Heart-Disease-Prediction.ipynb

{
 "cells": [
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   "cell_type": "markdown",
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   "source": [
    "# Heart Disease Prediciton"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objective\n",
    "\n",
    "To predict whether a patient is at risk for a heart attack. **This is a binary outcome.**\n",
    "\n",
    "Positive (+) = 1, patient is at risk Negative (-) = 0, patient is not at risk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Understanding the dataset avaliable\n",
    "\n",
    "We have 13 features to train our model\n",
    "\n",
    "1) age (#)\n",
    "\n",
    "2) sex : 1 = Male, 0 = Female (Binary)\n",
    "\n",
    "3) (cp) chest pain [type (4 values, Ordinal)]: 1: typical angina, 2: atypical angina, 3: non-anginal pain, 4: asymptomatic\n",
    "\n",
    "4) (trestbps) resting blood pressure (#)\n",
    "\n",
    "5) (chol) serum cholestoral in mg/dl (#)\n",
    "\n",
    "6) (fbs) fasting blood sugar > 120 mg/dl (Binary) [1 = true; 0 = false]\n",
    "\n",
    "7) (restecg) resting electrocardiographic results [values 0,1,2]\n",
    "\n",
    "8) (thalach) maximum heart rate achieved (#)\n",
    "\n",
    "9) (exang) exercise induced angina (Binary) [1 = yes; 0 = no]\n",
    "\n",
    "10) (oldpeak) = ST depression induced by exercise relative to rest (#)\n",
    "\n",
    "11) (slope) of the peak exercise ST segment (Ordinal) [ 1: upsloping, 2: flat , 3: downsloping]\n",
    "\n",
    "12) (ca) number of major vessels (0-3, Ordinal) colored by fluoroscopy\n",
    "\n",
    "13) (thal) maximum heart rate achieved (Ordinal) [3 = normal; 6 = fixed defect; 7 = reversable defect]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
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   "outputs": [],
   "source": [
    "# Importing the necessary libraries for the project\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing\n",
    "from sklearn.metrics import confusion_matrix,accuracy_score,classification_report #Metrics\n",
    "from sklearn.model_selection import train_test_split #Train_Test_split\n",
    "from sklearn.preprocessing import StandardScaler #Preprocessing Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring the Dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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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",
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       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
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       "      <th>target</th>\n",
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       "      <td>100</td>\n",
       "      <td>219</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
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       "      <th>5</th>\n",
       "      <td>32</td>\n",
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       "      <td>2</td>\n",
       "      <td>105</td>\n",
       "      <td>198</td>\n",
       "      <td>0</td>\n",
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       "      <td>125</td>\n",
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      "text/plain": [
