09-Heart-Disease-Detection/Heart_Disease_Detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c8727b1f",
   "metadata": {},
   "source": [
    "# Heart Disease Detection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4edc52f5",
   "metadata": {},
   "source": [
    "### Import dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "675b108b",
   "metadata": {},
   "outputs": [],
   "source": [
    "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.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d00f48f0",
   "metadata": {},
   "source": [
    "### Data Collection and Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f1bb06dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading dataset\n",
    "heart_dataset = pd.read_csv(\"data/heart_disease_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "664a60cc",
   "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>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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <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",
       "      <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",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# showing dataset\n",
    "heart_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5ade4c40",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x500 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot testing with single column\n",
    "\n",
    "num_rows, num_cols = 1, 5\n",
    "fig, axs = plt.subplots(num_rows, num_cols, figsize=(15, 5))\n",
    "\n",
    "col = heart_dataset[\"age\"]\n",
    "sns.countplot(x = col, ax = axs[0])\n",
    "axs[0].set_title(\"Count Plot for age\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "11bbe082",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x2000 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# automating for full dataset\n",
    "\n",
    "num_rows, num_cols = 5, 3\n",
    "fig, axs = plt.subplots(num_rows, num_cols, figsize=(15, 20))\n",
    "\n",
    "for index, col_name in enumerate(heart_dataset.columns):\n",
    "    #if(col_name != \"trestbps\" and col_name != \"chol\" and col_name != \"thalach\" and col_name != \"oldpeak\"):\n",
    "        col = heart_dataset[col_name]\n",
    "        \n",
    "        row_index = index // num_cols\n",
    "        col_index = index % num_cols\n",
    "        \n",
    "        sns.countplot(x = col, ax = axs[row_index, col_index])\n",
    "        axs[row_index, col_index].set_title(f\"Plot {col_name}\")\n",
    "        \n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "89bcd78e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(303, 14)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of rows and columns\n",
    "heart_dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3c9887d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age         0\n",
       "sex         0\n",
       "cp          0\n",
       "trestbps    0\n",
       "chol        0\n",
       "fbs         0\n",
       "restecg     0\n",
       "thalach     0\n",
       "exang       0\n",
       "oldpeak     0\n",
       "slope       0\n",
       "ca          0\n",
       "thal        0\n",
       "target      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check missing values\n",
    "heart_dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cd555943",
   "metadata": {},
   "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   cp        303 non-null    int64  \n",
      " 3   trestbps  303 non-null    int64  \n",
      " 4   chol      303 non-null    int64  \n",
      " 5   fbs       303 non-null    int64  \n",
      " 6   restecg   303 non-null    int64  \n",
      " 7   thalach   303 non-null    int64  \n",
      " 8   exang     303 non-null    int64  \n",
      " 9   oldpeak   303 non-null    float64\n",
      " 10  slope     303 non-null    int64  \n",
      " 11  ca        303 non-null    int64  \n",
      " 12  thal      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": [
    "# showing dataset information\n",
    "heart_dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2d546dbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    165\n",
       "0    138\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data distribution\n",
    "heart_dataset[\"target\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "951c0708",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>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>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          cp    trestbps        chol         fbs  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean    54.366337    0.683168    0.966997  131.623762  246.264026    0.148515   \n",
       "std      9.082101    0.466011    1.032052   17.538143   51.830751    0.356198   \n",
       "min     29.000000    0.000000    0.000000   94.000000  126.000000    0.000000   \n",
       "25%     47.500000    0.000000    0.000000  120.000000  211.000000    0.000000   \n",
