final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Heart attack possibility prediction \n",
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 1 - Data Preprocessing\n",
    "### Importing the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\saniy\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "C:\\Users\\saniy\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "C:\\Users\\saniy\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "C:\\Users\\saniy\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "C:\\Users\\saniy\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "C:\\Users\\saniy\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
      "C:\\Users\\saniy\\Anaconda3\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "C:\\Users\\saniy\\Anaconda3\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "C:\\Users\\saniy\\Anaconda3\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "C:\\Users\\saniy\\Anaconda3\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "C:\\Users\\saniy\\Anaconda3\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "C:\\Users\\saniy\\Anaconda3\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "import tensorflow.keras as keras\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb;\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('heart.csv')\n",
    "s_dataset = dataset.sample(frac =1).reset_index(drop=True)\n",
    "\n",
    "\n",
    "#X = s_dataset.iloc[:, [0,1,2,3,4,5,6,7,8,9,10,11,12]].values\n",
    "#X = s_dataset.iloc[:, [0,1,2,7,8,9,10,11,12]].values\n",
    "X = s_dataset.drop('target',axis=1)\n",
    "y = s_dataset.iloc[:, -1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>130</td>\n",
       "      <td>254</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>147</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>124</td>\n",
       "      <td>197</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>136</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>160</td>\n",
       "      <td>234</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>102</td>\n",
       "      <td>265</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>122</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>132</td>\n",
       "      <td>224</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>173</td>\n",
       "      <td>0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</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  \\\n",
       "0   63    1   0       130   254    0        0      147      0      1.4      1   \n",
       "1   63    0   0       124   197    0        1      136      1      0.0      1   \n",
       "2   69    1   3       160   234    1        0      131      0      0.1      1   \n",
       "3   42    0   0       102   265    0        0      122      0      0.6      1   \n",
       "4   58    1   2       132   224    0        0      173      0      3.2      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   1     3       0  \n",
       "1   0     2       0  \n",
       "2   1     2       1  \n",
       "3   0     2       1  \n",
       "4   2     3       0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_dataset.head()"
   ]
  },
  {
   "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>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.366337</td>\n",
       "      <td>0.683168</td>\n",
       "      <td>0.966997</td>\n",
       "      <td>131.623762</td>\n",
       "      <td>246.264026</td>\n",
       "      <td>0.148515</td>\n",
       "      <td>0.528053</td>\n",
       "      <td>149.646865</td>\n",
       "      <td>0.326733</td>\n",
       "      <td>1.039604</td>\n",
       "      <td>1.399340</td>\n",
       "      <td>0.729373</td>\n",
       "      <td>2.313531</td>\n",
       "      <td>0.544554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.082101</td>\n",
       "      <td>0.466011</td>\n",
       "      <td>1.032052</td>\n",
       "      <td>17.538143</td>\n",
       "      <td>51.830751</td>\n",
       "      <td>0.356198</td>\n",
       "      <td>0.525860</td>\n",
       "      <td>22.905161</td>\n",
       "      <td>0.469794</td>\n",
       "      <td>1.161075</td>\n",
       "      <td>0.616226</td>\n",
       "      <td>1.022606</td>\n",
       "      <td>0.612277</td>\n",
       "      <td>0.498835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>47.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>133.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>274.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age         sex          cp    trestbps        chol         fbs  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean    54.366337    0.683168    0.966997  131.623762  246.264026    0.148515   \n",
       "std      9.082101    0.466011    1.032052   17.538143   51.830751    0.356198   \n",
       "min     29.000000    0.000000    0.000000   94.000000  126.000000    0.000000   \n",
       "25%     47.500000    0.000000    0.000000  120.000000  211.000000    0.000000   \n",
       "50%     55.000000    1.000000    1.000000  130.000000  240.000000    0.000000   \n",
       "75%     61.000000    1.000000    2.000000  140.000000  274.500000    0.000000   \n",
       "max     77.000000    1.000000    3.000000  200.000000  564.000000    1.000000   \n",
       "\n",
       "          restecg     thalach       exang     oldpeak       slope          ca  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean     0.528053  149.646865    0.326733    1.039604    1.399340    0.729373   \n",
       "std      0.525860   22.905161    0.469794    1.161075    0.616226    1.022606   \n",
       "min      0.000000   71.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000  133.500000    0.000000    0.000000    1.000000    0.000000   \n",
       "50%      1.000000  153.000000    0.000000    0.800000    1.000000    0.000000   \n",
       "75%      1.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n",
       "max      2.000000  202.000000    1.000000    6.200000    2.000000    4.000000   \n",
       "\n",
       "             thal      target  \n",
       "count  303.000000  303.000000  \n",
       "mean     2.313531    0.544554  \n",
       "std      0.612277    0.498835  \n",
       "min      0.000000    0.000000  \n",
       "25%      2.000000    0.000000  \n",
       "50%      2.000000    1.000000  \n",
