model of Heart Attack.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "educational-alberta",
   "metadata": {},
   "source": [
    "## 1. Importing the required libraries for EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "local-terminal",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Packages imported...\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd \n",
    "import numpy as np                     # For mathematical calculations \n",
    "import seaborn as sns                  # For data visualization \n",
    "import matplotlib.pyplot as plt        # For plotting graphs \n",
    "%matplotlib inline \n",
    "sns.set(color_codes=True)\n",
    "import warnings  \n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# Scaling\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "\n",
    "# Train Test Split\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Models\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "# Metrics\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "\n",
    "# Cross Validation\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "print('Packages imported...')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "wound-michael",
   "metadata": {},
   "source": [
    "## Reading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "figured-president",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "</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>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>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  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",
       "\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  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv(\"heart.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "extensive-lying",
   "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   trtbps    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   thalachh  303 non-null    int64  \n",
      " 8   exng      303 non-null    int64  \n",
      " 9   oldpeak   303 non-null    float64\n",
      " 10  slp       303 non-null    int64  \n",
      " 11  caa       303 non-null    int64  \n",
      " 12  thall     303 non-null    int64  \n",
      " 13  output    303 non-null    int64  \n",
      "dtypes: float64(1), int64(13)\n",
      "memory usage: 33.3 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "tender-exclusion",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63.5 (303, 14)\n"
     ]
    }
   ],
   "source": [
    "Q1 = data.quantile(0.25).loc['chol']\n",
    "Q3 = data.quantile(0.75).loc['chol']\n",
    "IQR = Q3 - Q1\n",
    "print(IQR,data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "considerable-bearing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(298, 14)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data[~((data.chol < (Q1 - 1.5 * IQR)) | (data.chol > (Q3 + 1.5 * IQR)))]\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mexican-physics",
   "metadata": {},
   "source": [
    "##  Scaling and Encoding features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "precise-climate",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The first 5 rows of X are\n"
     ]
    },
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>trtbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>thalachh</th>\n",
       "      <th>sex_1</th>\n",
       "      <th>exng_1</th>\n",
       "      <th>caa_1</th>\n",
       "      <th>caa_2</th>\n",
       "      <th>caa_3</th>\n",
       "      <th>caa_4</th>\n",
       "      <th>cp_1</th>\n",
       "      <th>cp_2</th>\n",
       "      <th>cp_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.592593</td>\n",
       "      <td>0.75</td>\n",
       "      <td>-0.110236</td>\n",
       "      <td>-0.092308</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.333333</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.157480</td>\n",
       "      <td>1.046154</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.037037</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.566929</td>\n",
       "      <td>0.584615</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.074074</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>-0.062992</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.148148</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>1.795276</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        age  trtbps      chol  thalachh  sex_1  exng_1  caa_1  caa_2  caa_3  \\\n",
       "0  0.592593    0.75 -0.110236 -0.092308      1       0      0      0      0   \n",
       "1 -1.333333    0.00  0.157480  1.046154      1       0      0      0      0   \n",
       "2 -1.037037    0.00 -0.566929  0.584615      0       0      0      0      0   \n",
       "3  0.074074   -0.50 -0.062992  0.769231      1       0      0      0      0   \n",
       "4  0.148148   -0.50  1.795276  0.307692      0       1      0      0      0   \n",
       "\n",
       "   caa_4  cp_1  cp_2  cp_3  \n",
       "0      0     0     0     1  \n",
       "1      0     0     1     0  \n",
       "2      0     1     0     0  \n",
       "3      0     1     0     0  \n",
       "4      0     0     0     0  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# define the columns to be encoded and scaled\n",
    "cat_cols = ['sex','exng','caa','cp']\n",
    "con_cols = [\"age\",\"trtbps\",\"chol\",\"thalachh\"]\n",
    "\n",
    "# creating a copy of data\n",
