td>1</td>\n",
       "      <td>95</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>337000.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>138</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>146</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>63.0</td>\n",
       "      <td>1</td>\n",
       "      <td>122</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>267000.00</td>\n",
       "      <td>1.2</td>\n",
       "      <td>145</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>147</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>308</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>377000.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>136</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>186</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>70.0</td>\n",
       "      <td>0</td>\n",
       "      <td>97</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>220000.00</td>\n",
       "      <td>0.9</td>\n",
       "      <td>138</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>186</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>53.0</td>\n",
       "      <td>1</td>\n",
       "      <td>446</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>263358.03</td>\n",
       "      <td>1.0</td>\n",
       "      <td>139</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>215</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>50.0</td>\n",
       "      <td>0</td>\n",
       "      <td>582</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>147000.00</td>\n",
       "      <td>0.8</td>\n",
       "      <td>140</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>192</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      age  anaemia  creatinine_phosphokinase  diabetes  ejection_fraction  \\\n",
       "289  64.0        0                      1610         0                 60   \n",
       "290  62.0        0                        30         1                 60   \n",
       "291  53.0        0                       196         0                 60   \n",
       "292  70.0        1                       171         0                 60   \n",
       "293  60.0        1                        95         0                 60   \n",
       "294  63.0        1                       122         1                 60   \n",
       "295  45.0        0                       308         1                 60   \n",
       "296  70.0        0                        97         0                 60   \n",
       "297  53.0        1                       446         0                 60   \n",
       "298  50.0        0                       582         0                 62   \n",
       "\n",
       "     high_blood_pressure  platelets  serum_creatinine  serum_sodium  sex  \\\n",
       "289                    0  242000.00               1.0           137    1   \n",
       "290                    1  244000.00               0.9           139    1   \n",
       "291                    0  220000.00               0.7           133    1   \n",
       "292                    1  176000.00               1.1           145    1   \n",
       "293                    0  337000.00               1.0           138    1   \n",
       "294                    0  267000.00               1.2           145    1   \n",
       "295                    1  377000.00               1.0           136    1   \n",
       "296                    1  220000.00               0.9           138    1   \n",
       "297                    1  263358.03               1.0           139    1   \n",
       "298                    1  147000.00               0.8           140    1   \n",
       "\n",
       "     smoking  time  DEATH_EVENT  \n",
       "289        0   113            0  \n",
       "290        0   117            0  \n",
       "291        1   134            0  \n",
       "292        1   146            0  \n",
       "293        1   146            0  \n",
       "294        0   147            0  \n",
       "295        0   186            0  \n",
       "296        0   186            0  \n",
       "297        0   215            0  \n",
       "298        1   192            0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the bottom 10 data\n",
    "\n",
    "df.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Duplicate Dataset\n",
    "\n",
    "data_backup = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 299 entries, 0 to 298\n",
      "Data columns (total 13 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   age                       299 non-null    float64\n",
      " 1   anaemia                   299 non-null    int64  \n",
      " 2   creatinine_phosphokinase  299 non-null    int64  \n",
      " 3   diabetes                  299 non-null    int64  \n",
      " 4   ejection_fraction         299 non-null    int64  \n",
      " 5   high_blood_pressure       299 non-null    int64  \n",
      " 6   platelets                 299 non-null    float64\n",
      " 7   serum_creatinine          299 non-null    float64\n",
      " 8   serum_sodium              299 non-null    int64  \n",
      " 9   sex                       299 non-null    int64  \n",
      " 10  smoking                   299 non-null    int64  \n",
      " 11  time                      299 non-null    int64  \n",
      " 12  DEATH_EVENT               299 non-null    int64  \n",
      "dtypes: float64(3), int64(10)\n",
      "memory usage: 30.5 KB\n"
     ]
    }
   ],
   "source": [
    "# Check Dataset\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this dataset, there are 13 columns with 10 columns of integer data type and 3 columns of float data type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                         0\n",
       "anaemia                     0\n",
       "creatinine_phosphokinase    0\n",
       "diabetes                    0\n",
       "ejection_fraction           0\n",
       "high_blood_pressure         0\n",
       "platelets                   0\n",
       "serum_creatinine            0\n",
       "serum_sodium                0\n",
       "sex                         0\n",
       "smoking                     0\n",
       "time                        0\n",
       "DEATH_EVENT                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check Missing Value\n",
    "\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is no missing value in this dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Duplication Check \n",
    "\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There in no duplicate value in this dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of Dataset :  (299, 13)\n",
      "\n",
      "Col :  age\n",
      "Length Unique Value :  47\n",
      "\n",
      "Col :  anaemia\n",
      "Length Unique Value :  2\n",
      "\n",
      "Col :  creatinine_phosphokinase\n",
      "Length Unique Value :  208\n",
      "\n",
      "Col :  diabetes\n",
      "Length Unique Value :  2\n",
      "\n",
      "Col :  ejection_fraction\n",
      "Length Unique Value :  17\n",
      "\n",
      "Col :  high_blood_pressure\n",
      "Length Unique Value :  2\n",
      "\n",
      "Col :  platelets\n",
      "Length Unique Value :  176\n",
      "\n",
      "Col :  serum_creatinine\n",
      "Length Unique Value :  40\n",
      "\n",
      "Col :  serum_sodium\n",
      "Length Unique Value :  27\n",
      "\n",
      "Col :  sex\n",
      "Length Unique Value :  2\n",
      "\n",
      "Col :  smoking\n",
      "Length Unique Value :  2\n",
      "\n",
      "Col :  time\n",
      "Length Unique Value :  148\n",
      "\n",
      "Col :  DEATH_EVENT\n",
      "Length Unique Value :  2\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Cardinality Check\n",
    "\n",
    "print('Shape of Dataset : ', df.shape)\n",
    "print('')\n",
    "\n",
    "for col in df.columns.tolist():\n",
    "  print('Col : ', col)\n",
    "  print('Length Unique Value : ', df[col].nunique())\n",
    "  print('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of Dataset :  (299, 13)\n",
      "\n",
      "Col :  age\n",
      "Length Unique Value :  [42.    46.    65.    53.    50.    70.    60.    59.    72.    49.\n",
      " 75.    57.    48.    62.    52.    55.    58.    86.    66.    80.\n",
      " 45.    67.    90.    95.    63.    61.    60.667 40.    73.    51.\n",
      " 82.    43.    85.    64.    68.    54.    69.    47.    44.    81.\n",
      " 87.    94.    56.    41.    78.    77.    79.   ]\n",
      "\n",
      "Col :  anaemia\n",
      "Length Unique Value :  [1 0]\n",
      "\n",
      "Col :  creatinine_phosphokinase\n",
      "Length Unique Value :  [ 250  168  160   91  582  125   52  161   76  280  156 1896   56  211\n",
      "   80  128  159  124  129  328  482  167  131  166 2522 1051  249  281\n",
      "  291  335   58 2334  835  972   81  572   88  618  892  235  260  144\n",
      "   68   96  776  326  213   84  337 1820  112  318   69   61  400  719\n",
      "  151  101 2281  720 1185  207  655  336  233  244  855   53  358 1202\n",
      "  615  588   92   59  143  102  113  200   62  675  157 2060 3964  427\n",
      "  246  212  146  111  553  789  364   47   66  115 1199  231 1380  577\n",
      " 7702  110  154  514  305  898  369  646  943  176  395  145   57 2017\n",
      "  258  981   70 2656  371 5209  248 1548  185  132 2442  478  104  232\n",
      "  191  257   64  123  220   75  109 5882 1876  292   60  270 4540 2261\n",
      " 1846  130  198 1211  135 1021   86 2794   93   90  624  298  253  320\n",
      "  103 7861  149  148  203  936  805   99  707 1688  190 2413  607  754\n",
      "   72 3966 1419  121 2695   54  170  122   23  571  897  748 1082  418\n",
      " 1767  180  245  196  379   94  127  224   55   78  910  118  119  315\n",
      "   63  133  737 1808  193 1610   30  171   95  308   97  446]\n",
      "\n",
      "Col :  diabetes\n",
      "Length Unique Value :  [1 0]\n",
      "\n",
      "Col :  ejection_fraction\n",
      "Length Unique Value :  [15 17 20 25 30 35 38 40 45 50 55 60 62 65 70 80 14]\n",
      "\n",
      "Col :  high_blood_pressure\n",
      "Length Unique Value :  [0 1]\n",
      "\n",
      "Col :  platelets\n",
      "Length Unique Value :  [213000.   271000.   327000.   418000.   279000.   237000.   276000.\n",
      " 244000.   196000.   302000.   318000.   365000.   274000.   427000.\n",
      " 297000.   263358.03 153000.   395000.   621000.   127000.   329000.\n",
      " 259000.    62000.   404000.   232000.   319000.   221000.   348000.\n",
      " 235000.   277000.    75000.   305000.   268000.   371000.   533000.\n",
      " 231000.   236000.   255000.   162000.   228000.   192000.   294000.\n",
      " 215000.   422000.   451000.   390000.   270000.   216000.   293000.\n",
      " 164000.   229000.   201000.   226000.   283000.   257000.   220000.\n",
      " 223000.   324000.   275000.   362000.   321000.   351000.   286000.\n",
      " 132000.   358000.    87000.   543000.   222000.   194000.   317000.\n",
      " 507000.   203000.   300000.   172000.   309000.   265000.   208000.\n",
      " 742000.   151000.   166000.   389000.   210000.   140000.   254000.\n",
      " 119000.   266000.   204000.    70000.   189000.   126000.   253000.\n",
      " 497000.   181000.   298000.   149000.   252000.   338000.   262000.\n",
      " 219000.   212000.   130000.   504000.   314000.   246000.   233000.\n",
      " 198000.   136000.   200000.   461000.   267000.   211000.   218000.\n",
      " 225000.   334000.   303000.   160000.   173000.   249000.   150000.\n",
      " 388000.   289000.   385000.   122000.   243000.   850000.   227000.\n",
      " 174000.   281000.   350000.   290000.   141000.   185000.   301000.\n",
      " 133000.   179000.   368000.   310000.   304000.   224000.   186000.\n",
      " 330000.   264000.   282000.   147000.    25100.   382000.   155000.\n",
      " 374000.   328000.   217000.   242000.   448000.   325000.   105000.\n",
      " 260000.   241000.    51000.   336000.   284000.   360000.   263000.\n",
      " 250000.   184000.    73000.    47000.   188000.   406000.   481000.\n",
      " 248000.   308000.   454000.   306000.   295000.   176000.   337000.\n",
      " 377000.  ]\n",
      "\n",
      "Col :  serum_creatinine\n",
      "Length Unique Value :  [1.3  2.1  2.7  1.4  1.   1.2  2.5  5.   1.6  1.83 1.7  0.9  0.8  0.5\n",
      " 0.7  1.1  3.   2.2  0.75 1.18 2.3  0.6  1.9  3.2  1.5  6.8  9.   4.4\n",
      " 2.9  2.4  3.5  2.   3.8  9.4  3.4  1.8  4.   5.8  3.7  6.1 ]\n",
      "\n",
      "Col :  serum_sodium\n",
      "Length Unique Value :  [136 124 116 139 134 140 137 142 132 141 144 130 138 145 127 128 133 143\n",
      " 135 131 121 125 146 129 113 126 148]\n",
      "\n",
      "Col :  sex\n",
      "Length Unique Value :  [0 1]\n",
      "\n",
      "Col :  smoking\n",
      "Length Unique Value :  [0 1]\n",
      "\n",
      "Col :  time\n",
      "Length Unique Value :  [ 65 100   8  43 186  15  16  66  77  78  85 172 207  12  20  23  29  32\n",
      "  42  88  95 109 193 214 246  28 108 120 126 145 187 197 212 215 245 256\n",
      "  30  45  83 130 185 220 244 271  24  60  75  86  91 107 110 140 210 213\n",
      " 233  74 115 174  44  87  97 147 196 250 257  33  82  90  94 104 205 237\n",
      " 278  10  63  14 188   4   7  31  41  55  59  64  73 135 146 180 198 241\n",
      "  38  67  79 111 150 154 170 201 230 235  11  26  50 112 113 129 148 171\n",
      " 216  54  61  71  72 105 119 121 194 195 206 208 209 240 258 270   6  27\n",
      "  35  40 162 280  68  96 123 175 247  80 118 211 285  13  76 192 200 231\n",
      "  22 106 117 134]\n",
      "\n",
      "Col :  DEATH_EVENT\n",
      "Length Unique Value :  [1 0]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Cardinality Check\n",
    "\n",
    "print('Shape of Dataset : ', df.shape)\n",
    "print('')\n",
    "\n",
    "for col in df.columns.tolist():\n",
    "  print('Col : ', col)\n",
    "  print('Length Unique Value : ', df[col].unique())\n",
    "  print('')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the cardinality check, there is no problems."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the info from the source dataset, we can know that there are features that need to be converted to categorical:\n",
    "\n",
    "1. `anaemia`\n",
    "2. `diabetes`\n",
    "3. `high_blood_pressure`\n",
    "4. `sex`\n",
    "5. `smoker`\n",
    "6. `DEATH_EVENT`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IV. Exploratory Data Analysis (EDA) - NOT YET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>anaemia</th>\n",
       "      <th>creatinine_phosphokinase</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>ejection_fraction</th>\n",
       "      <th>high_blood_pressure</th>\n",
       "      <th>platelets</th>\n",
       "      <th>serum_creatinine</th>\n",
       "      <th>serum_sodium</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoking</th>\n",
       "      <th>time</th>\n",
       "      <th>DEATH_EVENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.00000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.00000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>60.833893</td>\n",
       "      <td>0.431438</td>\n",
       "      <td>581.839465</td>\n",
       "      <td>0.418060</td>\n",
       "      <td>38.083612</td>\n",
       "      <td>0.351171</td>\n",
       "      <td>263358.029264</td>\n",
       "      <td>1.39388</td>\n",
       "      <td>136.625418</td>\n",
       "      <td>0.648829</td>\n",
       "      <td>0.32107</td>\n",
       "      <td>130.260870</td>\n",
       "      <td>0.32107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11.894809</td>\n",
       "      <td>0.496107</td>\n",
       "      <td>970.287881</td>\n",
       "      <td>0.494067</td>\n",
       "      <td>11.834841</td>\n",
       "      <td>0.478136</td>\n",
       "      <td>97804.236869</td>\n",
       "      <td>1.03451</td>\n",
       "      <td>4.412477</td>\n",
       "      <td>0.478136</td>\n",
       "      <td>0.46767</td>\n",
       "      <td>77.614208</td>\n",
       "      <td>0.46767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25100.000000</td>\n",
       "      <td>0.50000</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>51.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>116.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>212500.000000</td>\n",
       "      <td>0.90000</td>\n",
       "      <td>134.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>262000.000000</td>\n",
       "      <td>1.10000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>70.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>582.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>303500.000000</td>\n",
       "      <td>1.40000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>203.000000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>95.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7861.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>850000.000000</td>\n",
       "      <td>9.40000</td>\n",
       "      <td>148.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>285.000000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age     anaemia  creatinine_phosphokinase    diabetes  \\\n",
       "count  299.000000  299.000000                299.000000  299.000000   \n",
       "mean    60.833893    0.431438                581.839465    0.418060   \n",
       "std     11.894809    0.496107                970.287881    0.494067   \n",
       "min     40.000000    0.000000                 23.000000    0.000000   \n",
       "25%     51.000000    0.000000                116.500000    0.000000   \n",
       "50%     60.000000    0.000000                250.000000    0.000000   \n",
       "75%     70.000000    1.000000                582.000000    1.000000   \n",
       "max     95.000000    1.000000               7861.000000    1.000000   \n",
       "\n",
       "       ejection_fraction  high_blood_pressure      platelets  \\\n",
       "count         299.000000           299.000000     299.000000   \n",
       "mean           38.083612             0.351171  263358.029264   \n",
       "std            11.834841             0.478136   97804.236869   \n",
       "min            14.000000             0.000000   25100.000000   \n",
       "25%            30.000000             0.000000  212500.000000   \n",
       "50%            38.000000             0.000000  262000.000000   \n",
       "75%            45.000000             1.000000  303500.000000   \n",
       "max            80.000000             1.000000  850000.000000   \n",
       "\n",
       "       serum_creatinine  serum_sodium         sex    smoking        time  \\\n",
       "count         299.00000    299.000000  299.000000  299.00000  299.000000   \n",
       "mean            1.39388    136.625418    0.648829    0.32107  130.260870   \n",
       "std             1.03451      4.412477    0.478136    0.46767   77.614208   \n",
       "min             0.50000    113.000000    0.000000    0.00000    4.000000   \n",
       "25%             0.90000    134.000000    0.000000    0.00000   73.000000   \n",
       "50%             1.10000    137.000000    1.000000    0.00000  115.000000   \n",
       "75%             1.40000    140.000000    1.000000    1.00000  203.000000   \n",
       "max             9.40000    148.000000    1.000000    1.00000  285.000000   \n",
       "\n",
       "       DEATH_EVENT  \n",
       "count    299.00000  \n",
       "mean       0.32107  \n",
       "std        0.46767  \n",
       "min        0.00000  \n",
       "25%        0.00000  \n",
       "50%        0.00000  \n",
       "75%        1.00000  \n",
       "max        1.00000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check Basic Statistic\n",
    "\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the basic statistics check above, we can know that:\n",