       "75%      3.000000    1.000000  \n",
       "max      3.000000    1.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(303, 14)\n"
     ]
    }
   ],
   "source": [
    "print(s_dataset.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    165\n",
       "0    138\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_dataset[\"target\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 474.375x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#histogram with respect to age\n",
    "\n",
    "sb.FacetGrid(s_dataset, hue=\"target\", size = 6)\\\n",
    "  .map(sb.distplot, \"age\")\\\n",
    "  .add_legend();  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 474.375x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.FacetGrid(dataset, hue=\"target\", size = 6)\\\n",
    "  .map(sb.distplot, \"trestbps\")\\\n",
    "  .add_legend(); "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 474.375x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.FacetGrid(dataset, hue=\"target\", size = 6)\\\n",
    "  .map(sb.distplot, \"restecg\")\\\n",
    "  .add_legend(); "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 474.375x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.FacetGrid(dataset, hue=\"target\", size = 6)\\\n",
    "  .map(sb.distplot, \"thalach\")\\\n",
    "  .add_legend(); "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 474.375x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.FacetGrid(dataset, hue=\"target\", size = 6)\\\n",
    "  .map(sb.distplot, \"oldpeak\")\\\n",
    "  .add_legend(); "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 474.375x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.FacetGrid(dataset, hue=\"target\", size = 6)\\\n",
    "  .map(sb.distplot, \"slope\")\\\n",
    "  .add_legend(); "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "sb.boxplot(x=\"target\", y=\"age\", data= dataset)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "sb.boxplot(x=\"sex\", y=\"target\", data= dataset)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    " def bar_plot(variable):\n",
    "    \"\"\"\n",
    "    input: variable ex: sex\n",
    "    output: barplot & value count\n",
    "    \"\"\"\n",
    "    # get features\n",
    "    var = s_dataset[variable]\n",
    "    # count number of categorical variable (value/sample)\n",
    "    varValue = var.value_counts()\n",
    "    # visualize\n",
    "    plt.figure(figsize=(9,3))\n",
    "    plt.bar(varValue.index, varValue)\n",
    "    plt.xticks(varValue.index, varValue.index.values)\n",
    "    plt.ylabel(\"Frequanc\")\n",
    "    plt.title(variable)\n",
    "    plt.show()\n",
    "    \n",
    "    print(\"{}\\n{}\".format(variable, varValue))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Categorical variable bar plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sex\n",
      "1    207\n",
      "0     96\n",
      "Name: sex, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cp\n",
      "0    143\n",
      "2     87\n",
      "1     50\n",
      "3     23\n",
      "Name: cp, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fbs\n",
      "0    258\n",
      "1     45\n",
      "Name: fbs, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "restecg\n",
      "1    152\n",
      "0    147\n",
      "2      4\n",
      "Name: restecg, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exang\n",
      "0    204\n",
      "1     99\n",
      "Name: exang, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiwAAADSCAYAAABpcJupAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAR0UlEQVR4nO3de6xlZX3G8e8jAyheijgHHefioJmi1NRIj0pra6xoq6KAUSvEWrTYqSlWrTYKtSk2kQRb66W1NY4FHdSCiFpooRekWtJY0QGpAiMyRWQG0DkWEe/j4K9/7DW6HfYwm5mz93rPnO8n2dlrvetd6/2dZOfkybtuqSokSZJadp++C5AkSdodA4skSWqegUWSJDXPwCJJkppnYJEkSc0zsEiSpOYZWCT1Ksmbknyw7zoktc3AIkmSmmdgkSRJzTOwSJqaJG9IckuSbye5PsnRI/ocm+TaJHck+VSSxwxtuynJaUmuS/LNJO9Lct+h7c9JcnW376eT/OK0/jZJk2VgkTQVSQ4HXgk8oaoeCPwmcNNOfX4eOBd4DTADXAL8U5IDhrq9uNv3UcDPA3/a7XskcDbw+8BDgPcAFyU5cHJ/laRpMbBImpa7gAOBI5LsX1U3VdX/7tTnRcDFVXVpVf0IeCtwP+BXhvq8q6o2V9XtwBnAiV377wHvqaorququqloP/BA4apJ/lKTpMLBImoqq2sRg5uRNwNYk5yV5+E7dHg58dWifHwObgeVDfTYPLX+12wfgEcDrutNBdyS5A1g5tF3SAmZgkTQ1VfUPVfWrDMJFAW/Zqcut3TYAkoRB6LhlqM/KoeVV3T4wCDJnVNXBQ5+Dqurc+f47JE2fgUXSVCQ5PMnTumtKfgB8n8FpomHnA8ckOTrJ/sDrGJzW+fRQn1OSrEhyCPAnwIe79vcCr0jypAzcP8kxSR440T9M0lQYWCRNy4HAmcA3gK8BhzIIHD9RVdcDvw38TdfvucBzq2rbULd/AP4duLH7vLnbdwOD61jeBXwT2AS8dGJ/jaSpSlX1XYMkjSXJTcDLq+oTfdciabqcYZEkSc0zsEiSpOZ5SkiSJDXPGRZJktQ8A4skSWrekr4L2BtLly6t1atX912GJEmaB1deeeU3qmpm1LYFHVhWr17Nhg0b+i5DkiTNgyRf3dU2TwlJkqTmGVgkSVLzDCySJKl5BhZJktS8iQWWJGcn2ZrkmhHb/jhJJVnarSfJXyfZlOQLSY6cVF2SJGnhmeRdQu9n8NbUc4Ybk6wEngHcPNT8LGBN93kS8O7uW5LUiNWnXtx3CWrETWceM/UxJzbDUlWXA7eP2PR24PXA8DsBjgPOqYHPAAcnWTap2iRJ0sIy1WtYkhwL3FJV/7PTpuXA5qH1LV2bJEnS9B4cl+Qg4I3Ab4zaPKJt5FsZk6wF1gKsWrVq3uqTWuU0vHboYxpeasU0Z1geBRwG/E+Sm4AVwFVJHsZgRmXlUN8VwK2jDlJV66pqtqpmZ2ZGPr1XkiTtY6YWWKrqi1V1aFWtrqrVDELKkVX1NeAi4He6u4WOAr5VVbdNqzZJktS2Sd7WfC7w38DhSbYkOfkeul8C3AhsAt4L/MGk6pIkSQvPxK5hqaoTd7N99dByAadMqhZJkrSw+aRbSZLUPAOLJElqnoFFkiQ1z8AiSZKaZ2CRJEnNM7BIkqTmGVgkSVLzDCySJKl5BhZJktQ8A4skSWqegUWSJDXPwCJJkppnYJEkSc2bWGBJcnaSrUmuGWr7yyRfSvKFJB9PcvDQttOSbEpyfZLfnFRdkiRp4ZnkDMv7gWfu1HYp8Niq+kXgy8BpAEmOAE4AfqHb5++S7DfB2iRJ0gIyscBSVZcDt+/U9u9Vtb1b/Qywols+Djivqn5YVV8BNgFPnFRtkiRpYenzGpbfBf6lW14ObB7atqVrkyRJ6iewJHkjsB340I6mEd1qF/uuTbIhyYa5ublJlShJkhoy9cCS5CTgOcCLq2pHKNkCrBzqtgK4ddT+VbWuqmaranZmZmayxUqSpCZMNbAkeSbwBuDYqvre0KaLgBOSHJjkMGAN8Nlp1iZJktq1ZFIHTnIu8FRgaZItwOkM7go6ELg0CcBnquoVVXVtkvOB6xicKjqlqu6aVG2SJGlhmVhgqaoTRzSfdQ/9zwDOmFQ9kiRp4fJJt5IkqXkGFkmS1DwDiyRJap6BRZIkNc/AIkmSmmdgkSRJzTOwSJKk5hlYJElS8wwskiSpeQYWSZLUPAOLJElqnoFFkiQ1z8AiSZKaN7HAkuTsJFuTXDPUdkiSS5Pc0H0/uGtPkr9OsinJF5IcOam6JEnSwjPJGZb3A8/cqe1U4LKqWgNc1q0DPAtY033WAu+eYF2SJGmBmVhgqarLgdt3aj4OWN8trweOH2o/pwY+AxycZNmkapMkSQvLtK9heWhV3QbQfR/atS8HNg/129K13U2StUk2JNkwNzc30WIlSVIbWrnoNiPaalTHqlpXVbNVNTszMzPhsiRJUgumHVi+vuNUT/e9tWvfAqwc6rcCuHXKtUmSpEZNO7BcBJzULZ8EXDjU/jvd3UJHAd/acepIkiRpyaQOnORc4KnA0iRbgNOBM4Hzk5wM3Ay8sOt+CfBsYBPwPeBlk6pLkiQtPBMLLFV14i42HT2ibwGnTKoWSZK0sLVy0a0kSdIujRVYktw/yX2G1u+T5KDJlSVJkvRT486wXAYMB5SDgE/MfzmSJEl3N25guW9VfWfHSrfsDIskSZqKcQPLd4dfSJjkl4DvT6YkSZKknzXuXUKvAT6SZMfD3JYBL5pMSZIkST9rrMBSVZ9L8mjgcAaP0f9SVf1oopVJkiR17s1zWJ4ArO72eXwSquqciVQlSZI0ZKzAkuQDwKOAq4G7uuYCDCySJGnixp1hmQWO6J5IK0mSNFXj3iV0DfCwSRYiSZK0K+POsCwFrkvyWeCHOxqr6tiJVCVJkjRk3MDypkkWIUmSdE/Gva35P+dz0CR/BLycwYW7XwRexuDZLucBhwBXAS+pqm3zOa4kSVqYxn354VFJPpfkO0m2JbkryZ17MmCS5cCrgNmqeiywH3AC8Bbg7VW1BvgmcPKeHF+SJO17xr3o9l3AicANwP0YzI68ay/GXQLcL8kSBu8kug14GnBBt309cPxeHF+SJO1Dxg0sVNUmYL+ququq3gc8dU8GrKpbgLcCNzMIKt8CrgTuqKrtXbctwPJR+ydZm2RDkg1zc3N7UoIkSVpgxg0s30tyAHB1kr/orkG5/54MmOTBwHHAYcDDu+M8a0TXkc98qap1VTVbVbMzMzN7UoIkSVpgxg0sL2Fwrckrge8CK4Hn7+GYTwe+UlVz3fuIPgb8CnBwd4oIYAVw664OIEmSFpdx7xL6arf4feDP93LMm4GjkhzUHe9oYAPwSeAFDO4UOgm4cC/HkSRJ+4hx3yX0FUacoqmqR97bAavqiiQXMLh1eTvweWAdcDFwXpI3d21n3dtjS5KkfdO9eZfQDvcFXsjgeSl7pKpOB07fqflG4Il7ekxJkrTvGusalqr6v6HPLVX1Dga3IUuSJE3cuKeEjhxavQ+DGZcHTqQiSZKknYx7Suivhpa3AzcBvzXv1UiSJI0w7l1Cvz7pQiRJknZl3FNCr72n7VX1tvkpR5Ik6e7uzV1CTwAu6tafC1wObJ5EUZIkScPGDSxLgSOr6tsASd4EfKSqXj6pwiRJknYY99H8q4BtQ+vbgNXzXo0kSdII486wfAD4bJKPM3ji7fOAcyZWlSRJ0pBx7xI6I8m/AL/WNb2sqj4/ubIkSZJ+atxTQgAHAXdW1TuBLUkOm1BNkiRJP2OswJLkdOANwGld0/7ABydVlCRJ0rBxZ1ieBxwLfBegqm7FR/NLkqQpGTewbKuqYnDBLUnuvzeDJjk4yQVJvpRkY5JfTnJIkkuT3NB9P3hvxpAkSfuOcQPL+UneAxyc5PeATwDv3Ytx3wn8a1U9GngcsBE4FbisqtYAl3XrkiRJY98l9NYkzwDuBA4H/qyqLt2TAZM8CHgK8NLu2NuAbUmOA57adVsPfIrBdTOSJGmR221gSbIf8G9V9XRgj0LKTh4JzAHvS/I44Erg1cBDq+o2gKq6Lcmhu6hnLbAWYNWqVfNQjiRJat1uTwlV1V3A95L83DyNuQQ4Enh3VT2ewYW8Y5/+qap1VTVbVbMzMzPzVJIkSWrZuE+6/QHwxSSX0t0pBFBVr9qDMbcAW6rqim79AgaB5etJlnWzK8uArXtwbEmStA8aN7Bc3H32WlV9LcnmJIdX1fXA0cB13eck4Mzu+8L5GE+SJC189xhYkqyqqpurav08j/uHwIeSHADcCLyMwemp85OcDNwMvHCex5QkSQvU7mZY/pHB9SYk+WhVPX8+Bq2qq4HZEZuOno/jS5KkfcvuLrrN0PIjJ1mIJEnSruwusNQuliVJkqZmd6eEHpfkTgYzLffrlunWq6oeNNHqJEmS2E1gqar9plWIJEnSroz7LiFJkqTeGFgkSVLzDCySJKl5BhZJktQ8A4skSWqegUWSJDXPwCJJkppnYJEkSc0zsEiSpOb1FliS7Jfk80n+uVs/LMkVSW5I8uEkB/RVmyRJakufMyyvBjYOrb8FeHtVrQG+CZzcS1WSJKk5vQSWJCuAY4C/79YDPA24oOuyHji+j9okSVJ7+ppheQfweuDH3fpDgDuqanu3vgVYPmrHJGuTbEiyYW5ubvKVSpKk3k09sCR5DrC1qq4cbh7RtUbtX1Xrqmq2qmZnZmYmUqMkSWrLkh7GfDJwbJJnA/cFHsRgxuXgJEu6WZYVwK091CZJkho09RmWqjqtqlZU1WrgBOA/qurFwCeBF3TdTgIunHZtkiSpTS09h+UNwGuTbGJwTctZPdcjSZIa0ccpoZ+oqk8Bn+qWbwSe2Gc9kiSpTS3NsEiSJI1kYJEkSc0zsEiSpOYZWCRJUvMMLJIkqXkGFkmS1DwDiyRJap6BRZIkNc/AIkmSmmdgkSRJzTOwSJKk5hlYJElS86YeWJKsTPLJJBuTXJvk1V37IUkuTXJD9/3gadcmSZLa1McMy3bgdVX1GOAo4JQkRwCnApdV1Rrgsm5dkiRp+oGlqm6rqqu65W8DG4HlwHHA+q7beuD4adcmSZLatKTPwZOsBh4PXAE8tKpug0GoSXJoj6Wx+tSL+xxejbjpzGP6LkGSRI8X3SZ5APBR4DVVdee92G9tkg1JNszNzU2uQEmS1IxeAkuS/RmElQ9V1ce65q8nWdZtXwZsHbVvVa2rqtmqmp2ZmZlOwZIkqVd93CUU4CxgY1W9bWjTRcBJ3fJJwIXTrk2SJLWpj2tYngy8BPhikqu7tj8BzgTOT3IycDPwwh5qkyRJDZp6YKmq/wKyi81HT7MWSZK0MPikW0mS1DwDiyRJap6BRZIkNc/AIkmSmmdgkSRJzTOwSJKk5hlYJElS8wwskiSpeQYWSZLUPAOLJElqnoFFkiQ1z8AiSZKaZ2CRJEnNay6wJHlmkuuTbEpyat/1SJKk/jUVWJLsB/wt8CzgCODEJEf0W5UkSepbU4EFeCKwqapurKptwHnAcT3XJEmSetZaYFkObB5a39K1SZKkRWxJ3wXsJCPa6mc6JGuBtd3qd5JcP/GqFq+lwDf6LqJPeUvfFaiz6H+L4O+xEf4Wmehv8RG72tBaYNkCrBxaXwHcOtyhqtYB66ZZ1GKVZENVzfZdh+RvUa3wt9if1k4JfQ5Yk+SwJAcAJwAX9VyTJEnqWVMzLFW1PckrgX8D9gPOrqprey5LkiT1rKnAAlBVlwCX9F2HAE+9qR3+FtUKf4s9SVXtvpckSVKPWruGRZIk6W4MLBrJVySoBUnOTrI1yTV916LFLcnKJJ9MsjHJtUle3XdNi42nhHQ33SsSvgw8g8Gt5p8DTqyq63otTItOkqcA3wHOqarH9l2PFq8ky4BlVXVVkgcCVwLH+39xepxh0Si+IkFNqKrLgdv7rkOqqtuq6qpu+dvARnwS+1QZWDSKr0iQpF1Ishp4PHBFv5UsLgYWjbLbVyRI0mKU5AHAR4HXVNWdfdezmBhYNMpuX5EgSYtNkv0ZhJUPVdXH+q5nsTGwaBRfkSBJQ5IEOAvYWFVv67uexcjAorupqu3AjlckbATO9xUJ6kOSc4H/Bg5PsiXJyX3XpEXrycBLgKclubr7PLvvohYTb2uWJEnNc4ZFkiQ1z8AiSZKaZ2CRJEnNM7BIkqTmGVgkSVLzDCySJKl5BhZJktQ8A4skSWre/wO3HZbny4YXYQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 648x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "slope\n",
      "2    142\n",
      "1    140\n",
      "0     21\n",
      "Name: slope, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ca\n",
      "0    175\n",
      "1     65\n",
      "2     38\n",
      "3     20\n",
      "4      5\n",
      "Name: ca, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "thal\n",
      "2    166\n",
      "3    117\n",
      "1     18\n",
      "0      2\n",
      "Name: thal, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "target\n",
      "1    165\n",
      "0    138\n",
      "Name: target, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "categorical = [\"sex\", \"cp\", \"fbs\", \"restecg\", \"exang\", \"slope\", \"ca\", \"thal\", \"target\"]\n",
    "for c in categorical:\n",
    "    bar_plot(c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Numerical variable bar plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_hist(variable):\n",
    "    plt.figure(figsize=(9,3))\n",
    "    plt.hist(s_dataset[variable], bins=40)\n",
    "    plt.xlabel(variable)\n",
    "    plt.ylabel(\"Frequancy\")\n",
    "    plt.title(\"{} distribution with histogram\".format(variable))\n",
    "    plt.show()\n",
    "numericVar = [\"age\", \"trestbps\", \"chol\", \"thalach\", \"oldpeak\"]\n",
    "for n in numericVar:\n",
    "    plot_hist(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### By observing the kaggle dataset there are no missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.098447</td>\n",
       "      <td>-0.068653</td>\n",
       "      <td>0.279351</td>\n",
       "      <td>0.213678</td>\n",
       "      <td>0.121308</td>\n",
       "      <td>-0.116211</td>\n",
       "      <td>-0.398522</td>\n",
       "      <td>0.096801</td>\n",
       "      <td>0.210013</td>\n",
       "      <td>-0.168814</td>\n",
       "      <td>0.276326</td>\n",
       "      <td>0.068001</td>\n",
       "      <td>-0.225439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>-0.098447</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.049353</td>\n",
       "      <td>-0.056769</td>\n",
       "      <td>-0.197912</td>\n",
       "      <td>0.045032</td>\n",
       "      <td>-0.058196</td>\n",
       "      <td>-0.044020</td>\n",
       "      <td>0.141664</td>\n",
       "      <td>0.096093</td>\n",
       "      <td>-0.030711</td>\n",
       "      <td>0.118261</td>\n",
       "      <td>0.210041</td>\n",
       "      <td>-0.280937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cp</th>\n",
       "      <td>-0.068653</td>\n",
       "      <td>-0.049353</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.047608</td>\n",
       "      <td>-0.076904</td>\n",
       "      <td>0.094444</td>\n",
       "      <td>0.044421</td>\n",
       "      <td>0.295762</td>\n",
       "      <td>-0.394280</td>\n",
       "      <td>-0.149230</td>\n",
       "      <td>0.119717</td>\n",
       "      <td>-0.181053</td>\n",
       "      <td>-0.161736</td>\n",
