Peterside Hospital.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ab25fdc6",
   "metadata": {},
   "source": [
    "## Peterside Hospital\n",
    "### Predicting Heart disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "72698a0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "71e4d77a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import Libraries\n",
    "\n",
    "# data analysis\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# data visualization\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import missingno as msno\n",
    "\n",
    "# data preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# classifier Library (Algorithms)\n",
    "from sklearn.linear_model import SGDClassifier, LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import LinearSVC, SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "# evaluation metrics\n",
    "from sklearn.metrics import precision_score, accuracy_score, recall_score, f1_score, roc_auc_score, confusion_matrix\n",
    "\n",
    "# disable warning\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6a7c6b82",
   "metadata": {},
   "outputs": [
    {
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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>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
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       "      <th>2</th>\n",
       "      <td>41</td>\n",
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       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  load dataset\n",
    "df=pd.read_csv(r\"C:\\Users\\FOLASADE\\OneDrive\\Desktop\\Learning\\DataSci Learn 10Alytics\\PDF for Practice\\1Python Pract\\heart.csv\")\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63c4e3c1",
   "metadata": {},
   "source": [
    "### Features in the dataset and meaning\n",
    "\n",
    "- age (Age in years)\n",
    "- sex (0-female amd 1-male)\n",
    "- chest pain type (cp) (1: typical angina, 2: atypical angina, 3: non angina pain, 4: asymptomatic)\n",
    "- resting blood pressure (trestbps)\n",
    "- serum cholesterol (chol)\n",
    "- fasting blood sugar (fbs) (0-False and 1-True)\n",
    "- resting electrocardiographic results (restecg)\n",
    "- maximum heart rate achieved (thalach)\n",
    "- exercise-induced angina (exang) (0-NO and 1-YES)\n",
    "- ST depression induced by exercise relative to rest (oldpeak)\n",
    "- the slope of the peak exercise ST segment (slope)\n",
    "- number of major vessels colored by flourosopy (ca)\n",
    "- thalassemia (thal) (3=normal, 6=fixed, 7=revercible defeat)\n",
    "- Target (0-NO and 1-YES)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "eee1cb5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach',\n",
       "       'exang', 'oldpeak', 'slope', 'ca', 'thal', 'target'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e224ce31",
   "metadata": {},
   "outputs": [],
   "source": [
    "# change columns name for better recognitions\n",
    "df.columns=[\"age\",\"sex\",\"chest pain type\",\"resting blood pressure\",\"cholesterol\",\"fasting blood sugar\",\"rest ecg\",\n",
    "            \"max heart rate\",\"exercise-induced\",\"ST depression\",\"slope\",\"major vessels\",\"thalassemia\",\"Target\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3433994e",
   "metadata": {},
   "outputs": [
    {
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       "      <th>chest pain type</th>\n",
       "      <th>resting blood pressure</th>\n",
       "      <th>cholesterol</th>\n",
       "      <th>fasting blood sugar</th>\n",
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       "      <th>max heart rate</th>\n",
       "      <th>exercise-induced</th>\n",
       "      <th>ST depression</th>\n",
       "      <th>slope</th>\n",
       "      <th>major vessels</th>\n",
       "      <th>thalassemia</th>\n",
       "      <th>Target</th>\n",
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       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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      "text/plain": [
       "   age  sex  chest pain type  resting blood pressure  cholesterol  \\\n",
       "0   63    1                3                     145          233   \n",
       "1   37    1                2                     130          250   \n",
       "2   41    0                1                     130          204   \n",
       "\n",
       "   fasting blood sugar  rest ecg  max heart rate  exercise-induced  \\\n",
       "0                    1         0             150                 0   \n",
       "1                    0         1             187                 0   \n",
       "2                    0         0             172                 0   \n",
       "\n",
       "   ST depression  slope  major vessels  thalassemia  Target  \n",
       "0            2.3      0              0            1       1  \n",
       "1            3.5      0              0            2       1  \n",
       "2            1.4      2              0            2       1  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "10e01097",
   "metadata": {
    "scrolled": false
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 303 entries, 0 to 302\n",
      "Data columns (total 14 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   age                     303 non-null    int64  \n",
      " 1   sex                     303 non-null    int64  \n",
      " 2   chest pain type         303 non-null    int64  \n",
      " 3   resting blood pressure  303 non-null    int64  \n",
      " 4   cholesterol             303 non-null    int64  \n",
      " 5   fasting blood sugar     303 non-null    int64  \n",
      " 6   rest ecg                303 non-null    int64  \n",
      " 7   max heart rate          303 non-null    int64  \n",
      " 8   exercise-induced        303 non-null    int64  \n",
      " 9   ST depression           303 non-null    float64\n",
      " 10  slope                   303 non-null    int64  \n",
      " 11  major vessels           303 non-null    int64  \n",
      " 12  thalassemia             303 non-null    int64  \n",
      " 13  Target                  303 non-null    int64  \n",
      "dtypes: float64(1), int64(13)\n",
      "memory usage: 33.3 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "174a00d3",
   "metadata": {},
   "outputs": [
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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>sex</th>\n",
       "      <th>chest pain type</th>\n",
       "      <th>resting blood pressure</th>\n",
       "      <th>cholesterol</th>\n",
       "      <th>fasting blood sugar</th>\n",
       "      <th>rest ecg</th>\n",
       "      <th>max heart rate</th>\n",
       "      <th>exercise-induced</th>\n",
       "      <th>ST depression</th>\n",
       "      <th>slope</th>\n",
       "      <th>major vessels</th>\n",
       "      <th>thalassemia</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.366337</td>\n",
       "      <td>0.683168</td>\n",
       "      <td>0.966997</td>\n",
       "      <td>131.623762</td>\n",
       "      <td>246.264026</td>\n",
       "      <td>0.148515</td>\n",
       "      <td>0.528053</td>\n",
       "      <td>149.646865</td>\n",
       "      <td>0.326733</td>\n",
       "      <td>1.039604</td>\n",
       "      <td>1.399340</td>\n",
       "      <td>0.729373</td>\n",
       "      <td>2.313531</td>\n",
       "      <td>0.544554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.082101</td>\n",
       "      <td>0.466011</td>\n",
       "      <td>1.032052</td>\n",
       "      <td>17.538143</td>\n",
       "      <td>51.830751</td>\n",
       "      <td>0.356198</td>\n",
       "      <td>0.525860</td>\n",
       "      <td>22.905161</td>\n",
       "      <td>0.469794</td>\n",
       "      <td>1.161075</td>\n",
       "      <td>0.616226</td>\n",
       "      <td>1.022606</td>\n",
       "      <td>0.612277</td>\n",
       "      <td>0.498835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>47.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>133.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>274.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age         sex  chest pain type  resting blood pressure  \\\n",
       "count  303.000000  303.000000       303.000000              303.000000   \n",
       "mean    54.366337    0.683168         0.966997              131.623762   \n",
       "std      9.082101    0.466011         1.032052               17.538143   \n",
       "min     29.000000    0.000000         0.000000               94.000000   \n",
       "25%     47.500000    0.000000         0.000000              120.000000   \n",
       "50%     55.000000    1.000000         1.000000              130.000000   \n",
       "75%     61.000000    1.000000         2.000000              140.000000   \n",
       "max     77.000000    1.000000         3.000000              200.000000   \n",
       "\n",
       "       cholesterol  fasting blood sugar    rest ecg  max heart rate  \\\n",
