{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1f41db61-4f75-4696-84a8-2a906dca3e3e",
   "metadata": {},
   "source": [
    "# Heart Failure analysis and detection using Machine Learning techniques\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b7897612-92c7-41bc-99ed-0f19a23d16ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import ConfusionMatrixDisplay, accuracy_score, classification_report, confusion_matrix, RocCurveDisplay\n",
    "from sklearn import metrics\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d59970e5-184f-4b1e-a269-71223a49dba9",
   "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>ChestPainType</th>\n",
       "      <th>RestingBP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>FastingBS</th>\n",
       "      <th>RestingECG</th>\n",
       "      <th>MaxHR</th>\n",
       "      <th>ExerciseAngina</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>ST_Slope</th>\n",
       "      <th>HeartDisease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "      <td>M</td>\n",
       "      <td>ATA</td>\n",
       "      <td>140</td>\n",
       "      <td>289</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>172</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>F</td>\n",
       "      <td>NAP</td>\n",
       "      <td>160</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>156</td>\n",
       "      <td>N</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>M</td>\n",
       "      <td>ATA</td>\n",
       "      <td>130</td>\n",
       "      <td>283</td>\n",
       "      <td>0</td>\n",
       "      <td>ST</td>\n",
       "      <td>98</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48</td>\n",
       "      <td>F</td>\n",
       "      <td>ASY</td>\n",
       "      <td>138</td>\n",
       "      <td>214</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>108</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>M</td>\n",
       "      <td>NAP</td>\n",
       "      <td>150</td>\n",
       "      <td>195</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>122</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  MaxHR  \\\n",
       "0   40   M           ATA        140          289          0     Normal    172   \n",
       "1   49   F           NAP        160          180          0     Normal    156   \n",
       "2   37   M           ATA        130          283          0         ST     98   \n",
       "3   48   F           ASY        138          214          0     Normal    108   \n",
       "4   54   M           NAP        150          195          0     Normal    122   \n",
       "\n",
       "  ExerciseAngina  Oldpeak ST_Slope  HeartDisease  \n",
       "0              N      0.0       Up             0  \n",
       "1              N      1.0     Flat             1  \n",
       "2              N      0.0       Up             0  \n",
       "3              Y      1.5     Flat             1  \n",
       "4              N      0.0       Up             0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Read the data\n",
    "data = pd.read_csv(\"heart.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "992daf53-ba71-44ee-93f9-bf9f74af90fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 918 entries, 0 to 917\n",
      "Data columns (total 12 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Age             918 non-null    int64  \n",
      " 1   Sex             918 non-null    object \n",
      " 2   ChestPainType   918 non-null    object \n",
      " 3   RestingBP       918 non-null    int64  \n",
      " 4   Cholesterol     918 non-null    int64  \n",
      " 5   FastingBS       918 non-null    int64  \n",
      " 6   RestingECG      918 non-null    object \n",
      " 7   MaxHR           918 non-null    int64  \n",
      " 8   ExerciseAngina  918 non-null    object \n",
      " 9   Oldpeak         918 non-null    float64\n",
      " 10  ST_Slope        918 non-null    object \n",
      " 11  HeartDisease    918 non-null    int64  \n",
      "dtypes: float64(1), int64(6), object(5)\n",
      "memory usage: 86.2+ KB\n"
     ]
    }
   ],
   "source": [
    "# Basic information about the dataset\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "df134866-a405-434d-8603-4cefd3181e4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(918, 12)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "effd23f1-8bc0-42a8-9dc4-1f1c9f4c3b6f",
   "metadata": {},
   "source": [
    "There are 918 rows and 12 columns\r\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f9b33bee-56a3-4448-ab53-9132732929c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11016"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ce154df8-9364-4988-b840-a8cd3d493c21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age               0\n",