       "      <td>0.433798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trestbps</th>\n",
       "      <td>0.279351</td>\n",
       "      <td>-0.056769</td>\n",
       "      <td>0.047608</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.123174</td>\n",
       "      <td>0.177531</td>\n",
       "      <td>-0.114103</td>\n",
       "      <td>-0.046698</td>\n",
       "      <td>0.067616</td>\n",
       "      <td>0.193216</td>\n",
       "      <td>-0.121475</td>\n",
       "      <td>0.101389</td>\n",
       "      <td>0.062210</td>\n",
       "      <td>-0.144931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chol</th>\n",
       "      <td>0.213678</td>\n",
       "      <td>-0.197912</td>\n",
       "      <td>-0.076904</td>\n",
       "      <td>0.123174</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.013294</td>\n",
       "      <td>-0.151040</td>\n",
       "      <td>-0.009940</td>\n",
       "      <td>0.067023</td>\n",
       "      <td>0.053952</td>\n",
       "      <td>-0.004038</td>\n",
       "      <td>0.070511</td>\n",
       "      <td>0.098803</td>\n",
       "      <td>-0.085239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fbs</th>\n",
       "      <td>0.121308</td>\n",
       "      <td>0.045032</td>\n",
       "      <td>0.094444</td>\n",
       "      <td>0.177531</td>\n",
       "      <td>0.013294</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.084189</td>\n",
       "      <td>-0.008567</td>\n",
       "      <td>0.025665</td>\n",
       "      <td>0.005747</td>\n",
       "      <td>-0.059894</td>\n",
       "      <td>0.137979</td>\n",
       "      <td>-0.032019</td>\n",
       "      <td>-0.028046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>restecg</th>\n",
       "      <td>-0.116211</td>\n",
       "      <td>-0.058196</td>\n",
       "      <td>0.044421</td>\n",
       "      <td>-0.114103</td>\n",
       "      <td>-0.151040</td>\n",
       "      <td>-0.084189</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.044123</td>\n",
       "      <td>-0.070733</td>\n",
       "      <td>-0.058770</td>\n",
       "      <td>0.093045</td>\n",
       "      <td>-0.072042</td>\n",
       "      <td>-0.011981</td>\n",
       "      <td>0.137230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thalach</th>\n",
       "      <td>-0.398522</td>\n",
       "      <td>-0.044020</td>\n",
       "      <td>0.295762</td>\n",
       "      <td>-0.046698</td>\n",
       "      <td>-0.009940</td>\n",
       "      <td>-0.008567</td>\n",
       "      <td>0.044123</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.378812</td>\n",
       "      <td>-0.344187</td>\n",
       "      <td>0.386784</td>\n",
       "      <td>-0.213177</td>\n",
       "      <td>-0.096439</td>\n",
       "      <td>0.421741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exang</th>\n",
       "      <td>0.096801</td>\n",
       "      <td>0.141664</td>\n",
       "      <td>-0.394280</td>\n",
       "      <td>0.067616</td>\n",
       "      <td>0.067023</td>\n",
       "      <td>0.025665</td>\n",
       "      <td>-0.070733</td>\n",
       "      <td>-0.378812</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.288223</td>\n",
       "      <td>-0.257748</td>\n",
       "      <td>0.115739</td>\n",
       "      <td>0.206754</td>\n",
       "      <td>-0.436757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oldpeak</th>\n",
       "      <td>0.210013</td>\n",
       "      <td>0.096093</td>\n",
       "      <td>-0.149230</td>\n",
       "      <td>0.193216</td>\n",
       "      <td>0.053952</td>\n",
       "      <td>0.005747</td>\n",
       "      <td>-0.058770</td>\n",
       "      <td>-0.344187</td>\n",
       "      <td>0.288223</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.577537</td>\n",
       "      <td>0.222682</td>\n",
       "      <td>0.210244</td>\n",
       "      <td>-0.430696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slope</th>\n",
       "      <td>-0.168814</td>\n",
       "      <td>-0.030711</td>\n",
       "      <td>0.119717</td>\n",
       "      <td>-0.121475</td>\n",
       "      <td>-0.004038</td>\n",
       "      <td>-0.059894</td>\n",
       "      <td>0.093045</td>\n",
       "      <td>0.386784</td>\n",
       "      <td>-0.257748</td>\n",
       "      <td>-0.577537</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.080155</td>\n",
       "      <td>-0.104764</td>\n",
       "      <td>0.345877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ca</th>\n",
       "      <td>0.276326</td>\n",
       "      <td>0.118261</td>\n",
       "      <td>-0.181053</td>\n",
       "      <td>0.101389</td>\n",
       "      <td>0.070511</td>\n",
       "      <td>0.137979</td>\n",
       "      <td>-0.072042</td>\n",
       "      <td>-0.213177</td>\n",
       "      <td>0.115739</td>\n",
       "      <td>0.222682</td>\n",
       "      <td>-0.080155</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.151832</td>\n",
       "      <td>-0.391724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thal</th>\n",
       "      <td>0.068001</td>\n",
       "      <td>0.210041</td>\n",
       "      <td>-0.161736</td>\n",
       "      <td>0.062210</td>\n",
       "      <td>0.098803</td>\n",
       "      <td>-0.032019</td>\n",
       "      <td>-0.011981</td>\n",
       "      <td>-0.096439</td>\n",
       "      <td>0.206754</td>\n",
       "      <td>0.210244</td>\n",
       "      <td>-0.104764</td>\n",
       "      <td>0.151832</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.344029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>-0.225439</td>\n",
       "      <td>-0.280937</td>\n",
       "      <td>0.433798</td>\n",
       "      <td>-0.144931</td>\n",
       "      <td>-0.085239</td>\n",
       "      <td>-0.028046</td>\n",
       "      <td>0.137230</td>\n",
       "      <td>0.421741</td>\n",
       "      <td>-0.436757</td>\n",
       "      <td>-0.430696</td>\n",
       "      <td>0.345877</td>\n",
       "      <td>-0.391724</td>\n",
       "      <td>-0.344029</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age       sex        cp  trestbps      chol       fbs  \\\n",
       "age       1.000000 -0.098447 -0.068653  0.279351  0.213678  0.121308   \n",
       "sex      -0.098447  1.000000 -0.049353 -0.056769 -0.197912  0.045032   \n",