    "- Based on the distribution, almost all features appear to be fairly symmetrical. This is because the mean and median (50th percentile) values are close together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pie chart `DEATH_EVENT`\n",
    "yes = df['DEATH_EVENT'][df['DEATH_EVENT'] == 1].count()\n",
    "no  = df['DEATH_EVENT'][df['DEATH_EVENT'] == 0].count()\n",
    "\n",
    "# Make pie chart\n",
    "plt.figure(figsize=(6, 6))\n",
    "plt.pie([yes,no], labels=['DEATH', 'NO DEATH'], autopct=\"%1.1f%%\", startangle=100, colors=[\"lightcoral\",\"lightblue\"])\n",
    "plt.title(\"Proporsion of DEATH EVENT\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the diagram above, we can understand that the surviving patients outnumber the deceased patients. It also shows that the proportion of the two classes is not balanced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pie chart `sex`\n",
    "woman = df['sex'][df['sex'] == 1].count()\n",
    "man   = df['sex'][df['sex'] == 0].count()\n",
    "\n",
    "# Make pie chart\n",
    "plt.figure(figsize=(6, 6))\n",
    "plt.pie([woman,man], labels=['WOMAN', 'MAN'], autopct=\"%1.1f%%\", startangle=100, colors=[\"green\",\"blue\"])\n",
    "plt.title(\"Proporsion of SEX\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the diagram above, we can understand that the woman patients outnumber the man patients. It also shows that the proportion of the two classes is not balanced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pie chart `diabetes`\n",
    "diabetes      = df['diabetes'][df['diabetes'] == 1].count()\n",
    "no_diabetes   = df['diabetes'][df['diabetes'] == 0].count()\n",
    "\n",
    "# Make pie chart\n",
    "plt.figure(figsize=(6, 6))\n",
    "plt.pie([diabetes,no_diabetes], labels=['DIABETES', 'NO DIABETES'], autopct=\"%1.1f%%\", startangle=100, colors=[\"red\",\"lightgreen\"])\n",
    "plt.title(\"Proporsion of DIABETES\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the diagram above, we can know that patients who do not have diabetes are more than patients who have diabetes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pie chart `anaemia`\n",
    "anaemia      = df['anaemia'][df['anaemia'] == 1].count()\n",
    "no_anaemia   = df['anaemia'][df['anaemia'] == 0].count()\n",
    "\n",
    "# Make pie chart\n",
    "plt.figure(figsize=(6, 6))\n",
    "plt.pie([anaemia,no_anaemia], labels=['ANAEMIA', 'NO ANAEMIA'], autopct=\"%1.1f%%\", startangle=100, colors=[\"red\",\"lightyellow\"])\n",
    "plt.title(\"Proporsion of ANAEMIA\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the diagram above, we can know that patients who do not have anaemia are more than patients who have anaemia."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pie chart `high_blood_pressure`\n",
    "high_blood_pressure      = df['high_blood_pressure'][df['high_blood_pressure'] == 1].count()\n",
    "no_high_blood_pressure   = df['high_blood_pressure'][df['high_blood_pressure'] == 0].count()\n",
    "\n",
    "# Make pie chart\n",
    "plt.figure(figsize=(6, 6))\n",
    "plt.pie([high_blood_pressure,no_high_blood_pressure], labels=['HIGH BLOOD PRESSURE', 'NO HIGH BLOOD PRESSURE'], autopct=\"%1.1f%%\", startangle=100, colors=[\"red\",\"lightblue\"])\n",
    "plt.title(\"Proporsion of HIGH BLOOD PRESSURE\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the diagram above, we can know that patients who do not have high blood pressure are more than patients who have high blood pressure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hYtCgQWLXrl11ym6z2cTTTz8tunfvLsLDw0VYWJjo3r27+Pjjj6s8p5/fgtyQ5/JLLVu2FNOnT6/28/Rz2dnZ4je/+Y2Ii4sTBoNBdOvWTcydO7fa49XlFuTs7Gzx5ptvigEDBoikpCSh0+lEdHS0uPPOO8WSJUsq7VvXf/Oazl1cXCxefvll0bVrVxESEiLCw8NFv379xLx586q9rbemz1VKSoqIi4sTMTEx4tixY7XegvxLL730kgBQ49fl0qVLxciRI0V8fLzQ6XQiKipK3H777WL27NmioKDgus+dKFgoQnBskQgov032kUceqXI5hYiI5OCcFCIiIlIllhQiIiJSJZYUIiIiUiXe3UP0E07PIiJSF46kEBERkSqxpBAREZEqsaQQERGRKrGkEBERkSqxpBAREZEqsaQQERGRKrGkEBERkSqxpBAREZEqsaQQERGRKrGkEBERkSqxpBAREZEqsaQQERGRKrGkEBERkSqxpBAREZEqsaQQERGRKrGkEBERkSqxpBAREZEqsaQQERGRKrGkEBERkSqxpBAREZEqsaQQERGRKrGkEBERkSqxpBAREZEqsaQQERGRKrGkEBERkSqxpBAREZEqsaQQERGRKrGkEBERkSqxpBAREZEqsaQQERGRKrGkEBERkSqxpBAREZEqsaQQERGRKrGkEBERkSqxpBAREZEqsaQQERGRKrGkEBERkSqxpBAREZEqsaQQERGRKrGkEBERkSqxpBAREZEqsaQQERGRKrGkEBERkSqxpBAREZEqsaQQERGRKrGkEBERkSqxpBAREZEqsaQQERGRKrGkEBERkSrpZAcgIu8QTifcRUUQRUUQpaUQDgeE3Q44HOV/dzgAux3C6Sz/89o2h6N8m6JA0ekArRaKVgv8/O8/+xM6HRStFkpICBSzGRqzGYrZDCUsDEpoKBRFkf2pICI/xZJC5IeE1Qp3URHcxcUQRUUVZcRdXPy/YmKxyI4JaDTlZSUsrKK8aMLCyv+MioImLg6amBgoGg7qElFVihBCyA5BRNVzFxfDnZMDV05OpT9VUUA8RauFJiYGmrg4aOPioImPL/8zLg6KXi87HRFJxJJCpAJu4UaeOw85zhzkuMrfLJZCjP4kQ3Y0qZTISGjj48sLTEICtMnJ0MbGyo5FRD7Cyz1EEtiEDZecl3DReREXHRdxxXUFLrgq76QHEBkOFBZLyagGorAQzsJCIC2tYpsSFgZt8+bQJSdD26IFtElJ5XNniCjgcCSFyAcsbkt5IXFeRJYzCzmuHAjU/l9vyndhMJy56IOEfkyrhbZpU2iTk8uLS3IyNGFhslMRkQfw1w8iLyh1lyLTmYmLjvJikufOa9BxiuNNiD3j4XCBxuWCKyMDrowM2H/apImJga5VK+g6dICuTRvObSHyUywpRB6S78rHGccZnLGfwWXXZY8cMzcG4AyM+nPn5cGelwf7gQOATgddmzbQd+gAXYcO0ISHy45HRHXEyz1EDSSEwGXXZZyxn8FZx1nku/M9fo5WeWYM/CLT48cNZpqkJOg7dIC+QwdokpK4jguRirGkENWDUziR4czAWftZnHWcRZko8+r5tC4F0z4pBNxur54nWCnh4dC1b18+ytK2LSfgEqkMSwpRLYQQSHem44TtBM45zsEBh0/PP/0rLZQruT49Z1AyGqHv0gWG7t2hbdGCIyxEKsCSQlSDAlcBTthPIMWWghJRIi3HfRsjEHIiXdr5g5ESGQlDt27Qd+8ObVyc7DhEQYslhehnHMKBVHsqjtuPI8uZJTsOAGD4oVgkbuctPrJomzeHoWdP6G+4AYrBIDsOUVBhSSECcNF5ESdsJ5BqT/X55Zza9EqPwY3LzsqOQQZD+eWgXr2gS06WnYYoKLCkUNCyCRuO247jqO0oCtwFsuPUKL7EhLvneOaWZvIMTXw8jLfcAn337pxsS+RFLCkUdIrdxThkPYRjtmOwVyz/pW4z/m0BrDbZMegXlLAwGPr0geHmm6EJCZEdhyjgsKRQ0Mhx5uCA7QBO20/DDf+6pXfaUiO0GdmyY1BN9HoYevSA8dZboYmOlp2GKGCwpFDAu+C4gAPWA0h3+u8dMvd8H43wg+dkx6DaKAp0nTrBeNtt0DVvLjsNkd/jxVQKSC7hwmn7aRywHcBV11XZcRotL1YDLubuB4SAMyUFzpQUaJOTy8tKx45cc4WogTiSQgHFLdw4bj+OHyw/SF3bxNM6X47ELYsuyI5BDaCJjYVxwIDyW5hZVojqhSWFAoIQAmmONOy27PbKa+jIFmbXYeK//H9EKJhpEhNhGjwY+nbtZEch8hssKeT30h3p2GnZiSuuK7KjeNWM+S6gsFh2DGokbevWMA0eDF2zZrKjEKkeSwr5rWxnNnZadiLDmSE7ik9MWR0GQ9pF2THIQ3RdusB0553QxsbKjkKkWiwp5HfyXfnYZdmFNEea7Cg+NeaHGMTs4cqzAUWjKb91eeBAaMI5NZrol3h3D/mNUncp9lj24IT9hN+tc+IJubEKYmSHIM9yu2E/cAD2o0dhvOUWGPv1g2IyyU5FpBocSSHVE0LgiO0Idll3wS78Y4VYb2iVb8bAzzNlxyAvUkJCYBo6FIaePWVHIVIFlhRStSvOK9hcthnZLq62qnUpmPZJIeAOvlGkYKNt1Qohd98NbVyc7ChEUrGkkCrZhR27Lbtx2HYYAvwSvWb6Qg2UnDzZMcgXtFoY+/WDsX9/voghBS2WFFKdVHsqtpVtQ6kolR1Fde7bGIGQE/67vD/VnyY2FiGjRkHXqpXsKEQ+x5JCqlHoKsSWsi244OTKqjUZfigOiduD664mKqfv3h2mu+6CJjRUdhQin+EYIknnEi7st+7HPus+OOGUHUfVrsS6kCg7BEnhOHwYztOnYbrrLhh69JAdh8gnOJJCUuW78rG2dG3ArxbrKU1KQjByziXZMUgybatWCBk9GtoY3pROgY0lhaQ5ajuK7WXbOXpSTzM+tQA2m+wYJJvBgJCRI2Ho3l12EiKv0cgOQMHH4rZgZclKbC7bzILSAK74KNkRSA3sdliWLUPZt99CsLRSgOKcFPKp847z2FC6AWWiTHYUv1UWH4pwrulGP3EcPQpXZiZCfvUr6Jo3lx2HyKM4kkI+4RRObC3biuUly1lQGikvlv9tqTJ3fj5K586F9fvvwSv4FEg4kkJel+PMwbrSdch158qOEhCyY11oKTsEqY/bDdvmzXCePYvQ8eOhiYiQnYio0ThxlrzqoPUgdlp2wgWX7CgBw2zXY8K/cmTHIBVTQkIQMmYM9J06yY5C1CgcNyavcAgH1pSswXbLdhYUDysxOICIcNkxSMWExYKyr7+GZdUqCCcnp5P/YkkhjytwFeDroq9x2nFadpSAZY/nUD7Vzr5/P0rnzYO7uFh2FKIGYUkhjzprP4uFxQs5/8TLSuJMsiOQn3BdvIiS//wHrqws2VGI6o0lhTxmr2UvVpauhF3YZUcJeLmxshOQPxHFxSiZOxf248dlRyGqF5YUajSncGJ1yWrsse6RHSVoXIzl4l1UT04nLEuWwLplC29TJr/BW5CpUYrdxVhVsoqvveNjGRGlgEYDuN2yo5CfsW3fDldODkLHj4ei18uOQ3RdHEmhBrvkvISvir5iQZHApRUQsVGyY5CfcqakoGTOHLgLC2VHIboulhRqkLP2s/i2+FuuHiuRNd4sOwL5Mfflyyj5z3/gzMiQHYWoRiwpVG8nbCfwXel3fHFAyQrjOFRPjSNKS1E6fz7sR47IjkJULZYUqpcD1gPYULYBbnAuhGw5MZz8SB7gcsGydClsP/wgOwlRFSwpVGc7ynbge8v3smPQTzJjLLIjUACxrlkD6/f8/03qwpJCtXILNzaWbsR+237ZUehnss0WwGiUHYMCiG3zZlg3bpQdg6gCSwpdl1M4sbp0NY7buQiUGrnio2RHoABj27kTltWruZYKqQJLCtXIJmxYXrIcZxxnZEehGpTFh8qOQAHIvm8fLCtWQHAdHpKMJYWqVeYuwzfF3yDTmSk7Cl1HfqxWdgQKUI5Dh1D2zTcQLr6KOcnDkkJVWN1WLC1ZihxXjuwoVIvLMQ7ZESiAOU+cQNlXX0E4+HVGcrCkUCU2YcPSkqW46roqOwrVQXoMF9Mj73KmpaH0yy8hbHy9KPI9lhSq4BAOLC9ezmXu/UiJwQFEcOVZ8i7XhQvlRYUjKuRjLCkEoPwunhUlK3DJdUl2FKone3yk7AgUBFwZGShbtIhzVMinWFIILuHCqpJVnCTrp0riTLIjUJBwpqXBsmwZb08mn2FJCXJu4caa0jW44LwgOwo1UG6s7AQUTBzHjsG6Zo3sGBQkWFKCmBAC60rXcR0UP5cVY5cdgYKMfd8+WLdskR2DggBLSpASQmBj2UacdpyWHYUaKTOyFNDwvzL5lm37dtj27pUdgwIcv7MFqR2WHThhPyE7BnmAQ+uGiI2SHYOCkHXtWtiPHJEdgwIYS0oQOmY7hgO2A7JjkAdZ43gbMslhWb4cjtMckSXvYEkJMumOdGwp47XkQFMYr5cdgYKV242yxYvhTE+XnYQCEEtKEMl35WN16Wq4wRcNCzQ5MbwllCRyOlG6cCFcV7lSNXkWS0qQsLgtWF6yHDbBpa0DUWaMRXYECnZWK8oWLoSw8GuRPIclJQi4hAvflX6HQneh7CjkJdlmC2A0yo5BQc6dl4eyJUsg3BytJc9gSQkCm8o24aLzouwY5GWu+CjZEYjgPHsW1nXrZMegAMGSEuD2WfYhxZ4iOwb5QFlcqOwIRAAA+w8/wL5/v+wYFABYUgJYqj0Vu6y7ZMcgH8mP1cqOQFTBsmYNnBkZsmOQn2NJCVB5rjysL10vOwb5UHasQ3YEov9xuVC2eDHcJSWyk5AfY0kJQE7hxOqS1XDCKTsK+dCFmDLZEYgqEcXFKFu8mBNpqcFYUgLQ1rKtyHXnyo5BPlZicAARXHmW1MWVng7reo7qUsOwpASYk/aTOG4/LjsGSeKIj5QdgagK+969sB89KjsG+SGWlACS78rH5tLNsmOQRMVxXCuF1MmyahXc+fmyY5CfYUkJEE7hxOrS1XCAkyeDWV6sIjsCUfXsdpQtXcr5KVQvLCkBYrtlO666+LoZwe5ijF12BKIauTIyYNuxQ3YM8iMsKQEg1Z6KozZe7yUgM7IU0PC/NamXbds2OC9yBWyqG34383OFrkJsLN0oOwaphEPrhoiNkh2DqGZuNyxLl0I4eGmaaseS4sfcwo01pWtgB4f46X+scbwNmdTNnZvL1/ehOmFJ8WMHbQeR7cqWHYNUpijOIDsCUa3s+/fDcfq07BikciwpfirflY89lj2yY5AK5cTy7gnyD5YVK+AuLZUdg1SMJcUPCSGwqWwTl72namVGW2VHIKoTUVoKy8qVsmOQirGk+KGj9qO46OTseKreZXMpYOAlH/IPzlOnYN+/X3YMUimWFD9T7C7GzrKdsmOQmikKXPFRslMQ1Zllwwa4i4tlxyAVYknxM5tLN/NuHqpVWXyY7AhEdWez8UUIqVosKX7kpO0kzjvPy45BfiA/Vis7AlG9OI4dg/PcOdkxSGVYUvxEmbsM2yzbZMcgP5Edy0nV5H8sq1dDuFyyY5CKsKT4ia1lW2EVvGuD6uZCNG/rJP/jvnoVtl27ZMcgFWFJ8QMXHBeQ6kiVHYP8SInRAYRz5VnyP7bvv4e7oEB2DFIJlhSVcws3vi/7XnYM8kOO+EjZEYjqz+GAZe1a2SlIJVhSVO6Y/Rhy3bmyY5AfKo4zyo5A1CDOU6e4ZD4BYElRNZuwcel7arC8OEV2BKIGs65dC+HkBPBgx5KiYvss+2ARFtkxyE9djOF6OuS/3Pn5sH3PS93BjiVFpQpdhThkOyQ7BvmxzMhSQMP/4uS/bDt3wp2fLzsGScTvYCq1w7IDLnC9AGo4h9YNERMlOwZRw7lcsG7j+lDBjCVFhS46LyLNkSY7BgUAWzxvQyb/5jhyBK6cHNkxSBKWFJURQvCWY/KYwji97AhEjSMEbFu3yk5BkrCkqMwp+ylku7Jlx6AAkRMjZEcgajTHiRNwXb4sOwZJwJKiIi7hwi4rl4Qmz8mM4UspUGCwbt4sOwJJwJKiIin2FBS7i2XHoABy2VwKGAyyYxA1mjM1Fc6MDNkxyMdYUlTCLdz40fqj7BgUaBQFrvho2SmIPIKjKcGHJUUlTttPo9BdKDsGBaCy+BDZEYg8wnX+PJxnz8qOQT5U75KSk5OD3//+92jRogWMRiMSExMxbNgw7Ny5EwDQqlUrKIqCr776qsrHdu3aFYqiYN68eZW279q1CyNHjkR0dDRMJhO6deuG9957Dy5X5XVCFEXBsmXLKt53OByYPHkymjVrhmPHjlW7j6IoMJlMuHDhQqVjjRs3DjNmzKi07fLly3jiiSfQrl07mEwmJCQkoF+/fvjkk09QVlZWz89U3QkhsM+6z2vHp+BWEKuTHYHIY6xbtsiOQD5U75Jyzz334ODBg5g/fz5Onz6NFStWYODAgcjN/d+L4CUnJ2Pu3LmVPm7Pnj24fPkywsLCKm1funQpBgwYgObNm2PLli04efIknnjiCbz22muYNGkShKj+7oSysjKMGTMG+/btw44dO3DDDTfUmFlRFPzlL3+57vM6e/YsevbsifXr1+P111/HwYMHsXv3bjzzzDNYtWoVNm7cWNunpsHSHGnIc+d57fgU3LJj+PonFDhcmZl88cEgUq9fsQoKCvD9999j69atGDBgAACgZcuW6NOnT6X9pk6divfffx8ZGRlITk4GAMyZMwdTp07FZ599VrFfaWkpHn74YYwZMwb//ve/K7Y/9NBDSEhIwJgxY7Bo0SLcd999VXLcfffdKCkpwY4dO5CYmHjd3I8++ijee+89PP300zWWmZkzZ0Kn0+HHH3+sVKTatGmDsWPH1liWPIGjKORN6TFluEl2CCIPsm7dCn2HDrJjkA/UayTFbDbDbDZj2bJlsNlsNe6XkJCAYcOGYf78+QDKRz2+/vprPPDAA5X2W79+PXJzc/HUU09VOcbo0aPRoUMHLFy4sNL2y5cvVxSkbdu21VpQAKBfv34YNWoUnnvuuWofz83Nxfr16/HII49UGem5RlG884qyZ+1nkePiaorkPUVGOxDOlWcpcLgvXYLz/HnZMcgH6lVSdDod5s2bh/nz5yMqKgr9+vXDCy+8gCNHjlTZ94EHHsC8efMghMCSJUvQtm1b9OjRo9I+p38asuvcuXO15+vUqVPFPtc88cQTsNvt2LBhA6Kiouqc/Y033sDatWvxfTWvqpmWlgYhBDp27Fhpe1xcXEUxe/bZZ+t8rvrgKAr5giM+UnYEIo+y7d0rOwL5QIPmpGRlZWHFihUYPnw4tm7dil69elWZDHvtcsz27dsxZ86cKqMoP1efSymjRo3C6dOn8emnn9Yrd5cuXXD//ffXOJpSnR9++AGHDh1C165drzty1FDpjnRcdnEVRfK+kjij7AhEHuU8dYqvkBwEGnQLsslkwtChQ/HnP/8Zu3btwowZM/DSSy9V2ken02HatGl46aWXsHfvXkydOrXKcTr8dE0xJSWl2vOkpKRU7HPNtGnTMGfOHDz11FN477336pX7lVdewYEDByrd/QMA7dq1g6IoOHXqVKXtbdq0Qbt27RAS4p1bODmKQr6SG+udy5VE0gjB0ZQg4JF1Urp06YLS0tIq2x944AFs27YNY8eORXR01QWl7rrrLsTExODdd9+t8tiKFSuQmpqKyZMnV3ls+vTpmDdvHp555hm88847dc6ZnJyMRx99FC+88EKl25tjY2MxdOhQfPTRR9U+D2/IceYg05npk3MRXYpxyI5A5HH2Q4cgvDDKTepRr5KSm5uLO++8E1988QWOHDmCc+fOYfHixXj77bcxduzYKvt37twZV69erXI78jVhYWH49NNPsXz5cvz2t7/FkSNHcP78efz3v//FjBkzMGHCBNx7773Vfuy0adMwf/58PPfcc5g9e3adn8Pzzz+PrKysKrcUf/zxx3A6nbjpppvw9ddfIyUlBadOncIXX3yBkydPQqvV1vkcdXHYdtijxyO6nvTIEkDDtRspwNhssB86JDsFeVG9bkE2m8245ZZb8P777+PMmTNwOBxITk7Gww8/jBdeeKHaj4mNjb3uMSdMmIAtW7bgb3/7G/r37w+r1Yr27dvjxRdfxKxZs657V83UqVOh0Wgwbdo0uN3uOk1ujYmJwbPPPlslb9u2bXHw4EG8/vrreP7555GZmQmj0YguXbrgqaeewsyZM2s9dl1Z3Vacsp+qfUciD3Ho3BAxkVCu8ho+BRb7Dz/A0KeP1+7AJLkU4c0FQKhaB6wH8L2l6l1GRN40aUMETCnpsmMQeVzopEnQ/+LuTAoMHP/1MSEEjtiq3rJN5G2FcXrZEYi8wrZnj+wI5CUsKT6W7kznCwmSFDkxHDSlwOQ6fx6u7GzZMcgLWFJ87KjtqOwIFKQyY6yyIxB5DUdTAhNLig+VuktxznFOdgwKUpfNpYDBIDsGkVc4TpyAcPBW+0DDkuJDJ+wn4IZbdgwKVooCV3yU7BRE3mG3w3HypOwU5GEsKT4ihMBx23HZMSjIWeJCZUcg8hrHUV5ODzQsKT6S6czkhFmSLj+uXksjEfkV55kzcPto1XDyDZYUHzltP137TkRelh3jlB2ByHvcbjiOHZOdgjyIJcUH3MKNNEea7BhESI8pkx2ByKt4ySewsKT4QIYzA1bB2z9JviKjHQg3y45B5DWuixfhys2VHYM8hCXFB3iph9TEER8hOwKRVzmOcFXvQMGS4mUu4cJZx1nZMYgqlMSZZEcg8ipe8gkcLClexks9pDZ5sfxvT4HNnZ8PZ0aG7BjkAfxu5WW81ENqkxVjkx2ByOt4yScwsKR4ES/1kBqlR5YCGv7Xp8DmOHECQvBFNf0dv1N5UbozHTbB31pJXRw6N0RMpOwYRF4lysrgysqSHYMaiSXFi1LtqbIjEFXLFh8uOwKR1zlT+T3Y37GkeIlLuHDGcUZ2DKJqFcXy1ZAp8DnTuIimv2NJ8ZLLzsuwC7vsGETVyonlq3FT4HNlZcFdxlWW/RlLipekO9NlRyCqUWYMb4unICAER1P8HEuKl6Q7WFJIvS6ZSwEDL/lQ4GNJ8W8sKV5gEzZku7JlxyCqmaLAFR8lOwWR1znPnOGtyH6MJcULMh2ZEOB/ClI3S1yo7AhEXifKyuC6eFF2DGognewAgSiQ56PsmLMDO+fsRF56HgAgsVMihj09DF2GdkFpfinWvrkWJ7ecREFmAcJiw9Dt7m4Y+cJIhESE1HjMwysPY9fcXcg4nIGy/DI8te0pNO/WvNI+S19cin0L98EQasCol0bhpok3VTx2aNkh7Pt6Hx5e+LB3nnSAyo/Tga+HTMHAmZYGXfPmte9IqsOS4gWBPB8lqmkURr80GvFt4iGEwL6v9uG/v/4vntr6FCCAwkuFGPvqWCR2TEReRh4WP7kYRZeK8Jv5v6nxmPYyO1r3bY0e43rg61lfV3n82NpjOPDNAfzum98h52wOvnrsK3S6sxPMsWZYiiz47m/fYebSmd582gHpSowTybJDEPmAMzUVGDhQdgxqAJYUDyt2F6PAXSA7htfcMPyGSu/f/ae7sXPOTlz48QL6TuuLBz57oOKxuNZxuPvFu/H57z6Hy+mCVqet9pg333czACA3Pbfax7NPZ6Ndv3Zo0bMFWvRsgaUvLEXehTyYY81Y8dIK9PtNP0Q3j/bQMwweF6LL0Ft2CCIfcGVlwV1aCk1YmOwoVE+ck+JhgTyK8ktulxsHvjkAW5kNrW5uVe0+liILTOGmGgtKXTTt2hQZhzJQVlCGjEMZcFgciGsTh7N7ziLzSCbu+H93NPjYwazIZAfM/KZNwcF14YLsCNQAHEnxsGAoKVknsvD3YX+H0+qEIcyABz9/EImdEqvsV5JbgvXvrMdt029r1Pk6D+6M3hN7473B70Fv0mPqx1NhCDVg8ZOLMeWfU7Bzzk5s/892mGPMuPf9e5HUOalR5wsmjvhI6EtKZccg8jpnZib0XbrIjkH1xJLiQUIIZDgzZMfwuibtmuDpbU/DWmTFoRWH8OXML/HYyscqFRVrkRX/vu/fSOiYgOHPDm/0OUc8NwIjnhtR8f7at9aiw4AO0Oq1WP/uejy741kcX3ccX878Ek9tearR5wsWJfEmRJ+TnYLI+3iHj3/i5R4PKnAXwCIssmN4nc6gQ3ybeCT3SMbov4xGsxuaYdun2yoetxZb8a+J/4Ip3IQHP38QWn3DL/VUJ/t0Nn5c/CNGvjASqTtS0fbWtjDHmdFjXA9kHs6EtZirqdZVXiy/BVBwcF26BOHmy0H4G36H8qArriuyI0gh3AJOuxNA+QjKJ/d8Aq1Bi4e+fAh6k96z5xICi/64CONeGwej2QjhEnA5XQBQ8aeb34jqLCvGJjsCkW84HHBnc5FNf8OS4kFXnIFfUla+uhJndp1Bbnousk5kYeWrK5G2Iw03TbipoqDYy+yY/I/JsBZbUZRdhKLsIrhd/ysOr9/yOo6sOlLxfml+KTKPZiL7VPk3kCupV5B5NBNF2UVVzr/nsz0wx5or7jJqfUtrpG5Pxfl957Ht421I7JiI0EguUlZX6ZGlgKLIjkHkE05e8vE7nJPiQcEwklKSU4Ivfv8FirKLEBIRgqZdm+J3S36HjoM6InVHKi7sL59B/1rv1yp93J8P/RmxLWIBlJcQS9H/LosdW3MMCx9dWPH+Zw99BgAY9sywSvNQiq8UY/176zFr7ayKbS17t8TARwbi35P+DXOcGVM/nurx5xzIHDo3REwUlNx82VGIvM518SJw002170iqoQi+qIFHCCHwr4J/wQ677ChE9TJpfQRMJwP/rjQiTVwcwh95RHYMqgde7vGQAncBCwr5paI4z84bIlIr99WrEFZOrPcnLCkeEgyXeigw5cRwMJWCB+el+BeWFA/JdnLWOPmnzFj+ZknBw5WZKTsC1QNLiodwJIX81SVzKWAwyI5B5BNc1M2/sKR4gBACOc4c2TGIGkZR4I6Lkp2CyCdcly/LjkD1wJLiAZw0S/6uLJ5ry1BwEMXFEDYuYugvWFI8IMfFURTybwWxXDKJgofr6lXZEaiOWFI8oMBVIDsCUaNkxzhlRyDyGTdLit9gSfGAQneh7AhEjZIeE/gvjEl0DUuK/2BJ8YAid9XXmCHyJ4UmG2AOkx2DyCd4ucd/sKR4AC/3UCBwxEfKjkDkE+7cXNkRqI5YUhrJKZwoFaWyYxA1WmmcSXYEIp9w5+eDL1vnH1hSGqnIXQQBfrGT/8uNVWRHIPINpxOiuFh2CqoDlpRG4qRZChSXYhyyIxD5jDs/X3YEqgOWlEYqdLGkUGC4EFUCKBxNoeDAkuIfWFIaiSMpFCgcOjdEDCfPUnBw5+XJjkB1wJLSSCwpFEhs8RGyIxD5hLuQ37v9AUtKI/FyDwWSoji97AhEPiHKymRHoDpgSWkk3n5MgeRqjOwERL7BkuIfWFIaQQgBm+CraVLgyOTy+BQk3KX8BdMfsKQ0glVYZUcg8qis8FJAz0s+FPg4kuIfWFIagSWFAo6iwB0fLTsFkfc5HBBOvvq32rGkNAJLCgWisrhQ2RGIfIKjKerHktIILCkUiAridLIjEPkES4r6saQ0gtXNkkKBJzuGQ+AUHFhS1I8lpRE4kkKBKJ13+FCQ4B0+6seS0ggsKRSICk02wBwmOwaR13EkRf1YUhrBIvgbJwUmRzxfw4cCH0uK+rGkNILNzYXcKDCVxplkRyDyOmHhL5pqx5LSCFxtlgJVbqwiOwKR1wmXS3YEqgVLSiO44ZYdgcgrLsU4ZEcg8j43v4erHUtKIwgI2RGIvOJCVAmgcDSFApzg93C1Y0lpBJYUClQOnRsiJkp2DCLvYklRPZaURmBJoUBmizfLjkDkXbzco3osKY0g2MIpgBXFGmRHIPIufg9XPZaURuBICgWyq7H8+qbAJjiSonosKY3Au3sokGXGcEVlCnAcSVE9lhQiqiLCZsCA3VrZMYi8iyMpqsfXZG8EjqRQIOqWFYXe664CxVdkRyHyLpYU1WNJaQTOSaFAonUpuPvHaMT8cI7D4BQc+HWueiwpjcC7eyhQNCsMw+D1dmgunZUdhchnOHFW/VhSGkGr8Jo9+b8Bp+LQevMFwMGl8Cm4KDr+CFQ7/gs1gl7Ry45A1GBmmx6jtplgOpkmOwqRFIqBawGpHUtKIxjAL3DyT90u/TQ5tihHdhQieYxG2QmoFiwpjWBQWFLIv2hdCkbuj0Hs3rOcNEhBT2FJUT2WlEZgSSF/0rQoFEPWOaC5dEZ2FCJVYElRP5aURmBJIX9xx6lYtNmSAdjtsqMQqQZLivqxpDQCSwqpndmux6htITClcPSE6JdYUtSPJaURWFJIzbpejsLN63KBQk6OJaoOS4r6saQ0AksKqZHW/dPk2D2cHEt0XSwpqseS0ggsKaQ2ScWhGLrOCU0WL+8Q1YYjKerHktIILCmkJv1T49B2UzonxxLVEUuK+rGkNAJLCqlBmF2HUdtCEZLClWOJ6oMlRf1YUhohTAmTHYGCXJfsKPRZmwcUXpUdhcjvKCEhsiNQLVhSGiFMw5JCcmjcwMgDsYjbzcmxRA2hhIZC0fP119SOJaURtIoWIUoILMIiOwoFkcTiUAxd74L2IifHEjWUJipKdgSqA5aURjJrzLC4WFLIN25Pi0W7TZmAzSY7CpFfU1hS/AJLSiOZNWbkuLhYFnlXqE2H0d+HIuQER0+IPEETGSk7AtUBS0ojmRWz7AgU4DpnR+KWdflAASfHEnkKS4p/YElppAhthOwIFKA0bmDEwVjE7+LkWCJP45wU/8CS0kgRGpYU8rzE4lAM3eCCNpOXd4i8gSMp/oElpZFYUsjTbkuLRQdOjiXyKo6k+AeWlEZiSSFPCbXrMOr7MIQe5+gJkVcZjVBMJtkpqA5YUhopVBMKPfRwwCE7CvmxjlcicevaAqDgguwoRAGPoyj+gyXFAyK1kbjq4p0XVH8aNzD8UCya7DoHuN2y4xAFBc5H8R8sKR4Qp41jSaF6a1ISgmEb3NBm8PIOkS9poqNlR6A6YknxgDhtnOwI5GduPROLjpsyASsnxxL5mjYxUXYEqiOWFA9gSaG6CrHrMGpHGMKOcfSESBaWFP/BkuIBLClUFx1yInDr2kIo+ZwcSySNVgtNfLzsFFRHLCkeEKYJQ6gSijJRJjsKqZAiyifHJuzk5Fgi2TTx8VC0WtkxqI5YUjwkThuHdGe67BikMvElJgzfAE6OJVIJXurxLywpHsKSQr/U92wsOm28CFitsqMQ0U9YUvwLS4qHcF4KXRNi12HUTjPCjnL0hEhtWFL8C0uKh7CkEPDzybHnZUchlckqKsLLGzZgQ1oaLA4H2sTE4J9jx6Jns2YAgKiXX672414dOhSP9+tX7WPFNhv+tnkzVp08iaulpbgxMRFvjhiBXj8dEwA+3LkTH+zcCQB44vbb8dhtt1U89mNmJp787jtseugh6IJkngZLin9hSfGQGG0MtNDCBZfsKCSBIoBhh2ORuIOTY6mqAosFw/77X/Rv3RpLpk5FbFgYzubmIiokpGKfU08+WeljNqSl4bHlyzGmc+caj/v4ihVIuXIFn44fj6TwcHx95AjGffYZ9jzyCJpGRODY5ct4fcsWfDVlCgDgvgULcGfbtuiakACny4U/rFqFD0aPDpqCoomOhmI0yo5B9cCS4iFaRYtobTRXng1C8SUmDNuoQJfOyztUvb/v2IHmkZH4eNy4im2tfrHqaUJ4eKX3V588if6tW6NVTEy1x7Q4HFhx4gQWTJ6Mfq1aAQCeHzQIa0+fxpx9+/CnwYORevUquiYkYECbNgCArgkJFdv+sWsXbmvZstKoS6DTcBTF77CkeFCSNoklJcjcci4WnTdeBCycHEs1W3PqFO5s1w7TFy3CzvPnkRQRgYduvhnTe/eudv8rJSVYn5qKT35Wan7J6XbDJQRMusrfxkN0OuxOL5/E3yUhAWm5ucgoKIAAkJabi85NmuBcXh6+PHgQW//f//PUU/QLvNTjf1hSPKi5vjmO2o/KjkE+YHKUT441H+HoCdXufH4+5uzbh0duvRV/7N8fBy9exLNr1kCv1WJKjx5V9l946BDMBgNGX+dST7jRiD7Nm+PtbdvQIS4OTcxmLDl6FD9kZqLNT6MvHePj8ZfBgzH+888BAC8NHoyO8fEYO38+Xhk6FJvT0vDm1q3QabV4c/jwihGZQKVt2lR2BKonlhQPaqYLnmHTYNbuagT6rS2CkndedhTyE24h0LNpU/xlyBAAQPekJJy4cgVzf/yx2pLyxcGDmHjjjTDp9dc97qe/+hUeWb4cnd97D1pFQfekJEy44QYcunSpYp8Hbr4ZD9x8c8X7Cw4dgtloRJ/kZNz04YfY8tvf4mJRER5csgSHZ82CURegPxY0GuhatJCdguopQL8a5QjThCFaE418d77sKOQNQmDY4Xgk7TjLybFULwnh4ej4i6XYO8bHY2VKSpV9d124gNTcXMyZOLHW47aOicHq3/wGpXY7im02JIaH4zeLF1eZ73JNbmkp3tq6Fat/8xv8mJmJdrGxaPvTm8PtRlpuLromJDTsSaqctlkzKAaD7BhUTxrZAQINR1MCU1xpCH69IhRJ29NYUKje+iYnIy03t9K2tNxcJEdGVtn38wMH0CMpCd3qMX8izGBAYng4CiwWbEpLw8iOHavd7/l16zDz1lvRLDISLiHg+NnXstPthiuAv7Z1rVvLjkANwJLiYc31zWVHIA/rcz4GoxbkQ3fhUu07E1Vj5q23Yl9mJt7dvh1nc3Ox+MgRzN+/Hw/16VNpvyKrFctPnMD9vXpVe5wx8+fj33v3Vry/KS0NG1NTcT4/H1vOnMGoefPQIS4OU3v2rPKxW86cwZncXDz806WfXk2bIvXqVWxITcW8H3+EVlHQPi5w13tiSfFPvNzjYRxJCRxGhxajd4XDfPis7Cjk53o1a4Yv7rsPr27ahLe3bUPL6Gi8MXw47r3xxkr7fXvsGIQQuKdbt2qPcy4vD7ll/3sh0yKrFa9s2oSsoiJEh4RgTOfO+NPgwdD/Yt0Ti8OBp1evxpwJE6DRlP9u2iwyEm+PGIFHli2DQafDJ+PHI6SWOTB+S6eDtjl/gfRHihBCyA4RaOYXzkeBu0B2DGqEtrnhuH1tCZRczi8i8ne6Nm0QNm2a7BjUABxJ8YJmumYosBfIjkENIQTuOhKPpjvOAS6uHkwUCLS81OO3WFK8oLmuOY7bj8uOQfUUW2rC8E0a6M+nyY5CRB7E+Sj+iyXFC5rpOS/F39x0IQY3bLgElFlkRyEiTzIaoU1Kkp2CGoglxQvCNeGI0cQgz50nOwrVwujUYtTuCIQf5ORYokCka9UKioY3svorlhQvaWtoizwrS4qatc2NwO3riqFcPSc7ChF5CS/1+DfWSy9pq28rOwLVRAgMPRKL/l9dhHKVd+8QBTJdW34v9mccSfGSBF0CzIoZJaJEdhT6mZgyI0Zs0kJ/ji8MSBToNPHx0AbwAnXBgCMpXtTWwAavJr3TYzBmQTH057JkRyEiH9B36SI7AjUSS4oX8ZKPOhidWvzq+2h0W3YW+NlqnUQU2FhS/B8v93hRM10zmBQTrMIqO0rQap0XjjvWlnJyLFGQ0cTFQdukiewY1EgcSfEijaJBaz1nlkshBIYci8OAhVlQrvIuK6Jgw1GUwMCRFC9rp2+HFHuK7BhBJbrMiJGbddCf5cqxRMFK37Wr7AjkASwpXtZC3wJ66OGAQ3aUoNArPQY3brgMlHLuCVGw4qWewMGS4mU6RYcW+hY44+Atr96kd2owek8kIg5w5ViiYMdLPYGDc1J8oIOhg+wIAa1VnhlTFgMRBzg5lohYUgIJR1J8oK2+Le/y8ZLBx+OQvO084HTKjkJEKqCJjYU2IUF2DPIQjqT4gFbRopOhk+wYASXKYsCU78KQvCmNBYWIKnAUJbCwpPhIVyNnmntKz4xojFtQAsOZi7KjEJHK6G+8UXYE8iBe7vGROG0cErQJyHZly47it/RODUbtjULkfk6OJaKqtC1a8LV6AgxLig91NXZFdhlLSkO0zA/HgHVl0FxhQSGi6hl69ZIdgTyMl3t8qIOhA3TshfV25/E4DFp4CZorubKjEJFamUycjxKA+BPTh4yKEe0N7bkCbR1FWQwYucUAQxpXjiWi6zN06wZFr5cdgzyMIyk+1tXACbR10SMzGuMWlsKQlik7ChH5AUPv3rIjkBdwJMXHmumbIUoThQJ3gewoqqR3aXD33ihE/ci5J0RUN9rkZK6NEqA4kiJBFyOvm1anRYEZk5doWFCIqF4Mffp47FgzZsyAoih48803K21ftmwZFEWptM3lcuH9999Ht27dYDKZEB0djREjRmDnzp21nmfbtm248847ERMTg9DQULRv3x7Tp0+H3W4HAGzduhWKoiA6OhpWa+WFQPft2wdFURqcZ968eYiKiqq0LSUlBcnJyZg4cSLsdnuVfebNmwdFUTB8+PBKH1dQUABFUbB169ZK27ds2YJRo0YhPj4eJpMJbdu2xX333Yft27fX+rn5OZYUCboaunIC7S8MOhGHOxdchib7quwoRORHFLMZ+s6dPXpMk8mEt956C/n5+TXuI4TApEmT8Oqrr+KJJ55ASkoKtm7diuTkZAwcOBDLli2r8WNPnDiB4cOH46abbsL27dtx9OhRfPjhhzAYDHC5XJX2DQ8Px9KlSytt++9//4sWLVp4LM++ffvQv39/DB8+HF9//TUMBkO1++l0OmzcuBFbtmyp8VgA8PHHH2Pw4MGIjY3F119/jVOnTmHp0qW47bbb8Ic//OG6H1vlnPXamzwiVBOKzobOOGo/KjuKdJFWI0Zu0cOYysmxRFR/hl69oGi1Hj3mkCFDkJaWhjfeeANvv/12tfssWrQIS5YswYoVKzB69OiK7f/+97+Rm5uLhx56CEOHDkVYWFiVj12/fj0SExMrHbtt27ZVRikAYPr06ZgzZw4mT54MALBYLPjqq6/w+OOP469//Wuj82zevBljx47FzJkz8dZbb1338xIWFoZ7770Xzz33HPbu3VvtPunp6Zg1axZmzZqF9957r9JjN954Ix5//PHrnuOXOJIiSS9TLyhQat8xgN14MRrjF5TAmMrJsUTUABoNDDfd5PHDarVavP766/jwww+RmVn996cFCxagQ4cOlQrBNU8++SRyc3OxYcOGaj82MTERly5dqtOlj2nTpuH7779Heno6AOCbb75Bq1at0OsXa8I0JM/SpUtx9913409/+lOtBeWal19+GUePHsWSJUuqffybb76Bw+HAM888U+3jv7xEVRuWFEmitFFoq28rO4YUepcGY/fEoNc354CSUtlxiMhP6bt0gSY83CvHHj9+PHr06IGXXnqp2sdPnz6NzjVcZrq2/fTp09U+PnHiREyePBkDBgxAUlISxo8fj48++ghFRUVV9m3SpAlGjBiBefPmAQDmzJmDBx54oNF5SkpKMHHiRDz99NN49tlnq/246jRt2hRPPPEEXnzxRTired2006dPIyIiAomJiRXbvvnmG5jN5oq3o0frfhWBJUWim0ye/w1A7ZILwjB5iQbRP3ByLBE1jvH22716/Lfeegvz589HSkr1a1sJIRp0XK1Wi7lz5yIzMxNvv/02mjVrhtdffx1du3bFpUuXquz/wAMPYN68eTh79ix2796NqVOnNjpPSEgIhg4div/85z81Pr+aPPvss8jJycGcOXOqffyXoyXDhg3DoUOH8N1336G0tLTKvJvrYUmRKEGXgOa65rJj+MzAlDgMXniFk2OJqNF0nTp5/bbjO+64A8OGDcPzzz9f5bEOHTrU+MP92vYOHTpc9/jNmjXDtGnT8NFHH+H48eOwWq3417/+VWW/ESNGwGKx4MEHH8To0aMRGxvb6DxarRbLli1Dr169MGjQoHoVlaioKDz//PN45ZVXUFZWVumx9u3bo7CwEJcvX67YZjab0a5dO7Rs2bLO57iGJUWy3qbAX4AowmrA5LXhaLUhDXA4ZMchogBguuMOn5znzTffxMqVK7F79+5K2ydNmoTU1FSsXLmyyse8++67iI2NxdChQ+t8nujoaCQlJaG0tOolcJ1Oh/vvvx9bt26t9lJPQ/MYjUZ8++23uPnmmzFo0CCcOHGiznkfe+wxaDQafPDBB5W2T5gwAXq9vs5zXGrDu3ska6VvhVhtLHJdgfm6NN2yotB73VWg+IrsKEQUIHTt20OblOSTc3Xr1g1Tp07FP/7xj0rbJ02ahMWLF2P69OmYPXs2Bg8ejKKiIvzzn//EihUrsHjx4mrv7AGATz/9FIcOHcL48ePRtm1bWK1WfPbZZzh+/Dg+/PDDaj/mr3/9K55++ulqR1Eak8doNOKbb77BxIkTMWjQIGzevBldu9a+MrrJZMIrr7yCRx55pNL2Fi1a4N1338UTTzyBvLw8zJgxA61bt0ZeXh6++OILAOWjOHXFkRQV6G0MvNEUrUvBmL0x6P3NBaC4RHYcIgogRh+Nolzz6quvwu12V9qmKAoWLVqEF154Ae+//z46duyI/v3748KFC9i6dSvGjRtX4/H69OmDkpIS/O53v0PXrl0xYMAA7NmzB8uWLcOAAQOq/RiDwYC4uLga745pTB6DwYAlS5bgtttuw6BBg3Ds2LFaPydA+e3Rbdq0qbL9sccew/r165GTk4MJEyagffv2GDlyJM6dO4e1a9eiW7dudTo+ACiioTN/yGPcwo25hXNRIgLjh3mzwjAMXmeH5nKO7ChEFGB0bdogbNo02THIRziSogIaRYNepl617+gHBpyMxdAFV1hQiMgrfD2KQnKxpKhEN2M3hGu8c7+/L5htekxaF47W689wciwReYW2ZUvoGnCHCPkvlhSV0Ck69DX1lR2jQbpdisKEhRaYTmXIjkJEAcxXd/SQerCkqEhnQ2fEaqufua1GWpeCMT/EoPeSC0BRYMynISJ10jZvDl01kzQpsLGkqIiiKOgX0k92jDppVhiGqd/qELPnLMC510TkZZyLEpy4TorKtNa3RjNdM1x0XpQdpUZ3nIpFmy0ZgN0uOwoRBQFdmzbQt28vOwZJwJEUFbo9xLuvR9FQZrsek9ZHoM26MywoROQbigLTsGGyU5AkHElRoURdItrq2+KM44zsKBW6Xo7CzWtzgSLeWkxEvmO46SZomzSRHYMkYUlRqX4h/XDOcQ5uuGvf2Yu0bgUjf4xB7F7OPSEi31JMJhgHDpQdgyTi5R6VitZGo4uhi9QMTYtCMfVbPWL3nGFBISKfMw4cCE1oqOwYJBFHUlSsb0hfnLKfggO+Xxyt/+lYtN3MybFEJIcmLg6Gm2+WHYMk40iKioVpwtAnpI9Pz2m26zFpQwTaruXkWCKSxzRsGBQNf0QFO34FqFwvYy/EaeN8cq4ulyMxYaEVppR0n5yPiKg6uvbtoW/XTnYMUgGWFJXTKBrcGXonFFT/8tyeoHUrGLUvFn0WpwOFxV47DxFRrTQamO66S3YKUgmWFD+QpEvCDYYbvHPs4lBMWapH3G5OjiUi+Qw33wxtnG9Gj0n9OHHWT/QL7YczjjMoE2UeO+btqbFot4mTY4lIHRSzGSbeckw/w5EUP2FUjBgQOsAjxwqz63Dfxgi0W8PJsUSkHiGjRkExmWTHIBVhSfEjHQwd0FLXslHH6Hw5EhMX2hFygpNjiUg99DfeCH3HjrJjkMqwpPiZQaGDoGvAVTqNG7j7x1jcsjgdKCzyQjIiooZRzGaEDB8uOwapEEuKn4nURtZ77ZTE4lBMXWZE/C5OjiUi9QkZNQpKSIjsGKRCnDjrh3obe+OU/RRyXbm17tsvLRbtN2UCNpsPkhER1Q8v89D1cCTFD2kUDYaFDoMW2hr3CbXpcN+mSLRffYYFhYhUiZd5qDYsKX4qXhePviF9q32sU3Yk7v3ajpDjF3ycioio7niZh2rDyz1+rLexN847zuOi8yKA8smxww/Gosnuc4DbLTkdEVHNeJmH6oIjKX5MURTcFXoXDDAgoSQEU5cb0WTnGRYUIlI1XuahuuJIip+L0EZgrOtOhH25lHNPiMgvhIwezcs8VCccSQkATeM6ctiUiPyC4dZboe/QQXYM8hMsKQEi5O67oYmJkR2DiKhG2hYtYBoyRHYM8iMsKQFCMRgQOmECoK35tmQiIlmUsDCETpgARcMfO1R3/GoJINqkJP6WQkTqoygIveceaMLDZSchP8OSEmCMfftCx/kpRKQixkGDoGvdWnYM8kMsKQEodPx4aOLjZccgIoKuQwcYb79ddgzyUywpAUgxGhE6aRJv8SMiqZSoKISOGwdFUWRHIT/FkhKgtDExCJ04EeAkNSKSQatF2MSJ/GWJGoU/wQKYrnVrmIYNkx2DiIKQafhwaJs2lR2D/BxLSoAz9ukDQ+/esmMQURDR9+gB4003yY5BAYAlJQiYRoyAtmVL2TGIKAjo2rZFyOjRsmNQgGBJCQKKVovQe++FEhUlOwoRBTBNYiJCJ07kgm3kMfxKChKa0FCETZoEGAyyoxBRAFIiIxE2ZQoUo1F2FAogLClBRJuQgNDx42XHIKJAYzIhbOpUrihLHseSEmT0nTrxjh8i8hytFmH33QctF5AkL2BJCULGvn1hHDBAdgwiCgAhY8dC16qV7BgUoFhSgpRp4EAY+vaVHYOI/Jhp8GAYunWTHYMCGEtKEAsZNgz6nj1lxyAiP2S46Sa+Jg95HUtKkAsZPRr6rl1lxyAiP6Lr1AmmESNkx6AgwJIS5BRFQcj48dC1ayc7ChH5AV2nTgidMIFroZBP8KuMKhZ746q0RHQ9FQVFq5UdhYIESwoBABS9HmGTJ0OblCQ7ChGpkK5zZxYU8jmWFKqgGI0I/fWvoeF6B0T0MywoJAtLClWiCQ1F2P33Q5OQIDsKEalARUHhHBSSQBFCCNkhSH2E1YrSBQvgysiQHYWIJGFBIdlYUqhGwuFA2aJFcKalyY5CRD6m69IFoffcw4JCUrGk0HUJlwuWpUvhOH5cdhQi8hF9ly4IYUEhFWBJoVoJIWBdtQr2AwdkRyEiL9P36IGQ0aNZUEgVWFKozqwbN8K2c6fsGETkJcaBA2Hii4+SirCkUL3YduyAddMm2TGIyJM0GoSMGQND9+6ykxBVwpJC9Wbfvx+W774D+KVD5P9MJoTdey90rVvLTkJUBUsKNYjjxAmULVsGOByyoxBRAymRkQibOhVaLuBIKsWSQg3munQJpV9/DVFYKDsKEdWTJikJYZMnQxMeLjsKUY1YUqhR3KWlKFu8GK4LF2RHIaI60rVvX75Im8EgOwrRdbGkUKMJlwvWNWtg379fdhQiqoWhd2+YRo7kLcbkF1hSyGNsP/4I65o1gNstOwoR/ZJGA9OQITDeeqvsJER1xpJCHuW8cAFlixdDlJbKjkJEP1HMZoROmABdy5ayoxDVC0sKeZy7sBClX30F9+XLsqMQBT1tixYInTCBE2TJL7GkkFcIhwOWFSvgOHZMdhSioGXo2xemoUM5/4T8FksKeZVt715YN24EnE7ZUYiCh9GI0DFjoO/SRXYSokZhSSGvc125grJvv4U7O1t2FKKAp23atPzyTnS07ChEjcaSQj4hnE5YN26Efe9e2VGIApahb1+YhgyBotXKjkLkESwp5FOOM2dgWbYMoqREdhSigKGEhCBk7FjoO3aUHYXIo1hSyOfcZWWwrFwJ58mTsqMQ+T1d584IGTkSGrNZdhQij2NJIWns+/fDsm4dX6SQqAGU0FCEjBwJfdeusqMQeQ1LCknlys2F5dtv4crKkh2FyG/ob7gBphEjoAkNlR2FyKtYUkg64XLBtnMnbN9/z1uVia5DMZsRcvfd0HfqJDsKkU+wpJBquPPzYVm9Gs60NNlRiFRH3707QoYNgxISIjsKkc+wpJDqOE6cgGXtWojiYtlRiKRTwsMRMmoU9B06yI5C5HMsKaRKwm6HdcsW2H/4ga+qTMFJUWDo2bN8WXuTSXYaIilYUkjVXNnZsKxaBVdmpuwoRD6jbdUKIcOGQZuYKDsKkVQsKaR6Qgg4Dh6EdeNGCItFdhwir9HExMA0dCgnxhL9hCWF/Ia7rAzWDRvgOHwY4JctBRKTCaY77oChTx8uaU/0Mywp5HdcOTmwbt7MFWvJ/ykKDL17wzhoENc8IaoGSwr5LefFi7Bt3gzn2bOyoxDVm65tW5juugvaJk1kRyFSLZYU8nvOc+dg3byZk2vJL2iaNIFpyBDo27eXHYVI9VhSKGA4Tp2CdfNmuK9ckR2FqApt06Yw9u8PXceOUBRFdhwiv8CSQgFFCAHHsWOwbd0Kd16e7DhE0LZoAWP//tC3ayc7CpHfYUmhgCTcbjgOHoRt1y6WFZJC16YNjHfcAV3LlrKjEPktlhQKaEIIOE+ehG3XLs5ZIZ/QdexYflmnWTPZUYj8HksKBQ1nejpsu3bBeeqU7CgUaBQF+i5dYOzfH9qEBNlpiAIGSwoFHVdeHuw//AD7oUOAzSY7DvkxJSwMhp49YejVC5roaNlxiAIOSwoFLWG3w37oEOw//AB3bq7sOORHtK1bw9i7N3SdOnGFWCIvYkmhoCeEgPPMGdgPHiy/FORyyY5EKqSEhkLfowcMvXtDGxMjOw5RUGBJIfoZYbXCcfw47EeOwJWeLjsOqYC2ZUsYeveGvnNnKDqd7DhEQYUlhagG7vx82I8cgePIEd7GHGSU8HDou3YtHzWJi5MdhyhosaQQ1YEzMxOOw4fhOH4cwmKRHYe8QImMhL5zZ+i7dIG2eXOuCkukAiwpRPUgXC44U1NhP3oUzrQ0wG6XHYkaQRMdDd1PxYTrmhCpD0sKUQMJlwuu9HQ4UlPhTE2F++pV2ZGoDjSxsf8bMUlKkh2HiK6DJYXIQ9z5+RWFxXn+POB0yo5EAKDVQpucDF2rVtB36sTF1oj8CEsKkRcIhwPOc+fgTE2FIy0NoqBAdqTgodFA26wZdK1aQde6NbTJybwrh8hPsaQQ+YArNxeuzEy4MjLgvHgR7uxsgP/1PENRoE1KgvanUqJr0QKKwSA7FRF5AEsKkQTCbocrKwuuzEw4MzPhysyEKC2VHcsvKKGh0CYmQpOQAF3LltC1bAnFZJIdi4i8gCWFSCXcBQXlhSUjA65Ll+C+ejW4b3dWFGhiYioKiTYxsfzv4eGykxGRj7CkEKmYu6wM7tzc8rerV+HKy4P76tXyxeUCaPl+JTwcmuhoaJs0+V8hSUiAotfLjkZEErGkEPkhIQREQQFcP5UXd14eRGkp3GVlEKWl5W8Wizrmveh00ERElBeRa39GR0MTFVXxxomtRFQdlhSiACWEgCgrK3/7qbhUKjFOZ3mJcbsBlwvC7S7/+09v4qftFds0GihGI2AwQDEaoej1/3v/2jaDAfjpT8VkgiY8HEpIiOxPBRH5KZYUIiIiUiWN7ABERERE1WFJISIiIlViSSEiIiJVYkkhIiIiVWJJISIiIlViSSEiIiJVYkkhIiIiVWJJISIiIlViSSEiIiJVYkkhIiIiVWJJISIiIlViSSEiIiJVYkkhIiIiVWJJISIiIlViSSEiIiJVYkkhIiIiVWJJISIiIlViSSEiIiJVYkkhIiIiVWJJISIiIlViSSEiIiJVYkkhIiIiVWJJISIiIlViSSEiIiJVYkkhIiIiVWJJISIiIlViSSEiIiJVYkkhIiIiVWJJISIiIlViSSEiIiJVYkkhIiIiVWJJISIiIlViSSEiIiJVYkkhIiIiVWJJISIiIlViSSEiIiJVYkkhIiIiVWJJISIiIlViSSEiIiJVYkkhIiIiVWJJISIiIlViSSEiIiJVYkkhIiIiVWJJISIiIlViSSEiIiJVYkkhIiIiVWJJISIiIlViSSEiIiJVYkkhIiIiVWJJISIiIlViSSEiIiJVYkkhIiIiVWJJISIiIlViSSEiIiJVYkkhIiIiVfr/jFQ+Ocm+N9MAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pie chart `smoking`\n",
    "smoking      = df['smoking'][df['smoking'] == 1].count()\n",
    "no_smoking   = df['smoking'][df['smoking'] == 0].count()\n",
    "\n",
    "# Make pie chart\n",
    "plt.figure(figsize=(6, 6))\n",
    "plt.pie([smoking,no_smoking], labels=['SMOKING', 'NO SMOKING'], autopct=\"%1.1f%%\", startangle=100, colors=[\"lightgreen\",\"lightcoral\"])\n",
    "plt.title(\"Proporsion of SMOKING\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the diagram above, we can know that patients who do not smoke are more than patients who smoke."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Count Plot `DEATH_EVENT` based on `sex`\n",
    "\n",
    "death = sns.countplot(data=df, x=\"sex\", hue=\"DEATH_EVENT\")\n",
    "\n",
    "for container in death.containers:\n",
    "    death.bar_label(container)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the diagram above, we can see that more man patients die than woman patients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Count Plot `DEATH_EVENT` based on `smoking`\n",
    "\n",
    "death_smoke = sns.countplot(data=df, x=\"smoking\", hue=\"DEATH_EVENT\")\n",
    "\n",
    "for container in death_smoke.containers:\n",
    "    death_smoke.bar_label(container)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the diagram above, we can see that the number of patients who do not smoke and die is equal to the number of patients who smoke and do not die."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Count Plot `DEATH_EVENT` based on `diabetes`\n",
    "\n",
    "death_diabetes = sns.countplot(data=df, x=\"diabetes\", hue=\"DEATH_EVENT\")\n",
    "\n",
    "for container in death_diabetes.containers:\n",
    "    death_diabetes.bar_label(container)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the diagram above, we can see that patients who do not die with non-diabetics are more than patients who die with diabetes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Count Plot `DEATH_EVENT` based on `anaemia`\n",
    "\n",
    "death_anaemia = sns.countplot(data=df, x=\"anaemia\", hue=\"DEATH_EVENT\")\n",
    "\n",
    "for container in death_anaemia.containers:\n",
    "    death_anaemia.bar_label(container)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the diagram above, we can see that patients who do not die with non-anaemia are more than patients who die with anaemia."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Count Plot `DEATH_EVENT` based on `high_blood_pressure`\n",
    "\n",
    "death_hbp = sns.countplot(data=df, x=\"high_blood_pressure\", hue=\"DEATH_EVENT\")\n",
    "\n",
    "for container in death_hbp.containers:\n",
    "    death_hbp.bar_label(container)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the diagram above, we can see that patients who do not die with non-high blood pressure are more than patients who die with high blood pressure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 3000x2500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create Histogram\n",
    "\n",
    "plt.figure(figsize=(30,25))\n",
    "\n",
    "plt.subplot(4,3,1)\n",
    "sns.histplot(df['age'], kde=True, bins = 30)\n",
    "plt.title('Histogram of Age')\n",
    "\n",
    "plt.subplot(4,3,2)\n",
    "sns.histplot(df['creatinine_phosphokinase'], kde=True, bins = 30)\n",
    "plt.title('Histogram of Creatinine Phosphokinase')\n",
    "\n",
    "plt.subplot(4,3,3)\n",
    "sns.histplot(df['ejection_fraction'], kde=True, bins = 30)\n",
    "plt.title('Histogram of Ejection Fraction')\n",
    "\n",
    "plt.subplot(4,3,4)\n",
    "sns.histplot(df['platelets'], kde=True, bins = 30)\n",
    "plt.title('Histogram of Platelets')\n",
    "\n",
    "plt.subplot(4,3,5)\n",
    "sns.histplot(df['serum_creatinine'], kde=True, bins = 30)\n",
    "plt.title('Histogram of Serum Creatinine')\n",
    "\n",
    "plt.subplot(4,3,6)\n",
    "sns.histplot(df['serum_sodium'], kde=True, bins = 30)\n",
    "plt.title('Histogram of Serum Sodium')\n",
    "\n",
    "plt.show;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FEATURES SKEWNESS\n",
      "-----------------\n",
      "\n",
      "Skewness Age                         :  0.4230619067286355\n",
      "Skewness Creatinine Phosphokinase    :  4.463110084653752\n",
      "Skewness Ejection Fraction           :  0.5553827516973213\n",
      "Skewness Platelets                   :  1.462320838275779\n",
      "Skewness Serum Creatinine            :  4.455995882049029\n",
      "Skewness Serum Sodium                :  -1.0481360160574988\n"
     ]
    }
   ],
   "source": [
    "# Check Skewness \n",
    "\n",
    "print('FEATURES SKEWNESS')\n",
    "print('-----------------')\n",
    "print('')\n",
    "print('Skewness Age                         : ',df['age'].skew())\n",
    "print('Skewness Creatinine Phosphokinase    : ',df['creatinine_phosphokinase'].skew())\n",
    "print('Skewness Ejection Fraction           : ',df['ejection_fraction'].skew())\n",
    "print('Skewness Platelets                   : ',df['platelets'].skew())\n",
    "print('Skewness Serum Creatinine            : ',df['serum_creatinine'].skew())\n",
    "print('Skewness Serum Sodium                : ',df['serum_sodium'].skew())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the histogram and skewness level, we can tell that the data tends to be not normally distributed in the features `creatinine_phosphokinase`, `platelets`, `serum_creatinine` and `serum_sodium`. In `age` and `ejection_fraction` features, the data tends to be normally distributed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlation feature checking\n",
    "\n",
    "fig,ax = plt.subplots(figsize=[20,10])\n",
    "\n",
    "corr = df.corr()\n",
    "\n",
    "ax = sns.heatmap(corr,annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 299 entries, 0 to 298\n",
      "Data columns (total 13 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   age                       299 non-null    float64\n",
      " 1   anaemia                   299 non-null    int64  \n",
      " 2   creatinine_phosphokinase  299 non-null    int64  \n",
      " 3   diabetes                  299 non-null    int64  \n",
      " 4   ejection_fraction         299 non-null    int64  \n",
      " 5   high_blood_pressure       299 non-null    int64  \n",
      " 6   platelets                 299 non-null    float64\n",
      " 7   serum_creatinine          299 non-null    float64\n",
      " 8   serum_sodium              299 non-null    int64  \n",
      " 9   sex                       299 non-null    int64  \n",
      " 10  smoking                   299 non-null    int64  \n",
      " 11  time                      299 non-null    int64  \n",
      " 12  DEATH_EVENT               299 non-null    int64  \n",
      "dtypes: float64(3), int64(10)\n",
      "memory usage: 30.5 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the correlation test of all features against the `DEATH_EVENT` feature using the `pearson` method, we can conclude that :\n",
    "\n",
    "1. There is a positive correlation between the `DEATH_EVENT` feature and the feature:\n",
    "- `age` of 0.25, which means a strong positive correlation.\n",
    "- `anaemia` of 0.066, which means a weak positive correlation.\n",
    "- `creatinine_phosphokinase` of 0.063, which means a weak positive correlation.\n",
    "- `high_blood_pressure` of 0.079, which means a weak positive correlation.\n",
    "- `serum_creatinine` of 0.29, which means a strong positive correlation.\n",
    "\n",
    "2. There is a negative correlation between the feature `DEATH_EVENT` and the feature:\n",
    "- `diabetes` of -0.0019, which means a weak negative correlation.\n",
    "- `ejection_fraction` of -0.27, which means a strong negative correlation.\n",
    "- `platelets` of -0.049, which means a weak negative correlation.\n",
    "- `serum_sodium` of -0.2, which means a strong negative correlation.\n",
    "- `sex` of -0.0043, which means a weak negative correlation.\n",
    "- `smoking` of -0.013, which means a weak negative correlation.\n",
    "- `time` of -0.53, which means a strong negative correlation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## V. Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### V.I. Split between X (Features) and y (Target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>anaemia</th>\n",
       "      <th>creatinine_phosphokinase</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>ejection_fraction</th>\n",
       "      <th>high_blood_pressure</th>\n",
       "      <th>platelets</th>\n",
       "      <th>serum_creatinine</th>\n",
       "      <th>serum_sodium</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoking</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42.0</td>\n",
       "      <td>1</td>\n",
       "      <td>250</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>213000.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>46.0</td>\n",
       "      <td>0</td>\n",
       "      <td>168</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>271000.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>124</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65.0</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>327000.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>116</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  anaemia  creatinine_phosphokinase  diabetes  ejection_fraction  \\\n",
       "0  42.0        1                       250         1                 15   \n",
       "1  46.0        0                       168         1                 17   \n",
       "2  65.0        1                       160         1                 20   \n",
       "\n",
       "   high_blood_pressure  platelets  serum_creatinine  serum_sodium  sex  \\\n",
       "0                    0   213000.0               1.3           136    0   \n",
       "1                    1   271000.0               2.1           124    0   \n",
       "2                    0   327000.0               2.7           116    0   \n",
       "\n",
       "   smoking  time  \n",
       "0        0    65  \n",
       "1        0   100  \n",
       "2        0     8  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Splitting between X and y\n",
    "\n",
    "X = df.drop(['DEATH_EVENT'], axis=1)\n",
    "y = df['DEATH_EVENT']\n",
    "\n",
    "X.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(299, 12)\n",
      "(299,)\n"
     ]
    }
   ],
   "source": [
    "# Show shape on X and y\n",
    "\n",
    "print(X.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### V.II. Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this feature selection, I will use all features that have strong correlation base on correlation checking above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>ejection_fraction</th>\n",
       "      <th>serum_creatinine</th>\n",
       "      <th>serum_sodium</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42.0</td>\n",
       "      <td>15</td>\n",
       "      <td>1.3</td>\n",
       "      <td>136</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>46.0</td>\n",
       "      <td>17</td>\n",
       "      <td>2.1</td>\n",
       "      <td>124</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65.0</td>\n",
       "      <td>20</td>\n",
       "      <td>2.7</td>\n",
       "      <td>116</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53.0</td>\n",
       "      <td>20</td>\n",
       "      <td>1.4</td>\n",
       "      <td>139</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50.0</td>\n",
       "      <td>20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>134</td>\n",
       "      <td>186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>63.0</td>\n",
       "      <td>60</td>\n",
       "      <td>1.2</td>\n",
       "      <td>145</td>\n",
       "      <td>147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>45.0</td>\n",
       "      <td>60</td>\n",
       "      <td>1.0</td>\n",
       "      <td>136</td>\n",
       "      <td>186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>70.0</td>\n",
       "      <td>60</td>\n",
       "      <td>0.9</td>\n",
       "      <td>138</td>\n",
       "      <td>186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>53.0</td>\n",
       "      <td>60</td>\n",
       "      <td>1.0</td>\n",
       "      <td>139</td>\n",
       "      <td>215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>50.0</td>\n",
       "      <td>62</td>\n",
