diff --git "a/anemia.ipynb" "b/anemia.ipynb"
new file mode 100644--- /dev/null
+++ "b/anemia.ipynb"
@@ -0,0 +1,1169 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.metrics import accuracy_score,RocCurveDisplay,PrecisionRecallDisplay,ConfusionMatrixDisplay\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.model_selection import train_test_split,GridSearchCV\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 1421 entries, 0 to 1420\n",
+      "Data columns (total 6 columns):\n",
+      " #   Column      Non-Null Count  Dtype  \n",
+      "---  ------      --------------  -----  \n",
+      " 0   Gender      1421 non-null   int64  \n",
+      " 1   Hemoglobin  1421 non-null   float64\n",
+      " 2   MCH         1421 non-null   float64\n",
+      " 3   MCHC        1421 non-null   float64\n",
+      " 4   MCV         1421 non-null   float64\n",
+      " 5   Result      1421 non-null   int64  \n",
+      "dtypes: float64(4), int64(2)\n",
+      "memory usage: 66.7 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "data = pd.read_csv(\"anemia.csv\")\n",
+    "data.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "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>Gender</th>\n",
+       "      <th>Hemoglobin</th>\n",
+       "      <th>MCH</th>\n",
+       "      <th>MCHC</th>\n",
+       "      <th>MCV</th>\n",
+       "      <th>Result</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>1421.000000</td>\n",
+       "      <td>1421.000000</td>\n",
+       "      <td>1421.000000</td>\n",
+       "      <td>1421.000000</td>\n",
+       "      <td>1421.000000</td>\n",
+       "      <td>1421.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.520760</td>\n",
+       "      <td>13.412738</td>\n",
+       "      <td>22.905630</td>\n",
+       "      <td>30.251232</td>\n",
+       "      <td>85.523786</td>\n",
+       "      <td>0.436312</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.499745</td>\n",
+       "      <td>1.974546</td>\n",
+       "      <td>3.969375</td>\n",
+       "      <td>1.400898</td>\n",
+       "      <td>9.636701</td>\n",
+       "      <td>0.496102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>6.600000</td>\n",
+       "      <td>16.000000</td>\n",
+       "      <td>27.800000</td>\n",
+       "      <td>69.400000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>11.700000</td>\n",
+       "      <td>19.400000</td>\n",
+       "      <td>29.000000</td>\n",
+       "      <td>77.300000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>13.200000</td>\n",
+       "      <td>22.700000</td>\n",
+       "      <td>30.400000</td>\n",
+       "      <td>85.300000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>15.000000</td>\n",
+       "      <td>26.200000</td>\n",
+       "      <td>31.400000</td>\n",
+       "      <td>94.200000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>16.900000</td>\n",
+       "      <td>30.000000</td>\n",
+       "      <td>32.500000</td>\n",
+       "      <td>101.600000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Gender   Hemoglobin          MCH         MCHC          MCV  \\\n",
+       "count  1421.000000  1421.000000  1421.000000  1421.000000  1421.000000   \n",
+       "mean      0.520760    13.412738    22.905630    30.251232    85.523786   \n",
+       "std       0.499745     1.974546     3.969375     1.400898     9.636701   \n",
+       "min       0.000000     6.600000    16.000000    27.800000    69.400000   \n",
+       "25%       0.000000    11.700000    19.400000    29.000000    77.300000   \n",
+       "50%       1.000000    13.200000    22.700000    30.400000    85.300000   \n",
+       "75%       1.000000    15.000000    26.200000    31.400000    94.200000   \n",
+       "max       1.000000    16.900000    30.000000    32.500000   101.600000   \n",
+       "\n",
+       "            Result  \n",
+       "count  1421.000000  \n",
+       "mean      0.436312  \n",
+       "std       0.496102  \n",
+       "min       0.000000  \n",
+       "25%       0.000000  \n",
+       "50%       0.000000  \n",
+       "75%       1.000000  \n",
+       "max       1.000000  "
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = data.drop_duplicates()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Class Distribution:\n",
+      "Result\n",
+      "0    0.537453\n",
+      "1    0.462547\n",
+      "Name: proportion, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "class_distribution = data['Result'].value_counts(normalize=True)\n",
+    "print(\"Class Distribution:\")\n",
+    "print(class_distribution)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Gender\n",
+       "1    279\n",
+       "0    255\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[\"Gender\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Gender        0\n",
