IT253 OS Project CVD Prediction model - Final .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CardioVascular Disease Prediction model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This model predicts whether a user has cardiovasular disease or is no longer under cardiovascular disease danger."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataset:  <a href=\"https://www.kaggle.com/sulianova/cardiovascular-disease-dataset?select=cardio_train.csv\"> Link to the dataset used for this model </a>\n",
    "This dataset has data of 70000 patients"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## About dataset\n",
    "* age in years int\n",
    "* gender in 0-men, 1-women\n",
    "* height int cm int\n",
    "* weight in kg  float\n",
    "* ap_hi(systolic BP) int\n",
    "* ap_lo(diastolic BP) int\n",
    "* cholestrol: 1: normal, 2: above normal, 3: well above normal\n",
    "* gluc:       1: normal, 2: above normal, 3: well above normal\n",
    "* smoke:      0: NO, 1: Yes\n",
    "* alco:       0: NO, 1: Yes\n",
    "* Active(exercise):  0: NO, 1: Yes\n",
    "* cardio:  0: Safe, 1: Danger"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing required libraries/modules\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Getting the dataset into a DataFrame using pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset=pd.read_csv(\"cardio_train.csv\",sep=\";\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70000, 13)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>ap_hi</th>\n",
       "      <th>ap_lo</th>\n",
       "      <th>cholesterol</th>\n",
       "      <th>gluc</th>\n",
       "      <th>smoke</th>\n",
       "      <th>alco</th>\n",
       "      <th>active</th>\n",
       "      <th>cardio</th>\n",
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       "      <td>156</td>\n",
       "      <td>85.0</td>\n",
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       "      <td>90</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>18857</td>\n",
       "      <td>1</td>\n",
       "      <td>165</td>\n",
       "      <td>64.0</td>\n",
       "      <td>130</td>\n",
       "      <td>70</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>17623</td>\n",
       "      <td>2</td>\n",
       "      <td>169</td>\n",
       "      <td>82.0</td>\n",
       "      <td>150</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>17474</td>\n",
       "      <td>1</td>\n",
       "      <td>156</td>\n",
       "      <td>56.0</td>\n",
       "      <td>100</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8</td>\n",
       "      <td>21914</td>\n",
       "      <td>1</td>\n",
       "      <td>151</td>\n",
       "      <td>67.0</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9</td>\n",
       "      <td>22113</td>\n",
       "      <td>1</td>\n",
       "      <td>157</td>\n",
       "      <td>93.0</td>\n",
       "      <td>130</td>\n",
       "      <td>80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>12</td>\n",
       "      <td>22584</td>\n",
       "      <td>2</td>\n",
       "      <td>178</td>\n",
       "      <td>95.0</td>\n",
       "      <td>130</td>\n",
       "      <td>90</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>13</td>\n",
       "      <td>17668</td>\n",
       "      <td>1</td>\n",
       "      <td>158</td>\n",
       "      <td>71.0</td>\n",
       "      <td>110</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <th>9</th>\n",
       "      <td>14</td>\n",
       "      <td>19834</td>\n",
       "      <td>1</td>\n",
       "      <td>164</td>\n",
       "      <td>68.0</td>\n",
       "      <td>110</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>10</th>\n",
       "      <td>15</td>\n",
       "      <td>22530</td>\n",
       "      <td>1</td>\n",
       "      <td>169</td>\n",
       "      <td>80.0</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <th>11</th>\n",
       "      <td>16</td>\n",
       "      <td>18815</td>\n",
       "      <td>2</td>\n",
       "      <td>173</td>\n",
       "      <td>60.0</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>18</td>\n",
       "      <td>14791</td>\n",
       "      <td>2</td>\n",
       "      <td>165</td>\n",
       "      <td>60.0</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>21</td>\n",
       "      <td>19809</td>\n",
       "      <td>1</td>\n",
       "      <td>158</td>\n",
       "      <td>78.0</td>\n",
       "      <td>110</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <th>14</th>\n",
       "      <td>23</td>\n",
       "      <td>14532</td>\n",
       "      <td>2</td>\n",
       "      <td>181</td>\n",
       "      <td>95.0</td>\n",
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       "      <td>90</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <th>15</th>\n",
       "      <td>24</td>\n",
       "      <td>16782</td>\n",
       "      <td>2</td>\n",
       "      <td>172</td>\n",
       "      <td>112.0</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>25</td>\n",
       "      <td>21296</td>\n",
       "      <td>1</td>\n",
       "      <td>170</td>\n",
       "      <td>75.0</td>\n",
       "      <td>130</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>27</td>\n",
       "      <td>16747</td>\n",
       "      <td>1</td>\n",
       "      <td>158</td>\n",
       "      <td>52.0</td>\n",
       "      <td>110</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>28</td>\n",
       "      <td>17482</td>\n",
       "      <td>1</td>\n",
       "      <td>154</td>\n",
       "      <td>68.0</td>\n",
       "      <td>100</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>29</td>\n",
       "      <td>21755</td>\n",
       "      <td>2</td>\n",
       "      <td>162</td>\n",
       "      <td>56.0</td>\n",
       "      <td>120</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>30</td>\n",
       "      <td>19778</td>\n",
       "      <td>2</td>\n",
       "      <td>163</td>\n",
       "      <td>83.0</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>31</td>\n",
       "      <td>21413</td>\n",
       "      <td>1</td>\n",
       "      <td>157</td>\n",
       "      <td>69.0</td>\n",
       "      <td>130</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>22</th>\n",
       "      <td>32</td>\n",
       "      <td>23046</td>\n",
       "      <td>1</td>\n",
       "      <td>158</td>\n",
       "      <td>90.0</td>\n",
       "      <td>145</td>\n",
       "      <td>85</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>33</td>\n",
       "      <td>23376</td>\n",
       "      <td>2</td>\n",
       "      <td>156</td>\n",
       "      <td>45.0</td>\n",
       "      <td>110</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>35</td>\n",
       "      <td>16608</td>\n",
       "      <td>1</td>\n",
       "      <td>170</td>\n",
       "      <td>68.0</td>\n",
       "      <td>150</td>\n",
       "      <td>90</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>25</th>\n",
       "      <td>36</td>\n",
       "      <td>14453</td>\n",
       "      <td>1</td>\n",
       "      <td>153</td>\n",
       "      <td>65.0</td>\n",
       "      <td>130</td>\n",
       "      <td>100</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>37</td>\n",
       "      <td>19559</td>\n",
       "      <td>1</td>\n",
       "      <td>156</td>\n",
       "      <td>59.0</td>\n",
       "      <td>130</td>\n",
       "      <td>90</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>38</td>\n",
       "      <td>18085</td>\n",
       "      <td>1</td>\n",
       "      <td>159</td>\n",
       "      <td>78.0</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>39</td>\n",
       "      <td>14574</td>\n",
       "      <td>2</td>\n",
       "      <td>166</td>\n",
       "      <td>66.0</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>40</td>\n",
       "      <td>21057</td>\n",
       "      <td>2</td>\n",
       "      <td>169</td>\n",
       "      <td>74.0</td>\n",
       "      <td>130</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69970</th>\n",
       "      <td>99958</td>\n",
       "      <td>22572</td>\n",
       "      <td>2</td>\n",
       "      <td>173</td>\n",
       "      <td>103.0</td>\n",
       "      <td>140</td>\n",
       "      <td>80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69971</th>\n",
       "      <td>99959</td>\n",
       "      <td>20136</td>\n",
       "      <td>2</td>\n",
       "      <td>177</td>\n",
       "      <td>80.0</td>\n",
       "      <td>130</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69972</th>\n",
       "      <td>99960</td>\n",
       "      <td>17065</td>\n",
       "      <td>1</td>\n",
       "      <td>165</td>\n",
       "      <td>76.0</td>\n",
       "      <td>140</td>\n",
       "      <td>90</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69973</th>\n",
       "      <td>99961</td>\n",
       "      <td>22142</td>\n",
       "      <td>2</td>\n",
       "      <td>175</td>\n",
