kidneydisease.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 436,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (2.2.3)\n",
      "Requirement already satisfied: numpy>=1.26.0 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from pandas) (2.1.3)\n",
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      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip3 install --upgrade pip\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: numpy in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (2.1.3)\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip3 install --upgrade pip\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install numpy\n"
   ]
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  {
   "cell_type": "code",
   "execution_count": 438,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: seaborn in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (0.13.2)\n",
      "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from seaborn) (2.1.3)\n",
      "Requirement already satisfied: pandas>=1.2 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from seaborn) (2.2.3)\n",
      "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from seaborn) (3.9.2)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.3.1)\n",
      "Requirement already satisfied: cycler>=0.10 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.55.0)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.7)\n",
      "Requirement already satisfied: packaging>=20.0 in /Users/alekhyakoduri/Library/Python/3.12/lib/python/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (24.1)\n",
      "Requirement already satisfied: pillow>=8 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (11.0.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.2.0)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /Users/alekhyakoduri/Library/Python/3.12/lib/python/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.9.0.post0)\n",
      "Requirement already satisfied: pytz>=2020.1 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from pandas>=1.2->seaborn) (2024.2)\n",
      "Requirement already satisfied: tzdata>=2022.7 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from pandas>=1.2->seaborn) (2024.2)\n",
      "Requirement already satisfied: six>=1.5 in /Users/alekhyakoduri/Library/Python/3.12/lib/python/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip3 install --upgrade pip\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 439,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: matplotlib in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (3.9.2)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from matplotlib) (1.3.1)\n",
      "Requirement already satisfied: cycler>=0.10 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from matplotlib) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from matplotlib) (4.55.0)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from matplotlib) (1.4.7)\n",
      "Requirement already satisfied: numpy>=1.23 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from matplotlib) (2.1.3)\n",
      "Requirement already satisfied: packaging>=20.0 in /Users/alekhyakoduri/Library/Python/3.12/lib/python/site-packages (from matplotlib) (24.1)\n",
      "Requirement already satisfied: pillow>=8 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from matplotlib) (11.0.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from matplotlib) (3.2.0)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /Users/alekhyakoduri/Library/Python/3.12/lib/python/site-packages (from matplotlib) (2.9.0.post0)\n",
      "Requirement already satisfied: six>=1.5 in /Users/alekhyakoduri/Library/Python/3.12/lib/python/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip3 install --upgrade pip\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: scikit-learn in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (1.5.2)\n",
      "Requirement already satisfied: numpy>=1.19.5 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from scikit-learn) (2.1.3)\n",
      "Requirement already satisfied: scipy>=1.6.0 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from scikit-learn) (1.14.1)\n",
      "Requirement already satisfied: joblib>=1.2.0 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from scikit-learn) (1.4.2)\n",
      "Requirement already satisfied: threadpoolctl>=3.1.0 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from scikit-learn) (3.5.0)\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip3 install --upgrade pip\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split,KFold,cross_val_score\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv(\"/Users/alekhyakoduri/Desktop/Repo /Chronic-Kidney-Disease-Prediction-main/Kidney_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.pandas.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "metadata": {},
   "outputs": [
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       "      <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>7</th>\n",
       "      <td>7</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.015</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>abnormal</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>410.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.4</td>\n",
       "      <td>44</td>\n",
       "      <td>6900</td>\n",
       "      <td>5</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>good</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>ckd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>52.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.015</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>abnormal</td>\n",
       "      <td>present</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>138.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.8</td>\n",
       "      <td>33</td>\n",
       "      <td>9600</td>\n",
       "      <td>4.0</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>good</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>ckd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>53.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>1.020</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>abnormal</td>\n",
       "      <td>abnormal</td>\n",
       "      <td>present</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>70.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>114.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>9.5</td>\n",
       "      <td>29</td>\n",
       "      <td>12100</td>\n",
       "      <td>3.7</td>\n",
       "      <td>yes</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id   age     bp     sg   al   su       rbc        pc         pcc  \\\n",
       "0   0  48.0   80.0  1.020  1.0  0.0       NaN    normal  notpresent   \n",
       "1   1   7.0   50.0  1.020  4.0  0.0       NaN    normal  notpresent   \n",
       "2   2  62.0   80.0  1.010  2.0  3.0    normal    normal  notpresent   \n",
       "3   3  48.0   70.0  1.005  4.0  0.0    normal  abnormal     present   \n",
       "4   4  51.0   80.0  1.010  2.0  0.0    normal    normal  notpresent   \n",
       "5   5  60.0   90.0  1.015  3.0  0.0       NaN       NaN  notpresent   \n",
       "6   6  68.0   70.0  1.010  0.0  0.0       NaN    normal  notpresent   \n",
       "7   7  24.0    NaN  1.015  2.0  4.0    normal  abnormal  notpresent   \n",
       "8   8  52.0  100.0  1.015  3.0  0.0    normal  abnormal     present   \n",
       "9   9  53.0   90.0  1.020  2.0  0.0  abnormal  abnormal     present   \n",
       "\n",
       "           ba    bgr     bu    sc    sod  pot  hemo pcv     wc   rc  htn   dm  \\\n",
       "0  notpresent  121.0   36.0   1.2    NaN  NaN  15.4  44   7800  5.2  yes  yes   \n",
       "1  notpresent    NaN   18.0   0.8    NaN  NaN  11.3  38   6000  NaN   no   no   \n",
       "2  notpresent  423.0   53.0   1.8    NaN  NaN   9.6  31   7500  NaN   no  yes   \n",
       "3  notpresent  117.0   56.0   3.8  111.0  2.5  11.2  32   6700  3.9  yes   no   \n",
       "4  notpresent  106.0   26.0   1.4    NaN  NaN  11.6  35   7300  4.6   no   no   \n",
       "5  notpresent   74.0   25.0   1.1  142.0  3.2  12.2  39   7800  4.4  yes  yes   \n",
       "6  notpresent  100.0   54.0  24.0  104.0  4.0  12.4  36    NaN  NaN   no   no   \n",
       "7  notpresent  410.0   31.0   1.1    NaN  NaN  12.4  44   6900    5   no  yes   \n",
       "8  notpresent  138.0   60.0   1.9    NaN  NaN  10.8  33   9600  4.0  yes  yes   \n",
       "9  notpresent   70.0  107.0   7.2  114.0  3.7   9.5  29  12100  3.7  yes  yes   \n",
       "\n",
       "  cad appet   pe  ane classification  \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",
       "5  no  good  yes   no            ckd  \n",
       "6  no  good   no   no            ckd  \n",
       "7  no  good  yes   no            ckd  \n",
       "8  no  good   no  yes            ckd  \n",
       "9  no  poor   no  yes            ckd  "
      ]
     },
     "execution_count": 444,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>bp</th>\n",
       "      <th>sg</th>\n",
       "      <th>al</th>\n",
       "      <th>su</th>\n",
       "      <th>rbc</th>\n",
       "      <th>pc</th>\n",
       "      <th>pcc</th>\n",
       "      <th>ba</th>\n",
       "      <th>bgr</th>\n",
       "      <th>bu</th>\n",
       "      <th>sc</th>\n",
       "      <th>sod</th>\n",
       "      <th>pot</th>\n",
       "      <th>hemo</th>\n",
       "      <th>pcv</th>\n",
       "      <th>wc</th>\n",
       "      <th>rc</th>\n",
       "      <th>htn</th>\n",
       "      <th>dm</th>\n",
       "      <th>cad</th>\n",
       "      <th>appet</th>\n",