       "   age  sex  cp trestbps chol fbs restecg thalach exang  oldpeak slope ca  \\\n",
       "0   28    1   2      130  132   0       2     185     0      0.0     ?  ?   \n",
       "1   29    1   2      120  243   0       0     160     0      0.0     ?  ?   \n",
       "2   29    1   2      140    ?   0       0     170     0      0.0     ?  ?   \n",
       "3   30    0   1      170  237   0       1     170     0      0.0     ?  ?   \n",
       "4   31    0   2      100  219   0       1     150     0      0.0     ?  ?   \n",
       "5   32    0   2      105  198   0       0     165     0      0.0     ?  ?   \n",
       "6   32    1   2      110  225   0       0     184     0      0.0     ?  ?   \n",
       "7   32    1   2      125  254   0       0     155     0      0.0     ?  ?   \n",
       "8   33    1   3      120  298   0       0     185     0      0.0     ?  ?   \n",
       "9   34    0   2      130  161   0       0     190     0      0.0     ?  ?   \n",
       "\n",
       "  thal  target  \n",
       "0    ?       0  \n",
       "1    ?       0  \n",
       "2    ?       0  \n",
       "3    6       0  \n",
       "4    ?       0  \n",
       "5    ?       0  \n",
       "6    ?       0  \n",
       "7    ?       0  \n",
       "8    ?       0  \n",
       "9    ?       0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reading the given dataset into data variable\n",
    "data=pd.read_csv('data.csv')\n",
    "\n",
    "#Renaming num as target\n",
    "data.rename(columns={'num       ': 'target'}, inplace=True) \n",
    "\n",
    "#printing the first 10 rows of the data\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age           int64\n",
       "sex           int64\n",
       "cp            int64\n",
       "trestbps     object\n",
       "chol         object\n",
       "fbs          object\n",
       "restecg      object\n",
       "thalach      object\n",
       "exang        object\n",
       "oldpeak     float64\n",
       "slope        object\n",
       "ca           object\n",
       "thal         object\n",
       "target        int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#printing the columns and their types\n",
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Vizualizing the Data\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "corrmat = data.corr()\n",
    "f, ax = plt.subplots(figsize=(12, 9))\n",
    "sns.heatmap(corrmat, vmax=.8, square=True, annot=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **OldPeak**(ST depression induced by exercise relative to rest) and **CP** ( Chest Pain Type ) have the most correlation with **target** ( diagnosis of heart disease)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>28</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>185</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>120</td>\n",
       "      <td>243</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>160</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>140</td>\n",
       "      <td>243</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>170</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>170</td>\n",