       "      <td>72.0</td>\n",
       "      <td>130</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69974</th>\n",
       "      <td>99962</td>\n",
       "      <td>18226</td>\n",
       "      <td>1</td>\n",
       "      <td>168</td>\n",
       "      <td>75.0</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69975</th>\n",
       "      <td>99963</td>\n",
       "      <td>21264</td>\n",
       "      <td>2</td>\n",
       "      <td>182</td>\n",
       "      <td>100.0</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69976</th>\n",
       "      <td>99964</td>\n",
       "      <td>21699</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>65.0</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69977</th>\n",
       "      <td>99965</td>\n",
       "      <td>16843</td>\n",
       "      <td>1</td>\n",
       "      <td>168</td>\n",
       "      <td>75.0</td>\n",
       "      <td>120</td>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69978</th>\n",
       "      <td>99967</td>\n",
       "      <td>18800</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>78.0</td>\n",
       "      <td>90</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69979</th>\n",
       "      <td>99969</td>\n",
       "      <td>22423</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>74.0</td>\n",
       "      <td>160</td>\n",
       "      <td>100</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69980</th>\n",
       "      <td>99971</td>\n",
       "      <td>17727</td>\n",
       "      <td>2</td>\n",
       "      <td>167</td>\n",
       "      <td>69.0</td>\n",
       "      <td>110</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69981</th>\n",
       "      <td>99972</td>\n",
       "      <td>17500</td>\n",
       "      <td>2</td>\n",
       "      <td>182</td>\n",
       "      <td>110.0</td>\n",
       "      <td>130</td>\n",
       "      <td>90</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69982</th>\n",
       "      <td>99973</td>\n",
       "      <td>18941</td>\n",
       "      <td>1</td>\n",
       "      <td>153</td>\n",
       "      <td>86.0</td>\n",
       "      <td>130</td>\n",
       "      <td>90</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69983</th>\n",
       "      <td>99974</td>\n",
       "      <td>19652</td>\n",
       "      <td>1</td>\n",
       "      <td>165</td>\n",
       "      <td>72.0</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69984</th>\n",
       "      <td>99975</td>\n",
       "      <td>17926</td>\n",
       "      <td>2</td>\n",
       "      <td>168</td>\n",
       "      <td>80.0</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69985</th>\n",
       "      <td>99977</td>\n",
       "      <td>18194</td>\n",
       "      <td>1</td>\n",
       "      <td>156</td>\n",
       "      <td>102.0</td>\n",
       "      <td>130</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69986</th>\n",
       "      <td>99978</td>\n",
       "      <td>18198</td>\n",
       "      <td>2</td>\n",
       "      <td>180</td>\n",
       "      <td>78.0</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69987</th>\n",
       "      <td>99979</td>\n",
       "      <td>18852</td>\n",
       "      <td>1</td>\n",
       "      <td>151</td>\n",
       "      <td>49.0</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69988</th>\n",
       "      <td>99981</td>\n",
       "      <td>21978</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>59.0</td>\n",
       "      <td>110</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69989</th>\n",
       "      <td>99985</td>\n",
       "      <td>21013</td>\n",
       "      <td>1</td>\n",
       "      <td>157</td>\n",
       "      <td>83.0</td>\n",
       "      <td>120</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69990</th>\n",
       "      <td>99986</td>\n",
       "      <td>15094</td>\n",
       "      <td>1</td>\n",
       "      <td>168</td>\n",
       "      <td>72.0</td>\n",
       "      <td>110</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69991</th>\n",
       "      <td>99988</td>\n",
       "      <td>20609</td>\n",
       "      <td>1</td>\n",
       "      <td>159</td>\n",
       "      <td>72.0</td>\n",
       "      <td>130</td>\n",
       "      <td>90</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69992</th>\n",
       "      <td>99990</td>\n",
       "      <td>18792</td>\n",
       "      <td>1</td>\n",
       "      <td>161</td>\n",
       "      <td>56.0</td>\n",
       "      <td>170</td>\n",
       "      <td>90</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69993</th>\n",
       "      <td>99991</td>\n",
       "      <td>19699</td>\n",
       "      <td>1</td>\n",
       "      <td>172</td>\n",
       "      <td>70.0</td>\n",
       "      <td>130</td>\n",
       "      <td>90</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69994</th>\n",
       "      <td>99992</td>\n",
       "      <td>21074</td>\n",
       "      <td>1</td>\n",
       "      <td>165</td>\n",
       "      <td>80.0</td>\n",
       "      <td>150</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69995</th>\n",
       "      <td>99993</td>\n",
       "      <td>19240</td>\n",
       "      <td>2</td>\n",
       "      <td>168</td>\n",
       "      <td>76.0</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69996</th>\n",
       "      <td>99995</td>\n",
       "      <td>22601</td>\n",
       "      <td>1</td>\n",
       "      <td>158</td>\n",
       "      <td>126.0</td>\n",
       "      <td>140</td>\n",
       "      <td>90</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69997</th>\n",
       "      <td>99996</td>\n",
       "      <td>19066</td>\n",
       "      <td>2</td>\n",
       "      <td>183</td>\n",
       "      <td>105.0</td>\n",
       "      <td>180</td>\n",
       "      <td>90</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69998</th>\n",
       "      <td>99998</td>\n",
       "      <td>22431</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>72.0</td>\n",
       "      <td>135</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69999</th>\n",
       "      <td>99999</td>\n",
       "      <td>20540</td>\n",
       "      <td>1</td>\n",
       "      <td>170</td>\n",
       "      <td>72.0</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>2</td>\n",
       "      <td>1</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>70000 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id    age  gender  height  weight  ap_hi  ap_lo  cholesterol  gluc  \\\n",
       "0          0  18393       2     168    62.0    110     80            1     1   \n",
       "1          1  20228       1     156    85.0    140     90            3     1   \n",
       "2          2  18857       1     165    64.0    130     70            3     1   \n",
       "3          3  17623       2     169    82.0    150    100            1     1   \n",
       "4          4  17474       1     156    56.0    100     60            1     1   \n",
       "5          8  21914       1     151    67.0    120     80            2     2   \n",
       "6          9  22113       1     157    93.0    130     80            3     1   \n",
       "7         12  22584       2     178    95.0    130     90            3     3   \n",
       "8         13  17668       1     158    71.0    110     70            1     1   \n",
       "9         14  19834       1     164    68.0    110     60            1     1   \n",
       "10        15  22530       1     169    80.0    120     80            1     1   \n",
       "11        16  18815       2     173    60.0    120     80            1     1   \n",
       "12        18  14791       2     165    60.0    120     80            1     1   \n",
       "13        21  19809       1     158    78.0    110     70            1     1   \n",
       "14        23  14532       2     181    95.0    130     90            1     1   \n",
       "15        24  16782       2     172   112.0    120     80            1     1   \n",
       "16        25  21296       1     170    75.0    130     70            1     1   \n",
       "17        27  16747       1     158    52.0    110     70            1     3   \n",
       "18        28  17482       1     154    68.0    100     70            1     1   \n",
       "19        29  21755       2     162    56.0    120     70            1     1   \n",
       "20        30  19778       2     163    83.0    120     80            1     1   \n",
       "21        31  21413       1     157    69.0    130     80            1     1   \n",
       "22        32  23046       1     158    90.0    145     85            2     2   \n",
       "23        33  23376       2     156    45.0    110     60            1     1   \n",
       "24        35  16608       1     170    68.0    150     90            3     1   \n",
       "25        36  14453       1     153    65.0    130    100            2     1   \n",
       "26        37  19559       1     156    59.0    130     90            1     1   \n",
