Article-3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "847d0210",
   "metadata": {},
   "source": [
    "<div style='font-size:100%;'>\n",
    "    <a id='import'></a>\n",
    "    <h1 style='color: black; font-weight: bold; font-family: Cascadia code;'>\n",
    "        <center> Importing necessary libraries 📚 </center>\n",
    "    </h1>\n",
    " \n",
    "</div> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "27deb794",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "from numpy import isnan\n",
    "from pandas import read_csv\n",
    "from datetime import date\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd86e4d5",
   "metadata": {},
   "source": [
    "<div style='font-size:100%;'>\n",
    "    <a id='clean'></a>\n",
    "    <h1 style='color: black; font-weight: bold; font-family: Cascadia code;'>\n",
    "        <center> Importing the dataset ⬇️ </center>\n",
    "    </h1>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "31f59fca",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('csv_result-chronic_kidney_disease.csv',na_values='?')\n",
    "df.drop('id', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79cb81c7",
   "metadata": {},
   "source": [
    "<div style='font-size:100%;'>\n",
    "    <a id='discover'></a>\n",
    "    <h1 style='color: black; font-weight: bold; font-family: Cascadia code;'>\n",
    "        <center> Discovering the dataset 🔎 </center>\n",
    "    </h1>\n",
    "</div> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "03157069",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>'age'</th>\n",
       "      <th>'bp'</th>\n",
       "      <th>'sg'</th>\n",
       "      <th>'al'</th>\n",
       "      <th>'su'</th>\n",
       "      <th>'rbc'</th>\n",
       "      <th>'pc'</th>\n",
       "      <th>'pcc'</th>\n",
       "      <th>'ba'</th>\n",
       "      <th>'bgr'</th>\n",
       "      <th>...</th>\n",
       "      <th>'pcv'</th>\n",
       "      <th>'wbcc'</th>\n",
       "      <th>'rbcc'</th>\n",
       "      <th>'htn'</th>\n",
       "      <th>'dm'</th>\n",
       "      <th>'cad'</th>\n",
       "      <th>'appet'</th>\n",
       "      <th>'pe'</th>\n",
       "      <th>'ane'</th>\n",
       "      <th>'class'</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>48.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.020</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>normal</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>121.0</td>\n",
       "      <td>...</td>\n",
       "      <td>44.0</td>\n",
       "      <td>7800.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>good</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>ckd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1.020</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>normal</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>38.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>good</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>ckd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.010</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>normal</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>423.0</td>\n",
       "      <td>...</td>\n",
       "      <td>31.0</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>poor</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>ckd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.005</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>abnormal</td>\n",
       "      <td>present</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>117.0</td>\n",
       "      <td>...</td>\n",
       "      <td>32.0</td>\n",
       "      <td>6700.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>poor</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>ckd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.010</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>normal</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>106.0</td>\n",
       "      <td>...</td>\n",
       "      <td>35.0</td>\n",
       "      <td>7300.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>good</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>ckd</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   'age'  'bp'   'sg'  'al'  'su'   'rbc'      'pc'       'pcc'        'ba'  \\\n",
       "0   48.0  80.0  1.020   1.0   0.0     NaN    normal  notpresent  notpresent   \n",
       "1    7.0  50.0  1.020   4.0   0.0     NaN    normal  notpresent  notpresent   \n",
       "2   62.0  80.0  1.010   2.0   3.0  normal    normal  notpresent  notpresent   \n",
       "3   48.0  70.0  1.005   4.0   0.0  normal  abnormal     present  notpresent   \n",
       "4   51.0  80.0  1.010   2.0   0.0  normal    normal  notpresent  notpresent   \n",
       "\n",
       "   'bgr'  ...  'pcv'  'wbcc'  'rbcc'  'htn'  'dm'  'cad'  'appet'  'pe' 'ane'  \\\n",
       "0  121.0  ...   44.0  7800.0     5.2    yes   yes     no     good    no    no   \n",
       "1    NaN  ...   38.0  6000.0     NaN     no    no     no     good    no    no   \n",
       "2  423.0  ...   31.0  7500.0     NaN     no   yes     no     poor    no   yes   \n",
       "3  117.0  ...   32.0  6700.0     3.9    yes    no     no     poor   yes   yes   \n",
       "4  106.0  ...   35.0  7300.0     4.6     no    no     no     good    no    no   \n",
       "\n",
       "  'class'  \n",
       "0     ckd  \n",
       "1     ckd  \n",
       "2     ckd  \n",
       "3     ckd  \n",
       "4     ckd  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()\n",
    "#print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "75180bfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([''age'', ''bp'', ''sg'', ''al'', ''su'', ''rbc'', ''pc'', ''pcc'',\n",
       "       ''ba'', ''bgr'', ''bu'', ''sc'', ''sod'', ''pot'', ''hemo'', ''pcv'',\n",
       "       ''wbcc'', ''rbcc'', ''htn'', ''dm'', ''cad'', ''appet'', ''pe'',\n",
       "       ''ane'', ''class''],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b8ee55ba",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 400 entries, 0 to 399\n",
      "Data columns (total 25 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   'age'    391 non-null    float64\n",
      " 1   'bp'     388 non-null    float64\n",
      " 2   'sg'     353 non-null    float64\n",
      " 3   'al'     354 non-null    float64\n",
      " 4   'su'     351 non-null    float64\n",
      " 5   'rbc'    248 non-null    object \n",
      " 6   'pc'     335 non-null    object \n",
      " 7   'pcc'    396 non-null    object \n",
      " 8   'ba'     396 non-null    object \n",
      " 9   'bgr'    356 non-null    float64\n",
      " 10  'bu'     381 non-null    float64\n",
      " 11  'sc'     383 non-null    float64\n",
      " 12  'sod'    313 non-null    float64\n",
      " 13  'pot'    312 non-null    float64\n",
      " 14  'hemo'   348 non-null    float64\n",
      " 15  'pcv'    329 non-null    float64\n",
      " 16  'wbcc'   294 non-null    float64\n",
      " 17  'rbcc'   269 non-null    float64\n",
      " 18  'htn'    398 non-null    object \n",
      " 19  'dm'     398 non-null    object \n",
      " 20  'cad'    398 non-null    object \n",
      " 21  'appet'  399 non-null    object \n",
      " 22  'pe'     399 non-null    object \n",
      " 23  'ane'    399 non-null    object \n",
      " 24  'class'  400 non-null    object \n",
      "dtypes: float64(14), object(11)\n",
      "memory usage: 78.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2e19ff6d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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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>'bp'</th>\n",
       "      <th>'sg'</th>\n",
       "      <th>'al'</th>\n",
       "      <th>'su'</th>\n",
       "      <th>'bgr'</th>\n",
       "      <th>'bu'</th>\n",
       "      <th>'sc'</th>\n",
       "      <th>'sod'</th>\n",
       "      <th>'pot'</th>\n",
       "      <th>'hemo'</th>\n",
       "      <th>'pcv'</th>\n",
       "      <th>'wbcc'</th>\n",
       "      <th>'rbcc'</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>391.000000</td>\n",