       "cp       -0.068653 -0.049353  1.000000  0.047608 -0.076904  0.094444   \n",
       "trestbps  0.279351 -0.056769  0.047608  1.000000  0.123174  0.177531   \n",
       "chol      0.213678 -0.197912 -0.076904  0.123174  1.000000  0.013294   \n",
       "fbs       0.121308  0.045032  0.094444  0.177531  0.013294  1.000000   \n",
       "restecg  -0.116211 -0.058196  0.044421 -0.114103 -0.151040 -0.084189   \n",
       "thalach  -0.398522 -0.044020  0.295762 -0.046698 -0.009940 -0.008567   \n",
       "exang     0.096801  0.141664 -0.394280  0.067616  0.067023  0.025665   \n",
       "oldpeak   0.210013  0.096093 -0.149230  0.193216  0.053952  0.005747   \n",
       "slope    -0.168814 -0.030711  0.119717 -0.121475 -0.004038 -0.059894   \n",
       "ca        0.276326  0.118261 -0.181053  0.101389  0.070511  0.137979   \n",
       "thal      0.068001  0.210041 -0.161736  0.062210  0.098803 -0.032019   \n",
       "target   -0.225439 -0.280937  0.433798 -0.144931 -0.085239 -0.028046   \n",
       "\n",
       "           restecg   thalach     exang   oldpeak     slope        ca  \\\n",
       "age      -0.116211 -0.398522  0.096801  0.210013 -0.168814  0.276326   \n",
       "sex      -0.058196 -0.044020  0.141664  0.096093 -0.030711  0.118261   \n",
       "cp        0.044421  0.295762 -0.394280 -0.149230  0.119717 -0.181053   \n",
       "trestbps -0.114103 -0.046698  0.067616  0.193216 -0.121475  0.101389   \n",
       "chol     -0.151040 -0.009940  0.067023  0.053952 -0.004038  0.070511   \n",
       "fbs      -0.084189 -0.008567  0.025665  0.005747 -0.059894  0.137979   \n",
       "restecg   1.000000  0.044123 -0.070733 -0.058770  0.093045 -0.072042   \n",
       "thalach   0.044123  1.000000 -0.378812 -0.344187  0.386784 -0.213177   \n",
       "exang    -0.070733 -0.378812  1.000000  0.288223 -0.257748  0.115739   \n",
       "oldpeak  -0.058770 -0.344187  0.288223  1.000000 -0.577537  0.222682   \n",
       "slope     0.093045  0.386784 -0.257748 -0.577537  1.000000 -0.080155   \n",
       "ca       -0.072042 -0.213177  0.115739  0.222682 -0.080155  1.000000   \n",
       "thal     -0.011981 -0.096439  0.206754  0.210244 -0.104764  0.151832   \n",
       "target    0.137230  0.421741 -0.436757 -0.430696  0.345877 -0.391724   \n",
       "\n",
       "              thal    target  \n",
       "age       0.068001 -0.225439  \n",
       "sex       0.210041 -0.280937  \n",
       "cp       -0.161736  0.433798  \n",
       "trestbps  0.062210 -0.144931  \n",
       "chol      0.098803 -0.085239  \n",
       "fbs      -0.032019 -0.028046  \n",
       "restecg  -0.011981  0.137230  \n",
       "thalach  -0.096439  0.421741  \n",
       "exang     0.206754 -0.436757  \n",
       "oldpeak   0.210244 -0.430696  \n",
       "slope    -0.104764  0.345877  \n",
       "ca        0.151832 -0.391724  \n",
       "thal      1.000000 -0.344029  \n",
       "target   -0.344029  1.000000  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_dataset.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plot = sb.heatmap(dataset.corr(), linewidths=0 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ML ALGORITHMS\n",
    "## Cross Validation Algorithms\n",
    "#### Decision tree classifier\n",
    "#### Support vector classifier\n",
    "#### Random Forest Classifier\n",
    "#### Logistic Regression\n",
    "#### KNeighbours Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split, StratifiedKFold, GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier, VotingClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting the dataset into the Training set and Test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train , X_test , y_train , y_test = train_test_split(X,y,test_size = 0.2 , random_state = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_state = 42\n",
    "classifier = [DecisionTreeClassifier(random_state=random_state),\n",
    "             SVC(random_state=random_state),\n",
    "             RandomForestClassifier(random_state=random_state),\n",
    "             LogisticRegression(random_state=random_state),\n",
    "             KNeighborsClassifier()]\n",
    "\n",
    "dt_param_grid = {\"min_samples_split\":range(10,500,20),\n",
    "                \"max_depth\":range(1,20,2)}\n",
    "\n",
    "svc_param_grid = {\"kernel\":[\"rbf\"],\n",
    "                 \"gamma\":[0.001,0.01,0.1,1],\n",
    "                 \"C\":[1,10,50,100,200,300,1000]}\n",
    "\n",
    "rf_param_grid = {\"max_features\":[1.3,10],\n",
    "                \"min_samples_split\":[2,3,10],\n",
    "                \"min_samples_leaf\":[1,3,10],\n",
    "                \"bootstrap\":[False],\n",
    "                \"n_estimators\":[100,300],\n",
    "                \"criterion\":[\"gini\"]}\n",
    "\n",
    "lr_param_grid = {\"C\":np.logspace(-3,3,7),\n",
    "                \"penalty\":[\"l1\",\"l2\"]}\n",
    "\n",
    "knn_param_grid = {\"n_neighbors\":np.linspace(1,19,10, dtype=int).tolist(),\n",
    "                 \"weights\":[\"uniform\",\"distance\"],\n",
    "                 \"metric\":[\"euclidean\",\"manhattan\"]}\n",
    "\n",
    "classifier_param = [dt_param_grid,\n",
    "                   svc_param_grid,\n",
    "                   rf_param_grid,\n",
    "                   lr_param_grid,\n",
    "                   knn_param_grid]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 250 candidates, totalling 2500 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    2.9s\n",
      "[Parallel(n_jobs=-1)]: Done 740 tasks      | elapsed:    3.7s\n",
      "[Parallel(n_jobs=-1)]: Done 2500 out of 2500 | elapsed:    4.5s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  28 tasks      | elapsed:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTree : 0.7686666666666667\n",
      "Fitting 10 folds for each of 28 candidates, totalling 280 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 280 out of 280 | elapsed:    0.2s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC :  0.8265\n",
      "Fitting 10 folds for each of 36 candidates, totalling 360 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  28 tasks      | elapsed:    0.5s\n",
      "[Parallel(n_jobs=-1)]: Done 308 tasks      | elapsed:    8.5s\n",
      "[Parallel(n_jobs=-1)]: Done 360 out of 360 | elapsed:   11.5s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForest :  0.76\n",
