disease_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#loading the required libraries.\n",
    "#This is my first time working with some dataset.\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as pyplot\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Proceeding towards data preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>age</th>\n",
       "      <th>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": [
    "#loading the data to panda data frame from the csv file present in root folder\n",
    "data = pd.read_csv('kidney_disease.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      " 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",
      " hypertension                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\n"
     ]
    }
   ],
   "source": [
    "header = pd.read_csv(\"header.txt\", sep=\"-\", header=None)\n",
    "data.columns = header[1]\n",
    "data.head()\n",
    "print(data.dtypes)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "float64\n",
      "float64\n",
      "float64\n"
     ]
    }
   ],
   "source": [
    "for i in [' packed cell volume', ' white blood cell count', ' red blood cell count']:\n",
    "    data[i] = pd.to_numeric(data[i], errors='coerce')\n",
    "    print(data[i].dtypes)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>1</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>hypertension</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": [
       "1   age   blood pressure   specific gravity   albumin   sugar  \\\n",
       "0  48.0             80.0              1.020       1.0     0.0   \n",
       "1   7.0             50.0              1.020       4.0     0.0   \n",
       "2  62.0             80.0              1.010       2.0     3.0   \n",
       "3  48.0             70.0              1.005       4.0     0.0   \n",
       "4  51.0             80.0              1.010       2.0     0.0   \n",
       "\n",
       "1  red blood cells  pus cell  pus cell clumps    bacteria  \\\n",
       "0              NaN    normal       notpresent  notpresent   \n",
       "1              NaN    normal       notpresent  notpresent   \n",
       "2           normal    normal       notpresent  notpresent   \n",
       "3           normal  abnormal          present  notpresent   \n",
       "4           normal    normal       notpresent  notpresent   \n",
       "\n",
       "1   blood glucose random  ...   packed cell volume   white blood cell count  \\\n",
       "0                  121.0  ...                 44.0                   7800.0   \n",
       "1                    NaN  ...                 38.0                   6000.0   \n",
       "2                  423.0  ...                 31.0                   7500.0   \n",
       "3                  117.0  ...                 32.0                   6700.0   \n",
       "4                  106.0  ...                 35.0                   7300.0   \n",
       "\n",
       "1   red blood cell count   hypertension   diabetes mellitus  \\\n",
       "0                    5.2            yes                 yes   \n",
       "1                    NaN             no                  no   \n",
       "2                    NaN             no                 yes   \n",
       "3                    3.9            yes                  no   \n",
       "4                    4.6             no                  no   \n",
       "\n",
       "1   coronary artery disease   appetite   pedal edema  anemia  class  \n",
       "0                        no       good            no      no    ckd  \n",
       "1                        no       good            no      no    ckd  \n",
       "2                        no       poor            no     yes    ckd  \n",
       "3                        no       poor           yes     yes    ckd  \n",
       "4                        no       good            no      no    ckd  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop(' id', axis=1, inplace=True)\n",
    "data.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Proceeding to clean the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#lets first categorise our features into categorial feature and numeric feature\n",
    "cat_features = [features for features in data.columns if data[features].dtypes=='object']\n",
    "num_features = [features for features in data.columns if data[features].dtypes!='object']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature  red blood cells: [nan 'normal' 'abnormal']\n",
      "Feature  pus cell: ['normal' 'abnormal' nan]\n",
      "Feature  pus cell clumps: ['notpresent' 'present' nan]\n",
      "Feature  bacteria: ['notpresent' 'present' nan]\n",
      "Feature  hypertension: ['yes' 'no' nan]\n",
      "Feature  diabetes mellitus: ['yes' 'no' ' yes' '\\tno' '\\tyes' nan]\n",
      "Feature  coronary artery disease: ['no' 'yes' '\\tno' nan]\n",
      "Feature  appetite: ['good' 'poor' nan]\n",
      "Feature  pedal edema: ['no' 'yes' nan]\n",
      "Feature  anemia: ['no' 'yes' nan]\n",
      "Feature  class: ['ckd' 'ckd\\t' 'notckd']\n"
     ]
    }
   ],
   "source": [