       "      <th>pe</th>\n",
       "      <th>ane</th>\n",
       "      <th>classification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>48.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.020</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>normal</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>121.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.4</td>\n",
       "      <td>44</td>\n",
       "      <td>7800</td>\n",
       "      <td>5.2</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>good</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>ckd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1.020</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>normal</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.3</td>\n",
       "      <td>38</td>\n",
       "      <td>6000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>good</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>ckd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.010</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>normal</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>423.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.6</td>\n",
       "      <td>31</td>\n",
       "      <td>7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>poor</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>ckd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.005</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>abnormal</td>\n",
       "      <td>present</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>117.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>111.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>11.2</td>\n",
       "      <td>32</td>\n",
       "      <td>6700</td>\n",
       "      <td>3.9</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>poor</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>ckd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.010</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>normal</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>106.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.6</td>\n",
       "      <td>35</td>\n",
       "      <td>7300</td>\n",
       "      <td>4.6</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>good</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>ckd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>60.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>1.015</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>74.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>142.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>12.2</td>\n",
       "      <td>39</td>\n",
       "      <td>7800</td>\n",
       "      <td>4.4</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>good</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>ckd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>68.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.010</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>normal</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>100.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.4</td>\n",
       "      <td>36</td>\n",
       "      <td>NaN</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>7</th>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.015</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>abnormal</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>410.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.4</td>\n",
       "      <td>44</td>\n",
       "      <td>6900</td>\n",
       "      <td>5</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>good</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>ckd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>52.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.015</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>abnormal</td>\n",
       "      <td>present</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>138.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.8</td>\n",
       "      <td>33</td>\n",
       "      <td>9600</td>\n",
       "      <td>4.0</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>good</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>ckd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>53.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>1.020</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>abnormal</td>\n",
       "      <td>abnormal</td>\n",
       "      <td>present</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>70.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>114.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>9.5</td>\n",
       "      <td>29</td>\n",
       "      <td>12100</td>\n",
       "      <td>3.7</td>\n",
       "      <td>yes</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age     bp     sg   al   su       rbc        pc         pcc          ba  \\\n",
       "0  48.0   80.0  1.020  1.0  0.0       NaN    normal  notpresent  notpresent   \n",
       "1   7.0   50.0  1.020  4.0  0.0       NaN    normal  notpresent  notpresent   \n",
       "2  62.0   80.0  1.010  2.0  3.0    normal    normal  notpresent  notpresent   \n",
       "3  48.0   70.0  1.005  4.0  0.0    normal  abnormal     present  notpresent   \n",
       "4  51.0   80.0  1.010  2.0  0.0    normal    normal  notpresent  notpresent   \n",
       "5  60.0   90.0  1.015  3.0  0.0       NaN       NaN  notpresent  notpresent   \n",
       "6  68.0   70.0  1.010  0.0  0.0       NaN    normal  notpresent  notpresent   \n",
       "7  24.0    NaN  1.015  2.0  4.0    normal  abnormal  notpresent  notpresent   \n",
       "8  52.0  100.0  1.015  3.0  0.0    normal  abnormal     present  notpresent   \n",
       "9  53.0   90.0  1.020  2.0  0.0  abnormal  abnormal     present  notpresent   \n",
       "\n",
       "     bgr     bu    sc    sod  pot  hemo pcv     wc   rc  htn   dm cad appet  \\\n",
       "0  121.0   36.0   1.2    NaN  NaN  15.4  44   7800  5.2  yes  yes  no  good   \n",
       "1    NaN   18.0   0.8    NaN  NaN  11.3  38   6000  NaN   no   no  no  good   \n",
       "2  423.0   53.0   1.8    NaN  NaN   9.6  31   7500  NaN   no  yes  no  poor   \n",
       "3  117.0   56.0   3.8  111.0  2.5  11.2  32   6700  3.9  yes   no  no  poor   \n",
       "4  106.0   26.0   1.4    NaN  NaN  11.6  35   7300  4.6   no   no  no  good   \n",
       "5   74.0   25.0   1.1  142.0  3.2  12.2  39   7800  4.4  yes  yes  no  good   \n",
       "6  100.0   54.0  24.0  104.0  4.0  12.4  36    NaN  NaN   no   no  no  good   \n",
       "7  410.0   31.0   1.1    NaN  NaN  12.4  44   6900    5   no  yes  no  good   \n",
       "8  138.0   60.0   1.9    NaN  NaN  10.8  33   9600  4.0  yes  yes  no  good   \n",
       "9   70.0  107.0   7.2  114.0  3.7   9.5  29  12100  3.7  yes  yes  no  poor   \n",
       "\n",
       "    pe  ane classification  \n",
       "0   no   no            ckd  \n",
       "1   no   no            ckd  \n",
       "2   no  yes            ckd  \n",
       "3  yes  yes            ckd  \n",
       "4   no   no            ckd  \n",
       "5  yes   no            ckd  \n",
       "6   no   no            ckd  \n",
       "7  yes   no            ckd  \n",
       "8   no  yes            ckd  \n",
       "9   no  yes            ckd  "
      ]
     },
     "execution_count": 445,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = dataset.drop('id', axis=1)\n",
    "dataset.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(400, 25)"
      ]
     },
     "execution_count": 446,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                 9\n",
       "bp                 12\n",
       "sg                 47\n",
       "al                 46\n",
       "su                 49\n",
       "rbc               152\n",
       "pc                 65\n",
       "pcc                 4\n",
       "ba                  4\n",
       "bgr                44\n",
       "bu                 19\n",
       "sc                 17\n",
       "sod                87\n",
       "pot                88\n",
       "hemo               52\n",
       "pcv                70\n",
       "wc                105\n",
       "rc                130\n",
       "htn                 2\n",
       "dm                  2\n",
       "cad                 2\n",
       "appet               1\n",
       "pe                  1\n",
       "ane                 1\n",
       "classification      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 447,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "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>bgr</th>\n",
       "      <th>bu</th>\n",
       "      <th>sc</th>\n",
       "      <th>sod</th>\n",
       "      <th>pot</th>\n",
       "      <th>hemo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>391.000000</td>\n",
       "      <td>388.000000</td>\n",
       "      <td>353.000000</td>\n",
       "      <td>354.000000</td>\n",
       "      <td>351.000000</td>\n",
       "      <td>356.000000</td>\n",
       "      <td>381.000000</td>\n",
       "      <td>383.000000</td>\n",
       "      <td>313.000000</td>\n",
       "      <td>312.000000</td>\n",
       "      <td>348.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>51.483376</td>\n",
       "      <td>76.469072</td>\n",
       "      <td>1.017408</td>\n",
       "      <td>1.016949</td>\n",
       "      <td>0.450142</td>\n",
       "      <td>148.036517</td>\n",
       "      <td>57.425722</td>\n",
       "      <td>3.072454</td>\n",
       "      <td>137.528754</td>\n",
       "      <td>4.627244</td>\n",
