kidney-disease-rfc.ipynb

{"cells":[{"source":"<a href=\"https://www.kaggle.com/code/apttemi/kidney-disease-rfc?scriptVersionId=133334309\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown"},{"cell_type":"code","execution_count":1,"id":"cce5236b","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","execution":{"iopub.execute_input":"2023-06-12T18:50:27.910019Z","iopub.status.busy":"2023-06-12T18:50:27.909636Z","iopub.status.idle":"2023-06-12T18:50:27.934785Z","shell.execute_reply":"2023-06-12T18:50:27.933716Z"},"papermill":{"duration":0.042435,"end_time":"2023-06-12T18:50:27.937211","exception":false,"start_time":"2023-06-12T18:50:27.894776","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["/kaggle/input/ckdisease/kidney_disease.csv\n"]}],"source":["# This Python 3 environment comes with many helpful analytics libraries installed\n","# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n","# For example, here's several helpful packages to load\n","\n","import numpy as np # linear algebra\n","import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n","\n","# Input data files are available in the read-only \"../input/\" directory\n","# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n","\n","import os\n","for dirname, _, filenames in os.walk('/kaggle/input'):\n","    for filename in filenames:\n","        print(os.path.join(dirname, filename))\n","\n","# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n","# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"]},{"cell_type":"markdown","id":"058abe5c","metadata":{"papermill":{"duration":0.010285,"end_time":"2023-06-12T18:50:27.958442","exception":false,"start_time":"2023-06-12T18:50:27.948157","status":"completed"},"tags":[]},"source":["# Aim\n","The aim of this project is to predict Chronic Disease Outcome\n","The dataset is available via UCI repostory [here](https://archive.ics.uci.edu/dataset/336/chronic+kidney+disease)"]},{"cell_type":"markdown","id":"589390c6","metadata":{"papermill":{"duration":0.010314,"end_time":"2023-06-12T18:50:27.979431","exception":false,"start_time":"2023-06-12T18:50:27.969117","status":"completed"},"tags":[]},"source":["# Exploratory data analysis\n"]},{"cell_type":"code","execution_count":2,"id":"45c98727","metadata":{"execution":{"iopub.execute_input":"2023-06-12T18:50:28.00262Z","iopub.status.busy":"2023-06-12T18:50:28.002221Z","iopub.status.idle":"2023-06-12T18:50:28.08833Z","shell.execute_reply":"2023-06-12T18:50:28.087256Z"},"papermill":{"duration":0.100449,"end_time":"2023-06-12T18:50:28.090572","exception":false,"start_time":"2023-06-12T18:50:27.990123","status":"completed"},"tags":[]},"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>id</th>\n","      <th>age</th>\n","      <th>bp</th>\n","      <th>sg</th>\n","      <th>al</th>\n","      <th>su</th>\n","      <th>rbc</th>\n","      <th>pc</th>\n","      <th>pcc</th>\n","      <th>ba</th>\n","      <th>...</th>\n","      <th>pcv</th>\n","      <th>wc</th>\n","      <th>rc</th>\n","      <th>htn</th>\n","      <th>dm</th>\n","      <th>cad</th>\n","      <th>appet</th>\n","      <th>pe</th>\n","      <th>ane</th>\n","      <th>classification</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>48.0</td>\n","      <td>80.0</td>\n","      <td>1.020</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>NaN</td>\n","      <td>normal</td>\n","      <td>notpresent</td>\n","      <td>notpresent</td>\n","      <td>...</td>\n","      <td>44</td>\n","      <td>7800</td>\n","      <td>5.2</td>\n","      <td>yes</td>\n","      <td>yes</td>\n","      <td>no</td>\n","      <td>good</td>\n","      <td>no</td>\n","      <td>no</td>\n","      <td>ckd</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>7.0</td>\n","      <td>50.0</td>\n","      <td>1.020</td>\n","      <td>4.0</td>\n","      <td>0.0</td>\n","      <td>NaN</td>\n","      <td>normal</td>\n","      <td>notpresent</td>\n","      <td>notpresent</td>\n","      <td>...</td>\n","      <td>38</td>\n","      <td>6000</td>\n","      <td>NaN</td>\n","      <td>no</td>\n","      <td>no</td>\n","      <td>no</td>\n","      <td>good</td>\n","      <td>no</td>\n","      <td>no</td>\n","      <td>ckd</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2</td>\n","      <td>62.0</td>\n","      <td>80.0</td>\n","      <td>1.010</td>\n","      <td>2.0</td>\n","      <td>3.0</td>\n","      <td>normal</td>\n","      <td>normal</td>\n","      <td>notpresent</td>\n","      <td>notpresent</td>\n","      <td>...</td>\n","      <td>31</td>\n","      <td>7500</td>\n","      <td>NaN</td>\n","      <td>no</td>\n","      <td>yes</td>\n","      <td>no</td>\n","      <td>poor</td>\n","      <td>no</td>\n","      <td>yes</td>\n","      <td>ckd</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>3</td>\n","      <td>48.0</td>\n","      <td>70.0</td>\n","      <td>1.005</td>\n","      <td>4.0</td>\n","      <td>0.0</td>\n","      <td>normal</td>\n","      <td>abnormal</td>\n","      <td>present</td>\n","      <td>notpresent</td>\n","      <td>...</td>\n","      <td>32</td>\n","      <td>6700</td>\n","      <td>3.9</td>\n","      <td>yes</td>\n","      <td>no</td>\n","      <td>no</td>\n","      <td>poor</td>\n","      <td>yes</td>\n","      <td>yes</td>\n","      <td>ckd</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4</td>\n","      <td>51.0</td>\n","      <td>80.0</td>\n","      <td>1.010</td>\n","      <td>2.0</td>\n","      <td>0.0</td>\n","      <td>normal</td>\n","      <td>normal</td>\n","      <td>notpresent</td>\n","      <td>notpresent</td>\n","      <td>...</td>\n","      <td>35</td>\n","      <td>7300</td>\n","      <td>4.6</td>\n","      <td>no</td>\n","      <td>no</td>\n","      <td>no</td>\n","      <td>good</td>\n","      <td>no</td>\n","      <td>no</td>\n","      <td>ckd</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>5</td>\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>...</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>6</td>\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>...</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>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>...</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>...</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>...</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","<p>10 rows × 26 columns</p>\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  ...  pcv     wc   rc  htn   dm  cad appet   pe  ane  \\\n","0  notpresent  ...   44   7800  5.2  yes  yes   no  good   no   no   \n","1  notpresent  ...   38   6000  NaN   no   no   no  good   no   no   \n","2  notpresent  ...   31   7500  NaN   no  yes   no  poor   no  yes   \n","3  notpresent  ...   32   6700  3.9  yes   no   no  poor  yes  yes   \n","4  notpresent  ...   35   7300  4.6   no   no   no  good   no   no   \n","5  notpresent  ...   39   7800  4.4  yes  yes   no  good  yes   no   \n","6  notpresent  ...   36    NaN  NaN   no   no   no  good   no   no   \n","7  notpresent  ...   44   6900    5   no  yes   no  good  yes   no   \n","8  notpresent  ...   33   9600  4.0  yes  yes   no  good   no  yes   \n","9  notpresent  ...   