       "      <td>388.000000</td>\n",
       "      <td>353.000000</td>\n",
       "      <td>354.000000</td>\n",
       "      <td>351.000000</td>\n",
       "      <td>356.000000</td>\n",
       "      <td>381.000000</td>\n",
       "      <td>383.000000</td>\n",
       "      <td>313.000000</td>\n",
       "      <td>312.000000</td>\n",
       "      <td>348.000000</td>\n",
       "      <td>329.000000</td>\n",
       "      <td>294.000000</td>\n",
       "      <td>269.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>51.483376</td>\n",
       "      <td>76.469072</td>\n",
       "      <td>1.017408</td>\n",
       "      <td>1.016949</td>\n",
       "      <td>0.450142</td>\n",
       "      <td>148.036517</td>\n",
       "      <td>57.425722</td>\n",
       "      <td>3.072454</td>\n",
       "      <td>137.528754</td>\n",
       "      <td>4.627244</td>\n",
       "      <td>12.526437</td>\n",
       "      <td>38.884498</td>\n",
       "      <td>8406.122449</td>\n",
       "      <td>4.707435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>17.169714</td>\n",
       "      <td>13.683637</td>\n",
       "      <td>0.005717</td>\n",
       "      <td>1.352679</td>\n",
       "      <td>1.099191</td>\n",
       "      <td>79.281714</td>\n",
       "      <td>50.503006</td>\n",
       "      <td>5.741126</td>\n",
       "      <td>10.408752</td>\n",
       "      <td>3.193904</td>\n",
       "      <td>2.912587</td>\n",
       "      <td>8.990105</td>\n",
       "      <td>2944.474190</td>\n",
       "      <td>1.025323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>1.005000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>3.100000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2200.000000</td>\n",
       "      <td>2.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>42.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>1.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>10.300000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>6500.000000</td>\n",
       "      <td>3.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.020000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>138.000000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>12.650000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>8000.000000</td>\n",
       "      <td>4.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>64.500000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.020000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>163.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>142.000000</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>9800.000000</td>\n",
       "      <td>5.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>1.025000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>490.000000</td>\n",
       "      <td>391.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>163.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>17.800000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>26400.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            'age'        'bp'        'sg'        'al'        'su'       'bgr'  \\\n",
       "count  391.000000  388.000000  353.000000  354.000000  351.000000  356.000000   \n",
       "mean    51.483376   76.469072    1.017408    1.016949    0.450142  148.036517   \n",
       "std     17.169714   13.683637    0.005717    1.352679    1.099191   79.281714   \n",
       "min      2.000000   50.000000    1.005000    0.000000    0.000000   22.000000   \n",
       "25%     42.000000   70.000000    1.010000    0.000000    0.000000   99.000000   \n",
       "50%     55.000000   80.000000    1.020000    0.000000    0.000000  121.000000   \n",
       "75%     64.500000   80.000000    1.020000    2.000000    0.000000  163.000000   \n",
       "max     90.000000  180.000000    1.025000    5.000000    5.000000  490.000000   \n",
       "\n",
       "             'bu'        'sc'       'sod'       'pot'      'hemo'       'pcv'  \\\n",
       "count  381.000000  383.000000  313.000000  312.000000  348.000000  329.000000   \n",
       "mean    57.425722    3.072454  137.528754    4.627244   12.526437   38.884498   \n",
       "std     50.503006    5.741126   10.408752    3.193904    2.912587    8.990105   \n",
       "min      1.500000    0.400000    4.500000    2.500000    3.100000    9.000000   \n",
       "25%     27.000000    0.900000  135.000000    3.800000   10.300000   32.000000   \n",
       "50%     42.000000    1.300000  138.000000    4.400000   12.650000   40.000000   \n",
       "75%     66.000000    2.800000  142.000000    4.900000   15.000000   45.000000   \n",
       "max    391.000000   76.000000  163.000000   47.000000   17.800000   54.000000   \n",
       "\n",
       "             'wbcc'      'rbcc'  \n",
       "count    294.000000  269.000000  \n",
       "mean    8406.122449    4.707435  \n",
       "std     2944.474190    1.025323  \n",
       "min     2200.000000    2.100000  \n",
       "25%     6500.000000    3.900000  \n",
       "50%     8000.000000    4.800000  \n",
       "75%     9800.000000    5.400000  \n",
       "max    26400.000000    8.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c516076b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'age'       2.25\n",
       "'bp'        3.00\n",
       "'sg'       11.75\n",
       "'al'       11.50\n",
       "'su'       12.25\n",
       "'rbc'      38.00\n",
       "'pc'       16.25\n",
       "'pcc'       1.00\n",
       "'ba'        1.00\n",
       "'bgr'      11.00\n",
       "'bu'        4.75\n",
       "'sc'        4.25\n",
       "'sod'      21.75\n",
       "'pot'      22.00\n",
       "'hemo'     13.00\n",
       "'pcv'      17.75\n",
       "'wbcc'     26.50\n",
       "'rbcc'     32.75\n",
       "'htn'       0.50\n",
       "'dm'        0.50\n",
       "'cad'       0.50\n",
       "'appet'     0.25\n",
       "'pe'        0.25\n",
       "'ane'       0.25\n",
       "'class'     0.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#percentage of Na values:\n",
    "(df.isnull().sum()/df.shape[0])*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d890af97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     158\n",
       "1      45\n",
       "3      37\n",
       "2      33\n",
       "5      33\n",
       "4      31\n",
       "7      20\n",
       "6      12\n",
       "9      12\n",
       "8       8\n",
       "11      7\n",
       "10      4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking count of row with missing values for columns\n",
    "df.isnull().sum(1).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "95a8acb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>'rbc'</th>\n",
       "      <th>'pc'</th>\n",
       "      <th>'pcc'</th>\n",
       "      <th>'ba'</th>\n",
       "      <th>'htn'</th>\n",
       "      <th>'dm'</th>\n",
       "      <th>'cad'</th>\n",
       "      <th>'appet'</th>\n",
       "      <th>'pe'</th>\n",
       "      <th>'ane'</th>\n",
       "      <th>'class'</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>248</td>\n",
       "      <td>335</td>\n",
       "      <td>396</td>\n",
       "      <td>396</td>\n",
       "      <td>398</td>\n",
       "      <td>398</td>\n",
       "      <td>398</td>\n",
       "      <td>399</td>\n",
       "      <td>399</td>\n",
       "      <td>399</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>normal</td>\n",
       "      <td>normal</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>good</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>ckd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>201</td>\n",
       "      <td>259</td>\n",
       "      <td>354</td>\n",
       "      <td>374</td>\n",
       "      <td>251</td>\n",
       "      <td>261</td>\n",
       "      <td>364</td>\n",
       "      <td>317</td>\n",
       "      <td>323</td>\n",
       "      <td>339</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         'rbc'    'pc'       'pcc'        'ba' 'htn' 'dm' 'cad' 'appet' 'pe'  \\\n",
       "count      248     335         396         396   398  398   398     399  399   \n",
