Kidney Disease Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Importing Libraries and Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>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>...</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>...</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>...</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>...</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>...</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>...</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",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "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",
       "   ...  pcv    wc   rc  htn   dm  cad appet   pe  ane classification  \n",
       "0  ...   44  7800  5.2  yes  yes   no  good   no   no            ckd  \n",
       "1  ...   38  6000  NaN   no   no   no  good   no   no            ckd  \n",
       "2  ...   31  7500  NaN   no  yes   no  poor   no  yes            ckd  \n",
       "3  ...   32  6700  3.9  yes   no   no  poor  yes  yes            ckd  \n",
       "4  ...   35  7300  4.6   no   no   no  good   no   no            ckd  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv(\"D:\\Ishty Folder\\Projects Python\\Chronic Kidney Disease\\kidney_disease.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Changing the columns names using the text description file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cols</th>\n",
       "      <th>Full_names</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>id</td>\n",
       "      <td>id</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>age</td>\n",
       "      <td>age</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bp</td>\n",
       "      <td>blood pressure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sg</td>\n",
       "      <td>specific gravity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>al</td>\n",
       "      <td>albumin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   cols        Full_names\n",
       "0   id                 id\n",
       "1  age                age\n",
       "2   bp     blood pressure\n",
       "3   sg   specific gravity\n",
       "4   al            albumin"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = pd.read_csv(\"D:\\Ishty Folder\\Projects Python\\Chronic Kidney Disease\\data_description.txt\",sep=\"-\")\n",
    "columns=columns.reset_index()\n",
    "columns.columns=[\"cols\",\"Full_names\"]\n",
    "columns.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>age</th>\n",
       "      <th>blood pressure</th>\n",
       "      <th>specific gravity</th>\n",
       "      <th>albumin</th>\n",
       "      <th>sugar</th>\n",
       "      <th>red blood cells</th>\n",
       "      <th>pus cell</th>\n",
       "      <th>pus cell clumps</th>\n",
       "      <th>bacteria</th>\n",
       "      <th>...</th>\n",
       "      <th>packed cell volume</th>\n",
       "      <th>white blood cell count</th>\n",
       "      <th>red blood cell count</th>\n",
       "      <th>ypertension</th>\n",
       "      <th>diabetes mellitus</th>\n",
       "      <th>coronary artery disease</th>\n",
       "      <th>appetite</th>\n",
       "      <th>pedal edema</th>\n",
       "      <th>anemia</th>\n",
       "      <th>class</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>...</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>...</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>...</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>...</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>...</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",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id   age  blood pressure  specific gravity  albumin  sugar red blood cells  \\\n",
       "0   0  48.0            80.0             1.020      1.0    0.0             NaN   \n",
       "1   1   7.0            50.0             1.020      4.0    0.0             NaN   \n",
       "2   2  62.0            80.0             1.010      2.0    3.0          normal   \n",
       "3   3  48.0            70.0             1.005      4.0    0.0          normal   \n",
       "4   4  51.0            80.0             1.010      2.0    0.0          normal   \n",
       "\n",
       "   pus cell pus cell clumps    bacteria  ...  packed cell volume  \\\n",
       "0    normal      notpresent  notpresent  ...                  44   \n",
       "1    normal      notpresent  notpresent  ...                  38   \n",
       "2    normal      notpresent  notpresent  ...                  31   \n",
       "3  abnormal         present  notpresent  ...                  32   \n",
       "4    normal      notpresent  notpresent  ...                  35   \n",
       "\n",
       "   white blood cell count  red blood cell count  ypertension  \\\n",
       "0                    7800                   5.2          yes   \n",
       "1                    6000                   NaN           no   \n",
       "2                    7500                   NaN           no   \n",
       "3                    6700                   3.9          yes   \n",
       "4                    7300                   4.6           no   \n",
       "\n",
       "   diabetes mellitus  coronary artery disease appetite pedal edema anemia  \\\n",
       "0                yes                       no     good          no     no   \n",
       "1                 no                       no     good          no     no   \n",
       "2                yes                       no     poor          no    yes   \n",
       "3                 no                       no     poor         yes    yes   \n",
       "4                 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 26 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns = columns[\"Full_names\"].values\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Changing the Data Types of Required Columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                           int64\n",
       "age                        float64\n",
       "blood pressure             float64\n",
       "specific gravity           float64\n",
       "albumin                    float64\n",