+       "Hemoglobin    0\n",
+       "MCH           0\n",
+       "MCHC          0\n",
+       "MCV           0\n",
+       "Result        0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = data[\"Result\"]\n",
+    "x = data.drop([\"Result\",\"MCH\",\"MCHC\"],axis=1)\n",
+    "# x_dropped = data.drop([\"MCH\",\"MCHC\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xtrain,xtest,ytrain,ytest = train_test_split(x,y,test_size=0.2,random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# scaler = StandardScaler()\n",
+    "\n",
+    "# X_train = scaler.fit_transform(xtrain)\n",
+    "\n",
+    "# X_test = scaler.transform(xtest)\n",
+    "\n",
+    "X_train = xtrain\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_test = xtest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "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",
+       "    .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>Gender</th>\n",
+       "      <th>Hemoglobin</th>\n",
+       "      <th>MCV</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>906</th>\n",
+       "      <td>0</td>\n",
+       "      <td>11.7</td>\n",
+       "      <td>71.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>716</th>\n",
+       "      <td>1</td>\n",
+       "      <td>15.2</td>\n",
+       "      <td>90.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>943</th>\n",
+       "      <td>1</td>\n",
+       "      <td>12.1</td>\n",
+       "      <td>86.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>930</th>\n",
+       "      <td>1</td>\n",
+       "      <td>11.6</td>\n",
+       "      <td>96.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>729</th>\n",
+       "      <td>1</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>71.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>1</td>\n",
+       "      <td>11.2</td>\n",
+       "      <td>93.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>106</th>\n",
+       "      <td>0</td>\n",
+       "      <td>14.8</td>\n",
+       "      <td>91.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>474</th>\n",
+       "      <td>1</td>\n",
+       "      <td>10.6</td>\n",
+       "      <td>83.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>852</th>\n",
+       "      <td>1</td>\n",
+       "      <td>10.8</td>\n",
+       "      <td>99.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>102</th>\n",
+       "      <td>1</td>\n",
+       "      <td>10.4</td>\n",
+       "      <td>90.6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>427 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Gender  Hemoglobin   MCV\n",
+       "906       0        11.7  71.6\n",
+       "716       1        15.2  90.2\n",
+       "943       1        12.1  86.9\n",
+       "930       1        11.6  96.7\n",
+       "729       1        14.0  71.1\n",
+       "..      ...         ...   ...\n",
+       "71        1        11.2  93.9\n",
+       "106       0        14.8  91.1\n",
+       "474       1        10.6  83.7\n",
+       "852       1        10.8  99.6\n",
+       "102       1        10.4  90.6\n",
+       "\n",
+       "[427 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "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>Gender</th>\n",
+       "      <th>Hemoglobin</th>\n",
+       "      <th>MCV</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>222</th>\n",
+       "      <td>0</td>\n",
+       "      <td>11.1</td>\n",
+       "      <td>73.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>131</th>\n",
+       "      <td>1</td>\n",
+       "      <td>13.9</td>\n",
+       "      <td>98.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>149</th>\n",
+       "      <td>1</td>\n",
+       "      <td>16.4</td>\n",
+       "      <td>77.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>290</th>\n",
+       "      <td>0</td>\n",
+       "      <td>10.5</td>\n",
+       "      <td>83.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>84</th>\n",
+       "      <td>1</td>\n",
+       "      <td>12.8</td>\n",
+       "      <td>100.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>738</th>\n",
+       "      <td>1</td>\n",
+       "      <td>13.2</td>\n",
+       "      <td>70.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177</th>\n",
+       "      <td>0</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>100.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>220</th>\n",
+       "      <td>1</td>\n",
+       "      <td>11.4</td>\n",
+       "      <td>70.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1396</th>\n",
+       "      <td>1</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>82.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>800</th>\n",
+       "      <td>0</td>\n",
+       "      <td>12.9</td>\n",