       "      <td>0.8</td>\n",
       "      <td>140</td>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>299 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age  ejection_fraction  serum_creatinine  serum_sodium  time\n",
       "0    42.0                 15               1.3           136    65\n",
       "1    46.0                 17               2.1           124   100\n",
       "2    65.0                 20               2.7           116     8\n",
       "3    53.0                 20               1.4           139    43\n",
       "4    50.0                 20               1.0           134   186\n",
       "..    ...                ...               ...           ...   ...\n",
       "294  63.0                 60               1.2           145   147\n",
       "295  45.0                 60               1.0           136   186\n",
       "296  70.0                 60               0.9           138   186\n",
       "297  53.0                 60               1.0           139   215\n",
       "298  50.0                 62               0.8           140   192\n",
       "\n",
       "[299 rows x 5 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove feature \n",
    "\n",
    "X.drop(['anaemia', 'creatinine_phosphokinase', 'diabetes', 'high_blood_pressure', 'platelets', 'sex', \n",
    "        'smoking'], axis=1, inplace=True)\n",
    "\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### V.III. Split between Train-set and Test-set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will divide the data into 2, namely data for train and data for test. In this case, I will use an 80/20 ratio with 80% being the data for train and 20% being the data for test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train size :  (239, 5)\n",
      "Test size :  (60, 5)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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>ejection_fraction</th>\n",
       "      <th>serum_creatinine</th>\n",
       "      <th>serum_sodium</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>55.0</td>\n",
       "      <td>38</td>\n",
       "      <td>1.1</td>\n",
       "      <td>136</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>70.0</td>\n",
       "      <td>60</td>\n",
       "      <td>1.3</td>\n",
       "      <td>137</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>60.0</td>\n",
       "      <td>60</td>\n",
       "      <td>1.1</td>\n",
       "      <td>131</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      age  ejection_fraction  serum_creatinine  serum_sodium  time\n",
       "205  55.0                 38               1.1           136     6\n",
       "91   70.0                 60               1.3           137    90\n",
       "281  60.0                 60               1.1           131    10"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Splitting between train set and test set\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.2, random_state = 20)\n",
    "\n",
    "print('Train size : ', X_train.shape)\n",
    "print('Test size : ', X_test.shape)\n",
    "X_train.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### V.IV. Handling Outlier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we will check for outliers in the numeric features/columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make function to show histogram & boxplot\n",
    "\n",
    "def diagnostic_plots(df, variable):\n",
    "    # Define figure size\n",
    "    plt.figure(figsize=(16, 4))\n",
    "\n",
    "    # Histogram\n",
    "    plt.subplot(1, 2, 1)\n",
    "    sns.histplot(df[variable],kde=True, bins=30)\n",
    "    plt.title('Histogram')\n",
    "\n",
    "    # Boxplot\n",
    "    plt.subplot(1, 2, 2)\n",
    "    sns.boxplot(y=df[variable])\n",
    "    plt.title('Boxplot')\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### V.III.I. Check Feature Scewness & Outlier on X using Histogram & Boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JYt34n7W648JWuiTc8dcDAAAAAABQj7n1iMbvv/9eV199taKjo2UwGPTxxx9Xme9wODRt2jRFRUXJx8dHAwcO1N69e90TFjWm0FahF1bu1YWzv9FzK/eqwFahpMgAPTeii76b1F9j+ibI30QHjsare3wzfXnfhRrRM1YOh/Tq9/v14NdWeYXFuzsaAAAAAADV5taisaioSJ07d9a8efNOO3/OnDl6/vnn9fLLL2vdunXy8/PToEGDuKFGA2WrqNRr3+/XhbO/0b9X7FF+aYXaNPfXS6O66cv7LtSQLi3k5cHZ/Gga/EyeempYJ716c3eF+nnrUF6FokbP1Z58oxwOjm4EAAAAADQ8bj1sbPDgwRo8ePBp5zkcDs2dO1ePPPKIhgwZIkl66623FBERoY8//lgjRoyoy6hwgcPh0OdbMzV72S4dPlkiSWoV5qf7B7bRVZ2iufYimrTL2keqa1wz/f2NH5SaKW3LlU5uPqJB7SM5shcAAAAA0KDU28PHDhw4oKysLA0cONA5LSgoSL169dKaNWvOuJ7NZlN+fn6VB9wn9VCOhs7/SePe26zDJ0sUEWjSnL910lcT+mlIlxaUjICk8ACT/tm3mU4se1EeBocOnyzRonUWHfj17usAAAAAADQE9bZozMrKkiRFRERUmR4REeGcdzqzZs1SUFCQ8xEbG1urOXF6mXklGrtok4bNX6PNllz5entowsC2+vaB/hreI1aenCINVGEwGFT48zJdElmucH+TSsor9enPR/XD3uOqtHMqNQAAAACg/mt0bc+UKVOUl5fnfGRkZLg7UpNSVmHXy6vSdcm/V+mLrZkyGKQbesTquwf66/6BbeTrzamgwJ8J8JKG94hR55ggSdImS67e35ih3OIyNycDAAAAAODP1dvWJzIyUpKUnZ2tqKgo5/Ts7Gx16dLljOuZTCaZTKbajofT+HGfVdM+2a7047+c7tk9vplmDmmv9tFBbk4GNCyeHkb1b9dcsSG+WrEzW8cKbHpvfYYuTmqudpEB7o4HAAAAAMBp1dsjGhMSEhQZGamVK1c6p+Xn52vdunVKSUlxYzL80fECm8a9t1mj/rNO6ceLFOrnrX9d31kf3JVCyQi4IDHcX6N6xSk6yKyySruW7cjS12nZKq+0uzsaAAAAAACncOsRjYWFhdq3b5/z+YEDB7RlyxaFhIQoLi5O48eP1+OPP642bdooISFBU6dOVXR0tK699lr3hYaTw+HQh5uO6LHPdyqvpFxGg3Rz73hNvKydgny83B0PaBQCzF4a1i1G6w7kaP3BHO04mq/MvFJd2TFKIX7e7o4HAAAAAICTW4vGjRs3asCAAc7nEydOlCSNHj1aCxcu1IMPPqiioiLdeeedys3NVd++fbVs2TKZzWZ3RcavMnKK9c8l2/TDXqskqX10oGYP66QOLTiCEahpRqNBKYmhimnmo2U7spRTVKbFGywamByhthGcSg0AAAAAqB/cWjT2799fDseZ76ZqMBg0c+ZMzZw5sw5T4c9U2h16a81BPb18t4rLKuXtadSEgW11+4UJ8uJO0kCtig3x1Y3nx2nZ9iwdzi3R0u1ZyswtVd82YfIwGtwdDwAAAADQxNXbm8Gg/tmbXaAHP9yqzZZcSdL5LUM0a1hHJYb7uzcY0IT4mTx1XdcWWrP/hDYeOqkth3OVlV+qKzpGujsaAAAAAKCJo2jEX6qotOvlVel6fuU+lVXa5W/y1OTBSRp1fpyMHEVV59LS0tyybk2zWCyyWq0ujVGf3k9dMhoNuqB1mKKCzFq+M1tZ+aV6b32Gugfz7xEAAAAA4D4UjfhTB6xFmvj+FudRjBcnNdfj13ZQdLCPe4M1Qfk5xyVJN910k8tjFRYWujyGKywWi5KSk1VSXFwj47n7/bhLq3B/3Xi+SV9sy9TxAptWH/dUUJ8Rsv/JJSkAAAAAAKgtFI04o2X7ivT2kh9UUl6pAJOnpl/TXkO7tZDBwFFT7lBSmC9JuvKuh9WuU/dqjZG2fpWWvvmcSktLazLaObNarSopLtaoyU8rIi6x2uPUl/fjTkE+XhrePUar9hzX9qP5Cr7wJj3+w0m9kVTGXakBAAAAAHWKohGnKKmUmv9tul7d9EuxldIqVP8a3lktOIqxXgiNjldMm/bVWjfbkl7DaVwTEZdY7fci1b/34y6eHkZdkhwhc2mO1mdXakuWdNXzP2jeqG7qGtfM3fEAAAAAAE0EtwlGFXuzC/R1ppd8EnvIyyg9cmWy3r29FyUj0ADE+9uV9fY/FOXvoaN5pRr+yhq9vfaQHJxKDQAAAACoAxSNkCTZKiq1fEeWvtyepTK7QbasfXr60jDdfmErbvgCNCDlxw9qzsAwXd4+UuWVDk39eLv+8f7PKimrdHc0AAAAAEAjR9EIZeaVaNE6i3ZlFcggqV1gpbLefkBxQV7ujgagGvy8jZp/UzdNGZwko0H6aPMRXffSjzp0osjd0QAAAAAAjRhFYxPmcDi04WCOPkg9rPzSCgWaPfW37jHqEFwp2SvcHQ+ACwwGg+66KFHv3N5LYf7e2pVVoKteWK2vd2a7OxoAAAAAoJGiaGyiimwVWrLliH5KPyGHQ2rb3F839opTNNdiBBqVPolh+nzcheoWF6yC0grd/tZG/Wv5blXauW4jAAAAAKBmcdfpJujgiSJ9tSNbJeWV8jQadFG7cLWPCpTBwLUYgeqyWCyyWq3VXj8tLa0G01QVGWTW4jtT9OSXaVr400G9+O0+/Xw4V8+N6KoQP+9aeU1Xt4ckhYWFKS4uroYSAQAAAABqG0VjE1Jpd+indKs2WXIlSWH+3hrcIarWigagqbBYLEpKTlZJcbHLYxUWFtZAolN5exo1/Zr26hoXrIc+3KYf9lp19Qur9dKobuocG1yjr1VT28PH11e70tIoGwEAAACggaBobCLySsr15bZMHSuwSZI6xQTpwtZh8vTg7HnAVVarVSXFxRo1+WlFxCVWa4y09au09M3nVFpaWsPpqhrSpYWSIgP193dSdcBapOtfXqNHrzlPN54fV2NHNdfE9si2pOvd2ZNktVopGgEAAACggaBobAL2Hy/UVzuzZauwy+Rp1KXnRSgx3N/dsYBGJyIuUTFt2ldr3WxLeg2nObN2kQH65N4L9MD7P+urndl6eMl2bTqUqyeu6yCzl0eNvY4r2wMAAAAA0PBwOFsjZrc7tHqfVZ9tzZStwq7IQLNu7BVHyQhAgWYvvXJzd02+PElGg/ThpsMa+tJPspxw/fRvAAAAAEDTRNHYSJVUSh9tPqLUQyclSV1ig/W37jEKNHu5ORmA+sJgMOju/ol6Z0wvhfp5a2dmvq564Qd9syvb3dEAAAAAAA0QRWMjZIrtqJWZXjqSWyIvD4Ou6BCpi9qGy8PIXaUBnKpP6zB9fl9fdY0LVn5phW5buFHPfLVblXaHu6MBQK2bPn26DAZDlUdSUpJzfmlpqcaOHavQ0FD5+/tr2LBhys7mFzIAAACnQ9HYiNjtDn2UVqiIEY/LZjco1M9bI3vGqU1EgLujAajnooJ89H93puj/pcRLkp7/Zp9uWbBeJ4vK3JwMAGpf+/btlZmZ6XysXr3aOW/ChAn67LPP9MEHH2jVqlU6evSohg4d6sa0AAAA9Rc3g2kk8krK9Y/3t+jrtAIZjB6K96vUlT1j5cVdpQGcJW9Po2YO6aCuccGa8tE2/bDXqqteWK35N3VTp5hgd8cDgFrj6empyMjIU6bn5eXp9ddf16JFi3TxxRdLkhYsWKDk5GStXbtWvXv3ruuo9Z7BXiGOhwfqmMMh2St++droKRk4kw2oa4bf/g2CorEx2JNdoLveTtUBa5G8jFLWF89p6J13UzICqJbrusYoKTJQd7+TqoMnivW3+Ws0c0h7jTg/zt3RAKBW7N27V9HR0TKbzUpJSdGsWbMUFxen1NRUlZeXa+DAgc5lk5KSFBcXpzVr1pyxaLTZbLLZbM7n+fn5tf4e6gv/Le+5OwIAAHAjisYaZLFYZLVaXRojLCxMcXFn/8P8l9sy9cAHP6u4rFItgn00vqefhs9aIYPhbpdyAGjakqMC9cm9ffXABz9rxc5sPfTRNm2ynNTMIR1k9vJwdzwAqDG9evXSwoUL1a5dO2VmZmrGjBm68MILtX37dmVlZcnb21vBwcFV1omIiFBWVtYZx5w1a5ZmzJhRy8kBAADqH4rGGmKxWJSUnKyS4mKXxvHx9dWutLS/LBsr7Q7966vdmv9duiTpgtahemFkNx3cvd2l1weA3wT5eOmVm7pr/qp0/fur3Xp/42FtPZynF2/sqtbNufYrgMZh8ODBzq87deqkXr16KT4+Xu+//758fHyqNeaUKVM0ceJE5/P8/HzFxsa6nLW+MpvNWrp0qbtjAE1WaWmprrvuOknSkiVLZDab3ZwIaNqa+r9BisYaYrVaVVJcrFGTn1ZEXGK1xsi2pOvd2ZNktVr/tGjMLS7TuPc264e9vxw9eWe/VnpwUDt5ehh1sFqvDACnZzQaNHZAa3WOCdb9izdrV1aBrn7hR02/5jwN7xErA9cAAtDIBAcHq23bttq3b58uvfRSlZWVKTc3t8pRjdnZ2ae9puNvTCaTTCZTHaStHwwGQ7VLWQA1y2w28+8RgFtRNNawiLhExbRpX2vj7zyar7ve2aiMnBKZvYya87fOuqZzdK29HgBIUt82YVo6/kJN/L+ftXqfVZM/3Kbv91r15HUdFeTj5e54AFBjCgsLlZ6erptvvlndu3eXl5eXVq5cqWHDhkmSdu/eLYvFopSUFDcnBQAAqH+4W0gD8tnPRzV0/o/KyClRXIivltxzASUjgDrTPMCst247Xw8NTpKn0aAvtmbqyud/UOqhk+6OBgDV9sADD2jVqlU6ePCgfvrpJ1133XXy8PDQyJEjFRQUpDFjxmjixIn69ttvlZqaqltvvVUpKSnccRoAAOA0OKKxAbDbHfr3it2a9+0v12Ps1zZcz4/oomBfbzcnA9DUGI0G/f2iRPVKCNF9izcrI6dEw19Zo4mXttXfL0qUh5FTqQE0LIcPH9bIkSN14sQJhYeHq2/fvlq7dq3Cw8MlSc8++6yMRqOGDRsmm82mQYMG6aWXXnJzagAAgPqJorGeK7RVaPziLfo6LVuSdFe/Vnrw8iR+mAfgVl3jmumL+y7UI0u269Ofj+rp5bv14z6r/j28s7ujAcA5Wbx48Z/ON5vNmjdvnubNm1dHiQAAABouTp2uxzJyijXspZ/0dVq2vD2NemZ4Z025IpmSEUC9EGj20nMjuujpv3WSj5eHfko/oUHPfq9Vh4rdHQ0AAAAA4AYc0VhPrUk/oXveTdXJ4nI1DzDplZu7q2tcM3fHAoAqDAaDru8Rq27xzTTx/Z/1c0aunluXp7Brp8hW6e50AAAAAIC6RNFYDy1cna4vMjNV6ZASm3npoQuC5LAe0Cbrgb9cNy0trQ4SAkBVieH++vDvKZr/Xbrmfr1Hfu0u0IpMhxRaqMRwf7flslgsslqtLo0RFhamuLi4GkoEAAAAAI0XRWM9knviuEIu/bs+PeorSSra+Z2+Xfq8vqkoO+exCgsLazoeAPwpTw+jxl3SRlHK0X2LNkrhLfX51ky1iwhQv7Zh8vWu2285FotFScnJKil27VRuH19f7UpLo2wEAAAAgL9A0VhPlJRXamNRsAK6XSU5HGofXKl2g/rIcHmfcxonbf0qLX3zOZWWltZSUgD4c62aeSnzzfG6dMaH2lvgod3ZBTqUU6SL2oSrXWSADIa6uc6s1WpVSXGxRk1+WhFxidUaI9uSrndnT5LVaqVoBAAAAIC/QNFYD5wotOnTn48qX36y24rVMaBEA3v0rtZY2Zb0Gk4HANVQWaGOzSrVtV1Lfb0rWycKy7R8Z7Z2ZRXo4qTmCvTxqrMoEXGJimnTvs5eDwAAAACaKu467Wb7rYV6f+Nh5ZdWyKQyZb0zSWGeNnfHAoAaERlk1siecUpJDJWH0aBDOcV6e+0hpR46qUq7w93xAAAAAAA1iKLRTRwOh1IPndRnP2eqrNKumGAfddFBlVsPuTsaANQoD6NB57cM0ajz49Qi2EcVdodW77Nq0TqLLDmuXT8RAAAAAFB/1Ouicfr06TIYDFUeSUlJ7o7lsgq7XSvSsrV63y93Qu0QHahru7aQlyrdnAwAak8zP28N69ZCA5Oby8fLQznFZVqy+Yi+3JapgtJyd8cDAAAAALio3l+jsX379vr666+dzz09633kP1VcVqHPt2YqM69UBkn92oarc0xQnd0cAQDcyWAwqH10kBLD/bV2/wltPZynvccKdcBapJ4tQ9Q1LlheHvX6d2AAAAAAgDOo962dp6enIiMj3R2jRhwvsOmzrUdVUFohb0+jrugQqfhQP3fHAoA6Z/byUP92zdU+Okjf7T6mo3mlWrP/hLYeyVVKq1AFcvlGAAAAAGhw6n3RuHfvXkVHR8tsNislJUWzZs1SXFzcGZe32Wyy2f53M5X8/Py6iPmX9h8v1LIdWSqvdCjIx0vXdI5WiJ+3u2PVKovFIqvVWu3109LSaiyLK2PVZA6gLrj6mQ0LC/vT/2drUniASX/rHqM92YX6Kd2q/NIKfZ12TIFenjK36iGHg8YRAAAAABqKel009urVSwsXLlS7du2UmZmpGTNm6MILL9T27dsVEBBw2nVmzZqlGTNm1HHSM/vtpi8/pp+QJMU089GVHaNk9vJwc7LaZbFYlJScrJJi12/0UFhYWO1183OOS5Juuukmt+YA6kJNfd59fH21Ky2tzspGg8GgdpEBSmzup62H87T+QI7yy6WI66fr0e9yNC0kR+cnhNRJFgAAAABA9dXronHw4MHOrzt16qRevXopPj5e77//vsaMGXPadaZMmaKJEyc6n+fn5ys2NrbWs55ORaVd3+w6prSsAklSxxZBuqhtuDyMjf96jFarVSXFxRo1+WlFxCVWa4y09au09M3nVFpaWu0cJYW/HNF65V0Pq12n7m7LAdSFmvi8Z1vS9e7sSbJarXVWNP7G02hUt7hmOi8qUN9u3qvdJyu1/bg0/JU1uqB1qMYPbKueLSkcAQAAAKC+qtdF4x8FBwerbdu22rdv3xmXMZlMMplMdZjq9IpsFfpi2683fTFIF7UNV+eYYHfHqnMRcYmKadO+WutmW9JrLEdodHy9yAHUBVc+7/WB2ctDHZtV6pvZd2rMv9/XNwdL9OO+E/px3xr1bR2m+we2oXAEAAAAgHqoQd3as7CwUOnp6YqKinJ3lD91vMCmxRsylJlXKpOnUdd2adEkS0YAcEVlgVV3dQ/Stw/018jz4+RpNGj1Pquuf3mN/jb/J321I0t2O9dwBAAAAID6ol4XjQ888IBWrVqlgwcP6qefftJ1110nDw8PjRw50t3RzuhIsUHvb8xQoa1Cwb5euqFnrOJCfN0dCwAarJhmvpo1tOOvhWOsvD2M2njopO58O1UDn12lxestKi2vdHdMAAAAAGjy6nXRePjwYY0cOVLt2rXT8OHDFRoaqrVr1yo8PNzd0U7hcDgU2Pt6rbV6qcLuUGyIj27oEatmvo37ztIAUFdiQ3w1a2gnrZ48QHf3T1SA2VP7jxfpoY+2qe/sb/XMij3KyuNaqgAAAADgLvX6Go2LFy92d4SzUlpeqefW5arZRaMlSZ1jgtSvTbiMTeCmLwBQ15oHmjX58iSNHdBai9db9PrqA8rMK9XzK/dq3rf7dHn7SN2cEi8vB6dVAwAAAEBdqtdFY0MxfvEWfW8plcNeqW6hDvVr19zdkQCg0fM3eer2C1tpdJ+WWr4jS2+tOaT1B3L0xbZMfbEtU3FBngrofrVsnFUNAAAAAHWCorEG3N0/Uev3H1PaG/9Uq8nT3R0HqBNpaWluWRf4Iy8Po67qFK2rOkVrV1a+3lpzSEs2HZElr0IhA+/Sl0ccSizP1HnRgYoL8ZXRwNHmAAAAAFAbKBprQOfYYM2/orl6T9/q7ihArcvPOS5Juummm1weq7Cw0OUxgN9LigzUk9d11EODk/TCJ2v14tJNMkW21t5jhdp7rFD+Jk+1iwxQu4gAhfl7y0DpCAAAAAA1hqKxhnh58MMqmoaSwnxJ0pV3Pax2nbpXa4y09au09M3nVFrKjTtQOwLNXhrcxk+PjBiv255dIqtnmHZlFajQVqHUQyeVeuikQny91TbSX+0iAhTMjbsAAAAAwGUUjQCqJTQ6XjFt2ldr3WxLeg2nAc4s2NuhDm2aq2/rMB2wFml3doEOnihWTnGZ1u7P0dr9OWoeYFK7yAC1bR4gfzPfGgEAAACgOvhpCgDQJHh6GNUmIkBtIgJkq6hU+vEi7c4qUMbJYh0rsOlYgU0/7LUqMtCsxOZ+Sgz3d3dkAAAAAGhQKBoBAE2OydND50UF6ryoQBWXVWhvdqF2ZxcoM69UWfm/PH7cd0IBnl4KvvBm7c0pUxe7Q0Yjl8kAAAAAgDOhaAQANGm+3p7qHBuszrHBKrRVaP/xQqUfL9Lhk8UqqDAoqM8Nmvz1CT27/hsNPK+5+rdtrj6tQ+XrzbdQAAAAAPg9fkoCAOBX/iZPdYoJVqeYYNnKK5W6Y4+++3GNQjv0U1Z+qd5Za9E7ay3y9jCqV6sQ9W/XXAPahSshzI87WAMAAABo8igaAQA4DZOXh+L87LJ+MlufPXy9igNi9M2uY/p213EdyS3RD3ut+mGvVY99LsWF+Kp/u3D1axOuXq1CFGD2cnd8AAAAAKhzFI04o7S0NLesC7gDn/eq6sv2qC850vfsUnKyQUPjpOtig3SkwE+bMm3alGnTTmuZLDnFemvNIb215pCMBqlNiJc6RZjUKcJbbUO8FRURrri4OJcyWCwWWa1Wl99LWFiYy1lqQk28n5p4L/UlBwAAANAYUDTiFPk5xyVJN910k8tjFRYWujwGUJv4vFdVX7ZHQ8ph8PaROa6TfFr1kLllZ3k1i9buE+XafaJcH+yU7GWlKs9cqvEjBuuqnq2VHBl4zjeVsVgsSkpOVklxcbXfy298fH21Ky3NrcVYTb0fV99LfckBAAAANBYUjThFSWG+JOnKux5Wu07dqzVG2vpVWvrmcyotLa3JaECN4/NeVX3ZHg05R1FFmY6VGnW81KBjpUbZvM0yxXfV/DVZmr8mSyF+3urdKkTntwxRz4QQJUUGyuMviker1aqS4mKNmvy0IuISq/1+si3penf2JFmtVreWYjXxfmrivdSXHAAAAEBjQdGIMwqNjldMm/bVWjfbkl7DaYDaxee9qvqyPRpqjna//ulwOLRzZ5ref3ehLr3pXu06UaGcojJ9uS1LX27LkiQFmD3VI76ZeiaEqFdCiDq2CJa3p/G040bEJVZ7e9RH9eX91JccAAAAQENH0QgAQC0xGAwK8naoYOMneviVaerYuYt+zsjVugM5Wn8gR6mHTqqgtELf7j6ub3f/cpq2ydOoLrHB6pXwyxGPnWOD3fsmAAAAAOAsUTQCAFBHvDyM6tEyRD1ahmjsAKmi0q5dWQVadyBHGw7kaP3BHOUUlWndgRytO5AjSTIYpJgAT4UOvl/7C40yFdgU6ud9ztd5BAAAAIDaRtEIAICbeHoY1aFFkDq0CNKYvglyOBxKP16kDQd/OeJx46EcZeSUKCO/Qv6dLtXmHGnzeou8PAxqHmBWZJBZkYG//Olv4ls6AAAAAPfipxIAAOoJg8Gg1s391bq5v0ae/8uNRayFNn34XaoemjNfrQcMV16Fp8oq7TqSW6IjuSXOdf28PRQeYFLzALPCA0wKDzAp0Owpg4EjHwEAAADUDYpGAADqsTB/k3pGm5X7wzvqN2KoWrRuq5yiMmXllyorr1RZ+aU6UVimorJKFZ0o1sETxc51TZ5GZ+nY3N8ke7kkw+lvNAMAAAAArqJoBACgATEYDAr1NynU36T20UGSpPJKu6yFNh3Lt+l4oU3HC2yyFtpkq7Dr8MkSHT7525GP3oqb+F+N/TRDbdaeVFyQl2KDPBUX6KlwPw8Zz/Lox7CwMMXFxdXSOwQAAADQUFE0AgDQwHl5GBUV5KOoIB/ntEq7QzlFZTpWUKrjBTYdK7DpWF6xKj29lVkqZVpKJZU6l7eXlajcmqHyE4dUdtyi8hMWVZw4rIr845LDXuX1fHx9tSstjbIRAAAAQBUUjQAANEIeRoPztOnfbFz5qd5/5d9KGfWAAlq0Vn65QfnlBhWUGyRvH5mi28oU3bbKOEY55Ocp+Xs5FODpkL3gmFa/O1d7M7IVGxvLNSDR6Dz11FOaMmWK7r//fs2dO1eSVFpaqn/84x9avHixbDabBg0apJdeekkRERHuDQsAAFDPUDQCANBEGCRV5GYpoXmguvRIdk632x3KKymXtcimnMIynSgqU05RmXJLylVplwoqpIIKgzIlSVGKHDVbYz47Jv+vvlJCmJ/iQnwVE+Kj2Ga+ig3xVWwzH7Vo5iOTp4eb3ilQPRs2bNArr7yiTp06VZk+YcIEffHFF/rggw8UFBSke++9V0OHDtWPP/7opqQAAAD1E0UjAABNnNFoUDM/bzXz85aa/2+6w+FQga1CJ4vKlFtcrtzicmVac3Q4M1umkCgV2iq07Uieth3JO2VMg0GKDDQrttmpJWRecSU3pUG9U1hYqFGjRum1117T448/7pyel5en119/XYsWLdLFF18sSVqwYIGSk5O1du1a9e7d212RgRrhcDhUWlr61wui3vr93x9/lw2f2WzmjBE0aBSNAADgtAwGgwLNXgo0eyk+9Jdph43H9MyMO7R2/UaFxrfV/uNFyjhZooycYh0+WSxLTrEyckpUUl6pzLxSZeaVav3BU8eOe2CJvjxiVHBehgJMnvI3eyrA7CV/k6cCzJ7yN3nK19uDHW3UmbFjx+rKK6/UwIEDqxSNqampKi8v18CBA53TkpKSFBcXpzVr1py2aLTZbLLZbM7n+fn5tRsecEFpaakGDx7s7hioIdddd527I8BFS5culY+Pz18vCNRTFI0AAOCceXkY1Lp5gFo3DzhlnsPh0ImiMmXkFFcpITNySmTJKdaRk8WqNHqopFIqySv99ZTsU3kYDPL/tXQM+P2fZk8VlxlkNPvL4XDU7htFk7B48WJt2rRJGzZsOGVeVlaWvL29FRwcXGV6RESEsrKyTjverFmzNGPGjNqICgAAUK9RNAIAgBplMBgU5m9SmL9JXeOanTJ/w8ZUpQy4TDfPfF0+4bEqsFWosLTid3+Wq8hWqUrHL9eOzCspP82reCn2/sW68aNstfjuO0UH+SgqyPzLI/iXr6N//TPA7FX7bxoNVkZGhu6//36tWLFCZrO5RsacMmWKJk6c6Hyen5+v2NjYGhkbqGlms1lLly51dwy4wOFwOI+iNplMnA3QwNXU9yLAXSgaAQBAnfIwGlRZmKMQk0MxEaceESlJlXaHimwVKvi1eKxaRFYor6hUZXaDbJUO7T9epP3Hi874egEmT0X+WkBGB5kVFeSjqGCzCo/Z5BkSowp7bb1TNASpqak6duyYunXr5pxWWVmp77//Xi+++KKWL1+usrIy5ebmVjmqMTs7W5GRkacd02QyyWQynXYeUN8YDAZO02wEfH193R0BACRRNAIAgHrIw2hQoI+XAn28JJ36A/DhvTv07P0j9OnXPyikRatfrwdZoqN5pcrMLXFeHzKvpFwFtgoVHCvU3mOFp4zT4o6X9clhyZSV7jw929/sqQCT1ymna3sauYFNY3TJJZdo27ZtVabdeuutSkpK0uTJkxUbGysvLy+tXLlSw4YNkyTt3r1bFotFKSkp7ogMAABQb1E0AgCABslRUaboAE91ax12xmWKbBXOEjIzt1RH80qUlVeqo3mlOpB1UoeO58to8pWtwi5bYZmshWVnHMvP5OG8OU6A2VOBPl6ylRjkGdJCdq4V2WAFBASoQ4cOVab5+fkpNDTUOX3MmDGaOHGiQkJCFBgYqHHjxiklJYU7TgMAAPwBRSMAAGi0/Eyeat3cX62b+58yb9OmTere/XKNe+EjBbZo/b9Ts393unbhr6dvV9gdKrJVqsj2y920/8dL0WNeUiWnXzdqzz77rIxGo4YNGyabzaZBgwbppZdecncsAACAeoeiEQAANGleRinU36RQ/9NfU8/hcKikvFL5pRUqKClXfmmF8kvLVVBaoRN5BbJmHpGXR0wdp0Zt+u6776o8N5vNmjdvnubNm+eeQAAAAA0ERSMAAMCfMBgM8vX2lK+3pyIDq94J8vDeHXrm8Xuke1LdlA4AAACoP7iqOQAAAAAAAACXUTQCAAAAAAAAcFmDKBrnzZunli1bymw2q1evXlq/fr27IwEAAAAAAAD4nXpfNP7f//2fJk6cqEcffVSbNm1S586dNWjQIB07dszd0QAAAAAAAAD8qt4Xjc8884zuuOMO3XrrrTrvvPP08ssvy9fXV2+88Ya7owEAAAAAAAD4Vb2+63RZWZlSU1M1ZcoU5zSj0aiBAwdqzZo1p13HZrPJZrM5n+fl5UmS8vPzazVrYWGhpF/uPmkrKa7WGNmWdElS1sE9SvfzZQzGYIwGkoUxGOPPHD98QJKUmprq/F5xrnbv3i3Jte8xUuN6P/UtR2FhYa3ua/w2tsPhqLXXQO357e+ttvdHAQAAasvZ7o8aHPV4j/Xo0aNq0aKFfvrpJ6WkpDinP/jgg1q1apXWrVt3yjrTp0/XjBkz6jImAABAncjIyFBMTIy7Y+AcHT58WLGxse6OAQAA4LK/2h+t10c0VseUKVM0ceJE53O73a6cnByFhobKYDDU2uvm5+crNjZWGRkZCgwMrLXXwf+wzese27zusc3rHtu87rHN/5rD4VBBQYGio6PdHQXVEB0drYyMDAUEBNTq/iiApovvpQBq29nuj9brojEsLEweHh7Kzs6uMj07O1uRkZGnXcdkMslkMlWZFhwcXFsRTxEYGMh/7HWMbV732OZ1j21e99jmdY9t/ueCgoLcHQHVZDQaORIVQJ3geymA2nQ2+6P1+mYw3t7e6t69u1auXOmcZrfbtXLlyiqnUgMAAAAAAABwr3p9RKMkTZw4UaNHj1aPHj10/vnna+7cuSoqKtKtt97q7mgAAAAAAAAAflXvi8YbbrhBx48f17Rp05SVlaUuXbpo2bJlioiIcHe0Kkwmkx599NFTTttG7WGb1z22ed1jm9c9tnndY5sDAOAavpcCqC/q9V2nAQAAAAAAADQM9foajQAAAAAAAAAaBopGAAAAAAAAAC6jaAQAAAAAAADgMopGAAAAAAAAAC6jaKymp556SgaDQePHj3dOKy0t1dixYxUaGip/f38NGzZM2dnZ7gvZCEyfPl0Gg6HKIykpyTmfbV47jhw5optuukmhoaHy8fFRx44dtXHjRud8h8OhadOmKSoqSj4+Pho4cKD27t3rxsQNW8uWLU/5nBsMBo0dO1YSn/PaUFlZqalTpyohIUE+Pj5KTEzUY489pt/fH43Pec0rKCjQ+PHjFR8fLx8fH/Xp00cbNmxwzmebAwAAAA0bRWM1bNiwQa+88oo6depUZfqECRP02Wef6YMPPtCqVat09OhRDR061E0pG4/27dsrMzPT+Vi9erVzHtu85p08eVIXXHCBvLy8tHTpUu3cuVP//ve/1axZM+cyc+bM0fPPP6+XX35Z69atk5+fnwYNGqTS0lI3Jm+4NmzYUOUzvmLFCknS9ddfL4nPeW2YPXu25s+frxdffFFpaWmaPXu25syZoxdeeMG5DJ/zmnf77bdrxYoVevvtt7Vt2zZddtllGjhwoI4cOSKJbQ4AAAA0eA6ck4KCAkebNm0cK1ascFx00UWO+++/3+FwOBy5ubkOLy8vxwcffOBcNi0tzSHJsWbNGjelbfgeffRRR+fOnU87j21eOyZPnuzo27fvGefb7XZHZGSk4+mnn3ZOy83NdZhMJsd7771XFxEbvfvvv9+RmJjosNvtfM5ryZVXXum47bbbqkwbOnSoY9SoUQ6Hg895bSguLnZ4eHg4Pv/88yrTu3Xr5nj44YfZ5gAAAEAjwBGN52js2LG68sorNXDgwCrTU1NTVV5eXmV6UlKS4uLitGbNmrqO2ajs3btX0dHRatWqlUaNGiWLxSKJbV5bPv30U/Xo0UPXX3+9mjdvrq5du+q1115zzj9w4ICysrKqbPegoCD16tWL7V4DysrK9M477+i2226TwWDgc15L+vTpo5UrV2rPnj2SpJ9//lmrV6/W4MGDJfE5rw0VFRWqrKyU2WyuMt3Hx0erV69mmwMAAACNgKe7AzQkixcv1qZNm6pcT+o3WVlZ8vb2VnBwcJXpERERysrKqqOEjU+vXr20cOFCtWvXTpmZmZoxY4YuvPBCbd++nW1eS/bv36/58+dr4sSJ+uc//6kNGzbovvvuk7e3t0aPHu3cthEREVXWY7vXjI8//li5ubm65ZZbJPF/S2156KGHlJ+fr6SkJHl4eKiyslJPPPGERo0aJUl8zmtBQECAUlJS9Nhjjyk5OVkRERF67733tGbNGrVu3ZptDgAAADQCFI1nKSMjQ/fff79WrFhxytEYqD2/HV0kSZ06dVKvXr0UHx+v999/Xz4+Pm5M1njZ7Xb16NFDTz75pCSpa9eu2r59u15++WWNHj3azekav9dff12DBw9WdHS0u6M0au+//77effddLVq0SO3bt9eWLVs0fvx4RUdH8zmvRW+//bZuu+02tWjRQh4eHurWrZtGjhyp1NRUd0cDAAAAUAM4dfospaam6tixY+rWrZs8PT3l6empVatW6fnnn5enp6ciIiJUVlam3NzcKutlZ2crMjLSPaEboeDgYLVt21b79u1TZGQk27wWREVF6bzzzqsyLTk52XnK+m/b9o93PWa7u+7QoUP6+uuvdfvttzun8TmvHZMmTdJDDz2kESNGqGPHjrr55ps1YcIEzZo1SxKf89qSmJioVatWqbCwUBkZGVq/fr3Ky8vVqlUrtjkAAADQCFA0nqVLLrlE27Zt05YtW5yPHj16aNSoUc6vvby8tHL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      "text/plain": [
       "<Figure size 1600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Skewness Value :  0.4830635489937634\n"
     ]
    }
   ],
   "source": [
    "# Set value histogram & boxplot and show skewness from feature `Age`\n",
    "\n",
    "diagnostic_plots(X_train, 'age')\n",
    "print('\\nSkewness Value : ', X_train['age'].skew())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Skewness Value :  0.49071073234719453\n"
     ]
    }
   ],
   "source": [
    "# Set value histogram & boxplot and show skewness from feature `ejection_fraction`\n",
    "\n",
    "diagnostic_plots(X_train, 'ejection_fraction')\n",
    "print('\\nSkewness Value : ', X_train['ejection_fraction'].skew())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Skewness Value :  0.13409512756629116\n"
     ]
    }
   ],
   "source": [
    "# Set value histogram & boxplot and show skewness from feature `time`\n",
    "\n",
    "diagnostic_plots(X_train, 'time')\n",