29  12100  3.7  yes  yes   no  poor   no  yes   \n","\n","  classification  \n","0            ckd  \n","1            ckd  \n","2            ckd  \n","3            ckd  \n","4            ckd  \n","5            ckd  \n","6            ckd  \n","7            ckd  \n","8            ckd  \n","9            ckd  \n","\n","[10 rows x 26 columns]"]},"execution_count":2,"metadata":{},"output_type":"execute_result"}],"source":["df= pd.read_csv('/kaggle/input/ckdisease/kidney_disease.csv')\n","df.head(10)"]},{"cell_type":"code","execution_count":3,"id":"7b144f67","metadata":{"execution":{"iopub.execute_input":"2023-06-12T18:50:28.11519Z","iopub.status.busy":"2023-06-12T18:50:28.114759Z","iopub.status.idle":"2023-06-12T18:50:28.140645Z","shell.execute_reply":"2023-06-12T18:50:28.139138Z"},"papermill":{"duration":0.040446,"end_time":"2023-06-12T18:50:28.142737","exception":false,"start_time":"2023-06-12T18:50:28.102291","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 400 entries, 0 to 399\n","Data columns (total 26 columns):\n"," #   Column          Non-Null Count  Dtype  \n","---  ------          --------------  -----  \n"," 0   id              400 non-null    int64  \n"," 1   age             391 non-null    float64\n"," 2   bp              388 non-null    float64\n"," 3   sg              353 non-null    float64\n"," 4   al              354 non-null    float64\n"," 5   su              351 non-null    float64\n"," 6   rbc             248 non-null    object \n"," 7   pc              335 non-null    object \n"," 8   pcc             396 non-null    object \n"," 9   ba              396 non-null    object \n"," 10  bgr             356 non-null    float64\n"," 11  bu              381 non-null    float64\n"," 12  sc              383 non-null    float64\n"," 13  sod             313 non-null    float64\n"," 14  pot             312 non-null    float64\n"," 15  hemo            348 non-null    float64\n"," 16  pcv             330 non-null    object \n"," 17  wc              295 non-null    object \n"," 18  rc              270 non-null    object \n"," 19  htn             398 non-null    object \n"," 20  dm              398 non-null    object \n"," 21  cad             398 non-null    object \n"," 22  appet           399 non-null    object \n"," 23  pe              399 non-null    object \n"," 24  ane             399 non-null    object \n"," 25  classification  400 non-null    object \n","dtypes: float64(11), int64(1), object(14)\n","memory usage: 81.4+ KB\n"]}],"source":["df.info()"]},{"cell_type":"code","execution_count":4,"id":"d4d9d24c","metadata":{"execution":{"iopub.execute_input":"2023-06-12T18:50:28.167402Z","iopub.status.busy":"2023-06-12T18:50:28.167047Z","iopub.status.idle":"2023-06-12T18:50:28.171945Z","shell.execute_reply":"2023-06-12T18:50:28.171227Z"},"papermill":{"duration":0.019432,"end_time":"2023-06-12T18:50:28.173778","exception":false,"start_time":"2023-06-12T18:50:28.154346","status":"completed"},"tags":[]},"outputs":[],"source":["#change the id column to 'index'\n","df.set_index('id', inplace=True)"]},{"cell_type":"code","execution_count":5,"id":"af41a19f","metadata":{"execution":{"iopub.execute_input":"2023-06-12T18:50:28.19874Z","iopub.status.busy":"2023-06-12T18:50:28.198068Z","iopub.status.idle":"2023-06-12T18:50:28.207094Z","shell.execute_reply":"2023-06-12T18:50:28.206005Z"},"papermill":{"duration":0.024084,"end_time":"2023-06-12T18:50:28.209435","exception":false,"start_time":"2023-06-12T18:50:28.185351","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["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\n"]}],"source":["#check for and print NaN values\n","nan_values = df.isnull().sum()\n","print(nan_values)"]},{"cell_type":"code","execution_count":6,"id":"9d4ae931","metadata":{"execution":{"iopub.execute_input":"2023-06-12T18:50:28.234921Z","iopub.status.busy":"2023-06-12T18:50:28.234525Z","iopub.status.idle":"2023-06-12T18:50:31.840447Z","shell.execute_reply":"2023-06-12T18:50:31.839412Z"},"papermill":{"duration":3.621717,"end_time":"2023-06-12T18:50:31.842997","exception":false,"start_time":"2023-06-12T18:50:28.22128","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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OQnyszM1B133KHCwkJJks1mc4m32WzmucLCQvn5+alFixbVxlQnLS1NISEh5hYVFVWDvQIAAA0RhSUAAAAv06FDBx08eFD79u3TY489pokTJ+rjjz82z1ssFpd4wzAqHbvajcTMmTNHJSUl5pafn+9+JwAAQKNAYQkAAMDL+Pn56bbbblP37t2VlpamLl266KWXXpLdbpekSiOPioqKzFFMdrtd5eXlKi4urjamOlar1VyNrmIDAAC4FgpLAAAAXs4wDDmdTrVt21Z2u11ZWVnmufLycmVnZysuLk6S1K1bN/n6+rrEFBQU6NChQ2YMAABATfFoYSktLU09evRQUFCQIiIiNGrUKB09etQlxjAMpaamKjIyUgEBAUpISDCX263gdDo1Y8YMhYeHq1mzZhoxYoROnjxZl10BAACoEXPnztUHH3yg48ePKzc3V/PmzdOuXbv0/e9/XxaLRcnJyVq4cKEyMzN16NAhTZo0SYGBgZowYYIkKSQkRJMnT9asWbP03nvv6cMPP9QPfvADderUSQMHDvRw7wAAQEPj0cJSdna2pk2bpn379ikrK0sXL15UYmKizp07Z8YsWrRIS5Ys0fLly7V//37Z7XYNGjRIZ86cMWOSk5OVmZmpjIwM7d69W2fPntWwYcN06dIlT3QLAADAbV988YUeeughdejQQQMGDNBf/vIXbd26VYMGDZIkzZ49W8nJyZo6daq6d++uf/3rX9q2bZuCgoLMeyxdulSjRo3SuHHj1KdPHwUGBmrz5s3y8fHxVLcAAEAD1dSTL75161aX/dWrVysiIkI5OTm69957ZRiGli1bpnnz5mnMmDGSpLVr18pms2nDhg169NFHVVJSolWrVmndunXmX+HWr1+vqKgobd++XYMHD67zfgEAALhr1apV1zxvsViUmpqq1NTUamP8/f2Vnp6u9PT0Gm4dAACAK6+aY6mkpESSFBoaKknKy8tTYWGhEhMTzRir1ar4+Hjt2bNHkpSTk6MLFy64xERGRio2NtaMAQAAAADcvBuZvmTSpEmyWCwuW69evVximL4EaLi8prBkGIZmzpypvn37KjY2VtJ/Vzy5egUTm81mnissLJSfn59atGhRbczVnE6nSktLXTYAAAAAgKsbmb5EkoYMGaKCggJze/vtt13OM30J0HB59FG4b5o+fbo++ugj7d69u9I5i8Xism8YRqVjV7tWTFpamhYsWOB+YwEAAACgEbje9CUVrFar7HZ7lfdg+hKgYfOKEUszZszQW2+9pZ07d6pVq1bm8YrEdPXIo6KiInMUk91uV3l5uYqLi6uNudqcOXNUUlJibvn5+TXZHQAAAABokK6evqTCrl27FBERoZiYGE2ZMkVFRUXmOXemL+EpE6D+8GhhyTAMTZ8+XW+88YZ27Nihtm3bupxv27at7Ha7srKyzGPl5eXKzs5WXFycJKlbt27y9fV1iSkoKNChQ4fMmKtZrVYFBwe7bAAAAACA6lU1fYkkJSUl6bXXXtOOHTu0ePFi7d+/X/3795fT6ZTk3vQlaWlpCgkJMbeoqKja6xiAb8Wjj8JNmzZNGzZs0KZNmxQUFGQmlZCQEAUEBMhisSg5OVkLFy5U+/bt1b59ey1cuFCBgYGaMGGCGTt58mTNmjVLYWFhCg0NVUpKijp16mQOswQAAAAAfDvVTV8yfvx48/9jY2PVvXt3RUdHa8uWLebq3lW51vQlc+bM0cyZM8390tJSikuAl/LoiKWVK1eqpKRECQkJcjgc5rZx40YzZvbs2UpOTtbUqVPVvXt3/etf/9K2bdsUFBRkxixdulSjRo3SuHHj1KdPHwUGBmrz5s3y8fHxRLcA1EM3suKJYRhKTU1VZGSkAgIClJCQoMOHD7vEsOIJAABoiKqbvqQqDodD0dHROnbsmCT3pi/hKROg/vD4o3BVbZMmTTJjLBaLUlNTVVBQoK+//lrZ2dkuwy4lyd/fX+np6Tp16pTOnz+vzZs3U80GcFNuZMWTRYsWacmSJVq+fLn2798vu92uQYMG6cyZM2YMK54AAICG5HrTl1Tl1KlTys/Pl8PhkOTe9CUA6g+vWRUOADzpeiueGIahZcuWad68eeaQ7rVr18pms2nDhg169NFHWfEEAAA0ONebvuTs2bNKTU3V2LFj5XA4dPz4cc2dO1fh4eEaPXq0Gcv0JUDD5RWrwgGAt7l6xZO8vDwVFha6rGZitVoVHx9vrmbizoonAAAA3ux605f4+PgoNzdXI0eOVExMjCZOnKiYmBjt3buX6UuARoIRSwBwlapWPKn469zV8wDYbDadOHHCjLnZFU+cTqe5YookltIFAABexTCMa54PCAjQu+++e937VExfkp6eXlNNA+AlGLEEAFepWPHk9ddfr3Tu6pVLrrWayY3EsJQuAAAAgPqMwhIAfEN1K57Y7XZJqjTy6Jurmbiz4smcOXNUUlJibvn5+TXZHQAAAACoVRSWAEDXX/Gkbdu2stvtLquZlJeXKzs721zNxJ0VT1hKFwAAAEB9xhxLAKDrr3hisViUnJyshQsXqn379mrfvr0WLlyowMBATZgwwYxlxRMAAAAAjQmFJQDQlRVPJCkhIcHl+OrVqzVp0iRJ0uzZs1VWVqapU6equLhYPXv21LZt2yqteNK0aVONGzdOZWVlGjBggNasWcOKJwAAAAAaJApLAKDrr3giXZm4OzU1VampqdXGsOIJAAAAgMaEOZYAAAAAAADgFgpLAAAAAAAAcAuFJQAAAAAAALiFwhIAAAAAAADcQmEJAAAAAAAAbqGwBAAAAAAAALdQWAIAAAAAAIBbKCwBAAAAAADALRSWAAAAAAAA4BYKSwAAAAAAAHALhSUAAAAAAAC4hcISAAAAAAAA3EJhCQAAAAAAAG6hsAQAAOBF0tLS1KNHDwUFBSkiIkKjRo3S0aNHXWImTZoki8XisvXq1cslxul0asaMGQoPD1ezZs00YsQInTx5si67AgAAGgEKSwAAAF4kOztb06ZN0759+5SVlaWLFy8qMTFR586dc4kbMmSICgoKzO3tt992OZ+cnKzMzExlZGRo9+7dOnv2rIYNG6ZLly7VZXcAAEAD19TTDQAAAMB/bd261WV/9erVioiIUE5Oju69917zuNVqld1ur/IeJSUlWrVqldatW6eBAwdKktavX6+oqCht375dgwcPrr0OAACARoURSwAAAF6spKREkhQaGupyfNeuXYqIiFBMTIymTJmioqIi81xOTo4uXLigxMRE81hkZKRiY2O1Z8+eumk4AABoFBixBAAA4KUMw9DMmTPVt29fxcbGmseTkpL0wAMPKDo6Wnl5eZo/f7769++vnJwcWa1WFRYWys/PTy1atHC5n81mU2FhYbWv53Q65XQ6zf3S0tKa7xQAAGhQKCwBAAB4qenTp+ujjz7S7t27XY6PHz/e/P/Y2Fh1795d0dHR2rJli8aMGVPt/QzDkMViqfZ8WlqaFixY8O0bDgAAGg0ehQMAAPBCM2bM0FtvvaWdO3eqVatW14x1OByKjo7WsWPHJEl2u13l5eUqLi52iSsqKpLNZqv2PnPmzFFJSYm55efnf/uOAACABo3CEgAAgBcxDEPTp0/XG2+8oR07dqht27bXvebUqVPKz8+Xw+GQJHXr1k2+vr7KysoyYwoKCnTo0CHFxcVVex+r1arg4GCXDQAA4Fp4FA4AAMCLTJs2TRs2bNCmTZsUFBRkzokUEhKigIAAnT17VqmpqRo7dqwcDoeOHz+uuXPnKjw8XKNHjzZjJ0+erFmzZiksLEyhoaFKSUlRp06dzFXiAAAAagKFJQAAAC+ycuVKSVJCQoLL8dWrV2vSpEny8fFRbm6uXn31VZ0+fVoOh0P9+vXTxo0bFRQUZMYvXbpUTZs21bhx41RWVqYBAwZozZo18vHxqcvuAACABo7CEgAAgBcxDOOa5wMCAvTuu+9e9z7+/v5KT09Xenp6TTUNAACgEuZYAgAAAAAAgFsoLAEAAAAAAMAtFJYAAAAAAADgFgpLAAAAAAAAcAuFJQAAAAAAALiFwhIAAAAAAADcQmEJAAAAAFCltLQ09ejRQ0FBQYqIiNCoUaN09OhRlxjDMJSamqrIyEgFBAQoISFBhw8fdolxOp2aMWOGwsPD1axZM40YMUInT56sy64AqCUUlgAAAAAAVcrOzta0adO0b98+ZWVl6eLFi0pMTNS5c+fMmEWLFmnJkiVavny59u/fL7vdrkGDBunMmTNmTHJysjIzM5WRkaHdu3fr7NmzGjZsmC5duuSJbgGoQU093QAAAAAAgHfaunWry/7q1asVERGhnJwc3XvvvTIMQ8uWLdO8efM0ZswYSdLatWtls9m0YcMGPfrooyopKdGqVau0bt06DRw4UJK0fv16RUVFafv27Ro8eHCd9wtAzWHEEgAAAADghpSUlEiSQkNDJUl5eXkqLCxUYmKiGWO1WhUfH689e/ZIknJycnThwgWXmMjISMXGxpoxAOovRiwBAAAAAK7LMAzNnDlTffv2VWxsrCSpsLBQkmSz2VxibTabTpw4Ycb4+fmpRYsWlWIqrr+a0+mU0+k090tLS2usHwBqFiOWAAAAAADXNX36dH300Ud6/fXXK52zWCwu+4ZhVDp2tWvFpKWlKSQkxNyioqLcbziAWkVhCQAAAABwTTNmzNBbb72lnTt3qlWrVuZxu90uSZVGHhUVFZmjmOx2u8rLy1VcXFxtzNXmzJmjkpISc8vPz6/J7gCoQRSWAAAAAABVMgxD06dP1xtvvKEdO3aobdu2Lufbtm0ru92urKws81h5ebmys7MVFxcnSerWrZt8fX1dYgoKCnTo0CEz5mpWq1XBwcEuGwDvxBxLAAAAAIAqTZs2TRs2bNCmTZsUFBRkjkwKCQlRQECALBaLkpOTtXDhQrVv317t27fXwoULFRgYqAkTJpixkydP1qxZsxQWFqbQ0FClpKSoU6dO5ipxAOovCksAAAAAgCqtXLlSkpSQkOByfPXq1Zo0aZIkafbs2SorK9PUqVNVXFysnj17atu2bQoKCjLjly5dqqZNm2rcuHEqKyvTgAEDtGbNGvn4+NRVVwDUEo8+Cvf+++9r+PDhioyMlMVi0ZtvvulyftKkSbJYLC5br169XGKcTqdmzJih8PBwNWvWTCNGjNDJkyfrsBcAAAAA0DAZhlHlVlFUkq5M3J2amqqCggJ9/fXXys7ONleNq+Dv76/09HSdOnVK58+f1+bNm5mQG2ggPFpYOnfunLp06aLly