       "Sex               0\n",
       "ChestPainType     0\n",
       "RestingBP         0\n",
       "Cholesterol       0\n",
       "FastingBS         0\n",
       "RestingECG        0\n",
       "MaxHR             0\n",
       "ExerciseAngina    0\n",
       "Oldpeak           0\n",
       "ST_Slope          0\n",
       "HeartDisease      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5c13c288-af55-498c-a5bb-edf9d289b569",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### visualize in the heatmap\n",
    "plt.figure(figsize = (16,8))\n",
    "sns.heatmap(data.isna(),cmap='BrBG')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "88eee4d5-03fa-4bee-bd8e-7233d215499b",
   "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>ChestPainType</th>\n",
       "      <th>RestingBP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>FastingBS</th>\n",
       "      <th>RestingECG</th>\n",
       "      <th>MaxHR</th>\n",
       "      <th>ExerciseAngina</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>ST_Slope</th>\n",
       "      <th>HeartDisease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "      <td>M</td>\n",
       "      <td>ATA</td>\n",
       "      <td>140</td>\n",
       "      <td>289</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>172</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>F</td>\n",
       "      <td>NAP</td>\n",
       "      <td>160</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>156</td>\n",
       "      <td>N</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>M</td>\n",
       "      <td>ATA</td>\n",
       "      <td>130</td>\n",
       "      <td>283</td>\n",
       "      <td>0</td>\n",
       "      <td>ST</td>\n",
       "      <td>98</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48</td>\n",
       "      <td>F</td>\n",
       "      <td>ASY</td>\n",
       "      <td>138</td>\n",
       "      <td>214</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>108</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>M</td>\n",
       "      <td>NAP</td>\n",
       "      <td>150</td>\n",
       "      <td>195</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>122</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  MaxHR  \\\n",
       "0   40   M           ATA        140          289          0     Normal    172   \n",
       "1   49   F           NAP        160          180          0     Normal    156   \n",
       "2   37   M           ATA        130          283          0         ST     98   \n",
       "3   48   F           ASY        138          214          0     Normal    108   \n",
       "4   54   M           NAP        150          195          0     Normal    122   \n",
       "\n",
       "  ExerciseAngina  Oldpeak ST_Slope  HeartDisease  \n",
       "0              N      0.0       Up             0  \n",
       "1              N      1.0     Flat             1  \n",
       "2              N      0.0       Up             0  \n",
       "3              Y      1.5     Flat             1  \n",
       "4              N      0.0       Up             0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "677f1259-f567-4629-8924-a15277bdefed",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e107b348-5817-42d9-bbb6-5c4c5afcffe3",
   "metadata": {},
   "source": [
    "## Data analysis\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "66174ff8-73c5-4a6c-bb61-4cd7beb1f308",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "M    725\n",
       "F    193\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Sex'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0d25c72c-aaf6-482f-b703-21c9ddffcd4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Sex', ylabel='count'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (16,8))\n",
    "sns.countplot(data = data, x = 'Sex')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7310d0a7-c361-4214-a3e5-708d934cfccb",
   "metadata": {},
   "source": [
    "## According to this dataset, which gender suffered more from heart disease?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4b019dff-6704-4df9-9fd3-7786dc9d95c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "F    143\n",
       "M    267\n",
       "Name: HeartDisease, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tot = data['HeartDisease'].groupby(data['Sex']).count()\n",
    "suff = data['HeartDisease'].groupby(data['Sex']).sum()\n",
    "not_suf = tot-suff\n",