       "27        38  18085       1     159    78.0    120     80            1     1   \n",
       "28        39  14574       2     166    66.0    120     80            1     1   \n",
       "29        40  21057       2     169    74.0    130     70            1     3   \n",
       "...      ...    ...     ...     ...     ...    ...    ...          ...   ...   \n",
       "69970  99958  22572       2     173   103.0    140     80            3     1   \n",
       "69971  99959  20136       2     177    80.0    130     80            1     1   \n",
       "69972  99960  17065       1     165    76.0    140     90            1     1   \n",
       "69973  99961  22142       2     175    72.0    130     80            1     1   \n",
       "69974  99962  18226       1     168    75.0    120     80            1     1   \n",
       "69975  99963  21264       2     182   100.0    120     80            1     1   \n",
       "69976  99964  21699       1     163    65.0    120     80            2     2   \n",
       "69977  99965  16843       1     168    75.0    120     79            1     1   \n",
       "69978  99967  18800       1     163    78.0     90     60            1     1   \n",
       "69979  99969  22423       1     163    74.0    160    100            2     2   \n",
       "69980  99971  17727       2     167    69.0    110     80            1     1   \n",
       "69981  99972  17500       2     182   110.0    130     90            2     2   \n",
       "69982  99973  18941       1     153    86.0    130     90            1     2   \n",
       "69983  99974  19652       1     165    72.0    120     80            1     1   \n",
       "69984  99975  17926       2     168    80.0    120     80            1     1   \n",
       "69985  99977  18194       1     156   102.0    130     80            1     1   \n",
       "69986  99978  18198       2     180    78.0    120     80            1     1   \n",
       "69987  99979  18852       1     151    49.0    120     80            1     1   \n",
       "69988  99981  21978       1     160    59.0    110     70            1     1   \n",
       "69989  99985  21013       1     157    83.0    120     70            1     1   \n",
       "69990  99986  15094       1     168    72.0    110     70            1     1   \n",
       "69991  99988  20609       1     159    72.0    130     90            2     2   \n",
       "69992  99990  18792       1     161    56.0    170     90            1     1   \n",
       "69993  99991  19699       1     172    70.0    130     90            1     1   \n",
       "69994  99992  21074       1     165    80.0    150     80            1     1   \n",
       "69995  99993  19240       2     168    76.0    120     80            1     1   \n",
       "69996  99995  22601       1     158   126.0    140     90            2     2   \n",
       "69997  99996  19066       2     183   105.0    180     90            3     1   \n",
       "69998  99998  22431       1     163    72.0    135     80            1     2   \n",
       "69999  99999  20540       1     170    72.0    120     80            2     1   \n",
       "\n",
       "       smoke  alco  active  cardio  \n",
       "0          0     0       1       0  \n",
       "1          0     0       1       1  \n",
       "2          0     0       0       1  \n",
       "3          0     0       1       1  \n",
       "4          0     0       0       0  \n",
       "5          0     0       0       0  \n",
       "6          0     0       1       0  \n",
       "7          0     0       1       1  \n",
       "8          0     0       1       0  \n",
       "9          0     0       0       0  \n",
       "10         0     0       1       0  \n",
       "11         0     0       1       0  \n",
       "12         0     0       0       0  \n",
       "13         0     0       1       0  \n",
       "14         1     1       1       0  \n",
       "15         0     0       0       1  \n",
       "16         0     0       0       0  \n",
       "17         0     0       1       0  \n",
       "18         0     0       0       0  \n",
       "19         1     0       1       0  \n",
       "20         0     0       1       0  \n",
       "21         0     0       1       0  \n",
       "22         0     0       1       1  \n",
       "23         0     0       1       0  \n",
       "24         0     0       1       1  \n",
       "25         0     0       1       0  \n",
       "26         0     0       1       0  \n",
       "27         0     0       1       0  \n",
       "28         0     0       1       0  \n",
       "29         0     0       0       0  \n",
       "...      ...   ...     ...     ...  \n",
       "69970      1     1       0       1  \n",
       "69971      0     0       1       0  \n",
       "69972      0     0       1       1  \n",
       "69973      0     0       1       0  \n",
       "69974      0     0       1       0  \n",
       "69975      0     0       1       1  \n",
       "69976      0     0       1       0  \n",
       "69977      0     0       1       0  \n",
       "69978      0     0       1       1  \n",
       "69979      0     0       1       1  \n",
       "69980      0     1       0       0  \n",
       "69981      0     0       1       1  \n",
       "69982      0     0       1       1  \n",
       "69983      0     0       1       0  \n",
       "69984      0     0       1       1  \n",
       "69985      0     1       0       1  \n",
       "69986      0     0       1       0  \n",
       "69987      0     0       1       0  \n",
       "69988      0     0       1       0  \n",
       "69989      0     0       1       1  \n",
       "69990      0     0       1       1  \n",
       "69991      0     0       1       0  \n",
       "69992      0     0       1       1  \n",
       "69993      0     0       1       1  \n",
       "69994      0     0       1       1  \n",
       "69995      1     0       1       0  \n",
       "69996      0     0       1       1  \n",
       "69997      0     1       0       1  \n",
       "69998      0     0       0       1  \n",
       "69999      0     0       1       0  \n",
       "\n",
       "[70000 rows x 13 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>id</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>ap_hi</th>\n",
       "      <th>ap_lo</th>\n",
       "      <th>cholesterol</th>\n",
       "      <th>gluc</th>\n",
       "      <th>smoke</th>\n",
       "      <th>alco</th>\n",
       "      <th>active</th>\n",
       "      <th>cardio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>70000.000000</td>\n",
       "      <td>70000.000000</td>\n",
       "      <td>70000.000000</td>\n",
       "      <td>70000.000000</td>\n",
       "      <td>70000.000000</td>\n",
       "      <td>70000.000000</td>\n",
       "      <td>70000.000000</td>\n",
       "      <td>70000.000000</td>\n",
       "      <td>70000.000000</td>\n",
       "      <td>70000.000000</td>\n",
       "      <td>70000.000000</td>\n",
       "      <td>70000.000000</td>\n",
       "      <td>70000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>49972.419900</td>\n",
       "      <td>19468.865814</td>\n",
       "      <td>1.349571</td>\n",
       "      <td>164.359229</td>\n",
       "      <td>74.205690</td>\n",
       "      <td>128.817286</td>\n",
       "      <td>96.630414</td>\n",
       "      <td>1.366871</td>\n",
       "      <td>1.226457</td>\n",
       "      <td>0.088129</td>\n",
       "      <td>0.053771</td>\n",
       "      <td>0.803729</td>\n",
       "      <td>0.499700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>28851.302323</td>\n",
       "      <td>2467.251667</td>\n",
       "      <td>0.476838</td>\n",
       "      <td>8.210126</td>\n",
       "      <td>14.395757</td>\n",
       "      <td>154.011419</td>\n",
       "      <td>188.472530</td>\n",
       "      <td>0.680250</td>\n",
       "      <td>0.572270</td>\n",
       "      <td>0.283484</td>\n",
       "      <td>0.225568</td>\n",
       "      <td>0.397179</td>\n",
       "      <td>0.500003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>10798.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>-150.000000</td>\n",
       "      <td>-70.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</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>25006.750000</td>\n",
       "      <td>17664.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>50001.500000</td>\n",
       "      <td>19703.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>74889.250000</td>\n",
       "      <td>21327.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>99999.000000</td>\n",
       "      <td>23713.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>16020.000000</td>\n",
       "      <td>11000.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id           age        gender        height        weight  \\\n",
       "count  70000.000000  70000.000000  70000.000000  70000.000000  70000.000000   \n",
       "mean   49972.419900  19468.865814      1.349571    164.359229     74.205690   \n",