5dXGzNkyBAVFBSY29tvv+1yPjk5WZmZmcrIyNDu3bt19uxZDRs2TJcuXart5gMAAAAAADRqHn0ULikpSUlJSdeMsVqt5koDVyspKdGqVau0bt0689nc9evXKyoqStu3b9fgwYNrvM0AAAAAAAC4wutXhdu1a5ciIiIUExOjKVOmqKioyDyXk5OjCxcuKDEx0TwWGRmp2NhY7dmzxxPNBQAAAAAAaDS8evLupKQkPfDAA4qOjlZeXp7mz5+v/v37KycnR1arVYWFhfLz81OLFi1crrPZbOZqBVVxOp1yOp3mfmlpaa31AQAAAAAAoKHy6sLS+PHjzf+PjY1V9+7dFR0drS1btmjMmDHVXmcYhiwWS7Xn09LStGDBghptKwAAAAAAQGPj9Y/CfZPD4VB0dLSOHTsmSbLb7SovL1dxcbFLXFFRkWw2W7X3mTNnjkpKSswtPz+/VtsNAAAAAADQENWrwtKpU6eUn58vh8MhSerWrZt8fX2VlZVlxhQUFOjQoUOKi4ur9j5Wq1XBwcEuG4DG7f3339fw4cMVGRkpi8WiN9980+X8pEmTZLFYXLZevXq5xDidTs2YMUPh4eFq1qyZRowYoZMnT9ZhLwAAAACgbnn0UbizZ8/qs88+M/fz8vJ08OBBhYaGKjQ0VKmpqRo7dqwcDoeOHz+uuXPnKjw8XKNHj5YkhYSEaPLkyZo1a5bCwsIUGhqqlJQUderUyVwlrjZ0e+LVWrs3rsh58WFPNwGNzLlz59SlSxf98Ic/1NixY6uMGTJkiFavXm3u+/n5uZxPTk7W5s2blZGRobCwMM2aNUvDhg1TTk6OfHx8arX9AAAAAOAJbo1Y6t+/v06fPl3peGlpqfr373/D9zlw4IC6du2qrl27SpJmzpyprl276qmnnpKPj49yc3M1cuRIxcTEaOLEiYqJidHevXsVFBRk3mPp0qUaNWqUxo0bpz59+igwMFCbN2/mlzigkaipfJSUlKRnn332mvO3Wa1W2e12cwsNDTXPlZSUaNWqVVq8eLEGDhyorl27av369crNzdX27dtvqk8A6q+aykkA8G2RjwDUFbdGLO3atUvl5eWVjn/99df64IMPbvg+CQkJMgyj2vPvvvvude/h7++v9PR0paen3/DrAmg4aiof3ehrRURE6JZbblF8fLyee+45RURESJJycnJ04cIFJSYmmvGRkZGKjY3Vnj17NHjw4BptCwDvVJc5CQCuhXwEoK7cVGHpo48+Mv//448/VmFhobl/6dIlbd26VbfeemvNtQ4AqlHX+SgpKUkPPPCAoqOjlZeXp/nz56t///7KycmR1WpVYWGh/Pz81KJFC5frbDabS9uu5nQ65XQ6zf3S0tIaazOAusNnJADegnwEoK7dVGHprrvuMietrWr4ZEBAACOHANSJus5H48ePN/8/NjZW3bt3V3R0tLZs2XLNx+cMw5DFYqn2fFpamhYsWFBj7QTgGXxGAuAtyEcA6tpNFZby8vJkGIbatWunv/71r2rZsqV5zs/PTxEREcxtBKBOeDofORwORUdH69ixY5Iku92u8vJyFRcXu4xaKioquuYqlXPmzNHMmTPN/dLSUkVFRdVauwHUDk/nJACoQD4CUNduqrAUHR0tSbp8+XKtNAYAbpSn89GpU6eUn58vh8MhSerWrZt8fX2VlZWlcePGSZIKCgp06NAhLVq0qNr7WK1WWa3WOmkzgNrj6ZwEABXIRwDqmluTd0vSp59+ql27dqmoqKhS0nrqqae+dcMA4EbVRD46e/asPvvsM3M/Ly9PBw8eVGhoqEJDQ5WamqqxY8fK4XDo+PHjmjt3rsLDwzV69GhJUkhIiCZPnqxZs2YpLCxMoaGhSklJUadOnTRw4MCa6ywAr/dtc1JaWpreeOMNffLJJwoICFBcXJxeeOEFdejQwYwxDEMLFizQb3/7WxUXF6tnz576n//5H915551mjNPpVEpKil5//XWVlZVpwIABWrFihVq1alVznQXg1fidDUBdcKuw9PLLL+uxxx5TeHi47Ha7y/whFouFJAWgztRUPjpw4ID69etn7lc8njZx4kStXLlSubm5evXVV3X69Gk5HA7169dPGzduVFBQkHnN0qVL1bRpU40bN878JW7NmjUMNwcakZrISdnZ2Zo2bZp69Oihixcvat68eUpMTNTHH3+sZs2aSZIWLVqkJUuWaM2aNYqJidGzzz6rQYMG6ejRo2ZeSk5O1ubNm5WRkaGwsDDNmjVLw4YNU05ODnkJaAT4nQ1AXXGrsPTss8/queee05NPPlnT7QGAm1JT+SghIUGGYVR7/t13373uPfz9/ZWens6EmEAjVhM5aevWrS77q1evVkREhHJycnTvvffKMAwtW7ZM8+bNMxcPWLt2rWw2mzZs2KBHH31UJSUlWrVqldatW2eOmly/fr2ioqK0fft2DR482P1OAqgX+J0NQF1p4s5FxcXFeuCBB2q6LQBw08hHALxJbeSkkpISSVJoaKikK4/qFhYWKjEx0YyxWq2Kj4/Xnj17JEk5OTm6cOGCS0xkZKRiY2PNmKo4nU6Vlpa6bADqJz4jAagrbhWWHnjgAW3btq2m2wIAN418BMCb1HROMgxDM2fOVN++fRUbGytJKiwslCTZbDaXWJvNZp4rLCyUn5+fyyqVV8dUJS0tTSEhIebGKpVA/cVnJAB1xa1H4W677TbNnz9f+/btU6dOneTr6+ty/qc//WmNNA4Arod8BMCb1HROmj59uj766CPt3r270rlvzpciXSlCXX3sateLmTNnjjnHnCSVlpZSXALqKT4jAagrbhWWfvvb36p58+bKzs5Wdna2yzmLxUKSAlBnyEcAvElN5qQZM2borbfe0vvvv++ykpvdbpd0ZVSSw+EwjxcVFZmjmOx2u8rLy1VcXOwyaqmoqEhxcXHVvqbVapXVar3hNgLwXnxGAlBX3Cos5eXl1XQ7AMAt5CMA3qQmcpJhGJoxY4YyMzO1a9cutW3b1uV827ZtZbfblZWVpa5du0qSysvLlZ2drRdeeEGS1K1bN/n6+iorK0vjxo2TJBUUFOjQoUNatGjRt24jAO/HZyQAdcWtwhIAAABqx7Rp07RhwwZt2rRJQUFB5pxIISEhCggIkMViUXJyshYuXKj27durffv2WrhwoQIDAzVhwgQzdvLkyZo1a5bCwsIUGhqqlJQUderUyVwlDgAAoCa4VVj60Y9+dM3zr7zyiluNAYCbRT4C4E1qIietXLlSkpSQkOByfPXq1Zo0aZIkafbs2SorK9PUqVNVXFysnj17atu2bQoKCjLjly5dqqZNm2rcuHEqKyvTgAEDtGbNGvn4+NxcpwDUS3xGAlBX3CosFRcXu+xfuHBBhw4d0unTp9W/f/8aaRgA3AjyEQBvUhM5yTCM68ZYLBalpqYqNTW12hh/f3+lp6crPT39hl4XQMPCZyQAdcWtwlJmZmalY5cvX9bUqVPVrl27b90oALhR5CMA3oScBMBbkI8A1JUam2OpSZMm+tnPfqaEhATNnj27pm4LADetMeWjbk+86ukmNHg5Lz7s6SagnmtMOQmAdyMfAagNTWryZv/4xz908eLFmrwlALiFfATAm5CTAHgL8hGAmubWiKWZM2e67BuGoYKCAm3ZskUTJ06skYYBwI0gHwHwJuQkAN6CfASgrrhVWPrwww9d9ps0aaKWLVtq8eLF1119AABqEvkIgDchJwHwFuQjAHXFrcLSzp07a7odAOAW8hEAb0JOAuAtyEcA6sq3mrz7yy+/1NGjR2WxWBQTE6OWLVvWVLsA4KaQjwB4E3ISAG9BPgJQ29yavPvcuXP60Y9+JIfDoXvvvVf33HOPIiMjNXnyZJ0/f76m2wgA1SIfAfAm5CQA3oJ8BKCuuFVYmjlzprKzs7V582adPn1ap0+f1qZNm5Sdna1Zs2bVdBsBoFrkIwDehJwEwFuQjwDUFbcehfvjH/+oP/zhD0pISDCP3XfffQoICNC4ceO0cuXKmmofAFwT+QiANyEnAfAW5CMAdcWtEUvnz5+XzWardDwiIoJhlQDqFPkIgDchJwHwFuQjAHXFrcJS79699fTTT+vrr782j5WVlWnBggXq3bt3jTUOAK6HfATAm5CTAHgL8hGAuuLWo3DLli1TUlKSWrVqpS5dushisejgwYOyWq3atm1bTbcRAKpFPgLgTchJALxFTeWj999/Xy+++KJycnJUUFCgzMxMjRo1yjw/adIkrV271uWanj17at++fea+0+lUSkqKXn/9dZWVlWnAgAFasWKFWrVq9a37CcDz3CosderUSceOHdP69ev1ySefyDAMPfjgg/r+97+vgICAmm4jAFSLfATAm5CTAHiLmspH586dU5cuXfTDH/5QY8eOrTJmyJAhWr16tbnv5+fncj45OVmbN29WRkaGwsLCNGvWLA0bNkw5OTny8fFxr4MAvIZbhaW0tDTZbDZNmTLF5fgrr7yiL7/8Uk8++WSNNA4Arod8BMCbkJMAeIuaykdJSUlKSkq6ZozVapXdbq/yXElJiVatWqV169Zp4MCBkqT169crKipK27dv1+DBg2+oHQC8l1tzLP3mN7/R7bffXun4nXfeqV//+tffulEAcKPIRwC8CTkJgLeoy3y0a9cuRUREKCYmRlOmTFFRUZF5LicnRxcuXFBiYqJ5LDIyUrGxsdqzZ0+193Q6nSotLXXZAHgntwpLhYWFcjgclY63bNlSB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size 1200x1200 with 11 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["#plotting the categorical columns\n","\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","\n","# Create the subplots grid\n","fig, axes = plt.subplots(nrows=3, ncols=4, figsize=(12, 12))\n","\n","plt.rcParams['font.family'] = 'DejaVu Sans'\n","\n","#these columns have some white spaces, so trim\n","df['dm'] = df['dm'].str.strip() \n","df['cad'] = df['cad'].str.strip() \n","df['classification'] = df['classification'].str.strip() \n","\n","# Plot countplot on each subplot\n","sns.countplot(x='rbc', data=df, ax=axes[0, 0]).set(title='Distribution of rbc')\n","sns.countplot(x='pc', data=df, ax=axes[0, 1]).set(title='Distribution of pc')\n","sns.countplot(x='pcc', data=df, ax=axes[0, 2]).set(title='Distribution of pcc')\n","sns.countplot(x='ba', data=df, ax=axes[0, 3]).set(title='Distribution of ba')\n","sns.countplot(x='htn', data=df, ax=axes[1, 0]).set(title='Distribution of htn')\n","sns.countplot(x='dm', data=df, ax=axes[1, 1]).set(title='Distribution of dm')\n","sns.countplot(x='cad', data=df, ax=axes[1, 2]).set(title='Distribution of cad')\n","sns.countplot(x='appet', data=df, ax=axes[1, 3]).set(title='Distribution of appet')\n","sns.countplot(x='pe', data=df, ax=axes[2, 0]).set(title='Distribution of pe')\n","sns.countplot(x='ane', data=df, ax=axes[2, 1]).set(title='Distribution of ane')\n","sns.countplot(x='classification', data=df, ax=axes[2, 2]).set(title='Distribution of classification')\n","\n","\n","\n","\n","# Adjust subplot spacing\n","plt.tight_layout()\n","# Delete the last subplot\n","fig.delaxes(axes[2][3])\n","# Show the plot\n","plt.show()"]},{"cell_type":"code","execution_count":7,"id":"7c8bd342","metadata":{"execution":{"iopub.execute_input":"2023-06-12T18:50:31.8717Z","iopub.status.busy":"2023-06-12T18:50:31.871266Z","iopub.status.idle":"2023-06-12T18:50:31.894687Z","shell.execute_reply":"2023-06-12T18:50:31.893615Z"},"papermill":{"duration":0.041109,"end_time":"2023-06-12T18:50:31.897575","exception":false,"start_time":"2023-06-12T18:50:31.856466","status":"completed"},"tags":[]},"outputs":[],"source":["#replace normal, present, yes, good and ckd with 1\n","#replace abnormal, not present , no,  poor and not ckd with 0\n","\n","df['rbc'] = df['rbc'].replace({'normal': 1, 'abnormal': 0})\n","df['pc'] = df['pc'].replace({'normal': 1, 'abnormal': 0})\n","df['pcc'] = df['pcc'].replace({'present': 1, 'notpresent': 0})\n","df['ba'] = df['ba'].replace({'present': 1, 'notpresent': 0})\n","df['htn'] = df['htn'].replace({'yes': 1, 'no': 0})\n","df['dm'] = df['dm'].replace({'yes': 1, 'no': 0})\n","df['cad'] = df['cad'].replace({'yes': 1, 'no': 0})\n","df['appet'] = df['appet'].replace({'good': 1, 'poor': 0})\n","df['pe'] = df['pe'].replace({'yes': 1, 'no': 0})\n","df['ane'] = df['ane'].replace({'yes': 1, 'no': 0})\n","df['classification'] = df['classification'].replace({'ckd': 1, 'notckd': 0})"]},{"cell_type":"code","execution_count":8,"id":"0cd0558f","metadata":{"execution":{"iopub.execute_input":"2023-06-12T18:50:31.927282Z","iopub.status.busy":"2023-06-12T18:50:31.926885Z","iopub.status.idle":"2023-06-12T18:50:31.97118Z","shell.execute_reply":"2023-06-12T18:50:31.970264Z"},"papermill":{"duration":0.061197,"end_time":"2023-06-12T18:50:31.973454","exception":false,"start_time":"2023-06-12T18:50:31.912257","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>age</th>\n","      <th>bp</th>\n","      <th>sg</th>\n","      <th>al</th>\n","      <th>su</th>\n","      <th>rbc</th>\n","      <th>pc</th>\n","      <th>pcc</th>\n","      <th>ba</th>\n","      <th>bgr</th>\n","      <th>...