api/Kidney.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Chronic Kidney Disease"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Set Information:\n",
    "\n",
    "We use the following representation to collect the dataset<br>\n",
    "age - age<br>\n",
    "bp - blood pressure<br>\n",
    "sg - specific gravity<br>\n",
    "al - albumin<br>\n",
    "su - sugar<br>\n",
    "rbc - red blood cells<br>\n",
    "pc - pus cell<br>\n",
    "pcc - pus cell clumps<br>\n",
    "ba - bacteria<br>\n",
    "bgr - blood glucose random<br>\n",
    "bu - blood urea<br>\n",
    "sc - serum creatinine<br>\n",
    "sod - sodium<br>\n",
    "pot - potassium<br>\n",
    "hemo - hemoglobin<br>\n",
    "pcv - packed cell volume<br>\n",
    "wc - white blood cell count<br>\n",
    "rc - red blood cell count<br>\n",
    "htn - hypertension<br>\n",
    "dm - diabetes mellitus<br>\n",
    "cad - coronary artery disease<br>\n",
    "appet - appetite<br>\n",
    "pe - pedal edema<br>\n",
    "ane - anemia<br>\n",
    "class - class<br>\n",
    "\n",
    "\n",
    "#### Attribute Information:\n",
    "\n",
    "We use 24 + class = 25 ( 11 numeric ,14 nominal)<br>\n",
    "1. Age(numerical)\n",
    "age in years<br>\n",
    "2. Blood Pressure(numerical)\n",
    "bp in mm/Hg<br>\n",
    "3. Specific Gravity(nominal)\n",
    "sg - (1.005,1.010,1.015,1.020,1.025)<br>\n",
    "4. Albumin(nominal)\n",
    "al - (0,1,2,3,4,5)\n",
    "5. Sugar(nominal)\n",
    "su - (0,1,2,3,4,5)\n",
    "6. Red Blood Cells(nominal)\n",
    "rbc - (normal,abnormal)\n",
    "7. Pus Cell (nominal)\n",
    "pc - (normal,abnormal)\n",
    "8. Pus Cell clumps(nominal)\n",
    "pcc - (present,notpresent)\n",
    "9. Bacteria(nominal)\n",
    "ba - (present,notpresent)\n",
    "10. Blood Glucose Random(numerical)\n",
    "bgr in mgs/dl\n",
    "11. Blood Urea(numerical)\n",
    "bu in mgs/dl\n",
    "12. Serum Creatinine(numerical)\n",
    "sc in mgs/dl\n",
    "13. Sodium(numerical)\n",
    "sod in mEq/L\n",
    "14. Potassium(numerical)\n",
    "pot in mEq/L\n",
    "15. Hemoglobin(numerical)\n",
    "hemo in gms\n",
    "16. Packed Cell Volume(numerical)\n",
    "17. White Blood Cell Count(numerical)\n",
    "wc in cells/cumm\n",
    "18. Red Blood Cell Count(numerical)\n",
    "rc in millions/cmm\n",
    "19. Hypertension(nominal)\n",
    "htn - (yes,no)\n",
    "20. Diabetes Mellitus(nominal)\n",
    "dm - (yes,no)\n",
    "21. Coronary Artery Disease(nominal)\n",
    "cad - (yes,no)\n",
    "22. Appetite(nominal)\n",
    "appet - (good,poor)\n",
    "23. Pedal Edema(nominal)\n",
    "pe - (yes,no)\n",
    "24. Anemia(nominal)\n",
    "ane - (yes,no)\n",
    "25. Class (nominal)\n",
    "class - (ckd,notckd)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing Libraries:\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for displaying all feature from dataset:\n",
    "pd.pandas.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>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>bu</th>\n",
       "      <th>sc</th>\n",
       "      <th>sod</th>\n",
       "      <th>pot</th>\n",
       "      <th>hemo</th>\n",
       "      <th>pcv</th>\n",
       "      <th>wc</th>\n",
       "      <th>rc</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>classification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\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>36.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.4</td>\n",
       "      <td>44</td>\n",
       "      <td>7800</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>1</td>\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>18.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.3</td>\n",
       "      <td>38</td>\n",
       "      <td>6000</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>2</td>\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>53.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.6</td>\n",
       "      <td>31</td>\n",
       "      <td>7500</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>3</td>\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>56.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>111.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>11.2</td>\n",
       "      <td>32</td>\n",
       "      <td>6700</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>4</td>\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>26.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.6</td>\n",
       "      <td>35</td>\n",
       "      <td>7300</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",
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      ],
      "text/plain": [
       "   id   age    bp     sg   al   su     rbc        pc         pcc          ba  \\\n",
       "0   0  48.0  80.0  1.020  1.0  0.0     NaN    normal  notpresent  notpresent   \n",
       "1   1   7.0  50.0  1.020  4.0  0.0     NaN    normal  notpresent  notpresent   \n",
       "2   2  62.0  80.0  1.010  2.0  3.0  normal    normal  notpresent  notpresent   \n",
       "3   3  48.0  70.0  1.005  4.0  0.0  normal  abnormal     present  notpresent   \n",