       "std    28851.302323   2467.251667      0.476838      8.210126     14.395757   \n",
       "min        0.000000  10798.000000      1.000000     55.000000     10.000000   \n",
       "25%    25006.750000  17664.000000      1.000000    159.000000     65.000000   \n",
       "50%    50001.500000  19703.000000      1.000000    165.000000     72.000000   \n",
       "75%    74889.250000  21327.000000      2.000000    170.000000     82.000000   \n",
       "max    99999.000000  23713.000000      2.000000    250.000000    200.000000   \n",
       "\n",
       "              ap_hi         ap_lo   cholesterol          gluc         smoke  \\\n",
       "count  70000.000000  70000.000000  70000.000000  70000.000000  70000.000000   \n",
       "mean     128.817286     96.630414      1.366871      1.226457      0.088129   \n",
       "std      154.011419    188.472530      0.680250      0.572270      0.283484   \n",
       "min     -150.000000    -70.000000      1.000000      1.000000      0.000000   \n",
       "25%      120.000000     80.000000      1.000000      1.000000      0.000000   \n",
       "50%      120.000000     80.000000      1.000000      1.000000      0.000000   \n",
       "75%      140.000000     90.000000      2.000000      1.000000      0.000000   \n",
       "max    16020.000000  11000.000000      3.000000      3.000000      1.000000   \n",
       "\n",
       "               alco        active        cardio  \n",
       "count  70000.000000  70000.000000  70000.000000  \n",
       "mean       0.053771      0.803729      0.499700  \n",
       "std        0.225568      0.397179      0.500003  \n",
       "min        0.000000      0.000000      0.000000  \n",
       "25%        0.000000      1.000000      0.000000  \n",
       "50%        0.000000      1.000000      0.000000  \n",
       "75%        0.000000      1.000000      1.000000  \n",
       "max        1.000000      1.000000      1.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### As we can see here, that min value of ap_hi and ap_lo is -ve which can not be negative so these erroneous data must be discarded from the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-100 80\n",
      "-115 70\n",
      "-100 70\n",
      "-140 90\n",
      "-120 80\n",
      "-150 80\n",
      "-120 80\n",
      "15 -70\n"
     ]
    }
   ],
   "source": [
    "for ind in dataset.index:\n",
    "    if dataset[\"ap_hi\"][ind]<0 or dataset[\"ap_lo\"][ind]<0:\n",
    "        print(dataset[\"ap_hi\"][ind],dataset[\"ap_lo\"][ind])\n",
    "        dataset.drop(ind,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(69992, 13)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting age into years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The age is given in days, we have to convert it into years.\n",
    "dataset[\"age\"] = dataset[\"age\"]/365\n",
    "dataset[\"age\"] = dataset[\"age\"].astype(\"int\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>ap_hi</th>\n",
       "      <th>ap_lo</th>\n",
       "      <th>cholesterol</th>\n",
       "      <th>gluc</th>\n",
       "      <th>smoke</th>\n",
       "      <th>alco</th>\n",
       "      <th>active</th>\n",
       "      <th>cardio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>168</td>\n",
       "      <td>62.0</td>\n",
       "      <td>110</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>156</td>\n",
       "      <td>85.0</td>\n",
       "      <td>140</td>\n",
       "      <td>90</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>165</td>\n",
       "      <td>64.0</td>\n",
       "      <td>130</td>\n",
       "      <td>70</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>48</td>\n",
       "      <td>2</td>\n",
       "      <td>169</td>\n",
       "      <td>82.0</td>\n",
       "      <td>150</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>156</td>\n",
       "      <td>56.0</td>\n",
       "      <td>100</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  age  gender  height  weight  ap_hi  ap_lo  cholesterol  gluc  smoke  \\\n",
       "0   0   50       2     168    62.0    110     80            1     1      0   \n",
       "1   1   55       1     156    85.0    140     90            3     1      0   \n",
       "2   2   51       1     165    64.0    130     70            3     1      0   \n",
       "3   3   48       2     169    82.0    150    100            1     1      0   \n",
       "4   4   47       1     156    56.0    100     60            1     1      0   \n",
       "\n",
       "   alco  active  cardio  \n",
       "0     0       1       0  \n",
       "1     0       1       1  \n",
       "2     0       0       1  \n",
       "3     0       1       1  \n",
       "4     0       0       0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dropping Column id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping id column, its of no use.\n",
    "dataset = dataset.drop(columns = [\"id\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(69992, 12)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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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>gender</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>ap_hi</th>\n",
       "      <th>ap_lo</th>\n",
       "      <th>cholesterol</th>\n",
       "      <th>gluc</th>\n",
       "      <th>smoke</th>\n",
       "      <th>alco</th>\n",
       "      <th>active</th>\n",
       "      <th>cardio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>168</td>\n",
       "      <td>62.0</td>\n",
       "      <td>110</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>156</td>\n",
       "      <td>85.0</td>\n",
       "      <td>140</td>\n",
       "      <td>90</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>165</td>\n",
       "      <td>64.0</td>\n",
       "      <td>130</td>\n",
       "      <td>70</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48</td>\n",
       "      <td>2</td>\n",
       "      <td>169</td>\n",
       "      <td>82.0</td>\n",
       "      <td>150</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>156</td>\n",
       "      <td>56.0</td>\n",
       "      <td>100</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  gender  height  weight  ap_hi  ap_lo  cholesterol  gluc  smoke  alco  \\\n",
       "0   50       2     168    62.0    110     80            1     1      0     0   \n",
       "1   55       1     156    85.0    140     90            3     1      0     0   \n",
       "2   51       1     165    64.0    130     70            3     1      0     0   \n",
       "3   48       2     169    82.0    150    100            1     1      0     0   \n",
       "4   47       1     156    56.0    100     60            1     1      0     0   \n",
       "\n",
       "   active  cardio  \n",
       "0       1       0  \n",
       "1       1       1  \n",
       "2       0       1  \n",
       "3       1       1  \n",
       "4       0       0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting gender values to 0 & 1\n",
    "* 0: Male\n",
    "* 1: Female"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset[\"gender\"] = dataset[\"gender\"] % 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>gender</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>ap_hi</th>\n",
       "      <th>ap_lo</th>\n",
       "      <th>cholesterol</th>\n",
       "      <th>gluc</th>\n",
       "      <th>smoke</th>\n",
       "      <th>alco</th>\n",
       "      <th>active</th>\n",
       "      <th>cardio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>168</td>\n",
       "      <td>62.0</td>\n",
       "      <td>110</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>1</th>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>156</td>\n",
       "      <td>85.0</td>\n",
       "      <td>140</td>\n",
       "      <td>90</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>165</td>\n",
       "      <td>64.0</td>\n",
       "      <td>130</td>\n",
       "      <td>70</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>169</td>\n",
       "      <td>82.0</td>\n",
       "      <td>150</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>156</td>\n",
       "      <td>56.0</td>\n",
       "      <td>100</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  gender  height  weight  ap_hi  ap_lo  cholesterol  gluc  smoke  alco  \\\n",
       "0   50       0     168    62.0    110     80            1     1      0     0   \n",
       "1   55       1     156    85.0    140     90            3     1      0     0   \n",
       "2   51       1     165    64.0    130     70            3     1      0     0   \n",
       "3   48       0     169    82.0    150    100            1     1      0     0   \n",
       "4   47       1     156    56.0    100     60            1     1      0     0   \n",
       "\n",
       "   active  cardio  \n",
       "0       1       0  \n",
       "1       1       1  \n",
       "2       0       1  \n",
       "3       1       1  \n",