    "not_suf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "26ef5d40-ac18-4ef8-b2c0-20f1aa154562",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='HeartDisease', ylabel='count'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data = data, x = not_suf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9ba7095a-b22a-4db7-9996-80adf8f096eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='HeartDisease', ylabel='count'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data = data, x = suff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6f5b5e9c-ffcd-4a3d-b3c1-750c101da277",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\prajw\\AppData\\Local\\Temp\\ipykernel_3804\\3227672255.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  female_suffered = suff[0]\n",
      "C:\\Users\\prajw\\AppData\\Local\\Temp\\ipykernel_3804\\3227672255.py:2: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  male_suffered = suff[1]\n"
     ]
    }
   ],
   "source": [
    "female_suffered = suff[0]\n",
    "male_suffered = suff[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1b51947d-0785-4f5b-bc9f-de56cedbb420",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Female suffered percent  5.446623093681917\n"
     ]
    }
   ],
   "source": [
    "total_gend = data['Sex'].count()\n",
    "print(\"Female suffered percent \", (female_suffered/total_gend)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c440e9cc-3288-45e7-afbb-a12c88d40018",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Male suffered percent  49.89106753812636\n"
     ]
    }
   ],
   "source": [
    "total_gend = data['Sex'].count()\n",
    "print(\"Male suffered percent \", (male_suffered/total_gend)*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a15fa1e-b7f6-4b8d-bd01-9325972c5e8b",
   "metadata": {},
   "source": [
    "## Analyze the chest pain type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "877fec62-c514-469f-a0e0-072037f7d0f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ChestPainType\n",
       "ASY    496\n",
       "NAP    203\n",
       "ATA    173\n",
       "TA      46\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['ChestPainType'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "49a43505-823a-4f83-a731-a71b7ccc75b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='ChestPainType', ylabel='count'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (16,8))\n",
    "sns.countplot(data=data, x= 'ChestPainType')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d3a464a2-e2ac-49a0-9cfe-13d4bb180afe",
   "metadata": {},
   "outputs": [],
   "source": [
    "count_me = data['HeartDisease'].groupby(data['ChestPainType']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "89559fe8-36e9-47be-8b3b-b9001e2f6a2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "sum_me = data['HeartDisease'].groupby(data['ChestPainType']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b46f0a50-7b75-4fe0-ae83-f8ad5c16d5cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ChestPainType\n",
       "ASY    392\n",
       "ATA     24\n",
       "NAP     72\n",
       "TA      20\n",
       "Name: HeartDisease, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum_me"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e05b646e-1ce1-45c3-9d68-b9170ca01547",
   "metadata": {},
   "outputs": [],
   "source": [
    "no_risk = count_me-sum_me"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a59db56a-b94f-40d2-8189-8e0412176a9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "392 of people who have ASY got heart disease and 104 of people who have ASY doesn't get heart disease.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\prajw\\AppData\\Local\\Temp\\ipykernel_3804\\2156176294.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  print(\"{} of people who have ASY got heart disease and {} of people who have ASY doesn't get heart disease.\".format(sum_me[0],no_risk[0]))\n"
     ]
    }
   ],
   "source": [
    "print(\"{} of people who have ASY got heart disease and {} of people who have ASY doesn't get heart disease.\".format(sum_me[0],no_risk[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ba4f0a8f-de32-43b5-8794-5aa477bdb2ba",
   "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>ChestPainType</th>\n",
       "      <th>RestingBP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>FastingBS</th>\n",
       "      <th>RestingECG</th>\n",
       "      <th>MaxHR</th>\n",
       "      <th>ExerciseAngina</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>ST_Slope</th>\n",
       "      <th>HeartDisease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "      <td>M</td>\n",
       "      <td>ATA</td>\n",
       "      <td>140</td>\n",