       "4   4  51.0  80.0  1.010  2.0  0.0  normal    normal  notpresent  notpresent   \n",
       "\n",
       "     bgr    bu   sc    sod  pot  hemo pcv    wc   rc  htn   dm cad appet   pe  \\\n",
       "0  121.0  36.0  1.2    NaN  NaN  15.4  44  7800  5.2  yes  yes  no  good   no   \n",
       "1    NaN  18.0  0.8    NaN  NaN  11.3  38  6000  NaN   no   no  no  good   no   \n",
       "2  423.0  53.0  1.8    NaN  NaN   9.6  31  7500  NaN   no  yes  no  poor   no   \n",
       "3  117.0  56.0  3.8  111.0  2.5  11.2  32  6700  3.9  yes   no  no  poor  yes   \n",
       "4  106.0  26.0  1.4    NaN  NaN  11.6  35  7300  4.6   no   no  no  good   no   \n",
       "\n",
       "   ane classification  \n",
       "0   no            ckd  \n",
       "1   no            ckd  \n",
       "2  yes            ckd  \n",
       "3  yes            ckd  \n",
       "4   no            ckd  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading Dataset:\n",
    "dataset = pd.read_csv(\"Kidney_data.csv\")\n",
    "# Top 5 records:\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping unneccsary feature :\n",
    "dataset = dataset.drop('id', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(400, 25)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Shape of dataset:\n",
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                 9\n",
       "bp                 12\n",
       "sg                 47\n",
       "al                 46\n",
       "su                 49\n",
       "rbc               152\n",
       "pc                 65\n",
       "pcc                 4\n",
       "ba                  4\n",
       "bgr                44\n",
       "bu                 19\n",
       "sc                 17\n",
       "sod                87\n",
       "pot                88\n",
       "hemo               52\n",
       "pcv                70\n",
       "wc                105\n",
       "rc                130\n",
       "htn                 2\n",
       "dm                  2\n",
       "cad                 2\n",
       "appet               1\n",
       "pe                  1\n",
       "ane                 1\n",
       "classification      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cheaking Missing (NaN) Values:\n",
    "dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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  \n",
       "count  381.000000  383.000000  313.000000  312.000000  348.000000  \n",
       "mean    57.425722    3.072454  137.528754    4.627244   12.526437  \n",
       "std     50.503006    5.741126   10.408752    3.193904    2.912587  \n",
       "min      1.500000    0.400000    4.500000    2.500000    3.100000  \n",
       "25%     27.000000    0.900000  135.000000    3.800000   10.300000  \n",
       "50%     42.000000    1.300000  138.000000    4.400000   12.650000  \n",
       "75%     66.000000    2.800000  142.000000    4.900000   15.000000  \n",
       "max    391.000000   76.000000  163.000000   47.000000   17.800000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Description:\n",
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age               float64\n",
       "bp                float64\n",
       "sg                float64\n",
       "al                float64\n",
       "su                float64\n",
       "rbc                object\n",
       "pc                 object\n",
       "pcc                object\n",
       "ba                 object\n",
       "bgr               float64\n",
       "bu                float64\n",
       "sc                float64\n",
       "sod               float64\n",
       "pot               float64\n",
       "hemo              float64\n",
       "pcv                object\n",
       "wc                 object\n",
       "rc                 object\n",
       "htn                object\n",
       "dm                 object\n",
       "cad                object\n",
       "appet              object\n",
       "pe                 object\n",
       "ane                object\n",
       "classification     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Datatypes:\n",
    "dataset.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>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>bu</th>\n",
       "      <th>sc</th>\n",
       "      <th>sod</th>\n",
       "      <th>pot</th>\n",
       "      <th>hemo</th>\n",
       "      <th>pcv</th>\n",
       "      <th>wc</th>\n",
       "      <th>rc</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>classification</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>36.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.4</td>\n",
       "      <td>44</td>\n",
       "      <td>7800</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>18.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.3</td>\n",
       "      <td>38</td>\n",
       "      <td>6000</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>53.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.6</td>\n",
       "      <td>31</td>\n",
       "      <td>7500</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>56.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>111.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>11.2</td>\n",