    "# Now we will check what are the different unique values of a particular feature\n",
    "# We are doing so as to check for unclean data values\n",
    "for i in cat_features:\n",
    "    print(\"Feature {}: {}\".format(i,data[i].unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature  red blood cells: [nan 'normal' 'abnormal']\n",
      "Feature  pus cell: ['normal' 'abnormal' nan]\n",
      "Feature  pus cell clumps: ['notpresent' 'present' nan]\n",
      "Feature  bacteria: ['notpresent' 'present' nan]\n",
      "Feature  hypertension: ['yes' 'no' nan]\n",
      "Feature  diabetes mellitus: ['yes' 'no' ' yes' nan]\n",
      "Feature  coronary artery disease: ['no' 'yes' nan]\n",
      "Feature  appetite: ['good' 'poor' nan]\n",
      "Feature  pedal edema: ['no' 'yes' nan]\n",
      "Feature  anemia: ['no' 'yes' nan]\n",
      "Feature  class: ['ckd' 'notckd']\n"
     ]
    }
   ],
   "source": [
    "data[' diabetes mellitus'].replace({'\\tno':'no', '\\tyes':'yes'}, inplace=True)\n",
    "data[' coronary artery disease'].replace({'\\tno':'no', '\\tyes':'yes'}, inplace=True)\n",
    "data[' class'].replace({'ckd\\t': 'ckd'}, inplace=True)\n",
    "for i in cat_features:\n",
    "    print(\"Feature {}: {}\".format(i,data[i].unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22df8cafbe0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "k=0\n",
    "fig = pyplot.figure(figsize=(20,20))\n",
    "for i in num_features:\n",
    "    pyplot.subplot(5,3,k+1)\n",
    "    data[i].hist()\n",
    "    pyplot.title(i)\n",
    "    k+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22df94fc8d0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "k=0\n",
    "fig = pyplot.figure(figsize=(15,15))\n",
    "for i in cat_features:\n",
    "    pyplot.subplot(4,3,k+1)\n",
    "    sns.countplot(data[i])\n",
    "    k+=1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22dfb1ec748>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22df93cc8d0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#checking out the correlation between different features\n",
    "fig = pyplot.figure(figsize=(10,10))\n",
    "data.corr()\n",
    "sns.heatmap(data.corr(), annot = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>red blood cells</th>\n",
       "      <th>class</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>abnormal</th>\n",
       "      <th>ckd</th>\n",
       "      <td>25</td>\n",
       "      <td>3.832000</td>\n",
       "      <td>3.7</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">normal</th>\n",
       "      <th>ckd</th>\n",
       "      <td>40</td>\n",
       "      <td>3.782500</td>\n",
       "      <td>3.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>notckd</th>\n",
       "      <td>134</td>\n",
       "      <td>5.368657</td>\n",
       "      <td>5.3</td>\n",
       "      <td>6.5</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         count      mean  median  max  min\n",
       " red blood cells  class                                   \n",
       "abnormal         ckd        25  3.832000     3.7  5.6  2.5\n",
       "normal           ckd        40  3.782500     3.8  8.0  2.1\n",
       "                 notckd    134  5.368657     5.3  6.5  4.4"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby([' red blood cells',' class'])[' red blood cell count'].agg(['count','mean', 'median', 'max', 'min'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22dfb177a20>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22dfb1eccc0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyplot.figure(figsize=(6,6))\n",
    "\n",
    "\n",
    "def v_plot(feature):\n",
    "    plot_= sns.violinplot(data=data, x= ' class' , y=feature )\n",
    "    return plot_\n",
    "\n",
    "v_plot(' red blood cell count')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([' age', ' blood pressure', ' specific gravity', ' albumin', ' sugar',\n",
       "       ' red blood cells', ' pus cell', ' pus cell clumps', ' bacteria',\n",
       "       ' blood glucose random', ' blood urea', ' serum creatinine', ' sodium',\n",
       "       ' potassium', ' haemoglobin', ' packed cell volume',\n",
       "       ' white blood cell count', ' red blood cell count', ' hypertension',\n",
       "       ' diabetes mellitus', ' coronary artery disease', ' appetite',\n",
       "       ' pedal edema', ' anemia', ' class'],\n",
       "      dtype='object', name=1)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x22df95cb630>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22df95cb9e8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# finding the relationship between heamoglobin and red blood cell count\n",
    "\n",
    "#automating the regression plot \n",
    "def reg_plot(feature_x, feature_y):\n",
    "    plot_ = sns.lmplot(data = data, x=feature_x, y=feature_y, hue = ' class')\n",
    "    return plot_\n",
    "\n",
    "reg_plot(' haemoglobin', ' red blood cell count')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x22df96c2f28>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22df96d9be0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22df9662668>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22df98326a0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#analysing the dsitribution of red blood cell count for ckd and non ckd class\n",