       "4       0       0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dropping cardio column \n",
    "### X & Y of shape 11 & 1 are input and output for model respectively"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       age  gender  height  weight  ap_hi  ap_lo  cholesterol  gluc  smoke  \\\n",
      "0       50       0     168    62.0    110     80            1     1      0   \n",
      "1       55       1     156    85.0    140     90            3     1      0   \n",
      "2       51       1     165    64.0    130     70            3     1      0   \n",
      "3       48       0     169    82.0    150    100            1     1      0   \n",
      "4       47       1     156    56.0    100     60            1     1      0   \n",
      "5       60       1     151    67.0    120     80            2     2      0   \n",
      "6       60       1     157    93.0    130     80            3     1      0   \n",
      "7       61       0     178    95.0    130     90            3     3      0   \n",
      "8       48       1     158    71.0    110     70            1     1      0   \n",
      "9       54       1     164    68.0    110     60            1     1      0   \n",
      "10      61       1     169    80.0    120     80            1     1      0   \n",
      "11      51       0     173    60.0    120     80            1     1      0   \n",
      "12      40       0     165    60.0    120     80            1     1      0   \n",
      "13      54       1     158    78.0    110     70            1     1      0   \n",
      "14      39       0     181    95.0    130     90            1     1      1   \n",
      "15      45       0     172   112.0    120     80            1     1      0   \n",
      "16      58       1     170    75.0    130     70            1     1      0   \n",
      "17      45       1     158    52.0    110     70            1     3      0   \n",
      "18      47       1     154    68.0    100     70            1     1      0   \n",
      "19      59       0     162    56.0    120     70            1     1      1   \n",
      "20      54       0     163    83.0    120     80            1     1      0   \n",
      "21      58       1     157    69.0    130     80            1     1      0   \n",
      "22      63       1     158    90.0    145     85            2     2      0   \n",
      "23      64       0     156    45.0    110     60            1     1      0   \n",
      "24      45       1     170    68.0    150     90            3     1      0   \n",
      "25      39       1     153    65.0    130    100            2     1      0   \n",
      "26      53       1     156    59.0    130     90            1     1      0   \n",
      "27      49       1     159    78.0    120     80            1     1      0   \n",
      "28      39       0     166    66.0    120     80            1     1      0   \n",
      "29      57       0     169    74.0    130     70            1     3      0   \n",
      "...    ...     ...     ...     ...    ...    ...          ...   ...    ...   \n",
      "69970   61       0     173   103.0    140     80            3     1      1   \n",
      "69971   55       0     177    80.0    130     80            1     1      0   \n",
      "69972   46       1     165    76.0    140     90            1     1      0   \n",
      "69973   60       0     175    72.0    130     80            1     1      0   \n",
      "69974   49       1     168    75.0    120     80            1     1      0   \n",
      "69975   58       0     182   100.0    120     80            1     1      0   \n",
      "69976   59       1     163    65.0    120     80            2     2      0   \n",
      "69977   46       1     168    75.0    120     79            1     1      0   \n",
      "69978   51       1     163    78.0     90     60            1     1      0   \n",
      "69979   61       1     163    74.0    160    100            2     2      0   \n",
      "69980   48       0     167    69.0    110     80            1     1      0   \n",
      "69981   47       0     182   110.0    130     90            2     2      0   \n",
      "69982   51       1     153    86.0    130     90            1     2      0   \n",
      "69983   53       1     165    72.0    120     80            1     1      0   \n",
      "69984   49       0     168    80.0    120     80            1     1      0   \n",
      "69985   49       1     156   102.0    130     80            1     1      0   \n",
      "69986   49       0     180    78.0    120     80            1     1      0   \n",
      "69987   51       1     151    49.0    120     80            1     1      0   \n",
      "69988   60       1     160    59.0    110     70            1     1      0   \n",
      "69989   57       1     157    83.0    120     70            1     1      0   \n",
      "69990   41       1     168    72.0    110     70            1     1      0   \n",
      "69991   56       1     159    72.0    130     90            2     2      0   \n",
      "69992   51       1     161    56.0    170     90            1     1      0   \n",
      "69993   53       1     172    70.0    130     90            1     1      0   \n",
      "69994   57       1     165    80.0    150     80            1     1      0   \n",
      "69995   52       0     168    76.0    120     80            1     1      1   \n",
      "69996   61       1     158   126.0    140     90            2     2      0   \n",
      "69997   52       0     183   105.0    180     90            3     1      0   \n",
      "69998   61       1     163    72.0    135     80            1     2      0   \n",
      "69999   56       1     170    72.0    120     80            2     1      0   \n",
      "\n",
      "       alco  active  \n",
      "0         0       1  \n",
      "1         0       1  \n",
      "2         0       0  \n",
      "3         0       1  \n",
      "4         0       0  \n",
      "5         0       0  \n",
      "6         0       1  \n",
      "7         0       1  \n",
      "8         0       1  \n",
      "9         0       0  \n",
      "10        0       1  \n",
      "11        0       1  \n",
      "12        0       0  \n",
      "13        0       1  \n",
      "14        1       1  \n",
      "15        0       0  \n",
      "16        0       0  \n",
      "17        0       1  \n",
      "18        0       0  \n",
      "19        0       1  \n",
      "20        0       1  \n",
      "21        0       1  \n",
      "22        0       1  \n",
      "23        0       1  \n",
      "24        0       1  \n",
      "25        0       1  \n",
      "26        0       1  \n",
      "27        0       1  \n",
      "28        0       1  \n",
      "29        0       0  \n",
      "...     ...     ...  \n",
      "69970     1       0  \n",
      "69971     0       1  \n",
      "69972     0       1  \n",
      "69973     0       1  \n",
      "69974     0       1  \n",
      "69975     0       1  \n",
      "69976     0       1  \n",
      "69977     0       1  \n",
      "69978     0       1  \n",
      "69979     0       1  \n",
      "69980     1       0  \n",
      "69981     0       1  \n",
      "69982     0       1  \n",
      "69983     0       1  \n",
      "69984     0       1  \n",
      "69985     1       0  \n",
      "69986     0       1  \n",
      "69987     0       1  \n",
      "69988     0       1  \n",
      "69989     0       1  \n",
      "69990     0       1  \n",
      "69991     0       1  \n",
      "69992     0       1  \n",
      "69993     0       1  \n",
      "69994     0       1  \n",
      "69995     0       1  \n",
      "69996     0       1  \n",
      "69997     1       0  \n",
      "69998     0       0  \n",
      "69999     0       1  \n",
      "\n",
      "[69992 rows x 11 columns] 0        0\n",
      "1        1\n",
      "2        1\n",
      "3        1\n",
      "4        0\n",
      "5        0\n",
      "6        0\n",
      "7        1\n",
      "8        0\n",
      "9        0\n",
      "10       0\n",
      "11       0\n",
      "12       0\n",
      "13       0\n",
      "14       0\n",
      "15       1\n",
      "16       0\n",
      "17       0\n",
      "18       0\n",
      "19       0\n",
      "20       0\n",
      "21       0\n",
      "22       1\n",
      "23       0\n",
      "24       1\n",
      "25       0\n",
      "26       0\n",
      "27       0\n",
      "28       0\n",
      "29       0\n",
      "        ..\n",
      "69970    1\n",
      "69971    0\n",
      "69972    1\n",
      "69973    0\n",
      "69974    0\n",
      "69975    1\n",
      "69976    0\n",
      "69977    0\n",
      "69978    1\n",
      "69979    1\n",
      "69980    0\n",
      "69981    1\n",
      "69982    1\n",
      "69983    0\n",
      "69984    1\n",
      "69985    1\n",
      "69986    0\n",
      "69987    0\n",
      "69988    0\n",
      "69989    1\n",
      "69990    1\n",
      "69991    0\n",
      "69992    1\n",
      "69993    1\n",
      "69994    1\n",
      "69995    0\n",