       "      <td>12.526437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>17.169714</td>\n",
       "      <td>13.683637</td>\n",
       "      <td>0.005717</td>\n",
       "      <td>1.352679</td>\n",
       "      <td>1.099191</td>\n",
       "      <td>79.281714</td>\n",
       "      <td>50.503006</td>\n",
       "      <td>5.741126</td>\n",
       "      <td>10.408752</td>\n",
       "      <td>3.193904</td>\n",
       "      <td>2.912587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>1.005000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>3.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>42.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>1.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>10.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.020000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>138.000000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>12.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>64.500000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.020000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>163.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>142.000000</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>1.025000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>490.000000</td>\n",
       "      <td>391.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>163.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>17.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age          bp          sg          al          su         bgr  \\\n",
       "count  391.000000  388.000000  353.000000  354.000000  351.000000  356.000000   \n",
       "mean    51.483376   76.469072    1.017408    1.016949    0.450142  148.036517   \n",
       "std     17.169714   13.683637    0.005717    1.352679    1.099191   79.281714   \n",
       "min      2.000000   50.000000    1.005000    0.000000    0.000000   22.000000   \n",
       "25%     42.000000   70.000000    1.010000    0.000000    0.000000   99.000000   \n",
       "50%     55.000000   80.000000    1.020000    0.000000    0.000000  121.000000   \n",
       "75%     64.500000   80.000000    1.020000    2.000000    0.000000  163.000000   \n",
       "max     90.000000  180.000000    1.025000    5.000000    5.000000  490.000000   \n",
       "\n",
       "               bu          sc         sod         pot        hemo  \n",
       "count  381.000000  383.000000  313.000000  312.000000  348.000000  \n",
       "mean    57.425722    3.072454  137.528754    4.627244   12.526437  \n",
       "std     50.503006    5.741126   10.408752    3.193904    2.912587  \n",
       "min      1.500000    0.400000    4.500000    2.500000    3.100000  \n",
       "25%     27.000000    0.900000  135.000000    3.800000   10.300000  \n",
       "50%     42.000000    1.300000  138.000000    4.400000   12.650000  \n",
       "75%     66.000000    2.800000  142.000000    4.900000   15.000000  \n",
       "max    391.000000   76.000000  163.000000   47.000000   17.800000  "
      ]
     },
     "execution_count": 448,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 449,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age               float64\n",
       "bp                float64\n",
       "sg                float64\n",
       "al                float64\n",
       "su                float64\n",
       "rbc                object\n",
       "pc                 object\n",
       "pcc                object\n",
       "ba                 object\n",
       "bgr               float64\n",
       "bu                float64\n",
       "sc                float64\n",
       "sod               float64\n",
       "pot               float64\n",
       "hemo              float64\n",
       "pcv                object\n",
       "wc                 object\n",
       "rc                 object\n",
       "htn                object\n",
       "dm                 object\n",
       "cad                object\n",
       "appet              object\n",
       "pe                 object\n",
       "ane                object\n",
       "classification     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 449,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rbc\n",
       "normal      201\n",
       "abnormal     47\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 450,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['rbc'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 451,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/72/469w_sss0yz0l7q6y9k2cdvw0000gn/T/ipykernel_2680/1868271888.py:1: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  dataset['rbc'] = dataset['rbc'].replace(to_replace = {'normal' : 0, 'abnormal' : 1})\n"
     ]
    }
   ],
   "source": [
    "dataset['rbc'] = dataset['rbc'].replace(to_replace = {'normal' : 0, 'abnormal' : 1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 452,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pc\n",
       "normal      259\n",
       "abnormal     76\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 452,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['pc'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 453,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/72/469w_sss0yz0l7q6y9k2cdvw0000gn/T/ipykernel_2680/659499331.py:1: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  dataset['pc'] = dataset['pc'].replace(to_replace = {'normal' : 0, 'abnormal' : 1})\n"
     ]
    }
   ],
   "source": [
    "dataset['pc'] = dataset['pc'].replace(to_replace = {'normal' : 0, 'abnormal' : 1})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pcc\n",
       "notpresent    354\n",
       "present        42\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 454,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['pcc'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 455,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/72/469w_sss0yz0l7q6y9k2cdvw0000gn/T/ipykernel_2680/3196457339.py:1: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  dataset['pcc'] = dataset['pcc'].replace(to_replace = {'notpresent':0,'present':1})\n"
     ]
    }
   ],
   "source": [
    "dataset['pcc'] = dataset['pcc'].replace(to_replace = {'notpresent':0,'present':1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 456,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ba\n",
       "notpresent    374\n",
       "present        22\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 456,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['ba'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 457,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/72/469w_sss0yz0l7q6y9k2cdvw0000gn/T/ipykernel_2680/2271382674.py:1: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  dataset['ba'] = dataset['ba'].replace(to_replace = {'notpresent':0,'present':1})\n"
     ]
    }
   ],
   "source": [
    "dataset['ba'] = dataset['ba'].replace(to_replace = {'notpresent':0,'present':1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "htn\n",
       "no     251\n",
       "yes    147\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 458,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['htn'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/72/469w_sss0yz0l7q6y9k2cdvw0000gn/T/ipykernel_2680/2228437743.py:1: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  dataset['htn'] = dataset['htn'].replace(to_replace = {'yes' : 1, 'no' : 0})\n"
     ]
    }
   ],
   "source": [
    "dataset['htn'] = dataset['htn'].replace(to_replace = {'yes' : 1, 'no' : 0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 460,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dm\n",
       "no       258\n",
       "yes      134\n",
       "\\tno       3\n",
       "\\tyes      2\n",
       " yes       1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 460,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['dm'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 461,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['dm'] = dataset['dm'].replace(to_replace = {'\\tyes':'yes', ' yes':'yes', '\\tno':'no'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 462,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/72/469w_sss0yz0l7q6y9k2cdvw0000gn/T/ipykernel_2680/2648021753.py:1: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  dataset['dm'] = dataset['dm'].replace(to_replace = {'yes' : 1, 'no' : 0})\n"
     ]
    }
   ],
   "source": [
    "dataset['dm'] = dataset['dm'].replace(to_replace = {'yes' : 1, 'no' : 0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 463,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cad\n",
       "no      362\n",
       "yes      34\n",
       "\\tno      2\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 463,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['cad'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 464,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/72/469w_sss0yz0l7q6y9k2cdvw0000gn/T/ipykernel_2680/3037767176.py:2: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  dataset['cad'] = dataset['cad'].replace(to_replace = {'yes' : 1, 'no' : 0})\n"
     ]
    }
   ],
   "source": [
    "dataset['cad'] = dataset['cad'].replace(to_replace = {'\\tno':'no'})\n",
    "dataset['cad'] = dataset['cad'].replace(to_replace = {'yes' : 1, 'no' : 0})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 465,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['good', 'poor', nan], dtype=object)"
      ]
     },
     "execution_count": 465,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['appet'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 466,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/72/469w_sss0yz0l7q6y9k2cdvw0000gn/T/ipykernel_2680/1559130877.py:1: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  dataset['appet'] = dataset['appet'].replace(to_replace={'good':1,'poor':0,'no':np.nan})\n"