    "\n",
    "# automating the distribution plot by just passing the feature\n",
    "def kde_plot(feature):\n",
    "    multi_plot = sns.FacetGrid(data = data, hue = ' class', aspect= 2)\n",
    "    multi_plot.map(sns.kdeplot, feature)\n",
    "    multi_plot.add_legend()\n",
    "    return multi_plot\n",
    "\n",
    "    \n",
    "kde_plot(' red blood cell count')\n",
    "kde_plot(' haemoglobin')\n",
    "kde_plot(' white blood cell count')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1\n",
       " 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",
       " hypertension                 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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filling up the rn values\n",
    "data.isnull().sum().sort_values(ascending= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def nan_filler(feature):\n",
    "    rn_sample = data[feature].dropna().sample(data[feature].isnull().sum())\n",
    "    rn_sample.index = data[data[feature].isnull()].index\n",
    "    data.loc[data[feature].isnull(), feature] = rn_sample\n",
    "    data[feature]\n",
    "for i in num_features:\n",
    "    nan_filler(i)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1\n",
       " red blood cells            152\n",
       " pus cell                    65\n",
       " pus cell clumps              4\n",
       " bacteria                     4\n",
       " hypertension                 2\n",
       " diabetes mellitus            2\n",
       " coronary artery disease      2\n",
       " appetite                     1\n",
       " pedal edema                  1\n",
       " anemia                       1\n",
       " class                        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[cat_features].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1\n",
       " red blood cells            0\n",
       " pus cell                   0\n",
       " pus cell clumps            0\n",
       " bacteria                   0\n",
       " hypertension               0\n",
       " diabetes mellitus          0\n",
       " coronary artery disease    0\n",
       " appetite                   0\n",
       " pedal edema                0\n",
       " anemia                     0\n",
       " class                      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def nan_cat_filler(feature):\n",
    "    mode = data[feature].mode()[0]\n",
    "    data[feature] = data[feature].fillna(mode)\n",
    "for i in cat_features:\n",
    "    nan_cat_filler(i)\n",
    "\n",
    "    \n",
    "data[cat_features].isnull().sum()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " red blood cells - 2 unique features\n",
      " pus cell - 2 unique features\n",
      " pus cell clumps - 2 unique features\n",
      " bacteria - 2 unique features\n",
      " hypertension - 2 unique features\n",
      " diabetes mellitus - 3 unique features\n",
      " coronary artery disease - 2 unique features\n",
      " appetite - 2 unique features\n",
      " pedal edema - 2 unique features\n",
      " anemia - 2 unique features\n",
      " class - 2 unique features\n"
     ]
    }
   ],
   "source": [
    "# applying feature encoding\n",
    "for i in cat_features:\n",
    "    print('{} - {} unique features'.format(i, data[i].nunique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# since no feature with large number diff categorical data, apply feature encoding on all\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "l = LabelEncoder()\n",
    "for i in cat_features:\n",
    "    data[i] = l.fit_transform(data[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>1</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>hypertension</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",
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       "      <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",
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       "      <td>7.0</td>\n",
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       "      <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>117.0</td>\n",
       "      <td>...</td>\n",
       "      <td>38.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>62.0</td>\n",
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       "      <td>1.010</td>\n",
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       "      <td>0</td>\n",
       "      <td>423.0</td>\n",
       "      <td>...</td>\n",
       "      <td>31.0</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>3.9</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",
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       "      <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",
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       "    <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": [
       "1   age   blood pressure   specific gravity   albumin   sugar  \\\n",
       "0  48.0             80.0              1.020       1.0     0.0   \n",
