Kidney.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kidney Disease Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import warnings\n",
    "import random\n",
    "from sklearn.model_selection import ShuffleSplit\n",
    "from sklearn.model_selection import cross_validate\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"kidney_disease.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                  0\n",
       "age                 9\n",
       "bp                 12\n",
       "sg                 47\n",
       "al                 46\n",
       "su                 49\n",
       "rbc               152\n",
       "pc                 65\n",
       "pcc                 4\n",
       "ba                  4\n",
       "bgr                44\n",
       "bu                 19\n",
       "sc                 17\n",
       "sod                87\n",
       "pot                88\n",
       "hemo               52\n",
       "pcv                70\n",
       "wc                105\n",
       "rc                130\n",
       "htn                 2\n",
       "dm                  2\n",
       "cad                 2\n",
       "appet               1\n",
       "pe                  1\n",
       "ane                 1\n",
       "classification      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>age</th>\n",
       "      <th>bp</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>400.000000</td>\n",
       "      <td>391.000000</td>\n",
       "      <td>388.000000</td>\n",
       "      <td>353.000000</td>\n",
       "      <td>354.000000</td>\n",
       "      <td>351.000000</td>\n",
       "      <td>356.000000</td>\n",
       "      <td>381.000000</td>\n",
       "      <td>383.000000</td>\n",
       "      <td>313.000000</td>\n",
       "      <td>312.000000</td>\n",
       "      <td>348.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>199.500000</td>\n",
       "      <td>51.483376</td>\n",
       "      <td>76.469072</td>\n",
       "      <td>1.017408</td>\n",
       "      <td>1.016949</td>\n",
       "      <td>0.450142</td>\n",
       "      <td>148.036517</td>\n",
       "      <td>57.425722</td>\n",
       "      <td>3.072454</td>\n",
       "      <td>137.528754</td>\n",
       "      <td>4.627244</td>\n",
       "      <td>12.526437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>115.614301</td>\n",
       "      <td>17.169714</td>\n",
       "      <td>13.683637</td>\n",
       "      <td>0.005717</td>\n",
       "      <td>1.352679</td>\n",
       "      <td>1.099191</td>\n",
       "      <td>79.281714</td>\n",
       "      <td>50.503006</td>\n",
       "      <td>5.741126</td>\n",
       "      <td>10.408752</td>\n",
       "      <td>3.193904</td>\n",
       "      <td>2.912587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>1.005000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>3.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>99.750000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>1.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>10.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>199.500000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.020000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>138.000000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>12.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>299.250000</td>\n",
       "      <td>64.500000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.020000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>163.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>142.000000</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>399.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>1.025000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>490.000000</td>\n",
       "      <td>391.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>163.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>17.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               id         age          bp          sg          al          su  \\\n",
       "count  400.000000  391.000000  388.000000  353.000000  354.000000  351.000000   \n",
       "mean   199.500000   51.483376   76.469072    1.017408    1.016949    0.450142   \n",
       "std    115.614301   17.169714   13.683637    0.005717    1.352679    1.099191   \n",
       "min      0.000000    2.000000   50.000000    1.005000    0.000000    0.000000   \n",
       "25%     99.750000   42.000000   70.000000    1.010000    0.000000    0.000000   \n",
       "50%    199.500000   55.000000   80.000000    1.020000    0.000000    0.000000   \n",
       "75%    299.250000   64.500000   80.000000    1.020000    2.000000    0.000000   \n",
       "max    399.000000   90.000000  180.000000    1.025000    5.000000    5.000000   \n",
       "\n",
       "              bgr          bu          sc         sod         pot        hemo  \n",
       "count  356.000000  381.000000  383.000000  313.000000  312.000000  348.000000  \n",
       "mean   148.036517   57.425722    3.072454  137.528754    4.627244   12.526437  \n",
       "std     79.281714   50.503006    5.741126   10.408752    3.193904    2.912587  \n",