       "      <td>237</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>170</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>219</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</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 slope ca  \\\n",
       "0   28    1   2      130  132   0       2     185     0      0.0     0  0   \n",
       "1   29    1   2      120  243   0       0     160     0      0.0     0  0   \n",
       "2   29    1   2      140  243   0       0     170     0      0.0     0  0   \n",
       "3   30    0   1      170  237   0       1     170     0      0.0     0  0   \n",
       "4   31    0   2      100  219   0       1     150     0      0.0     0  0   \n",
       "\n",
       "  thal  target  \n",
       "0    0       0  \n",
       "1    0       0  \n",
       "2    0       0  \n",
       "3    6       0  \n",
       "4    6       0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Replacing the empty data with zeros\n",
    "data=data.replace('?',None)\n",
    "data=data.replace('?',0)\n",
    "\n",
    "#printing the processed data\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_kg_hide-input": true,
    "_kg_hide-output": true
   },
   "outputs": [],
   "source": [
    "# Getting dummies for ['sex', 'cp', 'fbs', 'restecg', 'exang', 'slope', 'ca', 'thal'] using pd.get_dummies\n",
    "data = pd.get_dummies(data, columns = ['sex', 'cp', 'fbs', 'restecg', 'exang', 'slope', 'ca', 'thal'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scaling the other features using standard scaler."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "standardScaler = StandardScaler()\n",
    "columns_to_scale = ['age', 'trestbps', 'chol', 'thalach', 'oldpeak']\n",
    "data[columns_to_scale] = standardScaler.fit_transform(data[columns_to_scale])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Final Preprocessed Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>thalach</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>target</th>\n",
       "      <th>sex_0</th>\n",
       "      <th>sex_1</th>\n",
       "      <th>cp_1</th>\n",
       "      <th>cp_2</th>\n",
       "      <th>...</th>\n",
       "      <th>slope_0</th>\n",
       "      <th>slope_1</th>\n",
       "      <th>slope_2</th>\n",
       "      <th>slope_3</th>\n",
       "      <th>ca_0</th>\n",
       "      <th>ca_0</th>\n",
       "      <th>thal_0</th>\n",
       "      <th>thal_3</th>\n",
       "      <th>thal_6</th>\n",
       "      <th>thal_7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>-2.542347</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2.414117</td>\n",
       "      <td>-0.717561</td>\n",
       "      <td>-0.117167</td>\n",
       "      <td>0.889244</td>\n",
       "      <td>-0.646074</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.414117</td>\n",
       "      <td>0.420626</td>\n",
       "      <td>-0.117167</td>\n",
       "      <td>1.314346</td>\n",
       "      <td>-0.646074</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.285888</td>\n",
       "      <td>2.127905</td>\n",
       "      <td>-0.201700</td>\n",
       "      <td>1.314346</td>\n",
       "      <td>-0.646074</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.157658</td>\n",
       "      <td>-1.855747</td>\n",
       "      <td>-0.455300</td>\n",