       "      <td>32</td>\n",
       "      <td>6700</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>26.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.6</td>\n",
       "      <td>35</td>\n",
       "      <td>7300</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",
       "</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    bu   sc    sod  pot  hemo pcv    wc   rc  htn   dm cad appet   pe  \\\n",
       "0  121.0  36.0  1.2    NaN  NaN  15.4  44  7800  5.2  yes  yes  no  good   no   \n",
       "1    NaN  18.0  0.8    NaN  NaN  11.3  38  6000  NaN   no   no  no  good   no   \n",
       "2  423.0  53.0  1.8    NaN  NaN   9.6  31  7500  NaN   no  yes  no  poor   no   \n",
       "3  117.0  56.0  3.8  111.0  2.5  11.2  32  6700  3.9  yes   no  no  poor  yes   \n",
       "4  106.0  26.0  1.4    NaN  NaN  11.6  35  7300  4.6   no   no  no  good   no   \n",
       "\n",
       "   ane classification  \n",
       "0   no            ckd  \n",
       "1   no            ckd  \n",
       "2  yes            ckd  \n",
       "3  yes            ckd  \n",
       "4   no            ckd  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Replacing Categorical values with numbers:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. rbc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "normal      201\n",
       "abnormal     47\n",
       "Name: rbc, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['rbc'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['rbc'] = dataset['rbc'].replace(to_replace = {'normal' : 0, 'abnormal' : 1})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. pc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "normal      259\n",
       "abnormal     76\n",
       "Name: pc, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['pc'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['pc'] = dataset['pc'].replace(to_replace = {'normal' : 0, 'abnormal' : 1})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. pcc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "notpresent    354\n",
       "present        42\n",
       "Name: pcc, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['pcc'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['pcc'] = dataset['pcc'].replace(to_replace = {'notpresent':0,'present':1})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. ba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "notpresent    374\n",
       "present        22\n",
       "Name: ba, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['ba'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['ba'] = dataset['ba'].replace(to_replace = {'notpresent':0,'present':1})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5. htn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no     251\n",
       "yes    147\n",
       "Name: htn, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['htn'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['htn'] = dataset['htn'].replace(to_replace = {'yes' : 1, 'no' : 0})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6. dm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no       258\n",
       "yes      134\n",
       "\\tno       3\n",
       "\\tyes      2\n",
       " yes       1\n",
       "Name: dm, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['dm'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['dm'] = dataset['dm'].replace(to_replace = {'\\tyes':'yes', ' yes':'yes', '\\tno':'no'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['dm'] = dataset['dm'].replace(to_replace = {'yes' : 1, 'no' : 0})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 7. cad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no      362\n",
       "yes      34\n",
       "\\tno      2\n",
       "Name: cad, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['cad'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['cad'] = dataset['cad'].replace(to_replace = {'\\tno':'no'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['cad'] = dataset['cad'].replace(to_replace = {'yes' : 1, 'no' : 0})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 8. appet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['good', 'poor', nan], dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['appet'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['appet'] = dataset['appet'].replace(to_replace={'good':1,'poor':0,'no':np.nan})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 9. pe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no     323\n",
       "yes     76\n",
       "Name: pe, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['pe'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['pe'] = dataset['pe'].replace(to_replace = {'yes' : 1, 'no' : 0})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 10. ane"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no     339\n",
       "yes     60\n",
       "Name: ane, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['ane'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['ane'] = dataset['ane'].replace(to_replace = {'yes' : 1, 'no' : 0})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 11. classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ckd       248\n",