     ]
    }
   ],
   "source": [
    "dataset['appet'] = dataset['appet'].replace(to_replace={'good':1,'poor':0,'no':np.nan})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 467,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pe\n",
       "no     323\n",
       "yes     76\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 467,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['pe'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 468,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/72/469w_sss0yz0l7q6y9k2cdvw0000gn/T/ipykernel_2680/166025595.py:1: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  dataset['pe'] = dataset['pe'].replace(to_replace = {'yes' : 1, 'no' : 0})\n"
     ]
    }
   ],
   "source": [
    "dataset['pe'] = dataset['pe'].replace(to_replace = {'yes' : 1, 'no' : 0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 469,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ane\n",
       "no     339\n",
       "yes     60\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 469,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['ane'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 470,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/72/469w_sss0yz0l7q6y9k2cdvw0000gn/T/ipykernel_2680/1781658938.py:1: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  dataset['ane'] = dataset['ane'].replace(to_replace = {'yes' : 1, 'no' : 0})\n"
     ]
    }
   ],
   "source": [
    "dataset['ane'] = dataset['ane'].replace(to_replace = {'yes' : 1, 'no' : 0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 471,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "classification\n",
       "ckd       248\n",
       "notckd    150\n",
       "ckd\\t       2\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 471,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['classification'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 472,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['classification'] = dataset['classification'].replace(to_replace={'ckd\\t':'ckd'})\n",
    "dataset[\"classification\"] = [1 if i == \"ckd\" else 0 for i in dataset[\"classification\"]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 473,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>bp</th>\n",
       "      <th>sg</th>\n",
       "      <th>al</th>\n",
       "      <th>su</th>\n",
       "      <th>rbc</th>\n",
       "      <th>pc</th>\n",
       "      <th>pcc</th>\n",
       "      <th>ba</th>\n",
       "      <th>bgr</th>\n",
       "      <th>bu</th>\n",
       "      <th>sc</th>\n",
       "      <th>sod</th>\n",
       "      <th>pot</th>\n",
       "      <th>hemo</th>\n",
       "      <th>pcv</th>\n",
       "      <th>wc</th>\n",
       "      <th>rc</th>\n",
       "      <th>htn</th>\n",
       "      <th>dm</th>\n",
       "      <th>cad</th>\n",
       "      <th>appet</th>\n",
       "      <th>pe</th>\n",
       "      <th>ane</th>\n",
       "      <th>classification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>48.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.020</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.4</td>\n",
       "      <td>44</td>\n",
       "      <td>7800</td>\n",
       "      <td>5.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1.020</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.3</td>\n",
       "      <td>38</td>\n",
       "      <td>6000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.010</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>423.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.6</td>\n",
       "      <td>31</td>\n",
       "      <td>7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.005</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>111.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>11.2</td>\n",
       "      <td>32</td>\n",
       "      <td>6700</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.010</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.6</td>\n",
       "      <td>35</td>\n",
       "      <td>7300</td>\n",
       "      <td>4.6</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>60.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>1.015</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>142.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>12.2</td>\n",
       "      <td>39</td>\n",
       "      <td>7800</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>68.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.010</td>\n",
       "      <td>0.0</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>100.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.4</td>\n",
       "      <td>36</td>\n",
       "      <td>NaN</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",
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       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.015</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>410.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.4</td>\n",
       "      <td>44</td>\n",
       "      <td>6900</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>52.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.015</td>\n",
       "      <td>3.0</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>138.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.8</td>\n",
       "      <td>33</td>\n",
       "      <td>9600</td>\n",
       "      <td>4.0</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>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>53.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>1.020</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>114.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>9.5</td>\n",
       "      <td>29</td>\n",
       "      <td>12100</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age     bp     sg   al   su  rbc   pc  pcc   ba    bgr     bu    sc  \\\n",
       "0  48.0   80.0  1.020  1.0  0.0  NaN  0.0  0.0  0.0  121.0   36.0   1.2   \n",
       "1   7.0   50.0  1.020  4.0  0.0  NaN  0.0  0.0  0.0    NaN   18.0   0.8   \n",
       "2  62.0   80.0  1.010  2.0  3.0  0.0  0.0  0.0  0.0  423.0   53.0   1.8   \n",
       "3  48.0   70.0  1.005  4.0  0.0  0.0  1.0  1.0  0.0  117.0   56.0   3.8   \n",
       "4  51.0   80.0  1.010  2.0  0.0  0.0  0.0  0.0  0.0  106.0   26.0   1.4   \n",
       "5  60.0   90.0  1.015  3.0  0.0  NaN  NaN  0.0  0.0   74.0   25.0   1.1   \n",
       "6  68.0   70.0  1.010  0.0  0.0  NaN  0.0  0.0  0.0  100.0   54.0  24.0   \n",
       "7  24.0    NaN  1.015  2.0  4.0  0.0  1.0  0.0  0.0  410.0   31.0   1.1   \n",
       "8  52.0  100.0  1.015  3.0  0.0  0.0  1.0  1.0  0.0  138.0   60.0   1.9   \n",
       "9  53.0   90.0  1.020  2.0  0.0  1.0  1.0  1.0  0.0   70.0  107.0   7.2   \n",
       "\n",
       "     sod  pot  hemo pcv     wc   rc  htn   dm  cad  appet   pe  ane  \\\n",
       "0    NaN  NaN  15.4  44   7800  5.2  1.0  1.0  0.0    1.0  0.0  0.0   \n",
       "1    NaN  NaN  11.3  38   6000  NaN  0.0  0.0  0.0    1.0  0.0  0.0   \n",
       "2    NaN  NaN   9.6  31   7500  NaN  0.0  1.0  0.0    0.0  0.0  1.0   \n",
       "3  111.0  2.5  11.2  32   6700  3.9  1.0  0.0  0.0    0.0  1.0  1.0   \n",
       "4    NaN  NaN  11.6  35   7300  4.6  0.0  0.0  0.0    1.0  0.0  0.0   \n",
       "5  142.0  3.2  12.2  39   7800  4.4  1.0  1.0  0.0    1.0  1.0  0.0   \n",
       "6  104.0  4.0  12.4  36    NaN  NaN  0.0  0.0  0.0    1.0  0.0  0.0   \n",
       "7    NaN  NaN  12.4  44   6900    5  0.0  1.0  0.0    1.0  1.0  0.0   \n",
       "8    NaN  NaN  10.8  33   9600  4.0  1.0  1.0  0.0    1.0  0.0  1.0   \n",
       "9  114.0  3.7   9.5  29  12100  3.7  1.0  1.0  0.0    0.0  0.0  1.0   \n",
       "\n",
       "   classification  \n",
       "0               1  \n",
       "1               1  \n",
       "2               1  \n",
       "3               1  \n",
       "4               1  \n",
       "5               1  \n",
       "6               1  \n",
       "7               1  \n",
       "8               1  \n",
       "9               1  "
      ]
     },
     "execution_count": 473,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 474,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age               float64\n",
       "bp                float64\n",
       "sg                float64\n",
       "al                float64\n",
       "su                float64\n",
       "rbc               float64\n",
       "pc                float64\n",
       "pcc               float64\n",
       "ba                float64\n",
       "bgr               float64\n",
       "bu                float64\n",
       "sc                float64\n",
       "sod               float64\n",
       "pot               float64\n",
       "hemo              float64\n",
       "pcv                object\n",
       "wc                 object\n",
       "rc                 object\n",
       "htn               float64\n",
       "dm                float64\n",
       "cad               float64\n",
       "appet             float64\n",
       "pe                float64\n",
       "ane               float64\n",
       "classification      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 474,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 475,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age               float64\n",
       "bp                float64\n",
       "sg                float64\n",
       "al                float64\n",
