Chronic_Kidney_withoutcode (1).ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"xpbAcqxvZlXd"},"source":["![](https://www.miskawaanhealth.com/wp-content/uploads/2021/05/chronic-kidney-disease-stages.jpg)"]},{"cell_type":"code","execution_count":67,"metadata":{"id":"sqMyfMAFsqtr","executionInfo":{"status":"ok","timestamp":1701328049176,"user_tz":-330,"elapsed":14,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}}},"outputs":[],"source":["# import necessary libraries like numpy, pandas, pyplot and seaborn\n","import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import seaborn as sns"]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"EyJ4YuH_Lo0V","executionInfo":{"status":"ok","timestamp":1701328058207,"user_tz":-330,"elapsed":9043,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}},"outputId":"d6c09b89-645f-4790-d5d1-77e92f27d8d6"},"execution_count":68,"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"]}]},{"cell_type":"code","execution_count":69,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":256},"id":"s-kb-iiFs33N","outputId":"eebf9bb0-e58f-4b0c-90ae-7d3d8cb54679","executionInfo":{"status":"ok","timestamp":1701328058209,"user_tz":-330,"elapsed":81,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["   id   age    bp     sg   al   su     rbc        pc         pcc          ba  \\\n","0   0  48.0  80.0  1.020  1.0  0.0     NaN    normal  notpresent  notpresent   \n","1   1   7.0  50.0  1.020  4.0  0.0     NaN    normal  notpresent  notpresent   \n","2   2  62.0  80.0  1.010  2.0  3.0  normal    normal  notpresent  notpresent   \n","3   3  48.0  70.0  1.005  4.0  0.0  normal  abnormal     present  notpresent   \n","4   4  51.0  80.0  1.010  2.0  0.0  normal    normal  notpresent  notpresent   \n","\n","   ...  pcv    wc   rc  htn   dm  cad appet   pe  ane classification  \n","0  ...   44  7800  5.2  yes  yes   no  good   no   no            ckd  \n","1  ...   38  6000  NaN   no   no   no  good   no   no            ckd  \n","2  ...   31  7500  NaN   no  yes   no  poor   no  yes            ckd  \n","3  ...   32  6700  3.9  yes   no   no  poor  yes  yes            ckd  \n","4  ...   35  7300  4.6   no   no   no  good   no   no            ckd  \n","\n","[5 rows x 26 columns]"],"text/html":["\n","  <div id=\"df-4b300cb3-24c0-4a78-ae5b-cae0547c0bfb\" class=\"colab-df-container\">\n","    <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: 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count\n","-\t\t\thtn\t\t-\thypertension\n","-\t\t\tdm\t\t-\tdiabetes mellitus\n","-\t\t\tcad\t\t-\tcoronary artery disease\n","-\t\t\tappet\t\t-\tappetite\n","-\t\t\tpe\t\t-\tpedal edema\n","-\t\t\tane\t\t-\tanemia\n","-\t\t\tclass\t\t-\tclass\n","- ##################################################\n","%\n","- Number of Attributes: 24 + class = 25 ( 11  numeric ,14  nominal)\n","%\n","<h3>Attribute Information :</h3>\n","- 1.Age(numerical)\n","  \t  \tage in years\n","- \t2.Blood Pressure(numerical)\n","\t       \tbp in mm/Hg\n","-\t3.Specific Gravity(nominal)\n","\t  \tsg - (1.005,1.010,1.015,1.020,1.025)\n","- \t4.Albumin(nominal)\n","\t\t  al - (0,1,2,3,4,5)\n","-\t5.Sugar(nominal)\n","\t\t  su - (0,1,2,3,4,5)\n","- \t6.Red Blood Cells(nominal)\n","\t\t  rbc - (normal,abnormal)\n","- \t7.Pus Cell (nominal)\n","\t\t  pc - (normal,abnormal)\n","- \t8.Pus Cell clumps(nominal)\n","\t\t  pcc - (present,notpresent)\n","- \t9.Bacteria(nominal)\n","\t\t  ba  - (present,notpresent)\n","- \t10.Blood Glucose Random(numerical)\n","\t\t  bgr in mgs/dl\n","- \t11.Blood Urea(numerical)\n","\t\t  bu in mgs/dl\n","- \t12.Serum Creatinine(numerical)\n","\t\t  sc