       "notckd    150\n",
       "ckd\\t       2\n",
       "Name: classification, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['classification'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['classification'] = dataset['classification'].replace(to_replace={'ckd\\t':'ckd'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset[\"classification\"] = [1 if i == \"ckd\" else 0 for i in dataset[\"classification\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\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>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>bu</th>\n",
       "      <th>sc</th>\n",
       "      <th>sod</th>\n",
       "      <th>pot</th>\n",
       "      <th>hemo</th>\n",
       "      <th>pcv</th>\n",
       "      <th>wc</th>\n",
       "      <th>rc</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>classification</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.4</td>\n",
       "      <td>44</td>\n",
       "      <td>7800</td>\n",
       "      <td>5.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.3</td>\n",
       "      <td>38</td>\n",
       "      <td>6000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>423.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.6</td>\n",
       "      <td>31</td>\n",
       "      <td>7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</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>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>111.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>11.2</td>\n",
       "      <td>32</td>\n",
       "      <td>6700</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.6</td>\n",
       "      <td>35</td>\n",
       "      <td>7300</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age    bp     sg   al   su  rbc   pc  pcc   ba    bgr    bu   sc    sod  \\\n",
       "0  48.0  80.0  1.020  1.0  0.0  NaN  0.0  0.0  0.0  121.0  36.0  1.2    NaN   \n",
       "1   7.0  50.0  1.020  4.0  0.0  NaN  0.0  0.0  0.0    NaN  18.0  0.8    NaN   \n",
       "2  62.0  80.0  1.010  2.0  3.0  0.0  0.0  0.0  0.0  423.0  53.0  1.8    NaN   \n",
       "3  48.0  70.0  1.005  4.0  0.0  0.0  1.0  1.0  0.0  117.0  56.0  3.8  111.0   \n",
       "4  51.0  80.0  1.010  2.0  0.0  0.0  0.0  0.0  0.0  106.0  26.0  1.4    NaN   \n",
       "\n",
       "   pot  hemo pcv    wc   rc  htn   dm  cad  appet   pe  ane  classification  \n",
       "0  NaN  15.4  44  7800  5.2  1.0  1.0  0.0    1.0  0.0  0.0               1  \n",
       "1  NaN  11.3  38  6000  NaN  0.0  0.0  0.0    1.0  0.0  0.0               1  \n",
       "2  NaN   9.6  31  7500  NaN  0.0  1.0  0.0    0.0  0.0  1.0               1  \n",
       "3  2.5  11.2  32  6700  3.9  1.0  0.0  0.0    0.0  1.0  1.0               1  \n",
       "4  NaN  11.6  35  7300  4.6  0.0  0.0  0.0    1.0  0.0  0.0               1  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age               float64\n",
       "bp                float64\n",
       "sg                float64\n",
       "al                float64\n",
       "su                float64\n",
       "rbc               float64\n",
       "pc                float64\n",
       "pcc               float64\n",
       "ba                float64\n",
       "bgr               float64\n",
       "bu                float64\n",
       "sc                float64\n",
       "sod               float64\n",
       "pot               float64\n",
       "hemo              float64\n",
       "pcv                object\n",
       "wc                 object\n",
       "rc                 object\n",
       "htn               float64\n",
       "dm                float64\n",
       "cad               float64\n",
       "appet             float64\n",
       "pe                float64\n",
       "ane               float64\n",
       "classification      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Datatypes:\n",
    "dataset.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting Object values into Numeric values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['pcv'] = pd.to_numeric(dataset['pcv'], errors='coerce')\n",
    "dataset['wc'] = pd.to_numeric(dataset['wc'], errors='coerce')\n",
    "dataset['rc'] = pd.to_numeric(dataset['rc'], errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age               float64\n",
       "bp                float64\n",
       "sg                float64\n",
       "al                float64\n",
       "su                float64\n",
       "rbc               float64\n",
       "pc                float64\n",
       "pcc               float64\n",
       "ba                float64\n",
       "bgr               float64\n",
       "bu                float64\n",
       "sc                float64\n",
       "sod               float64\n",
       "pot               float64\n",
       "hemo              float64\n",
       "pcv               float64\n",
       "wc                float64\n",
       "rc                float64\n",