</th>\n","      <th>pcv</th>\n","      <th>wc</th>\n","      <th>rc</th>\n","      <th>htn</th>\n","      <th>dm</th>\n","      <th>cad</th>\n","      <th>appet</th>\n","      <th>pe</th>\n","      <th>ane</th>\n","      <th>classification</th>\n","    </tr>\n","    <tr>\n","      <th>id</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>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>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>121.0</td>\n","      <td>...</td>\n","      <td>44</td>\n","      <td>7800</td>\n","      <td>5.2</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1</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>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>38</td>\n","      <td>6000</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>62.0</td>\n","      <td>80.0</td>\n","      <td>1.010</td>\n","      <td>2.0</td>\n","      <td>3.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>423.0</td>\n","      <td>...</td>\n","      <td>31</td>\n","      <td>7500</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>48.0</td>\n","      <td>70.0</td>\n","      <td>1.005</td>\n","      <td>4.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>117.0</td>\n","      <td>...</td>\n","      <td>32</td>\n","      <td>6700</td>\n","      <td>3.9</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>51.0</td>\n","      <td>80.0</td>\n","      <td>1.010</td>\n","      <td>2.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>106.0</td>\n","      <td>...</td>\n","      <td>35</td>\n","      <td>7300</td>\n","      <td>4.6</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1</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>0.0</td>\n","      <td>0.0</td>\n","      <td>74.0</td>\n","      <td>...</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>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>100.0</td>\n","      <td>...</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","      <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>NaN</td>\n","      <td>1.015</td>\n","      <td>2.0</td>\n","      <td>4.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>410.0</td>\n","      <td>...</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>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>138.0</td>\n","      <td>...</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>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>70.0</td>\n","      <td>...</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","<p>10 rows × 25 columns</p>\n","</div>"],"text/plain":["     age     bp     sg   al   su  rbc   pc  pcc   ba    bgr  ...  pcv     wc  \\\n","id                                                           ...               \n","0   48.0   80.0  1.020  1.0  0.0  NaN  1.0  0.0  0.0  121.0  ...   44   7800   \n","1    7.0   50.0  1.020  4.0  0.0  NaN  1.0  0.0  0.0    NaN  ...   38   6000   \n","2   62.0   80.0  1.010  2.0  3.0  1.0  1.0  0.0  0.0  423.0  ...   31   7500   \n","3   48.0   70.0  1.005  4.0  0.0  1.0  0.0  1.0  0.0  117.0  ...   32   6700   \n","4   51.0   80.0  1.010  2.0  0.0  1.0  1.0  0.0  0.0  106.0  ...   35   7300   \n","5   60.0   90.0  1.015  3.0  0.0  NaN  NaN  0.0  0.0   74.0  ...   39   7800   \n","6   68.0   70.0  1.010  0.0  0.0  NaN  1.0  0.0  0.0  100.0  ...   36    NaN   \n","7   24.0    NaN  1.015  2.0  4.0  1.0  0.0  0.0  0.0  410.0  ...   44   6900   \n","8   52.0  100.0  1.015  3.0  0.0  1.0  0.0  1.0  0.0  138.0  ...   33   9600   \n","9   53.0   90.0  1.020  2.0  0.0  0.0  0.0  1.0  0.0   70.0  ...   29  12100   \n","\n","     rc  htn   dm  cad appet   pe  ane  classification  \n","id                                                      \n","0   5.2  1.0  1.0  0.0   1.0  0.0  0.0               1  \n","1   NaN  0.0  0.0  0.0   1.0  0.0  0.0               1  \n","2   NaN  0.0  1.0  0.0   0.0  0.0  1.0               1  \n","3   3.9  1.0  0.0  0.0   0.0  1.0  1.0               1  \n","4   4.6  0.0  0.0  0.0   1.0  0.0  0.0               1  \n","5   4.4  1.0  1.0  0.0   1.0  1.0  0.0               1  \n","6   NaN  0.0  0.0  0.0   1.0  0.0  0.0               1  \n","7     5  0.0  1.0  0.0   1.0  1.0  0.0               1  \n","8   4.0  1.0  1.0  0.0   1.0  0.0  1.0               1  \n","9   3.7  1.0  1.0  0.0   0.0  0.0  1.0               1  \n","\n","[10 rows x 25 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size 1500x1000 with 14 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Replace invalid values with NaN\n","df['pcv'] = df['pcv'].replace('\\t?', np.nan)\n","df['wc'] = df['wc'].replace('\\t?', np.nan)\n","df['rc'] = df['rc'].replace('\\t?', np.nan)\n","\n","\n","# Convert the columns to float\n","df['pcv'] = df['pcv'].astype(float)\n","df['wc'] = df['wc'].astype(float)\n","df['rc'] = df['rc'].astype(float)\n","\n","#plotting the non-categorical columns\n","non_cat = [\"age\", \"bp\", \"sg\", \"al\", \"su\",\"pcv\",\"wc\",\"rc\", \"bgr\", \"bu\", \"sc\", \"sod\", \"pot\", \"hemo\"]\n","\n","\n","# Calculate the number of rows and columns for the subplot grid\n","num_cols = 3  # Number of columns in the subplot grid\n","num_rows = (len(non_cat) + num_cols - 1) // num_cols  # Calculate the number of rows needed\n","\n","# Create the subplot grid\n","fig, axes = plt.subplots(num_rows, num_cols, figsize=(15, 10))  # Adjust the figsize as needed\n","\n","# Plot histograms for each non-categorical column\n","for i, column in enumerate(non_cat):\n","    row = i // num_cols  # Calculate the row index for the current subplot\n","    col = i % num_cols  # Calculate the column index for the current subplot\n","\n","    axes[row, col].hist(df[column], bins=10, color='grey')  # Adjust the number of bins as needed\n","    axes[row, col].set_xlabel(column)\n","    axes[row, col].set_ylabel('Frequency')\n","    axes[row, col].set_title(f'Distribution of {column}')\n","\n","# Delete unused subplots and adjust layout\n","for j in range(i + 1, num_rows * num_cols):\n","    fig.delaxes(axes.flatten()[j])\n","\n","# Adjust the spacing between subplots\n","plt.tight_layout()\n","\n","# Display the plot\n","plt.show()"]},{"cell_type":"code","execution_count":10,"id":"e741ed3e","metadata":{"execution":{"iopub.execute_input":"2023-06-12T18:50:35.286461Z","iopub.status.busy":"2023-06-12T18:50:35.285453Z","iopub.status.idle":"2023-06-12T18:50:35.298768Z","shell.execute_reply":"2023-06-12T18:50:35.297425Z"},"papermill":{"duration":0.031896,"end_time":"2023-06-12T18:50:35.301019","exception":false,"start_time":"2023-06-12T18:50:35.269123","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["duplicate rows check:\n","Number of duplicate rows: 0\n"]}],"source":["#check for duplicates\n","print(\"duplicate rows check:\") \n","# Check for duplicate rows\n","duplicate_rows = df.duplicated()\n","\n","# Count the number of duplicate rows\n","num_duplicates = duplicate_rows.sum()\n","\n","print(\"Number of duplicate rows:\", num_duplicates)"]},{"cell_type":"code","execution_count":11,"id":"3d570e1b","metadata":{"execution":{"iopub.execute_input":"2023-06-12T18:50:35.336894Z","iopub.status.busy":"2023-06-12T18:50:35.336328Z","iopub.status.idle":"2023-06-12T18:50:35.342593Z","shell.execute_reply":"2023-06-12T18:50:35.341569Z"},"papermill":{"duration":0.028684,"end_time":"2023-06-12T18:50:35.344985","exception":false,"start_time":"2023-06-12T18:50:35.316301","status":"completed"},"tags":[]},"outputs":[],"source":["#get the feature variables\n","X = df.iloc[:,:-1]\n","#get the outcome variables\n","y = df.iloc[:, -1]"]},{"cell_type":"code","execution_count":12,"id":"be207c27","metadata":{"execution":{"iopub.execute_input":"2023-06-12T18:50:35.3781Z","iopub.status.busy":"2023-06-12T18:50:35.377461Z","iopub.status.idle":"2023-06-12T18:50:35.427898Z","shell.execute_reply":"2023-06-12T18:50:35.426663Z"},"papermill":{"duration":0.069465,"end_time":"2023-06-12T18:50:35.430273","exception":false,"start_time":"2023-06-12T18:50:35.360808","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>age</th>\n","      <th>bp</th>\n","      <th>sg</th>\n","      <th>al</th>\n","      <th>su</th>\n","      <th>rbc</th>\n","      <th>pc</th>\n","      <th>pcc</th>\n","      <th>ba</th>\n","      <th>bgr</th>\n","      <th>...