       "min     22.000000    1.500000    0.400000    4.500000    2.500000    3.100000  \n",
       "25%     99.000000   27.000000    0.900000  135.000000    3.800000   10.300000  \n",
       "50%    121.000000   42.000000    1.300000  138.000000    4.400000   12.650000  \n",
       "75%    163.000000   66.000000    2.800000  142.000000    4.900000   15.000000  \n",
       "max    490.000000  391.000000   76.000000  163.000000   47.000000   17.800000  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>htn</th>\n",
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       "      <th>396</th>\n",
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      "text/plain": [
       "     htn   dm cad   pe  ane\n",
       "0    yes  yes  no   no   no\n",
       "1     no   no  no   no   no\n",
       "2     no  yes  no   no  yes\n",
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       "395   no   no  no   no   no\n",
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       "398   no   no  no   no   no\n",
       "399   no   no  no   no   no\n",
       "\n",
       "[400 rows x 5 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['htn','dm','cad','pe','ane']] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[['htn','dm','cad','pe','ane']]  = df[['htn','dm','cad','pe','ane']].replace(to_replace={'yes':1,'no':0})"
   ]
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  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "     htn dm cad   pe  ane\n",
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     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df[['htn','dm','cad','pe','ane']] "
   ]
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  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[['rbc','pc']] = df[['rbc','pc']].replace(to_replace={'abnormal':1,'normal':0})\n",
    "df[['pcc','ba']] = df[['pcc','ba']].replace(to_replace={'present':1,'notpresent':0})\n",
    "df[['appet']] = df[['appet']].replace(to_replace={'good':1,'poor':0,'no':np.nan})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
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     "execution_count": 50,
     "metadata": {},
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    "df.head()"
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  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         ckd\n",
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       "3         ckd\n",
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     "execution_count": 51,
     "metadata": {},
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  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['classification'] = df['classification'].replace(to_replace={'ckd':1.0,'ckd\\t':1.0,'notckd':0.0,'no':0.0})"
   ]
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  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 53,
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  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns={'classification':'class'},inplace=True)"
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  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <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",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>51.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.010</td>\n",
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       "      <td>35</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</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  ...  pcv    wc   rc  \\\n",
       "0   0  48.0  80.0  1.020  1.0  0.0  NaN  0.0  0.0  0.0  ...   44  7800  5.2   \n",
       "1   1   7.0  50.0  1.020  4.0  0.0  NaN  0.0  0.0  0.0  ...   38  6000  NaN   \n",
       "2   2  62.0  80.0  1.010  2.0  3.0  0.0  0.0  0.0  0.0  ...   31  7500  NaN   \n",
       "3   3  48.0  70.0  1.005  4.0  0.0  0.0  1.0  1.0  0.0  ...   32  6700  3.9   \n",
       "4   4  51.0  80.0  1.010  2.0  0.0  0.0  0.0  0.0  0.0  ...   35  7300  4.6   \n",
       "\n",
       "   htn  dm  cad appet   pe  ane  class  \n",
       "0  1.0   1    0   1.0  0.0  0.0    1.0  \n",
       "1  0.0   0    0   1.0  0.0  0.0    1.0  \n",
       "2  0.0   1    0   0.0  0.0  1.0    1.0  \n",
       "3  1.0   0    0   0.0  1.0  1.0    1.0  \n",
       "4  0.0   0    0   1.0  0.0  0.0    1.0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "      id   age    bp     sg   al   su  rbc   pc  pcc   ba  ...  pcv    wc  \\\n",
       "0      0  48.0  80.0  1.020  1.0  0.0  NaN  0.0  0.0  0.0  ...   44  7800   \n",
       "1      1   7.0  50.0  1.020  4.0  0.0  NaN  0.0  0.0  0.0  ...   38  6000   \n",
       "2      2  62.0  80.0  1.010  2.0  3.0  0.0  0.0  0.0  0.0  ...   31  7500   \n",
       "3      3  48.0  70.0  1.005  4.0  0.0  0.0  1.0  1.0  0.0  ...   32  6700   \n",
       "4      4  51.0  80.0  1.010  2.0  0.0  0.0  0.0  0.0  0.0  ...   35  7300   \n",
       "..   ...   ...   ...    ...  ...  ...  ...  ...  ...  ...  ...  ...   ...   \n",
       "395  395  55.0  80.0  1.020  0.0  0.0  0.0  0.0  0.0  0.0  ...   47  6700   \n",
       "396  396  42.0  70.0  1.025  0.0  0.0  0.0  0.0  0.0  0.0  ...   54  7800   \n",
