heart_attack_risk_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "5b976408",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\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 confusion_matrix, accuracy_score, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "9e40e692",
   "metadata": {},
   "outputs": [
    {
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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>trtbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalachh</th>\n",
       "      <th>exng</th>\n",
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       "      <th>slp</th>\n",
       "      <th>caa</th>\n",
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       "      <th>output</th>\n",
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       "      <th>0</th>\n",
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       "      <th>4</th>\n",
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       "      <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",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <th>5</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>192</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>148</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>56</td>\n",
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       "      <td>1</td>\n",
       "      <td>140</td>\n",
       "      <td>294</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>153</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>263</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>173</td>\n",
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       "      <td>1</td>\n",
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       "      <td>2</td>\n",
       "      <td>172</td>\n",
       "      <td>199</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>162</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trtbps  chol  fbs  restecg  thalachh  exng  oldpeak  slp  \\\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",
       "5   57    1   0     140   192    0        1       148     0      0.4    1   \n",
       "6   56    0   1     140   294    0        0       153     0      1.3    1   \n",
       "7   44    1   1     120   263    0        1       173     0      0.0    2   \n",
       "8   52    1   2     172   199    1        1       162     0      0.5    2   \n",
       "9   57    1   2     150   168    0        1       174     0      1.6    2   \n",
       "\n",
       "   caa  thall  output  \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  \n",
       "5    0      1       1  \n",
       "6    0      2       1  \n",
       "7    0      3       1  \n",
       "8    0      3       1  \n",
       "9    0      2       1  "
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv('heart.csv')\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f08d4e0c",
   "metadata": {},
   "source": [
    "Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "7f1a83e3",
   "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>trtbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalachh</th>\n",
       "      <th>exng</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slp</th>\n",
       "      <th>caa</th>\n",
       "      <th>thall</th>\n",
       "      <th>output</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      trtbps        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    thalachh        exng     oldpeak         slp         caa  \\\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",
       "            thall      output  \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": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11e85d2a",
   "metadata": {},
   "source": [
    "Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "884d81ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "from feature_engine.selection import DropConstantFeatures\n",
    "from feature_engine.selection import DropDuplicateFeatures\n",
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "dde27f5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=data.drop('output', axis=1)\n",
    "Y = data['output']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "4bc342e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(303, 13)\n",
      "(303,)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape)\n",
    "print(Y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "29338f1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "xtrain,xtest,ytrain, ytest= train_test_split(X,Y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "07e6c4c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe=Pipeline([('Constant', DropConstantFeatures(tol=0.998)), ('Duplicate', DropDuplicateFeatures())])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "5d2153f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('Constant', DropConstantFeatures(tol=0.998)),\n",
       "                ('Duplicate', DropDuplicateFeatures())])"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.fit(xtrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "0bfa842b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of constant features to drop: 0\n",
      "No. of duplicate features to drop: 0\n"
     ]
    }
   ],
   "source": [
    "print(\"No. of constant features to drop:\", len(pipe.named_steps['Constant'].features_to_drop_))\n",
    "print(\"No. of duplicate features to drop:\", len(pipe.named_steps['Duplicate'].features_to_drop_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "4a7de279",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(242, 13)\n",
      "(61, 13)\n"
     ]
    }
   ],
   "source": [
    "print(xtrain.shape)\n",
    "print(xtest.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bb31f5c",
   "metadata": {},
   "source": [
    "Remove three features named oldpeak, slp, thall from the training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "6dc8c049",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.drop(['oldpeak', 'slp', 'thall'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "87963bac",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trtbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalachh</th>\n",
       "      <th>exng</th>\n",
       "      <th>caa</th>\n",