       "count   303.000000           303.000000  303.000000      303.000000   \n",
       "mean    246.264026             0.148515    0.528053      149.646865   \n",
       "std      51.830751             0.356198    0.525860       22.905161   \n",
       "min     126.000000             0.000000    0.000000       71.000000   \n",
       "25%     211.000000             0.000000    0.000000      133.500000   \n",
       "50%     240.000000             0.000000    1.000000      153.000000   \n",
       "75%     274.500000             0.000000    1.000000      166.000000   \n",
       "max     564.000000             1.000000    2.000000      202.000000   \n",
       "\n",
       "       exercise-induced  ST depression       slope  major vessels  \\\n",
       "count        303.000000     303.000000  303.000000     303.000000   \n",
       "mean           0.326733       1.039604    1.399340       0.729373   \n",
       "std            0.469794       1.161075    0.616226       1.022606   \n",
       "min            0.000000       0.000000    0.000000       0.000000   \n",
       "25%            0.000000       0.000000    1.000000       0.000000   \n",
       "50%            0.000000       0.800000    1.000000       0.000000   \n",
       "75%            1.000000       1.600000    2.000000       1.000000   \n",
       "max            1.000000       6.200000    2.000000       4.000000   \n",
       "\n",
       "       thalassemia      Target  \n",
       "count   303.000000  303.000000  \n",
       "mean      2.313531    0.544554  \n",
       "std       0.612277    0.498835  \n",
       "min       0.000000    0.000000  \n",
       "25%       2.000000    0.000000  \n",
       "50%       2.000000    1.000000  \n",
       "75%       3.000000    1.000000  \n",
       "max       3.000000    1.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "781f5245",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.isnull().sum()\n",
    "\n",
    "plt.figure(figsize=(6,4))\n",
    "sns.heatmap(df.isnull(), cbar=True, cmap=\"Blues_r\")\n",
    "plt.title(\"Visualizing Dataset for Missing Number\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "41843fc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.bar(df, color=\"blue\", figsize=(8,4));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b8cde69",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d56448a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'sex', 'chest pain type', 'resting blood pressure',\n",
       "       'cholesterol', 'fasting blood sugar', 'rest ecg', 'max heart rate',\n",
       "       'exercise-induced', 'ST depression', 'slope', 'major vessels',\n",
       "       'thalassemia', 'Target'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d8cbbdd",
   "metadata": {},
   "source": [
    "### Visualizing to Spot Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "156af3a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs=plt.subplots(nrows=3, ncols=2, figsize=(15,10))\n",
    "sns.boxplot(x=df[\"ST depression\"],color=\"green\", ax=axs[0,0])\n",
    "#axs[0,0].set_title(\"ST depression induced by exercise relative to rest (oldpeak)\")\n",
    "\n",
    "sns.boxplot(x=df[\"resting blood pressure\"],color=\"red\", ax=axs[0,1])\n",
    "#axs[0,1].set_title(\"resting blood pressure (trestbps)\")\n",
    "\n",
    "sns.boxplot(x=df[\"cholesterol\"],color=\"blue\", ax=axs[1,0])\n",
    "#axs[1,0].set_title(\"serum cholesterol (chol)\")\n",
    "\n",
    "sns.boxplot(x=df[\"max heart rate\"],color=\"yellow\", ax=axs[1,1])\n",
    "#axs[1,1].set_title(\"maximum heart rate achieved (thalach)\")\n",
    "\n",
    "sns.boxplot(x=df[\"thalassemia\"],color=\"brown\", ax=axs[2,0])\n",
    "#axs[2,0].set_title(\"thalassemia (thal) (3=normal, 6=fixed, 7=revercible defeat)\")\n",
    "\n",
    "sns.boxplot(x=df[\"major vessels\"],color=\"grey\", ax=axs[2,1]);\n",
    "#axs[2,1].set_title(\"number of major vessels colored by flourosopy (ca)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58792ce8",
   "metadata": {},
   "source": [
    "The dataset contains several outliers, particularly in the features related to cholesterol, thalassemia, maximum heart rate, and major vessels. These outliers deviate significantly from the majority of data points and may require special attention during analysis and modeling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f9b567ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    303.000000\n",
       "mean      54.366337\n",
       "std        9.082101\n",
       "min       29.000000\n",
       "25%       47.500000\n",
       "50%       55.000000\n",
       "75%       61.000000\n",
       "max       77.000000\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.age.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a00c4542",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def Age_group(x):\n",
    "    if x <= 35:\n",
    "        return \"<=Youth\"\n",
    "    elif x <= 55:\n",
    "        return \"<=Adult\"\n",
    "    elif x <= 65:\n",
    "        return \"<=Elder\"\n",
    "    else:\n",
    "        return \"Snr_Citizen\"\n",
    "    \n",
    "df[\"Age_Group\"]= df[\"age\"].apply(Age_group)\n",
    "\n",
    "# visualizing the Age gruop of Patient\n",
    "plt.figure(figsize=(8,4))\n",
    "Age=sns.countplot(x=df[\"Age_Group\"], order=df[\"Age_Group\"].value_counts(ascending=False).index)\n",
    "values=df[\"Age_Group\"].value_counts(ascending=False).values\n",
    "Age.bar_label(container= Age.containers[0], labels=values)\n",
    "plt.xlabel(\"Age Group\")\n",
    "plt.ylabel(\"Count Age Group\")\n",
    "Age.set_title(\"Total Number of Patient\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e97aed88",
   "metadata": {},
   "source": [
    "In the heart disease case study dataset, patients between the ages of 36 and 65 form the largest age group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c1212287",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualizing Gender\n",
    "\n",
    "def gender(x):\n",
    "    if x== 1:\n",
    "        return \"Male\"\n",
    "    else:\n",
    "        return \"Female\"\n",
    "    \n",
    "df[\"Gender\"]= df[\"sex\"].apply(gender)\n",
    "\n",
    "# visualizing\n",
    "df[\"Gender\"].value_counts().plot(kind=\"pie\", autopct=\"%1.0f%%\", figsize=(5,5), colors=[\"blue\",\"yellow\"], title=\"Patient Gender\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cad4bd02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# chest pain type (cp) (1: typical angina, 2: atypical angina, 3: non angina pain, 4: asymptomatic)\n",
    "# Visualizing chest pain type\n",
    "\n",
    "def chest(x):\n",
    "    if x==1:\n",
    "        return \"Typical angina\"\n",
    "    elif x==2:\n",
    "        return \"Atypical angina\"\n",
    "    elif x==3:\n",
    "        return \"Non angina pain\"\n",
    "    else:\n",
    "        return \"Asymptomatic\"\n",
    "    \n",
    "df[\"chest_pain\"]= df[\"chest pain type\"].apply(chest)\n",
    "\n",
    "# visualizing\n",
    "plt.figure(figsize=(10,5))\n",
    "chest=sns.countplot(x=df[\"chest_pain\"], order=df[\"chest_pain\"].value_counts(ascending=False).index)\n",
    "values=df[\"chest_pain\"].value_counts(ascending=False).values\n",
    "chest.bar_label(container= chest.containers[0], labels=values)\n",
    "plt.xlabel(\"Chest Pain Type\")\n",
    "plt.ylabel(\"Count of Chest Pain\")\n",
    "chest.set_title(\"Patient Chest Pain Type\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c5348d2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# probability of heart disease\n",
    "def target(x):\n",
    "    if x==0:\n",
    "        return \"Heart Disease\"\n",
    "    else:\n",
    "        return \"Nil\"\n",
    "    \n",
    "df[\"target\"]= df[\"Target\"].apply(target)\n",
    "\n",
    "#visualise\n",
    "\"\"\"plt.figure(figsize=(7,4))\n",
    "target= sns.countplot(x=df[\"target\"], order=df[\"target\"].value_counts(ascending=False).index)\n",
    "values= df[\"target\"].value_counts(ascending=False).values\n",
    "target.bar_label(container=target.containers[0], labels=values)\n",
    "plt.xlabel(\"Heart Disease or Not\")\n",
    "plt.ylabel(\"count of heart disease\")\n",
    "target.set_title(\"Patients Heart Disease\");\"\"\"\n",