      "Fitting 10 folds for each of 14 candidates, totalling 140 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  28 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 140 out of 140 | elapsed:    0.2s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression :  0.8266666666666665\n",
      "Fitting 10 folds for each of 40 candidates, totalling 400 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  28 tasks      | elapsed:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN :  0.8473333333333333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 400 out of 400 | elapsed:    0.3s finished\n"
     ]
    }
   ],
   "source": [
    "model_names=[\"DecisionTree :\", \"SVC : \", \"RandomForest : \", \"LogisticRegression : \", \"KNN : \"]\n",
    "cv_result = []\n",
    "best_estimators = []\n",
    "for i in range(len(classifier)):\n",
    "    clf = GridSearchCV(classifier[i], param_grid=classifier_param[i], cv=StratifiedKFold(n_splits=10), scoring=\"accuracy\", n_jobs=-1, verbose=1)\n",
    "    clf.fit(X_train,y_train)\n",
    "    cv_result.append(clf.best_score_)\n",
    "    best_estimators.append(clf.best_estimator_)\n",
    "    print(model_names[i], cv_result[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.7686666666666667, 0.8265, 0.76, 0.8266666666666665, 0.8473333333333333]\n"
     ]
    },
    {
     "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": [
    "print(cv_result)\n",
    "cv_results = pd.DataFrame({\"Cross Validation Means\":cv_result, \"ML Models\":[\"DecisionTreeClassifier\", \"SVC\", \"RandomForestClassifier\", \"LogisticRegression\", \"KNeigborsClassifier\"]})\n",
    "\n",
    "g = sb.barplot(x=\"Cross Validation Means\", y = \"ML Models\", data=cv_results)\n",
    "g.set_xlabel(\"Means Accuracy\")\n",
    "g.set_title(\"Cross Validation Scores\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Artificial Neural Network(ANN)\n",
    "##  Building the ANN\n",
    "### Initialising the ANN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "ann = tf.keras.models.Sequential()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding the input layer and the first hidden layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "ann.add(tf.keras.layers.Dense(units=9, activation ='relu', kernel_regularizer=keras.regularizers.l2(0.001)))\n",
    "ann.add(tf.keras.layers.Dropout(0.45))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "ann.add(tf.keras.layers.Dense(units=6, activation ='relu', kernel_regularizer=keras.regularizers.l2(0.001)))\n",
    "ann.add(tf.keras.layers.Dropout(0.52))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding the output layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "ann.add(tf.keras.layers.Dense(units=1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 3 - Training the ANN\n",
    "### Compiling the ANN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = keras.optimizers.Adam(learning_rate= 0.01)\n",
    "ann.compile( optimizer=optimizer , loss = 'binary_crossentropy' , metrics = ['accuracy']  )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training the ANN on the Training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Large dropout rate: 0.52 (>0.5). In TensorFlow 2.x, dropout() uses dropout rate instead of keep_prob. Please ensure that this is intended.\n",
      "WARNING:tensorflow:From C:\\Users\\saniy\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\nn_impl.py:180: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "Train on 193 samples, validate on 49 samples\n",
      "Epoch 1/100\n",
      "193/193 - 0s - loss: 0.9421 - acc: 0.5389 - val_loss: 0.6713 - val_acc: 0.6735\n",
      "Epoch 2/100\n",
      "193/193 - 0s - loss: 0.6919 - acc: 0.5803 - val_loss: 0.6572 - val_acc: 0.7551\n",
      "Epoch 3/100\n",
      "193/193 - 0s - loss: 0.6752 - acc: 0.5959 - val_loss: 0.6429 - val_acc: 0.7959\n",
      "Epoch 4/100\n",
      "193/193 - 0s - loss: 0.6750 - acc: 0.6062 - val_loss: 0.6162 - val_acc: 0.7755\n",
      "Epoch 5/100\n",
      "193/193 - 0s - loss: 0.6197 - acc: 0.6321 - val_loss: 0.5849 - val_acc: 0.7755\n",
      "Epoch 6/100\n",
      "193/193 - 0s - loss: 0.6079 - acc: 0.6684 - val_loss: 0.5869 - val_acc: 0.7551\n",
      "Epoch 7/100\n",
      "193/193 - 0s - loss: 0.6454 - acc: 0.6684 - val_loss: 0.5648 - val_acc: 0.7755\n",
      "Epoch 8/100\n",
      "193/193 - 0s - loss: 0.6226 - acc: 0.6580 - val_loss: 0.5399 - val_acc: 0.7551\n",
      "Epoch 9/100\n",
      "193/193 - 0s - loss: 0.6017 - acc: 0.6788 - val_loss: 0.5192 - val_acc: 0.7959\n",
      "Epoch 10/100\n",
      "193/193 - 0s - loss: 0.5986 - acc: 0.6891 - val_loss: 0.5106 - val_acc: 0.8163\n",
      "Epoch 11/100\n",
      "193/193 - 0s - loss: 0.6087 - acc: 0.7047 - val_loss: 0.4999 - val_acc: 0.7959\n",
      "Epoch 12/100\n",
      "193/193 - 0s - loss: 0.5830 - acc: 0.6943 - val_loss: 0.4769 - val_acc: 0.7959\n",
      "Epoch 13/100\n",
      "193/193 - 0s - loss: 0.5358 - acc: 0.7565 - val_loss: 0.4546 - val_acc: 0.8163\n",
      "Epoch 14/100\n",
      "193/193 - 0s - loss: 0.5650 - acc: 0.7202 - val_loss: 0.4526 - val_acc: 0.7959\n",
      "Epoch 15/100\n",
      "193/193 - 0s - loss: 0.6171 - acc: 0.7047 - val_loss: 0.4735 - val_acc: 0.7959\n",
      "Epoch 16/100\n",
      "193/193 - 0s - loss: 0.5176 - acc: 0.7461 - val_loss: 0.4665 - val_acc: 0.8163\n",
      "Epoch 17/100\n",
      "193/193 - 0s - loss: 0.5619 - acc: 0.7565 - val_loss: 0.4719 - val_acc: 0.8367\n",
      "Epoch 18/100\n",
      "193/193 - 0s - loss: 0.5063 - acc: 0.7461 - val_loss: 0.4547 - val_acc: 0.8163\n",
      "Epoch 19/100\n",
      "193/193 - 0s - loss: 0.5121 - acc: 0.7461 - val_loss: 0.4304 - val_acc: 0.8163\n",
      "Epoch 20/100\n",
      "193/193 - 0s - loss: 0.5340 - acc: 0.7565 - val_loss: 0.4239 - val_acc: 0.8163\n",
      "Epoch 21/100\n",
      "193/193 - 0s - loss: 0.4950 - acc: 0.7461 - val_loss: 0.4129 - val_acc: 0.8163\n",
      "Epoch 22/100\n",
      "193/193 - 0s - loss: 0.5634 - acc: 0.7824 - val_loss: 0.4324 - val_acc: 0.7959\n",
      "Epoch 23/100\n",
      "193/193 - 0s - loss: 0.5754 - acc: 0.7617 - val_loss: 0.4368 - val_acc: 0.7959\n",
      "Epoch 24/100\n",
      "193/193 - 0s - loss: 0.5233 - acc: 0.7409 - val_loss: 0.4424 - val_acc: 0.7959\n",
      "Epoch 25/100\n",
      "193/193 - 0s - loss: 0.5051 - acc: 0.7668 - val_loss: 0.4256 - val_acc: 0.7959\n",