       "unique       2       2           2           2     2    2     2       2    2   \n",
       "top     normal  normal  notpresent  notpresent    no   no    no    good   no   \n",
       "freq       201     259         354         374   251  261   364     317  323   \n",
       "\n",
       "       'ane' 'class'  \n",
       "count    399     400  \n",
       "unique     2       2  \n",
       "top       no     ckd  \n",
       "freq     339     250  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Object type describe\n",
    "df.describe(include = 'O')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7625a6c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'age' : 76 values\n",
      "'bp' : 10 values\n",
      "'sg' : 5 values\n",
      "'al' : 6 values\n",
      "'su' : 6 values\n",
      "'rbc' : 2 values\n",
      "'pc' : 2 values\n",
      "'pcc' : 2 values\n",
      "'ba' : 2 values\n",
      "'bgr' : 146 values\n",
      "'bu' : 118 values\n",
      "'sc' : 84 values\n",
      "'sod' : 34 values\n",
      "'pot' : 40 values\n",
      "'hemo' : 115 values\n",
      "'pcv' : 42 values\n",
      "'wbcc' : 89 values\n",
      "'rbcc' : 45 values\n",
      "'htn' : 2 values\n",
      "'dm' : 2 values\n",
      "'cad' : 2 values\n",
      "'appet' : 2 values\n",
      "'pe' : 2 values\n",
      "'ane' : 2 values\n",
      "'class' : 2 values\n"
     ]
    }
   ],
   "source": [
    "for i in df.columns:\n",
    "    print(f'{i} : {df[i].nunique()} values')"
   ]
  },
  {
   "cell_type": "raw",
   "id": "8c702a2a",
   "metadata": {},
   "source": [
    "=> Viewing distinct unique values for each feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a45cb314",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"'age'\",\n",
       " \"'bp'\",\n",
       " \"'bgr'\",\n",
       " \"'bu'\",\n",
       " \"'sc'\",\n",
       " \"'sod'\",\n",
       " \"'pot'\",\n",
       " \"'hemo'\",\n",
       " \"'pcv'\",\n",
       " \"'wbcc'\",\n",
       " \"'rbcc'\"]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Extraction of categorical and numerical features into distinct data frames\n",
    "numerical_features = []\n",
    "categorical_features = []\n",
    "for i in df.columns:\n",
    "    if df[i].nunique()>6:\n",
    "        numerical_features.append(i)\n",
    "    else:\n",
    "        categorical_features.append(i)\n",
    "numerical_features"
   ]
  },
  {
   "cell_type": "raw",
   "id": "5a133808",
   "metadata": {},
   "source": [
    "There are 14 categorical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7e405b3b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Count of nan in DataSet  1012\n",
      "Count of nan in column  'age'  is :  9\n",
      "Count of nan in column  'bp'  is :  12\n",
      "Count of nan in column  'sg'  is :  47\n",
      "Count of nan in column  'al'  is :  46\n",
      "Count of nan in column  'su'  is :  49\n",
      "Count of nan in column  'rbc'  is :  152\n",
      "Count of nan in column  'pc'  is :  65\n",
      "Count of nan in column  'pcc'  is :  4\n",
      "Count of nan in column  'ba'  is :  4\n",
      "Count of nan in column  'bgr'  is :  44\n",
      "Count of nan in column  'bu'  is :  19\n",
      "Count of nan in column  'sc'  is :  17\n",
      "Count of nan in column  'sod'  is :  87\n",
      "Count of nan in column  'pot'  is :  88\n",
      "Count of nan in column  'hemo'  is :  52\n",
      "Count of nan in column  'pcv'  is :  71\n",
      "Count of nan in column  'wbcc'  is :  106\n",
      "Count of nan in column  'rbcc'  is :  131\n",
      "Count of nan in column  'htn'  is :  2\n",
      "Count of nan in column  'dm'  is :  2\n",
      "Count of nan in column  'cad'  is :  2\n",
      "Count of nan in column  'appet'  is :  1\n",
      "Count of nan in column  'pe'  is :  1\n",
      "Count of nan in column  'ane'  is :  1\n",
      "Count of nan in column  'class'  is :  0\n"
     ]
    }
   ],
   "source": [
    "#looking for missing values\n",
    "print('Count of nan in DataSet ', df.isnull().sum().sum())\n",
    "names=df.columns\n",
    "for column_name in names:\n",
    "    column = df[column_name]\n",
    "    # Get the count of Nan in column\n",
    "    count = column.isnull().sum()\n",
    "    print('Count of nan in column ', column_name, ' is : ', count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "07ecdef3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'age'      41\n",
       "'bp'       42\n",
       "'sg'        0\n",
       "'al'        0\n",
       "'su'        0\n",
       "'rbc'       2\n",
       "'pc'        2\n",
       "'pcc'      44\n",
       "'ba'       44\n",
       "'bgr'      40\n",
       "'bu'       44\n",
       "'sc'       44\n",
       "'sod'      35\n",
       "'pot'      34\n",
       "'hemo'     33\n",
       "'pcv'      26\n",
       "'wbcc'     18\n",
       "'rbcc'     16\n",
       "'htn'      44\n",
       "'dm'       44\n",
       "'cad'      44\n",
       "'appet'    44\n",
       "'pe'       44\n",
       "'ane'      44\n",
       "'class'    44\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking rows that have both al and sg missing\n",
    "df[(df[\"'al'\"].isnull()) & (df[\"'sg'\"].isnull())].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "51a7dad0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percent of chronic kidney disease sample:  62.5 %\n",
      "Percent of not a chronic kidney disease sample:  37.5 %\n",
      "Our dataset is imbalanced\n"
     ]
    }
   ],
   "source": [
    "sns.countplot(x=\"'class'\",data=df)\n",
    "plt.xlabel(\"class\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.title(\"target Class\")\n",
    "plt.show()\n",
    "print('Percent of chronic kidney disease sample: ',round(len(df[df[\"'class'\"]=='ckd'])/len(df[\"'class'\"])*100,2),\"%\")\n",
    "print('Percent of not a chronic kidney disease sample: ',round(len(df[df[\"'class'\"]=='notckd'])/len(df[\"'class'\"])*100,2),\"%\")\n",
    "print(\"Our dataset is imbalanced\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "87563c17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Hemoglobin')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZvQa4KiKul7QauELSp4F7KJrQSc9fTbceeZLiPm9mk8WfAZelXwQ+BHyYVG8knQg8AhxdYXxmWTmxMmtSRNxLcduRweUPUdyzbXD5r4D/2YLQzGonIlYC3UOMOrjFoZi1hP+E2czMzCwTJ1ZmZmZmmTixMjMzM8vEiZWZmZlZJk6szMzMzDJxYmVmZmaWiRMrMzMzs0ycWJmZmZll4huEmpmZTRIzx/MH7osPzxhJ5/IVKzMzM7NMnFiZmZmZZeLEyszMzCwTJ1ZmZmZmmTixMjMzM8vEiZWZmZlZJk6szMzMzDJxYmVmZmaWiRMrMzMzs0ycWJmZmZll4sTKzMzMLBMnVmZmZmaZOLEyMzMzy8SJlZmZmVkmTqzMzMzMMtm66gDM2o2kGcClQBcQwJKI+KKks4A/Bn6WJj0jIm5M85wOnAi8BHwsIr7T8sDNrCPMXHTDqKddOLufE5qY3sbPiZVZ8/qBhRFxt6SdgLsk3ZzGfT4iPleeWNJewDxgb2B34LuS3hIRL7U0arMKSFoLPEtxUtEfEd2SdgGuBGYCa4GjI2JzVTGa5eSmQLMmRcSGiLg7DT8LPABMG2aWucAVEfF8RDwMrAH2n/hIzWrj3RExJyK60+tFwPKImAUsT6/NOoKvWJmNg6SZwL7A7cCBwMmSjgfupLiqtZki6bqtNNs6hkjEJC0AFgB0dXXR29vb8H0Xzu4fc8zDLbesr69v1NNOlDrEUJc46hBDRnOBnjS8FOgFTqsqGLOcnFiZjZGkHYFrgFMj4hlJ5wNnU/S7Ohs4F/jIaJcXEUuAJQDd3d3R09PTcNrx9JlYe2zj5Zb19vYyXAytUIcY6hJHHWIYowBukhTAP6b9vCsiNqTxj1P0V9xCMycbOdU9iW3mxKpr+/GdiJWNZ5uMN4axrkcVn+OIidUwHXWHbCOXJOCLwGHAL4ATBppNzDqFpG0okqrLIuIbABGxsTT+AuD69HI9MKM0+/RUZjYZvCsi1kv6TeBmST8uj4yISEnXFpo52cip7klsMydWC2f3c+6qPNdQRntSNpTxdqAf63qMJ+axGk0fq4GOunsBBwAnpc64jdrIDwVmpccC4PzsUZtVKJ08XAQ8EBHnlcqnlib7EHBfGl4GzJO0naQ9KOrGHa2K16xKEbE+PW8CrqXoX7hxoL6k503VRWiW14iJ1TAddedStI2Tno9Mw3OBS6NwGzBl0BeOWbs7EDgOOEjSyvQ4DPhbSask3Qu8G/hzgIi4H7gKWA18GzjJvwi0yUDSDumXs0jaAXgfxQnHMmB+mmw+cF01EZrl19R1tUEddRu1kU8DHi3NNtBRd0OprGHbed3btofTzrFDNfHn6oTdytgj4lZAQ4y6cZh5zgHOmbCgzOqpC7i2uMjL1sDXIuLbkn4EXCXpROAR4OgKYzTLatSJ1RAddV8eN1wbeSON2s7r3rY9nHaOHaqJP1cn7Hbf9madKCIeAvYZovwJ4ODWR2Q28UZ1H6uhOurSuI3cHXXNzMxsUhoxsWrUUZfGbeTLgONVOAB4utRkaGZmZtaxRtMUONBRd5WklansDGAxQ7eR30hxq4U1FLdb+HDOgM3MzMzqasTEapiOujBEG3lEBHDSOOMyMzMzazu+87qZmZmNaOY4b/I5WfhPmM3MzMwycWJlZmZmlokTKzMzM7NMnFiZmZmZZeLEyszMzCwTJ1ZmZmZmmTixMjMzM8vEiZWZmZlZJk6szMzMzDJxYmVmZmaWiRMrMzMzs0ycWJmZmZll4sTKzMzMLBMnVmZmZmaZOLEyMzMzy8SJlZmZmVkmTqzMzMzMMnFiZWZmZpaJEyszMzOzTJxYmTVJ0gxJKyStlnS/pFNS+S6Sbpb0k/S8cyqXpC9JWiPpXkn7VbsGZq0laStJ90i6Pr3eQ9LtqU5cKWnbqmM0y8WJlVnz+oGFEbEXcABwkqS9gEXA8oiYBSxPrwEOBWalxwLg/NaHbFapU4AHSq8/C3w+IvYENgMnVhKV2QRwYmXWpIjYEBF3p+FnKb4wpgFzgaVpsqXAkWl4LnBpFG4Dpkia2tqozaohaTpwOHBhei3gIODqNEm5rpi1va2rDsCsnUmaCewL3A50RcSGNOpxoCsNTwMeLc22LpVtKJUhaQHFFS26urro7e1t+L4LZ/ePOebhllvW19c36mknSh1iqEscdYhhjL4A/CWwU3r9BuCpiBjYiQfqwxaaqRM51X1bN1P/u7Yf3/GiLsa6HlV8jk6szMZI0o7ANcCpEfFMcSJeiIiQFM0sLyKWAEsAuru7o6enp+G0Jyy6YSwhA7D22MbLLevt7WW4GFqhDjHUJY46xNAsSUcAmyLiLkk9zc7fTJ3Iqe7bupn6v3B2P+euav+v+rGux2iPdzm1/9Y2q4CkbSiSqssi4hupeKOkqRGxITX1bUrl64EZpdmnpzKzTncg8EFJhwGvBX4D+CJFc/jW6aqV64N1FPexMmtS6iNyEfBARJxXGrUMmJ+G5wPXlcqPT78OPAB4utRkaNaxIuL0iJgeETOBecD3IuJYYAVwVJqsXFfM2p4TK7PmHQgcBxwkaWV6HAYsBt4r6SfAe9JrgBuBh4A1wAXAn1YQs1mdnAb8haQ1FH2uLqo4HrNs3BRo1qSIuBVQg9EHDzF9ACdNaFBmNRcRvUBvGn4I2L/KeMwmiq9YmZmZmWXixMrMzMwsEydWZmZmZpmMmFhJuljSJkn3lcrOkrR+UMfdgXGnp/9/elDS+ycqcDMzM7O6Gc0Vq0uAQ4Yo/3xEzEmPGwHS/6XNA/ZO83xF0la5gjUzMzOrsxETq4i4BXhylMubC1wREc9HxMMUPy/3Lz/MzMxsUhjP7RZOlnQ8cCewMCI2U/zf022laZr+D6i6/0fTcNo5dqgm/lz/edfu297MzDrDWBOr84GzgUjP5wIfaWYBjf4Dqu7/0TScdo4dqok/13/etfu2NzOzzjCmXwVGxMaIeCkifk1xJ+mB5j7/J5qZmZlNWmNKrNIfzA74EDDwi8FlwDxJ20naA5gF3DG+EM3MzMzaw4hNgZIuB3qAXSWtA84EeiTNoWgKXAt8FCAi7pd0FbAa6AdOioiXJiRyMzMzs5oZMbGKiGOGKG74h5kRcQ5wzniCMjMzM2tHvvO6mZmZWSZOrMzMzMwycWJlZmZmlokTKzMzM7NMnFiZmZmZZeLEyszMzCwTJ1ZmZmZmmTixMjMzM8vEiZWZmZlZJk6szMzMzDJxYmVmZmaWiRMrMzMzs0xG/BNmM3s1SRcDRwCbIuLtqews4I+Bn6XJzoiIG9O404ETgZeAj0XEd1oetFkFJL0WuAXYjuL75uqIOFPSHsAVwBuAu4DjIuKF6iK1TjVz0Q1jnnft4sPHNJ+vWJk17xLgkCHKPx8Rc9JjIKnaC5gH7J3m+YqkrVoWqVm1ngcOioh9gDnAIZIOAD5LUV/2BDZTnHiYdQQnVmZNiohbgCdHOflc4IqIeD4iHgbWAPtPWHBmNRKFvvRym/QI4CDg6lS+FDiy9dGZTQw3BZrlc7Kk44E7gYURsRmYBtxWmmZdKtuCpAXAAoCuri56e3sbvtHC2f1jDnK45Zb19fWNetqJUocY6hJHHWIYi3SF9i5gT+DLwE+BpyJiYCduWCfM2pETK7M8zgfOpjgbPxs4F/hIMwuIiCXAEoDu7u7o6elpOO0J4+k3cGzj5Zb19vYyXAytUIcY6hJHHWIYi4h4CZgjaQpwLfC20c7bzMlGTnVPYps5serafnwnYnVRxXqMdR9wYmWWQURsHBiWdAFwfXq5HphRmnR6KjObVCLiKUkrgHcCUyRtna5aNawTzZxs5FT3JLaZE6uFs/s5d1X7f9VXsR6jPQkdzH2szDKQNLX08kPAfWl4GTBP0nbpl1CzgDtaHZ9ZFSTtlq5UIWl74L3AA8AK4Kg02XzgukoCNJsA7Z/GmrWYpMuBHmBXSeuAM4EeSXMomgLXAh8FiIj7JV0FrAb6gZNS04jZZDAVWJr6Wb0GuCoirpe0GrhC0qeBe4CLqgzSLCcnVmZNiohjhihu+MUQEecA50xcRGb1FBH3AvsOUf4Q/nWsdSg3BZqZmZll4sTKzMzMLBMnVmZmZmaZOLEyMzMzy8SJlZmZmVkmTqzMzMzMMnFiZWZmZpaJEyszMzOzTJxYmZmZmWXixMrMzMwskxETK0kXS9ok6b5S2S6Sbpb0k/S8cyqXpC9JWiPpXkn7TWTwZmZmZnUymv8KvAT4e+DSUtkiYHlELJa0KL0+DTgUmJUevwucn57NzMwsmbnohqpDsAky4hWriLgFeHJQ8VxgaRpeChxZKr80CrcBUyRNzRSrmZmZWa2NtY9VV0RsSMOPA11peBrwaGm6danMzMzMrOONpilwWBERkqLZ+SQtABYAdHV10dvbC0BfX9/Lw+2mnWOHauJfOLt/zPOWY233bW9mZp1hrInVRklTI2JDaurblMrXAzNK001PZVuIiCXAEoDu7u7o6ekBii/LgeF2086xQzXxnzCOfgZrj+15ebjdt72ZmXWGsTYFLgPmp+H5wHWl8uPTrwMPAJ4uNRmamZmZdbQRr1hJuhzoAXaVtA44E1gMXCXpROAR4Og0+Y3AYcAa4BfAhycgZjMzM7NaGjGxiohjGow6eIhpAzhpvEGZmZmZtSPfed3MzMwsEydWZmZmZpk4sTIzMzPLxImVmZmZWSZOrMzMzMwycWJlZmZmlokTK7MmSbpY0iZJ95XKdpF0s6SfpOedU7kkfUnSGkn3StqvusjNWkvSDEkrJK2WdL+kU1L5kPXFrBM4sTJr3iXAIYPKFgHLI2IWsDy9BjgUmJUeC4DzWxSjWR30AwsjYi/gAOAkSXvRuL6YtT0nVmZNiohbgCcHFc8FlqbhpcCRpfJLo3AbMCX9v6ZZx4uIDRFxdxp+FngAmEbj+mLW9sb6J8xm9mpdpf/FfBzoSsPTgEdL061LZVv8h6akBRRXtejq6qK3t7fhmy2c3T/mQIdbbllfX9+op50odYihLnHUIYbxkDQT2Be4ncb1ZfA8o64TObViW4+nDjeja/vWvddEqmI9xroPOLEyyywiQlKMYb4lwBKA7u7u6OnpaTjtCYtuGHN8a49tvNyy3t5ehouhFeoQQ13iqEMMYyVpR+Aa4NSIeEbSy+OGqy/N1ImcWrGtx1OHm7Fwdj/nrmr/r/oq1mO0x8rB3BRolsfGgSa+9Lwpla8HZpSmm57KzCYFSdtQJFWXRcQ3UnGj+mLW9pxYmeWxDJifhucD15XKj0+/DjwAeLrUBGLW0VRcmroIeCAiziuNalRfzNpe+18fNGsxSZcDPcCuktYBZwKLgasknQg8AhydJr8ROAxYA/wC+HDLAzarzoHAccAqSStT2Rk0ri9mbc+JlVmTIuKYBqMOHmLaAE6a2IjM6ikibgXUYPQW9cWsE7gp0MzMzCwTJ1ZmZmZmmTixMjMzM8vEiZWZmZlZJk6szMzMzDJxYmVmZmaWiRMrMzMzs0ycWJmZmZll4sTKzMzMLBMnVmZmZmaZOLEyMzMzy8SJlZmZmVkmTqzMzMzMMnFiZWZmZpaJEyszMzOzTJxYmZmZmWXixMrMzMwsk63HM7OktcCzwEtAf0R0S9oFuBKYCawFjo6IzeML08zMzKz+clyxendEzImI7vR6EbA8ImYBy9NrMzMzs443EU2Bc4GlaXgpcOQEvIeZmZlZ7YyrKRAI4CZJAfxjRCwBuiJiQxr/ONA11IySFgALALq6uujt7QWgr6/v5eF2086xQzXxL5zdP+Z5y7G2+7Y3M7POMN7E6l0RsV7SbwI3S/pxeWREREq6tpCSsCUA3d3d0dPTAxRflgPD7aadY4dq4j9h0Q1jnnftsT0vD7f7tjczs84wrqbAiFifnjcB1wL7AxslTQVIz5vGG6SZmZlZOxhzYiVpB0k7DQwD7wPuA5YB89Nk84HrxhukmZmZWTsYT1NgF3CtpIHlfC0ivi3pR8BVkk4EHgGOHn+YZu3BtyAxezVJFwNHAJsi4u2pzHXCOtaYr1hFxEMRsU967B0R56TyJyLi4IiYFRHviYgn84Vr1hZ8CxKzV1wCHDKozHXCOpbvvG428XwLEpu0IuIWYPAJtuuEdazx/irQzF4t+y1IhpLrNhXDqcMtLOoQQ13iqEMMGWWvEzm1YluPpw43o2v71r3XRKpiPca6DzixMssr+y1IhpLrNhXDqcMtLOoQQ13iqEMMEyFXncipFdt6PHW4GQtn93Puqvb/qq9iPUZ7rBzMTYFmGfkWJGaj4jphHcuJlVkmvgWJ2ai5TljHav/rg2b14VuQmA0i6XKgB9hV0jrgTGAxrhPWoZxYmWUSEQ8B+wxR/gRwcOsjMqteRBzTYJTrhHUkNwWamZmZZeIrVmZmZmMws0W/7LP24sTKxs0HFzMzs4KbAs3MzMwycWJlZmZmlokTKzMzM7NMnFiZmZmZZeLEyszMzCwT/yrQzMwmraF+1bxwdn/L/iTZOo+vWJmZmZll4sTKzMzMLBMnVmZmZmaZOLEyMzMzy8Sd161tlTudNtvZdO3iwyciJDMzm+R8xcrMzMwsEydWZmZmZpk4sTIzMzPLxImVmZmZWSZOrMzMzMwycWJlZmZmlokTKzMzM7NMnFiZmZmZZeIbhBpQ3GzT/+huZmY2Pr5iZWZmZpaJr1iZmVnbm+mr7VYTE3bFStIhkh6UtEbSool6H7N24Tph9grXB+tUE5JYSdoK+DJwKLAXcIykvSbivczageuE2StcH6yTTVRT4P7Amoh4CEDSFcBcYPVYFjaeS7xrFx8+5nnNMspaJ8ZjtPVp8I8ZXJdao9HnM9E/Lmnx51ub+mCWmyIi/0Klo4BDIuKP0uvjgN+NiJNL0ywAFqSXbwUeTMO7Aj/PHlRrtHPs0N7x54z9TRGxW6ZlAeOuE1Wpw/5QhxigHnFUGUPWOjGa+pDKq6oTdfi8c+mUdanbejSsE5V1Xo+IJcCSweWS7oyI7gpCGrd2jh3aO/52jn1AozpRlTps0zrEUJc46hBDq1VVJzppW3fKurTTekxU5/X1wIzS6+mpzGyycp0we4Xrg3WsiUqsfgTMkrSHpG2BecCyCXovs3bgOmH2CtcH61gT0hQYEf2STga+A2wFXBwR949y9to0hYxBO8cO7R1/rWMfZ52oSh22aR1igHrEUYcYsmiD+tAx25rOWZe2WY8J6bxuZmZmNhn5L23MzMzMMnFiZWZmZpZJrRKrdvuLA0lrJa2StFLSnalsF0k3S/pJet656jgBJF0saZOk+0plQ8aqwpfS53CvpP2qi7xh7GdJWp+2/UpJh5XGnZ5if1DS+6uJur00s39MYAwzJK2QtFrS/ZJOaXUckl4r6Q5J/5Zi+GQq30PS7Wm/ujJ1uJ5wkraSdI+k66uMY7IY6pjeLupQh3Np9phfN7VJrNS+f3Hw7oiYU7q/xiJgeUTMApan13VwCXDIoLJGsR4KzEqPBcD5LYqxkUvYMnaAz6dtPycibgRI+8w8YO80z1fSvmXDu4TR7x8TpR9YGBF7AQcAJ6XPs5VxPA8cFBH7AHOAQyQdAHyWYn/bE9gMnDiBMZSdAjxQel1VHJPJ4GN6u7iE6utwLpcwymN+HdUmsaL0FwcR8QIw8BcH7WYusDQNLwWOrC6UV0TELcCTg4obxToXuDQKtwFTJE1tSaBDaBB7I3OBKyLi+Yh4GFhDsW/ZMJrcPyYqhg0RcXcafpYioZjWyjjSPt+XXm6THgEcBFzdihgGSJoOHA5cmF6rijisPdShDufS5DG/duqUWE0DHi29XpfK6iyAmyTdpeKvFwC6ImJDGn4c6KomtFFpFGu7fBYnp6bKi0uXuNsl9nZQ2b4saSawL3B7q+NIzW8rgU3AzcBPgacioj9N0qp96gvAXwK/Tq/fUFEck8lQx/R21k7fR6Mx1DG/duqUWLWjd0XEfhRNZydJ+r3yyCjuZdEW97Nop1iT84H/QtFcswE4t9JoOlwr9w9JOwLXAKdGxDOtjiMiXoqIORR3A98feNtEvt9QJB0BbIqIu1r93pPcsMf0dtaGx/jB2uaYX6fEqu3+4iAi1qfnTcC1FAfhjQPNZul5U3URjqhRrLX/LCJiY/oC/DVwAa8099U+9jbS8n1Z0jYUSdVlEfGNquIAiIingBXAOymawwduqNyKfepA4IOS1lJ0izgI+GIFcUwqDY7p7aydvo+GNcwxv3bqlFi11V8cSNpB0k4Dw8D7gPsoYp6fJpsPXFdNhKPSKNZlwPHp14EHAE+XLifXwqA+Xx+i2PZQxD5P0naS9qDogH9Hq+PrEC3dl1MfoouAByLivCrikLSbpClpeHvgvRR9vVYAR7UiBoCIOD0ipkfETIpj4fci4thWxzGZDHNMb2ft9H00rGGO+fUTEbV5AIcB/07Rp+HjVcczQqxvBv4tPe4fiJeiH8Ry4CfAd4Fdqo41xXU5xeXTFyn6ZpzYKFZAFL/Q/CmwCuiuYexfTbHdS3HwmFqa/uMp9geBQ6ve9u3waGb/mMAY3kXRVHEvsDI9DmtlHMBvA/ekGO4D/jqVv5kiQV8DfB3YroWfTQ9wfdVxdPqj0TG9XR51qMMTvC4Nj/l1e/gvbczMzMwyqVNToJmZmVlbc2JlZmZmlokTKzMzM7NMnFiZmZmZZeLEyszMzCwTJ1ZmZmZmmTixMjMzM8vkPwFkyHnGp+tGnAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Understanding Potassimum(pot), Sodium(sod), Hemoglobin(hemo) data structure more closely\n",
    "fig, ax = plt.subplots(1,3, figsize = (10,4))\n",
    "df[\"'sod'\"].hist(ax = ax[0])\n",
    "ax[0].set_title('Sodium')\n",
    "df[\"'pot'\"].hist(ax = ax[1])\n",
    "ax[1].set_title('Potassium Distribution')\n",
    "df[\"'hemo'\"].hist(ax = ax[2])\n",
    "ax[2].set_title('Hemoglobin')"
   ]
  },
  {
   "cell_type": "raw",
   "id": "f1552780",
   "metadata": {},
   "source": [
    "there are some observations that lies outside the overall pattern of the sodium(0) distribution : outliers\n",
    "there are some observations that lies outside the overall pattern of the potassium(4) distribution : outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5b507a17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Checking abnormality of sodium and instances of ckd\n",
    "ckd = list(df[\"'class'\"].unique())\n",
    "plt.figure(figsize = (10,6))\n",
    "for c in ckd:\n",
    "    sns.distplot(df[\"'sod'\"][df[\"'class'\"] == c], label = c)\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "734db442",
   "metadata": {},
   "source": [
    "the ckd and notckd graphs are skewed to th left and show some abnormalities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a7cecf9e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1dad2399490>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Count plot of rbc\n",