in mgs/dl\n","- \t13.Sodium(numerical)\n","\t\t  sod in mEq/L\n","- \t14.Potassium(numerical)\n","\t\t  pot in mEq/L\n","- \t15.Hemoglobin(numerical)\n","\t\t  hemo in gms\n","- \t16.Packed  Cell Volume(numerical)\n","- \t17.White Blood Cell Count(numerical)\n","\t\t  wc in cells/cumm\n","- \t18.Red Blood Cell Count(numerical)\n","\t\t  rc in millions/cmm\n","- \t19.Hypertension(nominal)\n","\t\t  htn - (yes,no)\n","- \t20.Diabetes Mellitus(nominal)\n","\t\t  dm - (yes,no)\n","- \t21.Coronary Artery Disease(nominal)\n","\t\t  cad - (yes,no)\n","- \t22.Appetite(nominal)\n","\t    appet - (good,poor)\n","- \t23.Pedal Edema(nominal)\n","\t\t  pe - (yes,no)\n","- \t24.Anemia(nominal)\n","\t\t  ane - (yes,no)\n","- \t25.Class (nominal)\n","\t\t  class - (ckd,notckd)"]},{"cell_type":"code","execution_count":70,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MEvQhjvcuJVG","outputId":"94f72c06-ae05-4190-cf49-651b8a1f4a7c","executionInfo":{"status":"ok","timestamp":1701328058209,"user_tz":-330,"elapsed":74,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}}},"outputs":[{"output_type":"stream","name":"stdout","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":["# checking info of columns and null values\n","chronic_df.info()"]},{"cell_type":"markdown","metadata":{"id":"I-CZTlLyZu9A"},"source":["## **`Data Cleaning`**"]},{"cell_type":"code","execution_count":71,"metadata":{"id":"p_NXIjgcwX9u","colab":{"base_uri":"https://localhost:8080/","height":256},"executionInfo":{"status":"ok","timestamp":1701328058211,"user_tz":-330,"elapsed":72,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}},"outputId":"aec4f558-62ab-4696-924c-3d58290be72b"},"outputs":[{"output_type":"execute_result","data":{"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","\n","     bgr  ...  pcv    wc   rc  htn   dm cad appet   pe  ane classification  \n","0  121.0  ...   44  7800  5.2  yes  yes  no  good   no   no            ckd  \n","1    NaN  ...   38  6000  NaN   no   no  no  good   no   no            ckd  \n","2  423.0  ...   31  7500  NaN   no  yes  no  poor   no  yes            ckd  \n","3  117.0  ...   32  6700  3.9  yes   no  no  poor  yes  yes            ckd  \n","4  106.0  ...   35  7300  4.6   no   no  no  good   no   no            ckd  \n","\n","[5 rows x 25 columns]"],"text/html":["\n","  <div id=\"df-429e899f-d19a-45c6-b529-a5ee5cb15b67\" class=\"colab-df-container\">\n","    <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","  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<td>normal</td>\n","      <td>normal</td>\n","      <td>notpresent</td>\n","      <td>notpresent</td>\n","      <td>106.0</td>\n","      <td>...</td>\n","      <td>35</td>\n","      <td>7300</td>\n","      <td>4.6</td>\n","      <td>no</td>\n","      <td>no</td>\n","      <td>no</td>\n","      <td>good</td>\n","      <td>no</td>\n","      <td>no</td>\n","      <td>ckd</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5 rows × 25 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-429e899f-d19a-45c6-b529-a5ee5cb15b67')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 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'peda_edema',\n","              'aanemia', 'class']"]},{"cell_type":"code","execution_count":73,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":256},"id":"yjMhni_PfJ1-","outputId":"c75cb3b6-ae17-4096-eb3a-2f6310c2f8e6","executionInfo":{"status":"ok","timestamp":1701328058214,"user_tz":-330,"elapsed":73,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["    age  blood_pressure  specific_gravity  albumin  sugar red_blood_cells  \\\n","0  48.0            80.0             1.020      1.0    0.0             NaN   \n","1   7.0            50.0             1.020      4.0    0.0             NaN   \n","2  62.0            80.0             1.010      2.0    3.0          normal   \n","3  48.0            70.0             1.005      4.0    0.0          normal   \n","4  51.0            80.0             1.010      2.0    0.0          normal   \n","\n","   pus_cell pus_cell_clumps    bacteria  blood_glucose_random  ...  \\\n","0    normal      notpresent  notpresent                 121.0  ...   \n","1    normal      notpresent  notpresent                   NaN  ...   \n","2    normal      notpresent  notpresent                 423.0  ...   \n","3  abnormal         present  notpresent                 117.0  ...   \n","4    normal      notpresent  notpresent                 106.0  ...   \n","\n","   packed_cell_volume  white_blood_cell_count  red_blood_cell_count  \\\n","0                  44                    7800                   5.2   \n","1                  38                    6000                   NaN   \n","2                  31                    7500                   NaN   \n","3                  32                    6700                   3.9   \n","4                  35                    7300                   4.6   \n","\n","   hypertension  diabetes_mellitus coronary_artery_disease appetite  \\\n","0           yes                yes                      no     good   \n","1            no                 no                      no     good   \n","2            no                yes                      no     poor   \n","3           yes                 no                      no     poor   \n","4            no                 no                      no     good   \n","\n","  peda_edema aanemia class  \n","0         no      no   ckd  \n","1         no      no   ckd  \n","2         no     yes   ckd  \n","3        yes     yes   ckd  \n","4         no      no   ckd  \n","\n","[5 rows x 25 columns]"],"text/html":["\n","  <div id=\"df-20238c6d-6e98-4a26-9032-2b312b51be5e\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>age</th>\n","      <th>blood_pressure</th>\n","      <th>specific_gravity</th>\n","      <th>albumin</th>\n","      <th>sugar</th>\n","      <th>red_blood_cells</th>\n","      <th>pus_cell</th>\n","      <th>pus_cell_clumps</th>\n","      <th>bacteria</th>\n","      <th>blood_glucose_random</th>\n","      <th>...</th>\n","      <th>packed_cell_volume</th>\n","      <th>white_blood_cell_count</th>\n","      <th>red_blood_cell_count</th>\n","      <th>hypertension</th>\n","      <th>diabetes_mellitus</th>\n","      <th>coronary_artery_disease</th>\n","      <th>appetite</th>\n","      <th>peda_edema</th>\n","      <th>aanemia</th>\n","      <th>class</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>48.0</td>\n","      <td>80.0</td>\n","      <td>1.020</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>NaN</td>\n","      <td>normal</td>\n","      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   <td>2.0</td>\n","      <td>3.0</td>\n","      <td>normal</td>\n","      <td>normal</td>\n","      <td>notpresent</td>\n","      <td>notpresent</td>\n","      <td>423.0</td>\n","      <td>...</td>\n","      <td>31</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>...</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>...