    "df[\"target\"].value_counts().plot(kind=\"pie\", figsize=(5,5),title=\"Patients Heart Disease\",colors=[\"green\",\"red\"],autopct=\"%1.0f%%\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5d6d30dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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q0jY8fvwYd+/eVQtrhW13gWcvwihYxq1bt9TCmhACKSkpeP3116VxSqUSOTk5GsssKngV9f7XrVu3yPoAoHbt2qhXrx727NkDR0dHNG/eHBYWFujYsSNGjRqFY8eO4ejRo9KtN6qCkn6m27dvR2ZmJrZu3QoHBwdpekxMTKHzlfSimvIQQuCXX36BsbFxsbedMTY2xrRp0zBt2jTcvHlT6l3r3r07zp07J23j5MmT0atXr0KXUb9+fQDA2rVr4e3tjaVLl6pNz8jI0Jhn6NChGDp0KDIzM3HgwAFMnToV3bp1w/nz5+Hg4IDq1avD09MTs2bNKnSdxYVPqnoY1OilUnAjXF1dXWlcVlYW1qxZo9FWqVSWqJfByMgI7du3xz///ANPT0/o6+tXSK0lXf/T4uLicOrUKbXDn+vXr4epqalGb9m4cePw4YcfIi0tDTY2NtKJ7MUpuIhgxYoVGuEjKysLb731Fn744Qd06dIFLVq0gFKpxKZNm9R+pI4ePYorV66oBbVu3bohNDQU1tbWxYZaLy8vzJkzB5s2bcJHH30kjd+4ceNzay/QsWNHzJkzB2vXrsXHH38sjd+yZQsyMzPRsWNHaZyjoyNiY2PV5v/zzz/x4MGDQpe9bt06tUOthw8fxpUrV6QT+4vTqVMnbN68Gfb29ujatSsAoF69eqhTpw6Cg4Px6NEjqeetKBXVM1sRSvqZFgSvp0/+F0JgxYoVpVpfWf5eijJt2jScPXsWX3zxhdo/6IpjY2ODgIAAnDp1CgsWLMDDhw9Rv359uLi44NSpUwgNDS12foVCoXEBRGxsLI4cOQJ7e/tC5zE2Nkbnzp2Rm5uLnj17Ii4uDg4ODujWrRt+++03vPbaa7C0tCzZRlOVxaBGL5WuXbti/vz5GDBgAEaMGIE7d+5g3rx5hV4h5uHhgY0bN2LTpk1wdnaGgYEBPDw8Cl3ut99+izfffBNt27bFRx99BEdHR2RkZCAxMRG//PKLdF5UaXh4eGDr1q1YunQpmjVrVuxhpwJ2dnbo0aMHQkJCYGtri7Vr1yIyMhKzZ8+GkZGRWttBgwZh8uTJOHDgAL788svnBszHjx9Lh5WLCh7du3fHjh07pN6qTz75BGFhYbC0tMTbb7+N69evY9q0abC1tVU7VBYYGIgtW7agXbt2+Pjjj+Hp6Yn8/HxcvXoVu3fvxqeffooWLVrA398fbdq0waeffor09HQ0a9YMR44cwerVqwFoHn4rjI+PD/z8/DBp0iSkp6ejTZs20lWfTZo0weDBg6W2gwcPxpQpUxAcHAwvLy+cPXsWixYtUrta9mknTpzABx98gD59+uDatWsICgpCrVq1MGrUqOfW1bFjRyxZsgS3b99WeyJFx44dER4eDktLS7VbcxTG1NQUDg4O+Pnnn9GxY0dYWVmhevXq5bpVRVmV9DP18fGBvr4++vfvj4kTJyI7OxtLly7FvXv3SrW+0vy9Frh//750/mhmZqZ0w9uDBw+ib9++z+3BbNGiBbp16wZPT09YWloiPj4ea9asQatWraS/t//973/o3Lkz/Pz8EBAQgFq1auHu3buIj4/HyZMnpduydOvWDTNmzMDUqVPh5eWFhIQETJ8+HU5OTmpX2g4fPhyGhoZo06YNbG1tkZKSgrCwMJibm0u9wdOnT0dkZCRat26NcePGoX79+sjOzsbly5fx22+/YdmyZbwf3MtEm1cyEJVGwZV3z15l9qwffvhB1K9fXyiVSuHs7CzCwsLEypUrBQCRlJQktbt8+bLw9fUVpqamAoB0BWZhV30WjB82bJioVauW0NPTEzVq1BCtW7dWu+Kv4KrPH3/8UWPeZ5d59+5d8c477wgLCwuhUCjE8/4cC65i++mnn4S7u7vQ19cXjo6OYv78+UXOExAQIKpVqyauX79e7LKFEGL79u0CgFiwYEGRbQquqPz666+FEELk5+eLmTNnitq1awt9fX3h6ekpdu7cKRo1aqRxheSDBw/El19+KerXry/09fWFubm58PDwEB9//LHaFbt3794VQ4cOFRYWFsLIyEj4+PiIo0ePCgDi22+/ldoVXOV469YtjTqzsrLEpEmThIODg9DT0xO2trbio48+Evfu3VNrl5OTIyZOnCjs7e2FoaGh8PLyEjExMUVe9bl7924xePBgYWFhIQwNDUWXLl3EhQsXnvveCiHEvXv3hI6OjjA2Nha5ubnS+HXr1gkAolevXhrzPHvVpxBC7NmzRzRp0kQolUq1KzGLej8Kan963y/MkCFDhLGxcZHTjY2N1d4TIUr+mf7yyy+iUaNGwsDAQNSqVUt89tln4vfffxcARFRUlNr2uru7F7r+ov5ei+Lg4CBdrapQKISJiYmoX7++GDx4sPjjjz8KnQfPXPX5+eefi+bNmwtLS0vp++Tjjz8Wt2/fVpvv1KlTom/fvqJmzZpCT09PqFQq0aFDB7Fs2TKpTU5OjpgwYYKoVauWMDAwEE2bNhXbt28XQ4YMUduWVatWifbt2wsbGxuhr68v7OzsRN++fUVsbKzaOm/duiXGjRsnnJychJ6enrCyshLNmjUTQUFB4sGDB8W+N1S1KIQowaUvRFTl5ObmwtHREW+++abGzTcrU1JSElxdXTF16lSNm3SW1fr16zFw4EAcOnQIrVu3rpBllkZERASGDh2K48eP81FaRPRC8dAn0Uvm1q1bSEhIQHh4OG7evInPP/+80tZ16tQpbNiwAa1bt4aZmRkSEhIwZ84cmJmZ4f333y/TMjds2IAbN27Aw8MDOjo6OHr0KObOnYt27dppJaQREWkTgxrRS+bXX3/F0KFDYWtriyVLlpTolhxlZWxsjBMnTmDlypW4f/8+zM3N4e3tjVmzZhV7S43imJqaYuPGjZg5cyYyMzNha2uLgIAAzJw5s4KrJyKSPx76JCIiIpIpPpmAiIiISKYY1IiIiIhkikGNiIiISKZ4MQGA/Px8/PvvvzA1NX0hjy8hIiKiV5cQAhkZGbCzs3vujbwZ1AD8+++/RT7Cg4iIiKgyXLt27blPkWBQw5PbAQBP3jAzMzMtV0NEREQvs/T0dNjb20v5ozgMavjvocFmZmYMakRERPRClOR0K15MQERERCRTDGpEREREMsWgRkRERCRTPEeNiIjoGUIIPH78GHl5edouhaooPT096Orqlns5Wg1qBw4cwNy5cxEdHY3k5GRs27YNPXv2lKYLITBt2jQsX74c9+7dQ4sWLbB48WK4u7tLbXJycjBhwgRs2LABWVlZ6NixI5YsWfLcy12JiIgKk5ubi+TkZDx8+FDbpVAVplAoULt2bZiYmJRrOVoNapmZmWjUqBGGDh2K3r17a0yfM2cO5s+fj4iICNSrVw8zZ86Ej48PEhISpEtaAwMD8csvv2Djxo2wtrbGp59+im7duiE6OrpCkiwREb068vPzkZSUBF1dXdjZ2UFfX583QqdSE0Lg1q1buH79OlxcXMqVRxRCCFGBtZWZQqFQ61ETQsDOzg6BgYGYNGkSgCe9ZzY2Npg9ezZGjhyJtLQ01KhRA2vWrEG/fv0A/Hfz2t9++w1+fn6FrisnJwc5OTnScMH9TNLS0nh7DiKiV1h2djaSkpLg4OAAIyMjbZdDVVhWVhYuX74MJycnGBgYqE1LT0+Hubl5iXKHbC8mSEpKQkpKCnx9faVxSqUSXl5eOHz4MAAgOjoajx49UmtjZ2eHhg0bSm0KExYWBnNzc+nFpxIQEdHTnvdYH6LnqaieWNnuiSkpKQAAGxsbtfE2NjbStJSUFOjr68PS0rLINoWZPHky0tLSpNe1a9cquHoiIiKi8pP9VZ/PJlIhxHNT6vPaKJVKKJXKCqmPiIiIqLLItkdNpVIBgEbPWGpqqtTLplKpkJubi3v37hXZhoiIiKiqkm1Qc3JygkqlQmRkpDQuNzcX+/fvR+vWrQEAzZo1g56enlqb5ORknDlzRmpDREREgLe3NwIDA7VdhkRu9ciVVg99PnjwAImJidJwUlISYmJiYGVlhTp16iAwMBChoaFwcXGBi4sLQkNDYWRkhAEDBgAAzM3N8f777+PTTz+FtbU1rKysMGHCBHh4eKBTp07a2iwiIqKXUm5uLvT19bVdxqtFaFFUVJQAoPEaMmSIEEKI/Px8MXXqVKFSqYRSqRTt2rUTp0+fVltGVlaWGDNmjLCyshKGhoaiW7du4urVq6WqIy0tTQAQaWlpFbVpRERUBWVlZYmzZ8+KrKwsbZdSoYYMGaLxW5uYmCiGDRsmHB0dhYGBgahXr55YsGCBxnxvvfWWCA0NFba2tsLBwUEIIcShQ4dEo0aNhFKpFM2aNRPbtm0TAMQ///wjzRsXFyc6d+4sjI2NRc2aNcWgQYPErVu3iqwnKSnpBb0bL0Zx+1JpcodWe9S8vb0hirmNm0KhQEhICEJCQopsY2BggIULF2LhwoWVUCEREVHV9+233+L8+fNo2LAhpk+fDgCwtLRE7dq1sXnzZlSvXh2HDx/GiBEjYGtri759+0rz7t27F2ZmZoiMjIQQAhkZGejevTu6dOmC9evX48qVKxqHMJOTk+Hl5YXhw4dj/vz5yMrKwqRJk9C3b1/8+eefhdZTo0aNF/Z+VCWyv+pTzpp9tlrbJVSa6LnvabsEIiKqIObm5tDX14eRkZF0sR4ATJs2Tfp/JycnHD58GJs3b1YLasbGxvj++++lQ57Lli2DQqHAihUrYGBgADc3N9y4cQPDhw+X5lm6dCmaNm2K0NBQadwPP/wAe3t7nD9/HvXq1Su0HtLEoEZERPSKWrZsGb7//ntcuXIFWVlZyM3NRePGjdXaeHh4qJ2XlpCQAE9PT7W77b/xxhtq80RHRyMqKqrQ51xevHgR9erVq9gNeYkxqBEREb2CNm/ejI8//hhff/01WrVqBVNTU8ydOxfHjh1Ta2dsbKw2LAq5V+mzpzHl5+eje/fumD17tsZ6bW1tK2gLXg0MakRERK8AfX195OXlScMHDx5E69atMWrUKGncxYsXn7scV1dXrFu3Djk5OdLN40+cOKHWpmnTptiyZQscHR1RrVrhUePZeqhwsr2PGhEREVUcR0dHHDt2DJcvX8bt27dRt25dnDhxAn/88QfOnz+PKVOm4Pjx489dzoABA5Cfn48RI0YgPj4ef/zxB+bNmwfgv6cJjR49Gnfv3kX//v3x999/49KlS9i9ezeGDRsmhbNn68nPz6+8ja/CGNSIiIheARMmTICuri7c3NxQo0YN+Pv7o1evXujXrx9atGiBO3fuqPWuFcXMzAy//PILYmJi0LhxYwQFBSE4OBgApPPW7OzscOjQIeTl5cHPzw8NGzbE+PHjYW5uLj3w/tl6rl69WnkbX4UpRHH3x3hFpKenw9zcHGlpaTAzMyvxfLzqk4jo5ZKdnY2kpCQ4OTmpnSxPxVu3bh2GDh2KtLQ0GBoaarscWShuXypN7uA5akRERFQqq1evhrOzM2rVqoVTp05J90hjSKt4DGpERERUKikpKQgODkZKSgpsbW3Rp08fzJo1S9tlvZQY1IiIiKhUJk6ciIkTJ2q7jFcCLyYgIiIikikGNSIiIiKZYlAjIiIikikGNSIiIiKZYlAjIiIikikGNSIiIiKZ4u05iIiInuNFP4mmsp4OExERgcDAQNy/fx8AEBISgu3btyMmJqZS1kflxx41IiKil0BAQAAUCgW++uortfHbt2+XHpber18/nD9/XhvlURkxqBEREb0kDAwMMHv2bNy7d6/Q6YaGhqhZs+YLrorKg0GNiIjoJdGpUyeoVCqEhYUVOj0iIgIWFhYvtigqFwY1IiKil4Suri5CQ0OxcOFCXL9+XdvlUAVgUCMiInqJvP3222jcuDGmTp2q7VKoAjCoERERvWRmz56NVatW4ezZs9ouhcqJQY2IiOgl065dO/j5+eGLL77QdilUTryPGhER0Uvoq6++QuPGjVGvXj1tl0LlwB41IiKil5CHhwcGDhyIhQsXarsUKgf2qBERET1HZT0poLLNmDEDmzdv1nYZVA4KIYTQdhHalp6eDnNzc6SlpcHMzKzE873oR4q8SFX1S4mIqDyys7ORlJQEJycnGBgYaLscqsKK25dKkzt46JOIiIhIphjUiIiIiGSKQY2IiIhIphjUiIiIiGSKQY2IiIhIphjUiIiIiGSKQY2IiIhIphjUiIiIiGSKQY2IiIhIpvgIKSIioue4Ot3jha6vTvDpF7o+uXJ0dERgYCACAwO1XYrWsEeNiIioigsICEDPnj01xu/btw8KhQL379+v9BpCQkLQuHHjErVTKBRQKBSoVq0aqlevjnbt2mHBggXIyclRa3v8+HGMGDGikiquGhjUiIiIqMyEEHj8+HGp5nF3d0dycjKuXr2KqKgo9OnTB2FhYWjdujUyMjKkdjVq1ICRkVFFl1ylMKgRERG9Qg4fPox27drB0NAQ9vb2GDduHDIzM6Xpa9euRfPmzWFqagqVSoUBAwYgNTVVml7QS/fHH3+gefPmUCqVWLNmDaZNm4ZTp05JvWURERFF1lCtWjWoVCrY2dnBw8MDY8eOxf79+3HmzBnMnj1baufo6IgFCxZIwyEhIahTpw6USiXs7Owwbtw4aVpubi4mTpyIWrVqwdjYGC1atMC+ffuk6Xfu3EH//v1Ru3ZtGBkZwcPDAxs2bFCr66effoKHhwcMDQ1hbW2NTp06qb034eHhaNCgAQwMDODq6oolS5aU5q0vEwY1IiKiV8Tp06fh5+eHXr16ITY2Fps2bcJff/2FMWPGSG1yc3MxY8YMnDp1Ctu3b0dSUhICAgI0ljVx4kSEhYUhPj4evr6++PTTT6WesuTkZPTr169Utbm6uqJz587YunVrodN/+uknfPPNN/jf//6HCxcuYPv27fDw+O/cwaFDh+LQoUPYuHEjYmNj0adPH/j7++PChQsAgOzsbDRr1gw7d+7EmTNnMGLECAwePBjHjh0DACQnJ6N///4YNmwY4uPjsW/fPvTq1QtCCADAihUrEBQUhFmzZiE+Ph6hoaGYMmUKVq1aVartLC1eTEBERPQS2LlzJ0xMTNTG5eXlqQ3PnTsXAwYMkE7Od3FxwXfffQcvLy8sXboUBgYGGDZsmNTe2dkZ3333Hd544w08ePBAbfnTp0+Hj4+PNGxiYiL1lJWVq6srdu/eXei0q1evQqVSoVOnTtDT00OdOnXwxhtvAAAuXryIDRs24Pr167CzswMATJgwAbt27UJ4eDhCQ0NRq1YtTJgwQVre2LFjsWvXLvz4449o0aIFkpOT8fjxY/Tq1QsODg4AoBYEZ8yYga+//hq9evUCADg5OeHs2bP43//+hyFDhpR5m5+HQY2IiOgl0L59eyxdulRt3LFjxzBo0CBpODo6GomJiVi3bp00TgiB/Px8JCUloUGDBvjnn38QEhKCmJgY3L17F/n5+QCeBCU3NzdpvubNm1f4NgghoFAoCp3Wp08fLFiwAM7OzvD390eXLl3QvXt3VKtWDSdPnoQQAvXq1VObJycnB9bW1gCehNavvvoKmzZtwo0bN5CTk4OcnBwYGxsDABo1aoSOHTvCw8MDfn5+8PX1xTvvvANLS0vcunUL165dw/vvv4/hw4dLy3/8+DHMzc0r/H14GoMaERHRS8DY2Bh169ZVG3f9+nW14fz8fIwcOVLt3K4CderUQWZmJnx9feHr64u1a9eiRo0auHr1Kvz8/JCbm6uxvooWHx8PJyenQqfZ29sjISEBkZGR2LNnD0aNGoW5c+di//79yM/Ph66uLqKjo6Grq6s2X0Ev4Ndff41vvvkGCxYsgIeHB4yNjREYGChtl66uLiIjI3H48GHs3r0bCxcuRFBQEI4dOyZd0LBixQq0aNFCbfnPrq+iMagRERG9Ipo2bYq4uDiNQFfg9OnTuH37Nr766ivY29sDAE6cOFGiZevr62scai2Nc+fOYdeuXZg8eXKRbQwNDdGjRw/06NEDo0ePhqurK06fPo0mTZogLy8PqampaNu2baHzHjx4EG+99ZbUw5ifn48LFy6gQYMGUhuFQoE2bdqgTZs2CA4OhoODA7Zt24ZPPvkEtWrVwqVLlzBw4MAyb2NZMKgRERG9IiZNmoSWLVti9OjRGD58OIyNjREfH4/IyEgsXLgQderUgb6+PhYuXIgPP/wQZ86cwYwZM0q0bEdHRyQlJSEmJga1a9eGqakplEploW0fP36MlJQU5Ofn486dO9i3bx9mzpyJxo0b47PPPit0noiICOTl5aFFixYwMjLCmjVrYGhoCAcHB1hbW2PgwIF477338PXXX6NJkya4ffs2/vzzT3h4eKBLly6oW7cutmzZgsOHD8PS0hLz589HSkqKFNSOHTuGvXv3wtfXFzVr1sSxY8dw69YtaXpISAjGjRsHMzMzdO7cGTk5OThx4gTu3buHTz75pAyfRskwqBERET3Hy/KkAE9PT+zfvx9BQUFo27YthBB47bXXpCs0a9SogYiICHzxxRf47rvv0LRpU8ybNw89evR47rJ79+6NrVu3on379rh//z7Cw8MLvVoUAOLi4mBrawtdXV2Ym5vDzc0NkydPxkcffVRkuLOwsMBXX32FTz75BHl5efDw8MAvv/winYMWHh6OmTNn4tNPP8WNGzdgbW2NVq1aoUuXLgCAKVOmICkpCX5+fjAyMsKIESPQs2dPpKWlAQDMzMxw4MABLFiwAOnp6XBwcMDXX3+Nzp07AwA++OADGBkZYe7cuZg4cSKMjY3h4eFR6U9NUIiC605fYenp6TA3N0daWhrMzMxKPF+zz1ZXYlXaFT33PW2XQET0wmVnZyMpKQlOTk4wMDDQdjlUhRW3L5Umd/A+akREREQyxaBGREREJFMMakREREQyxaBGREREJFMMakRERM/gdXZUXhW1DzGoERER/T89PT0AwMOHD7VcCVV1Tz/xoDx4HzUiIqL/p6urCwsLC6SmpgIAjIyMinz2JFFR8vPzcevWLRgZGaFatfJFLQY1IiKip6hUKgCQwhpRWejo6KBOnTrlDvoMakRERE9RKBSwtbVFzZo18ejRI22XQ1WUvr4+dHTKf4YZgxoREVEhdHV1y31+EVF58WICIiIiIpliUCMiIiKSKQY1IiIiIpliUCMiIiKSKQY1IiIiIpmSdVB7/PgxvvzySzg5OcHQ0BDOzs6YPn068vPzpTZCCISEhMDOzg6Ghobw9vZGXFycFqsmIiIiqhiyDmqzZ8/GsmXLsGjRIsTHx2POnDmYO3cuFi5cKLWZM2cO5s+fj0WLFuH48eNQqVTw8fFBRkaGFisnIiIiKj9Z30ftyJEjeOutt9C1a1cAgKOjIzZs2IATJ04AeNKbtmDBAgQFBaFXr14AgFWrVsHGxgbr16/HyJEjC11uTk4OcnJypOH09PRK3hIiIiKi0pN1j9qbb76JvXv34vz58wCAU6dO4a+//kKXLl0AAElJSUhJSYGvr680j1KphJeXFw4fPlzkcsPCwmBubi697O3tK3dDiIiIiMpA1j1qkyZNQlpaGlxdXaGrq4u8vDzMmjUL/fv3BwCkpKQAAGxsbNTms7GxwZUrV4pc7uTJk/HJJ59Iw+np6QxrREREJDuyDmqbNm3C2rVrsX79eri7uyMmJgaBgYGws7PDkCFDpHbPPvBUCFHsQ1CVSiWUSmWl1U1ERERUEWQd1D777DN8/vnnePfddwEAHh4euHLlCsLCwjBkyBCoVCoAT3rWbG1tpflSU1M1etmIiIiIqhpZn6P28OFDjSfP6+rqSrfncHJygkqlQmRkpDQ9NzcX+/fvR+vWrV9orUREREQVTdY9at27d8esWbNQp04duLu7459//sH8+fMxbNgwAE8OeQYGBiI0NBQuLi5wcXFBaGgojIyMMGDAAC1XT0RERFQ+sg5qCxcuxJQpUzBq1CikpqbCzs4OI0eORHBwsNRm4sSJyMrKwqhRo3Dv3j20aNECu3fvhqmpqRYrJyIiIio/hRBCaLsIbUtPT4e5uTnS0tJgZmZW4vmafba6EqvSrui572m7BCIiopdSaXKHrM9RIyIiInqVMagRERERyRSDGhEREZFMMagRERERyRSDGhEREZFMMagRERERyRSDGhEREZFMMagRERERyRSDGhEREZFMMagRERERyRSDGhEREZFMMagRERERyRSDGhEREZFMMagRERERyRSDGhEREZFMMagRERERyRSDGhEREZFMMagRERERyVQ1bRdARFXL1eke2i6h0tQJPq3tEoiI1LBHjYiIiEimGNSIiIiIZIpBjYiIiEimGNSIiIiIZIpBjYiIiEimGNSIiIiIZIpBjYiIiEimGNSIiIiIZIpBjYiIiEimGNSIiIiIZIpBjYiIiEimGNSIiIiIZIpBjYiIiEimGNSIiIiIZIpBjYiIiEimGNSIiIiIZIpBjYiIiEimGNSIiIiIZIpBjYiIiEimGNSIiIiIZIpBjYiIiEimGNSIiIiIZIpBjYiIiEimGNSIiIiIZIpBjYiIiEimGNSIiIiIZIpBjYiIiEimGNSIiIiIZIpBjYiIiEimGNSIiIiIZIpBjYiIiEimGNSIiIiIZIpBjYiIiEimGNSIiIiIZIpBjYiIiEimGNSIiIiIZIpBjYiIiEimGNSIiIiIZIpBjYiIiEimGNSIiIiIZIpBjYiIiEimGNSIiIiIZIpBjYiIiEimGNSIiIiIZEr2Qe3GjRsYNGgQrK2tYWRkhMaNGyM6OlqaLoRASEgI7OzsYGhoCG9vb8TFxWmxYiIiIqKKIeugdu/ePbRp0wZ6enr4/fffcfbsWXz99dewsLCQ2syZMwfz58/HokWLcPz4cahUKvj4+CAjI0N7hRMRERFVgGraLqA4s2fPhr29PcLDw6Vxjo6O0v8LIbBgwQIEBQWhV69eAIBVq1bBxsYG69evx8iRI190yUREREQVRtY9ajt27EDz5s3Rp08f1KxZE02aNMGKFSuk6UlJSUhJSYGvr680TqlUwsvLC4cPHy5yuTk5OUhPT1d7EREREcmNrIPapUuXsHTpUri4uOCPP/7Ahx9+iHHjxmH16tUAgJSUFACAjY2N2nw2NjbStMKEhYXB3Nxcetnb21feRhARERGVkayDWn5+Ppo2bYrQ0FA0adIEI0eOxPDhw7F06VK1dgqFQm1YCKEx7mmTJ09GWlqa9Lp27Vql1E9ERERUHrIOara2tnBzc1Mb16BBA1y9ehUAoFKpAECj9yw1NVWjl+1pSqUSZmZmai8iIiIiuZF1UGvTpg0SEhLUxp0/fx4ODg4AACcnJ6hUKkRGRkrTc3NzsX//frRu3fqF1kpERERU0WR91efHH3+M1q1bIzQ0FH379sXff/+N5cuXY/ny5QCeHPIMDAxEaGgoXFxc4OLigtDQUBgZGWHAgAFarp6IiIiofGQd1F5//XVs27YNkydPxvTp0+Hk5IQFCxZg4MCBUpuJEyciKysLo0aNwr1799CiRQvs3r0bpqamWqyciIiIqPxkHdQAoFu3bujWrVuR0xUKBUJCQhASEvLiiiIiIiJ6AWR9jhoRERHRq4xBjYiIiEimGNSIiIiIZIpBjYiIiEimGNSIiIiIZIpBjYiIiEimGNSIiIiIZKpMQc3Z2Rl37tzRGH///n04OzuXuygiIiIiKmNQu3z5MvLy8jTG5+Tk4MaNG+UuioiIiIhK+WSCHTt2SP//xx9/wNzcXBrOy8vD3r174ejoWGHFEREREb3KShXUevbsCeDJY5uGDBmiNk1PTw+Ojo74+uuvK6w4IiIioldZqYJafn4+AMDJyQnHjx9H9erVK6UoIiIiIirjQ9mTkpIqug4iIiIiekaZghoA7N27F3v37kVqaqrU01bghx9+KHdhRERERK+6MgW1adOmYfr06WjevDlsbW2hUCgqui4iIiKiV16ZgtqyZcsQERGBwYMHV3Q9RERERPT/ynQftdzcXLRu3bqiayEiIiKip5QpqH3wwQdYv359RddCRERERE8p06HP7OxsLF++HHv27IGnpyf09PTUps+fP79CiiMiIiJ6lZUpqMXGxqJx48YAgDNnzqhN44UFRERERBWjTEEtKiqqousgIiIiomeU6Rw1IiIiIqp8ZepRa9++fbGHOP/8888yF0RERERET5QpqBWcn1bg0aNHiImJwZkzZzQe1k5EREREZVOmoPbNN98UOj4kJAQPHjwoV0FERERE9ESFnqM2aNAgPueTiIiIqIJUaFA7cuQIDAwMKnKRRERERK+sMh367NWrl9qwEALJyck4ceIEpkyZUiGFEREREb3qyhTUzM3N1YZ1dHRQv359TJ8+Hb6+vhVSGBEREdGrrkxBLTw8vKLrICIiIqJnlCmoFYiOjkZ8fDwUCgXc3NzQpEmTiqqLiIiI6JVXpqCWmpqKd999F/v27YOFhQWEEEhLS0P79u2xceNG1KhRo6LrJCIiInrllOmqz7FjxyI9PR1xcXG4e/cu7t27hzNnziA9PR3jxo2r6BqJiIiIXkll6lHbtWsX9uzZgwYNGkjj3NzcsHjxYl5MQERERFRBytSjlp+fDz09PY3xenp6yM/PL3dRRERERFTGoNahQweMHz8e//77rzTuxo0b+Pjjj9GxY8cKK46IiIjoVVamoLZo0SJkZGTA0dERr732GurWrQsnJydkZGRg4cKFFV0jERER0SupTOeo2dvb4+TJk4iMjMS5c+cghICbmxs6depU0fURERERvbJK1aP2559/ws3NDenp6QAAHx8fjB07FuPGjcPrr78Od3d3HDx4sFIKJSIiInrVlCqoLViwAMOHD4eZmZnGNHNzc4wcORLz58+vsOKIiIiIXmWlCmqnTp2Cv79/kdN9fX0RHR1d7qKIiIiIqJRB7ebNm4XelqNAtWrVcOvWrXIXRURERESlvJigVq1aOH36NOrWrVvo9NjYWNja2lZIYaRdV6d7aLuESlEn+LS2SyAiIiqxUvWodenSBcHBwcjOztaYlpWVhalTp6Jbt24VVhwRERHRq6xUPWpffvkltm7dinr16mHMmDGoX78+FAoF4uPjsXjxYuTl5SEoKKiyaiUiIiJ6pZQqqNnY2ODw4cP46KOPMHnyZAghAAAKhQJ+fn5YsmQJbGxsKqVQIiIioldNqW946+DggN9++w337t1DYmIihBBwcXGBpaVlZdRHRERE9Moq05MJAMDS0hKvv/56RdZCRERERE8p07M+iYiIiKjylblHjYiK1+yz1douoVJsM9V2BURErw72qBERERHJFIMaERERkUwxqBERERHJFIMaERERkUwxqBERERHJFIMaERERkUwxqBERERHJFIMaERERkUwxqBERERHJFIMaERERkUwxqBERERHJFIMaERERkUwxqBERERHJFIMaERERkUxVqaAWFhYGhUKBwMBAaZwQAiEhIbCzs4OhoSG8vb0RFxenvSKJiIiIKkiVCWrHjx/H8uXL4enpqTZ+zpw5mD9/PhYtWoTjx49DpVLBx8cHGRkZWqqUiIiIqGJUiaD24MEDDBw4ECtWrIClpaU0XgiBBQsWICgoCL169ULDhg2xatUqPHz4EOvXr9dixURERETlVyWC2ujRo9G1a1d06tRJbXxSUhJSUlLg6+srjVMqlfDy8sLhw4eLXF5OTg7S09PVXkRERERyU03bBTzPxo0bcfLkSRw/flxjWkpKCgDAxsZGbbyNjQ2uXLlS5DLDwsIwbdq0ii2UiIiIqILJukft2rVrGD9+PNauXQsDA4Mi2ykUCrVhIYTGuKdNnjwZaWlp0uvatWsVVjMRERFRRZF1j1p0dDRSU1PRrFkzaVxeXh4OHDiARYsWISEhAcCTnjVbW1upTWpqqkYv29OUSiWUSmXlFU5ERERUAWTdo9axY0ecPn0aMTEx0qt58+YYOHAgYmJi4OzsDJVKhcjISGme3Nxc7N+/H61bt9Zi5URERETlJ+seNVNTUzRs2FBtnLGxMaytraXxgYGBCA0NhYuLC1xcXBAaGgojIyMMGDBAGyUTERERVRhZB7WSmDhxIrKysjBq1Cjcu3cPLVq0wO7du2Fqaqrt0oiIiIjKpcoFtX379qkNKxQKhISEICQkRCv1EBEREVUWWZ+jRkRERPQqY1AjIiIikikGNSIiIiKZYlAjIiIikikGNSIiIiKZYlAjIiIikikGNSIiIiKZYlAjIiIikikGNSIiIiKZYlAjIiIikikGNSIiIiKZYlAjIiIikikGNSIiIiKZYlAjIiIikikGNSIiIiKZYlAjIiIikikGNSIiIiKZYlAjIiIikikGNSIiIiKZYlAjIiIikikGNSIiIiKZYlAjIiIikikGNSIiIiKZYlAjIiIikikGNSIiIiKZYlAjIiIikikGNSIiIiKZYlAjIiIikikGNSIiIiKZYlAjIiIikikGNSIiIiKZYlAjIiIikikGNSIiIiKZYlAjIiIikikGNSIiIiKZYlAjIiIikikGNSIiIiKZYlAjIiIikikGNSIiIiKZYlAjIiIikikGNSIiIiKZYlAjIiIikikGNSIiIiKZYlAjIiIikikGNSIiIiKZYlAjIiIikikGNSIiIiKZYlAjIiIikikGNSIiIiKZYlAjIiIikikGNSIiIiKZYlAjIiIikikGNSIiIiKZYlAjIiIikikGNSIiIiKZYlAjIiIikikGNSIiIiKZYlAjIiIikikGNSIiIiKZYlAjIiIikikGNSIiIiKZYlAjIiIikikGNSIiIiKZYlAjIiIikilZB7WwsDC8/vrrMDU1Rc2aNdGzZ08kJCSotRFCICQkBHZ2djA0NIS3tzfi4uK0VDERERFRxZF1UNu/fz9Gjx6No0ePIjIyEo8fP4avry8yMzOlNnPmzMH8+fOxaNEiHD9+HCqVCj4+PsjIyNBi5URERETlV03bBRRn165dasPh4eGoWbMmoqOj0a5dOwghsGDBAgQFBaFXr14AgFWrVsHGxgbr16/HyJEjtVE2ERERUYWQdY/as9LS0gAAVlZWAICkpCSkpKTA19dXaqNUKuHl5YXDhw8XuZycnBykp6ervYiIiIjkpsoENSEEPvnkE7z55pto2LAhACAlJQUAYGNjo9bWxsZGmlaYsLAwmJubSy97e/vKK5yIiIiojKpMUBszZgxiY2OxYcMGjWkKhUJtWAihMe5pkydPRlpamvS6du1ahddLREREVF6yPketwNixY7Fjxw4cOHAAtWvXlsarVCoAT3rWbG1tpfGpqakavWxPUyqVUCqVlVcwERERUQWQdY+aEAJjxozB1q1b8eeff8LJyUltupOTE1QqFSIjI6Vxubm52L9/P1q3bv2iyyUiIiKqULLuURs9ejTWr1+Pn3/+GaamptJ5Z+bm5jA0NIRCoUBgYCBCQ0Ph4uICFxcXhIaGwsjICAMGDNBy9URERETlI+ugtnTpUgCAt7e32vjw8HAEBAQAACZOnIisrCyMGjUK9+7dQ4sWLbB7926Ympq+4GqJiIiIKpasg5oQ4rltFAoFQkJCEBISUvkFEREREb1Asj5HjYiIiOhVxqBGREREJFMMakREREQyxaBGREREJFOyvpiAiOhl1eyz1douoVJEz31P2yUQvVTYo0ZEREQkUwxqRERERDLFoEZEREQkUzxHjYiIKszV6R7aLqFS1Ak+re0S6BXFHjUiIiIimWJQIyIiIpIpBjUiIiIimWJQIyIiIpIpBjUiIiIimWJQIyIiIpIpBjUiIiIimWJQIyIiIpIpBjUiIiIimWJQIyIiIpIpBjUiIiIimWJQIyIiIpIpBjUiIiIimWJQIyIiIpIpBjUiIiIimWJQIyIiIpIpBjUiIiIimWJQIyIiIpIpBjUiIiIimWJQIyIiIpIpBjUiIiIimWJQIyIiIpIpBjUiIiIimWJQIyIiIpIpBjUiIiIimWJQIyIiIpIpBjUiIiIimWJQIyIiIpIpBjUiIiIimWJQIyIiIpIpBjUiIiIimWJQIyIiIpIpBjUiIiIimWJQIyIiIpIpBjUiIiIimWJQIyIiIpIpBjUiIiIimWJQIyIiIpIpBjUiIiIimWJQIyIiIpKpatougIiIiP7T7LPV2i6h0kTPfU/bJVQ57FEjIiIikikGNSIiIiKZYlAjIiIikikGNSIiIiKZYlAjIiIikikGNSIiIiKZYlAjIiIikikGNSIiIiKZYlAjIiIikikGNSIiIiKZ4iOkiIiI6IW4Ot1D2yVUijrBpytt2exRIyIiIpIpBjUiIiIimWJQIyIiIpKplyaoLVmyBE5OTjAwMECzZs1w8OBBbZdEREREVC4vRVDbtGkTAgMDERQUhH/++Qdt27ZF586dcfXqVW2XRkRERFRmL0VQmz9/Pt5//3188MEHaNCgARYsWAB7e3ssXbpU26URERERlVmVvz1Hbm4uoqOj8fnnn6uN9/X1xeHDhwudJycnBzk5OdJwWloaACA9Pb1U687LySpltVVHhl6etkuoFKX9jMvjZd0/XtZ9A+D+URFe1v2D+0bF4P6h3l4I8dy2VT6o3b59G3l5ebCxsVEbb2Njg5SUlELnCQsLw7Rp0zTG29vbV0qNVVFDbRdQWcLMtV1BlffS7hsA948K8NLuH9w3KgT3D3UZGRkwNy9+3iof1AooFAq1YSGExrgCkydPxieffCIN5+fn4+7du7C2ti5ynldJeno67O3tce3aNZiZmWm7HJIR7htUHO4fVBzuH/8RQiAjIwN2dnbPbVvlg1r16tWhq6ur0XuWmpqq0ctWQKlUQqlUqo2zsLCorBKrLDMzs1f+j4kKx32DisP9g4rD/eOJ5/WkFajyFxPo6+ujWbNmiIyMVBsfGRmJ1q1ba6kqIiIiovKr8j1qAPDJJ59g8ODBaN68OVq1aoXly5fj6tWr+PDDD7VdGhEREVGZvRRBrV+/frhz5w6mT5+O5ORkNGzYEL/99hscHBy0XVqVpFQqMXXqVI3Dw0TcN6g43D+oONw/ykYhSnJtKBERERG9cFX+HDUiIiKilxWDGhEREZFMMagRERERyRSDGpXa5cuXoVAoEBMTU+J5QkJC0Lhx40qribQjICAAPXv2LLaNt7c3AgMDX0g9JB8l+dwjIiJ4D0sqt7L8JlUlDGoEAKhfvz709fVx48aNF7K+kvzAk3bt27cPCoWi0FdRj2ejFys1NRUjR45EnTp1oFQqoVKp4OfnhyNHjlTqenNzczFnzhw0atQIRkZGqF69Otq0aYPw8HA8evQIALB161bMmDFDmsfR0RELFixQW06/fv1w/vz5Sq2VXpw1a9bA2NgYiYmJauP//fdfWFpa4ttvvy33Ol7F346X4vYcr7p79+5BT08PJiYmZZr/r7/+QnZ2Nvr06YOIiAgEBQVVcIWkTeXdPxISEjTuIl6zZs2KKK1E8vLyoFAooKPDf1c+q3fv3nj06BFWrVoFZ2dn3Lx5E3v37sXdu3fLtDwhBPLy8lCtWtE/Dbm5ufDz88OpU6cwY8YMtGnTBmZmZjh69CjmzZuHJk2aoHHjxrCysnru+gwNDWFoaFimWqlyleV7Y/Dgwdi2bRuGDBmCgwcPSn+zI0aMQJMmTTBu3LjKKvflJqhKevTokdi5c6fo06ePUCqVIiYmpszLCggIEJ9//rn4/fffhbOzs8jPz1ebfuzYMdG4cWOhVCpFs2bNxNatWwUA8c8//wghhAgPDxfm5uZq82zbtk08vXtNnTpVNGrUSPp/AGqvqKioMtdPmipi/4iKihIAxL1794psM2TIEPHWW29Jww8ePBCDBw8WxsbGQqVSiXnz5gkvLy8xfvx4qU1OTo747LPPhJ2dnTAyMhJvvPGG2udfsD/98ssvokGDBkJXV1dcunSp1PW/7O7duycAiH379hXZBoBYsWKF6NmzpzA0NBR169YVP//8szS94DPetWuXaNasmdDT0xN//vlnseudPXu20NHRESdPntSYlpubKx48eCCEEGqfu5eXl8bfvBCa3x0ODg4a7Z7+Hrl+/bro27evsLCwEFZWVqJHjx4iKSlJml6wP86dO1eoVCphZWUlRo0aJXJzc4vdJnqiIr43UlNTRc2aNcXcuXOFEE8+YzMzM3H58mVx9+5dMXjwYGFhYSEMDQ2Fv7+/OH/+vDTv078TBb755hvh4OAgTS/styMpKUkAEFu2bBHe3t7C0NBQeHp6isOHD5f5vZAT/hO1ijl9+jQmTJiA2rVr47333oO1tTWioqLQqFEjAIC7uztMTEyKfLm7u6stLyMjAz/++CMGDRoEHx8fZGZmYt++fdL0zMxMdOvWDfXr10d0dDRCQkIwYcKEcm3DhAkT0LdvX/j7+yM5ORnJycl83FcFqej9o7Q+++wzREVFYdu2bdi9ezf27duH6OhotTZDhw7FoUOHsHHjRsTGxqJPnz7w9/fHhQsXpDYPHz5EWFgYvv/+e8TFxb3QHryqouAz2759O3JycopsN23aNPTt2xexsbHo0qULBg4cqNHjNnHiRISFhSE+Ph6enp7FrnfdunXo1KkTmjRpojFNT08PxsbGGuO3bt2K2rVrSzclT05OLnTZx48fl6Zfv34dLVu2RNu2bQE82Sfat28PExMTHDhwAH/99RdMTEzg7++P3NxcaRlRUVG4ePEioqKisGrVKkRERCAiIqLYbXrVVeT3Ro0aNfC///0PU6ZMQWRkJD7++GN8++23cHBwQEBAAE6cOIEdO3bgyJEjEEKgS5cu0uHy53neb0dQUBAmTJiAmJgY1KtXD/3798fjx48r9s3SAh76rALu3LmDdevWISIiAnFxcejcuTOWLFmCbt26QV9fX63tb7/9VuxOr6enpza8ceNGuLi4SH9o7777LlauXIn27dsDePKlnJeXhx9++AFGRkZwd3fH9evX8dFHH5V5e0xMTGBoaIicnByoVKoyL4eeqMz9AwBq166tNlyrVi0kJCRotHvw4AFWrlyJ1atXw8fHBwCwatUqtfkvXryIDRs24Pr167CzswPw5Mt3165dCA8PR2hoKADg0aNHWLJkifRDQZqqVauGiIgIDB8+HMuWLUPTpk3h5eWFd999Vy1sBQQEoH///gCA0NBQLFy4EH///Tf8/f2lNtOnT5c+s+e5cOECvL29S1WrlZUVdHV1YWpqWuzffI0aNaT/Hz9+PJKTk3H8+HEAT76rdHR08P3330OhUAAAwsPDYWFhgX379sHX1xcAYGlpiUWLFkFXVxeurq7o2rUr9u7di+HDh5eq5pddZX5v9OzZUwpU3bp1Q0BAAC5cuIAdO3bg0KFDUrhat24d7O3tsX37dvTp0+e5NT/vt2PChAno2rUrgCf/QHF3d0diYiJcXV2fu2w5Y1CrAhYuXIhp06ahbdu2SExMhL29fZFtS/vYrJUrV2LQoEHS8KBBg9CuXTvcv38fFhYWiI+Pl04YLtCqVavSbwRVmsrcPwDg4MGDMDU1lYaLOn/p4sWLyM3NVds/rKysUL9+fWn45MmTEEKgXr16avPm5OTA2tpaGtbX139uzw49OUeta9euOHjwII4cOYJdu3Zhzpw5+P777xEQEAAAau+jsbExTE1NkZqaqrac5s2bl3idQggpKFWW5cuXY+XKlTh06JAU3qKjo5GYmKi2LwJAdnY2Ll68KA27u7tDV1dXGra1tcXp06crtd6qqLK/N6ZMmYLVq1djypQpAID4+HhUq1YNLVq0kNpYW1ujfv36iI+PL/0GFOLpfd3W1hbAkwtuGNSo0o0YMQJ6enpYtWoV3Nzc0Lt3bwwePBjt27fXOMHa3d0dV65cKXJZDg4OiIuLAwCcPXsWx44dw/HjxzFp0iSpTV5eHjZs2ICPPvoIogRPGNPR0dFoV9KubCq/yto/Cjg5OZXoFgol2Vfy8/Ohq6uL6OhotR9TAGonLRsaGlZ6GHhZGBgYwMfHBz4+PggODsYHH3yAqVOnSkHt2d4OhUKB/Px8tXGFHa4sSr169Srsh7Uw+/btw9ixY7Fhwwa1HtX8/Hw0a9YM69at05jn6Z64kmwvVf73RsE/6Ar+W9T3w9PBv7y/JU9/9gXLfBk+ewa1KsDOzg5BQUEICgrC4cOHsWrVKvTu3RumpqYYOHAgBg8eLB26LE0X9cqVK9GuXTssXrxYrc2aNWuwcuVKfPTRR3Bzc8OaNWuQlZUlXZ119OhRtfY1atRARkYGMjMzpS/8593PRl9fH3l5eSV+D6holbV/lFbdunWhp6eHo0ePok6dOgCeXDl2/vx5eHl5AQCaNGmCvLw8pKamSuceUcVyc3PD9u3bK235AwYMwBdffIF//vlH4zy1x48fIycnp9DgV5K/+cTERPTu3RtffPEFevXqpTatadOm2LRpE2rWrKlxFTKV3ov+3nBzc8Pjx49x7Ngx6dDnnTt3cP78eTRo0ADAk9+SlJQUtfD27G/JK/nboa2rGKh8srKyxIYNG4S/v7/Q1dUVsbGxpZo/NzdX1KhRQyxdulRj2vnz5wUAERMTIzIyMkT16tVF//79RVxcnPj1119F3bp11a76vHPnjjA2Nhbjxo0TFy5cEOvWrRN2dnZFXvUphBCzZs0SderUEefOnRO3bt3iVVkVrLz7hxD/XRGYkJAgkpOT1V4Fn9ezV31++OGHok6dOmLPnj3i9OnTokePHsLExETtqs+BAwcKR0dHsWXLFnHp0iXx999/i6+++kr8+uuvQojCryImTbdv3xbt27cXa9asEadOnRKXLl0SmzdvFjY2NmLYsGFCiCdXfW7btk1tPnNzcxEeHi6EKNmVvc/Kzs4Wbdu2FZaWlmLRokUiJiZGXLx4UWzatEk0bdpU+l549mpfHx8f0aNHD3H9+nVx69YtIYT6Z/3w4UPh6uoqOnToIP7991+1/U0IITIzM4WLi4vw9vYWBw4cEJcuXRL79u0T48aNE9euXRNCaO6PQggxfvx44eXlVeLte5VVxPdGgYIrMQv2ByGEeOutt4Sbm5s4ePCgiImJEf7+/qJu3brS98nZs2eFQqEQX331lUhMTBSLFi0SlpaW0lWfQhT+21HYugquin4Z7ijAoPYSuHHjhkhLSyvVPD/99JPQ0dERKSkphU738PAQY8eOFUIIceTIEdGoUSOhr68vGjduLLZs2aLxR7Ft2zZRt25dYWBgILp16yaWL19ebFBLTU0VPj4+wsTE5KX5Y5KrsuwfQvz3I17Y68iRI0IIzR/GjIwMMWjQIGFkZCRsbGzEnDlzNH6wc3NzRXBwsHB0dBR6enpCpVKJt99+W/pRYFArmezsbPH555+Lpk2bCnNzc2FkZCTq168vvvzyS/Hw4UMhROUEtYJ1h4WFCQ8PD2FgYCCsrKxEmzZtREREhHj06JEQQjOoHTlyRHh6egqlUlno7TkKfmwLexVITk4W7733nqhevbpQKpXC2dlZDB8+XNq/GdQqTlm/NwoUFp4Kbs9hbm4uDA0NhZ+fn9rtOYQQYunSpcLe3l4YGxuL9957T8yaNUstqBX22/GyBzWFECU4sYSIiIiIXjjeR42IiIhIphjUiIhIUtzNTQu74pKIKhcPfRIRkeTKlStFXuFnY2OjcR8zIqpcDGpEREREMsVDn0REREQyxaBGREREJFMMakREREQyxaBGREREJFMMakQkK97e3ggMDNR2GUREssCgRkRlEhAQAIVCAYVCAT09PTg7O2PChAnIzMws0fz79u2DQqHA/fv31cZv3boVM2bMqPBae/bsWSHLun79OvT19eHq6lohyyspIQRWrFiBVq1awczMDCYmJnB3d8f48eORmJj4QmshoheHQY2Iyszf3x/Jycm4dOkSZs6ciSVLlmDChAnlWqaVlZWs79UVERGBvn374uHDhzh06NALWacQAgMGDMC4cePQpUsX7N69G7Gxsfjuu+9gaGiImTNnFjlvbm7uC6mRiCqJ9h4zSkRVWWEPwP7ggw+ESqUSQgixZs0a0axZM2FiYiJsbGxE//79xc2bN4UQhT+Ae8iQIUIIzYd55+TkiM8++0zY2dkJIyMj8cYbb6g9aLngwd67du0Srq6uwtjYWPj5+Yl///1XCCHE1KlTNdYVFRUlcnJyxOjRo4VKpRJKpVI4ODiI0NDQYrc5Pz9fODs7i127dolJkyaJoUOHarQ5dOiQaNSokVAqlaJZs2Zi27ZtGg+MjouLE507dxbGxsaiZs2aYtCgQeLWrVtFrnfDhg0CgPj555+LrKtAwecSGhoqbG1tpQdax8bGivbt20sPUR8+fLjIyMiQ5nv2fRdCiLfeekv6XIQQwsHBQUyfPl30799fGBsbC1tbW/Hdd98V844RUXmxR42IKoyhoaF0V/vc3FzMmDEDp06dwvbt25GUlISAgAAAgL29PbZs2QIASEhIQHJyMr799ttClzl06FAcOnQIGzduRGxsLPr06QN/f39cuHBBavPw4UPMmzcPa9aswYEDB3D16lWpZ2/ChAno27ev1PuXnJyM1q1b47vvvsOOHTuwefNmJCQkYO3atXB0dCx2+6KiovDw4UN06tQJgwcPxubNm5GRkSFNz8jIQPfu3eHh4YGTJ09ixowZmDRpktoykpOT4eXlhcaNG+PEiRPYtWsXbt68ib59+xa53g0bNqB+/fro0aNHodMVCoXa8N69exEfH4/IyEjs3LkTDx8+hL+/PywtLXH8+HH8+OOP2LNnD8aMGVPs9hZm7ty58PT0xMmTJzF58mR8/PHHiIyMLPVyiKiEtJ0UiahqerZH7dixY8La2lr07du30PZ///23ACD14kRFRQkA4t69e2rtnu7ZSUxMFAqFQty4cUOtTceOHcXkyZOFEE961ACIxMREafrixYuFjY1NkbUKIcTYsWNFhw4d1HqjnmfAgAEiMDBQGm7UqJFYsWKFNLx06VJhbW0tsrKypHErVqxQ61GbMmWK8PX1VVvutWvXBACRkJBQ6HpdXV1Fjx491MaNHz9eGBsbC2NjY1GrVi21bbWxsRE5OTnSuOXLlwtLS0vx4MEDadyvv/4qdHR0REpKihCi5D1q/v7+am369esnOnfuXGjdRFR+7FEjojLbuXMnTExMYGBggFatWqFdu3ZYuHAhAOCff/7BW2+9BQcHB5iamsLb2xsAcPXq1RIv/+TJkxBCoF69emoPB9+/fz8uXrwotTMyMsJrr70mDdva2iI1NbXYZQcEBCAmJgb169fHuHHjsHv37mLb379/H1u3bsWgQYOkcYMGDcIPP/wgDSckJMDT0xMGBgbSuDfeeENtOdHR0YiKilLbnoILE57epmc922sWFBSEmJgYBAcH48GDB2rTPDw8oK+vLw3Hx8ejUaNGMDY2lsa1adMG+fn5SEhIKHa7n9WqVSuN4fj4+FItg4hKrpq2CyCiqqt9+/ZYunQp9PT0YGdnBz09PQBAZmYmfH194evri7Vr16JGjRq4evUq/Pz8SnVye35+PnR1dREdHQ1dXV21aSYmJtL/F6y3gEKhgHjOY4ybNm2KpKQk/P7779izZw/69u2LTp064aeffiq0/fr165GdnY0WLVpI44QQyM/Px9mzZ+Hm5gYhhEageraO/Px8dO/eHbNnz9ZYh62tbaHrdnFxwblz59TG1ahRAzVq1EDNmjU12j8dyApqeLauAgXjdXR0NGot6uHsRS2DiCoee9SIqMyMjY1Rt25dODg4qIWlc+fO4fbt2/jqq6/Qtm1buLq6avRwFfT45OXlFbn8Jk2aIC8vD6mpqahbt67aS6VSlbhOfX39QtdjZmaGfv36YcWKFdi0aRO2bNmCu3fvFrqMlStX4tNPP0VMTIz0OnXqFNq3by/1qrm6uiI2NhY5OTnSfCdOnFBbTtOmTREXFwdHR0eNbXo2YBXo378/EhIS8PPPP5d4m5/m5uaGmJgYtVunHDp0CDo6OqhXrx6AJ8EvOTlZmp6Xl4czZ85oLOvo0aMawy/6ViVErxIGNSKqcHXq1IG+vj4WLlyIS5cuYceOHRr3RnNwcIBCocDOnTtx69YtjcN3AFCvXj0MHDgQ7733HrZu3YqkpCQcP34cs2fPxm+//VbiehwdHREbG4uEhATcvn0bjx49wjfffIONGzfi3LlzOH/+PH788UeoVCpYWFhozB8TE4OTJ0/igw8+QMOGDdVe/fv3x+rVq/Ho0SMMGDAA+fn5GDFiBOLj4/HHH39g3rx5AP7rdRo9ejTu3r2L/v374++//8alS5ewe/duDBs2rMjQ+u677+Kdd97Bu+++i+nTp+PYsWO4fPky9u/fj02bNmn0Nj5r4MCBMDAwwJAhQ3DmzBlERUVh7NixGDx4MGxsbAAAHTp0wK+//opff/0V586dw6hRozTucQc8CXhz5szB+fPnsXjxYvz4448YP358iT8LIiolLZ4fR0RVWGEn6D9t/fr1wtHRUSiVStGqVSuxY8cOjdtUTJ8+XahUKqFQKIq8PUdubq4IDg4Wjo6OQk9PT6hUKvH222+L2NhYIcR/t+d4WsEtMQqkpqYKHx8fYWJiIt2eY/ny5aJx48bC2NhYmJmZiY4dO4qTJ08Wui1jxowRbm5uhU5LTU0Vurq6YsuWLUKIJ7fn8PT0FPr6+qJZs2Zi/fr1AoA4d+6cNM/58+fF22+/LSwsLIShoaFwdXUVgYGBxV7YkJeXJ5YtWyZatGghjI2Nhb6+vnB2dhbDhw8XZ8+eldoV9bk87/Ycubm54qOPPhJWVlaiZs2aIiwsrNCLCaZNmyb69u0rjIyMhI2NjViwYEGRNRNR+SmEeM6JHEREVGbr1q3D0KFDkZaWBkNDQ22XUy6Ojo4IDAzkI76IXiBeTEBEVIFWr14NZ2dn1KpVC6dOncKkSZPQt2/fKh/SiEg7GNSIiCpQSkoKgoODkZKSAltbW/Tp0wezZs3SdllEVEXx0CcRERGRTPGqTyIiIiKZYlAjIiIikikGNSIiIiKZYlAjIiIikikGNSIiIiKZYlAjIiIikikGNSIiIiKZYlAjIiIikqn/A92ea++bLBgQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,5))\n",
    "Age_Grp= sns.countplot(x=\"Age_Group\", data=df, hue=\"target\", order=df[\"Age_Group\"].value_counts(ascending=False).index)\n",
    "#values= df[\"Age_Group\"].value_counts(ascending=False).values\n",
    "#Age_Grp.bar_label(container=Age_Grp.containers[0], labels=values)\n",
    "plt.xlabel(\"Patients Age Group\")\n",
    "plt.ylabel(\"Count\")\n",
    "Age_Grp.set_title(\"Patient by Agegroup with Heart Disease\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d672cab2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,5))\n",
    "Gender= sns.countplot(x=\"Gender\", data=df, hue=\"target\", order=df[\"Gender\"].value_counts(ascending=False).index)\n",
    "plt.xlabel(\"Patients Gender\")\n",
    "plt.ylabel(\"Count\")\n",
    "Age_Grp.set_title(\"Patient by Gender with Heart Disease\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "aa675f2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,5))\n",
    "Gender= sns.countplot(x=\"chest_pain\", data=df, hue=\"target\", order=df[\"chest_pain\"].value_counts(ascending=False).index)\n",
    "plt.xlabel(\"Patients chest pain type\")\n",
    "plt.ylabel(\"Count\")\n",
    "Age_Grp.set_title(\"Patient by chest pain type with Heart Disease\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b5f7a577",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Correlation Matrix for the Dataset')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "hm=sns.heatmap(df.corr(), cbar=True,cmap=\"magma\", vmax=1, vmin=-1, annot=True, square=True, fmt=\".2f\", annot_kws={\"size\": 8})\n",
    "hm.set_title(\"Correlation Matrix for the Dataset\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f056bee",
   "metadata": {},
   "source": [
    "### Feature Engineering / Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d7c01123",
   "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>sex</th>\n",
       "      <th>chest pain type</th>\n",
       "      <th>resting blood pressure</th>\n",
       "      <th>cholesterol</th>\n",
       "      <th>fasting blood sugar</th>\n",
       "      <th>rest ecg</th>\n",
       "      <th>max heart rate</th>\n",
       "      <th>exercise-induced</th>\n",
       "      <th>ST depression</th>\n",
       "      <th>slope</th>\n",
       "      <th>major vessels</th>\n",
       "      <th>thalassemia</th>\n",
       "      <th>Target</th>\n",
       "      <th>Age_Group</th>\n",
       "      <th>Gender</th>\n",
       "      <th>chest_pain</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt;=Elder</td>\n",
       "      <td>Male</td>\n",
       "      <td>Non angina pain</td>\n",
       "      <td>Nil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt;=Adult</td>\n",
       "      <td>Male</td>\n",
       "      <td>Atypical angina</td>\n",
       "      <td>Nil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt;=Adult</td>\n",
       "      <td>Female</td>\n",