      "69996    1\n",
      "69997    1\n",
      "69998    1\n",
      "69999    0\n",
      "Name: cardio, Length: 69992, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "X = dataset.drop(columns = ['cardio'])\n",
    "Y = dataset['cardio']\n",
    "print(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(69992, 11)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shape of input is 11 with 69992 rows\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(69992,)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shape of output Y is 1 with 69992 rows\n",
    "Y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting dataset into train and test in ratio 7:3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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 = 0.30, random_state = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(48994, 11) (48994,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape,Y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20998, 11) (20998,)\n"
     ]
    }
   ],
   "source": [
    "print(X_test.shape,Y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Different Binary Classification Algorithms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to store the accuracies of different algorithms\n",
    "Accuracy={}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing accuracy_score method from Scikit-Learn Library to calculate the accuracy score of the algorithms\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logisitic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "lr = LogisticRegression()\n",
    "lr.fit(X_train,Y_train)\n",
    "lr_pred = lr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy score for Logistic Regression Algorithm is 71.18%\n"
     ]
    }
   ],
   "source": [
    "acc_lr = round(accuracy_score(lr_pred,Y_test)*100,2)\n",
    "Accuracy[\"Logistic regression\"]=acc_lr\n",
    "print(\"The accuracy score for Logistic Regression Algorithm is \" + str(acc_lr) + \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Naive Bayes Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "nb = GaussianNB()\n",
    "nb.fit(X_train,Y_train)\n",
    "nb_pred = nb.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy score for Naive Bayes Algorithm is 59.0%\n"
     ]
    }
   ],
   "source": [
    "acc_nb = round(accuracy_score(nb_pred,Y_test)*100,2)\n",
    "Accuracy[\"Naive Bayes\"]=acc_nb\n",
    "print(\"The accuracy score for Naive Bayes Algorithm is \" + str(acc_nb)+\"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVM(Support vector machine) Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "sv = SVC(random_state=6)\n",
    "sv.fit(X_train, Y_train)\n",
    "svm_pred = sv.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy score for SVM Classifier Algorithm is 71.96 %\n"
     ]
    }
   ],
   "source": [
    "acc_svm = round(accuracy_score(svm_pred,Y_test)*100,2)\n",
    "Accuracy[\"SVM classifier\"]=acc_svm\n",
    "print(\"The accuracy score for SVM Classifier Algorithm is \" + str(acc_svm)+\" %\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K Nearest Neighbor Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors=16)\n",
    "knn.fit(X_train,Y_train)\n",
    "knn_pred=knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy score for KNN Classifier is 71.35 %\n"
     ]
    }
   ],
   "source": [
    "acc_knn = round(accuracy_score(knn_pred,Y_test)*100,2)\n",
    "Accuracy[\"K Nearest Neighbor\"]=acc_knn\n",
    "print(\"The accuracy score for KNN Classifier is \" + str(acc_knn)+\" %\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "299\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "max_acc = 0\n",
    "\n",
    "# getting best values of random_state\n",
    "for rs in range(300):\n",
    "    dt = DecisionTreeClassifier(random_state=rs)\n",
    "    dt.fit(X_train,Y_train)\n",
    "    dt_pred = dt.predict(X_test)\n",
    "    curr_acc = round(accuracy_score(dt_pred,Y_test)*100,2)\n",
    "    if(curr_acc>max_acc):\n",
    "        max_acc = curr_acc\n",
    "        best_rs = rs\n",
    "\n",
    "print(rs)\n",
    "dt = DecisionTreeClassifier(random_state=best_rs)\n",
    "dt.fit(X_train,Y_train)\n",
    "dt_pred = dt.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy score for Decision Tree is 63.35 %\n"
     ]
    }
   ],
   "source": [
    "acc_dt = round(accuracy_score(dt_pred,Y_test)*100,2)\n",
    "Accuracy[\"Decision Tree\"]=acc_dt\n",
    "print(\"The accuracy score for Decision Tree is \" + str(acc_dt)+\" %\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "70.63\n",
      "21\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "max_acc = 0\n",
    "\n",
    "# getting best value for random_state\n",
    "for rs in range(50):\n",
    "    rf = RandomForestClassifier(random_state=rs)\n",
    "    rf.fit(X_train,Y_train)\n",
    "    rf_pred = rf.predict(X_test)\n",
    "    curr_acc = round(accuracy_score(rf_pred,Y_test)*100,2)\n",
    "    if(curr_acc>max_acc):\n",
    "        max_acc = curr_acc\n",
    "        best_rs = rs\n",
    "\n",
    "print(max_acc)\n",
    "print(best_rs)\n",
    "\n",
    "rf = RandomForestClassifier(random_state=best_rs)\n",
    "rf.fit(X_train,Y_train)\n",
    "rf_pred = rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy score for Random Forest  Algorithm is 70.63 %\n"
     ]
    }
   ],
   "source": [
    "acc_rf = round(accuracy_score(rf_pred,Y_test)*100,2)\n",
    "Accuracy[\"Random Forest\"]=acc_rf\n",
    "print(\"The accuracy score for Random Forest  Algorithm is \" + str(acc_rf)+\" %\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(None, 15)\n",
      "(None, 7)\n"
     ]
    }
   ],
   "source": [
    "# Creating Keras Sequential Model\n",
    "model = Sequential()\n",
    "\n",
    "# 1st Hidden Layer\n",
    "model.add(Dense(15,activation='relu',input_dim=11))\n",
    "print(model.output_shape)\n",
    "\n",
    "# 2nd Hidden Layer\n",
    "model.add(Dense(7,activation='relu',input_dim=15))\n",
    "print(model.output_shape)\n",
    "\n",
    "# Output Layer\n",
    "model.add(Dense(1,activation='sigmoid'))\n",
    "\n",
    "model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 15)                180       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 7)                 112       \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 8         \n",
      "=================================================================\n",
      "Total params: 300\n",
      "Trainable params: 300\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/300\n",
      "2227/2227 [==============================] - 2s 932us/step - loss: 0.8432 - accuracy: 0.6778\n",
      "Epoch 2/300\n",
      "2227/2227 [==============================] - 2s 918us/step - loss: 0.6671 - accuracy: 0.6866\n",
      "Epoch 3/300\n",
      "2227/2227 [==============================] - 2s 925us/step - loss: 0.6374 - accuracy: 0.6912\n",
      "Epoch 4/300\n",
      "2227/2227 [==============================] - 2s 944us/step - loss: 0.6436 - accuracy: 0.6955\n",
      "Epoch 5/300\n",
      "2227/2227 [==============================] - 2s 961us/step - loss: 0.6295 - accuracy: 0.6947\n",
      "Epoch 6/300\n",
      "2227/2227 [==============================] - 2s 965us/step - loss: 0.6304 - accuracy: 0.6992\n",
      "Epoch 7/300\n",
      "2227/2227 [==============================] - 2s 1ms/step - loss: 0.6148 - accuracy: 0.6978\n",
      "Epoch 8/300\n",
      "2227/2227 [==============================] - 2s 1ms/step - loss: 0.6181 - accuracy: 0.7016\n",
      "Epoch 9/300\n",
      "2227/2227 [==============================] - 2s 996us/step - loss: 0.6128 - accuracy: 0.7062\n",
      "Epoch 10/300\n",
      "2227/2227 [==============================] - 3s 1ms/step - loss: 0.6127 - accuracy: 0.7028\n",
      "Epoch 11/300\n",
      "2227/2227 [==============================] - 2s 1ms/step - loss: 0.6020 - accuracy: 0.7066\n",
      "Epoch 12/300\n",
      "2227/2227 [==============================] - 2s 970us/step - loss: 0.5964 - accuracy: 0.7093\n",
      "Epoch 13/300\n",
      "2227/2227 [==============================] - 2s 1ms/step - loss: 0.5877 - accuracy: 0.7110\n",
      "Epoch 14/300\n",
      "2227/2227 [==============================] - 2s 1ms/step - loss: 0.5942 - accuracy: 0.7126\n",
      "Epoch 15/300\n",
      "2227/2227 [==============================] - 2s 1ms/step - loss: 0.5820 - accuracy: 0.7154\n",
      "Epoch 16/300\n",
      "2227/2227 [==============================] - 2s 1ms/step - loss: 0.5761 - accuracy: 0.7166\n",
      "Epoch 17/300\n",
      "2227/2227 [==============================] - 2s 1ms/step - loss: 0.5757 - accuracy: 0.7163\n",
      "Epoch 18/300\n",
      "2227/2227 [==============================] - 2s 1ms/step - loss: 0.5744 - accuracy: 0.7170\n",
      "Epoch 19/300\n",
      "2227/2227 [==============================] - 2s 1ms/step - loss: 0.5693 - accuracy: 0.7193\n",
      "Epoch 20/300\n",
      "2227/2227 [==============================] - 2s 980us/step - loss: 0.5683 - accuracy: 0.7205\n",
      "Epoch 21/300\n",