       "sugar                      float64\n",
       "red blood cells             object\n",
       " pus cell                   object\n",
       "pus cell clumps             object\n",
       "bacteria                    object\n",
       "blood glucose random       float64\n",
       "blood urea                 float64\n",
       "serum creatinine           float64\n",
       "sodium                     float64\n",
       "potassium                  float64\n",
       "haemoglobin                float64\n",
       "packed cell volume          object\n",
       "white blood cell count      object\n",
       "red blood cell count        object\n",
       "ypertension                 object\n",
       "diabetes mellitus           object\n",
       "coronary artery disease     object\n",
       "appetite                    object\n",
       "pedal edema                 object\n",
       "anemia                      object\n",
       "class                       object\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: plotly in c:\\users\\deepanshu\\anaconda3\\lib\\site-packages (4.14.3)\n",
      "Requirement already satisfied: retrying>=1.3.3 in c:\\users\\deepanshu\\anaconda3\\lib\\site-packages (from plotly) (1.3.3)\n",
      "Requirement already satisfied: six in c:\\users\\deepanshu\\anaconda3\\lib\\site-packages (from plotly) (1.15.0)\n"
     ]
    }
   ],
   "source": [
    "from Function_File import *\n",
    "features =['packed cell volume','white blood cell count','red blood cell count']\n",
    "for feature in features:\n",
    "    convert_dtypes(data,feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                           int64\n",
       "age                        float64\n",
       "blood pressure             float64\n",
       "specific gravity           float64\n",
       "albumin                    float64\n",
       "sugar                      float64\n",
       "red blood cells             object\n",
       " pus cell                   object\n",
       "pus cell clumps             object\n",
       "bacteria                    object\n",
       "blood glucose random       float64\n",
       "blood urea                 float64\n",
       "serum creatinine           float64\n",
       "sodium                     float64\n",
       "potassium                  float64\n",
       "haemoglobin                float64\n",
       "packed cell volume         float64\n",
       "white blood cell count     float64\n",
       "red blood cell count       float64\n",
       "ypertension                 object\n",
       "diabetes mellitus           object\n",
       "coronary artery disease     object\n",
       "appetite                    object\n",
       "pedal edema                 object\n",
       "anemia                      object\n",
       "class                       object\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Cleaning "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>blood pressure</th>\n",
       "      <th>specific gravity</th>\n",
       "      <th>albumin</th>\n",
       "      <th>sugar</th>\n",
       "      <th>red blood cells</th>\n",
       "      <th>pus cell</th>\n",
       "      <th>pus cell clumps</th>\n",
       "      <th>bacteria</th>\n",
       "      <th>blood glucose random</th>\n",
       "      <th>...</th>\n",
       "      <th>packed cell volume</th>\n",
       "      <th>white blood cell count</th>\n",
       "      <th>red blood cell count</th>\n",
       "      <th>ypertension</th>\n",
       "      <th>diabetes mellitus</th>\n",
       "      <th>coronary artery disease</th>\n",
       "      <th>appetite</th>\n",
       "      <th>pedal edema</th>\n",
       "      <th>anemia</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  blood pressure  specific gravity  albumin  sugar red blood cells  \\\n",
       "0  48.0            80.0             1.020      1.0    0.0             NaN   \n",
       "1   7.0            50.0             1.020      4.0    0.0             NaN   \n",
       "2  62.0            80.0             1.010      2.0    3.0          normal   \n",
       "3  48.0            70.0             1.005      4.0    0.0          normal   \n",
       "4  51.0            80.0             1.010      2.0    0.0          normal   \n",
       "\n",
       "   pus cell pus cell clumps    bacteria  blood glucose random  ...  \\\n",
       "0    normal      notpresent  notpresent                 121.0  ...   \n",
       "1    normal      notpresent  notpresent                   NaN  ...   \n",
       "2    normal      notpresent  notpresent                 423.0  ...   \n",
       "3  abnormal         present  notpresent                 117.0  ...   \n",
       "4    normal      notpresent  notpresent                 106.0  ...   \n",
       "\n",
       "   packed cell volume  white blood cell count  red blood cell count  \\\n",
       "0                44.0                  7800.0                   5.2   \n",
       "1                38.0                  6000.0                   NaN   \n",
       "2                31.0                  7500.0                   NaN   \n",
       "3                32.0                  6700.0                   3.9   \n",
       "4                35.0                  7300.0                   4.6   \n",
       "\n",
       "   ypertension  diabetes mellitus  coronary artery disease  appetite  \\\n",
       "0          yes                yes                       no      good   \n",
       "1           no                 no                       no      good   \n",
       "2           no                yes                       no      poor   \n",
       "3          yes                 no                       no      poor   \n",
       "4           no                 no                       no      good   \n",
       "\n",
       "   pedal edema anemia class  \n",
       "0           no     no   ckd  \n",
       "1           no     no   ckd  \n",
       "2           no    yes   ckd  \n",
       "3          yes    yes   ckd  \n",
       "4           no     no   ckd  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop('id',axis=1,inplace=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from Function_File import *\n",