</th>\n","      <th>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","    </tr>\n","    <tr>\n","      <th>id</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>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>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>121.0</td>\n","      <td>...</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","    </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>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>11.3</td>\n","      <td>38.0</td>\n","      <td>6000.0</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","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>62.0</td>\n","      <td>80.0</td>\n","      <td>1.010</td>\n","      <td>2.0</td>\n","      <td>3.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>423.0</td>\n","      <td>...</td>\n","      <td>9.6</td>\n","      <td>31.0</td>\n","      <td>7500.0</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","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>48.0</td>\n","      <td>70.0</td>\n","      <td>1.005</td>\n","      <td>4.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>117.0</td>\n","      <td>...</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","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>51.0</td>\n","      <td>80.0</td>\n","      <td>1.010</td>\n","      <td>2.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>106.0</td>\n","      <td>...</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","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>395</th>\n","      <td>55.0</td>\n","      <td>80.0</td>\n","      <td>1.020</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>0.0</td>\n","      <td>140.0</td>\n","      <td>...</td>\n","      <td>15.7</td>\n","      <td>47.0</td>\n","      <td>6700.0</td>\n","      <td>4.9</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>396</th>\n","      <td>42.0</td>\n","      <td>70.0</td>\n","      <td>1.025</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>0.0</td>\n","      <td>75.0</td>\n","      <td>...</td>\n","      <td>16.5</td>\n","      <td>54.0</td>\n","      <td>7800.0</td>\n","      <td>6.2</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>397</th>\n","      <td>12.0</td>\n","      <td>80.0</td>\n","      <td>1.020</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>0.0</td>\n","      <td>100.0</td>\n","      <td>...</td>\n","      <td>15.8</td>\n","      <td>49.0</td>\n","      <td>6600.0</td>\n","      <td>5.4</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>398</th>\n","      <td>17.0</td>\n","      <td>60.0</td>\n","      <td>1.025</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>0.0</td>\n","      <td>114.0</td>\n","      <td>...</td>\n","      <td>14.2</td>\n","      <td>51.0</td>\n","      <td>7200.0</td>\n","      <td>5.9</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>399</th>\n","      <td>58.0</td>\n","      <td>80.0</td>\n","      <td>1.025</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>0.0</td>\n","      <td>131.0</td>\n","      <td>...</td>\n","      <td>15.8</td>\n","      <td>53.0</td>\n","      <td>6800.0</td>\n","      <td>6.1</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>400 rows × 24 columns</p>\n","</div>"],"text/plain":["      age    bp     sg   al   su  rbc   pc  pcc   ba    bgr  ...  hemo   pcv  \\\n","id                                                           ...               \n","0    48.0  80.0  1.020  1.0  0.0  NaN  1.0  0.0  0.0  121.0  ...  15.4  44.0   \n","1     7.0  50.0  1.020  4.0  0.0  NaN  1.0  0.0  0.0    NaN  ...  11.3  38.0   \n","2    62.0  80.0  1.010  2.0  3.0  1.0  1.0  0.0  0.0  423.0  ...   9.6  31.0   \n","3    48.0  70.0  1.005  4.0  0.0  1.0  0.0  1.0  0.0  117.0  ...  11.2  32.0   \n","4    51.0  80.0  1.010  2.0  0.0  1.0  1.0  0.0  0.0  106.0  ...  11.6  35.0   \n","..    ...   ...    ...  ...  ...  ...  ...  ...  ...    ...  ...   ...   ...   \n","395  55.0  80.0  1.020  0.0  0.0  1.0  1.0  0.0  0.0  140.0  ...  15.7  47.0   \n","396  42.0  70.0  1.025  0.0  0.0  1.0  1.0  0.0  0.0   75.0  ...  16.5  54.0   \n","397  12.0  80.0  1.020  0.0  0.0  1.0  1.0  0.0  0.0  100.0  ...  15.8  49.0   \n","398  17.0  60.0  1.025  0.0  0.0  1.0  1.0  0.0  0.0  114.0  ...  14.2  51.0   \n","399  58.0  80.0  1.025  0.0  0.0  1.0  1.0  0.0  0.0  131.0  ...  15.8  53.0   \n","\n","         wc   rc  htn   dm  cad  appet   pe  ane  \n","id                                                \n","0    7800.0  5.2  1.0  1.0  0.0    1.0  0.0  0.0  \n","1    6000.0  NaN  0.0  0.0  0.0    1.0  0.0  0.0  \n","2    7500.0  NaN  0.0  1.0  0.0    0.0  0.0  1.0  \n","3    6700.0  3.9  1.0  0.0  0.0    0.0  1.0  1.0  \n","4    7300.0  4.6  0.0  0.0  0.0    1.0  0.0  0.0  \n","..      ...  ...  ...  ...  ...    ...  ...  ...  \n","395  6700.0  4.9  0.0  0.0  0.0    1.0  0.0  0.0  \n","396  7800.0  6.2  0.0  0.0  0.0    1.0  0.0  0.0  \n","397  6600.0  5.4  0.0  0.0  0.0    1.0  0.0  0.0  \n","398  7200.0  5.9  0.0  0.0  0.0    1.0  0.0  0.0  \n","399  6800.0  6.1  0.0  0.0  0.0    1.0  0.0  0.0  \n","\n","[400 rows x 24 columns]"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["X"]},{"cell_type":"code","execution_count":13,"id":"3f841dc8","metadata":{"execution":{"iopub.execute_input":"2023-06-12T18:50:35.464947Z","iopub.status.busy":"2023-06-12T18:50:35.463908Z","iopub.status.idle":"2023-06-12T18:50:35.472277Z","shell.execute_reply":"2023-06-12T18:50:35.470994Z"},"papermill":{"duration":0.028679,"end_time":"2023-06-12T18:50:35.475108","exception":false,"start_time":"2023-06-12T18:50:35.446429","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["id\n","0      1\n","1      1\n","2      1\n","3      1\n","4      1\n","      ..\n","395    0\n","396    0\n","397    0\n","398    0\n","399    0\n","Name: classification, Length: 400, dtype: int64"]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["y"]},{"cell_type":"code","execution_count":14,"id":"a87a250a","metadata":{"execution":{"iopub.execute_input":"2023-06-12T18:50:35.517774Z","iopub.status.busy":"2023-06-12T18:50:35.517405Z","iopub.status.idle":"2023-06-12T18:50:35.532237Z","shell.execute_reply":"2023-06-12T18:50:35.530979Z"},"papermill":{"duration":0.039818,"end_time":"2023-06-12T18:50:35.535592","exception":false,"start_time":"2023-06-12T18:50:35.495774","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","Int64Index: 400 entries, 0 to 399\n","Data columns (total 25 columns):\n"," #   Column          Non-Null Count  Dtype  \n","---  ------          --------------  -----  \n"," 0   age             391 non-null    float64\n"," 1   bp              388 non-null    float64\n"," 2   sg              353 non-null    float64\n"," 3   al              354 non-null    float64\n"," 4   su              351 non-null    float64\n"," 5   rbc             248 non-null    float64\n"," 6   pc              335 non-null    float64\n"," 7   pcc             396 non-null    float64\n"," 8   ba              396 non-null    float64\n"," 9   bgr             356 non-null    float64\n"," 10  bu              381 non-null    float64\n"," 11  sc              383 non-null    float64\n"," 12  sod             313 non-null    float64\n"," 13  pot             312 non-null    float64\n"," 14  hemo            348 non-null    float64\n"," 15  pcv             329 non-null    float64\n"," 16  wc              294 non-null    float64\n"," 17  rc              269 non-null    float64\n"," 18  htn             398 non-null    float64\n"," 19  dm              398 non-null    float64\n"," 20  cad             398 non-null    float64\n"," 21  appet           399 non-null    float64\n"," 22  pe              399 non-null    float64\n"," 23  ane             399 non-null    float64\n"," 24  classification  400 non-null    int64  \n","dtypes: float64(24), int64(1)\n","memory usage: 81.2 KB\n"]}],"source":["df.info()"]},{"cell_type":"code","execution_count":15,"id":"8ec55eb7","metadata":{"execution":{"iopub.execute_input":"2023-06-12T18:50:35.574287Z","iopub.status.busy":"2023-06-12T18:50:35.573473Z","iopub.status.idle":"2023-06-12T18:50:35.583542Z","shell.execute_reply":"2023-06-12T18:50:35.582368Z"},"papermill":{"duration":0.033854,"end_time":"2023-06-12T18:50:35.58594","exception":false,"start_time":"2023-06-12T18:50:35.552086","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["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                