    "sns.catplot(x = \"'rbc'\", estimator = None, data = df[categorical_features], kind = 'count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d9228413",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Coronary Artery Disease')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution of HyperTension and DM and CAD\n",
    "plt.figure(figsize = (15,4))\n",
    "plt.subplot(1,3,1)\n",
    "sns.countplot(x = \"'htn'\", data = df[categorical_features])\n",
    "plt.title('HyperTension')\n",
    "plt.subplot(1,3,2)\n",
    "sns.countplot(x = \"'dm'\", data = df[categorical_features])\n",
    "plt.title('Diabetes Mellitus')\n",
    "plt.subplot(1,3,3)\n",
    "sns.countplot(x = \"'cad'\", data = df[categorical_features])\n",
    "plt.title('Coronary Artery Disease')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c943e73e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel=\"'htn'\", ylabel=\"'age'\">"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Relation between hypertension and age\n",
    "sns.swarmplot(x = \"'htn'\", y = \"'age'\", data = df)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "674804f0",
   "metadata": {},
   "source": [
    "this figure explain the relationship between age and hypertension \n",
    "we can interpret it by saying if the age is above 25-30 there's a high chance of hypertension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1c4eea27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Anemia')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15,4))\n",
    "plt.subplot(1,3,1)\n",
    "sns.countplot(x = \"'appet'\", data = df[categorical_features])\n",
    "plt.title('Appetite')\n",
    "plt.subplot(1,3,2)\n",
    "sns.countplot(x = \"'pe'\", data = df[categorical_features])\n",
    "plt.title('Pedal Edema')\n",
    "plt.subplot(1,3,3)\n",
    "sns.countplot(x = \"'ane'\", data = df[categorical_features])\n",
    "plt.title('Anemia')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee81254f",
   "metadata": {},
   "source": [
    "=>Most of the people have a good appetite.\n",
    "Pedel Edema : indicates swolleness of our feet. This can be a direct indicator of kidney issues.\n",
    "Anemia: Indicates presence or absence of red blood cells/hemoglobin. Most of the people have 'no' anemia "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3e9b1dfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Anemia and Hemoglobine')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,4))\n",
    "plt.subplot(1,2,1)\n",
    "sns.swarmplot(x = \"'ane'\", y = \"'hemo'\", data=df)\n",
    "plt.title('Anemia and Hemoglobine')"
   ]
  },
  {
   "cell_type": "raw",
   "id": "e52c11d7",
   "metadata": {},
   "source": [
    "from this figure we could understand that starting from a certain value of hemoglobine there's a lower chance of anemia \n",
    "As an example if hemoglobine values are in [4-10] there's a higher chance of anemia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2ceda3b9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Pus Cells Clump')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Distirbution of Pus cell and Pus cells clump\n",
    "plt.figure(figsize = (10,5))\n",
    "plt.subplot(1,2,1)\n",
    "sns.countplot(x = \"'pc'\", data = df[categorical_features])\n",
    "plt.title('Pus Cell')\n",
    "plt.subplot(1,2,2)\n",
    "sns.countplot(x = \"'pcc'\", data = df[categorical_features])\n",
    "plt.title('Pus Cells Clump')"
   ]
  },
  {
   "cell_type": "raw",
   "id": "400f337a",
   "metadata": {},
   "source": [
    "the number of abnormal pus cell values is lower than the number of normal one .This indicates a very imbalanced sample which is not very strong in terms of prediction\n",
    "same for plus cell clumps. Pus cell clumps are less present "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ebe08484",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x1dad271ea00>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Proportion of Bacteria present or not\n",
    "df[categorical_features][\"'ba'\"].value_counts(normalize = True).plot(kind = 'bar', colormap = 'cool')\n",
    "plt.axhline(y = 0.93, color = 'r', label = '0.93')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2545584d",
   "metadata": {},
   "source": [
    "=>About 93 percent values belong to only one variable present. This indicates a very imbalanced sample which is not very strong in terms of predictive power"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0a887977",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Coronary Artery Disease')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution of HyperTension and DM and CAD\n",
    "plt.figure(figsize = (15,4))\n",
    "plt.subplot(1,3,1)\n",
    "sns.countplot(x = \"'htn'\", data = df[categorical_features])\n",
    "plt.title('HyperTension')\n",
    "plt.subplot(1,3,2)\n",
    "sns.countplot(x = \"'dm'\", data = df[categorical_features])\n",
    "plt.title('Diabetes Mellitus')\n",
    "plt.subplot(1,3,3)\n",
    "sns.countplot(x = \"'cad'\", data = df[categorical_features])\n",
    "plt.title('Coronary Artery Disease')"
   ]
  },
  {
   "cell_type": "raw",
   "id": "130e2084",
   "metadata": {},
   "source": [
    "for the coronary Artery disease only approximately 40 samples have cad. this means that this is an imbalanced sample and might not help in predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ba5eaaa9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1dad238edc0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 485.25x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Relation of dm with blood glucose variable\n",
    "g = sns.FacetGrid(df, hue = \"'dm'\", aspect = 2)\n",
    "g.map(sns.kdeplot, \"'bgr'\")\n",
    "g.add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "354a3321",
   "metadata": {},
   "source": [
    "We can see a separation above 170 mark. Most of the cases with dm as yes belong above 170. The cases of no dm are concentrated between 50 -170 , which can be taken as normal bgr range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4bbfba60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel=\"'su'\", ylabel='count'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution of dm with su(sugar)\n",
    "\n",
    "sns.countplot(x = \"'su'\", hue = \"'dm'\", data = df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63c703f1",
   "metadata": {},
   "source": [
    "We can see that most of the no status of diabetes concur with 0 as sugar level. So we can conclude that above 0 , there are more chances of dm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "29781581",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#Imputation : Replace Missing Values:  ****** float by mean and Object by mode********\n",
    "for column_name in df.columns:\n",
    "    if df[column_name].dtype == 'float64' :\n",
    "        df[column_name].fillna(df[column_name].mean(), inplace = True)\n",
    "    else :\n",
    "        df[column_name].fillna(df[column_name].mode()[0], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4e097c40",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 400 entries, 0 to 399\n",
      "Data columns (total 25 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   'age'    400 non-null    float64\n",
      " 1   'bp'     400 non-null    float64\n",
      " 2   'sg'     400 non-null    float64\n",
      " 3   'al'     400 non-null    float64\n",
      " 4   'su'     400 non-null    float64\n",
      " 5   'rbc'    400 non-null    object \n",
      " 6   'pc'     400 non-null    object \n",
      " 7   'pcc'    400 non-null    object \n",
      " 8   'ba'     400 non-null    object \n",
      " 9   'bgr'    400 non-null    float64\n",
      " 10  'bu'     400 non-null    float64\n",
      " 11  'sc'     400 non-null    float64\n",
      " 12  'sod'    400 non-null    float64\n",
      " 13  'pot'    400 non-null    float64\n",
      " 14  'hemo'   400 non-null    float64\n",
      " 15  'pcv'    400 non-null    float64\n",