+       "      <td>81.7</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>107 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Gender  Hemoglobin    MCV\n",
+       "222        0        11.1   73.3\n",
+       "131        1        13.9   98.5\n",
+       "149        1        16.4   77.4\n",
+       "290        0        10.5   83.2\n",
+       "84         1        12.8  100.9\n",
+       "...      ...         ...    ...\n",
+       "738        1        13.2   70.4\n",
+       "177        0        11.5  100.8\n",
+       "220        1        11.4   70.0\n",
+       "1396       1        13.0   82.8\n",
+       "800        0        12.9   81.7\n",
+       "\n",
+       "[107 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "906    1\n",
+       "716    0\n",
+       "943    1\n",
+       "930    1\n",
+       "729    0\n",
+       "      ..\n",
+       "71     1\n",
+       "106    0\n",
+       "474    1\n",
+       "852    1\n",
+       "102    1\n",
+       "Name: Result, Length: 427, dtype: int64"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ytrain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "222     1\n",
+       "131     0\n",
+       "149     0\n",
+       "290     1\n",
+       "84      1\n",
+       "       ..\n",
+       "738     1\n",
+       "177     1\n",
+       "220     1\n",
+       "1396    1\n",
+       "800     0\n",
+       "Name: Result, Length: 107, dtype: int64"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ytest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Models to be evaluated\n",
+    "models = [\n",
+    "    ('RandomForest', RandomForestClassifier()),\n",
+    "    ('GradientBoosting', GradientBoostingClassifier()),\n",
+    "    ('SVM', SVC()),\n",
+    "    (\"Logistic Regression\",LogisticRegression(class_weight='balanced'))\n",
+    "]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# Pipeline for preprocessing and model training\n",
+    "results = {}\n",
+    "roc_auc_scores = {}\n",
+    "for name, model in models:\n",
+    "    pipe = Pipeline(steps=[('classifier', model)])\n",
+    "    # Define hyperparameters grid for GridSearchCV\n",
+    "    param_grid = {}\n",
+    "    if name == 'RandomForest':\n",
+    "        param_grid = {\n",
+    "            'classifier__n_estimators': [5,10,20,50, 100, 200],\n",
+    "            'classifier__max_depth': [None, 5, 10,20,30],\n",
+    "            'classifier__min_samples_split': [2, 5, 10],\n",
+    "            'classifier__class_weight': ['balanced']\n",
+    "        }\n",
+    "    elif name == 'GradientBoosting':\n",
+    "        param_grid = {\n",
+    "            'classifier__n_estimators': [5,10,20,50, 100, 200],\n",
+    "            'classifier__learning_rate': [0.1, 0.01,0.001],\n",
+    "            'classifier__max_depth': [3, 5, 7],\n",
+    "        }\n",
+    "    elif name == 'SVM':\n",
+    "        param_grid = {\n",
+    "            'classifier__C': [0.1, 1, 10],\n",
+    "            'classifier__kernel': ['linear', 'rbf'],\n",
+    "            'classifier__class_weight': ['balanced']\n",
+    "        }\n",
+    "    \n",
+    "    \n",
+    "    # Perform GridSearchCV\n",
+    "    grid_search = GridSearchCV(pipe, param_grid, cv=5)\n",
+    "    grid_search.fit(X_train, ytrain)\n",
+    "    \n",
+    "    # Save results\n",
+    "    results[name] = {\n",
+    "        'best_params': grid_search.best_params_,\n",
+    "        'best_score': grid_search.best_score_,\n",
+    "        # 'test_accuracy': accuracy_score(ytest, grid_search.predict(X_test))\n",
+    "    }\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RandomForest:\n",
+      "  Best Parameters: {'classifier__class_weight': 'balanced', 'classifier__max_depth': None, 'classifier__min_samples_split': 2, 'classifier__n_estimators': 5}\n",
+      "  Best CV Score: 0.9976744186046511\n",
+      "GradientBoosting:\n",
+      "  Best Parameters: {'classifier__learning_rate': 0.1, 'classifier__max_depth': 3, 'classifier__n_estimators': 5}\n",
+      "  Best CV Score: 0.9953488372093023\n",
+      "SVM:\n",
+      "  Best Parameters: {'classifier__C': 10, 'classifier__class_weight': 'balanced', 'classifier__kernel': 'linear'}\n",
+      "  Best CV Score: 0.9859644322845418\n",
+      "Logistic Regression:\n",
+      "  Best Parameters: {}\n",
+      "  Best CV Score: 0.9789329685362518\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# Print results\n",
+    "for name, result in results.items():\n",
+    "    print(f\"{name}:\")\n",
+    "    print(\"  Best Parameters:\", result['best_params'])\n",
+    "    print(\"  Best CV Score:\", result['best_score'])\n",
+    "    # print(\"  Test Accuracy:\", result['test_accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "rf = RandomForestClassifier(n_estimators=5)\n",