    "print('\\nSkewness Value : ', X_train['time'].skew())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TAYATREdHKz4+3l1UNAxD8fHxioqKsjgZAE9FoREAAAA4hd27d+v5559Xp06d9Pnnn+vWW2/VxIkT9dprr1V6/rRp05STk+O+paSk1HNiAJ5k7NixCg0NlSSFhYUpPj7e4kQAPBlTpwEAANBkTJ06VQ8//LD8/f01derUU56bkJBQpT5dLpcGDBig2bNnS5L69u2rn3/+WQsXLjxhyqIk+fr6ytfX9/TDA0A1OBwOTZ06VfPnz9ekSZPkcDisjgTAg1FoBAAAQJOxfv16lZSUuO+fzOmsXda6dWt169atQlvXrl313nvvVS8kANSyuLg4xcXFWR0DACg0AgAAoOn45ptvKr1fE4MHD9b27dsrtO3YsUNt27atlf4BAACaCtZobALyi0uVX1yqolKnTKvDAAAANDFTpkzRTz/9pNmzZ+vXX39VYmKiXnzxRU2YMMHqaAAAAA0KIxobseJSl77adlA70/Pcbc3UXl4h0RamAgAAaBiOHj2qxx57TMuWLVN6erpcLleF41XdeTo2NlYffPCBpk2bplmzZql9+/aaN2+exo4dWxexAQAAGi0KjY3U4fxifbLpN2UdLa7Qni+HWl/3hLKcRRYlAwAAaBhuuukmLV++XNddd51at259Wusy/tGoUaM0atSoWkwHAADQ9FBobIRyC0r0dlKKikpd8ve166KerRUR6FB+sVPvfL9FuY7m2lzkrzYHj6hTeIDVcQEAACyxZMkSffrppxo8eLDVUQAAADwCazQ2MqZp6pvt6SoqdalVgK+uiY1R6yA/GYYhf18v9VCy8jYvk2Ro2S/pyi8utToyAACAJVq0aKGQkBCrYwAAAHgMCo2NzK5DR7U3M182QxrePUL+vhUHpdpkKnPJfDU3SlRU6tKKnRkWJQUAALDWww8/rBkzZig/P9/qKAAAAB6BqdONSHGpS8t3HJIkDWgbohB/n8pPNF3q5JOtDUUttT3tiLpGBKhtqH89JgUAALDek08+qV27dik8PFzt2rWTt7d3hePr1q2zKBkAAEDTRKGxEVm1J1N5RaUK8vNWbLsWpzw30F6i3tHB2pCarW+2H9K1g/zkZWcAKwAA8ByjR4+2OgIAAIBHodDYSBSVOrV5f44k6S9ntqxS0fDsDqHaeeiIcgpK9MvBI+oRGVTXMQEAABqMBx980OoIAAAAHoUhbo3Ett+OqMRpKsTfR+1Cm1XpOT5eNvVtUzbycUNytkzTrMuIAAAAAAAA8GCMaGwETNPUptRsSVKvqCAZhlHl5/aIDNSqPZnKPFqs5Kx81moEAABNWkhIiHbs2KGwsDC1aNHilNdNWVlZ9ZgMAACg6aPQ2AikHi7Q4fwSedsNdWkdcFrP9fW2q1vrQG1MzdH6lGwKjQAAoEl76qmnFBAQ4L5/On+gBQAAQM1QaGwENh4bzdglIlC+XvbTfn6fNsHamJqjfZn5yjpafPLdqgEAABq5cePGue+PHz/euiAAAAAeiDUaG7gjhSXanXFUktQ7unqbuQQ389EZYWUjGdenHK61bAAAAA2Z3W5Xenr6Ce2ZmZmy20//j7cAAAA4NQqNDdyOg3kyTSky2KHQ5r7V7qdPm2B3f6VOVy2lAwAAaLhOthFeUVGRfHyY4QEAAFDbmDrdwP2anidJ6hx+emsz/lF0Cz819/VSXlGp9mbmq2Or5rURDwAAoMF5+umnJUmGYejll19W8+a/X/c4nU6tWLFCXbp0sSoeAABAk0WhsQE7UliitNxCSVKHljUrDBqGoTPDm2tdcra2px2h0AgAAJqsp556SlLZiMaFCxdWmCbt4+Ojdu3aaeHChVbFAwAAaLIoNDZguw+Vrc3YOsghf9+av1VdIgK1LjlbezKPqqjUWa2NZQAAABq6PXv2SJKGDBmi999/Xy1atLA4EQAAgGdgjcYGrHzadG2NPgxr7qOQZj5yukztSj9aK30CAAA0VN988w1FRgAAgHpUrWFyZ5xxhpKSkhQaGlqhPTs7W/369dPu3btrJZwnyy8u1f7sAkk1nzZdzjAMdY4I0I+7M7X94BF1iwyslX4BAAAaqtTUVH388cdKTk5WcXFxhWMJCQkWpQIAAGiaqlVo3Lt3r5xO5wntRUVF2r9/f41DQdqdcVSmpFYBvgry8661fssLjSlZ+TpaVForU7IBAAAaomXLlumSSy7RGWecoV9++UU9evTQ3r17ZZqm+vXrZ3U8AACAJue0qkwff/yx+/7nn3+uoKAg92On06lly5apXbt2tRbOk5VPm66t0Yzlgvy8FRHoUFpuoX49lKfe0cG12j8AAEBDMW3aNP3zn//UzJkzFRAQoPfee0+tWrXS2LFjdeGFF1odDwAAoMk5rULj6NGjJZVNwR03blyFY97e3mrXrp2efPLJWgvnqUpdUmpW+bRp/1rvv0Mrf6XlFmpPxlEKjQAAoMnatm2b3nzzTUmSl5eXCgoK1Lx5c82aNUuXXnqpbr31VosTAgAANC2ntRmMy+WSy+VSTEyM0tPT3Y9dLpeKioq0fft2jRo1qq6yeoyMIkNO01RzXy+F+PvUev9nhJWNkkzNKlBxqavW+wcAAGgI/P393esytm7dWrt27XIfy8jIsCoWANS6lStX6uqrr9bKlSutjgLAw1Vr1+k9e/YoLCystrPgmPTCsrclJqSZDMOo9f5bNPNWkJ+3nKapfVnsPg0AAJqms846S99//70kaeTIkbrzzjv16KOP6oYbbtBZZ51lcToAqB2FhYVKSEjQwYMHlZCQoMLCQqsjAfBg1d4JZNmyZVq2bJl7ZOPxXnnllRoH82TphWXFxZiQZnXSv2EYOqOlv9YnZ2vPoaPq1CqgTl4HAADASgkJCcrLK1v3eubMmcrLy9Pbb7+tTp06seM0gCZj8eLFyszMlCRlZmYqMTFRN9xwg8WpAHiqahUaZ86cqVmzZmnAgAFq3bp1nYy681S2ZsHKKSkb0dgmxK/OXueMsGOFxsyjcpmmbLyHAACgiTnjjDPc9/39/bVw4UIL0wBA7UtNTVViYqJM05QkmaapxMREDRs2TNHR0RanA+CJqlVoXLhwoV599VVdd911tZ3H4zna9pYktWzuq2Y+1R5w+qcig/zk62VTYYlLv+UUKiq47oqaAAAAVsnOzta7776rXbt26a677lJISIjWrVun8PBwRUVFWR0PAKrNNE3Nnz//pO2PP/44g4IA1LtqrdFYXFysuLi42s4CSX7t+0iqu2nT5Ww2Q+3Dyna03nOIdRoBAEDTs2nTJp155pmaO3eunnjiCWVnZ0uS3n//fU2bNs3acABQQ8nJyUpKSpLT6azQ7nQ6lZSUpOTkZIuSAfBk1So03nTTTUpMTKztLB7PNE052vWVVLfTpsudcazQuDsjr85fCwAAoL5NnTpV48eP186dO+VwONztI0eO1IoVKyxMBgA1FxMTo9jYWNnt9grtdrtdAwcOVExMjEXJAHiyas3NLSws1IsvvqivvvpKvXr1kre3d4XjLK5dPfuPlMorIEw2mfUylTkmtJkMQzqcX6LcghIF+nn/+ZMAAAAaiaSkJL3wwgsntEdFRSktLc2CRABQewzD0KRJkzRu3LhK25k2DcAK1So0btq0SX369JEk/fzzzxWO8Z9Z9W08WCxJCnOY8rJXa7DpafH1sqt1oEMHcgq1LytfPaOC6vw1AQAA6ouvr69yc3NPaN+xY4datmxpQSIAqF3R0dGKj4/XG2+8IdM0ZRiG4uPjWYMWgGWqVWj85ptvajsHJG09VFZobOnrqrfXjAltVlZozDxKoREAADQpl1xyiWbNmqV33nlHUtkfxJOTk3XPPffoiiuusDgdANSOsWPHasmSJcrIyFBYWJji4+OtjgTAg9X9sDlUiWma2pZxbESjr1lvr9s2pGydxpTDBXK56u91AQAA6tqTTz6pvLw8tWrVSgUFBfrLX/6ijh07KiAgQI8++qjV8QCgVjgcDk2dOlXh4eGaMmVKhTVpAaC+VWtE45AhQ045Rfrrr7+udiBPtTczX9mFLpmlJWpRj4XGVoG+cnjZVFjqUlpuoSLrYW1IAACA+hAUFKQvv/xSP/zwgzZu3Ki8vDz169dPQ4cOtToaANSquLg4xcXFWR0DAKpXaCxfn7FcSUmJNmzYoJ9//vmEhWhRNUl7syRJRb/tkP2MTvX2ujbDUJuQZtqZnqd9WfkUGgEAQJNQUlIiPz8/bdiwQYMHD9bgwYOtjgQAANDkVavQ+NRTT1Xa/tBDDykvL69GgTxV0p5jhcbUrZLqr9AoSW1DywqNyZn5OvuM0Hp9bQAAgLrg7e2tmJgYOZ1Oq6MAAAB4jFpdo/Haa6/VK6+8UptdeozyEY2FqVvq/bVjQppJkg7mFqqwhItxAADQNNx///267777lJWVZXUUAAAAj1CtEY0n8+OPP7LwbDWkHynU3sx8GZKK9m+r99cPcHgrxN9HWUeLlZKVLyZPAwCApuDZZ5/Vr7/+qsjISLVt21b+/v4Vjq9bt86iZAAAAE1TtQqNl19+eYXHpmnqt99+05o1azR9+vRaCeZJ1uw9LEmKCfLS3qKjlmSICWmmrKPFSs7KV+daLT8DAABYY/To0VZHAAAA8CjVKikFBQVVeGyz2dS5c2fNmjVLw4YNq5VgnqR82nS3lj5aYVGGmJBm2pCSrZTDBerc0qIQAAAAtejBBx+0OgIAAIBHqVahcdGiRbWdw6OVFxq7hvlYliEq2E82Q8opKNHRUstiAAAA1JqkpCS5XC4NGjSoQvuqVatkt9s1YMAAi5IBAAA0TTXaDGbt2rV644039MYbb2j9+vW1lcmj5BWVauuBXEnWFhp9vGwKDyxbXzO9sFb3CAIAALDEhAkTlJKSckL7/v37NWHCBAsSAQAANG3Vqiilp6fr/PPPV2xsrCZOnKiJEyeqf//+uuCCC3To0KHaztikbUrNlsssG1EY2sxuaZby3afTCw1LcwAAANSGrVu3ql+/fie09+3bV1u3brUgEQDUjZUrV+rqq6/WypUrrY4CwMNVq9B4xx136MiRI9qyZYuysrKUlZWln3/+Wbm5uZo4cWJtZ2zS1idnS5L6xARbmkOS2rQoKzQeKrRJotgIAAAaN19fXx08ePCE9t9++01eXux+B6BpKCwsVEJCgg4ePKiEhAQVFhZaHQmAB6tWoXHp0qV67rnn1LVrV3dbt27dtGDBAi1ZsqTWwnmC8kJj3zbBluaQpIggh7zthopchrxbtrM6DgAAQI0MGzZM06ZNU05OjrstOztb9913n/76179amAwAas/ixYuVmZkpScrMzFRiYqLFiQB4smoVGl0ul7y9vU9o9/b2lsvlqnEoT2GapjakHJYk9Y1pYXEayW4zFBnsJ0lytOttcRoAAICaeeKJJ5SSkqK2bdtqyJAhGjJkiNq3b6+0tDQ9+eSTVscDgBpLTU1VYmKiTNOUVPY7ZmJiolJTUy1OBsBTVavQeP7552vSpEk6cOCAu23//v2aMmWKLrjggloL19SlHi5QRl6xvO2GukcGWh1H0u/rNPq17WNtEAAAgBqKiorSpk2b9Pjjj6tbt27q37+/5s+fr82bN6tNmzZWxwOAGjFNU/Pnzz9pe3nxEQDqU7UWp3n22Wd1ySWXqF27du6LtJSUFPXo0UNvvPFGrQZsytanZEuSurUOlMPb2o1gypWv0+jbprtKnPxgAgAAjZu/v79uvvnmU55z0UUX6eWXX1br1q3rKRUA1FxycrKSkpJOaHc6nUpKSlJycrLatm1rQTIAnqxahcY2bdpo3bp1+uqrr/TLL79Ikrp27aqhQ4fWarimbn1yw5k2XS6suY98baaKfPy0M6tEg6wOBAAAUMdWrFihgoICq2MAwGmJiYlRbGys1q1bJ6fT6W632+3q37+/YmJiLEwHwFOd1tTpr7/+Wt26dVNubq4Mw9Bf//pX3XHHHbrjjjsUGxur7t2767vvvqtWkMcee0yGYWjy5MnutsLCQk2YMEGhoaFq3ry5rrjiikp3Dmys3BvBNIAdp8sZhqGWjrJ1NjcdLLI4DQAAAACgMoZhaNKkSSdtNwzDglQAPN1pFRrnzZunv//97woMPHE9waCgIN1yyy1KSEg47RBJSUl64YUX1KtXrwrtU6ZM0f/+9z/997//1fLly3XgwAFdfvnlp91/Q1RU6tTWA7mSpL5tGs6IRklq5SibMr0pnUIjAAAAADRU0dHRio+PdxcVDcNQfHy8oqKiLE4GwFOdVqFx48aNuvDCC096fNiwYVq7du1pBcjLy9PYsWP10ksvqUWL3wtuOTk5+ve//62EhASdf/756t+/vxYtWqSVK1fqp59+Oq3XaIi2HMhVsdOlEH8ftQnxszpOBa2OjWjcmVmivKJSi9MAAAAAAE5m7NixCg0NlSSFhYUpPj7e4kQAPNlpFRoPHjwob2/vkx738vLSoUOHTivAhAkTdNFFF52wvuPatWtVUlJSob1Lly6KiYnRjz/+eNL+ioqKlJubW+HWEG0onzbdJrjBDWn395JKDv8mpymt3pNpdRwAAAAAwEk4HA5NnTpV4eHhmjJlihwOh9WRAHiw09oMJioqSj///LM6duxY6fFNmzad1m59b731ltatW1fpTllpaWny8fFRcHBwhfbw8HClpaWdtM85c+Zo5syZVc5glfIdp/u0CbY0x8kU7tso7xat9cOvmTq/S7jVcQAAAAAAJxEXF6e4uDirYwDA6Y1oHDlypKZPn67CwsITjhUUFOjBBx/UqFGjqtRXSkqKJk2apMWLF9fqX1ymTZumnJwc9y0lJaXW+q5Nm1OzJUm9G2yhcYMk6YdfM6wNAgAAUMfuu+8+hYSEWB0DAACg0TutEY0PPPCA3n//fZ155pm6/fbb1blzZ0nSL7/8ogULFsjpdOr++++vUl9r165Venq6+vXr525zOp1asWKFnn32WX3++ecqLi5WdnZ2hVGNBw8eVERExEn79fX1la+v7+l8WvUuJ79EezPzJUm9ooMsTlO5wn2bJEm/pB3RoSNFahnQsL+mAAAAlTlw4IC+//57paeny+VyVTg2ceJESWV/qAaAxmzlypWaP3++Jk2axMhGAJY6rUJjeHi4Vq5cqVtvvVXTpk2TaZbtTmwYhoYPH64FCxYoPLxq02wvuOACbd68uULb9ddfry5duuiee+5RmzZt5O3trWXLlumKK66QJG3fvl3Jyck6++yzTyd2g7N5f44kKSakmYKb+VicpnKugly1D/bSnuxSrdyVoUv7sGsZAABoXF599VXdcsst8vHxUWhoaIV1sQ3DcBcaAaAxKywsVEJCgjIyMpSQkKB+/fqxTiMAy5xWoVGS2rZtq88++0yHDx/Wr7/+KtM01alTpwo7RldFQECAevToUaHN399foaGh7vYbb7xRU6dOVUhIiAIDA3XHHXfo7LPP1llnnXW6sRuUjcemTTfU0YzlerbyLSs0/ppJoREAADQ606dP14wZMzRt2jTZbKe1YtBJPfbYY5o2bZomTZqkefPm1UqfAFATixcvVmZm2SaemZmZSkxM1A033GBxKgCe6rQLjeVatGih2NjY2sxygqeeeko2m01XXHGFioqKNHz4cD333HN1+pr1YXNq2YjGhl5o7BXuo493HNX3v2bINM0Gtzs2AADAqeTn52vMmDG1VmRMSkrSCy+8oF69etVKfwBQU6mpqUpMTHTPNjRNU4mJiRo2bJiio6MtTgfAE9XOVVct+fbbbyv8ZdjhcGjBggXKysrS0aNH9f77759yfcbGYpN7RGOwpTn+TLeWPvK2G9qfXaDkrHyr4wAAAJyWG2+8Uf/9739rpa+8vDyNHTtWL7300p/O5CkqKlJubm6FGwDUNtM0NX/+/JO2lxcfAaA+VXtEI6rn0JEiHcgplGFIPaIa9ohGh5dNfWNaaPWeLP3wa6bahvpbHQkAAKDK5syZo1GjRmnp0qXq2bOnvL29KxxPSEiocl8TJkzQRRddpKFDh+qRRx7509edOXNmtTIDQFUlJycrKSnphHan06mkpCQlJyerbdu2FiQD4MkoNNazzfuzJUkdWjZXc9+G/+Uf3CHsWKExQ/GDYqyOAwAAUGVz5szR559/rs6dO0vSCZvBVNVbb72ldevWVfoLfWWmTZumqVOnuh/n5uaqTZs2VX49AKiKmJgYxcbGat26dXI6ne52u92u/v37KyaG398A1L+GX+lqYjaVr8/YwEczljunU6ie+kpauStDLpcpm411GgEAQOPw5JNP6pVXXtH48eOr3UdKSoomTZqkL7/8ssq7uPr6+srX17farwkAVWEYhiZNmqRx48ZV2s4a+wCs0KDWaPQEmxrJRjDlekUHy9/HrsP5Jdr6G+sLAQCAxsPX11eDBw+uUR9r165Venq6+vXrJy8vL3l5eWn58uV6+umn5eXlVWEUEQDUt+joaF111VUV2q666ipFRUVZlAiAp6PQWI9M0/y90Ngm2NowVeRtt+msM0IllY1qBAAAaCwmTZqkZ555pkZ9XHDBBdq8ebM2bNjgvg0YMEBjx47Vhg0bZLfbayktAABA48fU6Xr0W06hMvKK5GUz1K11oNVxqiyuY5iW/ZKu73/N1M3ndrA6DgAAQJWsXr1aX3/9tT755BN17979hM1g3n///T/tIyAgQD169KjQ5u/vr9DQ0BPaAaC+paam6p133qnQ9s4772jkyJGKjo62KBUAT0ahsR6Vj2Y8MzxADu/G89fvczqGSZKS9mSpqNQpX6/Gkx0AAHiu4OBgXX755VbHAIA6YZqm5s+ff9L2xx9/nHUaAdQ7Co31aFNqtqTGsz5juTPDmyusua8y8oq0PjnbPZUaAACgIVu0aFGd9Pvtt9/WSb8AcDqSk5OVlJR0QrvT6VRSUpKSk5PVtm1bC5IB8GSs0ViPNu8v3wgm2Nogp8kwDA3ueGydxl9ZpxEAAAAArBYTE6PY2NgT1oq12+0aOHCgYmJiLEoGwJNRaKwnFTaCaWQjGiVpcIey6dPfU2gEAACNRPv27XXGGWec9AYAjZlhGJo0adJJ25k2DcAKTJ2uJ8lZ+copKJGPl01nhgdYHee0De5UVmjcmJqjI4UlCnB4/8kzAAAArDV58uQKj0tKSrR+/XotXbpUd911lzWhAKAWRUdHKz4+Xm+88YZM05RhGIqPj1dUVJTV0QB4KAqN9WTjsdGMXVsHyser8Q0kjQr2U7vQZtqbma9Vu7M0tFu41ZEAAABOqbKRPpK0YMECrVmzpp7TAEDdGDt2rJYsWaKMjAyFhYUpPj7e6kgAPFjjq3g1UpuPbQTTuxFOmy43+Nju0z/sYvo0AABovEaMGKH33nvP6hgAUCscDod69OghSerevbscDofFiQB4MgqN9aR8RGPPqCZQaGSdRgAA0Ii9++67CgkJsToGANSK7OxsrVixQpK0YsUKZWdnWxsIgEdj6nQ9cLpMbTm243TvNsHWhqmBs88IlWFIOw7mKf1IoVoF8JcyAADQcPXt27fCZgimaSotLU2HDh3Sc889Z2EyAKg906dPl8vlkiS5XC7NmDFDTz/9tMWpAHgqCo31YPehPB0tdqqZj10dWja3Ok61tfD3UffIQP28P1crf83U6L4sMAwAABqu0aNHV3hss9nUsmVLnXfeeerSpYs1oQCgFq1Zs0abN2+u0LZp0yatWbNGAwYMsCgVAE9GobEebDo2bbpHZJDsNuNPzm7YBncM08/7c/XDrxkUGgEAQINVWlqq9u3ba/jw4QoPZxM7AE2Py+XSrFmzKj02a9Ysffjhh7LZWC0NQP3if516sOnYRjA9G/FGMOUGd/h9nUbTNC1OAwAAUDkvLy/94x//UGFhodVRAKBOrFq1Srm5uZUey83N1apVq+o5EQBQaKwXm46tz9irCRQaY9uFyMdu04GcQu3NzLc6DgAAwEkNHDhQ69evtzoGANSJQYMGqXnzypfmat68uQYNGlTPiQCAqdN1rsTp0tYDZX9l6hUdbG2YWuDnY1e/tsH6aXeWvv81Q+3D/K2OBAAAUKnbbrtNd955p1JTU9W/f3/5+1e8bunVq5dFyQCg5gzDUGRkpHbs2HHCscjIyAqbYQFAfaHQWMe2px1RUalLgQ4vtQttZnWcWnFOxzD9tDtLK3/N0HVntbU6DgAAQKXGjBkjSZo4caK7zTAMmaYpwzDkdDqtigYANZacnFxpkVGSduzYoeTkZLVty+9rAOoXhcY6Vr4RTK/o4CbzF6W4jmHSFzu0clemnC6z0W9wAwAAmqY9e/ZYHQEA6kxMTIxiY2O1Zs2aCuvnG4ah2NhYxcTEWJgOgKei0FjHyjeCaQrrM5brFRWkAF8v5RSUaOuB3CaxyQ0AAGh6GMkDoCkzDEOTJk3SuHHjKozQttvtmjRpUpMZ6AKgcWEzmDr2+4jGplOM87LbNOiMUEnS979mWJwGAADg5P7zn/9o8ODBioyM1L59+yRJ8+bN00cffWRxMgCouejoaMXHx7uLioZhKD4+XlFRURYnA+CpKDTWocISp7YfPCKpaWwEc7xzOpYVGlfuotAIAAAapueff15Tp07VyJEjlZ2d7R7xExwcrHnz5lkbDgBqydixYxUaWvb7WVhYmOLj4y1OBMCTUWisQ1sO5MrpMhXW3FetgxxWx6lVgzuGSZJW78lSYQkLqQMAgIbnmWee0UsvvaT7779fdrvd3T5gwABt3rzZwmQAUHscDoemTp2q8PBwTZkyRQ5H0/rdE0DjwhqNdah8fcbe0UFNbn2Mjq2aq1WAr9KPFGndvsNlG8QAAAA0IHv27FHfvn1PaPf19dXRo0ctSAQAdSMuLk5xcXFWxwAARjTWpeN3nG5qDMPQOZ3Kiovf7jhkcRoAAIATtW/fXhs2bDihfenSperatWv9BwIAAGjiKDTWoY1NcMfp413QJVyS9NW2gxYnAQAAONHUqVM1YcIEvf322zJNU6tXr9ajjz6qadOm6e6777Y6HgDUmn//+986//zz9e9//9vqKAA8HFOn68iRwhLtPlQ2JaepFhr/78wwedkM7T50VHsyjqp9mL/VkQAAANxuuukm+fn56YEHHlB+fr57J9b58+drzJgxVscDgFqRnZ2txYsXy+VyafHixbriiisUHBxsdSwAHooRjXVk8/6yadNRwX4Kbe5rcZq6Eejw1qAzQiRJX/+SbnEaAACAigoKCnTZZZdp586dysvL008//aSpU6cqOjra6mgAUGumT58ul8slSXK5XJoxY4bFiQB4MgqNdaR8fcbebZrmaMZy5x+bPr2M6dMAAKCBufTSS/X6669LkoqLi3XJJZcoISFBo0eP1vPPP29xOgCouTVr1mjz5s0V2jZt2qQ1a9ZYlAiAp6PQWEfKd5zuGRVsaY66dkGXVpKk1XuylFtYYnEaAACA361bt07/93//J0l69913FR4ern379un111/X008/bXE6AKgZl8ulWbNmVXps1qxZ7lGOAFCfKDTWkY0px0Y0NtH1Gcu1C/NXh5b+KnWZWsHu0wAAoAHJz89XQECAJOmLL77Q5ZdfLpvNprPOOkv79u2zOB0A1MyqVauUm5tb6bHc3FytWrWqnhMBAIXGOpGZV6T92QWSpB5NvNAoSRd0LZs+/fU21mkEAAANR8eOHfXhhx8qJSVFn3/+uYYNGyZJSk9PV2BgoMXpAKBmBg0adNL/y4KCgjRo0KB6TgQAFBrrxKZjG8Gc0dJfgQ5vi9PUvfOPTZ/+enu6Sp0MzwcAAA3DjBkz9M9//lPt2rXToEGDdPbZZ0sqG93Yt29fi9MBQM3YbDbddtttlR677bbbZLPx6z6A+sf/PHVgk3vadLC1QerJgLYt1KKZt7LzS7R6T5bVcQAAACRJf/vb35ScnKw1a9Zo6dKl7vYLLrhATz31lIXJAKDmTNPUsmXLKj321VdfyTTNek4EABQa68TvG8E0/WnTkuRlt+mv3cqmTy/5Oc3iNAAAAL+LiIhQ3759K4zsGThwoLp06WJhKgCoueTkZCUlJVV6LCkpScnJyfWcCAAoNNY60zS1MfXYiMY2nlFolKQRPVpLkj7fkiaXi7+cAQAAAEBdiomJUWxsrOx2e4V2u92ugQMHKiYmxqJkADwZhcZa9ltOoTLyimS3GerW2nMKjXEdQxXg66X0I0Van3LY6jgAAAAA0KQZhqFJkyadMEXaNE1NmjRJhmFYlAyAJ6PQWMs2HRvNeGZ4gPx87H9ydtPh62XX+V3LNoVZspnp0wAAAABQ16Kjo+VwOCq0ORwORUVFWZQIgKej0FjLytdn7B3tOaMZy43oESGpbJ1GFh4GAAAAgLq1dOlS5efnV2jLz8+vsAEWANQnCo21rHxEY08PLDT+5cxWcnjbtD+7QD/vz7U6DgAAAAA0WU6nU//6178qPfavf/1LTqeznhMBAIXGWuVymceNaAy2NIsV/HzsOu/MsunTn27+zeI0AAAAANB0ffLJJyctJjqdTn3yySf1nAgAKDTWqt0ZR5VbWCqHt02dIwKsjmOJUb3Ldp/+38YD7D4NAAAAAHVk1KhRJ+w4Xc7Ly0ujRo2q50QAQKGxVq1PLtttuVdUsLztnvmlHdo1XM19vbQ/u0Brk9l9GgAAAADqgt1u11133VXpsbvvvvukRUgAqEueWQ2rI+tTsiVJfWOCLc1hJYe3XcO7l20K8+H6/RanAQAAAICm68ILL1RoaGiFtrCwMA0bNsyiRAA8HYXGWrQ+OVuSZxcaJWl030hJZes0Fpe6LE4DAAAAAE1XWFjYKR8DQH2i0FhL8opKtT2tbKflvjEtLE5jrbgOYQpr7qvs/BKt2HHI6jgAAAAA0CStWbNG27dvr9D2yy+/aM2aNRYlAuDpKDTWkk2p2XKZUmSQQ+GBDqvjWMpuM3TxsU1hPtp4wOI0AAAAAND0uFwuzZo1q9Jjs2bNksvF7DIA9Y9CYy1xT5tu69mjGcuN7hMlSfpya5qOFJZYnAYAAAAAmpZVq1YpNze30mO5ublatWpVPScCAAqNtcZdaGwTbGmOhqJXdJA6tPRXYYlL/9v4m9VxAAAAAKBJGTRokJo1a1bpsWbNmmnQoEH1nAgAKDTWCtM0tSHlsCTWZyxnGIbGxMZIkt5KSrY4DQAAAAAAAOoahcZakHq4QBl5xfK2G+oeGWh1nAbj8n5R8rYb2pSaoy0HcqyOAwAAAABNxqpVq5Sfn1/psfz8fKZOA7AEhcZasC65bDRjt8ggObztFqdpOEKb+2pYtwhJ0ttJKRanAQAAAICmY9CgQXI4Kt+I1M/Pj6nTACxBobEWlK/P2C8m2NIcDdGYgW0kSR+s36/CEqfFaQAAAACgaTBNU8XFxZUeKyoqkmma9ZwIACg01oq/9Y/WtBFdNLJna6ujNDiDO4QpuoWfjhSW6rPNbAoDAAAAALXhk08+kcvlqvSYy+XSJ598Us+JAEDysjpAU9AjKkg9ooKsjtHgJCcnKyMjQ/8XZdebh6WFy7aqndJPq4+wsDDFxMTUUUIAAAAAaJxGjRqlp59+Wk7niTPHvLy8NGrUKAtSAfB0lhYa58yZo/fff1+//PKL/Pz8FBcXp7lz56pz587ucwoLC3XnnXfqrbfeUlFRkYYPH67nnntO4eHhFibHn0lOTlaXrl1VkJ8vm3+wom9dpB2Z0tmjrlHxbzuq3I9fs2b6Zds2io0AAAAAcBy73a6bbrpJL7zwwgnHbrrpJtnt7B8AoP5ZWmhcvny5JkyYoNjYWJWWluq+++7TsGHDtHXrVvn7+0uSpkyZok8//VT//e9/FRQUpNtvv12XX365fvjhByuj409kZGSoID9fY+/5l8JjOigp06bko9LAf/xLA8OqtlbjweRdWjz3LmVkZFBoBAAAAIDjmKapdevWVXps7dq1uvrqq2UYRj2nAuDpLC00Ll26tMLjV199Va1atdLatWt17rnnKicnR//+97+VmJio888/X5K0aNEide3aVT/99JPOOuusE/osKipSUVGR+3Fubm7dfhLHKZ8qXF3btm2rxTQNQ3hMB0V36i6f8EIlJ6Vof4FdwW06qrmDWfsAAAAAUF3JyclKSkqq9FhSUpKSk5PVtm3bek4FwNM1qGpPTk6OJCkkJERS2V9hSkpKNHToUPc5Xbp0UUxMjH788cdKC41z5szRzJkz6yfwcY6fKlxTeXl5tZCoYWkV6FBksEMHsgu1aX+24jqEWR0JAAAA8HimaaqwsNDqGKiGli1bqn///lq/fn2FTWFsNpv69u2rli1bqqCgwMKEqA6Hw8FIVDRqDabQ6HK5NHnyZA0ePFg9evSQJKWlpcnHx0fBwcEVzg0PD1daWlql/UybNk1Tp051P87NzVWbNm3qLHe5P04Vro5tq5dryWvzm+wP+j5tgnUgO02b9+doYLsQednZ9BwAADR8VVlXHGisCgsLNWLECKtjoBa5XC6tXbtWI0eOtDoKqmHJkiXy8/OzOgZQbQ2m0DhhwgT9/PPP+v7772vUj6+vr3x9fWsp1ekrnypcHQeTd9VymoalQ1hzBTi8dKSwVFsO5Kp3m2CrIwEAAPypqqwrDgAAgAZSaLz99tv1ySefaMWKFYqOjna3R0REqLi4WNnZ2RVGNR48eFAREREWJEVN2GyG+se00Lc7DmnNvsPqERUku40h4QAAoGH7s3XFgcbM4XBoyZIlVsdADeTk5GjMmDGSpNDQUL388styOBwWp0J18d6hsbO00Giapu644w598MEH+vbbb9W+ffsKx/v37y9vb28tW7ZMV1xxhSRp+/btSk5O1tlnn21FZNRQ98hArd6bpbyiUm1Ly1WPyCCrIwEAAJyWP64r/kdWbk4InC7DMJim2YTcfvvtatGihdUxAHgwSxfJmzBhgt544w0lJiYqICBAaWlpSktLcy9YGxQUpBtvvFFTp07VN998o7Vr1+r666/X2WefXelGMGj4vOw29W9b9oNvzd7DcrlMixMBAABUXWXriv/RnDlzFBQU5L7Vx3rhACCJ35MBWM7SQuPzzz+vnJwcnXf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      "text/plain": [
       "<Figure size 1600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Skewness Value :  4.186283446552417\n"
     ]
    }
   ],
   "source": [
    "# Set value histogram & boxplot and show skewness from feature `serum_creatinine`\n",
    "\n",
    "diagnostic_plots(X_train, 'serum_creatinine')\n",
    "print('\\nSkewness Value : ', X_train['serum_creatinine'].skew())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Skewness Value :  -1.20374269292244\n"
     ]
    }
   ],
   "source": [
    "# Set value histogram & boxplot and show skewness from feature `serum_sodium`\n",
    "\n",
    "diagnostic_plots(X_train, 'serum_sodium')\n",
    "print('\\nSkewness Value : ', X_train['serum_sodium'].skew())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the histogram, boxplot and skewness level, we can know that : \n",
    "- There are outliers in `ejection_fraction`, `serum_creatinine` and `serum_sodium` features.\n",
    "- In the feature `age` and `time` the data distribution tends to be normal.\n",
    "- In the features `ejection_fraction` and `serum_creatinine` the data distribution has positive skewness if the tail of the distribution lies longer towards the right. This means that the distribution has higher values on the left with a long tail towards the right. Extreme values tend to be higher on the right. The graph of a distribution with positive skewness will look skewed to the left. A distribution with positive skewness may indicate that the data has many low values.\n",
    "- In the `serum_sodium` feature, the distribution has negative skewness if the tail of the distribution lies longer towards the left. This means that the distribution has higher values on the right with a long tail towards the left. Extreme values tend to be lower on the left. The graph of a distribution with negative skewness will look skewed to the right. A distribution with negative skewness may indicate that the data has many high values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### V.III.II. Outlier Detection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### V.III.II.I. Outlier Detection from `ejection_fraction`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make function to detection outlier on `ejection_fraction` feature\n",
    "\n",
    "def find_skewed_boundaries_ef(df, variable, distance):\n",
    "    IQR = df[variable].quantile(0.75) - df[variable].quantile(0.25)\n",
    "\n",
    "    lower_boundary_ef = df[variable].quantile(0.25) - (IQR * distance)\n",
    "    upper_boundary_ef = df[variable].quantile(0.75) + (IQR * distance)\n",
    "\n",
    "    return upper_boundary_ef, lower_boundary_ef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(67.5, 7.5)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show upper & lower boundary from `ejection_fraction` feature\n",
    "\n",
    "upper_boundary_ef, lower_boundary_ef = find_skewed_boundaries_ef(X_train, 'ejection_fraction', 1.5)\n",
    "upper_boundary_ef, lower_boundary_ef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Store outliers in a variable\n",
    "\n",
    "outliers_ef = X_train['ejection_fraction'][(X_train['ejection_fraction'] < lower_boundary_ef) | (X_train['ejection_fraction'] > upper_boundary_ef)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Ejection Fraction                                    :  239\n",
      "Ejection Fraction with more than 67.5 and less than 7.5    :  1\n",
      "% Ejection Fraction with more than 67.5 and less than 7.5  :  0.41841004184100417\n"
     ]
    }
   ],
   "source": [
    "# Show the number and percentage of outliers for `ejection_fraction`\n",
    "\n",
    "print('Total Ejection Fraction                                    : ', X_train['ejection_fraction'].count())\n",
    "print('Ejection Fraction with more than 67.5 and less than 7.5    : ', outliers_ef.count())\n",
    "print('% Ejection Fraction with more than 67.5 and less than 7.5  : ', outliers_ef.count() / X_train['ejection_fraction'].count() * 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the outliers are 0.4%, I will change the outliers value to median because the data distribution looks symetrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/xc/1vjvgyy51c76j9nfylh6sv180000gp/T/ipykernel_2022/156946657.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_train['ejection_fraction'][(X_train['ejection_fraction'] < lower_boundary_ef) | (X_train['ejection_fraction'] > upper_boundary_ef)] = np.nan\n"
     ]
    }
   ],
   "source": [
    "# Change the outlier's value\n",
    "\n",
    "mean_ef  = X_train[(X_train['ejection_fraction'] >= lower_boundary_ef) & (X_train['ejection_fraction'] <= upper_boundary_ef)].mean(numeric_only=True)\n",
    "\n",
    "X_train['ejection_fraction'][(X_train['ejection_fraction'] < lower_boundary_ef) | (X_train['ejection_fraction'] > upper_boundary_ef)] = np.nan\n",
    "\n",