    "cat_col,num_col=extract_cat_num(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " red blood cells has 2 numbers of values i.e. [nan 'normal' 'abnormal']\n",
      "  pus cell has 2 numbers of values i.e. ['normal' 'abnormal' nan]\n",
      " pus cell clumps has 2 numbers of values i.e. ['notpresent' 'present' nan]\n",
      " bacteria has 2 numbers of values i.e. ['notpresent' 'present' nan]\n",
      " ypertension has 2 numbers of values i.e. ['yes' 'no' nan]\n",
      " diabetes mellitus has 5 numbers of values i.e. ['yes' 'no' ' yes' '\\tno' '\\tyes' nan]\n",
      " coronary artery disease has 3 numbers of values i.e. ['no' 'yes' '\\tno' nan]\n",
      " appetite has 2 numbers of values i.e. ['good' 'poor' nan]\n",
      " pedal edema has 2 numbers of values i.e. ['no' 'yes' nan]\n",
      " anemia has 2 numbers of values i.e. ['no' 'yes' nan]\n",
      " class has 3 numbers of values i.e. ['ckd' 'ckd\\t' 'notckd']\n"
     ]
    }
   ],
   "source": [
    "for col in cat_col:\n",
    "    print(' {} has {} numbers of values i.e. {}'.format(col, data[col].nunique(),data[col].unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " red blood cells has 2 numbers of values i.e. [nan 'normal' 'abnormal']\n",
      "  pus cell has 2 numbers of values i.e. ['normal' 'abnormal' nan]\n",
      " pus cell clumps has 2 numbers of values i.e. ['notpresent' 'present' nan]\n",
      " bacteria has 2 numbers of values i.e. ['notpresent' 'present' nan]\n",
      " ypertension has 2 numbers of values i.e. ['yes' 'no' nan]\n",
      " diabetes mellitus has 3 numbers of values i.e. ['yes' 'no' ' yes' nan]\n",
      " coronary artery disease has 2 numbers of values i.e. ['no' 'yes' nan]\n",
      " appetite has 2 numbers of values i.e. ['good' 'poor' nan]\n",
      " pedal edema has 2 numbers of values i.e. ['no' 'yes' nan]\n",
      " anemia has 2 numbers of values i.e. ['no' 'yes' nan]\n",
      " class has 2 numbers of values i.e. ['ckd' 'notckd']\n"
     ]
    }
   ],
   "source": [
    "data[\"diabetes mellitus\"]= data['diabetes mellitus'].replace(to_replace ={'\\tno':'no','\\tyes':'yes'})\n",
    "data[\"coronary artery disease\"]= data['coronary artery disease'].replace(to_replace ={'\\tno':'no','\\tyes':'yes'})\n",
    "data['class'] = data['class'].replace(to_replace={'ckd\\t':'ckd'})\n",
    "for col in cat_col:\n",
    "    print(' {} has {} numbers of values i.e. {}'.format(col, data[col].nunique(),data[col].unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "red blood cells            152\n",
       "red blood cell count       131\n",
       "white blood cell count     106\n",
       "potassium                   88\n",
       "sodium                      87\n",
       "packed cell volume          71\n",
       " pus cell                   65\n",
       "haemoglobin                 52\n",
       "sugar                       49\n",
       "specific gravity            47\n",
       "albumin                     46\n",
       "blood glucose random        44\n",
       "blood urea                  19\n",
       "serum creatinine            17\n",
       "blood pressure              12\n",
       "age                          9\n",
       "bacteria                     4\n",
       "pus cell clumps              4\n",
       "ypertension                  2\n",
       "diabetes mellitus            2\n",
       "coronary artery disease      2\n",
       "anemia                       1\n",
       "appetite                     1\n",
       "pedal edema                  1\n",
       "class                        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum().sort_values(ascending =False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The missing values age  are  0\n",
      "The missing values blood pressure  are  0\n",
      "The missing values specific gravity  are  0\n",
      "The missing values albumin  are  0\n",
      "The missing values sugar  are  0\n",
      "The missing values blood glucose random  are  0\n",
      "The missing values blood urea  are  0\n",
      "The missing values serum creatinine  are  0\n",
      "The missing values sodium  are  0\n",
      "The missing values potassium  are  0\n",
      "The missing values haemoglobin  are  0\n",
      "The missing values packed cell volume  are  0\n",
      "The missing values white blood cell count  are  0\n",
      "The missing values red blood cell count  are  0\n"
     ]
    }
   ],
   "source": [
    "from Function_File import *\n",
    "data[num_col].isnull().sum()\n",
    "for f in num_col:\n",
    "    random_value_imputation(data,f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The missing values red blood cells  are  0\n",
      "The missing values  pus cell  are  0\n",
      "The missing values pus cell clumps  are  0\n",
      "The missing values bacteria  are  0\n",
      "The missing values ypertension  are  0\n",
      "The missing values diabetes mellitus  are  0\n",
      "The missing values coronary artery disease  are  0\n",
      "The missing values appetite  are  0\n",
      "The missing values pedal edema  are  0\n",
      "The missing values anemia  are  0\n",
      "The missing values class  are  0\n"
     ]
    }
   ],
   "source": [
    "from Function_File import *\n",
    "data[cat_col].isnull().sum()\n",
    "for f in cat_col:\n",
    "    random_value_imputation(data,f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x1440 with 14 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30,20))\n",
    "\n",
    "for i,feature in enumerate(num_col):\n",
    "    plt.subplot(5,3,i+1)\n",
    "    data[feature].hist()\n",
    "    plt.title(feature)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion 1: The age, sodium, haemoglobin and packed cell volume are little bit left skewed. Means there are some negative / low value outliers\n",
    "Whereas, white blood cell count, senum creatinine, BP, blood urea and blood glucose are right skewed means it is right means high positive outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DEEPANSHU\\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",