       "      <td>0.464142</td>\n",
       "      <td>-0.646074</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</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>5</th>\n",
       "      <td>-2.029429</td>\n",
       "      <td>-1.571200</td>\n",
       "      <td>-0.751165</td>\n",
       "      <td>1.101795</td>\n",
       "      <td>-0.646074</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-2.029429</td>\n",
       "      <td>-1.286654</td>\n",
       "      <td>-0.370767</td>\n",
       "      <td>1.909489</td>\n",
       "      <td>-0.646074</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-2.029429</td>\n",
       "      <td>-0.433014</td>\n",
       "      <td>0.037810</td>\n",
       "      <td>0.676693</td>\n",
       "      <td>-0.646074</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-1.901199</td>\n",
       "      <td>-0.717561</td>\n",
       "      <td>0.657719</td>\n",
       "      <td>1.951999</td>\n",
       "      <td>-0.646074</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-1.772970</td>\n",
       "      <td>-0.148467</td>\n",
       "      <td>-1.272453</td>\n",
       "      <td>2.164550</td>\n",
       "      <td>-0.646074</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        age  trestbps      chol   thalach   oldpeak  target  sex_0  sex_1  \\\n",
       "0 -2.542347 -0.148467 -1.681029  1.951999 -0.646074       0      0      1   \n",
       "1 -2.414117 -0.717561 -0.117167  0.889244 -0.646074       0      0      1   \n",
       "2 -2.414117  0.420626 -0.117167  1.314346 -0.646074       0      0      1   \n",
       "3 -2.285888  2.127905 -0.201700  1.314346 -0.646074       0      1      0   \n",
       "4 -2.157658 -1.855747 -0.455300  0.464142 -0.646074       0      1      0   \n",
       "5 -2.029429 -1.571200 -0.751165  1.101795 -0.646074       0      1      0   \n",
       "6 -2.029429 -1.286654 -0.370767  1.909489 -0.646074       0      0      1   \n",
       "7 -2.029429 -0.433014  0.037810  0.676693 -0.646074       0      0      1   \n",
       "8 -1.901199 -0.717561  0.657719  1.951999 -0.646074       0      0      1   \n",
       "9 -1.772970 -0.148467 -1.272453  2.164550 -0.646074       0      1      0   \n",
       "\n",
       "   cp_1  cp_2  ...  slope_0  slope_1  slope_2  slope_3  ca_0  ca_0  thal_0  \\\n",
       "0     0     1  ...        1        0        0        0     1     0       1   \n",
       "1     0     1  ...        1        0        0        0     1     0       1   \n",
       "2     0     1  ...        1        0        0        0     1     0       1   \n",
       "3     1     0  ...        1        0        0        0     1     0       0   \n",
       "4     0     1  ...        1        0        0        0     1     0       0   \n",
       "5     0     1  ...        1        0        0        0     1     0       0   \n",
       "6     0     1  ...        1        0        0        0     1     0       0   \n",
       "7     0     1  ...        1        0        0        0     1     0       0   \n",
       "8     0     0  ...        1        0        0        0     1     0       0   \n",
       "9     0     1  ...        1        0        0        0     1     0       0   \n",
       "\n",