       "su                float64\n",
       "rbc               float64\n",
       "pc                float64\n",
       "pcc               float64\n",
       "ba                float64\n",
       "bgr               float64\n",
       "bu                float64\n",
       "sc                float64\n",
       "sod               float64\n",
       "pot               float64\n",
       "hemo              float64\n",
       "pcv               float64\n",
       "wc                float64\n",
       "rc                float64\n",
       "htn               float64\n",
       "dm                float64\n",
       "cad               float64\n",
       "appet             float64\n",
       "pe                float64\n",
       "ane               float64\n",
       "classification      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 475,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['pcv'] = pd.to_numeric(dataset['pcv'], errors='coerce')\n",
    "dataset['wc'] = pd.to_numeric(dataset['wc'], errors='coerce')\n",
    "dataset['rc'] = pd.to_numeric(dataset['rc'], errors='coerce')\n",
    "dataset.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 476,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>bp</th>\n",
       "      <th>sg</th>\n",
       "      <th>al</th>\n",
       "      <th>su</th>\n",
       "      <th>rbc</th>\n",
       "      <th>pc</th>\n",
       "      <th>pcc</th>\n",
       "      <th>ba</th>\n",
       "      <th>bgr</th>\n",
       "      <th>bu</th>\n",
       "      <th>sc</th>\n",
       "      <th>sod</th>\n",
       "      <th>pot</th>\n",
       "      <th>hemo</th>\n",
       "      <th>pcv</th>\n",
       "      <th>wc</th>\n",
       "      <th>rc</th>\n",
       "      <th>htn</th>\n",
       "      <th>dm</th>\n",
       "      <th>cad</th>\n",
       "      <th>appet</th>\n",
       "      <th>pe</th>\n",
       "      <th>ane</th>\n",
       "      <th>classification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>391.000000</td>\n",
       "      <td>388.000000</td>\n",
       "      <td>353.000000</td>\n",
       "      <td>354.000000</td>\n",
       "      <td>351.000000</td>\n",
       "      <td>248.000000</td>\n",
       "      <td>335.000000</td>\n",
       "      <td>396.000000</td>\n",
       "      <td>396.000000</td>\n",
       "      <td>356.000000</td>\n",
       "      <td>381.000000</td>\n",
       "      <td>383.000000</td>\n",
       "      <td>313.000000</td>\n",
       "      <td>312.000000</td>\n",
       "      <td>348.000000</td>\n",
       "      <td>329.000000</td>\n",
       "      <td>294.000000</td>\n",
       "      <td>269.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>399.000000</td>\n",
       "      <td>399.000000</td>\n",
       "      <td>399.000000</td>\n",
       "      <td>400.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>51.483376</td>\n",
       "      <td>76.469072</td>\n",
       "      <td>1.017408</td>\n",
       "      <td>1.016949</td>\n",
       "      <td>0.450142</td>\n",
       "      <td>0.189516</td>\n",
       "      <td>0.226866</td>\n",
       "      <td>0.106061</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>148.036517</td>\n",
       "      <td>57.425722</td>\n",
       "      <td>3.072454</td>\n",
       "      <td>137.528754</td>\n",
       "      <td>4.627244</td>\n",
       "      <td>12.526437</td>\n",
       "      <td>38.884498</td>\n",
       "      <td>8406.122449</td>\n",
       "      <td>4.707435</td>\n",
       "      <td>0.369347</td>\n",
       "      <td>0.344221</td>\n",
       "      <td>0.085427</td>\n",
       "      <td>0.794486</td>\n",
       "      <td>0.190476</td>\n",
       "      <td>0.150376</td>\n",
       "      <td>0.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>17.169714</td>\n",
       "      <td>13.683637</td>\n",
       "      <td>0.005717</td>\n",
       "      <td>1.352679</td>\n",
       "      <td>1.099191</td>\n",
       "      <td>0.392711</td>\n",
       "      <td>0.419431</td>\n",
       "      <td>0.308305</td>\n",
       "      <td>0.229351</td>\n",
       "      <td>79.281714</td>\n",
       "      <td>50.503006</td>\n",
       "      <td>5.741126</td>\n",
       "      <td>10.408752</td>\n",
       "      <td>3.193904</td>\n",
       "      <td>2.912587</td>\n",
       "      <td>8.990105</td>\n",
       "      <td>2944.474190</td>\n",
       "      <td>1.025323</td>\n",
       "      <td>0.483235</td>\n",
       "      <td>0.475712</td>\n",
       "      <td>0.279868</td>\n",
       "      <td>0.404584</td>\n",
       "      <td>0.393170</td>\n",
       "      <td>0.357888</td>\n",
       "      <td>0.484729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>1.005000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>3.100000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2200.000000</td>\n",
       "      <td>2.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>42.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>1.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>10.300000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>6500.000000</td>\n",
       "      <td>3.900000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.020000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>138.000000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>12.650000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>8000.000000</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>64.500000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.020000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>163.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>142.000000</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>9800.000000</td>\n",
       "      <td>5.400000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>1.025000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>490.000000</td>\n",
       "      <td>391.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>163.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>17.800000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>26400.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age          bp          sg          al          su         rbc  \\\n",
       "count  391.000000  388.000000  353.000000  354.000000  351.000000  248.000000   \n",
       "mean    51.483376   76.469072    1.017408    1.016949    0.450142    0.189516   \n",
       "std     17.169714   13.683637    0.005717    1.352679    1.099191    0.392711   \n",
       "min      2.000000   50.000000    1.005000    0.000000    0.000000    0.000000   \n",
       "25%     42.000000   70.000000    1.010000    0.000000    0.000000    0.000000   \n",
       "50%     55.000000   80.000000    1.020000    0.000000    0.000000    0.000000   \n",
       "75%     64.500000   80.000000    1.020000    2.000000    0.000000    0.000000   \n",
       "max     90.000000  180.000000    1.025000    5.000000    5.000000    1.000000   \n",
       "\n",
       "               pc         pcc          ba         bgr          bu          sc  \\\n",
       "count  335.000000  396.000000  396.000000  356.000000  381.000000  383.000000   \n",
       "mean     0.226866    0.106061    0.055556  148.036517   57.425722    3.072454   \n",
       "std      0.419431    0.308305    0.229351   79.281714   50.503006    5.741126   \n",
       "min      0.000000    0.000000    0.000000   22.000000    1.500000    0.400000   \n",
       "25%      0.000000    0.000000    0.000000   99.000000   27.000000    0.900000   \n",
       "50%      0.000000    0.000000    0.000000  121.000000   42.000000    1.300000   \n",
       "75%      0.000000    0.000000    0.000000  163.000000   66.000000    2.800000   \n",
       "max      1.000000    1.000000    1.000000  490.000000  391.000000   76.000000   \n",
       "\n",
       "              sod         pot        hemo         pcv            wc  \\\n",
       "count  313.000000  312.000000  348.000000  329.000000    294.000000   \n",
       "mean   137.528754    4.627244   12.526437   38.884498   8406.122449   \n",
       "std     10.408752    3.193904    2.912587    8.990105   2944.474190   \n",
       "min      4.500000    2.500000    3.100000    9.000000   2200.000000   \n",
       "25%    135.000000    3.800000   10.300000   32.000000   6500.000000   \n",
       "50%    138.000000    4.400000   12.650000   40.000000   8000.000000   \n",
       "75%    142.000000    4.900000   15.000000   45.000000   9800.000000   \n",
       "max    163.000000   47.000000   17.800000   54.000000  26400.000000   \n",
       "\n",
       "               rc         htn          dm         cad       appet          pe  \\\n",
       "count  269.000000  398.000000  398.000000  398.000000  399.000000  399.000000   \n",
       "mean     4.707435    0.369347    0.344221    0.085427    0.794486    0.190476   \n",
       "std      1.025323    0.483235    0.475712    0.279868    0.404584    0.393170   \n",
       "min      2.100000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      3.900000    0.000000    0.000000    0.000000    1.000000    0.000000   \n",
       "50%      4.800000    0.000000    0.000000    0.000000    1.000000    0.000000   \n",
       "75%      5.400000    1.000000    1.000000    0.000000    1.000000    0.000000   \n",
       "max      8.000000    1.000000    1.000000    1.000000    1.000000    1.000000   \n",
       "\n",
       "              ane  classification  \n",
       "count  399.000000      400.000000  \n",
       "mean     0.150376        0.625000  \n",
       "std      0.357888        0.484729  \n",
       "min      0.000000        0.000000  \n",
       "25%      0.000000        0.000000  \n",