      "C:\\Users\\DEEPANSHU\\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",
      "C:\\Users\\DEEPANSHU\\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",
      "C:\\Users\\DEEPANSHU\\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",
      "C:\\Users\\DEEPANSHU\\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",
      "C:\\Users\\DEEPANSHU\\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",
      "C:\\Users\\DEEPANSHU\\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",
      "C:\\Users\\DEEPANSHU\\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",
      "C:\\Users\\DEEPANSHU\\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",
      "C:\\Users\\DEEPANSHU\\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",
      "C:\\Users\\DEEPANSHU\\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": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x1440 with 11 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30,20))\n",
    "for i,feature in enumerate(cat_col):\n",
    "    plt.subplot(4,3,i+1)\n",
    "    sns.countplot(data[feature])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since, the features are having imbalance situations hence, cor-relations are needed to be find. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18,8)) \n",
    "sns.heatmap(data.corr(),annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: plotly in c:\\users\\deepanshu\\anaconda3\\lib\\site-packages (4.14.3)\n",
      "Requirement already satisfied: retrying>=1.3.3 in c:\\users\\deepanshu\\anaconda3\\lib\\site-packages (from plotly) (1.3.3)\n",
      "Requirement already satisfied: six in c:\\users\\deepanshu\\anaconda3\\lib\\site-packages (from plotly) (1.15.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install plotly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 501.875x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 501.875x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 501.875x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 501.875x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 501.875x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 501.875x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 501.875x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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FSczMrII4vLPw3eZmZlZBMoW3pCmSFklaLOmSEsdPkzQv/XtU0sTur2qOSo7z9t3mZmaWj7LhLakWuBI4FhgHnCppXIdizwFHRcQBwGXA1d1d0Vz5srmZmVWQLC3vQ4HFEbEkIlqAm4ATigtExKMR8Wq6+RgwqnurmbOOQ8VqfLe5mZnlJ0t4jwReKNpuTvd15izgN6UOSJoqqUlS04oVK7LXMm+l5jYHj/U2M7NcZAlvldhXMrUkvZckvC8udTwiro6IxohoHD58ePZa5q3UJC3g1reZmeWiLkOZZmCPou1RwPKOhSQdAFwLHBsRL3dP9SpEtHW4YS39PRMFoLbkS8zMzHpKlpb3bGCspDGS6oFTgJnFBSTtCdwGnB4Rf+r+aubMLW8zM6sgZVveEdEq6XzgHpJm5vSIWCjp3PT4NODfgGHAT5S0SlsjorHnqt3LSk2PClBoBRpyqZKZmfVdWS6bExGzgFkd9k0ren42cHb3Vq2CRIfpUWv7JY9tG/Kpj5mZ9WmeYa2cQnppvLjPuyYN70Jr79fHzMz6PId3OYW0dV1TdJGiNn3ulreZmeXA4V1OW0vyWFu/ad/GlrfD28zMel+mPu8+rb113d7PXfR82cuv0/zKy7S0FdjvbUMYPtg3r5mZWc9zeJfT3q+dBnZrW4FHFr/KUcBnrn2ExbFpJth3jBjEmZNHc+K7RtG/n8d/m5lZz3B4l9N+2bymH/ObX+P/3jKX0S/9jaPq4YKj9mKXdzQiwcJlr/Precv5yu0LuOp3f+HbH5/Ae8ZW0SxyZmZWNdznXU562fyp5Ws4adqjrFq7gXOO3geAEyaMYPI7duGIvXfhnCPfzh3nTebGsyZRX1vD6dc9waUzF7KhzRO5mJlZ93J4l5OG94w/LOOAUUO564vv5uAxI5JjHYaKSeLdY3dh1oXv4czJo5nx6FJOu/ZxVq5e39u1NjOz7ZjDu4y75v4VgHGjhnHj2ZMYNqhh07CxToaK9e9Xy9c/Mp4rTj6QuS+s4vgfPcz85td6q8pmZradc3hvxc1NL3DV/X8E4Kyj96GhLr0JrTbbULGPHjSSWz9/BJI4adqj3P50c09W18zM+giHdyfumLOMi2+dR+MegwHo169oGFj7OO+28jOs7T9yKDPPn8xBe+7IP/1yLt+68xla3Q9uZmZvgcO7hLsX/I1/vnkuk8bszJeP2TvZudk47/SyecZJWoYNauBnZ03ijCNGc+3Dz3HmjNmsWtvSzbU2M7O+wuHdwQOLXuKCXzzFxFFDue4zh9BQ05YcqCkO73S2tS5Mj9qvtoZLjx/Pf5x0AI8veYXjfvAQjy3ZvpY9NzOz3uHwLvLoX1Zy7s+eZN+3DWbGZw9lYENd0QxrRdOj9huQPG5Y2+XP+GTjHtz6+SNo6FfLqdc8xmV3PsOa9V7gxMzMsnN4px5f8jJnX9/EXsMGcMNnJzGkf9rSbl2XPNYVhXfDkORx/Rvb9FkTRg3lzgvezWmT9uS6h5/jmP/6PXfMWUahEG/hG5iZWV/h8Cbp4z59+hPsNrQ/N549iZ0HFgV1y5rksX7Qpn3928P79S3frG0DrHq+7GcObKjjWx+dwC3nHs7QHfpx4U1z+PCPHubXc5fT5hA3M7Ot6NPhXSgEVz6wmC/8/EnG7TaEX517BCMG99+8UMvq5LE4vOsaksvo60qE9+8uhysmwMo/Z6pD4+idufOCd/NfJ09k3YY2LvjF00y+/Ld8+zfP8se/lXh/MzPr8/rs3ObLVr3JV26fz+8WreAjE3fnOydOYEB9idOxseU9cPP9DYNLt7zn3pQ8Ln0IdhmbqS41NeJjB43i+Ikjue+Zv/Orphe47qHn+O8Hl7Df2wZz9L4jOHzvYTTutVPSD29mZn1an0uC19dt4IZHl3LlA38hCC47YTyfOmwvJJV+wfq05d1+k1q7ncbA848nY703Dh1rg3WrkufLnoLGz3apbrU1Ysr+b2PK/m/j5dXr+fXc5dw1/0WufWgJ0x78C3U14p27DWHsroMYO2IwY0cMYvcdd2DXIQ3sNKCemppOvoOZmW1XMoW3pCnAD4Ba4NqIuLzDcaXHjwPWAmdExFPdXNdt1lYI5rywiplzlnHbU8t4Y30rH3jnrnz9I+PYY+cBW3/x6r/DwOFQ06GH4bDPw61nwWM/gclfTPat/POmy+zLn35LdR42qIEzJo/hjMljWNvSStPSV/nDkpdZsOw1Hlm8ktueWrZZ+X61YqcB9QxqqGNgQx0DG2oZ1FDHgPo6BtTXbnzcob423a5lh/o6BvSr3bh/YPraQQ11DKyvpa62T/eqmJlVrLLhLakWuBL4B6AZmC1pZkQ8U1TsWGBs+jcJuCp97FERQUtbgbXr21i7oY03W1pZ29LGy6tb+OvLa/jrK2tZsmINTz3/Km+sa6W+roZjxu3K547cmwmjhmb7kFXPw5Ddt9w/4SRo+inMvgYO+0LS+l76UHJs/MfhmTuSu9EbBr/l7zmgvo4j9xnOkftsWmL0tbUbWLxiNX9/fR0vvb6Ov7+xnldWt7CmpZU161tZs76N5avWsSY9J2+2tLG2pZWu3AvXv1/Nxh8DWz7WMrC+jkH9N+0bUF9LfW0N9XU1NNTVUl/X/jx5rK+toa5WCCGBSBZzKX5eIxDJjuL9kDxvV3yhROmRUhdPypXb/D07/5xOr8yYmeUgS8v7UGBxRCwBkHQTcAJQHN4nADdERACPSdpR0m4R8WK317jIuy67j1fXdj5RyoD6WvbceQAfPmA3Dnv7MN633wgG9+/XafktLHsKnvs9HHpO6eOTpsLNn4ZvjQACogAjxidhvvA2uPVs+Mdfdu1LZTR0QD8O3munLr0mIljfWkiCvOjHTnu4twf/G+uS8F/T0srq9cm+1euS5y+9sY41K9vSMq28uaGtR75ftdgY7pvty/ZjgxI/IrryY8OsEjR99R/Yob4272r0OUrydisFpJOAKRFxdrp9OjApIs4vKnMncHlEPJxu3w9cHBFNHd5rKjA13dwXWNRdX6SH7QKszLsSVcrnbtv53G07n7ttl8e5WxkRU3r5M6talpZ3qR/7HRM/Sxki4mrg6gyfWVEkNUVEY971qEY+d9vO527b+dxtO5+76pDljqRmYI+i7VHA8m0oY2ZmZt0gS3jPBsZKGiOpHjgFmNmhzEzg00ocBrzW0/3dZmZmfVXZy+YR0SrpfOAekqFi0yNioaRz0+PTgFkkw8QWkwwVO7PnqpyLqrvUX0F87radz92287nbdj53VaDsDWtmZmZWWTwLh5mZWZVxeJuZmVUZh/dWSJoiaZGkxZIuybs+lUzSHpIekPSspIWSLkz37yzpPkl/Th+7NrNMHyKpVtLT6bwJPnddkE4MdYukP6b/DR7u85eNpH9K/80ukPQLSf197iqfw7sTRdPCHguMA06VNC7fWlW0VuD/RMQ7gcOA89LzdQlwf0SMBe5Pt620C4Fni7Z97rL7AXB3ROwHTCQ5jz5/ZUgaCXwRaIyI/UluSj4Fn7uK5/Du3MZpYSOiBWifFtZKiIgX2xejiYg3SP7nOZLknF2fFrse+GguFaxwkkYBHwKuLdrtc5eBpCHAkcB1ABHREhGr8PnLqg7YQVIdMIBkjg6fuwrn8O7cSOCFou3mdJ+VIWk0cBDwOLBr+5j/9HFEjlWrZFcA/wIUivb53GXzdmAF8NO02+FaSQPx+SsrIpYB3wOeB14kmaPjXnzuKp7Du3OZpny1zUkaBNwKfCkiXs+7PtVA0oeBlyLiybzrUqXqgHcBV0XEQcAafJk3k7Qv+wRgDLA7MFDSp/KtlWXh8O6cp3ztIkn9SIL75xFxW7r775J2S4/vBryUV/0q2GTgeElLSbpn3ifpRnzusmoGmiPi8XT7FpIw9/kr7wPAcxGxIiI2ALcBR+BzV/Ec3p3LMi2spZSsg3kd8GxEfL/o0EzgM+nzzwB39HbdKl1EfDkiRkXEaJL/zn4bEZ/C5y6TiPgb8IKkfdNd7ydZstjnr7zngcMkDUj/Db+f5H4Vn7sK5xnWtkLScSR9ke3Twv57vjWqXJLeDTwEzGdTv+2/kvR73wzsSfI/ik9ExCu5VLIKSDoauCgiPixpGD53mUg6kORmv3pgCckUzTX4/JUl6RvAySQjRp4GzgYG4XNX0RzeZmZmVcaXzc3MzKqMw9vMzKzKOLzNzMyqjMPbzMysyji8zczMqozD26xKSfqSpAFF27Mk7VjmNd+U9IEer5yZ9SgPFTPrQFJdRLT28mfWRkRbF1+zlGQ1qJU9Uyszq1Ruedt2SdJASXdJmpuuU3xyuv9gSQ9KelLSPUVTQP5O0v+T9CBwoaQZkk4qer/V6ePR6etvlvQnSZdLOk3SE5LmS9q7RF0GSfppenyepBPb3zNtCT8OHC7pU+n7zJH03+mytEi6SlJTuubyN9J9XySZi/oBSQ+k+5ZK2kXS6HRN62vS19wraYe0zMbvlZb/hqSn0rrtV3TupkuanS704dX0zCqMw9u2V1OA5RExMV2n+O507vUfASdFxMHAdKB41rwdI+KoiPjPMu89kWTt7QnA6cA+EXEoyQxfF5Qo/zWS1ZomRMQBwG/T/QOBBRExCXiZZJaryRFxINAGnJaW+0pENAIHAEdJOiAifkgy1/57I+K9JT5zLHBlRIwHVgEndvJdVkbEu4CrgIvaP49kitZDgPcC301X6TKzClGXdwXMesh84HuSvgPcGREPSdof2B+4L5nGmVqSZRDb/TLje89uXy5R0l+Ae4s+s1SQfoBkznIAIuLV9GkbyUIukMwpfTAwO63bDmxaDOKTkqaS/HvdDRgHzCtTx+ciYk76/ElgdCflbisq8/H0+TEkC6W0h3l/kmkyny3zmWbWSxzetl2KiD9JOhg4Dvi2pHuB24GFEXF4Jy9bU/S8lfTKVLpgQ33RsfVFzwtF2wVK/5sSpZeTXVfUzy3g+oj48mYvlMaQtIgPiYhXJc0gCdNyiuvYRvJjYGvl2orqLuDEiFiU4XPMLAe+bG7bJUm7A2sj4kbgeyRLRC4Chks6PC3TT9L4Tt5iKUlLGJL1jvu9hercC5xfVLedSpS5HzhJ0oi0zM6S9gKGkPyoeE3SrsCxRa95Axj8FurVmXuAC9IfLUg6qAc+w8zeAoe3ba8mAE9ImkPSh/utiGgBTgK+I2kuMIdk7eJSriHpX34CmMTmrfKu+hawU3rj3FxKXFqPiGeArwL3SpoH3AfsFhFzSVZ6WkjSR/9I0cuuBn7TfsNaN7qM5MfKPEkL0m0zqyAeKmZmZlZl3PI2MzOrMg5vMzOzKuPwNjMzqzIObzMzsyrj8DYzM6syDm8zM7Mq4/A2MzOrMv8L7cMHnMuHyGMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 501.875x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 501.875x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 501.875x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 501.875x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 501.875x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 501.875x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 501.875x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from Function_File import *\n",
    "num_col\n",
    "for i in num_col:\n",
    "    kdeplot(data,i) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion 2:\n",
    "1. Age group of 50-70 is more likely to be suffering from ckd whereas age group between 20-80 are non ckd. The variance in non ckd data is more than the ckd. \n",
    "2. The BP for ckd is more variant and lie between 60-100 compared to non ckd \n",
    "3. The albumin content of non ckd lies less than 1 whereas the albumin content of ckd is more dispersed upto 5. \n",
    "4. The blood glucose level for non ckd lies 50 to 150 whereas less ckd suffers are having level in this normal range. Ckd sefferers are having more dispersed data. \n",
    "5. Similarly, for blood urea, only few ckd sufferers are having blood urea in normal range as non ckd sufferers.\n",
    "6. Maximum ckd sufferers are having serum creatinine in range of non ckd sufferers. \n",
    "7. The sodium and potassium levels of ckd and non ckd are in approximate same range \n",
    "8. The haemoglobin levels for non ckd is more in range of 12.5-17.5 than ckd in range of 5-17.5 having dispersed data\n",
    "9. The packed cell volume of ckd lies low in normal range i.e. 40-60.\n",
    "10. The wwhite blood count of non ckd and ckd are in approximately same range with approximately same dispersed data. \n",
    "11. The red blood count of non ckd is more in range of 4-7 whereas ckd sufferers are having less red blood count in range of 3-6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Enter the Feature (Having object dtype preferably) to study with red blood cell count:  anemia\n",
      "               count      mean  median  min  max\n",
      "anemia class                                    \n",
      "no     ckd       190  4.431579     4.4  2.4  8.0\n",
      "       notckd    150  5.341333     5.3  2.8  6.5\n",
      "yes    ckd        60  3.850000     3.7  2.1  6.5\n",
      " \n",
      " \n"
     ]
    }
   ],
   "source": [
    "#To study the RBC Count with respect to different features grouped with class\n",
    "Feature_Study = input(\" Enter the Feature (Having object dtype preferably) to study with red blood cell count:  \")\n",
    "for i in data.columns:\n",
    "    if i==Feature_Study:\n",
    "        print(data.groupby([i,'class'])['red blood cell count'].agg(['count','mean','median','min','max']), end='\\n \\n \\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Enter the Feature (Having object dtype preferably) to study with white blood cell count:  anemia\n",
      "               count         mean  median     min      max\n",
      "anemia class                                              \n",
      "no     ckd       190  9118.421053  8900.0  2200.0  26400.0\n",
      "       notckd    150  7654.666667  7400.0  4200.0  12300.0\n",
      "yes    ckd        60  8395.000000  8000.0  2600.0  21600.0\n",
      " \n",
      " \n"
     ]
    }
   ],
   "source": [
    "#To study the WBC Count with respect to different features grouped with class\n",
    "Feature_Study = input(\" Enter the Feature (Having object dtype preferably) to study with white blood cell count:  \")\n",
    "for i in data.columns:\n",
    "    if i==Feature_Study:\n",
    "        print(data.groupby([i,'class'])['white blood cell count'].agg(['count','mean','median','min','max']), end='\\n \\n \\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: tabulate in c:\\users\\deepanshu\\anaconda3\\lib\\site-packages (0.8.9)\n"
     ]
    }
   ],
   "source": [
    "!pip install tabulate\n",
    "from tabulate import tabulate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name                      count        mean    median       min        max\n",
      "----------------------  -------  ----------  --------  --------  ---------\n",
      "age                         400    51.4625      54.5      2         90\n",
      "blood pressure              400    76.65        80       50        180\n",
      "specific gravity            400     1.01739      1.02     1.005      1.025\n",
      "albumin                     400     0.9875       0        0          5\n",
      "sugar                       400     0.4475       0        0          5\n",