+    "rf.fit(X_train,ytrain)\n",
+    "rf_y_preds = rf.predict(X_test)\n",
+    "print(accuracy_score(ytest,rf_y_preds))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "lr = LogisticRegression()\n",
+    "lr.fit(X_train,ytrain)\n",
+    "lr_y_preds = rf.predict(X_test)\n",
+    "print(accuracy_score(ytest,lr_y_preds))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "svm = SVC(C= 10,kernel= 'linear')\n",
+    "svm.fit(X_train,ytrain)\n",
+    "svm_y_preds = rf.predict(X_test)\n",
+    "print(accuracy_score(ytest,svm_y_preds))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "gb = GradientBoostingClassifier(n_estimators=50)\n",
+    "gb.fit(X_train,ytrain)\n",
+    "gb_y_preds = rf.predict(X_test)\n",
+    "print(accuracy_score(ytest,gb_y_preds))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<sklearn.metrics._plot.precision_recall_curve.PrecisionRecallDisplay at 0x7a854452ec20>"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "PrecisionRecallDisplay.from_estimator(gb,X_test,ytest)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<sklearn.metrics._plot.precision_recall_curve.PrecisionRecallDisplay at 0x7a8529906d10>"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "PrecisionRecallDisplay.from_estimator(lr,X_test,ytest)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<sklearn.metrics._plot.precision_recall_curve.PrecisionRecallDisplay at 0x7a85266db220>"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "PrecisionRecallDisplay.from_estimator(svm,X_test,ytest)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<sklearn.metrics._plot.precision_recall_curve.PrecisionRecallDisplay at 0x7a8526810190>"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "PrecisionRecallDisplay.from_estimator(rf,X_test,ytest)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x7a85267e9f30>"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "RocCurveDisplay.from_estimator(lr,xtest,ytest)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x7a8527631ab0>"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "RocCurveDisplay.from_estimator(lr,X_test,ytest)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x7a85265ad5d0>"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "RocCurveDisplay.from_estimator(rf,X_test,ytest)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x7a85265ae380>"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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ePTAzM0O/fv1w+PBhdOzY0RD1ERERGYTkAOzTpw/effddbN26FT169IC5ubkh6iIiIjIoyQGYlpbGe34SEVGNp1MAZmVlwdbWFgAghEBWVla5fUv6ERERPc90CsDatWsjJSUFDg4OsLOzg0wmK9VHCAGZTFbmkyKIiIieNzoF4M8//4w6deoAAI4dO2bQgoiIiKqCTgHYqVMnzb/r168PNze3UkeBQggkJyfrtzoiIiIDkXwdYP369XH//v1S7RkZGahfv77kAlavXg13d3dYWFjA29sbv/32W4X9Hz58iIkTJ8LZ2RkKhQJNmjTBgQMHJG+XiIiMm+RRoCXf9T0pOzsbFhYWkta1fft2BAYGIiwsDN7e3ggNDYW/vz/i4uLg4OBQqr9KpULXrl3h4OCAH3/8Ea6urkhMTISdnZ3Ut0FEREZO5wAMDAwE8PgJ8HPmzIGVlZVmXnFxMc6dOwdPT09JG1++fDnGjBmDUaNGAQDCwsKwf/9+bNq0CdOnTy/Vf9OmTcjIyMCZM2c01x+6u7tL2iYREREgIQCjo6MBPD4CvHLlCuRyuWaeXC5Hq1atMG3aNJ03rFKpcOHCBcyYMUPTZmJiAj8/P5w9e7bMZfbu3QsfHx9MnDgRP/30E+rWrYvBgwfjiy++gKmpaZnLFBQUoKCgQPO6oks4iIjIeOgcgCWjP0eNGoUVK1Y88/V+6enpKC4uhqOjo1a7o6Mjbty4UeYy8fHx+PnnnzFkyBAcOHAAt27dwoQJE1BYWIjg4OAylwkJCeFNuomIqBTJg2A2b95cbRe7q9VqODg4YN26dWjbti0CAgIwa9YshIWFlbvMjBkzkJmZqZk4UpWIiAAdjwD79u2L8PBw2Nraom/fvhX23bVrl04btre3h6mpKdLS0rTa09LS4OTkVOYyzs7OMDc31zrd+corryA1NRUqlUrrtGwJhUIBhUKhU01ERGQ8dDoCVCqVmpGfSqWywklXcrkcbdu2RVRUlKZNrVYjKioKPj4+ZS7Tvn173Lp1C2q1WtN28+ZNODs7lxl+RERE5dHpCHDz5s1l/vtZBQYGYsSIEfDy8kK7du0QGhqKnJwczajQ4cOHw9XVFSEhIQCA8ePHY9WqVfj4448xefJk/PHHH1i0aBGmTJmit5qIiMg4SL4OMC8vD0IIzWUQiYmJ2L17Nzw8PNCtWzdJ6woICMD9+/cRFBSE1NRUeHp6IjIyUjMwJikpCSYm/ztIdXNzw6FDhzB16lS0bNkSrq6u+Pjjj/HFF19IfRtERGTkZEIIIWWBbt26oW/fvhg3bhwePnyIpk2bQi6XIz09HcuXL8f48eMNVateZGVlQalUIjMzE2YWVvAIOgQAiJ3vDyu55L8HiIioiv3z9/izDMqUPAr04sWL6NChAwDgxx9/hJOTExITE7F161Z8++23lS6EiIioKkkOwNzcXM0DcQ8fPoy+ffvCxMQEr7/+OhITE/VeIBERkSFIDsBGjRphz549SE5OxqFDhzTf+927d48PwyUiohpDcgAGBQVh2rRpcHd3R7t27TSXLBw+fBitW7fWe4FERESGIHnUR79+/fDGG28gJSUFrVq10rR36dIFffr00WtxREREhlKpYY9OTk5wcnLCn3/+CQB46aWX0K5dO70WRkREZEiST4Gq1WrMnz8fSqUS9erVQ7169WBnZ4cFCxZo3aGFiIjoeSb5CHDWrFnYuHEjFi9ejPbt2wMATp06hblz5yI/Px8LFy7Ue5FERET6JjkAt2zZgg0bNuC9997TtJXclWXChAkMQCIiqhEknwLNyMhAs2bNSrU3a9YMGRkZeimKiIjI0CQHYKtWrbBq1apS7atWrdIaFUpERPQ8k3wKdMmSJXjnnXdw9OhRzTWAZ8+eRXJyMg4cOKD3AomIiAxB8hFgp06dcPPmTfTt2xcPHz7Ew4cP0bdvX8TFxWnuEUpERPS8k3QEeOfOHRw5cgQqlQoDBw5E8+bNDVUXERGRQekcgMeOHcO7776LvLy8xwuamWHTpk0YOnSowYojIiIyFJ1Pgc6ZMwddu3bF3bt38ffff2PMmDH4/PPPDVkbERGRwegcgFevXsWiRYvg7OyM2rVrY+nSpbh37x7+/vtvQ9ZHRERkEDoHYFZWFuzt7TWvraysYGlpiczMTIMURkREZEiSBsEcOnQISqVS81qtViMqKgpXr17VtP3zDjFERETPK0kBOGLEiFJtH330kebfMpkMxcXFz14VERGRgekcgHzSAxERvUgkXwhPRET0ItApAH/99VedV5ibm4tr165VuiAiIqKqoFMADhs2DP7+/ti5cydycnLK7BMbG4uZM2eiYcOGuHDhgl6LJCIi0jedvgOMjY3F2rVrMXv2bAwePBhNmjSBi4sLLCws8ODBA9y4cQPZ2dno06cPDh8+jBYtWhi6biIiomciE0IIKQucP38ep06dQmJiIvLy8mBvb4/WrVvjrbfeQp06dQxVp95kZWVBqVQiMzMTZhZW8Ag6BACIne8PK7nkh2MQEVEV++fvcVtb20qvR/JvfC8vL3h5eVV6g0RERM8DjgIlIiKjxAAkIiKjxAAkIiKjxAAkIiKj9EwBmJ+fr686iIiIqpTkAFSr1ViwYAFcXV1hbW2N+Ph4AI8fmLtx40a9F0hERGQIkgPwyy+/RHh4OJYsWQK5XK5pb968OTZs2KDX4oiIiAxFcgBu3boV69atw5AhQ2Bqaqppb9WqFW7cuKHX4oiIiAxFcgDevXsXjRo1KtWuVqtRWFiol6KIiIgMTXIAenh44OTJk6Xaf/zxR7Ru3VovRRERERma5FuhBQUFYcSIEbh79y7UajV27dqFuLg4bN26Ffv27TNEjURERHon+QiwV69e+O9//4ujR4+iVq1aCAoKwvXr1/Hf//4XXbt2NUSNREREelepxx906NABR44c0XctREREVUbyEWCDBg3w999/l2p/+PAhGjRooJeiiIiIDE1yAN65cwfFxcWl2gsKCnD37l29FEVERGRoOp8C3bt3r+bfhw4dglKp1LwuLi5GVFQU3N3d9VocERGRoegcgL179wYAyGQyjBgxQmueubk53N3dsWzZMr0WR0REZCg6B6BarQYA1K9fH7///jvs7e0NVhQREZGhSR4FmpCQYIg6iIiIqlSlLoPIycnBiRMnkJSUBJVKpTVvypQpeimMiIjIkCQHYHR0NHr06IHc3Fzk5OSgTp06SE9Ph5WVFRwcHBiARERUI0i+DGLq1Kno2bMnHjx4AEtLS/z6669ITExE27Zt8fXXXxuiRiIiIr2THIAxMTH49NNPYWJiAlNTUxQUFMDNzQ1LlizBzJkzDVEjERGR3kkOQHNzc5iYPF7MwcEBSUlJAAClUonk5GT9VkdERGQgkr8DbN26NX7//Xc0btwYnTp1QlBQENLT0/Hdd9+hefPmhqiRiIhI7yQfAS5atAjOzs4AgIULF6J27doYP3487t+/j3//+996L5CIiMgQJB8Benl5af7t4OCAyMhIvRZERERUFSQfAZbn4sWLePfdd/W1OiIiIoOSFICHDh3CtGnTMHPmTMTHxwMAbty4gd69e+O1117T3C6NiIjoeafzKdCNGzdizJgxqFOnDh48eIANGzZg+fLlmDx5MgICAnD16lW88sorhqyViIhIb3Q+AlyxYgW++uorpKenY8eOHUhPT8eaNWtw5coVhIWFMfyIiKhG0TkAb9++jf79+wMA+vbtCzMzMyxduhQvvfSSwYojIiIyFJ0DMC8vD1ZWVgAePxNQoVBoLocgIiKqaSRdBrFhwwZYW1sDAIqKihAeHl7quYC8GTYREdUEMiGE0KWju7s7ZDJZxSuTyTSjQ6VYvXo1li5ditTUVLRq1QorV65Eu3btnrpcREQEBg0ahF69emHPnj06bSsrKwtKpRKZmZkws7CCR9AhAEDsfH9YySv1dCgiIqpC//w9bmtrW+n16Pwb/86dO5XeSEW2b9+OwMBAhIWFwdvbG6GhofD390dcXBwcHBwqrGfatGno0KGDQeoiIqIXm94uhK+s5cuXY8yYMRg1ahQ8PDwQFhYGKysrbNq0qdxliouLMWTIEMybNw8NGjSowmqJiOhFUa0BqFKpcOHCBfj5+WnaTExM4Ofnh7Nnz5a73Pz58+Hg4IDRo0c/dRsFBQXIysrSmoiIiKo1ANPT01FcXAxHR0etdkdHR6Smppa5zKlTp7Bx40asX79ep22EhIRAqVRqJjc3t2eum4iIar5qPwUqxaNHjzBs2DCsX7++1OjT8syYMQOZmZmaic8sJCIioBJPg9Ane3t7mJqaIi0tTas9LS0NTk5Opfrfvn0bd+7cQc+ePTVtJfcfNTMzQ1xcHBo2bKi1jEKhgEKhMED1RERUk1XqCPD27duYPXs2Bg0ahHv37gEADh48iGvXrklaj1wuR9u2bREVFaVpU6vViIqKgo+PT6n+zZo1w5UrVxATE6OZ3nvvPbz11luIiYnh6U0iItKZ5AA8ceIEWrRogXPnzmHXrl3Izs4GAFy6dAnBwcGSCwgMDMT69euxZcsWXL9+HePHj0dOTg5GjRoFABg+fDhmzJgBALCwsEDz5s21Jjs7O9jY2KB58+aQy+WSt09ERMZJ8inQ6dOn48svv0RgYCBsbGw07Z07d8aqVaskFxAQEID79+8jKCgIqamp8PT0RGRkpGZgTFJSEkxMatRXlUREVAPofCeYEtbW1rhy5Qrq168PGxsbXLp0CQ0aNMCdO3fQrFkz5OfnG6pWveCdYIiIajZ93QlG8qGVnZ0dUlJSSrVHR0fD1dW10oUQERFVJckBOHDgQHzxxRdITU2FTCaDWq3G6dOnMW3aNAwfPtwQNRIREemd5ABctGgRmjVrBjc3N2RnZ8PDwwMdO3aEr68vZs+ebYgaiYiI9E7yl15yuRzr16/HnDlzcPXqVWRnZ6N169Zo3LixIeojIiIyCMkBeOrUKbzxxht4+eWX8fLLLxuiJiIiIoOTfAq0c+fOqF+/PmbOnInY2FhD1ERERGRwkgPwr7/+wqeffooTJ06gefPm8PT0xNKlS/Hnn38aoj4iIiKDkByA9vb2mDRpEk6fPo3bt2+jf//+2LJlC9zd3dG5c2dD1EhERKR3z3SLlfr162P69OlYvHgxWrRogRMnTuirLiIiIoOqdACePn0aEyZMgLOzMwYPHozmzZtj//79+qyNiIjIYCSPAp0xYwYiIiLw119/oWvXrlixYgV69eoFKysrQ9RHRERkEJID8JdffsFnn32GAQMG6PxQWiIioueN5AA8ffq0IeogIiKqUjoF4N69e/H222/D3Nwce/furbDve++9p5fCiIiIDEmnAOzduzdSU1Ph4OCA3r17l9tPJpOhuLhYX7UREREZjE4BqFary/w3ERFRTSX5MoitW7eioKCgVLtKpcLWrVv1UhQREZGhSQ7AUaNGITMzs1T7o0ePMGrUKL0URUREZGiSA1AIAZlMVqr9zz//hFKp1EtRREREhqbzZRCtW7eGTCaDTCZDly5dYGb2v0WLi4uRkJCA7t27G6RIIiIifdM5AEtGf8bExMDf3x/W1taaeXK5HO7u7nj//ff1XiAREZEh6ByAwcHBAAB3d3cEBATAwsLCYEUREREZmuQ7wYwYMcIQdRAREVUpnQKwTp06uHnzJuzt7VG7du0yB8GUyMjI0FtxREREhqJTAH7zzTewsbHR/LuiACQiIqoJdArAf572HDlypKFqISIiqjKSrwO8ePEirly5onn9008/oXfv3pg5cyZUKpVeiyMiIjIUyQH40Ucf4ebNmwCA+Ph4BAQEwMrKCjt37sTnn3+u9wKJiIgMQXIA3rx5E56engCAnTt3olOnTvj+++8RHh6O//znP/quj4iIyCAqdSu0kidCHD16FD169AAAuLm5IT09Xb/VERERGYjkAPTy8sKXX36J7777DidOnMA777wDAEhISICjo6PeCyQiIjIEyQEYGhqKixcvYtKkSZg1axYaNWoEAPjxxx/h6+ur9wKJiIgMQfKdYFq2bKk1CrTE0qVLYWpqqpeiiIiIDE1yAJa4cOECrl+/DgDw8PBAmzZt9FYUERGRoUkOwHv37iEgIAAnTpyAnZ0dAODhw4d46623EBERgbp16+q7RiIiIr2T/B3g5MmTkZ2djWvXriEjIwMZGRm4evUqsrKyMGXKFEPUSEREpHeSjwAjIyNx9OhRvPLKK5o2Dw8PrF69Gt26ddNrcURERIYi+QhQrVbD3Ny8VLu5ubnm+kAiIqLnneQA7Ny5Mz7++GP89ddfmra7d+9i6tSp6NKli16LIyIiMhTJAbhq1SpkZWXB3d0dDRs2RMOGDVG/fn1kZWVh5cqVhqiRiIhI7yR/B+jm5oaLFy8iKipKcxnEK6+8Aj8/P70XR0REZCiSAnD79u3Yu3cvVCoVunTpgsmTJxuqLiIiIoPSOQDXrl2LiRMnonHjxrC0tMSuXbtw+/ZtLF261JD1ERERGYTO3wGuWrUKwcHBiIuLQ0xMDLZs2YI1a9YYsjYiIiKD0TkA4+PjMWLECM3rwYMHo6ioCCkpKQYpjIiIyJB0DsCCggLUqlXrfwuamEAulyMvL88ghRERERmSpEEwc+bMgZWVlea1SqXCwoULoVQqNW3Lly/XX3VEREQGonMAduzYEXFxcVptvr6+iI+P17yWyWT6q4yIiMiAdA7A48ePG7AMIiKiqiX5TjBEREQvAgYgEREZJQYgEREZJQYgEREZJQYgEREZpUoF4MmTJzF06FD4+Pjg7t27AIDvvvsOp06d0mtxREREhiI5AP/zn//A398flpaWiI6ORkFBAQAgMzMTixYt0nuBREREhiA5AL/88kuEhYVh/fr1MDc317S3b98eFy9e1GtxREREhiI5AOPi4tCxY8dS7UqlEg8fPtRHTURERAYnOQCdnJxw69atUu2nTp1CgwYN9FIUERGRoUkOwDFjxuDjjz/GuXPnIJPJ8Ndff2Hbtm2YNm0axo8fb4gaiYiI9E7S0yAAYPr06VCr1ejSpQtyc3PRsWNHKBQKTJs2DZMnTzZEjURERHonOQBlMhlmzZqFzz77DLdu3UJ2djY8PDxgbW1tiPqIiIgMotIXwsvlcnh4eKBdu3bPHH6rV6+Gu7s7LCws4O3