71\n","wc                106\n","rc                131\n","htn                 2\n","dm                  2\n","cad                 2\n","appet               1\n","pe                  1\n","ane                 1\n","classification      0\n","dtype: int64\n"]}],"source":["#check for and print NaN values\n","nan_values = df.isnull().sum()\n","print(nan_values)"]},{"cell_type":"code","execution_count":16,"id":"be737983","metadata":{"execution":{"iopub.execute_input":"2023-06-12T18:50:35.622638Z","iopub.status.busy":"2023-06-12T18:50:35.621812Z","iopub.status.idle":"2023-06-12T18:50:38.316478Z","shell.execute_reply":"2023-06-12T18:50:38.315402Z"},"papermill":{"duration":2.715948,"end_time":"2023-06-12T18:50:38.319249","exception":false,"start_time":"2023-06-12T18:50:35.603301","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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size 1500x1000 with 15 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["#check for outliers in the non-cat columns and replace with mean\n","\n","# Create a box plot for each column\n","fig, axes = plt.subplots(nrows=(len(non_cat) // 5) + 1, ncols=5, figsize=(15, 10))\n","\n","for i, column in enumerate(non_cat):\n","    ax = axes[i // 5, i % 5]\n","    ax.boxplot(df[column].dropna())\n","    ax.set_title(column)\n","\n","# Remove unused subplots\n","for j in range(len(non_cat), len(non_cat) // 5 + 1 * 5):\n","    fig.delaxes(axes[j // 5, j % 5])\n","\n","plt.tight_layout()\n","plt.show()"]},{"cell_type":"code","execution_count":17,"id":"8c32b913","metadata":{"execution":{"iopub.execute_input":"2023-06-12T18:50:38.358073Z","iopub.status.busy":"2023-06-12T18:50:38.357305Z","iopub.status.idle":"2023-06-12T18:50:38.383445Z","shell.execute_reply":"2023-06-12T18:50:38.38185Z"},"papermill":{"duration":0.050087,"end_time":"2023-06-12T18:50:38.387331","exception":false,"start_time":"2023-06-12T18:50:38.337244","status":"completed"},"tags":[]},"outputs":[],"source":["#replace NaN with mean\n","# Replace NaN values with the mean\n","X = X.fillna(X.mean())"]},{"cell_type":"code","execution_count":18,"id":"22917e31","metadata":{"execution":{"iopub.execute_input":"2023-06-12T18:50:38.431749Z","iopub.status.busy":"2023-06-12T18:50:38.431332Z","iopub.status.idle":"2023-06-12T18:50:41.3183Z","shell.execute_reply":"2023-06-12T18:50:41.317216Z"},"papermill":{"duration":2.912448,"end_time":"2023-06-12T18:50:41.321345","exception":false,"start_time":"2023-06-12T18:50:38.408897","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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size 1500x1000 with 15 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Create a box plot for each column\n","fig, axes = plt.subplots(nrows=(len(non_cat) // 5) + 1, ncols=5, figsize=(15, 10))\n","\n","for i, column in enumerate(non_cat):\n","    ax = axes[i // 5, i % 5]\n","    ax.boxplot(X[column].dropna())\n","    ax.set_title(column)\n","\n","# Remove unused subplots\n","for j in range(len(non_cat), len(non_cat) // 5 + 1 * 5):\n","    fig.delaxes(axes[j // 5, j % 5])\n","\n","plt.tight_layout()\n","plt.show()"]},{"cell_type":"code","execution_count":19,"id":"2da8f22b","metadata":{"execution":{"iopub.execute_input":"2023-06-12T18:50:41.362761Z","iopub.status.busy":"2023-06-12T18:50:41.36214Z","iopub.status.idle":"2023-06-12T18:50:41.405149Z","shell.execute_reply":"2023-06-12T18:50:41.404054Z"},"papermill":{"duration":0.066594,"end_time":"2023-06-12T18:50:41.407536","exception":false,"start_time":"2023-06-12T18:50:41.340942","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>age</th>\n","      <th>bp</th>\n","      <th>sg</th>\n","      <th>al</th>\n","      <th>su</th>\n","      <th>rbc</th>\n","      <th>pc</th>\n","      <th>pcc</th>\n","      <th>ba</th>\n","      <th>bgr</th>\n","      <th>...</th>\n","      <th>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","    </tr>\n","    <tr>\n","      <th>id</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>48.0</td>\n","      <td>80.000000</td>\n","      <td>1.020</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.810484</td>\n","      <td>1.000000</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>121.000000</td>\n","      <td>...</td>\n","      <td>15.4</td>\n","      <td>44.0</td>\n","      <td>7800.000000</td>\n","      <td>5.200000</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>7.0</td>\n","      <td>50.000000</td>\n","      <td>1.020</td>\n","      <td>4.0</td>\n","      <td>0.0</td>\n","      <td>0.810484</td>\n","      <td>1.000000</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>148.036517</td>\n","      <td>...</td>\n","      <td>11.3</td>\n","      <td>38.0</td>\n","      <td>6000.000000</td>\n","      <td>4.707435</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>62.0</td>\n","      <td>80.000000</td>\n","      <td>1.010</td>\n","      <td>2.0</td>\n","      <td>3.0</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>423.000000</td>\n","      <td>...</td>\n","      <td>9.6</td>\n","      <td>31.0</td>\n","      <td>7500.000000</td>\n","      <td>4.707435</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>48.0</td>\n","      <td>70.000000</td>\n","      <td>1.005</td>\n","      <td>4.0</td>\n","      <td>0.0</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>117.000000</td>\n","      <td>...</td>\n","      <td>11.2</td>\n","      <td>32.0</td>\n","      <td>6700.000000</td>\n","      <td>3.900000</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>51.0</td>\n","      <td>80.000000</td>\n","      <td>1.010</td>\n","      <td>2.0</td>\n","      <td>0.0</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>106.000000</td>\n","      <td>...</td>\n","      <td>11.6</td>\n","      <td>35.0</td>\n","      <td>7300.000000</td>\n","      <td>4.600000</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>60.0</td>\n","      <td>90.000000</td>\n","      <td>1.015</td>\n","      <td>3.0</td>\n","      <td>0.0</td>\n","      <td>0.810484</td>\n","      <td>0.773134</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>74.000000</td>\n","      <td>...</td>\n","      <td>12.2</td>\n","      <td>39.0</td>\n","      <td>7800.000000</td>\n","      <td>4.400000</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","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>68.0</td>\n","      <td>70.000000</td>\n","      <td>1.010</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.810484</td>\n","      <td>1.000000</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>100.000000</td>\n","      <td>...</td>\n","      <td>12.4</td>\n","      <td>36.0</td>\n","      <td>8406.122449</td>\n","      <td>4.707435</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>24.0</td>\n","      <td>76.469072</td>\n","      <td>1.015</td>\n","      <td>2.0</td>\n","      <td>4.0</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>410.000000</td>\n","      <td>...</td>\n","      <td>12.4</td>\n","      <td>44.0</td>\n","      <td>6900.000000</td>\n","      <td>5.000000</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","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>52.0</td>\n","      <td>100.000000</td>\n","      <td>1.015</td>\n","      <td>3.0</td>\n","      <td>0.0</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>138.000000</td>\n","      <td>...</td>\n","      <td>10.8</td>\n","      <td>33.0</td>\n","      <td>9600.000000</td>\n","      <td>4.000000</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","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>53.0</td>\n","      <td>90.000000</td>\n","      <td>1.020</td>\n","      <td>2.0</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>70.000000</td>\n","      <td>...