    "df1 = data[cat_cols + con_cols]\n",
    "\n",
    "# encoding the categorical columns\n",
    "X = pd.get_dummies(df1, columns = cat_cols, drop_first = True)\n",
    "\n",
    "# defining the features and target\n",
    "y = data[['output']]\n",
    "\n",
    "# instantiating the scaler\n",
    "scaler = RobustScaler()\n",
    "\n",
    "# scaling the continuous featuree\n",
    "X[con_cols] = scaler.fit_transform(X[con_cols])\n",
    "print(\"The first 5 rows of X are\")\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "following-pottery",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of X_train is       (242, 13)\n",
      "The shape of X_test is        (61, 13)\n",
      "The shape of y_train is       (242, 1)\n",
      "The shape of y_test is        (61, 1)\n"
     ]
    }
   ],
   "source": [
    "# Train and test split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.2, random_state = 42)\n",
    "print(\"The shape of X_train is      \", X_train.shape)\n",
    "print(\"The shape of X_test is       \",X_test.shape)\n",
    "print(\"The shape of y_train is      \",y_train.shape)\n",
    "print(\"The shape of y_test is       \",y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "running-south",
   "metadata": {},
   "source": [
    "## Modeling\n",
    "\n",
    "### 1. Support Vector Machines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "humanitarian-introduction",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The test accuracy score of SVM is  0.8688524590163934\n"
     ]
    }
   ],
   "source": [
    "# instantiating the object and fitting\n",
    "clf = SVC(kernel='linear', C=1, random_state=42).fit(X_train,y_train)\n",
    "\n",
    "# predicting the values\n",
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "# printing the test accuracy\n",
    "print(\"The test accuracy score of SVM is \", accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "excited-veteran",
   "metadata": {},
   "source": [
    "## Hyperparameter tuning of SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "strategic-separation",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best params are : {'C': 5, 'gamma': 1}\n",
      "The best score is   : 0.7935374149659864\n",
      "The test accuracy score of SVM after hyper-parameter tuning is  0.7868852459016393\n"
     ]
    }
   ],
   "source": [
    "# instantiating the object\n",
    "svm = SVC()\n",
    "\n",
    "# setting a grid - not so extensive\n",
    "parameters = {\"C\":np.arange(1,10,1),'gamma':[0.00001,0.00005, 0.0001,0.0005,0.001,0.005,0.01,0.05,0.1,0.5,1,5]}\n",
    "\n",
    "# instantiating the GridSearchCV object\n",
    "searcher = GridSearchCV(svm, parameters)\n",
    "\n",
    "# fitting the object\n",
    "searcher.fit(X_train, y_train)\n",
    "\n",
    "# the scores\n",
    "print(\"The best params are :\", searcher.best_params_)\n",
    "print(\"The best score is   :\", searcher.best_score_)\n",
    "\n",
    "# predicting the values\n",
    "y_pred = searcher.predict(X_test)\n",
    "\n",
    "# printing the test accuracy\n",
    "print(\"The test accuracy score of SVM after hyper-parameter tuning is \", accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "textile-arbitration",
   "metadata": {},
   "source": [
    "## Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "parental-survival",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The test accuracy score of Decision Tree is  0.7049180327868853\n"
     ]
    }
   ],
   "source": [
    "# instantiating the object\n",
    "dt = DecisionTreeClassifier(random_state = 42)\n",
    "\n",
    "# fitting the model\n",
    "dt.fit(X_train, y_train)\n",
    "\n",
    "# calculating the predictions\n",
    "y_pred = dt.predict(X_test)\n",
    "\n",
    "# printing the test accuracy\n",
    "print(\"The test accuracy score of Decision Tree is \", accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "local-catering",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "interesting-wallet",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The test accuracy score of Random Forest is  0.7049180327868853\n"
     ]
    }
   ],
   "source": [
    "# instantiating the object\n",
    "rf = RandomForestClassifier()\n",
    "\n",
    "# fitting the model\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "# calculating the predictions\n",
    "y_pred = dt.predict(X_test)\n",
    "\n",
    "# printing the test accuracy\n",
    "print(\"The test accuracy score of Random Forest is \", accuracy_score(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cellular-french",
   "metadata": {},
   "source": [
    " ## Gradient Boosting Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "racial-tension",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The test accuracy score of Gradient Boosting Classifier is  0.8032786885245902\n"
     ]
    }
   ],
   "source": [
    "# instantiate the classifier\n",
    "gbt = GradientBoostingClassifier(n_estimators = 300,max_depth=5,subsample=0.8,max_features=0.2,random_state=42)\n",
    "\n",
    "# fitting the model\n",
    "gbt.fit(X_train,y_train)\n",
    "\n",
    "# predicting values\n",
    "y_pred = gbt.predict(X_test)\n",
    "print(\"The test accuracy score of Gradient Boosting Classifier is \", accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "analyzed-stationery",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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