       "htn               float64\n",
       "dm                float64\n",
       "cad               float64\n",
       "appet             float64\n",
       "pe                float64\n",
       "ane               float64\n",
       "classification      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Datatypes:\n",
    "dataset.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>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>bu</th>\n",
       "      <th>sc</th>\n",
       "      <th>sod</th>\n",
       "      <th>pot</th>\n",
       "      <th>hemo</th>\n",
       "      <th>pcv</th>\n",
       "      <th>wc</th>\n",
       "      <th>rc</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>classification</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>248.000000</td>\n",
       "      <td>335.000000</td>\n",
       "      <td>396.000000</td>\n",
       "      <td>396.000000</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",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>399.000000</td>\n",
       "      <td>399.000000</td>\n",
       "      <td>399.000000</td>\n",
       "      <td>400.000000</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>0.189516</td>\n",
       "      <td>0.226866</td>\n",
       "      <td>0.106061</td>\n",
       "      <td>0.055556</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",
       "      <td>0.369347</td>\n",
       "      <td>0.344221</td>\n",
       "      <td>0.085427</td>\n",
       "      <td>0.794486</td>\n",
       "      <td>0.190476</td>\n",
       "      <td>0.150376</td>\n",
       "      <td>0.625000</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>0.392711</td>\n",
       "      <td>0.419431</td>\n",
       "      <td>0.308305</td>\n",
       "      <td>0.229351</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",
       "      <td>0.483235</td>\n",
       "      <td>0.475712</td>\n",
       "      <td>0.279868</td>\n",
       "      <td>0.404584</td>\n",
       "      <td>0.393170</td>\n",
       "      <td>0.357888</td>\n",
       "      <td>0.484729</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>0.000000</td>\n",
       "      <td>0.000000</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",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>0.000000</td>\n",
       "      <td>0.000000</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",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>0.000000</td>\n",
       "      <td>0.000000</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",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</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>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</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",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</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": [
       "              age          bp          sg          al          su         rbc  \\\n",
       "count  391.000000  388.000000  353.000000  354.000000  351.000000  248.000000   \n",
       "mean    51.483376   76.469072    1.017408    1.016949    0.450142    0.189516   \n",
       "std     17.169714   13.683637    0.005717    1.352679    1.099191    0.392711   \n",
       "min      2.000000   50.000000    1.005000    0.000000    0.000000    0.000000   \n",
       "25%     42.000000   70.000000    1.010000    0.000000    0.000000    0.000000   \n",
       "50%     55.000000   80.000000    1.020000    0.000000    0.000000    0.000000   \n",
       "75%     64.500000   80.000000    1.020000    2.000000    0.000000    0.000000   \n",
       "max     90.000000  180.000000    1.025000    5.000000    5.000000    1.000000   \n",
       "\n",
       "               pc         pcc          ba         bgr          bu          sc  \\\n",
       "count  335.000000  396.000000  396.000000  356.000000  381.000000  383.000000   \n",
       "mean     0.226866    0.106061    0.055556  148.036517   57.425722    3.072454   \n",
       "std      0.419431    0.308305    0.229351   79.281714   50.503006    5.741126   \n",
       "min      0.000000    0.000000    0.000000   22.000000    1.500000    0.400000   \n",
       "25%      0.000000    0.000000    0.000000   99.000000   27.000000    0.900000   \n",
       "50%      0.000000    0.000000    0.000000  121.000000   42.000000    1.300000   \n",
       "75%      0.000000    0.000000    0.000000  163.000000   66.000000    2.800000   \n",
       "max      1.000000    1.000000    1.000000  490.000000  391.000000   76.000000   \n",
       "\n",
       "              sod         pot        hemo         pcv            wc  \\\n",
       "count  313.000000  312.000000  348.000000  329.000000    294.000000   \n",
       "mean   137.528754    4.627244   12.526437   38.884498   8406.122449   \n",
       "std     10.408752    3.193904    2.912587    8.990105   2944.474190   \n",
       "min      4.500000    2.500000    3.100000    9.000000   2200.000000   \n",
       "25%    135.000000    3.800000   10.300000   32.000000   6500.000000   \n",
       "50%    138.000000    4.400000   12.650000   40.000000   8000.000000   \n",