    "X_train.fillna(mean_ef, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### V.III.II.IV. Outlier Detection from `serum_creatinine`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make function to detection outlier on `serum_creatinine` feature\n",
    "\n",
    "def find_skewed_boundaries_sc(df, variable, distance):\n",
    "    IQR = df[variable].quantile(0.75) - df[variable].quantile(0.25)\n",
    "\n",
    "    lower_boundary_sc = df[variable].quantile(0.25) - (IQR * distance)\n",
    "    upper_boundary_sc = df[variable].quantile(0.75) + (IQR * distance)\n",
    "\n",
    "    return upper_boundary_sc, lower_boundary_sc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2.1499999999999995, 0.15000000000000024)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show upper & lower boundary from `serum_creatinine` feature\n",
    "\n",
    "upper_boundary_sc, lower_boundary_sc = find_skewed_boundaries_sc(X_train, 'serum_creatinine', 1.5)\n",
    "upper_boundary_sc, lower_boundary_sc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Store outliers in a variable\n",
    "\n",
    "outliers_sc = X_train['serum_creatinine'][(X_train['serum_creatinine'] < lower_boundary_sc) | (X_train['serum_creatinine'] > upper_boundary_sc)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Serum Creatinine                                        :  239\n",
      "Serum Creatinine with higher than 2.15 and lower than 0.15    :  24\n",
      "% Serum Creatinine with higher than 2.15 and lower than 0.15  :  10.0418410041841\n"
     ]
    }
   ],
   "source": [
    "# Show the number and percentage of outliers for `serum_creatinine`\n",
    "\n",
    "print('Total Serum Creatinine                                        : ', X_train['serum_creatinine'].count())\n",
    "print('Serum Creatinine with higher than 2.15 and lower than 0.15    : ', outliers_sc.count())\n",
    "print('% Serum Creatinine with higher than 2.15 and lower than 0.15  : ', outliers_sc.count() / X_train['serum_creatinine'].count() * 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the outliers are 10%, I will change the outliers value to median because the data distribution looks not symetrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/xc/1vjvgyy51c76j9nfylh6sv180000gp/T/ipykernel_2022/127397580.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_train['serum_creatinine'][(X_train['serum_creatinine'] < lower_boundary_sc) | (X_train['serum_creatinine'] > upper_boundary_sc)] = np.nan\n"
     ]
    }
   ],
   "source": [
    "# Change the outlier's value\n",
    "\n",
    "median_sc  = X_train[(X_train['serum_creatinine'] >= lower_boundary_sc) & (X_train['serum_creatinine'] <= upper_boundary_sc)].median(numeric_only=True)\n",
    "\n",
    "X_train['serum_creatinine'][(X_train['serum_creatinine'] < lower_boundary_sc) | (X_train['serum_creatinine'] > upper_boundary_sc)] = np.nan\n",
    "\n",
    "X_train.fillna(median_sc, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### V.III.II.V. Outlier Detection from `serum_sodium`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make function to detection outlier on `serum_sodium` feature\n",
    "\n",
    "def find_skewed_boundaries_ss(df, variable, distance):\n",
    "    IQR = df[variable].quantile(0.75) - df[variable].quantile(0.25)\n",
    "\n",
    "    lower_boundary_ss = df[variable].quantile(0.25) - (IQR * distance)\n",
    "    upper_boundary_ss = df[variable].quantile(0.75) + (IQR * distance)\n",
    "\n",
    "    return upper_boundary_ss, lower_boundary_ss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(149.0, 125.0)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show upper & lower boundary from `serum_sodium` feature\n",
    "\n",
    "upper_boundary_ss, lower_boundary_ss = find_skewed_boundaries_ss(X_train, 'serum_sodium', 1.5)\n",
    "upper_boundary_ss, lower_boundary_ss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Store outliers in a variable\n",
    "\n",
    "outliers_ss = X_train['serum_sodium'][(X_train['serum_sodium'] < lower_boundary_ss) | (X_train['serum_sodium'] > upper_boundary_ss)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Serum Sodium                                      :  239\n",
      "Serum Sodium with higher than 149 and lower than 125    :  4\n",
      "% Serum Sodium with higher than 149 and lower than 125  :  1.6736401673640167\n"
     ]
    }
   ],
   "source": [
    "# Show the number and percentage of outliers for `serum_sodium`\n",
    "\n",
    "print('Total Serum Sodium                                      : ', X_train['serum_sodium'].count())\n",
    "print('Serum Sodium with higher than 149 and lower than 125    : ', outliers_ss.count())\n",
    "print('% Serum Sodium with higher than 149 and lower than 125  : ', outliers_ss.count() / X_train['serum_sodium'].count() * 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the outliers are 1.7%, I will change the outliers value to median because the data distribution looks not symetrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/xc/1vjvgyy51c76j9nfylh6sv180000gp/T/ipykernel_2022/728945182.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_train['serum_sodium'][(X_train['serum_sodium'] < lower_boundary_ss) | (X_train['serum_sodium'] > upper_boundary_ss)] = np.nan\n"
     ]
    }
   ],
   "source": [
    "# Change the outlier's value\n",
    "\n",
    "median_ss  = X_train[(X_train['serum_sodium'] >= lower_boundary_ss) & (X_train['serum_sodium'] <= upper_boundary_ss)].median(numeric_only=True)\n",
    "\n",
    "X_train['serum_sodium'][(X_train['serum_sodium'] < lower_boundary_ss) | (X_train['serum_sodium'] > upper_boundary_ss)] = np.nan\n",
    "\n",
    "X_train.fillna(median_ss, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### V.V. Handling Missing Value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here I will check the missing values of X_train, y_train, X_test and y_test, remove the missing values if any, and then equalize the indexes of X_train, y_train, X_test and y_test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                  0\n",
       "ejection_fraction    0\n",
       "serum_creatinine     0\n",
       "serum_sodium         0\n",
       "time                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Missing Value Checking for X_train\n",
    "\n",
    "X_train.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Missing Value Checking for y_train\n",
    "\n",
    "y_train.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                  0\n",
       "ejection_fraction    0\n",
       "serum_creatinine     0\n",
       "serum_sodium         0\n",
       "time                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Missing Value Checking for X_test\n",
    "\n",
    "X_test.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Missing Value Checking for y_test\n",
    "\n",
    "y_test.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### V.VI. Split between Numeric Columns and Category Columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since there are no categorical columns or features to process, we will process numerical columns or features only."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num Columns :  ['age', 'ejection_fraction', 'serum_creatinine', 'serum_sodium', 'time']\n"
     ]
    }
   ],
   "source": [
    "# Get Numeric Columns and Categorical Columns\n",
    "\n",
    "num_columns = X_train[['age', 'ejection_fraction', 'serum_creatinine', 'serum_sodium', 'time']].columns.tolist()\n",
    "\n",
    "print('Num Columns : ', num_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>ejection_fraction</th>\n",
       "      <th>serum_creatinine</th>\n",
       "      <th>serum_sodium</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>55.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>136.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>70.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>137.0</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>131.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>72.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>134.0</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>65.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>138.0</td>\n",
       "      <td>186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>62.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>133.0</td>\n",
       "      <td>233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>77.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>137.0</td>\n",
       "      <td>209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>85.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>136.0</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>61.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>141.0</td>\n",
       "      <td>237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>65.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>137.0</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>239 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age  ejection_fraction  serum_creatinine  serum_sodium  time\n",
       "205  55.0               38.0               1.1         136.0     6\n",
       "91   70.0               60.0               1.3         137.0    90\n",
       "281  60.0               60.0               1.1         131.0    10\n",
       "13   72.0               25.0               1.2         134.0   207\n",
       "23   65.0               30.0               0.8         138.0   186\n",
       "..    ...                ...               ...           ...   ...\n",
       "71   62.0               40.0               0.7         133.0   233\n",
       "278  77.0               50.0               1.1         137.0   209\n",
       "218  85.0               38.0               0.9         136.0   187\n",
       "223  61.0               38.0               1.2         141.0   237\n",
       "271  65.0               50.0               1.3         137.0    72\n",
       "\n",
       "[239 rows x 5 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split train set and test set based on column types\n",
    "\n",
    "X_train_num = X_train[num_columns]\n",
    "\n",
    "X_test_num = X_test[num_columns]\n",
    "\n",
    "X_train_num"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Columns that are numerical are `age`, `ejection_fraction`,`serum_creatinine`, `serum_sodium` and `time`.\n",
    "\n",
    "The numerical column will be scaled."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### V.VII. Feature Scalling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this step, I will use the StandardScaler because the average data distribution is normal. This is evidenced by the distance between the mean and median values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>ejection_fraction</th>\n",
       "      <th>serum_creatinine</th>\n",
       "      <th>serum_sodium</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>239.000000</td>\n",
       "      <td>239.000000</td>\n",
       "      <td>239.000000</td>\n",
       "      <td>239.000000</td>\n",
       "      <td>239.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>60.662485</td>\n",
       "      <td>37.655462</td>\n",
       "      <td>1.129038</td>\n",
       "      <td>136.937238</td>\n",
       "      <td>130.794979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11.902086</td>\n",
       "      <td>11.222679</td>\n",
       "      <td>0.323977</td>\n",
       "      <td>3.806263</td>\n",
       "      <td>78.886978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>40.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>71.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>115.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>68.500000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>205.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>95.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>2.100000</td>\n",
       "      <td>148.000000</td>\n",
       "      <td>285.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age  ejection_fraction  serum_creatinine  serum_sodium  \\\n",
       "count  239.000000         239.000000        239.000000    239.000000   \n",
       "mean    60.662485          37.655462          1.129038    136.937238   \n",
       "std     11.902086          11.222679          0.323977      3.806263   \n",
       "min     40.000000          14.000000          0.500000    126.000000   \n",
       "25%     50.000000          30.000000          0.900000    135.000000   \n",
       "50%     60.000000          38.000000          1.100000    137.000000   \n",
       "75%     68.500000          45.000000          1.200000    140.000000   \n",
       "max     95.000000          62.000000          2.100000    148.000000   \n",
       "\n",
       "             time  \n",
       "count  239.000000  \n",
       "mean   130.794979  \n",
       "std     78.886978  \n",
       "min      4.000000  \n",
       "25%     71.500000  \n",
       "50%    115.000000  \n",
       "75%    205.500000  \n",
       "max    285.000000  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show basic statistic\n",
    "\n",
    "X_train_num.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.47675416,  0.03076457, -0.08981697, -0.24675261, -1.58526644],\n",
       "       [ 0.78617403,  1.99519484,  0.52880712,  0.01652361, -0.51821725],\n",
       "       [-0.0557781 ,  1.99519484, -0.08981697, -1.5631337 , -1.53445458],\n",
       "       ...,\n",
       "       [ 2.04910221,  0.03076457, -0.70844106, -0.24675261,  0.7139705 ],\n",
       "       [ 0.02841712,  0.03076457,  0.21949508,  1.06962849,  1.34911883],\n",
       "       [ 0.36519797,  1.10227199,  0.52880712,  0.01652361, -0.74687065]])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Feature Scaling using StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train_num)\n",
    "X_train_num_scaled = scaler.transform(X_train_num)\n",
    "X_test_num_scaled = scaler.transform(X_test_num)\n",
    "X_train_num_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PCA(n_components=0.9)</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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PCA</label><div class=\"sk-toggleable__content\"><pre>PCA(n_components=0.9)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "PCA(n_components=0.9)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Setting and fit PCA\n",
    "\n",
    "pca = PCA(n_components=0.90)\n",
    "pca.fit(X_train_num_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transform PCA\n",
    "\n",
    "var_exp = pca.explained_variance_ratio_\n",
    "cum_var_exp = np.cumsum(var_exp)\n",
    "\n",
    "X_train_scaled_pca = pca.transform(X_train_num_scaled)\n",
    "X_test_scaled_pca  = pca.transform(X_test_num_scaled)\n",
    "\n",
    "X_train_scaled_pca = pd.DataFrame(X_train_scaled_pca)\n",
    "X_test_scaled_pca  = pd.DataFrame(X_test_scaled_pca)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### V.VIII. Feature Encoding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will skip this step because we don't process categorical or feature columns in this case."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### V.IX. Concate between Numeric Columns and Categorical Columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because we don't combine numeric columns with categorical columns, in this step I will convert the scaling result above into frame data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_final       = X_train_num_scaled\n",
    "X_train_final_pca   = X_train_scaled_pca\n",
    "\n",
    "X_test_final       = X_test_num_scaled\n",
    "X_test_final_pca   = X_test_scaled_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.425030</td>\n",
       "      <td>0.376891</td>\n",
       "      <td>1.320540</td>\n",
       "      <td>-0.385715</td>\n",
       "      <td>0.771341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.067226</td>\n",
       "      <td>1.764834</td>\n",
       "      <td>-0.893146</td>\n",
       "      <td>-0.660211</td>\n",
       "      <td>0.891393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.380161</td>\n",
       "      <td>1.154140</td>\n",
       "      <td>0.233570</td>\n",
       "      <td>-2.252179</td>\n",
       "      <td>1.478679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.954782</td>\n",
       "      <td>-0.868037</td>\n",
       "      <td>-0.741108</td>\n",
       "      <td>-0.023344</td>\n",
       "      <td>-1.247618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.530426</td>\n",
       "      <td>-0.320212</td>\n",
       "      <td>0.108307</td>\n",
       "      <td>0.123842</td>\n",
       "      <td>-1.339990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>-0.844700</td>\n",
       "      <td>-0.747472</td>\n",
       "      <td>-0.603456</td>\n",
       "      <td>-1.351715</td>\n",
       "      <td>-1.055550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>-0.242657</td>\n",
       "      <td>0.945589</td>\n",
       "      <td>-1.570347</td>\n",
       "      <td>-0.437462</td>\n",
       "      <td>-0.701418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>0.343958</td>\n",
       "      <td>0.780038</td>\n",
       "      <td>-0.912938</td>\n",
       "      <td>-0.540611</td>\n",
       "      <td>-1.849274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>-0.832855</td>\n",
       "      <td>-0.159158</td>\n",
       "      <td>-1.000937</td>\n",
       "      <td>1.075883</td>\n",
       "      <td>-0.368486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>0.312055</td>\n",
       "      <td>1.133831</td>\n",
       "      <td>-0.234357</td>\n",
       "      <td>-0.302219</td>\n",
       "      <td>0.810487</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>239 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1         2         3         4\n",
       "0    0.425030  0.376891  1.320540 -0.385715  0.771341\n",
       "1    0.067226  1.764834 -0.893146 -0.660211  0.891393\n",
       "2    0.380161  1.154140  0.233570 -2.252179  1.478679\n",
       "3    0.954782 -0.868037 -0.741108 -0.023344 -1.247618\n",
       "4   -0.530426 -0.320212  0.108307  0.123842 -1.339990\n",
       "..        ...       ...       ...       ...       ...\n",
       "234 -0.844700 -0.747472 -0.603456 -1.351715 -1.055550\n",
       "235 -0.242657  0.945589 -1.570347 -0.437462 -0.701418\n",
       "236  0.343958  0.780038 -0.912938 -0.540611 -1.849274\n",
       "237 -0.832855 -0.159158 -1.000937  1.075883 -0.368486\n",
       "238  0.312055  1.133831 -0.234357 -0.302219  0.810487\n",
       "\n",
       "[239 rows x 5 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create dataframe of X_train_final\n",
    "\n",
    "X_train_final_df     = pd.DataFrame(X_train_num_scaled, columns = [num_columns])\n",
    "\n",
    "X_train_final_df_pca = pd.DataFrame(X_train_scaled_pca)\n",
    "\n",
    "X_train_final_df_pca"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VI. Model Definition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training using Random Forest and AdaBoost\n",
    "\n",
    "rf  = RandomForestClassifier(random_state=50)\n",
    "gb  = GradientBoostingClassifier(random_state=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VII. Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train the Random Forest Classifier model - Not using PCA\n",
    "\n",
    "rf = rf.fit(X_train_final, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train the AdaBoost Classifier  model - Not using PCA\n",
    "\n",
    "gb = gb.fit(X_train_final, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train the Random Forest Classifier model - Using PCA\n",
    "\n",
    "rf_pca = rf.fit(X_train_final_pca, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train the AdaBoost Classifier  model - Using PCA\n",
    "\n",