       "      <th>output</th>\n",
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       "      <td>140</td>\n",
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       "      <td>1</td>\n",
       "      <td>148</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>140</td>\n",
       "      <td>294</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>153</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "   age  sex  cp  trtbps  chol  fbs  restecg  thalachh  exng  caa  output\n",
       "0   63    1   3     145   233    1        0       150     0    0       1\n",
       "1   37    1   2     130   250    0        1       187     0    0       1\n",
       "2   41    0   1     130   204    0        0       172     0    0       1\n",
       "3   56    1   1     120   236    0        1       178     0    0       1\n",
       "4   57    0   0     120   354    0        1       163     1    0       1\n",
       "5   57    1   0     140   192    0        1       148     0    0       1\n",
       "6   56    0   1     140   294    0        0       153     0    0       1\n",
       "7   44    1   1     120   263    0        1       173     0    0       1\n",
       "8   52    1   2     172   199    1        1       162     0    0       1\n",
       "9   57    1   2     150   168    0        1       174     0    0       1"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f9bdb0b",
   "metadata": {},
   "source": [
    "Standardization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "1db74f09",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "b642ac7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "data = scaler.fit_transform(data)\n",
    "data = pd.DataFrame(data, columns = ['age', 'sex', 'cp', 'trtbps', 'chol', 'fbs', 'restecg', 'thalachh', 'exng', 'caa', 'output'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "ecea8726",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
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       "      <th>trtbps</th>\n",
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       "      <th>restecg</th>\n",
       "      <th>thalachh</th>\n",
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       "  <tbody>\n",
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       "      <td>0.032031</td>\n",
       "      <td>-0.663867</td>\n",
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       "      <td>-0.714429</td>\n",
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       "      <td>-1.468418</td>\n",
       "      <td>-0.938515</td>\n",
       "      <td>-0.663867</td>\n",
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       "      <td>-0.417635</td>\n",
       "      <td>0.898962</td>\n",
       "      <td>0.583939</td>\n",
       "      <td>1.435481</td>\n",
       "      <td>-0.714429</td>\n",
       "      <td>0.914529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.290464</td>\n",
       "      <td>0.681005</td>\n",
       "      <td>-0.938515</td>\n",
       "      <td>0.478391</td>\n",
       "      <td>-1.048678</td>\n",
       "      <td>-0.417635</td>\n",
       "      <td>0.898962</td>\n",
       "      <td>-0.072018</td>\n",
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       "      <td>-0.714429</td>\n",
       "      <td>0.914529</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.180175</td>\n",
       "      <td>-1.468418</td>\n",
       "      <td>0.032031</td>\n",
       "      <td>0.478391</td>\n",
       "      <td>0.922521</td>\n",
       "      <td>-0.417635</td>\n",
       "      <td>-1.005832</td>\n",
       "      <td>0.146634</td>\n",
       "      <td>-0.696631</td>\n",
       "      <td>-0.714429</td>\n",
       "      <td>0.914529</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-1.143291</td>\n",
       "      <td>0.681005</td>\n",
       "      <td>0.032031</td>\n",
       "      <td>-0.663867</td>\n",
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       "      <td>-0.696631</td>\n",
       "      <td>-0.714429</td>\n",
       "      <td>0.914529</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.260980</td>\n",
       "      <td>0.681005</td>\n",
       "      <td>1.002577</td>\n",
       "      <td>2.306004</td>\n",
       "      <td>-0.913400</td>\n",
       "      <td>2.394438</td>\n",
       "      <td>0.898962</td>\n",
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       "      <td>-0.696631</td>\n",
       "      <td>-0.714429</td>\n",
       "      <td>0.914529</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.290464</td>\n",
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       "      <td>1.002577</td>\n",
       "      <td>1.049520</td>\n",
       "      <td>-1.512490</td>\n",
       "      <td>-0.417635</td>\n",
       "      <td>0.898962</td>\n",
       "      <td>1.064975</td>\n",
       "      <td>-0.696631</td>\n",
       "      <td>-0.714429</td>\n",
       "      <td>0.914529</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        age       sex        cp    trtbps      chol       fbs   restecg  \\\n",
       "0  0.952197  0.681005  1.973123  0.763956 -0.256334  2.394438 -1.005832   \n",
       "1 -1.915313  0.681005  1.002577 -0.092738  0.072199 -0.417635  0.898962   \n",
       "2 -1.474158 -1.468418  0.032031 -0.092738 -0.816773 -0.417635 -1.005832   \n",
       "3  0.180175  0.681005  0.032031 -0.663867 -0.198357 -0.417635  0.898962   \n",
       "4  0.290464 -1.468418 -0.938515 -0.663867  2.082050 -0.417635  0.898962   \n",
       "5  0.290464  0.681005 -0.938515  0.478391 -1.048678 -0.417635  0.898962   \n",
       "6  0.180175 -1.468418  0.032031  0.478391  0.922521 -0.417635 -1.005832   \n",
       "7 -1.143291  0.681005  0.032031 -0.663867  0.323431 -0.417635  0.898962   \n",
       "8 -0.260980  0.681005  1.002577  2.306004 -0.913400  2.394438  0.898962   \n",
       "9  0.290464  0.681005  1.002577  1.049520 -1.512490 -0.417635  0.898962   \n",
       "\n",
       "   thalachh      exng       caa    output  \n",
       "0  0.015443 -0.696631 -0.714429  0.914529  \n",
       "1  1.633471 -0.696631 -0.714429  0.914529  \n",
       "2  0.977514 -0.696631 -0.714429  0.914529  \n",
       "3  1.239897 -0.696631 -0.714429  0.914529  \n",