       "75%    142.000000    4.900000   15.000000   45.000000   9800.000000   \n",
       "max    163.000000   47.000000   17.800000   54.000000  26400.000000   \n",
       "\n",
       "               rc         htn          dm         cad       appet          pe  \\\n",
       "count  269.000000  398.000000  398.000000  398.000000  399.000000  399.000000   \n",
       "mean     4.707435    0.369347    0.344221    0.085427    0.794486    0.190476   \n",
       "std      1.025323    0.483235    0.475712    0.279868    0.404584    0.393170   \n",
       "min      2.100000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      3.900000    0.000000    0.000000    0.000000    1.000000    0.000000   \n",
       "50%      4.800000    0.000000    0.000000    0.000000    1.000000    0.000000   \n",
       "75%      5.400000    1.000000    1.000000    0.000000    1.000000    0.000000   \n",
       "max      8.000000    1.000000    1.000000    1.000000    1.000000    1.000000   \n",
       "\n",
       "              ane  classification  \n",
       "count  399.000000      400.000000  \n",
       "mean     0.150376        0.625000  \n",
       "std      0.357888        0.484729  \n",
       "min      0.000000        0.000000  \n",
       "25%      0.000000        0.000000  \n",
       "50%      0.000000        1.000000  \n",
       "75%      0.000000        1.000000  \n",
       "max      1.000000        1.000000  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Description:\n",
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rbc               152\n",
       "rc                131\n",
       "wc                106\n",
       "pot                88\n",
       "sod                87\n",
       "pcv                71\n",
       "pc                 65\n",
       "hemo               52\n",
       "su                 49\n",
       "sg                 47\n",
       "al                 46\n",
       "bgr                44\n",
       "bu                 19\n",
       "sc                 17\n",
       "bp                 12\n",
       "age                 9\n",
       "ba                  4\n",
       "pcc                 4\n",
       "htn                 2\n",
       "dm                  2\n",
       "cad                 2\n",
       "ane                 1\n",
       "appet               1\n",
       "pe                  1\n",
       "classification      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cheaking Missing (NaN) Values:\n",
    "dataset.isnull().sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handling Null Values:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- There is Outliers present in our dataset so We fill NaN values with Median."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'bp', 'sg', 'al', 'su', 'rbc', 'pc', 'pcc', 'ba', 'bgr', 'bu',\n",
       "       'sc', 'sod', 'pot', 'hemo', 'pcv', 'wc', 'rc', 'htn', 'dm', 'cad',\n",
       "       'appet', 'pe', 'ane', 'classification'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = ['age', 'bp', 'sg', 'al', 'su', 'rbc', 'pc', 'pcc', 'ba', 'bgr', 'bu',\n",
    "           'sc', 'sod', 'pot', 'hemo', 'pcv', 'wc', 'rc', 'htn', 'dm', 'cad',\n",
    "           'appet', 'pe', 'ane']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "for feature in features:\n",
    "    dataset[feature] = dataset[feature].fillna(dataset[feature].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isnull().any().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x1008 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(24,14))\n",
    "sns.heatmap(dataset.corr(), annot=True, cmap='YlGnBu')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We clearly see that 'pcv' and 'hemo' feature has **85% multicollinearity** \n",
    "- So we remove one of the feature. i.e **pcv**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.drop('pcv', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>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>bu</th>\n",
       "      <th>sc</th>\n",
       "      <th>sod</th>\n",
       "      <th>pot</th>\n",
       "      <th>hemo</th>\n",
       "      <th>wc</th>\n",
       "      <th>rc</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>classification</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>138.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>15.4</td>\n",
       "      <td>7800.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>138.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>11.3</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>423.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>138.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>9.6</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</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>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>111.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>11.2</td>\n",
       "      <td>6700.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>138.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>11.6</td>\n",
       "      <td>7300.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age    bp     sg   al   su  rbc   pc  pcc   ba    bgr    bu   sc    sod  \\\n",