       "50%      0.000000        1.000000  \n",
       "75%      0.000000        1.000000  \n",
       "max      1.000000        1.000000  "
      ]
     },
     "execution_count": 476,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 477,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rbc               152\n",
       "rc                131\n",
       "wc                106\n",
       "pot                88\n",
       "sod                87\n",
       "pcv                71\n",
       "pc                 65\n",
       "hemo               52\n",
       "su                 49\n",
       "sg                 47\n",
       "al                 46\n",
       "bgr                44\n",
       "bu                 19\n",
       "sc                 17\n",
       "bp                 12\n",
       "age                 9\n",
       "ba                  4\n",
       "pcc                 4\n",
       "htn                 2\n",
       "dm                  2\n",
       "cad                 2\n",
       "appet               1\n",
       "pe                  1\n",
       "ane                 1\n",
       "classification      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 477,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isnull().sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 478,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'bp', 'sg', 'al', 'su', 'rbc', 'pc', 'pcc', 'ba', 'bgr', 'bu',\n",
       "       'sc', 'sod', 'pot', 'hemo', 'pcv', 'wc', 'rc', 'htn', 'dm', 'cad',\n",
       "       'appet', 'pe', 'ane', 'classification'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 478,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 479,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = ['age', 'bp', 'sg', 'al', 'su', 'rbc', 'pc', 'pcc', 'ba', 'bgr', 'bu',\n",
    "           'sc', 'sod', 'pot', 'hemo', 'pcv', 'wc', 'rc', 'htn', 'dm', 'cad',\n",
    "           'appet', 'pe', 'ane']\n",
    "for feature in features:\n",
    "    dataset[feature] = dataset[feature].fillna(dataset[feature].median())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 480,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(0)"
      ]
     },
     "execution_count": 480,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isnull().any().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 481,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>bp</th>\n",
       "      <th>sg</th>\n",
       "      <th>al</th>\n",
       "      <th>su</th>\n",
       "      <th>rbc</th>\n",
       "      <th>pc</th>\n",
       "      <th>pcc</th>\n",
       "      <th>ba</th>\n",
       "      <th>bgr</th>\n",
       "      <th>bu</th>\n",
       "      <th>sc</th>\n",
       "      <th>sod</th>\n",
       "      <th>pot</th>\n",
       "      <th>hemo</th>\n",
       "      <th>pcv</th>\n",
       "      <th>wc</th>\n",
       "      <th>rc</th>\n",
       "      <th>htn</th>\n",
       "      <th>dm</th>\n",
       "      <th>cad</th>\n",
       "      <th>appet</th>\n",
       "      <th>pe</th>\n",
       "      <th>ane</th>\n",
       "      <th>classification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>48.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.020</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>138.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>15.4</td>\n",
       "      <td>44.0</td>\n",
       "      <td>7800.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1.020</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>138.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>11.3</td>\n",
       "      <td>38.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.010</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>423.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>138.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>9.6</td>\n",
       "      <td>31.0</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.005</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>111.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>11.2</td>\n",
       "      <td>32.0</td>\n",
       "      <td>6700.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.010</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>138.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>11.6</td>\n",
       "      <td>35.0</td>\n",
       "      <td>7300.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>60.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>1.015</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>0.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>142.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>12.2</td>\n",
       "      <td>39.0</td>\n",
       "      <td>7800.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>68.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.010</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>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.4</td>\n",
       "      <td>36.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>24.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.015</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>410.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>138.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>12.4</td>\n",
       "      <td>44.0</td>\n",
       "      <td>6900.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>52.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.015</td>\n",
       "      <td>3.0</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>138.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>138.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>10.8</td>\n",
       "      <td>33.0</td>\n",
       "      <td>9600.0</td>\n",
       "      <td>4.0</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>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>53.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>1.020</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>114.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>9.5</td>\n",
       "      <td>29.0</td>\n",
       "      <td>12100.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age     bp     sg   al   su  rbc   pc  pcc   ba    bgr     bu    sc  \\\n",
       "0  48.0   80.0  1.020  1.0  0.0  0.0  0.0  0.0  0.0  121.0   36.0   1.2   \n",
       "1   7.0   50.0  1.020  4.0  0.0  0.0  0.0  0.0  0.0  121.0   18.0   0.8   \n",
       "2  62.0   80.0  1.010  2.0  3.0  0.0  0.0  0.0  0.0  423.0   53.0   1.8   \n",
       "3  48.0   70.0  1.005  4.0  0.0  0.0  1.0  1.0  0.0  117.0   56.0   3.8   \n",
       "4  51.0   80.0  1.010  2.0  0.0  0.0  0.0  0.0  0.0  106.0   26.0   1.4   \n",
       "5  60.0   90.0  1.015  3.0  0.0  0.0  0.0  0.0  0.0   74.0   25.0   1.1   \n",
       "6  68.0   70.0  1.010  0.0  0.0  0.0  0.0  0.0  0.0  100.0   54.0  24.0   \n",
       "7  24.0   80.0  1.015  2.0  4.0  0.0  1.0  0.0  0.0  410.0   31.0   1.1   \n",
       "8  52.0  100.0  1.015  3.0  0.0  0.0  1.0  1.0  0.0  138.0   60.0   1.9   \n",
       "9  53.0   90.0  1.020  2.0  0.0  1.0  1.0  1.0  0.0   70.0  107.0   7.2   \n",
       "\n",
       "     sod  pot  hemo   pcv       wc   rc  htn   dm  cad  appet   pe  ane  \\\n",
       "0  138.0  4.4  15.4  44.0   7800.0  5.2  1.0  1.0  0.0    1.0  0.0  0.0   \n",
       "1  138.0  4.4  11.3  38.0   6000.0  4.8  0.0  0.0  0.0    1.0  0.0  0.0   \n",
       "2  138.0  4.4   9.6  31.0   7500.0  4.8  0.0  1.0  0.0    0.0  0.0  1.0   \n",
       "3  111.0  2.5  11.2  32.0   6700.0  3.9  1.0  0.0  0.0    0.0  1.0  1.0   \n",
       "4  138.0  4.4  11.6  35.0   7300.0  4.6  0.0  0.0  0.0    1.0  0.0  0.0   \n",
       "5  142.0  3.2  12.2  39.0   7800.0  4.4  1.0  1.0  0.0    1.0  1.0  0.0   \n",
       "6  104.0  4.0  12.4  36.0   8000.0  4.8  0.0  0.0  0.0    1.0  0.0  0.0   \n",
       "7  138.0  4.4  12.4  44.0   6900.0  5.0  0.0  1.0  0.0    1.0  1.0  0.0   \n",
       "8  138.0  4.4  10.8  33.0   9600.0  4.0  1.0  1.0  0.0    1.0  0.0  1.0   \n",
       "9  114.0  3.7   9.5  29.0  12100.0  3.7  1.0  1.0  0.0    0.0  0.0  1.0   \n",
       "\n",
       "   classification  \n",
       "0               1  \n",
       "1               1  \n",
       "2               1  \n",
       "3               1  \n",
       "4               1  \n",
       "5               1  \n",
       "6               1  \n",
       "7               1  \n",
       "8               1  \n",
       "9               1  "
      ]
     },
     "execution_count": 481,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 482,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2400x1400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(24,14))\n",
    "sns.heatmap(dataset.corr(), annot=True, cmap='YlGnBu')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 483,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>bp</th>\n",
       "      <th>sg</th>\n",
       "      <th>al</th>\n",
       "      <th>su</th>\n",
       "      <th>rbc</th>\n",
       "      <th>pc</th>\n",
       "      <th>pcc</th>\n",
       "      <th>ba</th>\n",
       "      <th>bgr</th>\n",
       "      <th>bu</th>\n",
       "      <th>sc</th>\n",
       "      <th>sod</th>\n",
       "      <th>pot</th>\n",
       "      <th>hemo</th>\n",
       "      <th>wc</th>\n",
       "      <th>rc</th>\n",
       "      <th>htn</th>\n",
       "      <th>dm</th>\n",
       "      <th>cad</th>\n",
       "      <th>appet</th>\n",
       "      <th>pe</th>\n",
       "      <th>ane</th>\n",
       "      <th>classification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>48.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.020</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>138.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>15.4</td>\n",