      "2227/2227 [==============================] - 2s 973us/step - loss: 0.5660 - accuracy: 0.7215\n",
      "Epoch 22/300\n",
      "2227/2227 [==============================] - 2s 992us/step - loss: 0.5648 - accuracy: 0.7225\n",
      "Epoch 23/300\n",
      "2227/2227 [==============================] - 2s 1ms/step - loss: 0.5617 - accuracy: 0.7223\n",
      "Epoch 24/300\n",
      "2227/2227 [==============================] - 2s 1ms/step - loss: 0.5621 - accuracy: 0.7215\n",
      "Epoch 25/300\n",
      "2227/2227 [==============================] - 2s 1ms/step - loss: 0.5613 - accuracy: 0.7232\n",
      "Epoch 26/300\n",
      "2227/2227 [==============================] - 2s 963us/step - loss: 0.5602 - accuracy: 0.7219\n",
      "Epoch 27/300\n",
      "2227/2227 [==============================] - 2s 963us/step - loss: 0.5584 - accuracy: 0.7234\n",
      "Epoch 28/300\n",
      "2227/2227 [==============================] - 2s 963us/step - loss: 0.5585 - accuracy: 0.7253\n",
      "Epoch 29/300\n",
      "2227/2227 [==============================] - 2s 988us/step - loss: 0.5582 - accuracy: 0.7240\n",
      "Epoch 30/300\n",
      "2227/2227 [==============================] - 2s 999us/step - loss: 0.5576 - accuracy: 0.7242\n",
      "Epoch 31/300\n",
      "2227/2227 [==============================] - 2s 1ms/step - loss: 0.5579 - accuracy: 0.7228\n",
      "Epoch 32/300\n",
      "2227/2227 [==============================] - 3s 1ms/step - loss: 0.5571 - accuracy: 0.7239\n",
      "Epoch 33/300\n",
      "2227/2227 [==============================] - 2s 973us/step - loss: 0.5567 - accuracy: 0.7261\n",
      "Epoch 34/300\n",
      "2227/2227 [==============================] - 2s 980us/step - loss: 0.5562 - accuracy: 0.7255\n",
      "Epoch 35/300\n",
      "2227/2227 [==============================] - 2s 979us/step - loss: 0.5570 - accuracy: 0.7231\n",
      "Epoch 36/300\n",
      "2227/2227 [==============================] - 2s 990us/step - loss: 0.5567 - accuracy: 0.7253\n",
      "Epoch 37/300\n",
      "2227/2227 [==============================] - 3s 1ms/step - loss: 0.5565 - accuracy: 0.7242\n",
      "Epoch 38/300\n",
      "2227/2227 [==============================] - 2s 1ms/step - loss: 0.5559 - accuracy: 0.7254\n",
      "Epoch 39/300\n",
      "2227/2227 [==============================] - 2s 1ms/step - loss: 0.5562 - accuracy: 0.7259\n",
      "Epoch 40/300\n",
      "2227/2227 [==============================] - 2s 1ms/step - loss: 0.5567 - accuracy: 0.7246\n",
      "Epoch 41/300\n",
      "2227/2227 [==============================] - 2s 1ms/step - loss: 0.5553 - accuracy: 0.7252\n",
      "Epoch 42/300\n",
      "2227/2227 [==============================] - 2s 1ms/step - loss: 0.5552 - accuracy: 0.7258\n",
      "Epoch 43/300\n",
      "2227/2227 [==============================] - 4s 2ms/step - loss: 0.5552 - accuracy: 0.7255\n",
      "Epoch 44/300\n",
      "2227/2227 [==============================] - 4s 2ms/step - loss: 0.5552 - accuracy: 0.7252\n",
      "Epoch 45/300\n",
      "2227/2227 [==============================] - 3s 2ms/step - loss: 0.5553 - accuracy: 0.7254\n",
      "Epoch 46/300\n",
      "2227/2227 [==============================] - 3s 1ms/step - loss: 0.5565 - accuracy: 0.7244\n",
      "Epoch 47/300\n",
      "2227/2227 [==============================] - 3s 1ms/step - loss: 0.5556 - accuracy: 0.7253\n",
      "Epoch 48/300\n",
      "2227/2227 [==============================] - 2s 1ms/step - loss: 0.5543 - accuracy: 0.7267\n",
      "Epoch 49/300\n",
      "2227/2227 [==============================] - 3s 1ms/step - loss: 0.5549 - accuracy: 0.7248\n",
      "Epoch 50/300\n",
      "2227/2227 [==============================] - 3s 1ms/step - loss: 0.5546 - accuracy: 0.7270\n",
      "Epoch 51/300\n",
      "2227/2227 [==============================] - 3s 1ms/step - loss: 0.5549 - accuracy: 0.7261\n",
      "Epoch 52/300\n",
      "2227/2227 [==============================] - 3s 1ms/step - loss: 0.5547 - accuracy: 0.7249\n",
      "Epoch 53/300\n",
      "2227/2227 [==============================] - 3s 1ms/step - loss: 0.5546 - accuracy: 0.7251\n",
      "Epoch 54/300\n",
      "2227/2227 [==============================] - 2s 1ms/step - loss: 0.5549 - accuracy: 0.7238\n",
      "Epoch 55/300\n",
      "2227/2227 [==============================] - 2s 1ms/step - loss: 0.5553 - accuracy: 0.7249\n",
      "Epoch 56/300\n",
      "2227/2227 [==============================] - 3s 1ms/step - loss: 0.5539 - accuracy: 0.7270\n",
      "Epoch 57/300\n",
      "2227/2227 [==============================] - 3s 1ms/step - loss: 0.5551 - accuracy: 0.7262\n",
      "Epoch 58/300\n",
      "2227/2227 [==============================] - 2s 980us/step - loss: 0.5536 - accuracy: 0.7259\n",
      "Epoch 59/300\n",
      "2227/2227 [==============================] - 2s 963us/step - loss: 0.5547 - accuracy: 0.7241\n",
      "Epoch 60/300\n",
      "2227/2227 [==============================] - 2s 950us/step - loss: 0.5533 - accuracy: 0.7274\n",
      "Epoch 61/300\n",
      "2227/2227 [==============================] - 2s 948us/step - loss: 0.5544 - accuracy: 0.7256\n",
      "Epoch 62/300\n",
      "2227/2227 [==============================] - 2s 976us/step - loss: 0.5534 - accuracy: 0.7265\n",
      "Epoch 63/300\n",
      "2227/2227 [==============================] - 2s 951us/step - loss: 0.5540 - accuracy: 0.7258\n",
      "Epoch 64/300\n",
      "2227/2227 [==============================] - 3s 1ms/step - loss: 0.5536 - accuracy: 0.7253\n",
      "Epoch 65/300\n",
      "2227/2227 [==============================] - 2s 992us/step - loss: 0.5537 - accuracy: 0.7259\n",
      "Epoch 66/300\n",
      "2227/2227 [==============================] - 2s 1ms/step - loss: 0.5539 - accuracy: 0.7273\n",
      "Epoch 67/300\n",
      "2227/2227 [==============================] - 2s 1ms/step - loss: 0.5536 - accuracy: 0.7264\n",
      "Epoch 68/300\n",
      "2227/2227 [==============================] - 2s 1ms/step - loss: 0.5536 - accuracy: 0.7254\n",
      "Epoch 69/300\n",
      "2227/2227 [==============================] - 3s 1ms/step - loss: 0.5528 - accuracy: 0.7278\n",
      "Epoch 70/300\n",
      "2227/2227 [==============================] - 3s 1ms/step - loss: 0.5530 - accuracy: 0.7261\n",
      "Epoch 71/300\n",
      "2227/2227 [==============================] - 3s 1ms/step - loss: 0.5533 - accuracy: 0.7266\n",
      "Epoch 72/300\n",
      "2227/2227 [==============================] - 3s 1ms/step - loss: 0.5541 - accuracy: 0.7270\n",
      "Epoch 73/300\n",
      "2227/2227 [==============================] - 3s 1ms/step - loss: 0.5533 - accuracy: 0.7274\n",
      "Epoch 74/300\n",
      "2227/2227 [==============================] - 2s 1ms/step - loss: 0.5531 - accuracy: 0.7250\n",
      "Epoch 75/300\n",
      "2227/2227 [==============================] - 2s 979us/step - loss: 0.5525 - accuracy: 0.7267\n",
      "Epoch 76/300\n",
      "2227/2227 [==============================] - 3s 1ms/step - loss: 0.5529 - accuracy: 0.7249\n",
      "Epoch 77/300\n",
      "2227/2227 [==============================] - 3s 1ms/step - loss: 0.5525 - accuracy: 0.7262\n",
      "Epoch 78/300\n",
      "2227/2227 [==============================] - 3s 1ms/step - loss: 0.5524 - accuracy: 0.7282\n",
      "Epoch 79/300\n",
      "2227/2227 [==============================] - 2s 977us/step - loss: 0.5522 - accuracy: 0.7276\n",
      "Epoch 80/300\n",
      "2227/2227 [==============================] - 2s 1ms/step - loss: 0.5530 - accuracy: 0.7261\n",
      "Epoch 81/300\n",
      "2227/2227 [==============================] - 2s 1ms/step - loss: 0.5533 - accuracy: 0.7258\n",
      "Epoch 82/300\n",
      "2227/2227 [==============================] - 2s 972us/step - loss: 0.5532 - accuracy: 0.7258\n",
      "Epoch 83/300\n",
      "2227/2227 [==============================] - 2s 1ms/step - loss: 0.5527 - accuracy: 0.7265\n",
      "Epoch 84/300\n",
      "2227/2227 [==============================] - 3s 1ms/step - loss: 0.5526 - accuracy: 0.7267\n",
      "Epoch 85/300\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "2227/2227 [==============================] - 2s 1ms/step - loss: 0.5498 - accuracy: 0.7292\n",
      "Epoch 252/300\n",
      "2227/2227 [==============================] - 3s 1ms/step - loss: 0.5496 - accuracy: 0.7281\n",
      "Epoch 253/300\n",
      "2227/2227 [==============================] - 3s 1ms/step - loss: 0.5498 - accuracy: 0.7281\n",
      "Epoch 254/300\n",
      "2227/2227 [==============================] - 2s 1ms/step - loss: 0.5497 - accuracy: 0.7291\n",
      "Epoch 255/300\n",
      "2227/2227 [==============================] - 2s 1ms/step - loss: 0.5494 - accuracy: 0.7295\n",
      "Epoch 256/300\n",
      "2227/2227 [==============================] - 3s 1ms/step - loss: 0.5499 - accuracy: 0.7294\n",
      "Epoch 257/300\n",
      "2227/2227 [==============================] - 3s 1ms/step - loss: 0.5493 - accuracy: 0.7304\n",
      "Epoch 258/300\n",
      "2227/2227 [==============================] - 3s 1ms/step - loss: 0.5491 - accuracy: 0.7286\n",