      "Epoch 26/100\n",
      "193/193 - 0s - loss: 0.4835 - acc: 0.7668 - val_loss: 0.3991 - val_acc: 0.8163\n",
      "Epoch 27/100\n",
      "193/193 - 0s - loss: 0.4974 - acc: 0.7617 - val_loss: 0.3881 - val_acc: 0.8163\n",
      "Epoch 28/100\n",
      "193/193 - 0s - loss: 0.4548 - acc: 0.7668 - val_loss: 0.4097 - val_acc: 0.8367\n",
      "Epoch 29/100\n",
      "193/193 - 0s - loss: 0.5254 - acc: 0.7668 - val_loss: 0.4309 - val_acc: 0.8367\n",
      "Epoch 30/100\n",
      "193/193 - 0s - loss: 0.5154 - acc: 0.7461 - val_loss: 0.4152 - val_acc: 0.8367\n",
      "Epoch 31/100\n",
      "193/193 - 0s - loss: 0.5208 - acc: 0.7772 - val_loss: 0.4168 - val_acc: 0.8367\n",
      "Epoch 32/100\n",
      "193/193 - 0s - loss: 0.4769 - acc: 0.7565 - val_loss: 0.4312 - val_acc: 0.8367\n",
      "Epoch 33/100\n",
      "193/193 - 0s - loss: 0.4501 - acc: 0.7876 - val_loss: 0.4240 - val_acc: 0.8367\n",
      "Epoch 34/100\n",
      "193/193 - 0s - loss: 0.4787 - acc: 0.7772 - val_loss: 0.4074 - val_acc: 0.8571\n",
      "Epoch 35/100\n",
      "193/193 - 0s - loss: 0.4749 - acc: 0.7409 - val_loss: 0.4024 - val_acc: 0.8776\n",
      "Epoch 36/100\n",
      "193/193 - 0s - loss: 0.4796 - acc: 0.7979 - val_loss: 0.4498 - val_acc: 0.8571\n",
      "Epoch 37/100\n",
      "193/193 - 0s - loss: 0.5067 - acc: 0.7254 - val_loss: 0.4807 - val_acc: 0.8163\n",
      "Epoch 38/100\n",
      "193/193 - 0s - loss: 0.4509 - acc: 0.7927 - val_loss: 0.4664 - val_acc: 0.8163\n",
      "Epoch 39/100\n",
      "193/193 - 0s - loss: 0.4466 - acc: 0.7979 - val_loss: 0.4621 - val_acc: 0.8163\n",
      "Epoch 40/100\n",
      "193/193 - 0s - loss: 0.4533 - acc: 0.8135 - val_loss: 0.4491 - val_acc: 0.7959\n",
      "Epoch 41/100\n",
      "193/193 - 0s - loss: 0.5050 - acc: 0.7358 - val_loss: 0.4172 - val_acc: 0.8367\n",
      "Epoch 42/100\n",
      "193/193 - 0s - loss: 0.5066 - acc: 0.7772 - val_loss: 0.4145 - val_acc: 0.8367\n",
      "Epoch 43/100\n",
      "193/193 - 0s - loss: 0.4884 - acc: 0.7927 - val_loss: 0.4380 - val_acc: 0.8163\n",
      "Epoch 44/100\n",
      "193/193 - 0s - loss: 0.4584 - acc: 0.7720 - val_loss: 0.4437 - val_acc: 0.8367\n",
      "Epoch 45/100\n",
      "193/193 - 0s - loss: 0.4472 - acc: 0.7927 - val_loss: 0.4282 - val_acc: 0.8367\n",
      "Epoch 46/100\n",
      "193/193 - 0s - loss: 0.4373 - acc: 0.7927 - val_loss: 0.4465 - val_acc: 0.8163\n",
      "Epoch 47/100\n",
      "193/193 - 0s - loss: 0.3936 - acc: 0.8187 - val_loss: 0.4733 - val_acc: 0.7755\n",
      "Epoch 48/100\n",
      "193/193 - 0s - loss: 0.4298 - acc: 0.8187 - val_loss: 0.5049 - val_acc: 0.7551\n",
      "Epoch 49/100\n",
      "193/193 - 0s - loss: 0.5435 - acc: 0.7720 - val_loss: 0.5089 - val_acc: 0.7755\n",
      "Epoch 50/100\n",
      "193/193 - 0s - loss: 0.4456 - acc: 0.7979 - val_loss: 0.4533 - val_acc: 0.8571\n",
      "Epoch 51/100\n",
      "193/193 - 0s - loss: 0.4810 - acc: 0.7772 - val_loss: 0.4651 - val_acc: 0.7959\n",
      "Epoch 52/100\n",
      "193/193 - 0s - loss: 0.4406 - acc: 0.7461 - val_loss: 0.4930 - val_acc: 0.7959\n",
      "Epoch 53/100\n",
      "193/193 - 0s - loss: 0.4837 - acc: 0.7461 - val_loss: 0.4844 - val_acc: 0.7959\n",
      "Epoch 54/100\n",
      "193/193 - 0s - loss: 0.4353 - acc: 0.7979 - val_loss: 0.4879 - val_acc: 0.7959\n",
      "Epoch 55/100\n",
      "193/193 - 0s - loss: 0.4583 - acc: 0.7876 - val_loss: 0.4904 - val_acc: 0.8163\n",
      "Epoch 56/100\n",
      "193/193 - 0s - loss: 0.4403 - acc: 0.8031 - val_loss: 0.5029 - val_acc: 0.8163\n",
      "Epoch 57/100\n",
      "193/193 - 0s - loss: 0.4719 - acc: 0.7461 - val_loss: 0.4908 - val_acc: 0.8367\n",
      "Epoch 58/100\n",
      "193/193 - 0s - loss: 0.3729 - acc: 0.8187 - val_loss: 0.4969 - val_acc: 0.8163\n",
      "Epoch 59/100\n",
      "193/193 - 0s - loss: 0.4779 - acc: 0.7513 - val_loss: 0.4713 - val_acc: 0.7959\n",
      "Epoch 60/100\n",
      "193/193 - 0s - loss: 0.4715 - acc: 0.8083 - val_loss: 0.4554 - val_acc: 0.7959\n",
      "Epoch 61/100\n",
      "193/193 - 0s - loss: 0.4834 - acc: 0.7617 - val_loss: 0.4630 - val_acc: 0.7959\n",
      "Epoch 62/100\n",
      "193/193 - 0s - loss: 0.4860 - acc: 0.7617 - val_loss: 0.4621 - val_acc: 0.8367\n",
      "Epoch 63/100\n",
      "193/193 - 0s - loss: 0.4449 - acc: 0.7668 - val_loss: 0.4535 - val_acc: 0.8367\n",
      "Epoch 64/100\n",
      "193/193 - 0s - loss: 0.4381 - acc: 0.7617 - val_loss: 0.4516 - val_acc: 0.8367\n",
      "Epoch 65/100\n",
      "193/193 - 0s - loss: 0.3914 - acc: 0.8135 - val_loss: 0.4441 - val_acc: 0.8367\n",
      "Epoch 66/100\n",
      "193/193 - 0s - loss: 0.4264 - acc: 0.7927 - val_loss: 0.4476 - val_acc: 0.8367\n",
      "Epoch 67/100\n",
      "193/193 - 0s - loss: 0.4976 - acc: 0.7565 - val_loss: 0.4479 - val_acc: 0.8367\n",
      "Epoch 68/100\n",
      "193/193 - 0s - loss: 0.4411 - acc: 0.7876 - val_loss: 0.4586 - val_acc: 0.8367\n",
      "Epoch 69/100\n",
      "193/193 - 0s - loss: 0.4257 - acc: 0.8187 - val_loss: 0.4493 - val_acc: 0.8367\n",
      "Epoch 70/100\n",
      "193/193 - 0s - loss: 0.4372 - acc: 0.7358 - val_loss: 0.4194 - val_acc: 0.8571\n",
      "Epoch 71/100\n",
      "193/193 - 0s - loss: 0.4244 - acc: 0.7824 - val_loss: 0.4232 - val_acc: 0.8367\n",
      "Epoch 72/100\n",
      "193/193 - 0s - loss: 0.5125 - acc: 0.7202 - val_loss: 0.4366 - val_acc: 0.8571\n",
      "Epoch 73/100\n",
      "193/193 - 0s - loss: 0.5398 - acc: 0.7720 - val_loss: 0.4573 - val_acc: 0.8367\n",
      "Epoch 74/100\n",
      "193/193 - 0s - loss: 0.4518 - acc: 0.7979 - val_loss: 0.4622 - val_acc: 0.8367\n",
      "Epoch 75/100\n",
      "193/193 - 0s - loss: 0.4853 - acc: 0.7306 - val_loss: 0.4593 - val_acc: 0.8163\n",
      "Epoch 76/100\n",
      "193/193 - 0s - loss: 0.4456 - acc: 0.7720 - val_loss: 0.4578 - val_acc: 0.8163\n",
      "Epoch 77/100\n",
      "193/193 - 0s - loss: 0.4200 - acc: 0.8135 - val_loss: 0.4805 - val_acc: 0.7959\n",
      "Epoch 78/100\n",
      "193/193 - 0s - loss: 0.4740 - acc: 0.7513 - val_loss: 0.4703 - val_acc: 0.7755\n",
      "Epoch 79/100\n",
      "193/193 - 0s - loss: 0.3343 - acc: 0.8549 - val_loss: 0.4819 - val_acc: 0.7755\n",
      "Epoch 80/100\n",
      "193/193 - 0s - loss: 0.4242 - acc: 0.7720 - val_loss: 0.4947 - val_acc: 0.8163\n",
      "Epoch 81/100\n",
      "193/193 - 0s - loss: 0.4304 - acc: 0.7720 - val_loss: 0.4890 - val_acc: 0.8163\n",