       "      <td>289</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>172</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>F</td>\n",
       "      <td>NAP</td>\n",
       "      <td>160</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>156</td>\n",
       "      <td>N</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>M</td>\n",
       "      <td>ATA</td>\n",
       "      <td>130</td>\n",
       "      <td>283</td>\n",
       "      <td>0</td>\n",
       "      <td>ST</td>\n",
       "      <td>98</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48</td>\n",
       "      <td>F</td>\n",
       "      <td>ASY</td>\n",
       "      <td>138</td>\n",
       "      <td>214</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>108</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>M</td>\n",
       "      <td>NAP</td>\n",
       "      <td>150</td>\n",
       "      <td>195</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>122</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  MaxHR  \\\n",
       "0   40   M           ATA        140          289          0     Normal    172   \n",
       "1   49   F           NAP        160          180          0     Normal    156   \n",
       "2   37   M           ATA        130          283          0         ST     98   \n",
       "3   48   F           ASY        138          214          0     Normal    108   \n",
       "4   54   M           NAP        150          195          0     Normal    122   \n",
       "\n",
       "  ExerciseAngina  Oldpeak ST_Slope  HeartDisease  \n",
       "0              N      0.0       Up             0  \n",
       "1              N      1.0     Flat             1  \n",
       "2              N      0.0       Up             0  \n",
       "3              Y      1.5     Flat             1  \n",
       "4              N      0.0       Up             0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d053d6e-60d0-4976-b713-2df3f6034a81",
   "metadata": {},
   "source": [
    "## Relation between RestingECG and the HeartDisease\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "beb409a5-332e-4b66-80ed-b16cf13a61bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RestingECG\n",
       "Normal    552\n",
       "LVH       188\n",
       "ST        178\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"RestingECG\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5be6b2df-af7e-4ee9-9d8e-1d98a90915a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([552.,   0.,   0.,   0.,   0., 178.,   0.,   0.,   0., 188.]),\n",
       " array([0. , 0.2, 0.4, 0.6, 0.8, 1. , 1.2, 1.4, 1.6, 1.8, 2. ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (16,8))\n",
    "plt.hist(data[\"RestingECG\"],color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77794355-8c15-4af6-a513-6f5f5c09bdbe",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "bc8675d2-03b7-49b7-a80d-99ce2f0ee43e",
   "metadata": {},
   "source": [
    "## Unique values to aid backend\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c8db5291-fdb5-4520-96c5-6aa4552bfb56",
   "metadata": {},
   "outputs": [],
   "source": [
    "s = data[\"Sex\"].unique()\n",
    "c = data[\"ChestPainType\"].unique()\n",
    "r = data[\"RestingECG\"].unique()\n",
    "e = data[\"ExerciseAngina\"].unique()\n",
    "sl = data[\"ST_Slope\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4fb5831f-8d53-4426-b84a-7eb3aa36b6ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['M' 'F']\n",
      "['ATA' 'NAP' 'ASY' 'TA']\n",
      "['Normal' 'ST' 'LVH']\n",
      "['N' 'Y']\n",
      "['Up' 'Flat' 'Down']\n"
     ]
    }
   ],
   "source": [
    "print(s)\n",
    "print(c)\n",
    "print(r)\n",
    "print(e)\n",
    "print(sl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f015da99-69f7-4195-9917-710d6f7fe634",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c4b35ad8-003a-42c3-a75b-c1ec01784ea2",
   "metadata": {},
   "source": [
    "## Label Encoding\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fefab506-4a13-4640-aa27-9ee0971a849a",
   "metadata": {},
   "outputs": [],
   "source": [
    "labelencoder = LabelEncoder()\n",
    "data[\"Sex\"] = labelencoder.fit_transform(data[\"Sex\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "6e8dcec9-7c3d-4e80-a4c3-9d4e81dca8a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"ChestPainType\"] = labelencoder.fit_transform(data[\"ChestPainType\"])\n",
    "data[\"RestingECG\"] = labelencoder.fit_transform(data[\"RestingECG\"])\n",
    "data[\"ExerciseAngina\"] = labelencoder.fit_transform(data[\"ExerciseAngina\"])\n",