       "50%     55.000000    1.000000    1.000000  130.000000  240.000000    0.000000   \n",
       "75%     61.000000    1.000000    2.000000  140.000000  274.500000    0.000000   \n",
       "max     77.000000    1.000000    3.000000  200.000000  564.000000    1.000000   \n",
       "\n",
       "          restecg     thalach       exang     oldpeak       slope          ca  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean     0.528053  149.646865    0.326733    1.039604    1.399340    0.729373   \n",
       "std      0.525860   22.905161    0.469794    1.161075    0.616226    1.022606   \n",
       "min      0.000000   71.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000  133.500000    0.000000    0.000000    1.000000    0.000000   \n",
       "50%      1.000000  153.000000    0.000000    0.800000    1.000000    0.000000   \n",
       "75%      1.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n",
       "max      2.000000  202.000000    1.000000    6.200000    2.000000    4.000000   \n",
       "\n",
       "             thal      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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# statistical measures of dataset\n",
    "heart_dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bff0f13c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.098447</td>\n",
       "      <td>-0.068653</td>\n",
       "      <td>0.279351</td>\n",
       "      <td>0.213678</td>\n",
       "      <td>0.121308</td>\n",
       "      <td>-0.116211</td>\n",
       "      <td>-0.398522</td>\n",
       "      <td>0.096801</td>\n",
       "      <td>0.210013</td>\n",
       "      <td>-0.168814</td>\n",
       "      <td>0.276326</td>\n",
       "      <td>0.068001</td>\n",
       "      <td>-0.225439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>-0.098447</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.049353</td>\n",
       "      <td>-0.056769</td>\n",
       "      <td>-0.197912</td>\n",
       "      <td>0.045032</td>\n",
       "      <td>-0.058196</td>\n",
       "      <td>-0.044020</td>\n",
       "      <td>0.141664</td>\n",
       "      <td>0.096093</td>\n",
       "      <td>-0.030711</td>\n",
       "      <td>0.118261</td>\n",
       "      <td>0.210041</td>\n",
       "      <td>-0.280937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cp</th>\n",
       "      <td>-0.068653</td>\n",
       "      <td>-0.049353</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.047608</td>\n",
       "      <td>-0.076904</td>\n",
       "      <td>0.094444</td>\n",
       "      <td>0.044421</td>\n",
       "      <td>0.295762</td>\n",
       "      <td>-0.394280</td>\n",
       "      <td>-0.149230</td>\n",
       "      <td>0.119717</td>\n",
       "      <td>-0.181053</td>\n",
       "      <td>-0.161736</td>\n",
       "      <td>0.433798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trestbps</th>\n",
       "      <td>0.279351</td>\n",
       "      <td>-0.056769</td>\n",
       "      <td>0.047608</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.123174</td>\n",
       "      <td>0.177531</td>\n",
       "      <td>-0.114103</td>\n",
       "      <td>-0.046698</td>\n",
       "      <td>0.067616</td>\n",
       "      <td>0.193216</td>\n",
       "      <td>-0.121475</td>\n",
       "      <td>0.101389</td>\n",
       "      <td>0.062210</td>\n",
       "      <td>-0.144931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chol</th>\n",
       "      <td>0.213678</td>\n",
       "      <td>-0.197912</td>\n",
       "      <td>-0.076904</td>\n",
       "      <td>0.123174</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.013294</td>\n",
       "      <td>-0.151040</td>\n",
       "      <td>-0.009940</td>\n",
       "      <td>0.067023</td>\n",
       "      <td>0.053952</td>\n",
       "      <td>-0.004038</td>\n",
       "      <td>0.070511</td>\n",
       "      <td>0.098803</td>\n",
       "      <td>-0.085239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fbs</th>\n",
       "      <td>0.121308</td>\n",
       "      <td>0.045032</td>\n",
       "      <td>0.094444</td>\n",
       "      <td>0.177531</td>\n",
       "      <td>0.013294</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.084189</td>\n",
       "      <td>-0.008567</td>\n",
       "      <td>0.025665</td>\n",
       "      <td>0.005747</td>\n",
       "      <td>-0.059894</td>\n",
       "      <td>0.137979</td>\n",
       "      <td>-0.032019</td>\n",
       "      <td>-0.028046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>restecg</th>\n",
       "      <td>-0.116211</td>\n",
       "      <td>-0.058196</td>\n",
       "      <td>0.044421</td>\n",
       "      <td>-0.114103</td>\n",
       "      <td>-0.151040</td>\n",
       "      <td>-0.084189</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.044123</td>\n",
       "      <td>-0.070733</td>\n",
       "      <td>-0.058770</td>\n",
       "      <td>0.093045</td>\n",