    "gb_pca = gb.fit(X_train_final_pca, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VIII. Model Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### VIII.I. Model Evaluation Result Checking"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### VIII.I.I. Without PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict Train-set and Test-set on Random Forest Classifier Model\n",
    "\n",
    "y_pred_train_rf = rf.predict(X_train_final)\n",
    "y_pred_test_rf  = rf.predict(X_test_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report of Random Forest Classifier Model\n",
      "\n",
      "Train\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.75      0.81      0.78       162\n",
      "           1       0.52      0.43      0.47        77\n",
      "\n",
      "    accuracy                           0.69       239\n",
      "   macro avg       0.63      0.62      0.62       239\n",
      "weighted avg       0.67      0.69      0.68       239\n",
      "\n",
      "\n",
      "Test\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.80      0.80        41\n",
      "           1       0.58      0.58      0.58        19\n",
      "\n",
      "    accuracy                           0.73        60\n",
      "   macro avg       0.69      0.69      0.69        60\n",
      "weighted avg       0.73      0.73      0.73        60\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classification Report of Random Forest Classifier Model\n",
    "\n",
    "print('Classification Report of Random Forest Classifier Model\\n')\n",
    "print('Train')\n",
    "print(classification_report(y_train,y_pred_train_rf))\n",
    "print('')\n",
    "print('Test')\n",
    "print(classification_report(y_test,y_pred_test_rf))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix \n",
      " <sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x7fc0857ec130>\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Confusion Matrix \\n', ConfusionMatrixDisplay.from_estimator(rf, X_train_final, y_train, cmap='Reds'));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix \n",
      " <sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x7fc085dd5390>\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Confusion Matrix \\n', ConfusionMatrixDisplay.from_estimator(rf, X_test_final, y_test, cmap='Reds'));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict Train-set and Test-set on Gradient Boost Classifier Model\n",
    "\n",
    "y_pred_train_gb = gb.predict(X_train_final)\n",
    "y_pred_test_gb  = gb.predict(X_test_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report of Gradient Boost Classifier Model\n",
      "\n",
      "Train\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.75      0.76      0.75       162\n",
      "           1       0.48      0.47      0.47        77\n",
      "\n",
      "    accuracy                           0.67       239\n",
      "   macro avg       0.61      0.61      0.61       239\n",
      "weighted avg       0.66      0.67      0.66       239\n",
      "\n",
      "\n",
      "Test\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.71      0.75        41\n",
      "           1       0.50      0.63      0.56        19\n",
      "\n",
      "    accuracy                           0.68        60\n",
      "   macro avg       0.65      0.67      0.66        60\n",
      "weighted avg       0.71      0.68      0.69        60\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classification Report of Gradient Boost Classifier Model\n",
    "\n",
    "print('Classification Report of Gradient Boost Classifier Model\\n')\n",
    "print('Train')\n",
    "print(classification_report(y_train,y_pred_train_gb))\n",
    "print('')\n",
    "print('Test')\n",
    "print(classification_report(y_test,y_pred_test_gb))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix \n",
      " <sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x7fc084e851e0>\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Confusion Matrix \\n', ConfusionMatrixDisplay.from_estimator(gb, X_train_final, y_train, cmap='Reds'));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix \n",
      " <sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x7fc08743a890>\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Confusion Matrix \\n', ConfusionMatrixDisplay.from_estimator(gb, X_test_final, y_test, cmap='Reds'));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### VIII.I.II. With PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict Train-set and Test-set on Random Forest Classifier Model\n",
    "\n",
    "y_pred_train_rf = rf_pca.predict(X_train_final_pca)\n",
    "y_pred_test_rf  = rf_pca.predict(X_test_final_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report of Random Forest Classifier Model\n",
      "\n",
      "Train\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00       162\n",
      "           1       1.00      1.00      1.00        77\n",
      "\n",
      "    accuracy                           1.00       239\n",
      "   macro avg       1.00      1.00      1.00       239\n",
      "weighted avg       1.00      1.00      1.00       239\n",
      "\n",
      "\n",
      "Test\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.88      0.88        41\n",
      "           1       0.74      0.74      0.74        19\n",
      "\n",
      "    accuracy                           0.83        60\n",
      "   macro avg       0.81      0.81      0.81        60\n",
      "weighted avg       0.83      0.83      0.83        60\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classification Report of Random Forest Classifier Model\n",
    "\n",
    "print('Classification Report of Random Forest Classifier Model\\n')\n",
    "print('Train')\n",
    "print(classification_report(y_train,y_pred_train_rf))\n",
    "print('')\n",
    "print('Test')\n",
    "print(classification_report(y_test,y_pred_test_rf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix \n",
      " <sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x7fc08588c100>\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Confusion Matrix \\n', ConfusionMatrixDisplay.from_estimator(rf_pca, X_train_final_pca, y_train, cmap='Reds'));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix \n",
      " <sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x7fc083f39c60>\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Confusion Matrix \\n', ConfusionMatrixDisplay.from_estimator(rf_pca, X_test_final_pca, y_test, cmap='Reds'));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict Train-set and Test-set on Gradient Boost Classifier Model\n",
    "\n",
    "y_pred_train_gb = gb_pca.predict(X_train_final_pca)\n",
    "y_pred_test_gb  = gb_pca.predict(X_test_final_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report of Gradient Boost Classifier Model\n",
      "\n",
      "Train\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00       162\n",
      "           1       1.00      1.00      1.00        77\n",
      "\n",
      "    accuracy                           1.00       239\n",
      "   macro avg       1.00      1.00      1.00       239\n",
      "weighted avg       1.00      1.00      1.00       239\n",
      "\n",
      "\n",
      "Test\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.85      0.88        41\n",
      "           1       0.71      0.79      0.75        19\n",
      "\n",
      "    accuracy                           0.83        60\n",
      "   macro avg       0.81      0.82      0.81        60\n",
      "weighted avg       0.84      0.83      0.84        60\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classification Report of Gradient Boost Classifier Model\n",
    "\n",
    "print('Classification Report of Gradient Boost Classifier Model\\n')\n",
    "print('Train')\n",
    "print(classification_report(y_train,y_pred_train_gb))\n",
    "print('')\n",
    "print('Test')\n",
    "print(classification_report(y_test,y_pred_test_gb))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix \n",
      " <sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x7fc0857c4730>\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Confusion Matrix \\n', ConfusionMatrixDisplay.from_estimator(gb_pca, X_train_final_pca, y_train, cmap='Reds'));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix \n",
      " <sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x7fc084e3ccd0>\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Confusion Matrix \\n', ConfusionMatrixDisplay.from_estimator(gb_pca, X_test_final_pca, y_test, cmap='Reds'));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### VIII.I.III. Result Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the classification report results above, we can find out that in:\n",
    "\n",
    "**Random Forest Classifier**\n",
    "\n",
    "- Not using PCA:\n",
    "\n",
    "1. On the training data, the model has an accuracy of about 69%, with an average F1-Score of 0.68. This shows that the model is able to classify the data with a reasonable balance between precision and recall.\n",
    "2. However, on the test data, the model accuracy increased to about 73%, with an average F1-Score of 0.73. This may indicate that the model has a better ability to generalize beyond the training data.\n",
    "\n",
    "- Using PCA:\n",
    "\n",
    "1. The use of PCA here led to excellent performance on both training and test data, with accuracy and F1-Score reaching 100% for training data and 83% for test data.\n",
    "2. There is a possibility of overcomplicating or overfitting the model on the training data, resulting in very high results on the training data but slightly lower performance on the testing data.\n",
    "\n",
    "\n",
    "**GradientBoost Classifier**\n",
    "\n",
    "- Does not use PCA:\n",
    "\n",
    "1. This model has relatively lower performance compared to Random Forest. The accuracy on the training data was around 67%, with an average F1-Score of 0.66.\n",
    "2. Although the performance on the test data was better than the training data, the accuracy and F1-Score only reached about 68% and 0.69, respectively. This suggests that the model may have problems in generalization.\n",
    "\n",
    "- Using PCA:\n",
    "1. As in Random Forest, the use of PCA here also resulted in excellent performance on both training and testing data, with accuracy and F1-Score reaching 100% for training data and 83% for testing data.\n",
    "2. As before, there are indications of over-complexity or overfitting of the model on the training data.\n",
    "\n",
    "\n",
    "**Conclusion**\n",
    "\n",
    "- Using PCA significantly improves performance on training data, but can show signs of overfitting on testing data.\n",
    "- The Random Forest model seems to be better at overfitting than GradientBoost, as the difference between training and testing data is not too large in Random Forest."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### VIII.II. Cross Validation Model Checking"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### VIII.II.I. Without PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score - All - Cross Validation  :  [0.75       0.75       0.91666667 0.875      0.83333333 0.91666667\n",
      " 0.875      0.875      0.83333333 0.82608696]\n",
      "F1 Score - Mean - Cross Validation :  0.8451086956521738\n",
      "F1 Score - Std - Cross Validation  :  0.056260136429453315\n",
      "F1 Score - Range of Test-Set       :  0.7888485592227206 - 0.9013688320816271\n"
     ]
    }
   ],
   "source": [
    "# Cross Validation using `cross_val_score` - Random Forest Classifier\n",
    "\n",
    "f1_score_train_cross_val = cross_val_score(rf, \n",
    "                                           X_train_final, \n",
    "                                           y_train, \n",
    "                                           cv=10)\n",
    "\n",
    "print('F1 Score - All - Cross Validation  : ', f1_score_train_cross_val)\n",
    "print('F1 Score - Mean - Cross Validation : ', f1_score_train_cross_val.mean())\n",
    "print('F1 Score - Std - Cross Validation  : ', f1_score_train_cross_val.std())\n",
    "print('F1 Score - Range of Test-Set       : ', (f1_score_train_cross_val.mean()-f1_score_train_cross_val.std()) , '-', (f1_score_train_cross_val.mean()+f1_score_train_cross_val.std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score - All - Cross Validation  :  [0.70833333 0.875      0.83333333 0.79166667 0.875      0.91666667\n",
      " 0.875      0.91666667 0.83333333 0.82608696]\n",
      "F1 Score - Mean - Cross Validation :  0.8451086956521738\n",
      "F1 Score - Std - Cross Validation  :  0.059265716677375305\n",
      "F1 Score - Range of Test-Set       :  0.7858429789747985 - 0.9043744123295492\n"
     ]
    }
   ],
   "source": [
    "# Cross Validation using `cross_val_score` - GradientBoost Classifier\n",
    "\n",
    "f1_score_train_cross_val = cross_val_score(gb, \n",
    "                                           X_train_final, \n",
    "                                           y_train, \n",
    "                                           cv=10)\n",
    "\n",
    "print('F1 Score - All - Cross Validation  : ', f1_score_train_cross_val)\n",
    "print('F1 Score - Mean - Cross Validation : ', f1_score_train_cross_val.mean())\n",
    "print('F1 Score - Std - Cross Validation  : ', f1_score_train_cross_val.std())\n",
    "print('F1 Score - Range of Test-Set       : ', (f1_score_train_cross_val.mean()-f1_score_train_cross_val.std()) , '-', (f1_score_train_cross_val.mean()+f1_score_train_cross_val.std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### VIII.II.II. With PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score - All - Cross Validation  :  [0.70833333 0.66666667 0.83333333 0.66666667 0.83333333 0.875\n",
      " 0.83333333 0.70833333 0.875      0.7826087 ]\n",
      "F1 Score - Mean - Cross Validation :  0.7782608695652173\n",
      "F1 Score - Std - Cross Validation  :  0.0790702244784712\n",
      "F1 Score - Range of Test-Set       :  0.6991906450867461 - 0.8573310940436886\n"
     ]
    }
   ],
   "source": [
    "# Cross Validation using `cross_val_score` - Random Forest Classifier\n",
    "\n",
    "f1_score_train_cross_val = cross_val_score(rf_pca, \n",
    "                                           X_train_final_pca, \n",
    "                                           y_train, \n",
    "                                           cv=10)\n",
    "\n",
    "print('F1 Score - All - Cross Validation  : ', f1_score_train_cross_val)\n",
    "print('F1 Score - Mean - Cross Validation : ', f1_score_train_cross_val.mean())\n",
    "print('F1 Score - Std - Cross Validation  : ', f1_score_train_cross_val.std())\n",
    "print('F1 Score - Range of Test-Set       : ', (f1_score_train_cross_val.mean()-f1_score_train_cross_val.std()) , '-', (f1_score_train_cross_val.mean()+f1_score_train_cross_val.std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score - All - Cross Validation  :  [0.66666667 0.625      0.79166667 0.70833333 0.83333333 0.91666667\n",
      " 0.79166667 0.75       0.875      0.7826087 ]\n",
      "F1 Score - Mean - Cross Validation :  0.7740942028985507\n",
      "F1 Score - Std - Cross Validation  :  0.0856638902332662\n",
      "F1 Score - Range of Test-Set       :  0.6884303126652844 - 0.859758093131817\n"
     ]
    }
   ],
   "source": [
    "# Cross Validation using `cross_val_score` - GradientBoost Classifier\n",
    "\n",
    "f1_score_train_cross_val = cross_val_score(gb_pca, \n",
    "                                            X_train_final_pca, \n",
    "                                            y_train, \n",
    "                                            cv=10)\n",
    "\n",
    "print('F1 Score - All - Cross Validation  : ', f1_score_train_cross_val)\n",
    "print('F1 Score - Mean - Cross Validation : ', f1_score_train_cross_val.mean())\n",
    "print('F1 Score - Std - Cross Validation  : ', f1_score_train_cross_val.std())\n",
    "print('F1 Score - Range of Test-Set       : ', (f1_score_train_cross_val.mean()-f1_score_train_cross_val.std()) , '-', (f1_score_train_cross_val.mean()+f1_score_train_cross_val.std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### VIII.II.III. Result Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the cross validation results above, we can see that at:\n",
    "\n",
    "**Random Forest Classifier**\n",
    "\n",
    "- Does not use PCA:\n",
    "\n",
    "1. Average F1 Score from cross validation: 0.8451\n",
    "2. Standard deviation of F1 Score from cross validation: 0.0563\n",
    "3. F1 Score range on the test set: 0.7888 to 0.9014\n",
    "4. The high average F1 Score indicates that the Random Forest Classifier model without PCA does a pretty good job of classifying your data.\n",
    "5. The low standard deviation indicates that the model has good consistency in performance on each fold of cross validation.\n",
    "\n",
    "- Using PCA:\n",
    "\n",
    "1. Average F1 Score from cross validation: 0.7783\n",
    "2. Standard deviation of F1 Score from cross validation: 0.0791\n",
    "3. F1 Score range on the test set: 0.6992 to 0.8573\n",
    "4. The model with PCA has a slightly lower average F1 Score than without PCA.\n",
    "5. Higher standard deviation indicates greater performance variation between cross validation folds.\n",
    "\n",
    "\n",
    "**GradientBoost Classifier**\n",
    "\n",
    "- Does not use PCA:\n",
    "\n",
    "1. Average F1 Score from cross validation: 0.8451\n",
    "2. Standard deviation of F1 Score from cross validation: 0.0593\n",
    "3. F1 Score range on the test set: 0.7858 to 0.9044\n",
    "4. As with Random Forest, the GradientBoost Classifier model without PCA also has a good average F1 Score.\n",
    "5. The moderate standard deviation indicates good stability in the performance of this model.\n",
    "\n",
    "- Using PCA:\n",
    "1. Average F1 Score from cross validation: 0.7741\n",
    "2. Standard deviation of F1 Score from cross validation: 0.0857\n",
    "3. F1 Score range on the test set: 0.6884 to 0.8598\n",
    "4. The model with PCA had a lower average F1 Score than without PCA, suggesting that in this context, PCA may not provide significant benefits."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### VIII.III. Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### VIII.III.I. Hyperparameter Tuning - Random Forest Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this step, I will perform hyperparameter tuning with the best Random Forest Classifier model results based on the cross validation results above.\n",
    "\n",
    "In this situation, the model without PCA has a slight advantage in average F1 Score. Both models have relatively similar performance. However, based on the average F1 Score, the model without PCA has a slightly higher average F1 Score (0.845 vs 0.778) and the standard deviation in the model with PCA is higher, this difference may not be significant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define Hyperparameters\n",
    "\n",
    "param_grid_rf = {'n_estimators'     : [10, 50, 100, 150, 200],\n",
    "                'max_depth'         : [2, 4, 6, 8, 10, 12],\n",
    "                'criterion'         : ['gini','entropy'],\n",
    "                'random_state'      : [50]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
       "                         &#x27;max_depth&#x27;: [2, 4, 6, 8, 10, 12],\n",
       "                         &#x27;n_estimators&#x27;: [10, 50, 100, 150, 200],\n",
       "                         &#x27;random_state&#x27;: [50]})</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-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(estimator=RandomForestClassifier(),\n",
       "             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
       "                         &#x27;max_depth&#x27;: [2, 4, 6, 8, 10, 12],\n",