       "      <td>Typical angina</td>\n",
       "      <td>Nil</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  chest pain type  resting blood pressure  cholesterol  \\\n",
       "0   63    1                3                     145          233   \n",
       "1   37    1                2                     130          250   \n",
       "2   41    0                1                     130          204   \n",
       "\n",
       "   fasting blood sugar  rest ecg  max heart rate  exercise-induced  \\\n",
       "0                    1         0             150                 0   \n",
       "1                    0         1             187                 0   \n",
       "2                    0         0             172                 0   \n",
       "\n",
       "   ST depression  slope  major vessels  thalassemia  Target Age_Group  Gender  \\\n",
       "0            2.3      0              0            1       1   <=Elder    Male   \n",
       "1            3.5      0              0            2       1   <=Adult    Male   \n",
       "2            1.4      2              0            2       1   <=Adult  Female   \n",
       "\n",
       "        chest_pain target  \n",
       "0  Non angina pain    Nil  \n",
       "1  Atypical angina    Nil  \n",
       "2   Typical angina    Nil  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fb17358c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1= df[['age', 'sex', 'chest pain type', 'resting blood pressure',\n",
    "       'cholesterol', 'fasting blood sugar', 'rest ecg', 'max heart rate',\n",
    "       'exercise-induced', 'ST depression', 'slope', 'major vessels',\n",
    "       'thalassemia']]\n",
    "results= df[[\"Target\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b9f3da6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>age</th>\n",
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       "      <th>chest pain type</th>\n",
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       "      <th>fasting blood sugar</th>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
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       "      <td>1</td>\n",
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       "      <td>236</td>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  chest pain type  resting blood pressure  cholesterol  \\\n",
       "0   63    1                3                     145          233   \n",
       "1   37    1                2                     130          250   \n",
       "2   41    0                1                     130          204   \n",
       "3   56    1                1                     120          236   \n",
       "4   57    0                0                     120          354   \n",
       "\n",
       "   fasting blood sugar  rest ecg  max heart rate  exercise-induced  \\\n",
       "0                    1         0             150                 0   \n",
       "1                    0         1             187                 0   \n",
       "2                    0         0             172                 0   \n",
       "3                    0         1             178                 0   \n",
       "4                    0         1             163                 1   \n",
       "\n",
       "   ST depression  slope  major vessels  thalassemia  \n",
       "0            2.3      0              0            1  \n",
       "1            3.5      0              0            2  \n",
       "2            1.4      2              0            2  \n",
       "3            0.8      2              0            2  \n",
       "4            0.6      2              0            2  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8d2de00e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>1</td>\n",
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       "      <th>4</th>\n",
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      "text/plain": [
       "   Target\n",
       "0       1\n",
       "1       1\n",
       "2       1\n",
       "3       1\n",
       "4       1"
      ]
     },
     "execution_count": 25,
     "metadata": {},
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   ],
   "source": [
    "results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fa8341fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 303 entries, 0 to 302\n",
      "Data columns (total 13 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   age                     303 non-null    int64  \n",
      " 1   sex                     303 non-null    int64  \n",
      " 2   chest pain type         303 non-null    int64  \n",
      " 3   resting blood pressure  303 non-null    int64  \n",
      " 4   cholesterol             303 non-null    int64  \n",
      " 5   fasting blood sugar     303 non-null    int64  \n",
      " 6   rest ecg                303 non-null    int64  \n",
      " 7   max heart rate          303 non-null    int64  \n",
      " 8   exercise-induced        303 non-null    int64  \n",
      " 9   ST depression           303 non-null    float64\n",
      " 10  slope                   303 non-null    int64  \n",
      " 11  major vessels           303 non-null    int64  \n",
      " 12  thalassemia             303 non-null    int64  \n",
      "dtypes: float64(1), int64(12)\n",
      "memory usage: 30.9 KB\n"
     ]
    }
   ],
   "source": [
    "df1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8c249043",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'sex', 'chest pain type', 'resting blood pressure',\n",
       "       'cholesterol', 'fasting blood sugar', 'rest ecg', 'max heart rate',\n",
       "       'exercise-induced', 'ST depression', 'slope', 'major vessels',\n",
       "       'thalassemia'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "231d6d67",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>chest pain type</th>\n",
       "      <th>fasting blood sugar</th>\n",
       "      <th>rest ecg</th>\n",
       "      <th>exercise-induced</th>\n",
       "      <th>ST depression</th>\n",
       "      <th>slope</th>\n",
       "      <th>major vessels</th>\n",
       "      <th>Scaled_RBP</th>\n",
       "      <th>Scaled_chol</th>\n",
       "      <th>Scaled_MHR</th>\n",
       "      <th>Scaled_thal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.481132</td>\n",
       "      <td>0.244292</td>\n",
       "      <td>0.603053</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.339623</td>\n",
       "      <td>0.283105</td>\n",
       "      <td>0.885496</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.339623</td>\n",
       "      <td>0.178082</td>\n",
       "      <td>0.770992</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  chest pain type  fasting blood sugar  rest ecg  exercise-induced  \\\n",
       "0   63    1                3                    1         0                 0   \n",
       "1   37    1                2                    0         1                 0   \n",
       "2   41    0                1                    0         0                 0   \n",
       "\n",
       "   ST depression  slope  major vessels  Scaled_RBP  Scaled_chol  Scaled_MHR  \\\n",
       "0            2.3      0              0    0.481132     0.244292    0.603053   \n",
       "1            3.5      0              0    0.339623     0.283105    0.885496   \n",
       "2            1.4      2              0    0.339623     0.178082    0.770992   \n",
       "\n",
       "   Scaled_thal  \n",
       "0     0.333333  \n",
       "1     0.666667  \n",
       "2     0.666667  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dealing with outlier ('resting blood pressure','cholesterol', 'max heart rate','thalassemia')\n",
    "\n",
    "# Normalise the data\n",
    "scaler= MinMaxScaler()\n",
    "\n",
    "df1[\"Scaled_RBP\"]= scaler.fit_transform(df1[\"resting blood pressure\"].values.reshape(-1,1))\n",
    "df1[\"Scaled_chol\"]= scaler.fit_transform(df1[\"cholesterol\"].values.reshape(-1,1))\n",
    "df1[\"Scaled_MHR\"]= scaler.fit_transform(df1[\"max heart rate\"].values.reshape(-1,1))\n",
    "df1[\"Scaled_thal\"]= scaler.fit_transform(df1[\"thalassemia\"].values.reshape(-1,1))\n",
    "\n",
    "df1.drop([\"resting blood pressure\",\"cholesterol\",\"max heart rate\",\"thalassemia\"], axis=1, inplace=True)\n",
    "\n",
    "df1.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8283d8cb",
   "metadata": {},
   "source": [
    "## Machine Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "38c06df8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# split dataset into training and test set X= Question y= Answer\n",
    "X_train, X_test, y_train, y_test= train_test_split(df1,results,test_size=0.2,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c40555df",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.328767</td>\n",
       "      <td>0.305344</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.528302</td>\n",
       "      <td>0.239726</td>\n",
       "      <td>0.580153</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.386792</td>\n",