       "      <td>-0.072042</td>\n",
       "      <td>-0.011981</td>\n",
       "      <td>0.137230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thalach</th>\n",
       "      <td>-0.398522</td>\n",
       "      <td>-0.044020</td>\n",
       "      <td>0.295762</td>\n",
       "      <td>-0.046698</td>\n",
       "      <td>-0.009940</td>\n",
       "      <td>-0.008567</td>\n",
       "      <td>0.044123</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.378812</td>\n",
       "      <td>-0.344187</td>\n",
       "      <td>0.386784</td>\n",
       "      <td>-0.213177</td>\n",
       "      <td>-0.096439</td>\n",
       "      <td>0.421741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exang</th>\n",
       "      <td>0.096801</td>\n",
       "      <td>0.141664</td>\n",
       "      <td>-0.394280</td>\n",
       "      <td>0.067616</td>\n",
       "      <td>0.067023</td>\n",
       "      <td>0.025665</td>\n",
       "      <td>-0.070733</td>\n",
       "      <td>-0.378812</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.288223</td>\n",
       "      <td>-0.257748</td>\n",
       "      <td>0.115739</td>\n",
       "      <td>0.206754</td>\n",
       "      <td>-0.436757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oldpeak</th>\n",
       "      <td>0.210013</td>\n",
       "      <td>0.096093</td>\n",
       "      <td>-0.149230</td>\n",
       "      <td>0.193216</td>\n",
       "      <td>0.053952</td>\n",
       "      <td>0.005747</td>\n",
       "      <td>-0.058770</td>\n",
       "      <td>-0.344187</td>\n",
       "      <td>0.288223</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.577537</td>\n",
       "      <td>0.222682</td>\n",
       "      <td>0.210244</td>\n",
       "      <td>-0.430696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slope</th>\n",
       "      <td>-0.168814</td>\n",
       "      <td>-0.030711</td>\n",
       "      <td>0.119717</td>\n",
       "      <td>-0.121475</td>\n",
       "      <td>-0.004038</td>\n",
       "      <td>-0.059894</td>\n",
       "      <td>0.093045</td>\n",
       "      <td>0.386784</td>\n",
       "      <td>-0.257748</td>\n",
       "      <td>-0.577537</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.080155</td>\n",
       "      <td>-0.104764</td>\n",
       "      <td>0.345877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ca</th>\n",
       "      <td>0.276326</td>\n",
       "      <td>0.118261</td>\n",
       "      <td>-0.181053</td>\n",
       "      <td>0.101389</td>\n",
       "      <td>0.070511</td>\n",
       "      <td>0.137979</td>\n",
       "      <td>-0.072042</td>\n",
       "      <td>-0.213177</td>\n",
       "      <td>0.115739</td>\n",
       "      <td>0.222682</td>\n",
       "      <td>-0.080155</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.151832</td>\n",
       "      <td>-0.391724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thal</th>\n",
       "      <td>0.068001</td>\n",
       "      <td>0.210041</td>\n",
       "      <td>-0.161736</td>\n",
       "      <td>0.062210</td>\n",
       "      <td>0.098803</td>\n",
       "      <td>-0.032019</td>\n",
       "      <td>-0.011981</td>\n",
       "      <td>-0.096439</td>\n",
       "      <td>0.206754</td>\n",
       "      <td>0.210244</td>\n",
       "      <td>-0.104764</td>\n",
       "      <td>0.151832</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.344029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>-0.225439</td>\n",
       "      <td>-0.280937</td>\n",
       "      <td>0.433798</td>\n",
       "      <td>-0.144931</td>\n",
       "      <td>-0.085239</td>\n",
       "      <td>-0.028046</td>\n",
       "      <td>0.137230</td>\n",
       "      <td>0.421741</td>\n",
       "      <td>-0.436757</td>\n",
       "      <td>-0.430696</td>\n",
       "      <td>0.345877</td>\n",
       "      <td>-0.391724</td>\n",
       "      <td>-0.344029</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age       sex        cp  trestbps      chol       fbs  \\\n",
       "age       1.000000 -0.098447 -0.068653  0.279351  0.213678  0.121308   \n",
       "sex      -0.098447  1.000000 -0.049353 -0.056769 -0.197912  0.045032   \n",
       "cp       -0.068653 -0.049353  1.000000  0.047608 -0.076904  0.094444   \n",
       "trestbps  0.279351 -0.056769  0.047608  1.000000  0.123174  0.177531   \n",
       "chol      0.213678 -0.197912 -0.076904  0.123174  1.000000  0.013294   \n",
       "fbs       0.121308  0.045032  0.094444  0.177531  0.013294  1.000000   \n",
       "restecg  -0.116211 -0.058196  0.044421 -0.114103 -0.151040 -0.084189   \n",
       "thalach  -0.398522 -0.044020  0.295762 -0.046698 -0.009940 -0.008567   \n",
       "exang     0.096801  0.141664 -0.394280  0.067616  0.067023  0.025665   \n",
       "oldpeak   0.210013  0.096093 -0.149230  0.193216  0.053952  0.005747   \n",
       "slope    -0.168814 -0.030711  0.119717 -0.121475 -0.004038 -0.059894   \n",