    "data[\"ST_Slope\"] = labelencoder.fit_transform(data[\"ST_Slope\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "08d1ae90-5b55-4e82-acb4-c5ee40bc9b77",
   "metadata": {},
   "outputs": [],
   "source": [
    "s = data[\"Sex\"].unique()\n",
    "c = data[\"ChestPainType\"].unique()\n",
    "r = data[\"RestingECG\"].unique()\n",
    "e = data[\"ExerciseAngina\"].unique()\n",
    "sl = data[\"ST_Slope\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "60195969-f008-430e-a0cc-08ddcfbcc382",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0]\n",
      "[1 2 0 3]\n",
      "[1 2 0]\n",
      "[0 1]\n",
      "[2 1 0]\n"
     ]
    }
   ],
   "source": [
    "print(s)\n",
    "print(c)\n",
    "print(r)\n",
    "print(e)\n",
    "print(sl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f253193-4257-45f9-ab18-0d23978847f5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "23306642-b970-4271-bc46-484b4f286be6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([40, 49, 37, 48, 54, 39, 45, 58, 42, 38, 43, 60, 36, 44, 53, 52, 51,\n",
       "       56, 41, 32, 65, 35, 59, 50, 47, 31, 46, 57, 55, 63, 66, 34, 33, 61,\n",
       "       29, 62, 28, 30, 74, 68, 72, 64, 69, 67, 73, 70, 77, 75, 76, 71],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#label = data[\"HeartDisease\"].copy()\n",
    "\n",
    "data[\"Age\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "507b778b-2cea-4e41-af16-2a4cadf41fac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data[\"RestingBP\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6f549d13-c001-4e9f-b08a-f6ca16225a23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xeFz9/f2GJwNgAoECwDjXdXXw4MEp99+MFgCzB4ECwLje3l4lEglNTExk7Z+YmFAikVBvb6+hyQCYQqAAMK6iokI1NTW3PbZo0SJVVFQ84IkAmEagALAaL+8AsxOBAsC43t5enT9//rbHzp8/z0s8wCyUZ3oAwDTXdbnfhmFlZWUqKSlROp2edKykpERlZWUaHR01MBlu8vv9chzH9BiYRe4pUFpbW/WjH/1IW7du1YEDByTd+GX/F3/xFzpy5IiGh4dVV1env/3bv9VXvvIV7/vGxsa0Y8cOnTx5UqOjo1qxYoVefvlllZeX39ODAe5GJpNRY2Oj6TEwhXQ6rW9961umx5j1Tp8+raKiItNjYBa565d4EomEjhw5okWLFmXt37dvn9ra2nTo0CElEgmFQiGtWrVKIyMj3ppYLKaOjg61t7fr7Nmzunr1qtasWTPpCn4AADA7Oe5dXIF29epVLV68WC+//LJ+/OMf64knntCBAwfkuq7C4bBisZhefPFFSTfOlgSDQe3du1ebNm1SKpXSww8/rOPHj6upqUmSdPnyZUUiEZ06dUqrV6/+1D8/nU4rEAgolUqppKQk1/GBLLzEY4fLly/rj//4j7P+oeLz+XTkyBGFw2GDk0HiJR7cH7k8f9/VSzwvvPCCvvWtb2nlypX68Y9/7O2/dOmSksmkGhoavH2FhYVatmyZuru7tWnTJvX09OjatWtZa8LhsKqrq9Xd3f2ZAgW4nxzH4dS1Bb70pS9p3bp1OnnypKQb/1/Wr1+vL33pS4YnA2BCzoHS3t6u9957T4lEYtKxZDIpSQoGg1n7g8GgPvjgA29NQUGB5s2bN2nNze+/1djYmMbGxryvb3chHYDp75OBUlpaqmg0angiAKbkdA1KX1+ftm7dqtdee01+v3/KdbeeBnRd91NPDd5pTWtrqwKBgLdFIpFcxgYwTXzy98oLL7xwx98zAGa2nAKlp6dHg4ODqq2tVV5envLy8tTV1aW/+Zu/UV5ennfm5NYzIYODg96xUCik8fFxDQ8PT7nmVrt27VIqlfK2vr6+XMYGMA197WtfMz0CAINyCpQVK1bo/PnzOnfunLctWbJE69ev17lz5/Sbv/mbCoVC6uzs9L5nfHxcXV1dqq+vlyTV1tYqPz8/a83AwIAuXLjgrblVYWGhSkpKsjYAADBz5XQNSnFxsaqrq7P2zZ07V6Wlpd7+WCymlpYWVVVVqaqqSi0tLZozZ473WnIgENDGjRu1fft2lZaWav78+dqxY4dqamq0cuXK+/SwAADAdHbf7yS7c+dOjY6OavPmzd6N2s6cOaPi4mJvzf79+5WXl6d169Z5N2o7evSofD7f/R4HAABMQ3d1HxTTuA8KMDONjo56d/XlzqXAzJPL8zcfFggAAKxDoAAAAOsQKAAAwDoECgAAsA6BAgAArEOgAAAA6xAoAADAOgQKAACwDoECAACsQ6AAAADrECgAAMA6BAoAALAOgQIAAKxDoAAAAOsQKAAAwDoECgAAsA6BAgAArEOgAAAA6xAoAADAOgQKAACwDoECAACsQ6AAAADrECgAAMA6BAoAALAOgQIAAKxDoAAAAOsQKAAAwDoECgAAsA6BAgAArEOgAAAA6xAoAADAOgQKAACwDoECAACsQ6AAAADrECgAAMA6BAoAALAOgQIAAKxDoAAAAOsQKAAAwDoECgAAsA6BAgAArEOgAAAA6xAoAADAOgQKAACwDoECAACsk1OgHD58WIsWLVJJSYlKSkr01FNP6fTp095x13W1Z88ehcNhFRUVafny5bp48WLWzxgbG9OWLVtUVlamuXPnau3aterv778/jwYAAMwIOQVKeXm5/vIv/1Lvvvuu3n33XX3zm9/U7/3e73kRsm/fPrW1tenQoUNKJBIKhUJatWqVRkZGvJ8Ri8XU0dGh9vZ2nT17VlevXtWaNWs0MTFxfx8ZAACYthzXdd17+QHz58/XX/3VX+kP//APFQ6HFYvF9OKLL0q6cbYkGAxq79692rRpk1KplB5++GEdP35cTU1NkqTLly8rEono1KlTWr169Wf6M9PptAKBgFKplEpKSu5lfAAWGR0dVWNjoyTp9OnTKioqMjwRgPspl+fvu74GZWJiQu3t7froo4/01FNP6dKlS0omk2poaPDWFBYWatmyZeru7pYk9fT06Nq1a1lrwuGwqqurvTW3MzY2pnQ6nbUBAICZK+dAOX/+vL7whS+osLBQzz//vDo6OvTYY48pmUxKkoLBYNb6YDDoHUsmkyooKNC8efOmXHM7ra2tCgQC3haJRHIdGwAATCM5B8pv/dZv6dy5c3rnnXf0J3/yJ9qwYYN+8YtfeMcdx8la77rupH23+rQ1u3btUiqV8ra+vr5cxwYAANNIzoFSUFCgL3/5y1qyZIlaW1v1+OOP6+DBgwqFQpI06UzI4OCgd1YlFAppfHxcw8PDU665ncLCQu+dQzc3AAAwc93zfVBc19XY2JgqKysVCoXU2dnpHRsfH1dXV5fq6+slSbW1tcrPz89aMzAwoAsXLnhrAAAA8nJZ/KMf/UiNjY2KRCIaGRlRe3u73nrrLb3xxhtyHEexWEwtLS2qqqpSVVWVWlpaNGfOHEWjUUlSIBDQxo0btX37dpWWlmr+/PnasWOHampqtHLlys/lAQIAgOknp0C5cuWKnnvuOQ0MDCgQCGjRokV64403tGrVKknSzp07NTo6qs2bN2t4eFh1dXU6c+aMiouLvZ+xf/9+5eXlad26dRodHdWKFSt09OhR+Xy++/vIAADAtHXP90ExgfugADMT90EBZrYHch8UAACAzwuBAgAArEOgAAAA6xAoAADAOgQKAACwDoECAACsQ6AAAADrECgAAMA6BAoAALAOgQIAAKyT02fx4P5xXVeZTMb0GIBVPvl3gr8fwO35/X45jmN6jM8dgWJIJpPxPnMEwGTPPPOM6REAK82Wz6niJR4AAGAdzqBY4OoT35P7EP8rALmudP3jG//9UJ40C05jA5+Fc/1jfeHcSdNjPFA8K1rAfShP8uWbHgOwRIHpAQDruKYHMICXeAAAgHUIFAAAYB0CBQAAWIdAAQAA1iFQAACAdQgUAABgHQIFAABYh0ABAADWIVAAAIB1CBQAAGAdAgUAAFiHQAEAANYhUAAAgHUIFAAAYB0CBQAAWIdAAQAA1iFQAACAdQgUAABgHQIFAABYJ8/0AJA0cc30BAAAm83C5wkCxQLF/6/d9AgAAFiFl3gAAIB1OINigZHHvyv58k2PAQCw1cS1WXe2nUCxgS+fQAEA4BN4iQcAAFiHQAEAANYhUAAAgHUIFAAAYB0CBQAAWIdAAQAA1skpUFpbW7V06VIVFxfrkUce0dNPP61f/vKXWWtc19WePXsUDodVVFSk5cuX6+LFi1lrxsbGtGXLFpWVlWnu3Llau3at+vv77/3RAACAGSGnQOnq6tILL7ygd955R52dnfr444/V0NCgjz76yFuzb98+tbW16dChQ0okEgqFQlq1apVGRka8NbFYTB0dHWpvb9fZs2d19epVrVmzRhMTE/fvkQEAgGkrpxu1vfHGG1lf//SnP9Ujjzyinp4e/c7v/I5c19WBAwe0e/duPfvss5KkY8eOKRgMKh6Pa9OmTUqlUnrllVd0/PhxrVy5UpL02muvKRKJ6M0339Tq1avv00MDAADT1T1dg5JKpSRJ8+fPlyRdunRJyWRSDQ0N3prCwkItW7ZM3d3dkqSenh5du3Yta004HFZ1dbW35lZjY2NKp9NZGwAAmLnuOlBc11Vzc7N++7d/W9XV1ZKkZDIpSQoGg1lrg8GgdyyZTKqgoEDz5s2bcs2tWltbFQgEvC0Sidzt2AAAYBq460D54Q9/qP/6r//SyZMnJx1zHCfra9d1J+271Z3W7Nq1S6lUytv6+vrudmwAADAN3FWgbNmyRT/72c/0r//6ryovL/f2h0IhSZp0JmRwcNA7qxIKhTQ+Pq7h4eEp19yqsLBQJSUlWRsAAJi5cgoU13X1wx/+UH//93+vf/mXf1FlZWXW8crKSoVCIXV2dnr7xsfH1dXVpfr6eklSbW2t8vPzs9YMDAzowoUL3hoAADC75fQunhdeeEHxeFz/+I//qOLiYu9MSSAQUFFRkRzHUSwWU0tLi6qqqlRVVaWWlhbNmTNH0WjUW7tx40Zt375dpaWlmj9/vnbs2KGamhrvXT0AAGB2yylQDh8+LElavnx51v6f/vSn+sEPfiBJ2rlzp0ZHR7V582YNDw+rrq5OZ86cUXFxsbd+//79ysvL07p16zQ6OqoVK1bo6NGj8vl89/ZoAADAjOC4ruuaHiJX6XRagUBAqVRq2l6PMjo6qsbGRknSyOLnJF++4YkAANaauKbi945Lkk6fPq2ioiLDA92dXJ6/+SweAABgHQIFAABYh0ABAADWIVAAAIB1CBQAAGAdAgUAAFgnp/ug4PPhXP9Y0+693sDnwXWl6x/f+O+H8qRP+QwvYLZwbv69mEUIFAt84dzkD1wEAGA24yUeAABgHc6gGOL3+3X69GnTYwBWyWQyeuaZZyRJHR0d8vv9hicC7DNb/l4QKIY4jjNtb1UMPAh+v5+/I8Asxks8AADAOgQKAACwDoECAACsQ6AAAADrECgAAMA6BAoAALAOgQIAAKxDoAAAAOsQKAAAwDoECgAAsA6BAgAArEOgAAAA6xAoAADAOgQKAACwDoECAACsQ6AAAADrECgAAMA6BAoAALAOgQIAAKxDoAAAAOsQKAAAwDoECgAAsA6BAgAArEOgAAAA6xAoAADAOgQKAACwDoECAACsQ6AAAADrECgAAMA6BAoAALAOgQIAAKxDoAAAAOsQKAAAwDoECgAAsE7OgfJv//Zv+va3v61wOCzHcfQP//APWcdd19WePXsUDodVVFSk5cuX6+LFi1lrxsbGtGXLFpWVlWnu3Llau3at+vv77+mBAACAmSPnQPnoo4/0+OOP69ChQ7c9vm/fPrW1tenQoUNKJBIKhUJatWqVRkZGvDWxWEwdHR1qb2/X2bNndfXqVa1Zs0YTExN3/0gAAMCMkZfrNzQ2NqqxsfG2x1zX1YEDB7R79249++yzkqRjx44pGAwqHo9r06ZNSqVSeuWVV3T8+HGtXLlSkvTaa68pEonozTff1OrVq+/h4QAAgJngvl6DcunSJSWTSTU0NHj7CgsLtWzZMnV3d0uSenp6dO3ataw14XBY1dXV3ppbjY2NKZ1OZ20AAGDmuq+BkkwmJUnBYDBrfzAY9I4lk0kVFBRo3rx5U665VWtrqwKBgLdFIpH7OTYAALDM5/IuHsdxsr52XXfSvlvdac2uXbuUSqW8ra+v777NCgAA7HNfAyUUCknSpDMhg4OD3lmVUCik8fFxDQ8PT7nmVoWFhSopKcnaAADAzHVfA6WyslKhUEidnZ3evvHxcXV1dam+vl6SVFtbq/z8/Kw1AwMDunDhgrcGAADMbjm/i+fq1av63//9X+/rS5cu6dy5c5o/f74qKioUi8XU0tKiqqoqVVVVqaWlRXPmzFE0GpUkBQIBbdy4Udu3b1dpaanmz5+vHTt2qKamxntXDwAAmN1yDpR3331Xv/u7v+t93dzcLEnasGGDjh49qp07d2p0dFSbN2/W8PCw6urqdObMGRUXF3vfs3//fuXl5WndunUaHR3VihUrdPToUfl8vvvwkAAAwHTnuK7rmh4iV+l0WoFAQKlUiutRcM9c11UmkzE9BiRlMhk988wzkqSOjg75/X7DE+Emv9//qW92AD5NLs/fOZ9BAWaaTCYz5c0HYc7NUIEdTp8+raKiItNjYBbhwwIBAIB1OIOCWc/v9+v06dOmx4BuvNw2NjYm6cbtBXhJwR683IYHjUDBrOc4DqeuLTJnzhzTIwCwAC/xAAAA6xAoAADAOgQKAACwDoECAACsQ6AAAADrECgAAMA6BAoAALAOgQIAAKxDoAAAAOsQKAAAwDoECgAAsA6BAgAArEOgAAAA6xAoAADAOgQKAACwDoECAACsQ6AAAADrECgAAMA6BAoAALAOgQIAAKxDoAAAAOsQKAAAwDoECgAAsA6BAgAArEOgAAAA6xAoAADAOgQKAACwDoECAACsQ6AAAADrECgAAMA6BAoAALAOgQIAAKxDoAAAAOsQKAAAwDoECgAAsA6BAgAArEOgAAAA6xAoAADAOgQKAACwTp7pAQDgk5YvX+7991tvvWVsDgBmGT2D8vLLL6uyslJ+v1+1tbV6++23TY4DwLBPxsntvgYwexgLlNdff12xWEy7d+/W+++/r2984xtqbGxUb2+vqZEAAIAlHNd1XRN/cF1dnRYvXqzDhw97+x599FE9/fTTam1tveP3ptNpBQIBpVIplZSUfN6jAngA7nS2hJd6gJkhl+dvI2dQxsfH1dPTo4aGhqz9DQ0N6u7unrR+bGxM6XQ6awMwc3znO9+5p+MAZh4jgfLhhx9qYmJCwWAwa38wGFQymZy0vrW1VYFAwNsikciDGhXAA/Dhhx/e03EAM4/Ri2Qdx8n62nXdSfskadeuXUqlUt7W19f3oEYE8ACUlZXd03EAM4+RQCkrK5PP55t0tmRwcHDSWRVJKiwsVElJSdYGYOb4u7/7u3s6DmDmMRIoBQUFqq2tVWdnZ9b+zs5O1dfXmxgJgGFTXQjLBbLA7GTsRm3Nzc167rnntGTJEj311FM6cuSIent79fzzz5saCQAAWMJYoDQ1NWloaEgvvfSSBgYGVF1drVOnTmnhwoWmRgJg2FtvvcWdZAFIMngflHvBfVAAAJh+rL8PCgAAwJ0QKAAAwDoECgAAsA6BAgAArEOgAAAA6xAoAADAOgQKAACwDoECAACsQ6AAAADrGLvV/b24efPbdDpteBIAAPBZ3Xze/iw3sZ+WgTIyMiJJikQihicBAAC5GhkZUSAQuOOaaflZPNevX9fly5dVXFwsx3FMjwPgPkqn04pEIurr6+OztoAZxnVdjYyMKBwO66GH7nyVybQMFAAzFx8GCkDiIlkAAGAhAgUAAFiHQAFglcLCQv35n/+5CgsLTY8CwCCuQQEAANbhDAoAALAOgQIAAKxDoAAAAOsQKAAAwDoECgCrvPzyy6qsrJTf71dtba3efvtt0yMBMIBAAWCN119/XbFYTLt379b777+vb3zjG2psbFRvb6/p0QA8YLzNGIA16urqtHjxYh0+fNjb9+ijj+rpp59Wa2urwckAPGicQQFghfHxcfX09KihoSFrf0NDg7q7uw1NBcAUAgWAFT788ENNTEwoGAxm7Q8Gg0omk4amAmAKgQLAKo7jZH3tuu6kfQBmPgIFgBXKysrk8/kmnS0ZHBycdFYFwMxHoACwQkFBgWpra9XZ2Zm1v7OzU/X19YamAmBKnukBAOCm5uZmPffcc1qyZImeeuopHTlyRL29vXr++edNjwbgASNQAFijqalJQ0NDeumllzQwMKDq6mqdOnVKCxcuND0agAeM+6AAAADrcA0KAACwDoECAACsQ6AAAADrECgAAMA6BAoAALAOgQIAAKxDoAAAAOsQKAAAwDoECgAAsA6BAgAArEOgAAAA6xAoAADAOv8fk1n5z2sAns0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data[\"Cholesterol\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e18b333b-6651-48f1-9ec8-b18d44e923f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data[\"MaxHR\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0a92e979-1c5c-4cf1-991b-5f89c9082a69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age               13.00\n",