       "      <td>0.283105</td>\n",
       "      <td>0.687023</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.216981</td>\n",
       "      <td>0.237443</td>\n",
       "      <td>0.679389</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.433962</td>\n",
       "      <td>0.244292</td>\n",
       "      <td>0.702290</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.230594</td>\n",
       "      <td>0.633588</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>69</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.622642</td>\n",
       "      <td>0.246575</td>\n",
       "      <td>0.458015</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.245283</td>\n",
       "      <td>0.280822</td>\n",
       "      <td>0.557252</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.433962</td>\n",
       "      <td>0.157534</td>\n",
       "      <td>0.824427</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>242 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  chest pain type  fasting blood sugar  rest ecg  \\\n",
       "132   42    1                1                    0         1   \n",
       "202   58    1                0                    0         0   \n",
       "196   46    1                2                    0         1   \n",
       "75    55    0                1                    0         0   \n",
       "176   60    1                0                    1         1   \n",
       "..   ...  ...              ...                  ...       ...   \n",
       "188   50    1                2                    0         1   \n",
       "71    51    1                2                    0         1   \n",
       "106   69    1                3                    1         0   \n",
       "270   46    1                0                    0         0   \n",
       "102   63    0                1                    0         1   \n",
       "\n",
       "     exercise-induced  ST depression  slope  major vessels  Scaled_RBP  \\\n",
       "132                 0            0.0      2              0    0.245283   \n",
       "202                 1            0.8      2              0    0.528302   \n",
       "196                 0            3.6      1              0    0.528302   \n",
       "75                  0            1.4      1              0    0.386792   \n",
       "176                 1            1.4      2              2    0.216981   \n",
       "..                ...            ...    ...            ...         ...   \n",
       "188                 0            0.6      1              1    0.433962   \n",
       "71                  1            0.0      2              1    0.000000   \n",
       "106                 0            0.1      1              1    0.622642   \n",
       "270                 0            0.8      2              0    0.245283   \n",
       "102                 0            0.0      2              2    0.433962   \n",
       "\n",
       "     Scaled_chol  Scaled_MHR  Scaled_thal  \n",
       "132     0.385845    0.694656     0.666667  \n",
       "202     0.328767    0.305344     1.000000  \n",
       "196     0.239726    0.580153     0.666667  \n",
       "75      0.283105    0.687023     0.666667  \n",
       "176     0.237443    0.679389     1.000000  \n",
       "..           ...         ...          ...  \n",
       "188     0.244292    0.702290     1.000000  \n",
       "71      0.230594    0.633588     1.000000  \n",
       "106     0.246575    0.458015     0.666667  \n",
       "270     0.280822    0.557252     1.000000  \n",
       "102     0.157534    0.824427     0.666667  \n",
       "\n",
       "[242 rows x 13 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "cda0e7f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression\n",
      "Accuracy: 0.8524590163934426\n",
      "Precision: 0.896551724137931\n",
      "Recall: 0.8125\n",
      "f1_score: 0.8524590163934426\n",
      "AUC ROC: 0.8545258620689655\n"
     ]
    }
   ],
   "source": [
    "# model building\n",
    "\n",
    "# Logistic Regression\n",
    "\n",
    "logreg= LogisticRegression()\n",
    "\n",
    "logreg.fit(X_train, y_train)\n",
    "\n",
    "pred_y= logreg.predict(X_test)\n",
    "\n",
    "print(\"Logistic Regression\")\n",
    "print(\"Accuracy:\", accuracy_score(y_test, pred_y))\n",
    "print(\"Precision:\", precision_score(y_test, pred_y))\n",
    "print(\"Recall:\", recall_score(y_test, pred_y))\n",
    "print(\"f1_score:\", f1_score(y_test, pred_y))\n",
    "print(\"AUC ROC:\", roc_auc_score(y_test, pred_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4adf2350",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0,\n",
       "       0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1,\n",
       "       1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d1ea0ba0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Target\n",
       "179       0\n",
       "228       0\n",
       "111       1\n",
       "246       0\n",
       "60        1\n",
       "..      ...\n",
       "249       0\n",
       "104       1\n",
       "300       0\n",
       "193       0\n",
       "184       0\n",
       "\n",
       "[61 rows x 1 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "20fd4bcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a logistic confusion matrix\n",
    "\n",
    "logCM= confusion_matrix(y_test, pred_y)\n",
    "\n",
    "#visualise LogCM\n",
    "sns.heatmap(logCM, cmap=\"Blues\", annot=True,fmt=\"g\")\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Actual\")\n",
    "plt.title(\"Confussion Matrix\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "721fc477",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Classifier\n",
      "Accuracy: 0.8524590163934426\n",
      "Precision: 0.84375\n",
      "Recall: 0.8709677419354839\n",
      "F1 Score: 0.8571428571428571\n",
      "AUC ROC: 0.8521505376344087\n"
     ]
    }
   ],
   "source": [
    "# model building\n",
    "# random forest classifier\n",
    "\n",
    "rfc= RandomForestClassifier()\n",
    "rfc.fit(X_train, y_train)\n",
    "rfy_pred = rfc.predict(X_test)\n",
    "\n",
    "print(\"Random Forest Classifier\")\n",
    "print(\"Accuracy:\", accuracy_score(rfy_pred, y_test))\n",
    "print(\"Precision:\", precision_score(rfy_pred, y_test))\n",
    "print(\"Recall:\", recall_score(rfy_pred, y_test))\n",
    "print(\"F1 Score:\", f1_score(rfy_pred, y_test))\n",
    "print(\"AUC ROC:\", roc_auc_score(rfy_pred, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "107591cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0,\n",
       "       1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfy_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "65d39b6c",
   "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>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Target\n",
       "179       0\n",
       "228       0\n",
       "111       1\n",
       "246       0\n",
       "60        1\n",
       "..      ...\n",
       "249       0\n",
       "104       1\n",
       "300       0\n",
       "193       0\n",
       "184       0\n",
       "\n",
       "[61 rows x 1 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "1975adf0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a random forest Confusion matrix\n",
    "RanCM= confusion_matrix(y_test, rfy_pred)\n",
    "\n",
    "sns.heatmap(RanCM, annot=True, cmap=\"Blues\", fmt=\"g\")\n",
    "plt.xlabel(\"Predict\")\n",
    "plt.ylabel(\"Actual\")\n",
    "plt.title(\"Confusion Matrix (Random Forest)\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2c943535",
   "metadata": {},
   "outputs": [],
   "source": [
    "# applied different machine learning algorithms\n",
    "\n",
    "classifier=[[XGBClassifier(),\"XGB Classifier\"],\n",
    "           [SGDClassifier(),\"SGD Classifier\"],\n",
    "            [LogisticRegression(), \"Logistic Regression\"],\n",
    "            [KNeighborsClassifier(),\"KNeighbors Classifier\"],\n",
    "           [RandomForestClassifier(),\"Random Forest Classifier\"],\n",
    "            [SVC(),\"SVC\"],\n",
    "            [GaussianNB(),\"Naive Bayes\"],\n",
    "            [DecisionTreeClassifier(),\"Decision Tree Classifier\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e40afc71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "               colsample_bylevel=None, colsample_bynode=None,\n",
       "               colsample_bytree=None, early_stopping_rounds=None,\n",
       "               enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "               gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "               interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "               max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "               max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "               min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "               n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "               predictor=None, random_state=None, ...),\n",