      " 16  'wbcc'   400 non-null    float64\n",
      " 17  'rbcc'   400 non-null    float64\n",
      " 18  'htn'    400 non-null    object \n",
      " 19  'dm'     400 non-null    object \n",
      " 20  'cad'    400 non-null    object \n",
      " 21  'appet'  400 non-null    object \n",
      " 22  'pe'     400 non-null    object \n",
      " 23  'ane'    400 non-null    object \n",
      " 24  'class'  400 non-null    object \n",
      "dtypes: float64(14), object(11)\n",
      "memory usage: 78.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7484c2a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 400 entries, 0 to 399\n",
      "Data columns (total 25 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   'age'    400 non-null    float64\n",
      " 1   'bp'     400 non-null    float64\n",
      " 2   'sg'     400 non-null    float64\n",
      " 3   'al'     400 non-null    float64\n",
      " 4   'su'     400 non-null    float64\n",
      " 5   'rbc'    400 non-null    float64\n",
      " 6   'pc'     400 non-null    float64\n",
      " 7   'pcc'    400 non-null    float64\n",
      " 8   'ba'     400 non-null    float64\n",
      " 9   'bgr'    400 non-null    float64\n",
      " 10  'bu'     400 non-null    float64\n",
      " 11  'sc'     400 non-null    float64\n",
      " 12  'sod'    400 non-null    float64\n",
      " 13  'pot'    400 non-null    float64\n",
      " 14  'hemo'   400 non-null    float64\n",
      " 15  'pcv'    400 non-null    float64\n",
      " 16  'wbcc'   400 non-null    float64\n",
      " 17  'rbcc'   400 non-null    float64\n",
      " 18  'htn'    400 non-null    float64\n",
      " 19  'dm'     400 non-null    float64\n",
      " 20  'cad'    400 non-null    float64\n",
      " 21  'appet'  400 non-null    float64\n",
      " 22  'pe'     400 non-null    float64\n",
      " 23  'ane'    400 non-null    float64\n",
      " 24  'class'  400 non-null    float64\n",
      "dtypes: float64(25)\n",
      "memory usage: 78.2 KB\n"
     ]
    }
   ],
   "source": [
    "#data Transformation : Replace categorical data with Ordinal Encoder\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "categorical_feature_mask = df.dtypes==object\n",
    "categorical_cols = df.columns[categorical_feature_mask].tolist()\n",
    "categorical_cols\n",
    "encoder = OrdinalEncoder()\n",
    "for column_name in categorical_cols:\n",
    "    df[column_name] = encoder.fit_transform(df[column_name].values.reshape(-1, 1))\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89ddb776",
   "metadata": {},
   "source": [
    "<div style='font-size:100%;'>\n",
    "    <a id='clean'></a>\n",
    "    <h1 style='color: black; font-weight: bold; font-family: Cascadia code;'>\n",
    "        <center> Visualizing and cleaning Outliers 📊</center>\n",
    "    </h1>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85a7f857",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Visualizing outliers in numerical features\n",
    "for col in df.columns:\n",
    "    fig, ax = plt.subplots(1,3, figsize = (13,5))\n",
    "    df[col].plot(kind = 'kde', ax = ax[0])\n",
    "    ax[0].set_ylim(bottom = 0)\n",
    "    sns.boxplot(col, data = df, ax = ax[1], orient = 'v')\n",
    "    sns.swarmplot(col, data = df, ax = ax[2], orient = 'v')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ffbceaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "ax = sns.boxplot(data = df[\"'wbcc'\"], orient = \"h\", palette = \"Set2\")\n",
    "#add labels\n",
    "ax.set_xlabel(\"Value\", fontsize = 20, labelpad = 20)\n",
    "ax.set_ylabel(\"Attributes\", fontsize = 20, labelpad = 20)\n",
    "ax.set_title(\"Boxplot of Outliers\", fontsize = 20,pad = 20)\n",
    "#edit ticks\n",
    "ax.tick_params(which = \"both\", labelsize = 15)\n",
    "print(\" we recognize that wbcc have outliers\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b331f82",
   "metadata": {},
   "outputs": [],
   "source": [
    "ax = sns.boxplot(data = df[\"'sg'\"], orient = \"h\", palette = \"Set2\")\n",
    "#add labels\n",
    "ax.set_xlabel(\"Value\", fontsize = 20, labelpad = 20)\n",
    "ax.set_ylabel(\"Attributes\", fontsize = 20, labelpad = 20)\n",
    "ax.set_title(\"Boxplot of Outliers\", fontsize = 20,pad = 20)\n",
    "#edit ticks\n",
    "ax.tick_params(which = \"both\", labelsize = 15)\n",
    "print(' in this case sg seem to have an insignificant number of outliers ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7c19b15",
   "metadata": {},
   "outputs": [],
   "source": [
    "column=[\"'bp'\",\"'age'\",\"'sod'\"]\n",
    "ax = sns.boxplot(data = df[column], orient = \"h\", palette = \"Set2\")\n",
    "#add labels\n",
    "ax.set_xlabel(\"Value\", fontsize = 20, labelpad = 20)\n",
    "ax.set_ylabel(\"Attributes\", fontsize = 20, labelpad = 20)\n",
    "ax.set_title(\"Boxplot of Outliers\", fontsize = 20,pad = 20)\n",
    "#edit ticks\n",
    "ax.tick_params(which = \"both\", labelsize = 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b52cb41",
   "metadata": {},
   "outputs": [],
   "source": [
    "column=[\"'bgr'\", \"'bu'\"]\n",
    "\n",
    "ax = sns.boxplot(data = df[column], orient = \"h\", palette = \"Set2\")\n",
    "#add labels\n",
    "ax.set_xlabel(\"Value\", fontsize = 20, labelpad = 20)\n",
    "ax.set_ylabel(\"Attributes\", fontsize = 20, labelpad = 20)\n",
    "ax.set_title(\"Boxplot of Outliers\", fontsize = 20,pad = 20)\n",
    "#edit ticks\n",
    "ax.tick_params(which = \"both\", labelsize = 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a54e2c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "column=[\"'sc'\",  \"'pot'\", \"'hemo'\", \"'pcv'\" , \"'sg'\"]\n",
    "ax = sns.boxplot(data = df[column], orient = \"h\", palette = \"Set2\")\n",
    "#add labels\n",
    "ax.set_xlabel(\"Value\", fontsize = 20, labelpad = 20)\n",
    "ax.set_ylabel(\"Attributes\", fontsize = 20, labelpad = 20)\n",
    "ax.set_title(\"Boxplot of Outliers\", fontsize = 20,pad = 20)\n",
    "#edit ticks\n",
    "ax.tick_params(which = \"both\", labelsize = 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "596bfd3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "column=[\"'rbcc'\", \"'htn'\", \"'dm'\", \"'cad'\",\"'appet'\", \"'pe'\",\n",
    "       \"'ane'\", \"'class'\" ]\n",
    "ax = sns.boxplot(data = df[column], orient = \"h\", palette = \"Set2\")\n",
    "#add labels\n",
    "ax.set_xlabel(\"Value\", fontsize = 20, labelpad = 20)\n",
    "ax.set_ylabel(\"Attributes\", fontsize = 20, labelpad = 20)\n",
    "ax.set_title(\"Boxplot of Outliers\", fontsize = 20,pad = 20)\n",
    "#edit ticks\n",
    "ax.tick_params(which = \"both\", labelsize = 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e7847c7",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ee2a07f",
   "metadata": {},
   "source": [
    "<div style='font-size:85%;'>\n",
    "    <a id='discover'></a>\n",
    "    <h1 style='color: black; font-weight: bold; font-family: Cascadia code;'>\n",
    "        <center> Modeling without feature selection and without cleaning outliers</center>\n",
    "    </h1>\n",
    "</div> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ba06994c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(300, 24)\n",
      "(100, 24)\n"
     ]
    }
   ],
   "source": [
    "# splitting the dataset into train and test set \n",
    "from sklearn.model_selection import train_test_split\n",
    "df1 = df.copy()\n",
    "ind_col = [col for col in df1.columns if col != \"'class'\"]\n",
    "dep_col = \"'class'\"\n",
    "X = df[ind_col]\n",
    "y = df[dep_col]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4c6017d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing libraries\n",
    "\n",
    "from sklearn import svm, tree, naive_bayes, ensemble , neighbors\n",
    "\n",
    "#Common Model Helpers\n",
    "from sklearn import feature_selection\n",
    "from sklearn import model_selection\n",
    "from sklearn import metrics\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.feature_selection import VarianceThreshold\n",
    "\n",