       "ca        0.276326  0.118261 -0.181053  0.101389  0.070511  0.137979   \n",
       "thal      0.068001  0.210041 -0.161736  0.062210  0.098803 -0.032019   \n",
       "target   -0.225439 -0.280937  0.433798 -0.144931 -0.085239 -0.028046   \n",
       "\n",
       "           restecg   thalach     exang   oldpeak     slope        ca  \\\n",
       "age      -0.116211 -0.398522  0.096801  0.210013 -0.168814  0.276326   \n",
       "sex      -0.058196 -0.044020  0.141664  0.096093 -0.030711  0.118261   \n",
       "cp        0.044421  0.295762 -0.394280 -0.149230  0.119717 -0.181053   \n",
       "trestbps -0.114103 -0.046698  0.067616  0.193216 -0.121475  0.101389   \n",
       "chol     -0.151040 -0.009940  0.067023  0.053952 -0.004038  0.070511   \n",
       "fbs      -0.084189 -0.008567  0.025665  0.005747 -0.059894  0.137979   \n",
       "restecg   1.000000  0.044123 -0.070733 -0.058770  0.093045 -0.072042   \n",
       "thalach   0.044123  1.000000 -0.378812 -0.344187  0.386784 -0.213177   \n",
       "exang    -0.070733 -0.378812  1.000000  0.288223 -0.257748  0.115739   \n",
       "oldpeak  -0.058770 -0.344187  0.288223  1.000000 -0.577537  0.222682   \n",
       "slope     0.093045  0.386784 -0.257748 -0.577537  1.000000 -0.080155   \n",
       "ca       -0.072042 -0.213177  0.115739  0.222682 -0.080155  1.000000   \n",
       "thal     -0.011981 -0.096439  0.206754  0.210244 -0.104764  0.151832   \n",
       "target    0.137230  0.421741 -0.436757 -0.430696  0.345877 -0.391724   \n",
       "\n",
       "              thal    target  \n",
       "age       0.068001 -0.225439  \n",
       "sex       0.210041 -0.280937  \n",
       "cp       -0.161736  0.433798  \n",
       "trestbps  0.062210 -0.144931  \n",
       "chol      0.098803 -0.085239  \n",
       "fbs      -0.032019 -0.028046  \n",
       "restecg  -0.011981  0.137230  \n",
       "thalach  -0.096439  0.421741  \n",
       "exang     0.206754 -0.436757  \n",
       "oldpeak   0.210244 -0.430696  \n",
       "slope    -0.104764  0.345877  \n",
       "ca        0.151832 -0.391724  \n",
       "thal      1.000000 -0.344029  \n",
       "target   -0.344029  1.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# correlation \n",
    "correlation = heart_dataset.corr()\n",
    "correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b37b2928",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualise correlation\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.heatmap(correlation, cbar = True, square = True, fmt = '.1f', annot = True, annot_kws = {\"size\": 10}, cmap = \"Blues\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cd85c7f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# separating features and target\n",
    "X = heart_dataset.drop([\"target\"], axis = 1)\n",
    "Y = heart_dataset[\"target\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "614e68db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
      "0     63    1   3       145   233    1        0      150      0      2.3   \n",
      "1     37    1   2       130   250    0        1      187      0      3.5   \n",
      "2     41    0   1       130   204    0        0      172      0      1.4   \n",
      "3     56    1   1       120   236    0        1      178      0      0.8   \n",
      "4     57    0   0       120   354    0        1      163      1      0.6   \n",
      "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
      "298   57    0   0       140   241    0        1      123      1      0.2   \n",
      "299   45    1   3       110   264    0        1      132      0      1.2   \n",
      "300   68    1   0       144   193    1        1      141      0      3.4   \n",
      "301   57    1   0       130   131    0        1      115      1      1.2   \n",
      "302   57    0   1       130   236    0        0      174      0      0.0   \n",
      "\n",
      "     slope  ca  thal  \n",
      "0        0   0     1  \n",
      "1        0   0     2  \n",
      "2        2   0     2  \n",
      "3        2   0     2  \n",
      "4        2   0     2  \n",
      "..     ...  ..   ...  \n",
      "298      1   0     3  \n",
      "299      1   0     3  \n",
      "300      1   2     3  \n",
      "301      1   1     3  \n",
      "302      1   1     2  \n",
      "\n",
      "[303 rows x 13 columns]\n",
      "0      1\n",
      "1      1\n",
      "2      1\n",
      "3      1\n",
      "4      1\n",
      "      ..\n",
      "298    0\n",
      "299    0\n",
      "300    0\n",
      "301    0\n",
      "302    0\n",
      "Name: target, Length: 303, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(X)\n",
    "print(Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "468bc6de",
   "metadata": {},
   "source": [
    "### Split Train and Test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5493eaa7",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, stratify = Y, random_state = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "98efdefd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total data:  (303, 13)\n",