      "Epoch 82/100\n",
      "193/193 - 0s - loss: 0.4467 - acc: 0.7617 - val_loss: 0.4984 - val_acc: 0.7959\n",
      "Epoch 83/100\n",
      "193/193 - 0s - loss: 0.5181 - acc: 0.7565 - val_loss: 0.4652 - val_acc: 0.8163\n",
      "Epoch 84/100\n",
      "193/193 - 0s - loss: 0.4261 - acc: 0.8083 - val_loss: 0.4425 - val_acc: 0.8163\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 85/100\n",
      "193/193 - 0s - loss: 0.4621 - acc: 0.7668 - val_loss: 0.4510 - val_acc: 0.8367\n",
      "Epoch 86/100\n",
      "193/193 - 0s - loss: 0.3911 - acc: 0.7979 - val_loss: 0.4681 - val_acc: 0.7959\n",
      "Epoch 87/100\n",
      "193/193 - 0s - loss: 0.4584 - acc: 0.8031 - val_loss: 0.4600 - val_acc: 0.8163\n",
      "Epoch 88/100\n",
      "193/193 - 0s - loss: 0.4853 - acc: 0.7461 - val_loss: 0.4639 - val_acc: 0.7959\n",
      "Epoch 89/100\n",
      "193/193 - 0s - loss: 0.4342 - acc: 0.7824 - val_loss: 0.4756 - val_acc: 0.7959\n",
      "Epoch 90/100\n",
      "193/193 - 0s - loss: 0.4533 - acc: 0.7979 - val_loss: 0.4875 - val_acc: 0.7755\n",
      "Epoch 91/100\n",
      "193/193 - 0s - loss: 0.4400 - acc: 0.7927 - val_loss: 0.4970 - val_acc: 0.7959\n",
      "Epoch 92/100\n",
      "193/193 - 0s - loss: 0.4253 - acc: 0.7927 - val_loss: 0.5293 - val_acc: 0.7755\n",
      "Epoch 93/100\n",
      "193/193 - 0s - loss: 0.5001 - acc: 0.7461 - val_loss: 0.5258 - val_acc: 0.7755\n",
      "Epoch 94/100\n",
      "193/193 - 0s - loss: 0.4458 - acc: 0.7565 - val_loss: 0.5052 - val_acc: 0.8163\n",
      "Epoch 95/100\n",
      "193/193 - 0s - loss: 0.4490 - acc: 0.7979 - val_loss: 0.5158 - val_acc: 0.7959\n",
      "Epoch 96/100\n",
      "193/193 - 0s - loss: 0.4080 - acc: 0.7772 - val_loss: 0.5212 - val_acc: 0.8163\n",
      "Epoch 97/100\n",
      "193/193 - 0s - loss: 0.4665 - acc: 0.7617 - val_loss: 0.5315 - val_acc: 0.8163\n",
      "Epoch 98/100\n",
      "193/193 - 0s - loss: 0.4067 - acc: 0.7979 - val_loss: 0.5300 - val_acc: 0.7755\n",
      "Epoch 99/100\n",
      "193/193 - 0s - loss: 0.4090 - acc: 0.7876 - val_loss: 0.5334 - val_acc: 0.7959\n",
      "Epoch 100/100\n",
      "193/193 - 0s - loss: 0.3981 - acc: 0.8238 - val_loss: 0.5649 - val_acc: 0.7755\n",
      "61/61 - 0s - loss: 0.4361 - acc: 0.8361\n",
      "\n",
      "Test accuracy: 0.8360656\n"
     ]
    }
   ],
   "source": [
    "ann.fit(X_train,y_train , validation_split=0.2,batch_size= 16 , verbose=2,shuffle = True,epochs  = 100)\n",
    "test_loss, test_acc = ann.evaluate(X_test, y_test, verbose=2)\n",
    "print('\\nTest accuracy:', test_acc)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XGBOoost Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10:43:02] WARNING: ..\\src\\learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "confussion matrix\n",
      "[[24  7]\n",
      " [ 0 30]]\n",
      "\n",
      "\n",
      "Accuracy of Extreme Gradient Boost: 88.52459016393442 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
    "from xgboost import XGBClassifier\n",
    "xgb = XGBClassifier(learning_rate=0.01, n_estimators=25, max_depth=15,gamma=0.6, subsample=0.52,colsample_bytree=0.6,seed=27, \n",
    "                    reg_lambda=2, booster='dart', colsample_bylevel=0.6, colsample_bynode=0.5)\n",
    "xgb.fit(X_train, y_train)\n",
    "xgb_predicted = xgb.predict(X_test)\n",
    "xgb_conf_matrix = confusion_matrix(y_test, xgb_predicted)\n",
    "xgb_acc_score = accuracy_score(y_test, xgb_predicted)\n",
    "print(\"confussion matrix\")\n",
    "print(xgb_conf_matrix)\n",
    "print(\"\\n\")\n",
    "print(\"Accuracy of Extreme Gradient Boost:\",xgb_acc_score*100,'\\n')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Extreme Gradient Boost</td>\n",
       "      <td>88.524590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ANN</td>\n",
       "      <td>83.606559</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Model   Accuracy\n",
       "0  Extreme Gradient Boost  88.524590\n",
       "1                     ANN  83.606559"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_ev = pd.DataFrame({'Model': ['Extreme Gradient Boost','ANN'], 'Accuracy': [xgb_acc_score*100,test_acc*100]})\n",
    "model_ev"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Making the predictions and evaluating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter your age: 63\n",
      "Enter 0 if you are female, 1 if you are male: 1\n",
      "Enter your chest pain type 1/2/3/4: 3\n",
      "Enter your resting blood pressure: 145\n",
      "Enter your serum cholestrol in mg/dl: 233\n",
      "Enter 1 if your fbs is greater than 120mg/dl1\n",
      "Enter your resting ecg 0/1/2: 0\n",
      "Enter maximum heart rate achieved : 150\n",
      "Do you have exercise induced agnia? 0/10\n",
      "Enter ST depression induced by exercise relative to rest? 2.3\n",
      "Slope of the peak exercise ST segment: 0/1/20\n",
      "number of major vessels (0-3) 0\n",
      "0 = normal; 1 = fixed defect; 2 = reversable defect1\n"
     ]
    }
   ],
   "source": [
    "age = int(input(\"Enter your age: \"))\n",
    "sex = int(input(\"Enter 0 if you are female, 1 if you are male: \"))\n",
    "cp = int(input(\"Enter your chest pain type 1/2/3/4: \"))\n",
    "trestbps = int(input(\"Enter your resting blood pressure: \"))\n",
    "chol = int(input(\"Enter your serum cholestrol in mg/dl: \"))\n",
    "fbs = int(input(\"Enter 1 if your fbs is greater than 120mg/dl\"))\n",
    "restecg = int(input(\"Enter your resting ecg 0/1/2: \"))\n",
    "thalach = int(input(\"Enter maximum heart rate achieved : \"))\n",
    "exang = int(input(\"Do you have exercise induced agnia? 0/1\"))\n",
    "oldpeak = float(input(\"Enter ST depression induced by exercise relative to rest? \"))\n",
    "slope = int(input(\"Slope of the peak exercise ST segment: 0/1/2\"))\n",
    "ca = int(input(\"number of major vessels (0-3) \"))\n",
    "thal = int(input(\"0 = normal; 1 = fixed defect; 2 = reversable defect\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You have a possibility of heart attack\n"
     ]
    }
   ],
   "source": [
    "if(ann.predict(sc.transform([[age,sex,cp,trestbps,chol,fbs,restecg,thalach,exang,oldpeak,slope,ca,thal]]))>0.5):\n",
    "    print(\"You have a possibility of heart attack\")\n",
    "else:\n",
    "    print(\"No worries\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}