      "Epoch 259/300\n",
      "2227/2227 [==============================] - 2s 973us/step - loss: 0.5490 - accuracy: 0.7286\n",
      "Epoch 260/300\n",
      "2227/2227 [==============================] - 2s 1ms/step - loss: 0.5499 - accuracy: 0.7278\n",
      "Epoch 261/300\n",
      "2227/2227 [==============================] - 2s 1ms/step - loss: 0.5492 - accuracy: 0.7291\n",
      "Epoch 262/300\n",
      "2227/2227 [==============================] - 2s 1ms/step - loss: 0.5490 - accuracy: 0.7297\n",
      "Epoch 263/300\n",
      "2227/2227 [==============================] - 3s 1ms/step - loss: 0.5494 - accuracy: 0.7298\n",
      "Epoch 264/300\n",
      "2227/2227 [==============================] - 3s 1ms/step - loss: 0.5496 - accuracy: 0.7297\n",
      "Epoch 265/300\n",
      "2227/2227 [==============================] - 2s 995us/step - loss: 0.5499 - accuracy: 0.7292\n",
      "Epoch 266/300\n",
      "2227/2227 [==============================] - 2s 952us/step - loss: 0.5498 - accuracy: 0.7286\n",
      "Epoch 267/300\n",
      "2227/2227 [==============================] - 2s 1ms/step - loss: 0.5496 - accuracy: 0.7274\n",
      "Epoch 268/300\n",
      "2227/2227 [==============================] - 2s 1ms/step - loss: 0.5492 - accuracy: 0.7299\n",
      "Epoch 269/300\n",
      "2227/2227 [==============================] - 3s 1ms/step - loss: 0.5488 - accuracy: 0.7287\n",
      "Epoch 270/300\n",
      "2227/2227 [==============================] - 3s 1ms/step - loss: 0.5494 - accuracy: 0.7289\n",
      "Epoch 271/300\n",
      "2227/2227 [==============================] - 2s 1ms/step - loss: 0.5499 - accuracy: 0.7286\n",
      "Epoch 272/300\n",
      "2227/2227 [==============================] - 2s 1ms/step - loss: 0.5498 - accuracy: 0.7293\n",
      "Epoch 273/300\n",
      "2227/2227 [==============================] - 3s 1ms/step - loss: 0.5497 - accuracy: 0.7275\n",
      "Epoch 274/300\n",
      "2227/2227 [==============================] - 3s 1ms/step - loss: 0.5491 - accuracy: 0.7287\n",
      "Epoch 275/300\n",
      "2227/2227 [==============================] - 3s 1ms/step - loss: 0.5495 - accuracy: 0.7289\n",
      "Epoch 276/300\n",
      "2227/2227 [==============================] - 2s 1ms/step - loss: 0.5504 - accuracy: 0.7277\n",
      "Epoch 277/300\n",
      "2227/2227 [==============================] - 2s 1ms/step - loss: 0.5492 - accuracy: 0.7299\n",
      "Epoch 278/300\n",
      "2227/2227 [==============================] - 3s 1ms/step - loss: 0.5497 - accuracy: 0.7295\n",
      "Epoch 279/300\n",
      "2227/2227 [==============================] - 3s 1ms/step - loss: 0.5496 - accuracy: 0.7294\n",
      "Epoch 280/300\n",
      "2227/2227 [==============================] - 4s 2ms/step - loss: 0.5493 - accuracy: 0.7272\n",
      "Epoch 281/300\n",
      "2227/2227 [==============================] - 3s 1ms/step - loss: 0.5496 - accuracy: 0.7302\n",
      "Epoch 282/300\n",
      "2227/2227 [==============================] - 3s 1ms/step - loss: 0.5487 - accuracy: 0.7292\n",
      "Epoch 283/300\n",
      "2227/2227 [==============================] - 3s 1ms/step - loss: 0.5495 - accuracy: 0.7275\n",
      "Epoch 284/300\n",
      "2227/2227 [==============================] - 3s 1ms/step - loss: 0.5492 - accuracy: 0.7295\n",
      "Epoch 285/300\n",
      "2227/2227 [==============================] - 3s 1ms/step - loss: 0.5496 - accuracy: 0.7286\n",
      "Epoch 286/300\n",
      "2227/2227 [==============================] - 3s 1ms/step - loss: 0.5492 - accuracy: 0.7298\n",
      "Epoch 287/300\n",
      "2227/2227 [==============================] - 2s 1ms/step - loss: 0.5500 - accuracy: 0.7281\n",
      "Epoch 288/300\n",
      "2227/2227 [==============================] - 3s 1ms/step - loss: 0.5492 - accuracy: 0.7294\n",
      "Epoch 289/300\n",
      "2227/2227 [==============================] - 2s 1ms/step - loss: 0.5496 - accuracy: 0.7288\n",
      "Epoch 290/300\n",
      "2227/2227 [==============================] - 3s 1ms/step - loss: 0.5492 - accuracy: 0.7306\n",
      "Epoch 291/300\n",
      "2227/2227 [==============================] - 3s 1ms/step - loss: 0.5490 - accuracy: 0.7294\n",
      "Epoch 292/300\n",
      "2227/2227 [==============================] - 3s 2ms/step - loss: 0.5490 - accuracy: 0.7292\n",
      "Epoch 293/300\n",
      "2227/2227 [==============================] - 4s 2ms/step - loss: 0.5489 - accuracy: 0.7289\n",
      "Epoch 294/300\n",
      "2227/2227 [==============================] - 4s 2ms/step - loss: 0.5489 - accuracy: 0.7296\n",
      "Epoch 295/300\n",
      "2227/2227 [==============================] - 3s 1ms/step - loss: 0.5492 - accuracy: 0.7305\n",
      "Epoch 296/300\n",
      "2227/2227 [==============================] - 4s 2ms/step - loss: 0.5492 - accuracy: 0.7278\n",
      "Epoch 297/300\n",
      "2227/2227 [==============================] - 3s 1ms/step - loss: 0.5494 - accuracy: 0.7295\n",
      "Epoch 298/300\n",
      "2227/2227 [==============================] - 2s 1ms/step - loss: 0.5485 - accuracy: 0.7282\n",
      "Epoch 299/300\n",
      "2227/2227 [==============================] - 3s 1ms/step - loss: 0.5492 - accuracy: 0.7302\n",
      "Epoch 300/300\n",
      "2227/2227 [==============================] - 3s 1ms/step - loss: 0.5489 - accuracy: 0.7295\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train,Y_train,epochs=300,batch_size=22)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[70.044136]\n",
      " [24.909237]\n",
      " [29.962713]\n",
      " ...\n",
      " [37.5975  ]\n",
      " [59.53579 ]\n",
      " [37.312145]] (20998, 1)\n",
      "The accuracy score for Neural Network model is 72.72 %\n"
     ]
    }
   ],
   "source": [
    "nn_pred = model.predict(X_test)\n",
    "\n",
    "# rounding the prediction nn_pred to zeros and ones, and rehaping into 1d array\n",
    "rounded = [round(x[0]) for x in nn_pred]\n",
    "print(nn_pred*100,nn_pred.shape)\n",
    "\n",
    "nn_pred = rounded\n",
    "\n",
    "# finding accuracy of the model\n",
    "from sklearn.metrics import accuracy_score\n",
    "acc_nn = round(accuracy_score(nn_pred,Y_test)*100,2)\n",
    "Accuracy[\"Neural Network\"]=acc_nn\n",
    "\n",
    "print(\"The accuracy score for Neural Network model is \" + str(acc_nn)+\" %\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing the accuracy of all the above algorithms and using the best Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Logistic regression': 71.18, 'Naive Bayes': 59.0, 'SVM classifier': 71.96, 'K Nearest Neighbor': 71.35, 'Decision Tree': 63.35, 'Random Forest': 70.63, 'Neural Network': 72.72}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(Accuracy)\n",
    "plt.figure(figsize=(20, 9))\n",
    "fig=plt.bar(range(len(Accuracy)), list(Accuracy.values()), align='center', width=0.45)\n",
    "plt.xticks(range(len(Accuracy)), list(Accuracy.keys()),fontsize=12)\n",
    "plt.xlabel(\"Binary Classification models\", fontsize=20)\n",
    "plt.ylabel(\"Accuracy Score\", fontsize=20)\n",
    "plt.title(\"Comapring the Accuray of the models\", fontsize=25)\n",
    "\n",
    "for p in fig:\n",
    "    width = p.get_width()\n",
    "    height = p.get_height()\n",
    "    x, y = p.get_xy() \n",
    "    plt.annotate(f'{height/100.0:.0%}', (x + width/2, y + height*1.02), ha='center')\n",
    "\n",
    "# saving the plot for using in th report\n",
    "plt.savefig('compAlgo.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### As we can see maximum Accuracy is for Neural Networks i.e. 72.72% also for using ML model in mobile we can convert keras neural network to lightweight mobile model which has low latency using Tensorflow Lite developer tool"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Converting Neural Network model to tflite model\n",
    "### TensorFlow Lite is TensorFlow's lightweight solution for mobile and embedded devices. It lets you run machine-learned models on mobile devices with low latency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow import lite"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\training\\tracking\\tracking.py:111: Model.state_updates (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n",
      "WARNING:tensorflow:From C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\training\\tracking\\tracking.py:111: Layer.updates (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n",
      "INFO:tensorflow:Assets written to: C:\\Users\\mihir\\AppData\\Local\\Temp\\tmpuhhg9m8w\\assets\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2860"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keras_file=\"model.h5\"\n",
    "tf.keras.models.save_model(model,keras_file)\n",
    "converter = lite.TFLiteConverter.from_keras_model(model)\n",
    "tfmodel = converter.convert()\n",
    "open(\"model.tflite\",\"wb\").write(tfmodel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## model.tflite is used in Android studio to create an android application"
   ]
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}