    "#Import SelectKBest for Feature Selection\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import mutual_info_classif\n",
    "\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.feature_selection import RFECV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4fc44772",
   "metadata": {},
   "outputs": [],
   "source": [
    "# List of different Machine learning Algorithm we will use \n",
    "MLA = [\n",
    "    ensemble.RandomForestClassifier(random_state=0),\n",
    "    #Navies Bayes\n",
    "    naive_bayes.BernoulliNB(),\n",
    "    naive_bayes.GaussianNB(),\n",
    "    #Trees    \n",
    "    tree.DecisionTreeClassifier(random_state=0),\n",
    "    tree.ExtraTreeClassifier(random_state=0),\n",
    "    #Nearest Neighbor\n",
    "    neighbors.KNeighborsClassifier(),\n",
    "    #SVM\n",
    "    svm.LinearSVC(random_state=0), \n",
    "    svm.NuSVC(probability=True),\n",
    "    svm.SVC(probability=True) \n",
    "    ]\n",
    "MLA1 = [\n",
    "    #Navies Bayes\n",
    "    naive_bayes.GaussianNB(),\n",
    "    naive_bayes.BernoulliNB(),\n",
    "    #SVM\n",
    "    svm.NuSVC(probability=True),\n",
    "    svm.SVC(probability=True, kernel='linear')\n",
    "    ]\n",
    "MLA2=[\n",
    "     naive_bayes.BernoulliNB(),\n",
    "     svm.LinearSVC(random_state=0), \n",
    "     ensemble.RandomForestClassifier(random_state=0),\n",
    "     tree.DecisionTreeClassifier(random_state=0),\n",
    "     tree.ExtraTreeClassifier(random_state=0)\n",
    "] \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3d743c9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(400, 24)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ind_col = [col for col in df.columns if col != \"'class'\"]\n",
    "X = ind_col\n",
    "Y =\"'class'\"\n",
    "x = df[ind_col]\n",
    "a=df[ind_col]\n",
    "y =df[\"'class'\"]\n",
    "scaler = MinMaxScaler()\n",
    "x=scaler.fit_transform(x)\n",
    "y.shape\n",
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "093889e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(300, 24)\n",
      "(100, 24)\n"
     ]
    }
   ],
   "source": [
    "#training the model\n",
    "X_train, X_test, y_train, y_test=model_selection.train_test_split(df[X], df[Y], test_size = 0.25, random_state = 0)\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "74c7aea9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MLA Name</th>\n",
       "      <th>MLA Test Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ExtraTreeClassifier</td>\n",
       "      <td>0.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BernoulliNB</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NuSVC</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LinearSVC</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 MLA Name MLA Test Accuracy\n",
       "0  RandomForestClassifier               1.0\n",
       "3  DecisionTreeClassifier              0.99\n",
       "2              GaussianNB              0.97\n",
       "4     ExtraTreeClassifier              0.94\n",
       "1             BernoulliNB               0.9\n",
       "7                   NuSVC              0.85\n",
       "5    KNeighborsClassifier              0.71\n",
       "6               LinearSVC              0.65\n",
       "8                     SVC              0.62"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MLA_columns = ['MLA Name', 'MLA Test Accuracy' ]\n",
    "MLA_compare = pd.DataFrame(columns = MLA_columns)\n",
    "\n",
    "\n",
    "row_index = 0\n",
    "for alg in MLA:\n",
    "    alg.fit(X_train, y_train)\n",
    "\n",
    "    alg_acc = accuracy_score(y_test, alg.predict(X_test))\n",
    "    MLA_name = alg.__class__.__name__\n",
    "    MLA_compare.loc[row_index, 'MLA Name'] = MLA_name\n",
    "    \n",
    "    MLA_compare.loc[row_index, 'MLA Test Accuracy'] = alg_acc\n",
    "\n",
    "   \n",
    "    \n",
    "    row_index+=1\n",
    "\n",
    "    \n",
    "MLA_compare.sort_values(by = ['MLA Test Accuracy'], ascending = False, inplace = True)\n",
    "MLA_compare"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba564fe0",
   "metadata": {},
   "source": [
    "<div style='font-size:95%;'>\n",
    "    <a id='clean'></a>\n",
    "    <h1 style='color: Black; font-weight: bold; font-family: Cascadia code;'>\n",
    "        <center> Normalization </center>\n",
    "    </h1>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "11493518",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Normalization\n",
    "scaler = MinMaxScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "ca5da610",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MLA Name</th>\n",
       "      <th>MLA Test Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>SVC</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LinearSVC</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NuSVC</td>\n",
       "      <td>0.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ExtraTreeClassifier</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BernoulliNB</td>\n",
       "      <td>0.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 MLA Name MLA Test Accuracy\n",
       "8                     SVC               1.0\n",
       "5    KNeighborsClassifier              0.99\n",
       "6               LinearSVC              0.99\n",
       "7                   NuSVC              0.98\n",
       "0  RandomForestClassifier              0.96\n",
       "3  DecisionTreeClassifier              0.95\n",
       "4     ExtraTreeClassifier              0.93\n",
       "1             BernoulliNB              0.91\n",
       "2              GaussianNB              0.71"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# views each algorithm and its accuracy \n",
    "\n",
    "MLA_columns = ['MLA Name', 'MLA Test Accuracy']\n",
    "MLA_compare = pd.DataFrame(columns = MLA_columns)\n",
    "\n",
    "#fitting and traing the model\n",
    "row_index = 0\n",
    "for alg in MLA:\n",
    "    alg.fit(X_train, y_train)\n",
    "\n",
    "    alg_acc = accuracy_score(y_test, alg.predict(X_test))\n",
    "    MLA_name = alg.__class__.__name__\n",
    "    MLA_compare.loc[row_index, 'MLA Name'] = MLA_name\n",
    "    \n",
    "    MLA_compare.loc[row_index, 'MLA Test Accuracy'] = alg_acc\n",
    "\n",
    "   \n",
    "    \n",
    "    row_index+=1\n",
    "MLA_compare.sort_values(by = ['MLA Test Accuracy'], ascending = False, inplace = True)\n",
    "MLA_compare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d57cf270",
   "metadata": {},
   "outputs": [],
   "source": [
    "#xb have all the feature minus the target class\n",
    "Xb=df[ind_col]\n",
    "Yb=df[\"'class'\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f0b82bf6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Treating imbalanced dataset with Oversampling method \n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "\n",
    "rus = RandomOverSampler(sampling_strategy=1) # Numerical value\n",
    "# rus = RandomUnderSampler(sampling_strategy=\"not minority\") # String\n",
    "X_res, y_res = rus.fit_resample(Xb, Yb)\n",
    "\n",
    "ax = y_res.value_counts().plot.pie(autopct='%.2f')\n",
    "\n",
    "_ = ax.set_title(\"Over-sampling\")\n",
    "     "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aeadc2eb",
   "metadata": {},
   "source": [
    "0 : ckd \n",
    "1 : notckd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "84bda31f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    250\n",
       "1.0    250\n",
       "Name: 'class', dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#verifying if the dataset now is balanced \n",
    "y_res.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "277cef68",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Normalization\n",
    "scaler = MinMaxScaler()\n",
    "X_res=scaler.fit_transform(X_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "f4e9653d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.52272727, 0.23076923, 0.75      , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.05681818, 0.        , 0.75      , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.68181818, 0.23076923, 0.25      , ..., 1.        , 0.        ,\n",