       " 'XGB Classifier']"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "17534267",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "acc_list={}\n",
    "pre_list={}\n",
    "recall_list={}\n",
    "#f1_list={}\n",
    "roc_list={}\n",
    "\n",
    "for unique in classifier:\n",
    "    model= unique[0]\n",
    "    model.fit(X_train, y_train)\n",
    "    model_name= unique[1]\n",
    "    \n",
    "    pred= model.predict(X_test)\n",
    "    \n",
    "    acc_score= accuracy_score(y_test,pred)\n",
    "    pre_score= precision_score(y_test,pred)\n",
    "    recal_score= recall_score(y_test,pred)\n",
    "    #f1_score= f1_score(y_test,pred)\n",
    "    roc_score= roc_auc_score(y_test,pred)\n",
    "    \n",
    "    acc_list[model_name] = ([str(round(acc_score*100,2)) + \"%\"])\n",
    "    pre_list[model_name] = ([str(round(pre_score*100,2)) + \"%\"])\n",
    "    recall_list[model_name] = ([str(round(recal_score*100,2)) + \"%\"])\n",
    "    #f1_list[model_name] = ([str(round(f1_score*100,2)) + \"%\"])\n",
    "    roc_list[model_name] = ([str(round(roc_score*100,2)) + \"%\"])\n",
    "    \n",
    "    if model_name != classifier[-1][1]:\n",
    "        print(\"\")\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "edcbdf51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'XGB Classifier': ['83.61%'],\n",
       " 'SGD Classifier': ['77.05%'],\n",
       " 'Logistic Regression': ['85.25%'],\n",
       " 'KNeighbors Classifier': ['75.41%'],\n",
       " 'Random Forest Classifier': ['83.61%'],\n",
       " 'SVC': ['65.57%'],\n",
       " 'Naive Bayes': ['86.89%'],\n",
       " 'Decision Tree Classifier': ['80.33%']}"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c74ae548",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'XGB Classifier': ['86.67%'],\n",
       " 'SGD Classifier': ['70.45%'],\n",
       " 'Logistic Regression': ['89.66%'],\n",
       " 'KNeighbors Classifier': ['79.31%'],\n",
       " 'Random Forest Classifier': ['84.38%'],\n",
       " 'SVC': ['65.71%'],\n",
       " 'Naive Bayes': ['90.0%'],\n",
       " 'Decision Tree Classifier': ['91.67%']}"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pre_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "e607df10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'XGB Classifier': ['81.25%'],\n",
       " 'SGD Classifier': ['96.88%'],\n",
       " 'Logistic Regression': ['81.25%'],\n",
       " 'KNeighbors Classifier': ['71.88%'],\n",
       " 'Random Forest Classifier': ['84.38%'],\n",
       " 'SVC': ['71.88%'],\n",
       " 'Naive Bayes': ['84.38%'],\n",
       " 'Decision Tree Classifier': ['68.75%']}"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "dad93f68",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'XGB Classifier': ['83.73%'],\n",
       " 'SGD Classifier': ['76.02%'],\n",
       " 'Logistic Regression': ['85.45%'],\n",
       " 'KNeighbors Classifier': ['75.59%'],\n",
       " 'Random Forest Classifier': ['83.57%'],\n",
       " 'SVC': ['65.25%'],\n",
       " 'Naive Bayes': ['87.02%'],\n",
       " 'Decision Tree Classifier': ['80.93%']}"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e18c5eec",
   "metadata": {},
   "source": [
    "##### In conclusion, the precision scores from various algorithms are as follows:\n",
    "- Decision Tree Classifier: 91.67%\n",
    "- SGD Classifier: 90.62%\t\n",
    "- Logistic Regression\t: 89.66%\t\n",
    "- XGB Classifier: 86.67%\n",
    "\n",
    "##### Among these algorithms, the Decision Tree Classifier achieved the highest precision score of 91.67%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "c6ca59a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score\n"
     ]
    },
    {
     "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>XGB Classifier</th>\n",
       "      <th>SGD Classifier</th>\n",
       "      <th>Logistic Regression</th>\n",
       "      <th>KNeighbors Classifier</th>\n",
       "      <th>Random Forest Classifier</th>\n",
       "      <th>SVC</th>\n",
       "      <th>Naive Bayes</th>\n",
       "      <th>Decision Tree Classifier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>83.61%</td>\n",
       "      <td>77.05%</td>\n",
       "      <td>85.25%</td>\n",
       "      <td>75.41%</td>\n",
       "      <td>83.61%</td>\n",
       "      <td>65.57%</td>\n",
       "      <td>86.89%</td>\n",
       "      <td>80.33%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  XGB Classifier SGD Classifier Logistic Regression KNeighbors Classifier  \\\n",
       "0         83.61%         77.05%              85.25%                75.41%   \n",
       "\n",
       "  Random Forest Classifier     SVC Naive Bayes Decision Tree Classifier  \n",
       "0                   83.61%  65.57%      86.89%                   80.33%  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Accuracy Score\")\n",
    "acc_pd = pd.DataFrame(acc_list)\n",
    "acc_pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "4e34dff2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision Score\n"
     ]
    },
    {
     "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>XGB Classifier</th>\n",
       "      <th>SGD Classifier</th>\n",
       "      <th>Logistic Regression</th>\n",
       "      <th>KNeighbors Classifier</th>\n",
       "      <th>Random Forest Classifier</th>\n",
       "      <th>SVC</th>\n",
       "      <th>Naive Bayes</th>\n",
       "      <th>Decision Tree Classifier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>86.67%</td>\n",
       "      <td>70.45%</td>\n",
       "      <td>89.66%</td>\n",
       "      <td>79.31%</td>\n",
       "      <td>84.38%</td>\n",
       "      <td>65.71%</td>\n",
       "      <td>90.0%</td>\n",
       "      <td>91.67%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  XGB Classifier SGD Classifier Logistic Regression KNeighbors Classifier  \\\n",
       "0         86.67%         70.45%              89.66%                79.31%   \n",
       "\n",
       "  Random Forest Classifier     SVC Naive Bayes Decision Tree Classifier  \n",
       "0                   84.38%  65.71%       90.0%                   91.67%  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Precision Score\")\n",
    "pre_pd = pd.DataFrame(pre_list)\n",
    "pre_pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "3bfc3f3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall Score\n"
     ]
    },
    {
     "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>XGB Classifier</th>\n",
       "      <th>SGD Classifier</th>\n",
       "      <th>Logistic Regression</th>\n",
       "      <th>KNeighbors Classifier</th>\n",
       "      <th>Random Forest Classifier</th>\n",
       "      <th>SVC</th>\n",
       "      <th>Naive Bayes</th>\n",
       "      <th>Decision Tree Classifier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>81.25%</td>\n",
       "      <td>96.88%</td>\n",
       "      <td>81.25%</td>\n",
       "      <td>71.88%</td>\n",
       "      <td>84.38%</td>\n",
       "      <td>71.88%</td>\n",
       "      <td>84.38%</td>\n",
       "      <td>68.75%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  XGB Classifier SGD Classifier Logistic Regression KNeighbors Classifier  \\\n",
       "0         81.25%         96.88%              81.25%                71.88%   \n",
       "\n",
       "  Random Forest Classifier     SVC Naive Bayes Decision Tree Classifier  \n",
       "0                   84.38%  71.88%      84.38%                   68.75%  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Recall Score\")\n",
    "recall_pd = pd.DataFrame(recall_list)\n",
    "recall_pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "37c13e0f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Roc_Auc Score\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>XGB Classifier</th>\n",
       "      <th>SGD Classifier</th>\n",
       "      <th>Logistic Regression</th>\n",
       "      <th>KNeighbors Classifier</th>\n",
       "      <th>Random Forest Classifier</th>\n",
       "      <th>SVC</th>\n",
       "      <th>Naive Bayes</th>\n",
       "      <th>Decision Tree Classifier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>83.73%</td>\n",
       "      <td>76.02%</td>\n",
       "      <td>85.45%</td>\n",
       "      <td>75.59%</td>\n",
       "      <td>83.57%</td>\n",
       "      <td>65.25%</td>\n",
       "      <td>87.02%</td>\n",
       "      <td>80.93%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  XGB Classifier SGD Classifier Logistic Regression KNeighbors Classifier  \\\n",
       "0         83.73%         76.02%              85.45%                75.59%   \n",
       "\n",
       "  Random Forest Classifier     SVC Naive Bayes Decision Tree Classifier  \n",
       "0                   83.57%  65.25%      87.02%                   80.93%  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Roc_Auc Score\")\n",
    "roc_pd = pd.DataFrame(roc_list)\n",
    "roc_pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "510a81b6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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