       "   thal_3  thal_6  thal_7  \n",
       "0       0       0       0  \n",
       "1       0       0       0  \n",
       "2       0       0       0  \n",
       "3       0       1       0  \n",
       "4       0       1       0  \n",
       "5       0       1       0  \n",
       "6       0       1       0  \n",
       "7       0       1       0  \n",
       "8       0       1       0  \n",
       "9       0       1       0  \n",
       "\n",
       "[10 rows x 29 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Splitting data as Train and Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data['target']\n",
    "X = data.drop('target',axis=1)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(235, 28)\n",
      "(235,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEHCAYAAABBW1qbAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAARGUlEQVR4nO3dfbBdVX3G8e9DolCqFphcMBJi0Im0gaLolfrSOlZKpVUJo8UJIzWDtKkV3zqtFmqndNqhw4z2xVHpTEYjobXQiCip9Y2mKmMr4A1KeZOSisKVSC5SxbfGBn/94+wsL+lJcok551xyvp+ZO2fvtdY++5eZ5D5ZZ++9TqoKSZIADhp1AZKk+cNQkCQ1hoIkqTEUJEmNoSBJagwFSVKzcFBvnGQd8FJgW1WdMKv9DcDrgR3AP1fVW7v2C4BzgYeAN1bVJ/d2jkWLFtWyZcsGUL0kHbg2b958f1VN9OsbWCgAlwLvBi7b2ZDkl4GVwIlVtT3JkV37CmAVcDzwJOBfkjytqh7a0wmWLVvG1NTUgMqXpANTkq/trm9gHx9V1bXAA7s0/y5wcVVt78Zs69pXAldU1faqugvYApw8qNokSf0N+5rC04BfSnJ9ks8meXbXfjRwz6xx012bJGmIBvnx0e7OdzjwHODZwIYkTwHSZ2zf9TeSrAHWACxdunRAZUrSeBr2TGEauKp6bgB+BCzq2o+ZNW4JcG+/N6iqtVU1WVWTExN9r5NIkvbRsEPhI8CLAJI8DXgscD+wEViV5OAkxwLLgRuGXJskjb1B3pJ6OfBCYFGSaeBCYB2wLsktwA+B1dVbpvXWJBuA2+jdqnre3u48kiTtf3k0L509OTlZ3pIqSY9Mks1VNdmvzyeaJUmNoSBJaoZ9S+q886y3XLb3QRo7m9/+6lGXII2EMwVJUmMoSJIaQ0GS1BgKkqTGUJAkNYaCJKkxFCRJjaEgSWoMBUlSYyhIkhpDQZLUGAqSpMZQkCQ1hoIkqTEUJEnNwEIhybok27rvY9617w+SVJJFs9ouSLIlyR1JXjyouiRJuzfImcKlwGm7NiY5BjgVuHtW2wpgFXB8d8wlSRYMsDZJUh8DC4WquhZ4oE/XXwNvBWpW20rgiqraXlV3AVuAkwdVmySpv6FeU0hyOvD1qrppl66jgXtm7U93bZKkIRradzQnORR4G/Cr/br7tFWfNpKsAdYALF26dL/VJ0ka7kzhqcCxwE1JvgosAW5M8kR6M4NjZo1dAtzb702qam1VTVbV5MTExIBLlqTxMrRQqKqbq+rIqlpWVcvoBcEzq+obwEZgVZKDkxwLLAduGFZtkqSeQd6SejnweeC4JNNJzt3d2Kq6FdgA3AZ8Ajivqh4aVG2SpP4Gdk2hqs7aS/+yXfYvAi4aVD2SpL3ziWZJUmMoSJIaQ0GS1BgKkqTGUJAkNYaCJKkxFCRJjaEgSWoMBUlSYyhIkhpDQZLUGAqSpMZQkCQ1hoIkqTEUJEmNoSBJagwFSVJjKEiSmkF+R/O6JNuS3DKr7e1JvpzkP5J8OMlhs/ouSLIlyR1JXjyouiRJuzfImcKlwGm7tF0DnFBVJwL/CVwAkGQFsAo4vjvmkiQLBlibJKmPgYVCVV0LPLBL26eqake3ex2wpNteCVxRVdur6i5gC3DyoGqTJPU3ymsKrwE+3m0fDdwzq2+6a/t/kqxJMpVkamZmZsAlStJ4GUkoJHkbsAP4wM6mPsOq37FVtbaqJqtqcmJiYlAlStJYWjjsEyZZDbwUOKWqdv7inwaOmTVsCXDvsGuTpHE31JlCktOAPwROr6rvz+raCKxKcnCSY4HlwA3DrE2SNMCZQpLLgRcCi5JMAxfSu9voYOCaJADXVdVrq+rWJBuA2+h9rHReVT00qNokSf0NLBSq6qw+ze/bw/iLgIsGVY8kae98olmS1BgKkqTGUJAkNYaCJKkxFCRJjaEgSWoMBUlSYyhIkhpDQZLUGAqSpMZQkCQ1hoIkqTEUJEmNoSBJagwFSVJjKEiSGkNBktQMLBSSrEuyLckts9qOSHJNkju718Nn9V2QZEuSO5K8eFB1SZJ2b5AzhUuB03ZpOx/YVFXLgU3dPklWAKuA47tjLkmyYIC1SZL6GFgoVNW1wAO7NK8E1nfb64EzZrVfUVXbq+ouYAtw8qBqkyT1N+xrCkdV1VaA7vXIrv1o4J5Z46a7NknSEM2XC83p01Z9ByZrkkwlmZqZmRlwWZI0XoYdCvclWQzQvW7r2qeBY2aNWwLc2+8NqmptVU1W1eTExMRAi5WkcTPsUNgIrO62VwNXz2pfleTgJMcCy4EbhlybJI29hYN64ySXAy8EFiWZBi4ELgY2JDkXuBs4E6Cqbk2yAbgN2AGcV1UPDao2SVJ/AwuFqjprN12n7Gb8RcBFg6pHkrR38+VCsyRpHjAUJEmNoSBJagwFSVJjKEiSGkNBktQYCpKkxlCQJDVzCoUkm+bSJkl6dNvjE81JDgEOpbdUxeH8eDXTJwBPGnBtkqQh29syF78DvJleAGzmx6HwIPCewZUlSRqFPYZCVb0TeGeSN1TVu4ZUkyRpROa0IF5VvSvJ84Bls4+pqssGVJckaQTmFApJ/g54KvAlYOeS1gUYCpJ0AJnr0tmTwIqq6vsVmZKkA8Ncn1O4BXjiIAuRJI3eXGcKi4DbktwAbN/ZWFWnD6QqSdJIzDUU/nSQRUiS5oe53n302f150iS/B/wWvYvVNwPn0HtI7h/p3eH0VeCVVfXf+/O8kqQ9m+syF99J8mD38z9JHkry4L6cMMnRwBuByao6AVgArALOBzZV1XJgU7cvSRqiOYVCVT2+qp7Q/RwCvAJ4909w3oXATyVZSG+GcC+wEljf9a8HzvgJ3l+StA/2aZXUqvoI8KJ9PPbrwDuAu4GtwLer6lPAUVW1tRuzFThyX95fkrTv5vrw2stn7R5E77mFfXpmoVtYbyVwLPAt4INJzn4Ex68B1gAsXbp0X0qQHhXu/rOfH3UJmoeW/snNA33/ud599LJZ2zvoXQheuY/n/BXgrqqaAUhyFfA84L4ki6tqa5LFwLZ+B1fVWmAtwOTkpA/TSdJ+NNe7j87Zj+e8G3hOkkOBHwCnAFPA94DVwMXd69X78ZySpDmY691HS5J8OMm2JPcl+VCSJftywqq6HrgSuJHe7agH0fuf/8XAqUnuBE7t9iVJQzTXj4/eD/wDcGa3f3bXduq+nLSqLgQu3KV5O71ZgyRpROZ699FEVb2/qnZ0P5cCEwOsS5I0AnMNhfuTnJ1kQfdzNvDNQRYmSRq+uYbCa4BXAt+g92zBb9BbmkKSdACZ6zWFPwdW71yLKMkR9B5Ae82gCpMkDd9cZwonzl6crqoeAE4aTEmSpFGZaygc1D2JDLSZwlxnGZKkR4m5/mL/S+Dfk1xJb3mLVwIXDawqSdJIzPWJ5suSTNFbBC/Ay6vqtoFWJkkaujl/BNSFgEEgSQewfVo6W5J0YDIUJEmNoSBJagwFSVJjKEiSGkNBktQYCpKkxlCQJDWGgiSpGUkoJDksyZVJvpzk9iTPTXJEkmuS3Nm9Hr73d5Ik7U+jmim8E/hEVf0s8HTgduB8YFNVLQc2dfuSpCEaeigkeQLwAuB9AFX1w6r6FrASWN8NWw+cMezaJGncjWKm8BRgBnh/ki8meW+SnwaOqqqtAN3rkSOoTZLG2ihCYSHwTOBvq+ok4Hs8go+KkqxJMpVkamZmZlA1StJYGkUoTAPTVXV9t38lvZC4L8ligO51W7+Dq2ptVU1W1eTExMRQCpakcTH0UKiqbwD3JDmuazqF3vc0bARWd22rgauHXZskjbtRfc/yG4APJHks8BXgHHoBtSHJucDdwJkjqk2SxtZIQqGqvgRM9uk6ZcilSJJm8YlmSVJjKEiSGkNBktQYCpKkxlCQJDWGgiSpMRQkSY2hIElqDAVJUmMoSJIaQ0GS1BgKkqTGUJAkNYaCJKkxFCRJjaEgSWoMBUlSYyhIkpqRhUKSBUm+mOSj3f4RSa5Jcmf3evioapOkcTXKmcKbgNtn7Z8PbKqq5cCmbl+SNEQjCYUkS4CXAO+d1bwSWN9trwfOGHJZkjT2RjVT+BvgrcCPZrUdVVVbAbrXI/sdmGRNkqkkUzMzMwMvVJLGydBDIclLgW1VtXlfjq+qtVU1WVWTExMT+7k6SRpvC0dwzucDpyf5deAQ4AlJ/h64L8niqtqaZDGwbQS1SdJYG/pMoaouqKolVbUMWAX8a1WdDWwEVnfDVgNXD7s2SRp38+k5hYuBU5PcCZza7UuShmgUHx81VfUZ4DPd9jeBU0ZZjySNu/k0U5AkjZihIElqDAVJUmMoSJIaQ0GS1BgKkqTGUJAkNYaCJKkxFCRJjaEgSWoMBUlSYyhIkhpDQZLUGAqSpMZQkCQ1hoIkqTEUJEnN0EMhyTFJPp3k9iS3JnlT135EkmuS3Nm9Hj7s2iRp3I1iprAD+P2q+jngOcB5SVYA5wObqmo5sKnblyQN0dBDoaq2VtWN3fZ3gNuBo4GVwPpu2HrgjGHXJknjbqTXFJIsA04CrgeOqqqt0AsO4MgRliZJY2lkoZDkccCHgDdX1YOP4Lg1SaaSTM3MzAyuQEkaQyMJhSSPoRcIH6iqq7rm+5Is7voXA9v6HVtVa6tqsqomJyYmhlOwJI2JUdx9FOB9wO1V9VezujYCq7vt1cDVw65NksbdwhGc8/nAbwI3J/lS1/ZHwMXAhiTnAncDZ46gNkkaa0MPhar6HJDddJ8yzFokSQ/nE82SpMZQkCQ1hoIkqTEUJEmNoSBJagwFSVJjKEiSGkNBktQYCpKkxlCQJDWGgiSpMRQkSY2hIElqDAVJUmMoSJIaQ0GS1BgKkqTGUJAkNfMuFJKcluSOJFuSnD/qeiRpnMyrUEiyAHgP8GvACuCsJCtGW5UkjY95FQrAycCWqvpKVf0QuAJYOeKaJGlszLdQOBq4Z9b+dNcmSRqChaMuYBfp01YPG5CsAdZ0u99NcsfAqxofi4D7R13EfJB3rB51CXo4/27udGG/X5OP2JN31zHfQmEaOGbW/hLg3tkDqmotsHaYRY2LJFNVNTnqOqRd+XdzeObbx0dfAJYnOTbJY4FVwMYR1yRJY2NezRSqakeS1wOfBBYA66rq1hGXJUljY16FAkBVfQz42KjrGFN+LKf5yr+bQ5Kq2vsoSdJYmG/XFCRJI2QoyKVFNG8lWZdkW5JbRl3LuDAUxpxLi2ieuxQ4bdRFjBNDQS4tonmrqq4FHhh1HePEUJBLi0hqDAXtdWkRSePDUNBelxaRND4MBbm0iKTGUBhzVbUD2Lm0yO3ABpcW0XyR5HLg88BxSaaTnDvqmg50PtEsSWqcKUiSGkNBktQYCpKkxlCQJDWGgiSpMRSkPUhyWJLXDeE8Z7gQoeYDQ0Has8OAOYdCevbl39UZ9FaplUbK5xSkPUiyc9XYO4BPAycChwOPAf64qq5Osgz4eNf/XHq/4F8NvIreYoP3A5ur6h1JnkpvqfIJ4PvAbwNHAB8Fvt39vKKq/mtIf0TpYebddzRL88z5wAlV9YwkC4FDq+rBJIuA65LsXBLkOOCcqnpdkkngFcBJ9P6N3Qhs7satBV5bVXcm+QXgkqp6Ufc+H62qK4f5h5N2ZShIcxfgL5K8APgRvSXGj+r6vlZV13XbvwhcXVU/AEjyT93r44DnAR9M2uK0Bw+pdmlODAVp7l5F72OfZ1XV/yb5KnBI1/e9WeP6LUcOvWt436qqZwysQukn5IVmac++Azy+2/4ZYFsXCL8MPHk3x3wOeFmSQ7rZwUsAqupB4K4kZ0K7KP30PueRRsZQkPagqr4J/Fv3xfHPACaTTNGbNXx5N8d8gd7y4zcBVwFT9C4g0x13bpKbgFv58VefXgG8JckXu4vR0kh495E0AEkeV1XfTXIocC2wpqpuHHVd0t54TUEajLXdw2iHAOsNBD1aOFOQJDVeU5AkNYaCJKkxFCRJjaEgSWoMBUlSYyhIkpr/A+kFZSGZ5ncYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the Target (Heart Disease)\n",