       "        1.        ],\n",
       "       ...,\n",
       "       [0.52272727, 0.23076923, 1.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.56818182, 0.23076923, 0.75      , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.375     , 0.20360825, 0.75      , ..., 0.        , 0.        ,\n",
       "        0.        ]])"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_res"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bd4867b",
   "metadata": {},
   "source": [
    "<div style='font-size:85%;'>\n",
    "    <a id='discover'></a>\n",
    "    <h1 style='color: black; font-weight: bold; font-family: Cascadia code;'>\n",
    "        <center> Modeling +grid search</center>\n",
    "    </h1>\n",
    "</div> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "74678a5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(375, 24)\n",
      "(125, 24)\n"
     ]
    }
   ],
   "source": [
    "#splitting the dataset\n",
    "X_train, X_test, y_train, y_test=model_selection.train_test_split(X_res, y_res, test_size = 0.25, random_state = 0)\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "11685918",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MLA Name</th>\n",
       "      <th>MLA Test Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ExtraTreeClassifier</td>\n",
       "      <td>0.984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LinearSVC</td>\n",
       "      <td>0.976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>0.968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>0.968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NuSVC</td>\n",
       "      <td>0.936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BernoulliNB</td>\n",
       "      <td>0.904</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 MLA Name MLA Test Accuracy\n",
       "0  RandomForestClassifier               1.0\n",
       "3  DecisionTreeClassifier               1.0\n",
       "4     ExtraTreeClassifier             0.984\n",
       "8                     SVC             0.984\n",
       "6               LinearSVC             0.976\n",
       "2              GaussianNB             0.968\n",
       "5    KNeighborsClassifier             0.968\n",
       "7                   NuSVC             0.936\n",
       "1             BernoulliNB             0.904"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list of ml algorithm and its accuracy \n",
    "MLA_columns = ['MLA Name', 'MLA Test Accuracy' ]\n",
    "MLA_compare = pd.DataFrame(columns = MLA_columns)\n",
    "\n",
    "#Training , fiting and predicting \n",
    "row_index = 0\n",
    "for alg in MLA:\n",
    "    alg.fit(X_train, y_train)\n",
    "\n",
    "    alg_acc = accuracy_score(y_test, alg.predict(X_test))\n",
    "    MLA_name = alg.__class__.__name__\n",
    "    MLA_compare.loc[row_index, 'MLA Name'] = MLA_name\n",
    "    \n",
    "    MLA_compare.loc[row_index, 'MLA Test Accuracy'] = alg_acc\n",
    "\n",
    "   \n",
    "    \n",
    "    row_index+=1\n",
    "\n",
    "    \n",
    "MLA_compare.sort_values(by = ['MLA Test Accuracy'], ascending = False, inplace = True)\n",
    "MLA_compare"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "006b4fdc",
   "metadata": {},
   "source": [
    "grid search \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "6d28547b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "dd354574",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'leaf_size': 1, 'n_neighbors': 2, 'p': 2} KNeighborsClassifier(leaf_size=1, n_neighbors=2) 0.9919630156472262\n"
     ]
    }
   ],
   "source": [
    "#Applying GridSearch with knn\n",
    "param_grid = {\"n_neighbors\": range(1, 20), \"leaf_size\": range(1,20), \"p\": [1,2]}\n",
    "knn_cv = GridSearchCV(neighbors.KNeighborsClassifier(), param_grid, cv=10)\n",
    "knn_cv.fit(X_train, y_train)\n",
    "print(knn_cv.best_params_, knn_cv.best_estimator_, knn_cv.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "291cb525",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C': 100, 'gamma': 1, 'kernel': 'rbf'} SVC(C=100, gamma=1) 0.9945945945945945\n"
     ]
    }
   ],
   "source": [
    "#Applying GridSearch with svc\n",
    "param_grid = {'C': [0.1, 1, 10, 100, 1000],\n",
    "              'gamma': [1, 0.1, 0.01, 0.001, 0.0001],\n",
    "              'kernel': ['rbf', 'linear', 'poly', 'sigmoid']}\n",
    "svc_cv = GridSearchCV(svm.SVC(), param_grid, cv=10)\n",
    "svc_cv.fit(X_train, y_train)\n",
    "print(svc_cv.best_params_, svc_cv.best_estimator_, svc_cv.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "a4dbc621",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'criterion': 'gini', 'max_depth': 4, 'max_features': 'auto', 'n_estimators': 200} RandomForestClassifier(max_depth=4, max_features='auto', n_estimators=200,\n",
      "                       random_state=42) 0.9920341394025606\n"
     ]
    }
   ],
   "source": [
    "#Applying GridSearch with RandomForest\n",
    "param_grid = { \n",
    "    'n_estimators': [200, 500],\n",
    "    'max_features': ['auto', 'sqrt', 'log2'],\n",
    "    'max_depth' : [4,5,6,7,8],\n",
    "    'criterion' :['gini', 'entropy']}\n",
    "RFC_cv = GridSearchCV( ensemble.RandomForestClassifier(random_state=42), param_grid, cv=10)\n",
    "RFC_cv.fit(X_train, y_train)\n",
    "print(RFC_cv.best_params_, RFC_cv.best_estimator_, RFC_cv.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "18460228",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alpha': 0.01, 'binarize': 0.0, 'class_prior': None, 'fit_prior': True} BernoulliNB(alpha=0.01) 0.95\n"
     ]
    }
   ],
   "source": [
    "#Applying GridSearch with Naive Bayes \n",
    "param_grid = { \n",
    "          'alpha': [0.01, 0.1, 0.5, 1.0, 10.0],\n",
    "          'fit_prior': [True, False],\n",
    "          'class_prior': [None, [0.1,]* 2, ],\n",
    "          'binarize': [None, 0.0, 8.5, 10.0]}\n",
    "B_cv = GridSearchCV( naive_bayes.BernoulliNB(), param_grid, cv=10)\n",
    "B_cv.fit(X_train, y_train)\n",
    "print(B_cv.best_params_, B_cv.best_estimator_, B_cv.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "5539417d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# All grid search examples in one list\n",
    "MLA_gs = [\n",
    "    ensemble.RandomForestClassifier(max_depth=4, max_features='auto', n_estimators=200,random_state=0),\n",
    "    naive_bayes.BernoulliNB(alpha=0.01, binarize=None),\n",
    "    neighbors.KNeighborsClassifier(leaf_size=1, n_neighbors=2, p=2),\n",
    "    svm.SVC(C=100,gamma=1,kernel='rbf') \n",
    "    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "2ea049ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MLA Name</th>\n",
       "      <th>MLA Test Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BernoulliNB</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 MLA Name MLA Test Accuracy\n",
       "2    KNeighborsClassifier               1.0\n",
       "1             BernoulliNB              0.96\n",
       "0  RandomForestClassifier              0.95\n",
       "3                     SVC              0.89"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Applying Different machine learning algorithms \n",
    "MLA_columns = ['MLA Name', 'MLA Test Accuracy' ]\n",
    "MLA_compare = pd.DataFrame(columns = MLA_columns)\n",
    "\n",
    "\n",
    "row_index = 0\n",
    "for alg in MLA_gs:\n",
    "    alg.fit(X_train, y_train)\n",
    "\n",
    "    alg_acc = accuracy_score(y_test, alg.predict(X_test))\n",
    "    MLA_name = alg.__class__.__name__\n",
    "    MLA_compare.loc[row_index, 'MLA Name'] = MLA_name\n",
    "    \n",
    "    MLA_compare.loc[row_index, 'MLA Test Accuracy'] = alg_acc\n",
    "\n",
    "   \n",
    "    \n",
    "    row_index+=1\n",
    "MLA_compare.sort_values(by = ['MLA Test Accuracy'], ascending = False, inplace = True)\n",
    "MLA_compare"
   ]
  },
  {
   "cell_type": "raw",
   "id": "43a3c972",
   "metadata": {},
   "source": [
    "In this article we chose the models that have the best accuracy"
   ]
  }
 ],
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