       "                         &#x27;n_estimators&#x27;: [10, 50, 100, 150, 200],\n",
       "                         &#x27;random_state&#x27;: [50]})</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-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" 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-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" 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(estimator=RandomForestClassifier(),\n",
       "             param_grid={'criterion': ['gini', 'entropy'],\n",
       "                         'max_depth': [2, 4, 6, 8, 10, 12],\n",
       "                         'n_estimators': [10, 50, 100, 150, 200],\n",
       "                         'random_state': [50]})"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train with Grid Search\n",
    "\n",
    "grid_rf = GridSearchCV(estimator=RandomForestClassifier(), \n",
    "                        param_grid=param_grid_rf\n",
    "                        )\n",
    "\n",
    "grid_rf.fit(X_train_num_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'criterion': 'gini', 'max_depth': 8, 'n_estimators': 100, 'random_state': 50}\n",
      "RandomForestClassifier(max_depth=8, random_state=50)\n"
     ]
    }
   ],
   "source": [
    "# Print best parameter after tuning\n",
    "print(grid_rf.best_params_)\n",
    "\n",
    "# Print how our model looks after hyper-parameter tuning\n",
    "print(grid_rf.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      1.00      0.98       162\n",
      "           1       1.00      0.94      0.97        77\n",
      "\n",
      "    accuracy                           0.98       239\n",
      "   macro avg       0.99      0.97      0.98       239\n",
      "weighted avg       0.98      0.98      0.98       239\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Predict using selected hyper-parameter\n",
    "grid_pred_test_rf = grid_rf.predict(X_train_final)\n",
    "\n",
    "# Print classification report\n",
    "print(classification_report(y_train, grid_pred_test_rf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix \n",
      " <sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x7fc0857ffd30>\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Confusion Matrix \\n', ConfusionMatrixDisplay.from_estimator(grid_rf, X_train_final, y_train, cmap='Reds'));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.85      0.88        41\n",
      "           1       0.71      0.79      0.75        19\n",
      "\n",
      "    accuracy                           0.83        60\n",
      "   macro avg       0.81      0.82      0.81        60\n",
      "weighted avg       0.84      0.83      0.84        60\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Predict using selected hyper-parameter\n",
    "\n",
    "grid_pred_test_rf = grid_rf.predict(X_test_final)\n",
    "\n",
    "# Print classification report\n",
    "\n",
    "print(classification_report(y_test, grid_pred_test_rf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix \n",
      " <sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x7fc08587feb0>\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Confusion Matrix \\n', ConfusionMatrixDisplay.from_estimator(grid_rf, X_test_final, y_test, cmap='Reds'));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "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>actual</th>\n",
       "      <th>pred_rf</th>\n",
       "      <th>grid_pred_rf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     actual  pred_rf  grid_pred_rf\n",
       "102       1        1             1\n",
       "294       0        0             0\n",
       "29        0        0             0\n",
       "129       0        0             1\n",
       "160       1        0             0\n",
       "0         1        1             1\n",
       "221       0        0             0\n",
       "24        1        0             0\n",
       "270       0        1             1\n",
       "122       1        1             1"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prediction Checking\n",
    "\n",
    "eval_pred_rfc = pd.DataFrame({\n",
    "    'actual': y_test,\n",
    "    'pred_rf': y_pred_test_rf,\n",
    "    'grid_pred_rf': grid_pred_test_rf\n",
    "})\n",
    "eval_pred_rfc.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### VIII.III.II. Hyperparameter Tuning - GradientBoost Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this step, I will perform hyperparameter tuning with the best AdaBoost Classifier model results based on the cross validation results above.\n",
    "\n",
    "In this case, I chose to perform hyperparameter tuning on the GradientBoost Classifier model without PCA because the performance of the model without PCA (0.845 average F1 Score) has a slightly higher average F1 Score value than the model with PCA (0.774 average F1 Score) and the standard deviation of GradientBoost without PCA is lower than the standard deviation of the model with PCA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define Hyperparameters\n",
    "\n",
    "param_grid_gb = {'n_estimators'     : [5, 10, 50, 100, 250, 300, 350, 400],\n",
    "                'learning_rate'     : [0.00001, 0.0001, 0.001, 0.01, 0.1, 1.0],\n",
    "                'max_depth'         : [1, 3, 5, 7, 9],\n",
    "                'random_state'      : [50]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(estimator=GradientBoostingClassifier(),\n",
       "             param_grid={&#x27;learning_rate&#x27;: [1e-05, 0.0001, 0.001, 0.01, 0.1,\n",
       "                                           1.0],\n",
       "                         &#x27;max_depth&#x27;: [1, 3, 5, 7, 9],\n",
       "                         &#x27;n_estimators&#x27;: [5, 10, 50, 100, 250, 300, 350, 400],\n",
       "                         &#x27;random_state&#x27;: [50]})</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-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(estimator=GradientBoostingClassifier(),\n",
       "             param_grid={&#x27;learning_rate&#x27;: [1e-05, 0.0001, 0.001, 0.01, 0.1,\n",
       "                                           1.0],\n",
       "                         &#x27;max_depth&#x27;: [1, 3, 5, 7, 9],\n",
       "                         &#x27;n_estimators&#x27;: [5, 10, 50, 100, 250, 300, 350, 400],\n",
       "                         &#x27;random_state&#x27;: [50]})</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-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier()</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-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(estimator=GradientBoostingClassifier(),\n",
       "             param_grid={'learning_rate': [1e-05, 0.0001, 0.001, 0.01, 0.1,\n",
       "                                           1.0],\n",
       "                         'max_depth': [1, 3, 5, 7, 9],\n",
       "                         'n_estimators': [5, 10, 50, 100, 250, 300, 350, 400],\n",
       "                         'random_state': [50]})"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train with Grid Search\n",
    "\n",
    "grid_gb = GridSearchCV(estimator=GradientBoostingClassifier(), \n",
    "                        param_grid=param_grid_gb)\n",
    "\n",
    "grid_gb.fit(X_train_final, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'learning_rate': 1.0, 'max_depth': 5, 'n_estimators': 10, 'random_state': 50}\n",
      "GradientBoostingClassifier(learning_rate=1.0, max_depth=5, n_estimators=10,\n",
      "                           random_state=50)\n"
     ]
    }
   ],
   "source": [
    "# Print best parameter after tuning\n",
    "print(grid_gb.best_params_)\n",
    "# Print how our model looks after hyper-parameter tuning\n",
    "print(grid_gb.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00       162\n",
      "           1       1.00      1.00      1.00        77\n",
      "\n",
      "    accuracy                           1.00       239\n",
      "   macro avg       1.00      1.00      1.00       239\n",
      "weighted avg       1.00      1.00      1.00       239\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Predict using selected hyper-parameter\n",
    "grid_pred_train_gb = grid_gb.predict(X_train_final)\n",
    "\n",
    "# Print classification report\n",
    "print(classification_report(y_train, grid_pred_train_gb))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix \n",
      " <sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x7fc085e13d60>\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Confusion Matrix \\n', ConfusionMatrixDisplay.from_estimator(grid_gb, X_train_final, y_train, cmap='Reds'));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.83      0.88        41\n",
      "           1       0.71      0.89      0.79        19\n",
      "\n",
      "    accuracy                           0.85        60\n",
      "   macro avg       0.83      0.86      0.84        60\n",
      "weighted avg       0.87      0.85      0.85        60\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Predict using selected hyper-parameter\n",
    "grid_pred_test_gb = grid_gb.predict(X_test_final)\n",
    "# Print classification report\n",
    "print(classification_report(y_test, grid_pred_test_gb))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix \n",
      " <sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x7fc085eaab30>\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Confusion Matrix \\n', ConfusionMatrixDisplay.from_estimator(grid_gb, X_test_final, y_test, cmap='Reds'));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "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>actual</th>\n",
       "      <th>pred_rf</th>\n",
       "      <th>grid_pred_rf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     actual  pred_rf  grid_pred_rf\n",
       "102       1        1             1\n",
       "294       0        0             0\n",
       "29        0        0             0\n",
       "129       0        0             1\n",
       "160       1        0             1\n",
       "0         1        1             1\n",
       "221       0        0             0\n",
       "24        1        0             0\n",
       "270       0        1             1\n",
       "122       1        1             1"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prediction Checking\n",
    "\n",
    "eval_pred_gb = pd.DataFrame({\n",
    "    'actual': y_test,\n",
    "    'pred_rf': y_pred_test_gb,\n",
    "    'grid_pred_rf': grid_pred_test_gb\n",
    "})\n",
    "eval_pred_gb.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### VIII.III.III. Result Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are some insights that can be drawn from the test results given for the Random Forest Classifier and GradientBoost Classifier models before and after hyperparameter tuning using GridSearchCV:\n",
    "\n",
    "**Random Forest Classifier**\n",
    "\n",
    "- Before Hyperparameter Tuning:\n",
    "\n",
    "1. Train Set: The model has an accuracy of about 0.69, with higher precision for class 0 and better recall for class 1. F1-score for class 0 is higher than class 1.\n",
    "2. Test Set: The model accuracy is about 0.73. Again, the precision for class 0 is higher, while the recall is better for class 1. The F1-score performance for both classes still looks balanced.\n",
    "\n",
    "- After Hyperparameter Tuning:\n",
    "\n",
    "1. Train Set: After hyperparameter tuning, the model has a very high accuracy (around 0.98). The model achieves perfect precision and recall for both classes, which results in a very high F1-score.\n",
    "2. Test Set: The model accuracy dropped slightly to about 0.83 after hyperparameter tuning. The model still performed well with high precision for class 0 and better recall for class 1. The F1-score also remained high.\n",
    "\n",
    "**GradientBoost Classifier**\n",
    "\n",
    "- Before Hyperparameter Tuning:\n",
    "\n",
    "1. Train Set: The model has an accuracy of about 0.67, with higher precision for class 0 and better recall for class 1. However, the F1-score performance for both classes is not optimal.\n",
    "2. Test Set: The model accuracy is about 0.68. Same as above, precision is better for class 0 and recall is better for class 1. F1-score still has potential improvements.\n",
    "\n",
    "- After Hyperparameter Tuning:\n",
    "\n",
    "1. Train Set: After hyperparameter tuning, the model has perfect accuracy (1.00) in the training set. Precision and recall are also perfect for both classes, resulting in a high F1-score.\n",
    "2. Test Set: The model accuracy increased to about 0.85 after hyperparameter tuning. The model still had good precision for class 0 and better recall for class 1. The F1-score also improved, although there was still room for improvement."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IX. Model Saving"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the files\n",
    "\n",
    "with open('list_num_cols.txt', 'w') as file_1:\n",
    "  json.dump(num_columns, file_1)\n",
    "\n",
    "with open('model_scaler.pkl', 'wb') as file_2:\n",
    "  pickle.dump(scaler, file_2)\n",
    "\n",
    "with open('model_rfc.pkl', 'wb') as file_3:\n",
    "  pickle.dump(rf, file_3)\n",
    "  \n",
    "with open('model_gbc.pkl', 'wb') as file_4:\n",
    "  pickle.dump(gb, file_4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## X. Model Inference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This step can be seen in : h8dsft_P1G3_THEO_INF.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XI. Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The model without PCA had a better average F1 Score in both cases (Random Forest and GradientBoost).\n",
    "- Models without PCA also tend to have lower variation in performance.\n",
    "- Hyperparameter tuning significantly improves the performance of both models on the training set, but on some metrics, the performance on the testing set may slightly decrease after tuning. This indicates potential overfitting after tuning.\n",
    "- The Random Forest Classifier model performed better after tuning than the GradientBoost Classifier model, especially in terms of class 1 accuracy and precision.\n",
    "- Despite the hyperparameter tuning, the performance on the test set still varies between class 0 and class 1, indicating an imbalance in the dataset. Further improvements may be needed to address this imbalance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conceptual Problems"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Explain the background of bagging and how bagging works!\n",
    "\n",
    "**Background of Bagging**:\n",
    "\n",
    "- Bagging, which stands for Bootstrap Aggregating, is one of the ensemble learning techniques used in machine learning. Ensemble learning involves combining results from multiple learning models to improve prediction performance. Bagging was developed to address the problem of overfitting that often occurs in complex single learning models.\n",
    "\n",
    "- Overfitting occurs when the learning model is too complex and can \"memorize\" the training data, but performs poorly on data it has never seen before (test data). Bagging aims to reduce its variance and improve prediction performance by combining multiple models that may differ or have lower variance.\n",
    "\n",
    "**How Bagging Works**:\n",
    "\n",
    "- Bootstrap Sampling: Bagging starts by generating a number of new datasets obtained by bootstrap sampling method. Bootstrap sampling involves random sampling with replacement from the original training dataset. This means some data may appear more than once in a bootstrap set, while some data may not appear at all. This step creates variation in the new dataset that will be used to train the models in the ensemble.\n",
    "\n",
    "- Model Base: Each bootstrap dataset is used to train the same or similar learning model, but on each different dataset. This model is known as the base model or base classifier. It can be a decision tree, linear regression, SVM, or any other type of model.\n",
    "\n",
    "- Aggregation Prediction: After the base models are trained on different bootstrap datasets, individual predictions of each base model are generated on never-before-seen test data. These predictions are then aggregated to produce the final prediction. Typically, in the case of classification, majority voting is used (e.g., through majority voting), whereas in the case of regression, the average of the predictions of the base models is used.\n",
    "\n",
    "- Reducing Variance: One of the main advantages of bagging is that through bootstrapping methods and merging of prediction results, the variance tends to be reduced. Lower variance leads to a more stable ensemble model and better performance on test data that has never been seen.\n",
    "\n",
    "- Prevents Overfitting: Bagging also helps reduce the risk of overfitting as different base models will tend to have lower variance. This reduces the likelihood that one overly complex and overfit model will dominate the ensemble results.\n",
    "\n",
    "In conclusion, bagging is an ensemble learning technique that is effective in reducing overfitting and improving prediction performance by combining results from multiple models trained on different bootstrap datasets.\n",
    "\n",
    "---\n",
    "\n",
    "2. Explain the difference between the Random Forest algorithm and your chosen boosting algorithm!\n",
    "\n",
    "**Random Forest**:\n",
    "\n",
    "- Approach: Random Forest is an aggregation-based ensemble learning. This means it combines predictions from multiple base models to produce the final prediction.\n",
    "- Model Building Process: RF uses a large number of independently created decision trees.\n",
    "- Bootstrap Sampling: Each tree in RF is created using a bootstrap sampling technique. Each tree is trained on a random subset of training data taken from the original dataset with replacement.\n",
    "- Feature Selection: At each tree generation, only a random subset of features are selected to be used in dividing the nodes in the tree. This helps reduce the correlation between the trees in the ensemble.\n",
    "- Final Prediction: The final prediction of Random Forest is obtained by aggregating the prediction results from all the trees and using majority vote (in case of classification) or average (in case of regression).\n",
    "\n",
    "**Boosting**:\n",
    "\n",
    "- Approach: Boosting is an adaptation-based ensemble learning. This means the model-base is generated sequentially, where each model tries to correct the error of the previous model.\n",
    "- Model Generation Process: Boosting involves creating model-bases that depend on each other sequentially.\n",
    "- Emphasis on Difficult Data: Data that is difficult to recognize by the previous model is given a higher weight, so that subsequent models focus more on those difficult cases.\n",
    "- Error Correction: Each model-base is focused on reducing the errors made by the previous model. This is achieved by giving higher weights to data that was misrecognized by the previous model.\n",
    "- Weight Adjustment: After each iteration, the weights of the data are changed so that the misrecognized data has a greater influence on subsequent model building.\n",
    "- Final Prediction: The final prediction of Boosting is obtained by combining the prediction results of all the base models, by assigning weights to each base model according to their performance.\n",
    "\n",
    "In conclusion, the fundamental difference between Random Forest and Boosting is that Random Forest uses many independent decision trees with random feature retrieval, while Boosting generates model-bases adaptively with a focus on difficult cases and error correction from previous models."
   ]
  }
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