       "0  48.0  80.0  1.020  1.0  0.0  0.0  0.0  0.0  0.0  121.0  36.0  1.2  138.0   \n",
       "1   7.0  50.0  1.020  4.0  0.0  0.0  0.0  0.0  0.0  121.0  18.0  0.8  138.0   \n",
       "2  62.0  80.0  1.010  2.0  3.0  0.0  0.0  0.0  0.0  423.0  53.0  1.8  138.0   \n",
       "3  48.0  70.0  1.005  4.0  0.0  0.0  1.0  1.0  0.0  117.0  56.0  3.8  111.0   \n",
       "4  51.0  80.0  1.010  2.0  0.0  0.0  0.0  0.0  0.0  106.0  26.0  1.4  138.0   \n",
       "\n",
       "   pot  hemo      wc   rc  htn   dm  cad  appet   pe  ane  classification  \n",
       "0  4.4  15.4  7800.0  5.2  1.0  1.0  0.0    1.0  0.0  0.0               1  \n",
       "1  4.4  11.3  6000.0  4.8  0.0  0.0  0.0    1.0  0.0  0.0               1  \n",
       "2  4.4   9.6  7500.0  4.8  0.0  1.0  0.0    0.0  0.0  1.0               1  \n",
       "3  2.5  11.2  6700.0  3.9  1.0  0.0  0.0    0.0  1.0  1.0               1  \n",
       "4  4.4  11.6  7300.0  4.6  0.0  0.0  0.0    1.0  0.0  0.0               1  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\LENOVO\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='classification', ylabel='count'>"
      ]
     },
     "execution_count": 48,
     "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": [
    "# Target feature:\n",
    "sns.countplot(dataset['classification'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Independent and Dependent Feature:\n",
    "X = dataset.iloc[:, :-1]\n",
    "y = dataset.iloc[:, -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>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>bu</th>\n",
       "      <th>sc</th>\n",
       "      <th>sod</th>\n",
       "      <th>pot</th>\n",
       "      <th>hemo</th>\n",
       "      <th>wc</th>\n",
       "      <th>rc</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",
       "    </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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>138.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>15.4</td>\n",
       "      <td>7800.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>138.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>11.3</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>423.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>138.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>9.6</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</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>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>111.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>11.2</td>\n",
       "      <td>6700.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>138.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>11.6</td>\n",
       "      <td>7300.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age    bp     sg   al   su  rbc   pc  pcc   ba    bgr    bu   sc    sod  \\\n",
       "0  48.0  80.0  1.020  1.0  0.0  0.0  0.0  0.0  0.0  121.0  36.0  1.2  138.0   \n",
       "1   7.0  50.0  1.020  4.0  0.0  0.0  0.0  0.0  0.0  121.0  18.0  0.8  138.0   \n",
       "2  62.0  80.0  1.010  2.0  3.0  0.0  0.0  0.0  0.0  423.0  53.0  1.8  138.0   \n",
       "3  48.0  70.0  1.005  4.0  0.0  0.0  1.0  1.0  0.0  117.0  56.0  3.8  111.0   \n",
       "4  51.0  80.0  1.010  2.0  0.0  0.0  0.0  0.0  0.0  106.0  26.0  1.4  138.0   \n",
       "\n",
       "   pot  hemo      wc   rc  htn   dm  cad  appet   pe  ane  \n",
       "0  4.4  15.4  7800.0  5.2  1.0  1.0  0.0    1.0  0.0  0.0  \n",
       "1  4.4  11.3  6000.0  4.8  0.0  0.0  0.0    1.0  0.0  0.0  \n",
       "2  4.4   9.6  7500.0  4.8  0.0  1.0  0.0    0.0  0.0  1.0  \n",
       "3  2.5  11.2  6700.0  3.9  1.0  0.0  0.0    0.0  1.0  1.0  \n",
       "4  4.4  11.6  7300.0  4.6  0.0  0.0  0.0    1.0  0.0  0.0  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Feature Importance:\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "import matplotlib.pyplot as plt\n",
    "model=ExtraTreesClassifier()\n",
    "model.fit(X,y)\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "ranked_features=pd.Series(model.feature_importances_,index=X.columns)\n",
    "ranked_features.nlargest(24).plot(kind='barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We take top 8 feature only."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['sg', 'htn', 'hemo', 'dm', 'al', 'appet', 'pc', 'rc'], dtype='object')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ranked_features.nlargest(8).index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>sg</th>\n",
       "      <th>htn</th>\n",
       "      <th>hemo</th>\n",
       "      <th>dm</th>\n",
       "      <th>al</th>\n",
       "      <th>appet</th>\n",
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       "      <th>pc</th>\n",
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       "      <th>0</th>\n",
       "      <td>1.020</td>\n",
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       "      <td>15.4</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.0</td>\n",
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      ],