      "blood glucose random        400   148.98       121       22        490\n",
      "blood urea                  400    58.0682      42        1.5      391\n",
      "serum creatinine            400     3.04387      1.3      0.4       76\n",
      "sodium                      400   137.471      138        4.5      163\n",
      "potassium                   400     4.67325      4.4      2.5       47\n",
      "haemoglobin                 400    12.5098      12.6      3.1       17.8\n",
      "packed cell volume          400    38.965       41        9         54\n",
      "white blood cell count      400  8461         8100     2200      26400\n",
      "red blood cell count        400     4.6855       4.7      2.1        8\n"
     ]
    }
   ],
   "source": [
    "g=[['Name','count','mean','median','min','max']]\n",
    "for i in data.columns:\n",
    "    if data[i].dtypes=='float64':\n",
    "        d=[i, data[i].count(), data[i].mean(), data[i].median(), data[i].min(), data[i].max()]\n",
    "        g.append(d)      \n",
    "print(tabulate(g, headers='firstrow'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selecting Best Feature From the List"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>blood pressure</th>\n",
       "      <th>specific gravity</th>\n",
       "      <th>albumin</th>\n",
       "      <th>sugar</th>\n",
       "      <th>red blood cells</th>\n",
       "      <th>pus cell</th>\n",
       "      <th>pus cell clumps</th>\n",
       "      <th>bacteria</th>\n",
       "      <th>blood glucose random</th>\n",
       "      <th>...</th>\n",
       "      <th>packed cell volume</th>\n",
       "      <th>white blood cell count</th>\n",
       "      <th>red blood cell count</th>\n",
       "      <th>ypertension</th>\n",
       "      <th>diabetes mellitus</th>\n",
       "      <th>coronary artery disease</th>\n",
       "      <th>appetite</th>\n",
       "      <th>pedal edema</th>\n",
       "      <th>anemia</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>...</td>\n",
       "      <td>38.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>423.0</td>\n",
       "      <td>...</td>\n",
       "      <td>31.0</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  blood pressure  specific gravity  albumin  sugar  red blood cells  \\\n",
       "0  48.0            80.0             1.020      1.0    0.0                1   \n",
       "1   7.0            50.0             1.020      4.0    0.0                1   \n",
       "2  62.0            80.0             1.010      2.0    3.0                1   \n",
       "3  48.0            70.0             1.005      4.0    0.0                1   \n",
       "4  51.0            80.0             1.010      2.0    0.0                1   \n",
       "\n",
       "    pus cell  pus cell clumps  bacteria  blood glucose random  ...  \\\n",
       "0          1                0         0                 121.0  ...   \n",
       "1          1                0         0                  70.0  ...   \n",
       "2          1                0         0                 423.0  ...   \n",
       "3          0                1         0                 117.0  ...   \n",
       "4          1                0         0                 106.0  ...   \n",
       "\n",
       "   packed cell volume  white blood cell count  red blood cell count  \\\n",
       "0                44.0                  7800.0                   5.2   \n",
       "1                38.0                  6000.0                   3.9   \n",
       "2                31.0                  7500.0                   4.7   \n",
       "3                32.0                  6700.0                   3.9   \n",
       "4                35.0                  7300.0                   4.6   \n",
       "\n",
       "   ypertension  diabetes mellitus  coronary artery disease  appetite  \\\n",
       "0            1                  2                        0         0   \n",
       "1            0                  1                        0         0   \n",
       "2            0                  2                        0         1   \n",
       "3            1                  1                        0         1   \n",
       "4            0                  1                        0         0   \n",
       "\n",
       "   pedal edema  anemia  class  \n",
       "0            0       0      0  \n",
       "1            0       0      0  \n",
       "2            0       1      0  \n",
       "3            1       1      0  \n",
       "4            0       0      0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le=LabelEncoder()\n",
    "for col in cat_col:\n",
    "    data[col]=le.fit_transform(data[col])\n",
    "data.head()\n",
    "#0 is ckd and 1 is non ckd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import chi2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "ind_col=[col for col in data.columns if col!='class']\n",
    "dep_col='class'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=data[ind_col]\n",
    "y=data[dep_col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.10871026e+02, 7.16407915e+01, 5.38801997e-03, 2.12091308e+02,\n",
       "       1.01095345e+02, 8.56296296e+00, 1.38744086e+01, 2.52000000e+01,\n",
       "       1.32000000e+01, 2.16899027e+03, 2.54218979e+03, 3.61269918e+02,\n",
       "       2.85239644e+01, 6.19265286e+00, 1.21405966e+02, 3.13562787e+02,\n",
       "       1.84424568e+04, 2.20314090e+01, 8.31145414e+01, 1.93699565e+01,\n",
       "       2.04000000e+01, 4.92000000e+01, 4.56000000e+01, 3.60000000e+01])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ordered_rank_feature=SelectKBest(score_func=chi2,k=20)\n",
    "ordered_feature=ordered_rank_feature.fit(x,y)\n",
    "ordered_feature.scores_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_score=pd.DataFrame(ordered_feature.scores_,columns=['score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>feature</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>age</td>\n",
       "      <td>110.871026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>blood pressure</td>\n",
       "      <td>71.640791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>specific gravity</td>\n",