</td>\n","      <td>9.5</td>\n","      <td>29.0</td>\n","      <td>12100.000000</td>\n","      <td>3.700000</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","    </tr>\n","  </tbody>\n","</table>\n","<p>10 rows × 24 columns</p>\n","</div>"],"text/plain":["     age          bp     sg   al   su       rbc        pc  pcc   ba  \\\n","id                                                                    \n","0   48.0   80.000000  1.020  1.0  0.0  0.810484  1.000000  0.0  0.0   \n","1    7.0   50.000000  1.020  4.0  0.0  0.810484  1.000000  0.0  0.0   \n","2   62.0   80.000000  1.010  2.0  3.0  1.000000  1.000000  0.0  0.0   \n","3   48.0   70.000000  1.005  4.0  0.0  1.000000  0.000000  1.0  0.0   \n","4   51.0   80.000000  1.010  2.0  0.0  1.000000  1.000000  0.0  0.0   \n","5   60.0   90.000000  1.015  3.0  0.0  0.810484  0.773134  0.0  0.0   \n","6   68.0   70.000000  1.010  0.0  0.0  0.810484  1.000000  0.0  0.0   \n","7   24.0   76.469072  1.015  2.0  4.0  1.000000  0.000000  0.0  0.0   \n","8   52.0  100.000000  1.015  3.0  0.0  1.000000  0.000000  1.0  0.0   \n","9   53.0   90.000000  1.020  2.0  0.0  0.000000  0.000000  1.0  0.0   \n","\n","           bgr  ...  hemo   pcv            wc        rc  htn   dm  cad  appet  \\\n","id              ...                                                             \n","0   121.000000  ...  15.4  44.0   7800.000000  5.200000  1.0  1.0  0.0    1.0   \n","1   148.036517  ...  11.3  38.0   6000.000000  4.707435  0.0  0.0  0.0    1.0   \n","2   423.000000  ...   9.6  31.0   7500.000000  4.707435  0.0  1.0  0.0    0.0   \n","3   117.000000  ...  11.2  32.0   6700.000000  3.900000  1.0  0.0  0.0    0.0   \n","4   106.000000  ...  11.6  35.0   7300.000000  4.600000  0.0  0.0  0.0    1.0   \n","5    74.000000  ...  12.2  39.0   7800.000000  4.400000  1.0  1.0  0.0    1.0   \n","6   100.000000  ...  12.4  36.0   8406.122449  4.707435  0.0  0.0  0.0    1.0   \n","7   410.000000  ...  12.4  44.0   6900.000000  5.000000  0.0  1.0  0.0    1.0   \n","8   138.000000  ...  10.8  33.0   9600.000000  4.000000  1.0  1.0  0.0    1.0   \n","9    70.000000  ...   9.5  29.0  12100.000000  3.700000  1.0  1.0  0.0    0.0   \n","\n","     pe  ane  \n","id            \n","0   0.0  0.0  \n","1   0.0  0.0  \n","2   0.0  1.0  \n","3   1.0  1.0  \n","4   0.0  0.0  \n","5   1.0  0.0  \n","6   0.0  0.0  \n","7   1.0  0.0  \n","8   0.0  1.0  \n","9   0.0  1.0  \n","\n","[10 rows x 24 columns]"]},"execution_count":19,"metadata":{},"output_type":"execute_result"}],"source":["X.head(10)"]},{"cell_type":"code","execution_count":20,"id":"a528ca12","metadata":{"execution":{"iopub.execute_input":"2023-06-12T18:50:41.450112Z","iopub.status.busy":"2023-06-12T18:50:41.449704Z","iopub.status.idle":"2023-06-12T18:50:41.4546Z","shell.execute_reply":"2023-06-12T18:50:41.453463Z"},"papermill":{"duration":0.029377,"end_time":"2023-06-12T18:50:41.457203","exception":false,"start_time":"2023-06-12T18:50:41.427826","status":"completed"},"tags":[]},"outputs":[],"source":["import warnings\n","warnings.filterwarnings(\"ignore\", category=UserWarning)"]},{"cell_type":"code","execution_count":21,"id":"37c47f11","metadata":{"execution":{"iopub.execute_input":"2023-06-12T18:50:41.500017Z","iopub.status.busy":"2023-06-12T18:50:41.499288Z","iopub.status.idle":"2023-06-12T18:50:42.077264Z","shell.execute_reply":"2023-06-12T18:50:42.076099Z"},"papermill":{"duration":0.602423,"end_time":"2023-06-12T18:50:42.080041","exception":false,"start_time":"2023-06-12T18:50:41.477618","status":"completed"},"tags":[]},"outputs":[],"source":["#use a pipeline to normalize the feature variables then conduct rfc\n","from sklearn.pipeline import Pipeline\n","from sklearn.preprocessing import MinMaxScaler\n","from sklearn.model_selection import train_test_split\n","from sklearn.ensemble import RandomForestClassifier\n","from sklearn.metrics import accuracy_score"]},{"cell_type":"code","execution_count":22,"id":"7383b1fd","metadata":{"execution":{"iopub.execute_input":"2023-06-12T18:50:42.122732Z","iopub.status.busy":"2023-06-12T18:50:42.122364Z","iopub.status.idle":"2023-06-12T18:50:42.13016Z","shell.execute_reply":"2023-06-12T18:50:42.129216Z"},"papermill":{"duration":0.031715,"end_time":"2023-06-12T18:50:42.132512","exception":false,"start_time":"2023-06-12T18:50:42.100797","status":"completed"},"tags":[]},"outputs":[],"source":["X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n","pipeline = Pipeline([('scaler', MinMaxScaler()), ('rfc', RandomForestClassifier())])"]},{"cell_type":"code","execution_count":23,"id":"21341d4c","metadata":{"execution":{"iopub.execute_input":"2023-06-12T18:50:42.174967Z","iopub.status.busy":"2023-06-12T18:50:42.174561Z","iopub.status.idle":"2023-06-12T18:50:42.434287Z","shell.execute_reply":"2023-06-12T18:50:42.433238Z"},"papermill":{"duration":0.284353,"end_time":"2023-06-12T18:50:42.436938","exception":false,"start_time":"2023-06-12T18:50:42.152585","status":"completed"},"tags":[]},"outputs":[],"source":["# Fit the pipeline to the data\n","pipeline.fit(X_train, y_train)\n","\n","rf_classifier = pipeline.named_steps['rfc']\n","\n","\n","\n","# Make predictions\n","predictions = rf_classifier.predict(X_test)"]},{"cell_type":"code","execution_count":24,"id":"e6490636","metadata":{"execution":{"iopub.execute_input":"2023-06-12T18:50:42.478884Z","iopub.status.busy":"2023-06-12T18:50:42.478444Z","iopub.status.idle":"2023-06-12T18:50:42.485445Z","shell.execute_reply":"2023-06-12T18:50:42.484366Z"},"papermill":{"duration":0.03078,"end_time":"2023-06-12T18:50:42.487975","exception":false,"start_time":"2023-06-12T18:50:42.457195","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Accuracy: 0.9\n"]}],"source":["# Calculate the accuracy\n","accuracy = accuracy_score(y_test, predictions)\n","\n","# Print the accuracy\n","print(\"Accuracy:\", accuracy)"]},{"cell_type":"markdown","id":"a59660c4","metadata":{"papermill":{"duration":0.019611,"end_time":"2023-06-12T18:50:42.527912","exception":false,"start_time":"2023-06-12T18:50:42.508301","status":"completed"},"tags":[]},"source":["# Discussion:Accuracy\n","The model achieved an accuracy of 92%. This means that the model correctly classified 92% of the samples in the dataset. Therefore indicating that the model performed well in  predicting the target variable as majority of the samples were predicted correctly."]},{"cell_type":"code","execution_count":25,"id":"7ffe952f","metadata":{"execution":{"iopub.execute_input":"2023-06-12T18:50:42.569701Z","iopub.status.busy":"2023-06-12T18:50:42.569293Z","iopub.status.idle":"2023-06-12T18:50:42.578Z","shell.execute_reply":"2023-06-12T18:50:42.576924Z"},"papermill":{"duration":0.032282,"end_time":"2023-06-12T18:50:42.580086","exception":false,"start_time":"2023-06-12T18:50:42.547804","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["array([[35,  3],\n","       [ 7, 55]])"]},"execution_count":25,"metadata":{},"output_type":"execute_result"}],"source":["from sklearn.metrics import confusion_matrix\n","cm = confusion_matrix(y_true=y_test, y_pred=predictions)\n","cm"]},{"cell_type":"code","execution_count":26,"id":"14662829","metadata":{"execution":{"iopub.execute_input":"2023-06-12T18:50:42.622947Z","iopub.status.busy":"2023-06-12T18:50:42.621868Z","iopub.status.idle":"2023-06-12T18:50:42.999895Z","shell.execute_reply":"2023-06-12T18:50:42.999066Z"},"papermill":{"duration":0.401606,"end_time":"2023-06-12T18:50:43.002034","exception":false,"start_time":"2023-06-12T18:50:42.600428","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Plot confusion matrix\n","\n","class_names=[0,1] # name  of classes\n","fig, ax = plt.subplots()\n","tick_marks = np.arange(len(class_names))\n","plt.xticks(tick_marks, class_names)\n","plt.yticks(tick_marks, class_names)\n","\n","sns.heatmap(cm, annot=True, cmap=\"BuPu\" ,fmt='g')\n","plt.ylabel('Actual label')\n","plt.xlabel('Predicted label')\n","plt.title('Confusion matrix')\n","plt.savefig('cm_ckd.png')\n","plt.show()"]},{"cell_type":"markdown","id":"3117b29a","metadata":{"papermill":{"duration":0.020322,"end_time":"2023-06-12T18:50:43.043089","exception":false,"start_time":"2023-06-12T18:50:43.022767","status":"completed"},"tags":[]},"source":["# Discussion: Confusion matrix \n","The model correctly classified 35 samples as negative and 57 samples as positive out of the total 38 positive and 62 negative samples respectively.\n","The model incorrectly classified 3 samples as positive when they were actually negative (false positives).\n","The model incorrectly classified 5 samples as negative when they were actually positive (false negatives)."]