       "397  397  12.0  80.0  1.020  0.0  0.0  0.0  0.0  0.0  0.0  ...   49  6600   \n",
       "398  398  17.0  60.0  1.025  0.0  0.0  0.0  0.0  0.0  0.0  ...   51  7200   \n",
       "399  399  58.0  80.0  1.025  0.0  0.0  0.0  0.0  0.0  0.0  ...   53  6800   \n",
       "\n",
       "      rc  htn  dm  cad appet   pe  ane  class  \n",
       "0    5.2  1.0   1    0   1.0  0.0  0.0    1.0  \n",
       "1    NaN  0.0   0    0   1.0  0.0  0.0    1.0  \n",
       "2    NaN  0.0   1    0   0.0  0.0  1.0    1.0  \n",
       "3    3.9  1.0   0    0   0.0  1.0  1.0    1.0  \n",
       "4    4.6  0.0   0    0   1.0  0.0  0.0    1.0  \n",
       "..   ...  ...  ..  ...   ...  ...  ...    ...  \n",
       "395  4.9  0.0   0    0   1.0  0.0  0.0    0.0  \n",
       "396  6.2  0.0   0    0   1.0  0.0  0.0    0.0  \n",
       "397  5.4  0.0   0    0   1.0  0.0  0.0    0.0  \n",
       "398  5.9  0.0   0    0   1.0  0.0  0.0    0.0  \n",
       "399  6.1  0.0   0    0   1.0  0.0  0.0    0.0  \n",
       "\n",
       "[400 rows x 26 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Further cleaning\n",
    "df['pe'] = df['pe'].replace(to_replace='good',value=0) # Not having pedal edema is good\n",
    "df['appet'] = df['appet'].replace(to_replace='no',value=0)\n",
    "df['cad'] = df['cad'].replace(to_replace='\\tno',value=0)\n",
    "df['dm'] = df['dm'].replace(to_replace={'\\tno':0,'\\tyes':1,' yes':1, '':np.nan})\n",
    "df.drop('id',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
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       "      <td>7800</td>\n",
       "      <td>5.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</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>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>38</td>\n",
       "      <td>6000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.010</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>423.0</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.005</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>6700</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.010</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>...</td>\n",
       "      <td>35</td>\n",
       "      <td>7300</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    age    bp     sg   al   su  rbc   pc  pcc   ba    bgr  ...  pcv    wc  \\\n",
       "0  48.0  80.0  1.020  1.0  0.0  NaN  0.0  0.0  0.0  121.0  ...   44  7800   \n",
       "1   7.0  50.0  1.020  4.0  0.0  NaN  0.0  0.0  0.0    NaN  ...   38  6000   \n",
       "2  62.0  80.0  1.010  2.0  3.0  0.0  0.0  0.0  0.0  423.0  ...   31  7500   \n",
       "3  48.0  70.0  1.005  4.0  0.0  0.0  1.0  1.0  0.0  117.0  ...   32  6700   \n",
       "4  51.0  80.0  1.010  2.0  0.0  0.0  0.0  0.0  0.0  106.0  ...   35  7300   \n",
       "\n",
       "    rc  htn   dm  cad appet   pe  ane  class  \n",
       "0  5.2  1.0  1.0  0.0   1.0  0.0  0.0    1.0  \n",
       "1  NaN  0.0  0.0  0.0   1.0  0.0  0.0    1.0  \n",
       "2  NaN  0.0  1.0  0.0   0.0  0.0  1.0    1.0  \n",
       "3  3.9  1.0  0.0  0.0   0.0  1.0  1.0    1.0  \n",
       "4  4.6  0.0  0.0  0.0   1.0  0.0  0.0    1.0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>bp</th>\n",
       "      <th>sg</th>\n",
       "      <th>al</th>\n",
       "      <th>su</th>\n",
       "      <th>rbc</th>\n",
       "      <th>pc</th>\n",
       "      <th>pcc</th>\n",
       "      <th>ba</th>\n",
       "      <th>bgr</th>\n",
       "      <th>...</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>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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>...</td>\n",
       "      <td>44</td>\n",
       "      <td>7800</td>\n",
       "      <td>5.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</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>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>38</td>\n",
       "      <td>6000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.010</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>423.0</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.005</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>6700</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.010</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>...</td>\n",
       "      <td>35</td>\n",
       "      <td>7300</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    age    bp     sg   al   su  rbc   pc  pcc   ba    bgr  ...  pcv    wc  \\\n",
       "0  48.0  80.0  1.020  1.0  0.0  NaN  0.0  0.0  0.0  121.0  ...   44  7800   \n",
       "1   7.0  50.0  1.020  4.0  0.0  NaN  0.0  0.0  0.0    NaN  ...   38  6000   \n",
       "2  62.0  80.0  1.010  2.0  3.0  0.0  0.0  0.0  0.0  423.0  ...   31  7500   \n",
       "3  48.0  70.0  1.005  4.0  0.0  0.0  1.0  1.0  0.0  117.0  ...   32  6700   \n",
       "4  51.0  80.0  1.010  2.0  0.0  0.0  0.0  0.0  0.0  106.0  ...   35  7300   \n",
       "\n",
       "    rc  htn   dm  cad appet   pe  ane  class  \n",
       "0  5.2  1.0  1.0  0.0   1.0  0.0  0.0    1.0  \n",
       "1  NaN  0.0  0.0  0.0   1.0  0.0  0.0    1.0  \n",
       "2  NaN  0.0  1.0  0.0   0.0  0.0  1.0    1.0  \n",