      "Train data:  (242, 13)\n",
      "Test data:  (61, 13)\n"
     ]
    }
   ],
   "source": [
    "print(\"Total data: \", X.shape)\n",
    "print(\"Train data: \", X_train.shape)\n",
    "print(\"Test data: \", X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5aa4d62a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
      "61    54    1   1       108   309    0        1      156      0      0.0   \n",
      "238   77    1   0       125   304    0        0      162      1      0.0   \n",
      "160   56    1   1       120   240    0        1      169      0      0.0   \n",
      "158   58    1   1       125   220    0        1      144      0      0.4   \n",
      "289   55    0   0       128   205    0        2      130      1      2.0   \n",
      "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
      "100   42    1   3       148   244    0        0      178      0      0.8   \n",
      "49    53    0   0       138   234    0        0      160      0      0.0   \n",
      "300   68    1   0       144   193    1        1      141      0      3.4   \n",
      "194   60    1   2       140   185    0        0      155      0      3.0   \n",
      "131   49    0   1       134   271    0        1      162      0      0.0   \n",
      "\n",
      "     slope  ca  thal  \n",
      "61       2   0     3  \n",
      "238      2   3     2  \n",
      "160      0   0     2  \n",
      "158      1   4     3  \n",
      "289      1   1     3  \n",
      "..     ...  ..   ...  \n",
      "100      2   2     2  \n",
      "49       2   0     2  \n",
      "300      1   2     3  \n",
      "194      1   0     2  \n",
      "131      1   0     2  \n",
      "\n",
      "[242 rows x 13 columns]\n",
      "61     1\n",
      "238    0\n",
      "160    1\n",
      "158    1\n",
      "289    0\n",
      "      ..\n",
      "100    1\n",
      "49     1\n",
      "300    0\n",
      "194    0\n",
      "131    1\n",
      "Name: target, Length: 242, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(X_train)\n",
    "print(Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55968ff6",
   "metadata": {},
   "source": [
    "### Model Training using Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c09f9f12",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LogisticRegression(max_iter = 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9e11d857",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(max_iter=1000)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1132c19c",
   "metadata": {},
   "source": [
    "### Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4944e76e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training data:  0.8553719008264463\n"
     ]
    }
   ],
   "source": [
    "# accuracy on training data\n",
    "X_train_prediction = model.predict(X_train)\n",
    "training_data_accuracy = accuracy_score(X_train_prediction, Y_train)\n",
    "print(\"Accuracy on training data: \", training_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "737353b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training data:  0.8032786885245902\n"
     ]
    }
   ],
   "source": [
    "# accuracy on test data\n",
    "X_test_prediction = model.predict(X_test)\n",
    "test_data_accuracy = accuracy_score(X_test_prediction, Y_test)\n",
    "print(\"Accuracy on training data: \", test_data_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3627396b",
   "metadata": {},
   "source": [
    "### Predictive System"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "704571c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 39. ,   1. ,   0. , 118. , 219. ,   0. ,   1. , 140. ,   0. ,\n",
       "         1.2,   1. ,   0. ,   3. ])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# taking a sample value for test\n",
    "X_test.iloc[18].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a06d1623",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking sample label\n",
    "Y_test.iloc[18]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c03eb790",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\n",
      "Diagnosis shows no Heart Disease\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\TanveerKader\\anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "input_data = X_test.iloc[18].values\n",
    "\n",
    "# reshape the numpy array as we're predicting for one instance\n",
    "input_data_reshaped = input_data.reshape(1,-1)\n",
    "\n",
    "prediction = model.predict(input_data_reshaped)\n",
    "print(prediction)\n",
    "\n",
    "if(prediction[0] == 0):\n",
    "    print(\"Diagnosis shows no Heart Disease\")\n",
    "else:\n",
    "    print(\"Diagnosis detects Heart Disease\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e0b9ab07",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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