tjd9++63cvuvXr0eHDh1Qu3Zt1K5dG35+fhX2JyIiKovkI8C33nqrwuf+/fzzz5LWt337dgQGBiIsLAze3t4IDQ2Fv78/4uLi4ODgUKr/8ePHMWjQIPj6+sLCwgJfffUVunXrhmvXrsHV1VXq2yEiIiMl+QjQ09MTrVq10kweHh5QqVS4ePEiWrRoIbmA5cuXY8yYMRg1ahQ8PDwQFhYGKysrbNq0qcz+27Ztw4QJE+Dp6YlmzZphw4YNUKvViIqKkrxtIiIyXpKPAL/55psy2+fOnYvs7GxJ61KpVLhw4QJmzJihaTMxMYGfnx/Onj2r0zpyc3NRWFiIOnXqlDm/oKBAc7caAMjKypJUIxERvZj0djPsoUOHlnvUVp709HQUFxfD0dFRq93R0RGpqak6reOLL76Ai4sL/Pz8ypwfEhICpVKpmdzc3CTVSERELya9BeDZs2dhYWGhr9XpZPHixYiIiMDu3bvL3faMGTOQmZmpmZKTk6u0RiIiej5JPgXat29frddCCKSkpOD8+fOYM2eOpHXZ29vD1NQUaWlpWu1paWlwcnKqcNmvv/4aixcvxtGjR9GyZcty+ykUCigUCkl1ERHRi0/yEeA/TycqlUrUqVMHb775Jg4cOIDg4GBJ65LL5Wjbtq3WAJaSAS0+Pj7lLrdkyRIsWLAAkZGR8PLykvoWiIiIpB0BFhcXY9SoUWjRogVq166tlwICAwMxYsQIeHl5oV27dggNDUVOTg5GjRoFABg+fDhcXV0REhICAPjqq68QFBSE77//Hu7u7prvCq2trXkxPhER6UxSAJqamqJbt264fv263gIwICAA9+/fR1BQEFJTU+Hp6YnIyEjNwJikpCSYmPzvQHXt2rVQqVTo16+f1nqCg4Mxd+5cvdREREQvPsnfATZv3hzx8fGoX7++3oqYNGkSJk2aVOa848ePa72+c+eO3rZLRETGq1IPxJ02bRr27duHlJQUZGVlaU1EREQ1gc5HgPPnz8enn36KHj16AADee+89rVuiCSEgk8lQXFys/yqJiIj0TOcAnDdvHsaNG4djx44Zsh4iIqIqoXMACiEAAJ06dTJYMURERFVF0neAFT0FgoiIqCaRNAq0SZMmTw3BjIyMZyqIiIioKkgKwHnz5kGpVBqqFiIioiojKQAHDhxY5kNqiYiIahqdvwPk939ERPQi0TkAS0aBEhERvQh0PgWqVqsNWQcREVGV0tsDcYmIiGoSBiARERklBiARERklBiARERklBiARERklBiARERklBiARERklBiARERklBiARERklBiARERklBiARERklBiARERklBiARERklBiARERklBiARERklBiARERklBiARERklBiARERklBiARERklBiARERklBiARERklBiARERklBiARERklBiARERklBiARERkls+ougIiqhhACRUVFKC4uru5SiCpkamoKMzMzyGQyg26HAUhkBFQqFVJSUpCbm1vdpRDpxMrKCs7OzpDL5QbbBgOQ6AWnVquRkJAAU1NTuLi4QC6XG/wva6LKEkJApVLh/v37SEhIQOPGjWFiYphv6xiARC84lUoFtVoNNzc3WFlZVXc5RE9laWkJc3NzJCYmQqVSwcLCwiDb4SAYIiNhqL+iiQyhKj6v/IkgIiKjxAAkIiKjxAAkInqKYcOGYdGiRdVdxgsjMjISnp6eUKvV1VoHA5CInlv379/H+PHj8fLLL0OhUMDJyQn+/v44ffo0VCoV7O3tsXjx4jKXXbBgARwdHVFYWAjg8WCgJUuWoFWrVrCysoK9vT3at2+PzZs3a/qU5dKlSzhw4ACmTJlSat4PP/wAU1NTTJw4sdS88PBw2NnZlblOmUyGPXv2aLX95z//wZtvvgmlUglra2u0bNkS8+fPR0ZGRrm1PauFCxfC19cXVlZW5db6JCEEgoKC4OzsDEtLS/j5+eGPP/7Q6pORkYEhQ4bA1tYWdnZ2GD16NLKzszXzu3fvDnNzc2zbtk2fb0cyBiARPbfef/99REdHY8uWLbh58yb27t2LN998E3///TfkcjmGDh2KzZs3l1pOCIHw8HAMHz4c5ubmUKlU8Pf3x+LFizF27FicOXMGv/32GyZOnIiVK1fi2rVr5dawcuVK9O/fH9bW1qXmbdy4EZ9//jl++OEH5OfnV/p9zpo1CwEBAXjttddw8OBBXL16FcuWLcOlS5fw3XffVXq9T6NSqdC/f3+MHz9e52WWLFmCb7/9FmFhYTh37hxq1aoFf39/rfc/ZMgQXLt2DUeOHMG+ffvwyy+/YOzYsVrrGTlyJL799lu9vZdKEUYmMzNTABCZmZkip6BQ1Ptin6j3xT6RU1BY3aURGUReXp6IjY0VeXl5mja1Wi1yCgqrZVKr1TrV/eDBAwFAHD9+vNw+ly9fFgDEyZMntdqPHTsmAIjr168LIYT46quvhImJibh48WKpdahUKpGdnV3m+ouKioRSqRT79u0rNS8+Pl5YWlqKhw8fCm9vb7Ft2zat+Zs3bxZKpbLM9QIQu3fvFkIIce7cOQFAhIaGltn3wYMHZbbrU0W1/pNarRZOTk5i6dKlmraHDx8KhUIhfvjhByGEELGxsQKA+P333zV9Dh48KGQymbh7966mLTExUQAQt27dKnNbZX1uS/zz9/iz4HWAREYor7AYHkGHqmXbsfP9YSV/+q8ea2trWFtbY8+ePXj99dehUChK9WnRogVee+01bNq0CW+88YamffPmzfD19UWzZs0AANu2bYOfnx9at25dah3m5uYwNzcvs4bLly8jMzMTXl5epeZt3rwZ77zzDpRKJYYOHYqNGzdi8ODBT31fT9q2bRusra0xYcKEMudXdGry1VdfRWJiYrnzO3TogIMHD0quqTwJCQlITU2Fn5+fpk2pVMLb2xtnz57FwIEDcfbsWdjZ2WntMz8/P5iYmODcuXPo06cPAODll1+Go6MjTp48iYYNG+qtRikYgET0XDIzM0N4eDjGjBmDsLAwtGnTBp06dcLAgQPRsmVLTb/Ro0dj2rRp+Pbbb2FtbY1Hjx7hxx9/1Dq99scff+DNN9+UXENiYiJMTU3h4OCg1a5WqxEeHo6VK1cCAAYOHIhPP/0UCQkJqF+/vqRt/PHHH2jQoEG5IVyRAwcOVPj9paWlpeR1ViQ1NRUA4OjoqNXu6OiomZeamlpqf5mZmaFOnTqaPiVcXFwqDHBDYwASGSFLc1PEzvevtm3r6v3338c777yDkydP4tdff8XBgwexZMkSbNiwASNHjgQADBo0CFOnTsWOHTvwwQcfYPv27TAxMUFAQIBmPUKIStWal5cHhUJR6tZxR44cQU5ODnr06AEAsLe3R9euXbFp0yYsWLBA0jYqWxsA1KtXr9LLPg8sLS2r9f60HARDZIRkMhms5GbVMkm9D6mFhQW6du2KOXPm4MyZMxg5ciSCg4M1821tbdGvXz/NYJjNmzdjwIABWoNWmjRpghs3bkjeT/b29sjNzYVKpdJq37hxIzIyMmBpaQkzMzOYmZnhwIED2LJli2Zov62tLXJyckoN9X/48CGAx6cOS2qLj4+v8EiuPK+++qrmVHFZ09tvvy15nRVxcnICAKSlpWm1p6WlaeY5OTnh3r17WvOLioqQkZGh6VMiIyMDdevW1WuNUjAAiahG8fDwQE5Ojlbb6NGjcerUKezbtw9nzpzB6NGjteYPHjwYR48eRXR0dKn1FRYWllpfCU9PTwBAbGyspu3vv//GTz/9hIiICMTExGim6OhoPHjwAIcPHwYANG3aFEVFRYiJidFa58WLFwE8Dr6S2rKzs7FmzZoyaygJzLIcOHBAq4Ynpw0bNpS7bGXUr18fTk5OiIqK0rRlZWXh3Llz8PHxAQD4+Pjg4cOHuHDhgqbPzz//DLVaDW9vb01bfn4+bt++Xeb3slXmmYbQ1EAcBUrGpqLRdM+z9PR08dZbb4nvvvtOXLp0ScTHx4sdO3YIR0dH8cEHH2j1VavVolGjRqJ27dqiWbNmpdaVn58vOnToIGrXri1WrVolYmJixO3bt8X27dtFmzZtRHR0dLl1tGnTRqxcuVLz+ptvvhHOzs5ljmYdMGCA6Nevn+Z1t27dRKtWrcTRo0dFfHy8OHjwoGjatKkICAjQWu7zzz8Xpqam4rPPPhNnzpwRd+7cEUePHhX9+vUrd3SoPiQmJoro6Ggxb948YW1tLaKjo0V0dLR49OiRpk/Tpk3Frl27NK8XL14s7OzsxE8//SQuX74sevXqJerXr6/1+erevbto3bq1OHfunDh16pRo3LixGDRokNa2jx07JqytrUVOTk6ZtVXFKFAGIAOQXnA1NQDz8/PF9OnTRZs2bYRSqRRWVlaiadOmYvbs2SI3N7dU/0WLFgkAYsmSJeWuLyQkRLRo0UJYWFiIOnXqiPbt24vw8HBRWFj+z/+aNWvE66+/rnndokULMWHChDL7bt++XcjlcnH//n0hxONLGKZMmSIaNmwoLC0tRePGjcXnn3+uFTD/XLZjx47CxsZG1KpVS7Rs2VLMnz/foJdBjBgxQgAoNR07dkzTB4DYvHmz5rVarRZz5swRjo6OQqFQiC5duoi4uDit9f79999i0KBBwtraWtja2opRo0aVes9jx44VH330Ubm1VUUAyoR4hm9ga6CsrCwolUpkZmbCzMJKMxRc16HZRDVNfn6+ZnSioR4r8yLLy8tD06ZNsX37ds1pPno26enpaNq0Kc6fP1/uqNmKPrf//D1ua2tb6Tr4HSARUQUsLS2xdetWpKenV3cpL4w7d+5gzZo1ki8Z0Tce8hARPUVlriGk8nl5eZV5c4GqxiNAIiIySgxAIiIySgxAIiNhZOPdqIaris8rA5DoBVdyj8nqvOUUkVQln9fK3CNVV8/FIJjVq1dj6dKlSE1NRatWrbBy5Uq0a9eu3P47d+7EnDlzcOfOHTRu3BhfffWV5p58RKTN1NQUdnZ2mttTWVlZSb4dGVFVEUIgNzcX9+7dg52dHUxNdb93rFTVHoDbt29HYGAgwsLC4O3tjdDQUPj7+yMuLq7UHcUB4MyZMxg0aBBCQkLw7rvv4vvvv0fv3r1x8eJFNG/evBreAdHzr+QejE/eo5HoeWVnZ1fq3qH6Vu0Xwnt7e+O1117DqlWrADx+zIibmxsmT56M6dOnl+ofEBCAnJwc7Nu3T9P2+uuvw9PTE2FhYU/dHi+EJ2NWXFxcqZsuE1Ulc3PzCo/89HUhfLX+xlepVLhw4QJmzJihaTMxMYGfnx/Onj1b5jJnz55FYGCgVpu/vz/27NlTZv+CggIUFBRoXmdlZT174UQ1lKmpqUFPKRHVJNU6CCY9PR3FxcUVPlzxSampqZL6h4SEQKlUaiY3Nzf9FE9ERDXaCz8KdMaMGcjMzNRMycnJmnklDwWNne8v6SGdRERU81XrKVB7e3uYmppW+HDFJzk5OUnqr1AooFAoypxX8lBQIiIyPtX6218ul6Nt27aIiopC7969ATweBBMVFYVJkyaVuYyPjw+ioqLwySefaNqOHDmi813aS8b88LtAIqKaqeT39zOP4XymhynpQUREhFAoFCI8PFzExsaKsWPHCjs7O5GamiqEEGLYsGFi+vTpmv6nT58WZmZm4uuvvxbXr18XwcHBwtzcXFy5ckWn7SUnJ5f5/CtOnDhx4lSzpuTk5GfKn2o//xcQEID79+8jKCgIqamp8PT0RGRkpGagS1JSEkxM/vdVpa+vL77//nvMnj0bM2fOROPGjbFnzx6drwF0cXFBcnIybGxsIJPJkJWVBTc3NyQnJz/TcNoXFffP03EfVYz75+m4jyr25P4RQuDRo0dwcXF5pvVW+3WA1U1f15O8qLh/no77qGLcP0/HfVQxQ+2fF34UKBERUVkYgEREZJSMPgAVCgWCg4PLvVTC2HH/PB33UcW4f56O+6hihto/Rv8dIBERGSejPwIkIiLjxAAkIiKjxAAkIiKjxAAkIiKjZBQBuHr1ari7u8PCwgLe3t747bffKuy/c+dONGvWDBYWFmjRogUOHDhQRZVWDyn7Z/369ejQoQNq166N2rVrw8/P76n780Ug9TNUIiIiAjKZTHOv2xeV1P3z8OFDTJw4Ec7OzlAoFGjSpAl/zp4QGhqKpk2bwtLSEm5ubpg6dSry8/OrqNqq9csvv6Bnz55wcXGBTCYr9/mu/3T8+HG0adMGCoUCjRo1Qnh4uPQNP9ON1GqAiIgIIZfLxaZNm8S1a9fEmDFjhJ2dnUhLSyuz/+nTp4WpqalYsmSJiI2NFbNnz5Z0r9GaRur+GTx4sFi9erWIjo4W169fFyNHjhRKpVL8+eefVVx51ZG6j0okJCQIV1dX0aFDB9GrV6+qKbYaSN0/BQUFwsvLS/To0UOcOnVKJCQkiOPHj4uYmJgqrrzqSN1H27ZtEwqFQmzbtk0kJCSIQ4cOCWdnZzF16tQqrrxqHDhwQMyaNUvs2rVLABC7d++usH98fLywsrISgYGBIjY2VqxcuVKYmpqKyMhISdt94QOwXbt2YuLEiZrXxcXFwsXFRYSEhJTZf8CAAeKdd97RavP29hYfffSRQeusLlL3z5OKioqEjY2N2LJli6FKrHaV2UdFRUXC19dXbNiwQYwYMeKFDkCp+2ft2rWiQYMGQqVSVVWJ1U7qPpo4caLo3LmzVltgYKBo3769Qet8HugSgJ9//rl49dVXtdoCAgKEv7+/pG290KdAVSoVLly4AD8/P02biYkJ/Pz8cPbs2TKXOXv2rFZ/APD39y+3f01Wmf3zpNzcXBQWFqJOnTqGKrNaVXYfzZ8/Hw4ODhg9enRVlFltKrN/9u7dCx8fH0ycOBGOjo5o3rw5Fi1ahOLi4qoqu0pVZh/5+vriwoULmtOk8fHxOHDgAHr06FElNT/v9PV7utqfBmFI6enpKC4u1jxZooSjoyNu3LhR5jKpqall9k9NTTVYndWlMvvnSV988QVcXFxKfRhfFJXZR6dOncLGjRsRExNTBRVWr8rsn/j4ePz8888YMmQIDhw4gFu3bmHChAkoLCxEcHBwVZRdpSqzjwYPHoz09HS88cYbEEKgqKgI48aNw8yZM6ui5Odeeb+ns7KykJeXB0tLS53W80IfAZJhLV68GBEREdi9ezcsLCyqu5znwqNHjzBs2DCsX78e9vb21V3Oc0mtVsPBwQHr1q1D27ZtERAQgFmzZiEsLKy6S3tuHD9+HIsWLcKaNWtw8eJF7Nq1C/v378eCBQuqu7QXygt9BGhvbw9TU1OkpaVptaelpcHJyanMZZycnCT1r8kqs39KfP3111i8eDGOHj2Kli1bGrLMaiV1H92+fRt37txBz549NW1qtRoAYGZmhri4ODRs2NCwRVehynyGnJ2dYW5uDlNTU03bK6+8gtTUVKhUKsjlcoPWXNUqs4/mzJmDYcOG4cMPPwQAtGjRAjk5ORg7dixmzZql9YxUY1Te72lbW1udj/6AF/wIUC6Xo23btoiKitK0qdVqREVFwcfHp8xlfHx8tPoDwJEjR8rtX5NVZv8AwJIlS7BgwQJERkbCy8urKkqtNlL3UbNmzXDlyhXExMRopvfeew9vvfUWYmJi4ObmVpXlG1xlPkPt27fHrVu3NH8YAMDNmzfh7Oz8woUfULl9lJubWyrkSv5gELx9s/5+T0sbn1PzRERECIVCIcLDw0VsbKwYO3assLOzE6mpqUIIIYYNGyamT5+u6X/69GlhZmYmvv76a3H9+nURHBz8wl8GIWX/LF68WMjlcvHjjz+KlJQUzfTo0aPqegsGJ3UfPelFHwUqdf8kJSUJGxsbMWnSJBEXFyf27dsnHBwcxJdfflldb8HgpO6j4OBgYWNjI3744QcRHx8vDh8+LBo2bCgGDBhQXW/BoB49eiSio6NFdHS0ACCWL18uoqOjRWJiohBCiOnTp4thw4Zp+pdcBvHZZ5+J69evi9WrV/MyiPKsXLlSvPzyy0Iul4t27dqJX3/9VTOvU6dOYsSIEVr9d+zYIZo0aSLkcrl49dVXxf79+6u44qolZf/Uq1dPACg1BQcHV33hVUjqZ+ifXvQAFEL6/jlz5ozw9vYWCoVCNGjQQCxcuFAUFRVVcdVVS8o+KiwsFHPnzhUNGzYUFhYWws3NTUyYMEE8ePCg6guvAseOHSvz90rJPhkxYoTo1KlTqWU8PT2FXC4XDRo0EJs3b5a8XT4OiYiIjNIL/R0gERFReRiARERklBiARERklBiARERklBiARERklBiARERklBiARERklBiARERklBiAVK7w8HDY2dlVdxmVJpPJsGfPngr7jBw5Er17966Sep43c+bMwdixY6tkW8ePH4dMJsPDhw8r7Ofu7o7Q0FCD1iJ1G/r6OdDl8yhVbGwsXnrpJeTk5Oh1vcaCAfiCGzlyJGQyWanp1q1b1V0awsPDNfWYmJjgpZdewqhRo3Dv3j29rD8lJQVvv/02AODOnTuQyWSlntG3YsUKhIeH62V75Zk7d67mfZqamsLNzQ1jx45FRkaGpPXoM6xTU1OxYsUKzJo1S2v9JXXK5XI0atQI8+fPR1FR0TNvz9fXFykpKVAqlQDKD5Xff/+9ykK5Jli4cCF8fX1hZWVV5v7y8PDA66+/juXLl1d9cS8ABqAR6N69O1JSUrSm+vXrV3dZAABbW1ukpKTgzz//xPr163Hw4EEMGzZML+t2cnKCQqGosI9SqaySo9xXX30VKSkpSEpKwubNmxEZGYnx48cbfLvl2bBhA3x9fVGvXj2t9pLPyh9//IFPP/0Uc+fOxdKlS595e3K5HE5OTpDJZBX2q1u3LqysrJ55ey8KlUqF/v37V/hZGTVqFNauXauXP1SMDQPQCCgUCjg5OWlNpqamWL58OVq0aIFatWrBzc0NEyZMQHZ2drnruXTpEt566y3Y2NjA1tYWbdu2xfnz5zXzT506hQ4dOsDS0hJubm6YMmXKU0/NyGQyODk5wcXFBW+//TamTJmCo0ePIi8vD2q1GvPnz8dLL70EhUIBT09PREZGapZVqVSYNGkSnJ2dYWFhgXr16iEkJERr3SWnnEoCv3Xr1pDJZHjzzTcBaB9VrVu3Di4uLlqP6QGAXr164YMPPtC8/umnn9CmTRtYWFigQYMGmDdv3lN/+ZiZmcHJyQmurq7w8/ND//79ceTIEc384uJijB49GvXr14elpSWaNm2KFStWaObPnTsXW7ZswU8//aQ5Sjt+/DgAIDk5GQMGDICdnR3q1KmDXr164c6dOxXWExERofXMwhIln5V69eph/Pjx8PPzw969ewEADx48wPDhw1G7dm1YWVnh7bffxh9//KFZNjExET179kTt2rVRq1YtvPrqqzhw4AAA7VOgx48fx6hRo5CZmal5L3PnzgWgfXpy8ODBCAgI0KqvsLAQ9vb22Lp1K4DHjxUKCQnR7LdWrVrhxx9/rPC9P0nXn4M9e/agcePGsLCwgL+/P5KTk7XmV+Zz8TTz5s3D1KlT0aJFi3L7dO3aFRkZGThx4sQzbcsYMQCNmImJCb799ltcu3YNW7Zswc8//4zPP/+83P5DhgzBSy+9hN9//x0XLlzA9OnTYW5uDuDxg2C7d++O999/H5cvX8b27dtx6tQpTJo0SVJNlpaWUKvVKCoqwooVK7Bs2TJ8/fXXuHz5Mvz9/fHee+9pful+++232Lt3L3bs2IG4uDhs27YN7u7uZa73t99+AwAcPXoUKSkp2LVrV6k+/fv3x99//41jx45p2jIyMhAZGYkhQ4YAAE6ePInhw4fj448/RmxsLP79738jPDwcCxcu1Pk93rlzB4cOHdJ69p1arcZLL72EnTt3IjY2FkFBQZg5cyZ27NgBAJg2bRoGDBigdTTv6+uLwsJC+Pv7w8bGBidPnsTp06dhbW2N7t27Q6VSlbn9jIwMxMbG6vQsR0tLS816Ro4cifPnz2Pv3r04e/YshBDo0aMHCgsLAQATJ05EQUEBfvnlF1y5cgVfffUVrK2tS63T19cXoaGhmqP/lJQUTJs2rVS/IUOG4L///a9WGB06dAi5ubno06cPACAkJARbt25FWFgYrl27hqlTp2Lo0KGSwkCXn4Pc3FwsXLgQW7duxenTp/Hw4UMMHDhQM78yn4s333wTI0eO1LnO8sjlcnh6euLkyZPPvC6j84xPsaDn3IgRI4SpqamoVauWZurXr1+ZfXfu3Cn+9a9/aV5v3rxZKJVKzWsbGxsRHh5e5rKjR48WY8eO1Wo7efKkMDExEXl5eWUu8+T6b968KZo0aSK8vLyEEEK4uLiIhQsXai3z2muviQkTJgghhJg8ebLo3LmzUKvVZa4fgNi9e7cQQoiEhAQBQERHR2v1efJRRb169RIffPCB5vW///1v4eLiIoqLi4UQQnTp0kUsWrRIax3fffedcHZ2LrMGIR4/283ExETUqlVLWFhYaB71snz58nKXEUKIiRMnivfff7/cWku23bRpU619UFBQICwtLcWhQ4fKXG/JM9eSkpK02v+5frVaLY4cOSIUCoWYNm2auHnzpgAgTp8+remfnp4uLC0txY4dO4QQQrRo0ULMnTu3zG2WPO6m5HE+T/7fl6hXr5745ptvhBCPHwlkb28vtm7dqpk/aNAgERAQIIQQIj8/X1hZWYkzZ85orWP06NFi0KBBZdbx5DbKUtbPAQCtxxddv35dABDnzp0TQuj2ufjn51GIpz9H8p/K218l+vTpI0aOHKnTuuh/zKoreKnqvPXWW1i7dq3mda1atQA8PhoKCQnBjRs3kJWVhaKiIuTn5yM3N7fM72ECAwPx4Ycf4rvvvtOcxmvYsCGAx6dHL1++jG3btmn6CyGgVquRkJCAV155pczaMjMzYW1tDbVajfz8fLzxxhvYsGEDsrKy8Ndff6F9+/Za/du3b49Lly4BeHxE0rVrVzRt2hTdu3fHu+++i27duj3TvhoyZAjGjBmDNWvWQKFQYNu2bRg4cKDm6dyXLl3C6dOntf6yLy4urnC/AUDTpk2xd+9e5Ofn4//+7/8QExODyZMna/VZvXo1Nm3ahKSkJOTl5UGlUsHT07PCei9duoRbt27BxsZGqz0/Px+3b98uc5m8vDwAgIWFRal5+/btg7W1NQoLC6FWqzF48GDMnTsXUVFRMDMzg7e3t6bvv/71LzRt2hTXr18HAEyZMgXjx4/H4cOH4efnh/fffx8tW7assP6KmJmZYcCAAdi2bRuGDRuGnJwc/PTTT4iIiAAA3Lp1C7m5uejatavWciqVCq1bt9Z5O7r8HJiZmeG1117TLNOsWTPY2dnh+vXraNeuXaU+FyWncfXB0tISubm5elufsWAAGoFatWqhUaNGWm137tzBu+++i/Hjx2PhwoWoU6cOTp06hdGjR0OlUpX5Azt37lwMHjwY+/fvx8GDBxEcHIyIiAj06dMH2dnZ+OijjzBlypRSy7388svl1mZjY4OLFy/CxMQEzs7OsLS0BABkZWU99X21adMGCQkJOHjwII4ePYoBAwbAz89P8ndA/9SzZ08IIbB//3689tprOHnyJL755hvN/OzsbMybNw99+/YttWxZgVKiZFQlACxevBjvvPMO5s2bhwULFgB4/J3ctGnTsGzZMvj4+MDGxgZLly7FuXPnKqw3Ozsbbdu21frDo0TdunXLXMbe3h7A4+/0nuxT8seSXC6Hi4sLzMx0/xXx4Ycfwt/fH/v378fhw4cREhKCZcuWlQp6KYYMGYJOnTrh3r17OHLkCCwtLdG9e3cA0Jwa3b9/P1xdXbWWe9rgpxKV+TkoS2U/F/qSkZGh+WOUdMcANFIXLlyAWq3GsmXLNEc3Jd83VaRJkyZo0qQJpk6dikGDBmHz5s3o06cP2rRpg9jY2FJB+zQmJiZlLmNrawsXFxecPn0anTp10rSfPn0a7dq10+oXEBCAgIAA9OvXD927d0dGRgbq1Kmjtb6S79uKi4srrMfCwgJ9+/bFtm3bcOvWLTRt2hRt2rTRzG/Tpg3i4uIkv88nzZ49G507d8b48eM179PX1xcTJkzQ9HnyCE4ul5eqv02bNti+fTscHBxga2ur07YbNmwIW1tbxMbGokmTJlrzyvpjCQBeeeUVFBUV4dy5c/D19QUA/P3334iLi4OHh4emn5ubG8aNG4dx48ZhxowZWL9+fZkBWNZ7KYuvry/c3Nywfft2HDx4EP3799d87+zh4QGFQoGkpCStz4gUuv4cFBUV4fz585rPXlxcHB4+fKg5s6Gvz0VlXb16Ff369auWbddkHARjpBo1aoTCwkKsXLkS8fHx+O677xAWFlZu/7y8PEyaNAnHjx9HYmIiTp8+jd9//13zC+CLL77AmTNnMGnSJMTExOCPP/7ATz/9JHkQzD999tln+Oqrr7B9+3bExcVh+vTpiImJwccffwzg8ei9H374ATdu3MDNmzexc+dOODk5lXlZg4ODAywtLREZGYm0tDRkZmaWu90hQ4Zg//792LRpk2bwS4mgoCBs3boV8+bNw7Vr13D9+nVERERg9uzZkt6bj48PWrZsiUWLFgEAGjdujPPnz+PQoUO4efMm5syZg99//11rGXd3d1y+fBlxcXFIT09HYWEhhgwZAnt7e/Tq1QsnT55EQkICjh8/jilTpuDPP/8sc9smJibw8/PDqVOndK63cePG6NWrF8aMGYNTp07h0qVLGDp0KFxdXdGrVy8AwCeffIJDhw4hISEBFy9exLFjx8o99e3u7o7s7GxERUUhPT29wtN3gwcPRlhYGI4cOaL1/2FjY4Np06Zh6tSp2LJlC27fvo2LFy9i5cqV2LJli07vS9efA3Nzc0yePBnnzp3DhQsXMHLkSLz++uuaQKzM52L48OGYMWNGhfUlJSUhJiYGSUlJKC4uRkxMDGJiYrQGBt25cwd3796Fn5+fTu+Z/qG6v4Qkwypr4ESJ5cuXC2dnZ2FpaSn8/f3F1q1byx2oUFBQIAYOHCjc3NyEXC4XLi4uYtKkSVoDXH777TfRtWtXYW1tLWrVqiVatmxZahDLPz3ti/3i4mIxd+5c4erqKszNzUWrVq3EwYMHNfPXrVsnPD09Ra1atYStra3o0qWLuHjxomY+nhh0sH79euHm5iZMTExEp06dyt0/xcXFwtnZWQAQt2/fLlVXZGSk8PX1FZaWlsLW1la0a9dOrFu3rtz3ERwcLFq1alWq/YcffhAKhUIkJSWJ/Px8MXLkSKFUKoWdnZ0YP368mD59utZy9+7d0+xfAOLYsWNCCCFSUlLE8OHDhb29vVAoFKJBgwZizJgxIjMzs9yaDhw4IFxdXTWDe8rbF/+UkZEhhg0bJpRKpeYzc/PmTc38SZMmiYYNGwqFQiHq1q0rhg0bJtLT04UQpQfBCCHEuHHjxL/+9S8BQAQHBwshyh6gEhsbKwCIevXqlRrwpFarRWhoqGjatKkwNzcXdevWFf7+/uLEiRPlvo8nt6Hrz8F//vMf0aBBA6FQKISfn59ITEzUWu/TPhdPfh47deokRowYUW6dQjz+P8H/HzT1z6nk/14IIRYtWiT8/f0rXA+VTSaEENURvERUfYQQ8Pb21pzKpppJpVKhcePG+P7770sNGKOn4ylQIiMkk8mwbt063j2khktKSsLMmTMZfpXEI0AiIjJKPAIkIiKjxAAkIiKjxAAkIiKjxAAkIiKjxAAkIiKjxAAkIiKjxAAkIiKjxAAkIiKjxAAkIiKj9P8A6v2SxKVWmv0AAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "RocCurveDisplay.from_estimator(svm,X_test,ytest)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x7a85267f3580>"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "RocCurveDisplay.from_estimator(gb,X_test,ytest)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['models/svm.pkl']"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import joblib\n",
+    "\n",
+    "joblib.dump(svm,\"models/svm.pkl\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(534, 6)"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(427, 3)"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train.shape"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}