</td>\n","      <td>35</td>\n","      <td>7300</td>\n","      <td>4.6</td>\n","      <td>no</td>\n","      <td>no</td>\n","      <td>no</td>\n","      <td>good</td>\n","      <td>no</td>\n","      <td>no</td>\n","      <td>ckd</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5 rows × 25 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-20238c6d-6e98-4a26-9032-2b312b51be5e')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" 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,'sugar']].astype('object')"]},{"cell_type":"code","execution_count":75,"metadata":{"id":"mnWy9ZfC1BLo","executionInfo":{"status":"ok","timestamp":1701328058215,"user_tz":-330,"elapsed":70,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}}},"outputs":[],"source":["# converting necessary columns like packed_cell_volume, white-blood_cell_count and red_blood_cell_count\n","# currently it is in object type and converting back to numerical type\n","chronic_df['packed_cell_volume']=pd.to_numeric(chronic_df['packed_cell_volume'],errors='coerce')\n","chronic_df['white_blood_cell_count']=pd.to_numeric(chronic_df['white_blood_cell_count'],errors='coerce')\n","chronic_df['red_blood_cell_count']=pd.to_numeric(chronic_df['red_blood_cell_count'],errors='coerce')\n"]},{"cell_type":"code","execution_count":76,"metadata":{"id":"o0YFzgHIj1sd","executionInfo":{"status":"ok","timestamp":1701328058216,"user_tz":-330,"elapsed":70,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}}},"outputs":[],"source":["# Extracting categorical and numerical columns\n","\n","cat_cols =[col for col in chronic_df.columns if chronic_df[col].dtype=='object']\n","num_cols =[col for col in chronic_df.columns if chronic_df[col].dtype!='object']\n"]},{"cell_type":"code","source":["cat_cols"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9Muk4Bh6pfQq","executionInfo":{"status":"ok","timestamp":1701328058216,"user_tz":-330,"elapsed":69,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}},"outputId":"581efdaa-57fb-4bdc-b0f7-b375b142b8a9"},"execution_count":77,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['specific_gravity',\n"," 'albumin',\n"," 'sugar',\n"," 'red_blood_cells',\n"," 'pus_cell',\n"," 'pus_cell_clumps',\n"," 'bacteria',\n"," 'hypertension',\n"," 'diabetes_mellitus',\n"," 'coronary_artery_disease',\n"," 'appetite',\n"," 'peda_edema',\n"," 'aanemia',\n"," 'class']"]},"metadata":{},"execution_count":77}]},{"cell_type":"code","source":["num_cols"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"CyxZ7GjWphYT","executionInfo":{"status":"ok","timestamp":1701328058217,"user_tz":-330,"elapsed":66,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}},"outputId":"7f41387d-dfb3-4bcb-ebce-5c6613575faa"},"execution_count":78,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['age',\n"," 'blood_pressure',\n"," 'blood_glucose_random',\n"," 'blood_urea',\n"," 'serum_creatinine',\n"," 'sodium',\n"," 'potassium',\n"," 'haemoglobin',\n"," 'packed_cell_volume',\n"," 'white_blood_cell_count',\n"," 'red_blood_cell_count']"]},"metadata":{},"execution_count":78}]},{"cell_type":"code","execution_count":79,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"y_1ShLVGlJjn","outputId":"f48356a4-d233-4adf-88c1-c9210373c971","executionInfo":{"status":"ok","timestamp":1701328058218,"user_tz":-330,"elapsed":64,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["specific_gravity has [1.02 1.01 1.005 1.015 nan 1.025] values\n","\n","albumin has [1.0 4.0 2.0 3.0 0.0 nan 5.0] values\n","\n","sugar has [0.0 3.0 4.0 1.0 nan 2.0 5.0] values\n","\n","red_blood_cells has [nan 'normal' 'abnormal'] values\n","\n","pus_cell has ['normal' 'abnormal' nan] values\n","\n","pus_cell_clumps has ['notpresent' 'present' nan] values\n","\n","bacteria has ['notpresent' 'present' nan] values\n","\n","hypertension has ['yes' 'no' nan] values\n","\n","diabetes_mellitus has ['yes' 'no' ' yes' '\\tno' '\\tyes' nan] values\n","\n","coronary_artery_disease