       "      <td>7800.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1.020</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>138.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>11.3</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.010</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>423.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>138.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>9.6</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.005</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>111.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>11.2</td>\n",
       "      <td>6700.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.010</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>138.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>11.6</td>\n",
       "      <td>7300.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>60.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>1.015</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>0.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>142.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>12.2</td>\n",
       "      <td>7800.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>68.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.010</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>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.4</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>24.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.015</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>410.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>138.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>12.4</td>\n",
       "      <td>6900.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>52.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.015</td>\n",
       "      <td>3.0</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>138.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>138.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>10.8</td>\n",
       "      <td>9600.0</td>\n",
       "      <td>4.0</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>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>53.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>1.020</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>114.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>9.5</td>\n",
       "      <td>12100.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age     bp     sg   al   su  rbc   pc  pcc   ba    bgr     bu    sc  \\\n",
       "0  48.0   80.0  1.020  1.0  0.0  0.0  0.0  0.0  0.0  121.0   36.0   1.2   \n",
       "1   7.0   50.0  1.020  4.0  0.0  0.0  0.0  0.0  0.0  121.0   18.0   0.8   \n",
       "2  62.0   80.0  1.010  2.0  3.0  0.0  0.0  0.0  0.0  423.0   53.0   1.8   \n",
       "3  48.0   70.0  1.005  4.0  0.0  0.0  1.0  1.0  0.0  117.0   56.0   3.8   \n",
       "4  51.0   80.0  1.010  2.0  0.0  0.0  0.0  0.0  0.0  106.0   26.0   1.4   \n",
       "5  60.0   90.0  1.015  3.0  0.0  0.0  0.0  0.0  0.0   74.0   25.0   1.1   \n",
       "6  68.0   70.0  1.010  0.0  0.0  0.0  0.0  0.0  0.0  100.0   54.0  24.0   \n",
       "7  24.0   80.0  1.015  2.0  4.0  0.0  1.0  0.0  0.0  410.0   31.0   1.1   \n",
       "8  52.0  100.0  1.015  3.0  0.0  0.0  1.0  1.0  0.0  138.0   60.0   1.9   \n",
       "9  53.0   90.0  1.020  2.0  0.0  1.0  1.0  1.0  0.0   70.0  107.0   7.2   \n",
       "\n",
       "     sod  pot  hemo       wc   rc  htn   dm  cad  appet   pe  ane  \\\n",
       "0  138.0  4.4  15.4   7800.0  5.2  1.0  1.0  0.0    1.0  0.0  0.0   \n",
       "1  138.0  4.4  11.3   6000.0  4.8  0.0  0.0  0.0    1.0  0.0  0.0   \n",
       "2  138.0  4.4   9.6   7500.0  4.8  0.0  1.0  0.0    0.0  0.0  1.0   \n",
       "3  111.0  2.5  11.2   6700.0  3.9  1.0  0.0  0.0    0.0  1.0  1.0   \n",
       "4  138.0  4.4  11.6   7300.0  4.6  0.0  0.0  0.0    1.0  0.0  0.0   \n",
       "5  142.0  3.2  12.2   7800.0  4.4  1.0  1.0  0.0    1.0  1.0  0.0   \n",
       "6  104.0  4.0  12.4   8000.0  4.8  0.0  0.0  0.0    1.0  0.0  0.0   \n",
       "7  138.0  4.4  12.4   6900.0  5.0  0.0  1.0  0.0    1.0  1.0  0.0   \n",
       "8  138.0  4.4  10.8   9600.0  4.0  1.0  1.0  0.0    1.0  0.0  1.0   \n",
       "9  114.0  3.7   9.5  12100.0  3.7  1.0  1.0  0.0    0.0  0.0  1.0   \n",
       "\n",
       "   classification  \n",
       "0               1  \n",
       "1               1  \n",
       "2               1  \n",
       "3               1  \n",
       "4               1  \n",
       "5               1  \n",
       "6               1  \n",
       "7               1  \n",
       "8               1  \n",
       "9               1  "
      ]
     },
     "execution_count": 483,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.drop('pcv', axis=1, inplace=True)\n",
    "dataset.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 484,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dataset.iloc[:, :-1]\n",
    "y = dataset.iloc[:, -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 485,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>bp</th>\n",
       "      <th>sg</th>\n",
       "      <th>al</th>\n",
       "      <th>su</th>\n",
       "      <th>rbc</th>\n",
       "      <th>pc</th>\n",
       "      <th>pcc</th>\n",
       "      <th>ba</th>\n",
       "      <th>bgr</th>\n",
       "      <th>bu</th>\n",
       "      <th>sc</th>\n",
       "      <th>sod</th>\n",
       "      <th>pot</th>\n",
       "      <th>hemo</th>\n",
       "      <th>wc</th>\n",
       "      <th>rc</th>\n",
       "      <th>htn</th>\n",
       "      <th>dm</th>\n",
       "      <th>cad</th>\n",
       "      <th>appet</th>\n",
       "      <th>pe</th>\n",
       "      <th>ane</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>48.0</td>\n",
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       "      <td>121.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>138.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>15.4</td>\n",
       "      <td>7800.0</td>\n",
       "      <td>5.2</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1.020</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>138.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>11.3</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>4.8</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.010</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>423.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>138.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>9.6</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.005</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>111.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>11.2</td>\n",
       "      <td>6700.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>138.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>11.6</td>\n",
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      ],
      "text/plain": [
       "    age    bp     sg   al   su  rbc   pc  pcc   ba    bgr    bu   sc    sod  \\\n",
       "0  48.0  80.0  1.020  1.0  0.0  0.0  0.0  0.0  0.0  121.0  36.0  1.2  138.0   \n",
       "1   7.0  50.0  1.020  4.0  0.0  0.0  0.0  0.0  0.0  121.0  18.0  0.8  138.0   \n",
       "2  62.0  80.0  1.010  2.0  3.0  0.0  0.0  0.0  0.0  423.0  53.0  1.8  138.0   \n",
       "3  48.0  70.0  1.005  4.0  0.0  0.0  1.0  1.0  0.0  117.0  56.0  3.8  111.0   \n",
       "4  51.0  80.0  1.010  2.0  0.0  0.0  0.0  0.0  0.0  106.0  26.0  1.4  138.0   \n",
       "\n",
       "   pot  hemo      wc   rc  htn   dm  cad  appet   pe  ane  \n",
       "0  4.4  15.4  7800.0  5.2  1.0  1.0  0.0    1.0  0.0  0.0  \n",
       "1  4.4  11.3  6000.0  4.8  0.0  0.0  0.0    1.0  0.0  0.0  \n",
       "2  4.4   9.6  7500.0  4.8  0.0  1.0  0.0    0.0  0.0  1.0  \n",
       "3  2.5  11.2  6700.0  3.9  1.0  0.0  0.0    0.0  1.0  1.0  \n",
       "4  4.4  11.6  7300.0  4.6  0.0  0.0  0.0    1.0  0.0  0.0  "
      ]
     },
     "execution_count": 485,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 486,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: scikit-learn in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (1.5.2)\n",
      "Requirement already satisfied: numpy>=1.19.5 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from scikit-learn) (2.1.3)\n",
      "Requirement already satisfied: scipy>=1.6.0 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from scikit-learn) (1.14.1)\n",
      "Requirement already satisfied: joblib>=1.2.0 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from scikit-learn) (1.4.2)\n",
      "Requirement already satisfied: threadpoolctl>=3.1.0 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from scikit-learn) (3.5.0)\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip3 install --upgrade pip\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install scikit-learn\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 487,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model=RandomForestClassifier()\n",
    "model.fit(X,y)\n",
    "plt.figure(figsize=(8,6))\n",
    "ranked_features=pd.Series(model.feature_importances_,index=X.columns)\n",
    "ranked_features.nlargest(24).plot(kind='barh')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 488,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['hemo', 'sc', 'sg', 'rc', 'al', 'dm', 'htn', 'bu'], dtype='object')"
      ]
     },
     "execution_count": 488,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ranked_features.nlargest(8).index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 489,