    "plt.figure(figsize=(6,4))\n",
    "sns.countplot(x=y_train)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " # MODELS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1) Support Vector Machine - with various kernals\n",
    "\n",
    "2) Logistic Regression\n",
    "\n",
    "3) Random Forest\n",
    "\n",
    "4) Multi-layer Perceptron classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1) Using SVM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Running SVM model with Various Kernals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "svc_scores = []\n",
    "kernel_type = ['linear', 'poly', 'rbf', 'sigmoid']\n",
    "for type in kernel_type:\n",
    "    svc_classifier = SVC(kernel = type)\n",
    "    svc_classifier.fit(X_train, y_train)\n",
    "    svc_scores.append(svc_classifier.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 4 artists>"
      ]
     },
     "execution_count": 13,
     "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": [
    "#Plotting the scores of the various kernals\n",
    "\n",
    "for i in range(len(kernel_type)):\n",
    "    label = round(svc_scores[i], 5)\n",
    "    plt.text(i, svc_scores[i], label)\n",
    "plt.xlabel('Kernels')\n",
    "plt.ylabel('Scores')\n",
    "plt.title('Support Vector Classifier scores for different kernels')\n",
    "plt.bar(kernel_type, svc_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the **Radial Basis Function** kernel gives the maximum accuracy. Training the final model in rbf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "confussion matrix\n",
      "[[31  2]\n",
      " [ 1 25]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.94      0.95        33\n",
      "           1       0.93      0.96      0.94        26\n",
      "\n",
      "    accuracy                           0.95        59\n",
      "   macro avg       0.95      0.95      0.95        59\n",
      "weighted avg       0.95      0.95      0.95        59\n",
      "\n",
      "\n",
      "\n",
      "Accuracy of Support Vector Classifier: 94.91525423728814 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "svc =  SVC(kernel='rbf')\n",
    "svc.fit(X_train, y_train)\n",
    "svc_predicted = svc.predict(X_test)\n",
    "svc_conf_matrix = confusion_matrix(y_test, svc_predicted)\n",
    "svc_acc_score = accuracy_score(y_test, svc_predicted)\n",
    "\n",
    "print(\"confussion matrix\")\n",
    "print(svc_conf_matrix)\n",
    "print(classification_report(y_test, svc_predicted))\n",
    "print(\"\\n\")\n",
    "print(\"Accuracy of Support Vector Classifier:\",svc_acc_score*100,'\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2) Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "confussion matrix\n",
      "[[30  3]\n",
      " [ 0 26]]\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.91      0.95        33\n",
      "           1       0.90      1.00      0.95        26\n",
      "\n",
      "    accuracy                           0.95        59\n",