       "4  0.583939  1.435481 -0.714429  0.914529  \n",
       "5 -0.072018 -0.696631 -0.714429  0.914529  \n",
       "6  0.146634 -0.696631 -0.714429  0.914529  \n",
       "7  1.021244 -0.696631 -0.714429  0.914529  \n",
       "8  0.540209 -0.696631 -0.714429  0.914529  \n",
       "9  1.064975 -0.696631 -0.714429  0.914529  "
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f335991",
   "metadata": {},
   "source": [
    "Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "61fa7c64",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn import svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "6a4cc245",
   "metadata": {},
   "outputs": [],
   "source": [
    "LR_model = LogisticRegression(fit_intercept=True)\n",
    "DT_model=DecisionTreeClassifier(max_depth=5, random_state=1)\n",
    "RFC_model = RandomForestClassifier(n_estimators = 100) \n",
    "KNN_model = KNeighborsClassifier(n_neighbors=5, metric='euclidean')\n",
    "SVM_model = svm.SVC()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2b09f2e",
   "metadata": {},
   "source": [
    "Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "d6b05b7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy 0.8524590163934426\n",
      "Test Classification Report\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.81      0.83        27\n",
      "           1       0.86      0.88      0.87        34\n",
      "\n",
      "    accuracy                           0.85        61\n",
      "   macro avg       0.85      0.85      0.85        61\n",
      "weighted avg       0.85      0.85      0.85        61\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mac/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:818: 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",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "LR_model.fit(xtrain, ytrain)\n",
    "LR_ypred = LR_model.predict(xtest)\n",
    "print(\"Test Accuracy\", accuracy_score(ytest, LR_ypred))\n",
    "print(\"Test Classification Report\\n\", classification_report(ytest,LR_ypred))\n",
    "mat=confusion_matrix(ytest,LR_ypred)\n",
    "sns.heatmap(mat,annot=True, cbar=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2f7fae3",
   "metadata": {},
   "source": [
    "Decission Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "9b42d225",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy 0.7704918032786885\n",
      "Test Classification Report\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.72      0.78      0.75        27\n",
      "           1       0.81      0.76      0.79        34\n",
      "\n",
      "    accuracy                           0.77        61\n",
      "   macro avg       0.77      0.77      0.77        61\n",
      "weighted avg       0.77      0.77      0.77        61\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DT_model.fit(xtrain, ytrain)\n",
    "DT_ypred = DT_model.predict(xtest)\n",
    "print(\"Test Accuracy\", accuracy_score(ytest, DT_ypred))\n",
    "print(\"Test Classification Report\\n\", classification_report(ytest,DT_ypred))\n",
    "mat=confusion_matrix(ytest,DT_ypred)\n",
    "sns.heatmap(mat,annot=True, cbar=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b08dc588",
   "metadata": {},
   "source": [
    "Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "18894849",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy 0.8524590163934426\n",
      "Test Classification Report\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.85      0.84        27\n",
      "           1       0.88      0.85      0.87        34\n",
      "\n",
      "    accuracy                           0.85        61\n",
      "   macro avg       0.85      0.85      0.85        61\n",
      "weighted avg       0.85      0.85      0.85        61\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "RFC_model.fit(xtrain, ytrain)\n",
    "RFC_ypred = RFC_model.predict(xtest)\n",
    "print(\"Test Accuracy\", accuracy_score(ytest, RFC_ypred))\n",
    "print(\"Test Classification Report\\n\", classification_report(ytest,RFC_ypred))\n",
    "mat=confusion_matrix(ytest,RFC_ypred)\n",
    "sns.heatmap(mat,annot=True, cbar=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efd9f3e7",
   "metadata": {},
   "source": [
    "KNN Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "5a686a0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy 0.639344262295082\n",
      "Test Classification Report\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.59      0.63      0.61        27\n",
      "           1       0.69      0.65      0.67        34\n",
      "\n",
      "    accuracy                           0.64        61\n",
      "   macro avg       0.64      0.64      0.64        61\n",
      "weighted avg       0.64      0.64      0.64        61\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "KNN_model.fit(xtrain, ytrain)\n",
    "KNN_ypred = KNN_model.predict(xtest)\n",
    "print(\"Test Accuracy\", accuracy_score(ytest, KNN_ypred))\n",
    "print(\"Test Classification Report\\n\", classification_report(ytest,KNN_ypred))\n",
    "mat=confusion_matrix(ytest,KNN_ypred)\n",
    "sns.heatmap(mat,annot=True, cbar=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31785675",
   "metadata": {},
   "source": [
    "SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "3b2eeac9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy 0.6885245901639344\n",
      "Test Classification Report\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.72      0.48      0.58        27\n",
      "           1       0.67      0.85      0.75        34\n",
      "\n",
      "    accuracy                           0.69        61\n",
      "   macro avg       0.70      0.67      0.67        61\n",
      "weighted avg       0.70      0.69      0.68        61\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "SVM_model.fit(xtrain, ytrain)\n",
    "SVM_ypred = SVM_model.predict(xtest)\n",
    "print(\"Test Accuracy\", accuracy_score(ytest, SVM_ypred))\n",
    "print(\"Test Classification Report\\n\", classification_report(ytest,SVM_ypred))\n",
    "mat=confusion_matrix(ytest,SVM_ypred)\n",
    "sns.heatmap(mat,annot=True, cbar=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b6a8e3c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}