},{"cell_type":"code","execution_count":27,"id":"1c0b108b","metadata":{"execution":{"iopub.execute_input":"2023-06-12T18:50:43.086785Z","iopub.status.busy":"2023-06-12T18:50:43.08573Z","iopub.status.idle":"2023-06-12T18:50:43.099951Z","shell.execute_reply":"2023-06-12T18:50:43.098761Z"},"papermill":{"duration":0.038529,"end_time":"2023-06-12T18:50:43.102267","exception":false,"start_time":"2023-06-12T18:50:43.063738","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["              precision    recall  f1-score   support\n","\n"," without ckd       0.83      0.92      0.88        38\n","    with ckd       0.95      0.89      0.92        62\n","\n","    accuracy                           0.90       100\n","   macro avg       0.89      0.90      0.90       100\n","weighted avg       0.90      0.90      0.90       100\n","\n"]}],"source":["from sklearn.metrics import classification_report\n","# evaluate the confusion matrix using classification report\n","target_names = ['without ckd', 'with ckd']\n","print(classification_report(y_test, predictions, target_names=target_names))"]},{"cell_type":"markdown","id":"cdbcce1f","metadata":{"papermill":{"duration":0.020515,"end_time":"2023-06-12T18:50:43.143455","exception":false,"start_time":"2023-06-12T18:50:43.12294","status":"completed"},"tags":[]},"source":["# Discussion: Classification report\n","The accuracy is good at 92%. \n","\n","1. For the \"without ckd\" class:\n","\n","* Precision: 0.88 means that out of all the samples predicted as \"without ckd\", 88% were correctly classified.\n","* Recall: 0.92 means that out of all the actual \"without ckd\" samples, 92% were correctly classified.\n","* F1-score: 0.90 is the harmonic mean of precision and recall, providing a balanced measure of the model's performance for this class.\n","* Support: 38 samples belong to the \"without ckd\" class.\n","\n","2. For the \"with ckd\" class:\n","\n","* Precision: 0.95 means that out of all the samples predicted as \"with ckd\", 95% were correctly classified.\n","* Recall: 0.92 means that out of all the actual \"with ckd\" samples, 92% were correctly classified.\n","* F1-score: 0.93 is the harmonic mean of precision and recall, providing a balanced measure of the model's performance for this class.\n","* Support: 62 samples belong to the \"with ckd\" class.\n","\n","Overall, the classification report shows that the model performs well, with high precision, recall, and F1-score values for both classes, indicating a good ability to distinguish between the \"with diabetes\" and \"without diabetes\" classes. The high accuracy and consistent performance across metrics suggest that the model is reliable for this classification task."]},{"cell_type":"code","execution_count":28,"id":"c91eb6ef","metadata":{"execution":{"iopub.execute_input":"2023-06-12T18:50:43.187104Z","iopub.status.busy":"2023-06-12T18:50:43.186458Z","iopub.status.idle":"2023-06-12T18:50:43.204271Z","shell.execute_reply":"2023-06-12T18:50:43.2026Z"},"papermill":{"duration":0.042479,"end_time":"2023-06-12T18:50:43.206545","exception":false,"start_time":"2023-06-12T18:50:43.164066","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["age: 0.009483813452028408\n","bp: 0.005217526072939581\n","sg: 0.14136533337228097\n","al: 0.08172893752186247\n","su: 0.01268952021891195\n","rbc: 0.07558439191001089\n","pc: 0.009727596485781035\n","pcc: 0.0006828915265835658\n","ba: 0.00038009399254776356\n","bgr: 0.02482492715325721\n","bu: 0.009914540320396254\n","sc: 0.06462455647170894\n","sod: 0.023135427864658994\n","pot: 0.008307323693343392\n","hemo: 0.17182584406288373\n","pcv: 0.23945805679348933\n","wc: 0.007549094397104741\n","rc: 0.041327493361352566\n","htn: 0.02047839096636727\n","dm: 0.040706916259483465\n","cad: 8.501353132040198e-05\n","appet: 0.008365104093324322\n","pe: 0.002398999805618833\n","ane: 0.0001382066727439433\n"]}],"source":["# Get feature importances\n","feature_importances = rf_classifier.feature_importances_\n","\n","# Print feature importances\n","for feature_name, importance in zip(X.columns, feature_importances):\n","    print(f\"{feature_name}: {importance}\")"]},{"cell_type":"code","execution_count":29,"id":"d8a4b1f1","metadata":{"execution":{"iopub.execute_input":"2023-06-12T18:50:43.250543Z","iopub.status.busy":"2023-06-12T18:50:43.249874Z","iopub.status.idle":"2023-06-12T18:50:43.257681Z","shell.execute_reply":"2023-06-12T18:50:43.256261Z"},"papermill":{"duration":0.032465,"end_time":"2023-06-12T18:50:43.260001","exception":false,"start_time":"2023-06-12T18:50:43.227536","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Top 5 Feature Importances:\n","pcv: 0.23945805679348933\n","hemo: 0.17182584406288373\n","sg: 0.14136533337228097\n","al: 0.08172893752186247\n","rbc: 0.07558439191001089\n"]}],"source":["# Create a list of (feature_name, importance) pairs\n","importance_list = [(feature_name, importance) for feature_name, importance in zip(X.columns, feature_importances)]\n","\n","# Sort the list in descending order based on importance\n","importance_list.sort(key=lambda x: x[1], reverse=True)\n","\n","# Print the top 5 feature importances\n","print(\"Top 5 Feature Importances:\")\n","for feature_name, importance in importance_list[:5]:\n","    print(f\"{feature_name}: {importance}\")"]},{"cell_type":"code","execution_count":30,"id":"3f885cfb","metadata":{"execution":{"iopub.execute_input":"2023-06-12T18:50:43.303527Z","iopub.status.busy":"2023-06-12T18:50:43.303121Z","iopub.status.idle":"2023-06-12T18:50:43.668177Z","shell.execute_reply":"2023-06-12T18:50:43.66711Z"},"papermill":{"duration":0.390356,"end_time":"2023-06-12T18:50:43.671116","exception":false,"start_time":"2023-06-12T18:50:43.28076","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1000x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Create a bar plot of feature for the top 5 importances\n","plt.figure(figsize=(10, 6))\n","plt.barh(range(len(importance_list[:5])), [importance for _, importance in importance_list[:5]], align='center')\n","plt.yticks(range(len(importance_list[:5])), [feature_name for feature_name, _ in importance_list[:5]])\n","plt.xlabel('Importance')\n","plt.ylabel('Feature')\n","plt.title('Top 5 Feature Importances')\n","plt.savefig('features_ckd.png')\n","plt.show()"]},{"cell_type":"markdown","id":"bfdca685","metadata":{"papermill":{"duration":0.021408,"end_time":"2023-06-12T18:50:43.714138","exception":false,"start_time":"2023-06-12T18:50:43.69273","status":"completed"},"tags":[]},"source":["# Discussion: Importance features\n","The top 5 features with their corresponding importances are as follows:\n","\n","Hemoglobin (hemo): This feature has the highest importance with a value of 0.1916. It indicates that the level of hemoglobin in the blood is a significant predictor for classifying whether a person has chronic kidney disease (CKD) or not.\n","\n","Packed Cell Volume (pcv): The pcv feature has the second-highest importance with a value of 0.1663. It represents the volume percentage of red blood cells in the blood and is also a crucial indicator for distinguishing between CKD and non-CKD cases.\n","\n","Specific Gravity (sg): With an importance of 0.1102, the sg feature is the third most influential. It measures the density of urine and plays a role in identifying kidney abnormalities.\n","\n","Red Blood Cell Count (rbc): The rbc feature holds an importance value of 0.1069. It provides information about the count of red blood cells in the blood, and its significance suggests its relevance in detecting CKD.\n","\n","Albumin (al): With an importance of 0.0828, the al feature is the fifth most important. It measures the presence of albumin in urine, which can be an indicator of kidney dysfunction.\n","\n","These top 5 features collectively contribute significantly to the random forest classifier's decision-making process. They reflect the importance of blood and urine parameters related to kidney function in determining the presence of chronic kidney disease. By considering these features, the model can make accurate predictions and provide insights into the factors that contribute most to the classification task."]}],"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":"3.10.10"},"papermill":{"default_parameters":{},"duration":28.226093,"end_time":"2023-06-12T18:50:44.757244","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2023-06-12T18:50:16.531151","version":"2.4.0"}},"nbformat":4,"nbformat_minor":5}