      "   macro avg       0.95      0.95      0.95        59\n",
      "weighted avg       0.95      0.95      0.95        59\n",
      "\n",
      "Accuracy of Logistic Regression: 94.915 %\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "lr = LogisticRegression()\n",
    "model = lr.fit(X_train, y_train)\n",
    "lr_predict = lr.predict(X_test)\n",
    "lr_conf_matrix = confusion_matrix(y_test, lr_predict)\n",
    "lr_acc_score = accuracy_score(y_test, lr_predict)\n",
    "print(\"confussion matrix\")\n",
    "print(lr_conf_matrix)\n",
    "print(\"\\n\")\n",
    "print(classification_report(y_test,lr_predict))\n",
    "print(\"Accuracy of Logistic Regression: {:.3f}\".format(lr_acc_score*100),'%\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3) Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "confussion matrix\n",
      "[[31  2]\n",
      " [ 0 26]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.94      0.97        33\n",
      "           1       0.93      1.00      0.96        26\n",
      "\n",
      "    accuracy                           0.97        59\n",
      "   macro avg       0.96      0.97      0.97        59\n",
      "weighted avg       0.97      0.97      0.97        59\n",
      "\n",
      "\n",
      "\n",
      "Accuracy of Random Forest Classifier: 96.61016949152543 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Trying with Random forest model\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "model=RandomForestClassifier(n_estimators=500,random_state=1)\n",
    "model.fit(X_train,y_train)\n",
    "rfpred=model.predict(X_test)\n",
    "RF_conf_matrix = confusion_matrix(y_test, rfpred)\n",
    "rf_acc_score = accuracy_score(y_test, rfpred)\n",
    "print(\"confussion matrix\")\n",
    "print(RF_conf_matrix)\n",
    "print(classification_report(y_test, rfpred))\n",
    "print(\"\\n\")\n",
    "print(\"Accuracy of Random Forest Classifier:\",rf_acc_score*100,'\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4) Multi-layer Perceptron classifier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "confussion matrix\n",
      "[[31  2]\n",
      " [ 2 24]]\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.94      0.94        33\n",
      "           1       0.92      0.92      0.92        26\n",
      "\n",
      "    accuracy                           0.93        59\n",
      "   macro avg       0.93      0.93      0.93        59\n",
      "weighted avg       0.93      0.93      0.93        59\n",
      "\n",
      "Accuracy of Multilayer Perceptron classifier: 93.220 %\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "MLP = MLPClassifier(hidden_layer_sizes=(32), learning_rate_init=0.0001, max_iter=5000)\n",
    "model = MLP.fit(X_train, y_train)\n",
    "MLP_predict = MLP.predict(X_test)\n",
    "MLP_conf_matrix = confusion_matrix(y_test, MLP_predict)\n",
    "MLP_acc_score = accuracy_score(y_test, MLP_predict)\n",
    "print(\"confussion matrix\")\n",
    "print(MLP_conf_matrix)\n",
    "print(\"\\n\")\n",
    "print(classification_report(y_test,MLP_predict))\n",
    "print(\"Accuracy of Multilayer Perceptron classifier: {:.3f}\".format(MLP_acc_score*100),'%\\n')\n"
   ]
  }
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