   "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>hemo</th>\n",
       "      <th>sg</th>\n",
       "      <th>rc</th>\n",
       "      <th>sc</th>\n",
       "      <th>al</th>\n",
       "      <th>htn</th>\n",
       "      <th>bu</th>\n",
       "      <th>dm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15.4</td>\n",
       "      <td>1.020</td>\n",
       "      <td>5.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.3</td>\n",
       "      <td>1.020</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.6</td>\n",
       "      <td>1.010</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>1.005</td>\n",
       "      <td>3.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11.6</td>\n",
       "      <td>1.010</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12.2</td>\n",
       "      <td>1.015</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12.4</td>\n",
       "      <td>1.010</td>\n",
       "      <td>4.8</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>12.4</td>\n",
       "      <td>1.015</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10.8</td>\n",
       "      <td>1.015</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9.5</td>\n",
       "      <td>1.020</td>\n",
       "      <td>3.7</td>\n",
       "      <td>7.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   hemo     sg   rc    sc   al  htn     bu   dm\n",
       "0  15.4  1.020  5.2   1.2  1.0  1.0   36.0  1.0\n",
       "1  11.3  1.020  4.8   0.8  4.0  0.0   18.0  0.0\n",
       "2   9.6  1.010  4.8   1.8  2.0  0.0   53.0  1.0\n",
       "3  11.2  1.005  3.9   3.8  4.0  1.0   56.0  0.0\n",
       "4  11.6  1.010  4.6   1.4  2.0  0.0   26.0  0.0\n",
       "5  12.2  1.015  4.4   1.1  3.0  1.0   25.0  1.0\n",
       "6  12.4  1.010  4.8  24.0  0.0  0.0   54.0  0.0\n",
       "7  12.4  1.015  5.0   1.1  2.0  0.0   31.0  1.0\n",
       "8  10.8  1.015  4.0   1.9  3.0  1.0   60.0  1.0\n",
       "9   9.5  1.020  3.7   7.2  2.0  1.0  107.0  1.0"
      ]
     },
     "execution_count": 489,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = dataset[['hemo', 'sg', 'rc', 'sc', 'al', 'htn', 'bu', 'dm']]\n",
    "X.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 490,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X,y, test_size=0.3, random_state=33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 491,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(280, 8)\n",
      "(120, 8)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 492,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.95\n",
      "[[58  0]\n",
      " [ 6 56]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      1.00      0.95        58\n",
      "           1       1.00      0.90      0.95        62\n",
      "\n",
      "    accuracy                           0.95       120\n",
      "   macro avg       0.95      0.95      0.95       120\n",
      "weighted avg       0.95      0.95      0.95       120\n",
      "\n"
     ]
    }
   ],
   "source": [
    "n = GaussianNB()\n",
    "n= n.fit(X_train,y_train)\n",
    "y_pred = n.predict(X_test)\n",
    "print('Accuracy:', accuracy_score(y_test,y_pred))\n",
    "nbayes=(accuracy_score(y_test,y_pred))*100\n",
    "print(confusion_matrix(y_test,y_pred))\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: [0.95]\n",
      "True Negative Rate or Specificity or Selectivity: 1.0\n",
      "False Positive Rate or Fall Out: 0.0\n",
      "False Negative Rate or The Miss Rate: 0.0967741935483871\n",
      "True Positive Rate or Recall or Hit Rate or Sensitivity Rate: 0.9032258064516129\n",
      "Precision or Positive Predictive Value: 1.0\n",
      "False Discovery Rate: 0.0\n",
      "False Omission Rate: 0.09375\n",
      "F1_score or Harmonic Mean: 0.9032258064516129\n",
      "Multi Class Classification Rate: 0.904736639634305\n"
     ]
    }
   ],
   "source": [
    "k=3\n",
    "acc_score=[]\n",
    "k_fold=KFold(n_splits=k,random_state=None)\n",
    "score=cross_val_score(n,X,y,cv=k_fold)\n",
    "acc=accuracy_score(y_pred,y_test)\n",
    "acc_score.append(acc)\n",
    "print(\"Accuracy:\",acc_score)\n",
    "c=confusion_matrix(y_test,y_pred)\n",
    "tn,fp,fn,tp=c[0][0],c[0][1],c[1][0],c[1][1]\n",
    "print(\"True Negative Rate or Specificity or Selectivity:\",tn/(tn+fp))\n",
    "print(\"False Positive Rate or Fall Out:\",fp/(fp+tn))\n",
    "print(\"False Negative Rate or The Miss Rate:\",fn/(fn+tp))\n",
    "print(\"True Positive Rate or Recall or Hit Rate or Sensitivity Rate:\",tp/(tp+fn))\n",
    "p=(tp/(tp+fp))\n",
    "print(\"Precision or Positive Predictive Value:\",p)\n",
    "se=(tp/(tp+fn))\n",
    "sp=(tn/(tn+fp))\n",
    "print(\"False Discovery Rate:\",(fp/(fp+tp)))\n",
    "print(\"False Omission Rate:\",(fn/(fn+tn)))\n",
    "print(\"F1_score or Harmonic Mean:\",((2*p*se)/(p+sp)))     \n",
    "w=(tp+fp)*(tp+fn)*(tn+fp)*(tn+fn) \n",
    "mcc=((tp*tn)-(fp*fn))/math.sqrt(w)\n",
    "print(\"Multi Class Classification Rate:\",mcc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 494,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "auc=metrics.roc_auc_score(y_test, y_pred)\n",
    "false_positive_rate, true_positive_rate, thresolds = metrics.roc_curve(y_test, y_pred)\n",
    "plt.figure(figsize=(10, 8), dpi=100)\n",
    "plt.axis('scaled')\n",
    "plt.xlim([0, 1])\n",
    "plt.ylim([0, 1])\n",
    "plt.title(\"AUC & ROC Curve\")\n",
    "plt.plot(false_positive_rate, true_positive_rate, 'g')\n",
    "plt.fill_between(false_positive_rate, true_positive_rate, facecolor='lightgreen', alpha=0.7)\n",
    "plt.text(0.95, 0.05, 'AUC = %0.4f' % auc, ha='right', fontsize=12, weight='bold', color='blue')\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 495,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.975\n",
      "[[55  3]\n",
      " [ 0 62]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.95      0.97        58\n",
      "           1       0.95      1.00      0.98        62\n",
      "\n",
      "    accuracy                           0.97       120\n",
      "   macro avg       0.98      0.97      0.97       120\n",
      "weighted avg       0.98      0.97      0.97       120\n",
      "\n"
     ]
    }
   ],
   "source": [
    "RandomForest = RandomForestClassifier()\n",
    "RandomForest = RandomForest.fit(X_train,y_train)\n",
    "# Predictions:\n",
    "y_pred = RandomForest.predict(X_test)\n",
    "\n",
    "# Performance:\n",
    "print('Accuracy:', accuracy_score(y_test,y_pred))\n",
    "rforest=(accuracy_score(y_test,y_pred))*100\n",
    "print(confusion_matrix(y_test,y_pred))\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 496,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: [0.975]\n",
      "True Negative Rate or Specificity or Selectivity: 0.9482758620689655\n",
      "False Positive Rate or Fall Out: 0.05172413793103448\n",
      "False Negative Rate or The Miss Rate: 0.0\n",
      "True Positive Rate or Recall or Hit Rate or Sensitivity Rate: 1.0\n",
      "Precision or Positive Predictive Value: 0.9538461538461539\n",
      "False Discovery Rate: 0.046153846153846156\n",
      "False Omission Rate: 0.0\n",
      "F1_score or Harmonic Mean: 1.0029284618602705\n",
      "Multi Class Classification Rate: 0.9510569298520613\n"
     ]
    }
   ],
   "source": [
    "k=3\n",
    "acc_score=[]\n",
    "k_fold=KFold(n_splits=k,random_state=None)\n",
    "score=cross_val_score(RandomForest,X,y,cv=k_fold)\n",
    "acc=accuracy_score(y_pred,y_test)\n",
    "acc_score.append(acc)\n",
    "print(\"Accuracy:\",acc_score)\n",
    "c=confusion_matrix(y_test,y_pred)\n",
    "tn,fp,fn,tp=c[0][0],c[0][1],c[1][0],c[1][1]\n",
    "print(\"True Negative Rate or Specificity or Selectivity:\",tn/(tn+fp))\n",
    "print(\"False Positive Rate or Fall Out:\",fp/(fp+tn))\n",
    "print(\"False Negative Rate or The Miss Rate:\",fn/(fn+tp))\n",
    "print(\"True Positive Rate or Recall or Hit Rate or Sensitivity Rate:\",tp/(tp+fn))\n",
    "p=(tp/(tp+fp))\n",
    "print(\"Precision or Positive Predictive Value:\",p)\n",
    "se=(tp/(tp+fn))\n",
    "sp=(tn/(tn+fp))\n",
    "print(\"False Discovery Rate:\",(fp/(fp+tp)))\n",
    "print(\"False Omission Rate:\",(fn/(fn+tn)))\n",
    "print(\"F1_score or Harmonic Mean:\",((2*p*se)/(p+sp)))     \n",
    "w=(tp+fp)*(tp+fn)*(tn+fp)*(tn+fn) \n",
    "mcc=((tp*tn)-(fp*fn))/math.sqrt(w)\n",
    "print(\"Multi Class Classification Rate:\",mcc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 497,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "auc = metrics.roc_auc_score(y_test, y_pred)\n",
    "\n",
    "false_positive_rate, true_positive_rate, thresolds = metrics.roc_curve(y_test, y_pred)\n",
    "\n",
    "plt.figure(figsize=(10, 8), dpi=100)\n",
    "plt.axis('scaled')\n",
    "plt.xlim([0, 1])\n",
    "plt.ylim([0, 1])\n",
    "plt.title(\"AUC & ROC Curve\")\n",
    "plt.plot(false_positive_rate, true_positive_rate, 'g')\n",
    "plt.fill_between(false_positive_rate, true_positive_rate, facecolor='lightgreen', alpha=0.7)\n",
    "plt.text(0.95, 0.05, 'AUC = %0.4f' % auc, ha='right', fontsize=12, weight='bold', color='blue')\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 498,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=['Naive Bayes','Random Forest']\n",
    "y=[nbayes,rforest]\n",
    "plt.bar(x,y,color=['red','blue'])\n",
    "plt.xlabel('Algorithms')\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.title('Comparison of models accuracy')\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "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",
   "version": "3.12.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}