       "Sex                0.00\n",
       "ChestPainType      2.00\n",
       "RestingBP         20.00\n",
       "Cholesterol       93.75\n",
       "FastingBS          0.00\n",
       "RestingECG         0.00\n",
       "MaxHR             36.00\n",
       "ExerciseAngina     1.00\n",
       "Oldpeak            1.50\n",
       "ST_Slope           1.00\n",
       "HeartDisease       1.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#### Remove the outlier\n",
    "Q1 = data.quantile(0.25)\n",
    "Q3 = data.quantile(0.75)\n",
    "IQR = Q3-Q1\n",
    "IQR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ecc614ef-39c9-43f3-bc46-8b1763721b20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(278, 12)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data[~((data < (Q1 - 1.5 * IQR)) | (data > (Q3 + 1.5 * IQR))).any(axis = 1)]\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b6e0e66c-3014-4d13-a7b8-e29eca3c03f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data[\"RestingBP\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d5482366-f3d5-4c7a-b3e0-01daaf022efc",
   "metadata": {},
   "outputs": [],
   "source": [
    "label = data[\"HeartDisease\"].copy()\n",
    "data = data.drop(\"HeartDisease\",axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b54c2525-5e46-4dfb-9eea-07186ec88519",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "35e25dfd-4b17-4049-8bd8-d9db61c43574",
   "metadata": {},
   "source": [
    "## Train Test Split\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e68d6405-3820-4eb6-bb2b-8b0b7cf5b9a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(data, label, test_size = 0.2, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c117be1c-dc30-4ea8-b48b-fd5f25fdd4ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "scale = StandardScaler()\n",
    "X_train = scale.fit_transform(X_train)\n",
    "X_test = scale.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c2a87620-4461-4b3d-af02-915739ae12f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c125509e-99e7-4484-be96-b9d46f8a6f1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('scaler.pkl','wb') as f:\n",
    "    pickle.dump(scale, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "7eb74d73-76ea-4225-a245-30245b9e2217",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.90      0.87        29\n",
      "           1       0.88      0.81      0.85        27\n",
      "\n",
      "    accuracy                           0.86        56\n",
      "   macro avg       0.86      0.86      0.86        56\n",
      "weighted avg       0.86      0.86      0.86        56\n",
      "\n"
     ]
    }
   ],
   "source": [
    "lor = LogisticRegression()\n",
    "lor.fit(X_train, y_train)\n",
    "y_pred = lor.predict(X_test)\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64875722-03a3-42a5-a0c6-0efe349442df",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "75a96873-9c98-48a1-9c22-b2a9c52a688a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x123469c8890>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred, labels=lor.classes_)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=lor.classes_)\n",
    "disp.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "5bf01287-c2cc-49a5-afe0-dc48a6ccf9bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Logistic Regression model is 85.71428571428571\n"
     ]
    }
   ],
   "source": [
    "#printing the accuracy for test set\n",
    "print('Accuracy of Logistic Regression model is {}'.format(accuracy_score(y_test,y_pred)*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "67ffcb57-1ecc-4898-9dd3-7b2684b4109f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display = RocCurveDisplay.from_estimator(lor, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33af49d3-3daf-4b9a-95fd-c1ded3c2c037",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "26c8a6dd-7d3e-4165-b802-013a646bb851",
   "metadata": {},
   "source": [
    "## Decision Tree\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "609d8e9c-b92e-4d7d-ac40-f8edca2020ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "mdl = DecisionTreeClassifier(criterion=\"entropy\", max_depth=6)\n",
    "mdl.fit(X_train,y_train)\n",
    "y_p = mdl.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "cda9a8e7-83f6-43c7-841e-ce6f85d92d84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.90      0.90        29\n",
      "           1       0.89      0.89      0.89        27\n",