has ['no' 'yes' '\\tno' nan] values\n","\n","appetite has ['good' 'poor' nan] values\n","\n","peda_edema has ['no' 'yes' nan] values\n","\n","aanemia has ['no' 'yes' nan] values\n","\n","class has ['ckd' 'ckd\\t' 'notckd'] values\n","\n"]}],"source":["# by looping & looking at unique values in categorical columns\n","for col in cat_cols:\n","  print(f\"{col} has {chronic_df[col].unique()} values\\n\")\n"]},{"cell_type":"code","execution_count":80,"metadata":{"id":"n483Q9Drld9V","executionInfo":{"status":"ok","timestamp":1701328058218,"user_tz":-330,"elapsed":61,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}}},"outputs":[],"source":["# replace incorrect values like '\\tno', '\\tyes', ' yes', '\\tno', 'ckd\\t', 'notckd'  in categorical cols\n","chronic_df['diabetes_mellitus'].replace({' yes':'yes','\\tno':'no','\\tyes':'yes'},inplace=True)\n","chronic_df['coronary_artery_disease'].replace({'\\tno':'no',},inplace=True)\n","chronic_df['class'].replace({'ckd\\t':'ckd'},inplace=True)\n"]},{"cell_type":"code","source":["chronic_df['class'].unique()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6a7s31-kxVc6","executionInfo":{"status":"ok","timestamp":1701328058218,"user_tz":-330,"elapsed":61,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}},"outputId":"a8c822fd-f6e3-4624-a4c5-22c0dfcf469b"},"execution_count":81,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array(['ckd', 'notckd'], dtype=object)"]},"metadata":{},"execution_count":81}]},{"cell_type":"code","execution_count":82,"metadata":{"id":"ZtHkmq_jl1bu","executionInfo":{"status":"ok","timestamp":1701328058219,"user_tz":-330,"elapsed":58,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}}},"outputs":[],"source":["# Converting target col class into 0(chronic kidney) and 1(not a chronic kidney)\n","chronic_df['class']=chronic_df['class'].map({'ckd':0,'notckd':1})\n","\n","# coverting target col into numeric to check correlation\n","chronic_df['class']=pd.to_numeric(chronic_df['class'],errors='coerce')\n"]},{"cell_type":"code","source":["chronic_df['class'].unique()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"tl0yRGwUxdJ8","executionInfo":{"status":"ok","timestamp":1701328058219,"user_tz":-330,"elapsed":58,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}},"outputId":"49e03c61-af93-4803-cc81-09d1497bab92"},"execution_count":83,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0, 1])"]},"metadata":{},"execution_count":83}]},{"cell_type":"code","source":["chronic_df.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":256},"id":"a7u2FFEWvcH8","executionInfo":{"status":"ok","timestamp":1701328058220,"user_tz":-330,"elapsed":56,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}},"outputId":"d8cc68f1-dc32-446d-f45d-8fead1070241"},"execution_count":84,"outputs":[{"output_type":"execute_result","data":{"text/plain":["    age  blood_pressure specific_gravity albumin sugar red_blood_cells  \\\n","0  48.0            80.0             1.02     1.0   0.0             NaN   \n","1   7.0            50.0             1.02     4.0   0.0             NaN   \n","2  62.0            80.0             1.01     2.0   3.0          normal   \n","3  48.0            70.0            1.005     4.0   0.0          normal   \n","4  51.0            80.0             1.01     2.0   0.0          normal   \n","\n","   pus_cell pus_cell_clumps    bacteria  blood_glucose_random  ...  \\\n","0    normal      notpresent  notpresent                 121.0  ...   \n","1    normal      notpresent  notpresent                   NaN  ...   \n","2    normal      notpresent  notpresent                 423.0  ...   \n","3  abnormal         present  notpresent                 117.0  ...   \n","4    normal      notpresent  notpresent                 106.0  ...   \n","\n","   packed_cell_volume  white_blood_cell_count  red_blood_cell_count  \\\n","0                44.0                  7800.0                   5.2   \n","1                38.0                  6000.0                   NaN   \n","2                31.0                  7500.0                   NaN   \n","3                32.0                  6700.0                   3.9   \n","4                35.0                  7300.0                   4.6   \n","\n","   hypertension  diabetes_mellitus  coronary_artery_disease  appetite  \\\n","0           yes                yes                       no      good   \n","1            no                 no                       no      good   \n","2            no                yes                       no      poor   \n","3           yes                 no                       no      poor   \n","4            no                 no                       no      good   \n","\n","   peda_edema aanemia class  \n","0          no      no     0  \n","1          no      no     0  \n","2          no     yes     0  \n","3         yes     yes     0  \n","4          no      no     0  \n","\n","[5 rows x 25 columns]"],"text/html":["\n","  <div id=\"df-e6857b63-472a-4cbf-9188-6a088876e777\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>age</th>\n","      <th>blood_pressure</th>\n","      <th>specific_gravity</th>\n","      <th>albumin</th>\n","      <th>sugar</th>\n","      <th>red_blood_cells</th>\n","      <th>pus_cell</th>\n","      <th>pus_cell_clumps</th>\n","      <th>bacteria</th>\n","      <th>blood_glucose_random</th>\n","      <th>...</th>\n","      <th>packed_cell_volume</th>\n","      <th>white_blood_cell_count</th>\n","      <th>red_blood_cell_count</th>\n","      <th>hypertension</th>\n","      <th>diabetes_mellitus</th>\n","      <th>coronary_artery_disease</th>\n","      <th>appetite</th>\n","      <th>peda_edema</th>\n","      <th>aanemia</th>\n","      <th>class</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>48.0</td>\n","      <td>80.0</td>\n","      <td>1.02</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>NaN</td>\n","      <td>normal</td>\n","      <td>notpresent</td>\n","      <td>notpresent</td>\n","      <td>121.0</td>\n","      <td>...</td>\n","      <td>44.0</td>\n","      <td>7800.0</td>\n","      <td>5.2</td>\n","      <td>yes</td>\n","      <td>yes</td>\n","      <td>no</td>\n","      <td>good</td>\n","      <td>no</td>\n","      <td>no</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>7.0</td>\n","      <td>50.0</td>\n","      <td>1.02</td>\n","      <td>4.0</td>\n","      <td>0.0</td>\n","      <td>NaN</td>\n","      <td>normal</td>\n","      <td>notpresent</td>\n","      <td>notpresent</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>38.0</td>\n","      <td>6000.0</td>\n","      <td>NaN</td>\n","      <td>no</td>\n","      <td>no</td>\n","      <td>no</td>\n","      <td>good</td>\n","      <td>no</td>\n","      <td>no</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>62.0</td>\n","      <td>80.0</td>\n","      <td>1.01</td>\n","      <td>2.0</td>\n","      <td>3.0</td>\n","      <td>normal</td>\n","      <td>normal</td>\n","      <td>notpresent</td>\n","      <td>notpresent</td>\n","      <td>423.0</td>\n","      <td>...