       "1   7.0             50.0              1.020       4.0     0.0   \n",
       "2  62.0             80.0              1.010       2.0     3.0   \n",
       "3  48.0             70.0              1.005       4.0     0.0   \n",
       "4  51.0             80.0              1.010       2.0     0.0   \n",
       "\n",
       "1   red blood cells   pus cell   pus cell clumps   bacteria  \\\n",
       "0                 1          1                 0          0   \n",
       "1                 1          1                 0          0   \n",
       "2                 1          1                 0          0   \n",
       "3                 1          0                 1          0   \n",
       "4                 1          1                 0          0   \n",
       "\n",
       "1   blood glucose random  ...   packed cell volume   white blood cell count  \\\n",
       "0                  121.0  ...                 44.0                   7800.0   \n",
       "1                  117.0  ...                 38.0                   6000.0   \n",
       "2                  423.0  ...                 31.0                   7500.0   \n",
       "3                  117.0  ...                 32.0                   6700.0   \n",
       "4                  106.0  ...                 35.0                   7300.0   \n",
       "\n",
       "1   red blood cell count   hypertension   diabetes mellitus  \\\n",
       "0                    5.2              1                   2   \n",
       "1                    2.8              0                   1   \n",
       "2                    3.9              0                   2   \n",
       "3                    3.9              1                   1   \n",
       "4                    4.6              0                   1   \n",
       "\n",
       "1   coronary artery disease   appetite   pedal edema   anemia   class  \n",
       "0                         0          0             0        0       0  \n",
       "1                         0          0             0        0       0  \n",
       "2                         0          1             0        1       0  \n",
       "3                         0          1             1        1       0  \n",
       "4                         0          0             0        0       0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "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_features = [i for i in data.columns if i!=' class']\n",
    "dep_features = ' class'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=data[ind_features]\n",
    "y=data[dep_features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>blood pressure</th>\n",
       "      <th>specific gravity</th>\n",
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       "      <th>sugar</th>\n",
       "      <th>red blood cells</th>\n",
       "      <th>pus cell</th>\n",
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       "      <th>bacteria</th>\n",
       "      <th>blood glucose random</th>\n",
       "      <th>...</th>\n",
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       "      <th>packed cell volume</th>\n",
       "      <th>white blood cell count</th>\n",
       "      <th>red blood cell count</th>\n",
       "      <th>hypertension</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",
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       "  <tbody>\n",
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       "      <td>0</td>\n",
       "      <td>117.0</td>\n",
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       "      <td>11.3</td>\n",
       "      <td>38.0</td>\n",
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       "      <td>31.0</td>\n",
       "      <td>7500.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <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",
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       "      <td>117.0</td>\n",
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       "      <th>4</th>\n",
       "      <td>51.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.010</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>7300.0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "1   age   blood pressure   specific gravity   albumin   sugar  \\\n",
       "0  48.0             80.0              1.020       1.0     0.0   \n",
       "1   7.0             50.0              1.020       4.0     0.0   \n",
       "2  62.0             80.0              1.010       2.0     3.0   \n",
       "3  48.0             70.0              1.005       4.0     0.0   \n",
       "4  51.0             80.0              1.010       2.0     0.0   \n",
       "\n",
       "1   red blood cells   pus cell   pus cell clumps   bacteria  \\\n",
       "0                 1          1                 0          0   \n",
       "1                 1          1                 0          0   \n",
       "2                 1          1                 0          0   \n",
       "3                 1          0                 1          0   \n",
       "4                 1          1                 0          0   \n",