       "3  3.9  1.0  0.0  0.0   0.0  1.0  1.0    1.0  \n",
       "4  4.6  0.0  0.0  0.0   1.0  0.0  0.0    1.0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    115\n",
       "1.0     43\n",
       "Name: class, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.dropna(axis=0)\n",
    "df2['class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop([\"su\",\"rbc\",\"rc\",\"wc\",\"pot\",\"sod\"],axis=1)\n",
    "df[\"pcv\"]=df[\"pcv\"].fillna(method=\"ffill\")\n",
    "df.drop([\"pc\"],axis=1,inplace=True)\n",
    "df[\"hemo\"]=df[\"hemo\"].fillna(method=\"ffill\")\n",
    "df.drop([\"sg\"],axis=1,inplace=True)\n",
    "df=df.fillna(method=\"ffill\")\n",
    "df.drop([\"ba\"],axis=1,inplace=True)\n",
    "df.drop([\"pe\"],axis=1,inplace=True)\n",
    "df.drop([\"cad\"],axis=1,inplace=True)\n",
    "df.drop([\"ane\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['age', 'bp', 'al', 'pcc', 'bgr', 'bu', 'sc', 'hemo', 'pcv', 'htn', 'dm',\n",
      "       'appet', 'class'],\n",
      "      dtype='object')\n",
      "13\n"
     ]
    }
   ],
   "source": [
    "df=df.replace(\"\\t?\",31)\n",
    "print(df.columns)\n",
    "print(df.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop([\"class\"],axis=1)\n",
    "y = df[\"class\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(360, 12)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(360,)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sm=SMOTE()\n",
    "# X_train, y_train =sm.fit_sample(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Put the models in dictonary \n",
    "\n",
    "models = {\"Logistic Regression\": LogisticRegression(),\n",
    "         \"Random Forest\": RandomForestClassifier()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Create a function to fit and score models \n",
    "\n",
    "def fit_and_score(models, X_train, X_test, y_train, y_test):\n",
    "    \n",
    "    # Random Seed\n",
    "    np.random.seed(42)\n",
    "    # Make a list to keep model scores\n",
    "    model_scores = {}\n",
    "    # Loop through Models \n",
    "    for name, model in models.items():\n",
    "        model.fit(X_train, y_train)\n",
    "        model_scores[name] = model.score(X_test, y_test)\n",
    "    return model_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/vishwasmore/opt/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:939: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html.\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'Logistic Regression': 0.925, 'Random Forest': 1.0}"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_scores = fit_and_score(models = models,\n",
    "                             X_train = X_train,\n",
    "                             X_test = X_test,\n",
    "                             y_train = y_train,\n",
    "                             y_test = y_test)\n",
    "model_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 30 candidates, totalling 150 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    0.5s finished\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "# Different hyperparameters for our LogisticRegression model\n",
    "log_reg_grid = {\"C\": np.logspace(-4, 4, 30),\n",
    "                \"solver\": [\"liblinear\"]}\n",
    "\n",
    "# Setup grid hyperparameter search for LogisticRegression\n",
    "gs_log_reg = GridSearchCV(LogisticRegression(),\n",
    "                          param_grid=log_reg_grid,\n",
    "                          cv=5,\n",
    "                          verbose=True)\n",
    "\n",
    "# Fit grid hyperparameter search model\n",
    "gs_log_reg.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 4.893900918477489, 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the best hyperparmaters\n",
    "gs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.925"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the grid search LogisticRegression model\n",
    "gs_log_reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=2.592943797404667, class_weight=None, dual=False,\n",
       "                   fit_intercept=True, intercept_scaling=1, l1_ratio=None,\n",
       "                   max_iter=100, multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='liblinear', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = LogisticRegression(C=2.592943797404667, solver='liblinear')\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions with tuned model\n",
    "y_preds = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 0., 1., 1., 0., 0., 1., 1., 0., 1., 1., 1., 0., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 0., 1., 1., 1., 1., 1., 1., 1., 0., 1., 1., 1., 1., 1.,\n",
       "       1., 0., 0., 1., 0., 1.])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.925\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "print(accuracy_score(y_test,y_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['kidney_model']"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.externals import joblib\n",
    "\n",
    "joblib.dump(clf,\"kidney_model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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