       "      <td>0.005388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>albumin</td>\n",
       "      <td>212.091308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sugar</td>\n",
       "      <td>101.095345</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            feature       score\n",
       "0               age  110.871026\n",
       "1    blood pressure   71.640791\n",
       "2  specific gravity    0.005388\n",
       "3           albumin  212.091308\n",
       "4             sugar  101.095345"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_x=pd.DataFrame(x.columns,columns=['feature'])\n",
    "features_rank=pd.concat([data_x,data_score],axis=1)\n",
    "features_rank.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18442.45676240003"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_rank['score'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>feature</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>white blood cell count</td>\n",
       "      <td>18442.456762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>blood urea</td>\n",
       "      <td>2542.189789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>blood glucose random</td>\n",
       "      <td>2168.990272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>serum creatinine</td>\n",
       "      <td>361.269918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>packed cell volume</td>\n",
       "      <td>313.562787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>albumin</td>\n",
       "      <td>212.091308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>haemoglobin</td>\n",
       "      <td>121.405966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>age</td>\n",
       "      <td>110.871026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sugar</td>\n",
       "      <td>101.095345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>ypertension</td>\n",
       "      <td>83.114541</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   feature         score\n",
       "16  white blood cell count  18442.456762\n",
       "10              blood urea   2542.189789\n",
       "9     blood glucose random   2168.990272\n",
       "11        serum creatinine    361.269918\n",
       "15      packed cell volume    313.562787\n",
       "3                  albumin    212.091308\n",
       "14             haemoglobin    121.405966\n",
       "0                      age    110.871026\n",
       "4                    sugar    101.095345\n",
       "18             ypertension     83.114541"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_rank.nlargest(10,'score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['white blood cell count', 'blood urea', 'blood glucose random',\n",
       "       'serum creatinine', 'packed cell volume', 'albumin', 'haemoglobin',\n",
       "       'age', 'sugar', 'ypertension'], dtype=object)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_columns=features_rank.nlargest(10,'score')['feature'].values\n",
    "selected_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>white blood cell count</th>\n",
       "      <th>blood urea</th>\n",
       "      <th>blood glucose random</th>\n",
       "      <th>serum creatinine</th>\n",
       "      <th>packed cell volume</th>\n",
       "      <th>albumin</th>\n",
       "      <th>haemoglobin</th>\n",
       "      <th>age</th>\n",
       "      <th>sugar</th>\n",
       "      <th>ypertension</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7800.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.4</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6000.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>38.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.3</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7500.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>423.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.6</td>\n",
       "      <td>62.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6700.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>32.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.2</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7300.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   white blood cell count  blood urea  blood glucose random  serum creatinine  \\\n",
       "0                  7800.0        36.0                 121.0               1.2   \n",
       "1                  6000.0        18.0                  70.0               0.8   \n",
       "2                  7500.0        53.0                 423.0               1.8   \n",
       "3                  6700.0        56.0                 117.0               3.8   \n",
       "4                  7300.0        26.0                 106.0               1.4   \n",
       "\n",
       "   packed cell volume  albumin  haemoglobin   age  sugar  ypertension  \n",
       "0                44.0      1.0         15.4  48.0    0.0            1  \n",
       "1                38.0      4.0         11.3   7.0    0.0            0  \n",
       "2                31.0      2.0          9.6  62.0    3.0            0  \n",
       "3                32.0      4.0         11.2  48.0    0.0            1  \n",
       "4                35.0      2.0         11.6  51.0    0.0            0  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_new=data[selected_columns]\n",
    "x_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "400"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(400, 10)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_new.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prediction Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "xtrain,xtest,ytrain,ytest=train_test_split(x_new,y,random_state=0,test_size=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    188\n",
       "1    112\n",
       "Name: class, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ytrain.value_counts() #Balanced Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: xgboost in c:\\users\\deepanshu\\anaconda3\\lib\\site-packages (1.4.1)\n",
      "Requirement already satisfied: scipy in c:\\users\\deepanshu\\anaconda3\\lib\\site-packages (from xgboost) (1.5.2)\n",