      "\n",
      "    accuracy                           0.89        56\n",
      "   macro avg       0.89      0.89      0.89        56\n",
      "weighted avg       0.89      0.89      0.89        56\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_p))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "7e85f163-fb7d-4fef-bc5e-cf27645dba8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x1234959c9d0>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_p, labels=mdl.classes_)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=mdl.classes_)\n",
    "disp.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "30867fc9-15c0-4513-9f31-5c11c2aa0518",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Decision Tree model is 89.28571428571429\n"
     ]
    }
   ],
   "source": [
    "#printing the accuracy for test set\n",
    "print('Accuracy of Decision Tree model is {}'.format(accuracy_score(y_test,y_p)*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "8ca87bb0-5907-4b65-8bf1-10449cb933ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display = RocCurveDisplay.from_estimator(mdl, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "384b3609-330d-400d-a53a-e98885bd1566",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "71b6fedb-87d0-4b24-924a-17a0b0f97337",
   "metadata": {},
   "source": [
    "## Random Forest\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "61aec462-a4c1-4093-b2be-b5b16d537c74",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = RandomForestClassifier(n_estimators =100)\n",
    "clf.fit(X_train, y_train)\n",
    "pp = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "20e279ea-a247-48f9-8267-7327aa9cface",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.90      0.88        29\n",
      "           1       0.88      0.85      0.87        27\n",
      "\n",
      "    accuracy                           0.88        56\n",
      "   macro avg       0.88      0.87      0.87        56\n",
      "weighted avg       0.88      0.88      0.87        56\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, pp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "2c3d055e-cdc4-413e-a944-b54d5eec27e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x12349532810>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, pp, labels=clf.classes_)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=clf.classes_)\n",
    "disp.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "eeed3511-1c37-47bc-b18b-bcd5a7545d07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Random forest classifier model is 87.5\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy of Random forest classifier model is {}'.format(accuracy_score(y_test,pp)*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38bb3b07-3826-4095-8ac5-a0f758168a95",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "8a59d445-be1c-4e4e-91fe-89f2f82051cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display = RocCurveDisplay.from_estimator(clf, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "a51764c6-c1cf-40c6-9d5b-8b775fac0e6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "a132fcd8-0865-4e5f-8844-8dfd103d6f65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['heart_model.sav']"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart_file = 'heart_model.sav'\n",
    "joblib.dump(mdl, heart_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "1f0a6616-21ff-48cc-9443-0b2d0e2b4765",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"heart_model.pkl\", 'wb') as f:\n",
    "    pickle.dump(mdl,f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e3aa27c-1050-46cc-9c0e-a65f8fd60453",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ed45717-3cb6-4f7c-bee6-c43c8e9e3af7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e163807a-71d8-4095-a812-31ab4adcdafa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15f6f610-9422-40c1-a05c-12bc3c9dec85",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b2bd7b4-1696-46b3-a652-1b506f370919",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5790ec8e-9a36-449a-817d-d9986898e970",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d130da8-a439-4ed3-9e0f-9b2d8b17d075",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a4bd3e7-9468-4b53-9fed-863182d3509b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59f85a06-fad5-49d6-85ec-d2a0ef8cef0e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e043b11-daa3-4189-ad8c-adc956080216",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b6f8ad7-1aa1-4bcf-88c9-a1b977d87466",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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