      "text/plain": [
       "      sg  htn  hemo   dm   al  appet   rc   pc\n",
       "0  1.020  1.0  15.4  1.0  1.0    1.0  5.2  0.0\n",
       "1  1.020  0.0  11.3  0.0  4.0    1.0  4.8  0.0\n",
       "2  1.010  0.0   9.6  1.0  2.0    0.0  4.8  0.0\n",
       "3  1.005  1.0  11.2  0.0  4.0    0.0  3.9  1.0\n",
       "4  1.010  0.0  11.6  0.0  2.0    1.0  4.6  0.0"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = dataset[['sg', 'htn', 'hemo', 'dm', 'al', 'appet', 'rc', 'pc']]\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>sg</th>\n",
       "      <th>htn</th>\n",
       "      <th>hemo</th>\n",
       "      <th>dm</th>\n",
       "      <th>al</th>\n",
       "      <th>appet</th>\n",
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       "      <th>pc</th>\n",
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       "      <th>395</th>\n",
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       "      <td>15.7</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>4.9</td>\n",
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       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>1.025</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>397</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>1.025</td>\n",
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       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>1.025</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        sg  htn  hemo   dm   al  appet   rc   pc\n",
       "395  1.020  0.0  15.7  0.0  0.0    1.0  4.9  0.0\n",
       "396  1.025  0.0  16.5  0.0  0.0    1.0  6.2  0.0\n",
       "397  1.020  0.0  15.8  0.0  0.0    1.0  5.4  0.0\n",
       "398  1.025  0.0  14.2  0.0  0.0    1.0  5.9  0.0\n",
       "399  1.025  0.0  15.8  0.0  0.0    1.0  6.1  0.0"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    1\n",
       "Name: classification, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train Test Split:\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y, test_size=0.3, random_state=33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(280, 8)\n",
      "(120, 8)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### There is no need of Standardization and Normalization of our dataset, as we using Ensemble Technique."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing Performance Metrics:\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.975\n",
      "[[55  3]\n",
      " [ 0 62]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.95      0.97        58\n",
      "           1       0.95      1.00      0.98        62\n",
      "\n",
      "    accuracy                           0.97       120\n",
      "   macro avg       0.98      0.97      0.97       120\n",
      "weighted avg       0.98      0.97      0.97       120\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# RandomForestClassifier:\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "RandomForest = RandomForestClassifier()\n",
    "RandomForest = RandomForest.fit(X_train,y_train)\n",
    "\n",
    "# Predictions:\n",
    "y_pred = RandomForest.predict(X_test)\n",
    "\n",
    "# Performance:\n",
    "print('Accuracy:', accuracy_score(y_test,y_pred))\n",
    "print(confusion_matrix(y_test,y_pred))\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.975\n",
      "[[55  3]\n",
      " [ 0 62]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.95      0.97        58\n",
      "           1       0.95      1.00      0.98        62\n",
      "\n",
      "    accuracy                           0.97       120\n",
      "   macro avg       0.98      0.97      0.97       120\n",
      "weighted avg       0.98      0.97      0.97       120\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# AdaBoostClassifier:\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "AdaBoost = AdaBoostClassifier()\n",
    "AdaBoost = AdaBoost.fit(X_train,y_train)\n",
    "\n",
    "# Predictions:\n",
    "y_pred = AdaBoost.predict(X_test)\n",
    "\n",
    "# Performance:\n",
    "print('Accuracy:', accuracy_score(y_test,y_pred))\n",
    "print(confusion_matrix(y_test,y_pred))\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.975\n",
      "[[55  3]\n",
      " [ 0 62]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.95      0.97        58\n",
      "           1       0.95      1.00      0.98        62\n",
      "\n",
      "    accuracy                           0.97       120\n",
      "   macro avg       0.98      0.97      0.97       120\n",
      "weighted avg       0.98      0.97      0.97       120\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# GradientBoostingClassifier:\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "GradientBoost = GradientBoostingClassifier()\n",
    "GradientBoost = GradientBoost.fit(X_train,y_train)\n",
    "\n",
    "# Predictions:\n",
    "y_pred = GradientBoost.predict(X_test)\n",
    "\n",
    "# Performance:\n",
    "print('Accuracy:', accuracy_score(y_test,y_pred))\n",
    "print(confusion_matrix(y_test,y_pred))\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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