      "Requirement already satisfied: numpy in c:\\users\\deepanshu\\anaconda3\\lib\\site-packages (from xgboost) (1.19.2)\n"
     ]
    }
   ],
   "source": [
    "!pip install xgboost\n",
    "\n",
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier=XGBClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "param={'learning_rate':[0.05,0.20,0.25],\n",
    "       'max_depth':[5,8,10],\n",
    "        'min_child_weight':[1,3,5,7],\n",
    "         'gamma':[0.0,0.1,0.2,0.4],\n",
    "         'colsample_bytree':[0.3,0.4,0.7]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "random_search=RandomizedSearchCV(classifier,param_distributions=param, n_iter=5, scoring='roc_auc', n_jobs=-1,cv=5,verbose=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 5 candidates, totalling 25 fits\n",
      "[00:58:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DEEPANSHU\\anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5,\n",
       "                   estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                           colsample_bylevel=None,\n",
       "                                           colsample_bynode=None,\n",
       "                                           colsample_bytree=None, gamma=None,\n",
       "                                           gpu_id=None, importance_type='gain',\n",
       "                                           interaction_constraints=None,\n",
       "                                           learning_rate=None,\n",
       "                                           max_delta_step=None, max_depth=None,\n",
       "                                           min_child_weight=None, missing=nan,\n",
       "                                           monotone_constraints=None,\n",
       "                                           n_estimators=100,...\n",
       "                                           random_state=None, reg_alpha=None,\n",
       "                                           reg_lambda=None,\n",
       "                                           scale_pos_weight=None,\n",
       "                                           subsample=None, tree_method=None,\n",
       "                                           validate_parameters=None,\n",
       "                                           verbosity=None),\n",
       "                   n_iter=5, n_jobs=-1,\n",
       "                   param_distributions={'colsample_bytree': [0.3, 0.4, 0.7],\n",
       "                                        'gamma': [0.0, 0.1, 0.2, 0.4],\n",
       "                                        'learning_rate': [0.05, 0.2, 0.25],\n",
       "                                        'max_depth': [5, 8, 10],\n",
       "                                        'min_child_weight': [1, 3, 5, 7]},\n",
       "                   scoring='roc_auc', verbose=3)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=0.7, gamma=0.4, gpu_id=-1,\n",
       "              importance_type='gain', interaction_constraints='',\n",
       "              learning_rate=0.05, max_delta_step=0, max_depth=8,\n",
       "              min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "              n_estimators=100, n_jobs=4, num_parallel_tree=1, random_state=0,\n",
       "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
       "              tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'min_child_weight': 1,\n",
       " 'max_depth': 8,\n",
       " 'learning_rate': 0.05,\n",
       " 'gamma': 0.4,\n",
       " 'colsample_bytree': 0.7}"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier=XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
    "              colsample_bynode=1, colsample_bytree=0.4, gamma=0.4, gpu_id=-1,\n",
    "              importance_type='gain', interaction_constraints='',\n",
    "              learning_rate=0.05, max_delta_step=0, max_depth=10,\n",
    "              min_child_weight=1, monotone_constraints='()',\n",
    "              n_estimators=100, n_jobs=4, num_parallel_tree=1, random_state=0,\n",
    "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
    "              tree_method='exact', validate_parameters=1, verbosity=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[00:58:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=0.4, gamma=0.4, gpu_id=-1,\n",
       "              importance_type='gain', interaction_constraints='',\n",
       "              learning_rate=0.05, max_delta_step=0, max_depth=10,\n",
       "              min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "              n_estimators=100, n_jobs=4, num_parallel_tree=1, random_state=0,\n",
       "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
       "              tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DEEPANSHU\\anaconda3\\lib\\site-packages\\xgboost\\data.py:112: UserWarning: Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1,\n",
       "       0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0,\n",
       "       1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1,\n",
       "       0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1,\n",
       "       1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ypred=classifier.predict(xtest)\n",
    "ypred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[59,  3],\n",
       "       [ 1, 37]], dtype=int64)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix,accuracy_score\n",
    "confusion_matrix(ytest,ypred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.96"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(ytest,ypred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 0.96\n",
      "Precision score: 0.925\n",
      "Recall score: 0.9736842105263158\n",
      "F1 Score : 0.9487179487179489\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "print(\"Accuracy score:\",metrics.accuracy_score(ytest, ypred))\n",
    "print(\"Precision score:\",metrics.precision_score(ytest, ypred))\n",
    "print(\"Recall score:\",metrics.recall_score(ytest, ypred))\n",
    "print(\"F1 Score :\",metrics.f1_score(ytest, ypred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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