       "\n",
       "1   blood glucose random  ...   haemoglobin   packed cell volume  \\\n",
       "0                  121.0  ...          15.4                 44.0   \n",
       "1                  117.0  ...          11.3                 38.0   \n",
       "2                  423.0  ...           9.6                 31.0   \n",
       "3                  117.0  ...          11.2                 32.0   \n",
       "4                  106.0  ...          11.6                 35.0   \n",
       "\n",
       "1   white blood cell count   red blood cell count   hypertension  \\\n",
       "0                   7800.0                    5.2              1   \n",
       "1                   6000.0                    2.8              0   \n",
       "2                   7500.0                    3.9              0   \n",
       "3                   6700.0                    3.9              1   \n",
       "4                   7300.0                    4.6              0   \n",
       "\n",
       "1   diabetes mellitus   coronary artery disease   appetite   pedal edema  \\\n",
       "0                   2                         0          0             0   \n",
       "1                   1                         0          0             0   \n",
       "2                   2                         0          1             0   \n",
       "3                   1                         0          1             1   \n",
       "4                   1                         0          0             0   \n",
       "\n",
       "1   anemia  \n",
       "0        0  \n",
       "1        0  \n",
       "2        1  \n",
       "3        1  \n",
       "4        0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "Name:  class, dtype: int32"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SelectKBest(k=20, score_func=<function chi2 at 0x0000022DFB1A4400>)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_features = SelectKBest(chi2, k=20)\n",
    "top_features.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "f_score = pd.DataFrame(top_features.scores_, columns=['Score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_feature_df = pd.DataFrame(X.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_scores = pd.concat([top_feature_df, f_score],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([' white blood cell count', ' blood urea', ' blood glucose random',\n",
       "       ' packed cell volume', ' serum creatinine', ' albumin',\n",
       "       ' haemoglobin', ' age', ' sugar', ' hypertension'], dtype=object)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scores.columns = ['feature', 'score']\n",
    "df_scores.nlargest(10, 'score')['feature'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(400, 10)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_new = data[df_scores.nlargest(10, 'score')['feature'].values]\n",
    "X_new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>1</th>\n",
       "      <th>white blood cell count</th>\n",
       "      <th>blood urea</th>\n",
       "      <th>blood glucose random</th>\n",
       "      <th>packed cell volume</th>\n",
       "      <th>serum creatinine</th>\n",
       "      <th>albumin</th>\n",
       "      <th>haemoglobin</th>\n",
       "      <th>age</th>\n",
       "      <th>sugar</th>\n",
       "      <th>hypertension</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>44.0</td>\n",
       "      <td>1.2</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>117.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.8</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>31.0</td>\n",
       "      <td>1.8</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>32.0</td>\n",
       "      <td>3.8</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>35.0</td>\n",
       "      <td>1.4</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": [
       "1   white blood cell count   blood urea   blood glucose random  \\\n",
       "0                   7800.0         36.0                  121.0   \n",
       "1                   6000.0         18.0                  117.0   \n",
       "2                   7500.0         53.0                  423.0   \n",
       "3                   6700.0         56.0                  117.0   \n",
       "4                   7300.0         26.0                  106.0   \n",
       "\n",
       "1   packed cell volume   serum creatinine   albumin   haemoglobin   age  \\\n",
       "0                 44.0                1.2       1.0          15.4  48.0   \n",
       "1                 38.0                0.8       4.0          11.3   7.0   \n",
       "2                 31.0                1.8       2.0           9.6  62.0   \n",
       "3                 32.0                3.8       4.0          11.2  48.0   \n",
       "4                 35.0                1.4       2.0          11.6  51.0   \n",
       "\n",
       "1   sugar   hypertension  \n",
       "0     0.0              1  \n",
       "1     0.0              0  \n",
       "2     3.0              0  \n",
       "3     0.0              1  \n",
