heart_attack_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "raw",
   "id": "48023f6c",
   "metadata": {},
   "source": [
    "Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "59093654",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "raw",
   "id": "7ea354b7",
   "metadata": {},
   "source": [
    "# data collection and processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5b994430",
   "metadata": {},
   "outputs": [],
   "source": [
    "#loading heart.csv data\n",
    "heart_data= pd.read_csv(\"Heart.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e4f0ae50",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>37</td>\n",
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       "      <td>130</td>\n",
       "      <td>250</td>\n",
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       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>41</td>\n",
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       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print of top 5 rows of data\n",
    "heart_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "781a26d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>298</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>241</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>123</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
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       "      <td>3</td>\n",
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       "      <th>299</th>\n",
       "      <td>45</td>\n",
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       "      <td>110</td>\n",
       "      <td>264</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
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       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "      <th>300</th>\n",
       "      <td>68</td>\n",
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       "      <td>144</td>\n",
       "      <td>193</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>130</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>115</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\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  target  \n",
       "298      1   0     3       0  \n",
       "299      1   0     3       0  \n",
       "300      1   2     3       0  \n",
       "301      1   1     3       0  \n",
       "302      1   1     2       0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print of last 5 rows of data\n",
    "heart_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "201b9353",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(303, 14)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#shape of the dataset\n",
    "heart_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d7b32eda",
   "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": [
    "#print info of dataset\n",
    "heart_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c422a3a5",
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for missing values\n",
    "heart_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "68246e75",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#some of statistical measures of dataset\n",
    "heart_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "3f5abcbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#heatmap\n",
    "corr_matric = heart_data.corr()\n",
    "top_corr_features = corr_matric.index\n",
    "plt.figure(figsize=(20,20))\n",
    "sns.heatmap(heart_data[top_corr_features].corr(),annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "fce96b9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x1440 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualizing the frequency \n",
    "heart_data.hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "4936094d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='target', ylabel='count'>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# count plot of target column\n",
    "sns.countplot(x=\"target\",data=heart_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "61992a09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    165\n",
       "0    138\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart_data.target.value_counts()\n",
    "# when target is 1 it means the person has a chance of heart attack \n",
    "# when target is 0 it means the person might not have a heart attack"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9ba15f69",
   "metadata": {},
   "outputs": [],
   "source": [
    "# splitting of dataset \n",
    "x= heart_data.drop(columns=\"target\", axis=1)\n",
    "y= heart_data[\"target\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "737fe4eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# splitting the data into train and test data\n",
    "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": 15,
   "id": "9f0c0b8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#model training\n",
    "#logistic regression\n",
    "model=LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "02824527",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\deept\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#training the model using train data\n",
    "model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "a439b94a",
   "metadata": {},
   "source": [
    "#model evaluation"
   ]
  },
  {
   "cell_type": "raw",
   "id": "ceecaeb6",
   "metadata": {},
   "source": [
    "#accuracy score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3a5b6c1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#accuracy score on train data\n",
    "x_train_prediction =model.predict(x_train)\n",
    "training_data_accuracy = accuracy_score(x_train_prediction, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e30a99c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy score for training data :  0.8512396694214877\n"
     ]
    }
   ],
   "source": [
    "print(\"accuracy score for training data : \",training_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "318802c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#accuracy score on test data\n",
    "x_test_prediction =model.predict(x_test)\n",
    "test_data_accuracy = accuracy_score(x_test_prediction, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a9944031",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy score for test data :  0.819672131147541\n"
     ]
    }
   ],
   "source": [
    "print(\"accuracy score for test data : \",test_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4d0830a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The person does not have a heart disease\n"
     ]
    }
   ],
   "source": [
    "# prediction of the model with some random values\n",
    "input_data=(43,1,0,120,1,1,157,1,0.5,1,0,1,0)\n",
    "input_data_asarray=np.asarray(input_data)\n",
    "input_data_reshape=input_data_asarray.reshape(1,-1)\n",
    "prediction= model.predict(input_data_reshape)\n",
    "if (prediction[0]==0):\n",
    "    print(\"The person does not have a heart disease\")\n",
    "else:\n",
    "    print(\"The person does not have a heart disease\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d51e2d04",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The person does not have a heart disease\n"
     ]
    }
   ],
   "source": [
    "# prediction of the model with some random values\n",
    "input_data=(44,1,1,120,263,0,1,173,0,0,2,0,3)\n",
    "input_data_asarray=np.asarray(input_data)\n",
    "input_data_reshape=input_data_asarray.reshape(1,-1)\n",
    "prediction= model.predict(input_data_reshape)\n",
    "if (prediction[0]==0):\n",
    "    print(\"The person does not have a heart disease\")\n",
    "else:\n",
    "    print(\"The person does not have a heart disease\")"
   ]
  }
 ],
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