       "4     0.0              0  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_new.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(300, 10)\n",
      "(100, 10)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_new,y, random_state = 0,test_size = 0.25)\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    188\n",
       "1    112\n",
       "Name:  class, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "params ={\n",
    "    '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]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier = XGBClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 5 candidates, totalling 25 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  19 out of  25 | elapsed:    6.1s remaining:    1.8s\n",
      "[Parallel(n_jobs=-1)]: Done  25 out of  25 | elapsed:    6.2s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[13:10:03] WARNING: ..\\src\\learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\envs\\machine_learning\\lib\\site-packages\\xgboost\\sklearn.py:888: 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, error_score='raise-deprecating',\n",
       "          estimator=XGBClassifier(base_score=None, booster=None, colsample_bylevel=None,\n",
       "       colsample_bynode=None, colsample_bytree=None, gamma=None,\n",
       "       gpu_id=None, importance_type='gain', interaction_constraints=None,\n",
       "       learning_rate=None, max_delta_step=None, max_depth=None,\n",
       "       min_child_w...e,\n",
       "       tree_method=None, use_label_encoder=True, validate_parameters=None,\n",
       "       verbosity=None),\n",
       "          fit_params=None, iid='warn', n_iter=5, n_jobs=-1,\n",
       "          param_distributions={'learning_rate': [0.05, 0.2, 0.25], 'max_depth': [5, 8, 10], 'min_child_weight': [1, 3, 5, 7], 'gamma': [0.0, 0.1, 0.2, 0.4], 'colsample_bytree': [0.3, 0.4, 0.7]},\n",
       "          pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "          return_train_score='warn', scoring='roc_auc', verbose=3)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "ran_search = RandomizedSearchCV(classifier, params,n_iter=5,scoring='roc_auc', n_jobs = -1, cv = 5, verbose=3)\n",
    "ran_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bynode=1, colsample_bytree=0.7, gamma=0.2, 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=3, missing=nan, monotone_constraints='()',\n",
       "       n_estimators=100, n_jobs=8, num_parallel_tree=1,\n",
       "       objective='binary:logistic', random_state=0, reg_alpha=0,\n",
       "       reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact',\n",
       "       use_label_encoder=True, validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ran_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'min_child_weight': 3,\n",
       " 'max_depth': 10,\n",
       " 'learning_rate': 0.05,\n",
       " 'gamma': 0.2,\n",
       " 'colsample_bytree': 0.7}"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ran_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier =XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
    "       colsample_bynode=1, colsample_bytree=0.7, gamma=0.2, 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=3, monotone_constraints='()',\n",
    "       n_estimators=100, n_jobs=8, num_parallel_tree=1,\n",
    "       objective='binary:logistic', random_state=0, reg_alpha=0,\n",
    "       reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact',\n",
    "       use_label_encoder=True, validate_parameters=1, verbosity=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[13:12:32] WARNING: ..\\src\\learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\envs\\machine_learning\\lib\\site-packages\\xgboost\\sklearn.py:888: 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": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bynode=1, colsample_bytree=0.7, gamma=0.2, 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=3, missing=nan, monotone_constraints='()',\n",
       "       n_estimators=100, n_jobs=8, num_parallel_tree=1,\n",
       "       objective='binary:logistic', random_state=0, reg_alpha=0,\n",
       "       reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact',\n",
       "       use_label_encoder=True, validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0,\n",
       "       1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0,\n",
       "       1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0,\n",
       "       1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1,\n",
       "       0, 0, 0, 0, 0, 1, 1, 1])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = classifier.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix, accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[61,  1],\n",
       "       [ 1, 37]], dtype=int64)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.97999999999999998"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
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  },
  "kernelspec": {
   "display_name": "Python 3.6.4 64-bit ('machine_learning': conda)",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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