</td>\n","      <td>31.0</td>\n","      <td>7500.0</td>\n","      <td>NaN</td>\n","      <td>no</td>\n","      <td>yes</td>\n","      <td>no</td>\n","      <td>poor</td>\n","      <td>no</td>\n","      <td>yes</td>\n","      <td>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>normal</td>\n","      <td>abnormal</td>\n","      <td>present</td>\n","      <td>notpresent</td>\n","      <td>117.0</td>\n","      <td>...</td>\n","      <td>32.0</td>\n","      <td>6700.0</td>\n","      <td>3.9</td>\n","      <td>yes</td>\n","      <td>no</td>\n","      <td>no</td>\n","      <td>poor</td>\n","      <td>yes</td>\n","      <td>yes</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>51.0</td>\n","      <td>80.0</td>\n","      <td>1.01</td>\n","      <td>2.0</td>\n","      <td>0.0</td>\n","      <td>normal</td>\n","      <td>normal</td>\n","      <td>notpresent</td>\n","      <td>notpresent</td>\n","      <td>106.0</td>\n","      <td>...</td>\n","      <td>35.0</td>\n","      <td>7300.0</td>\n","      <td>4.6</td>\n","      <td>no</td>\n","      <td>no</td>\n","      <td>no</td>\n","      <td>good</td>\n","      <td>no</td>\n","      <td>no</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5 rows × 25 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e6857b63-472a-4cbf-9188-6a088876e777')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            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**`EDA`**"]},{"cell_type":"code","execution_count":86,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"dkhRR-PRrfI1","outputId":"9dbbf5aa-ec84-433d-b81b-f2805c424dc7","executionInfo":{"status":"ok","timestamp":1701328064886,"user_tz":-330,"elapsed":6717,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 2000x1200 with 0 Axes>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure size 500x500 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAekAAAHqCAYAAAAgWrY5AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABZlklEQVR4nO3de3xT9f0/8FcuTdJrem/a0htQKJdCgdJSRBHpBMVLFRUQBZF52byA/amIU9A5V9EvfNHpZLip4yuI4hgqYygWFJFy6YWrUK5tKTS90qRN78n5/ZEmUinQS9KTy+v5eOShS06S92HSVz93iSAIAoiIiMjhSMUugIiIiDrHkCYiInJQDGkiIiIHxZAmIiJyUAxpIiIiB8WQJiIiclAMaSIiIgfFkCYiInJQDGkiIiIHxZAmIiJyUA4R0u+99x5iY2OhUqmQmpqKffv2XfX6DRs2ICEhASqVComJidiyZYv1tdbWVixatAiJiYnw9vZGREQE5syZgwsXLnT4jNjYWEgkkg6PN954o8s1C4IAvV4P7qpKRET2InpIf/bZZ8jMzMTSpUuRn5+PkSNHYsqUKaioqOj0+t27d2PWrFmYP38+CgoKkJGRgYyMDBw5cgQA0NDQgPz8fLz88svIz8/Hxo0bUVhYiDvuuOOyz/rjH/+IsrIy6+Opp57qct11dXVQq9Woq6vr2Y0TERFdg0TsAzZSU1MxduxYvPvuuwAAk8mEqKgoPPXUU3jhhRcuu37GjBkwGAzYvHmz9blx48YhKSkJq1at6vQ79u/fj5SUFBQXFyM6OhqAuSW9cOFCLFy4sEd16/V6qNVq6HQ6+Pn59egziIiIrkbUlnRLSwvy8vKQnp5ufU4qlSI9PR05OTmdvicnJ6fD9QAwZcqUK14PADqdDhKJBP7+/h2ef+ONNxAUFIRRo0bhrbfeQltb2xU/o7m5GXq9vsODiIjInuRifnlVVRWMRiPCwsI6PB8WFobjx493+h6tVtvp9VqtttPrm5qasGjRIsyaNatDi/fpp5/G6NGjERgYiN27d2Px4sUoKyvDihUrOv2crKwsvPrqq925PSIiol4RNaTtrbW1Fffddx8EQcD777/f4bXMzEzrv48YMQIKhQKPPfYYsrKyoFQqL/usxYsXd3iPXq9HVFSU/YonIiK3J2pIBwcHQyaToby8vMPz5eXl0Gg0nb5Ho9F06XpLQBcXF2P79u3XHDdOTU1FW1sbioqKMHjw4MteVyqVnYY3ERGRvYg6Jq1QKDBmzBhkZ2dbnzOZTMjOzkZaWlqn70lLS+twPQBs27atw/WWgD558iS+++47BAUFXbOWAwcOQCqVIjQ0tId3Q0REZFuid3dnZmZi7ty5SE5ORkpKClauXAmDwYB58+YBAObMmYPIyEhkZWUBABYsWICJEydi+fLlmDZtGtavX4/c3FysXr0agDmg77nnHuTn52Pz5s0wGo3W8erAwEAoFArk5ORg7969mDRpEnx9fZGTk4NnnnkGDzzwAAICAsT5gyAiIvo1wQH85S9/EaKjowWFQiGkpKQIe/bssb42ceJEYe7cuR2u//zzz4VBgwYJCoVCGDZsmPCf//zH+trZs2cFAJ0+duzYIQiCIOTl5QmpqamCWq0WVCqVMGTIEOHPf/6z0NTU1OWadTqdAEDQ6XS9unciIqIrEX2dtLPiOmkiIrI30XccIyIios4xpImIiBwUQ5qIiMhBMaSJiIgcFEOaiIjIQTGkiYiIHBRDmoiIyEExpImIiBwUQ5qIiMhBib53NxGRmKrrm7HrVBXajAICvRW4Pj4YchnbL+QYGNJE5JZ0Da1469vj2JBbiuY2k/X5mCAvZP5mEO5MihSxOiIzhjQRuZ0LtY2Y++E+nKyoBwAkaHwR4qvE0Qt6FFc3YMH6AzhbZcDC9EEiV0rujiFNRG7lfG0jpv91N7T6Jmj8VFhx30ikDQiCRCKBobkNf/3+FN7bcRorvzsJqUSCpyfHi10yuTEOvBCR22hqNeJ3n+RBq29CfKgPNv5+PMYPDIZEIgEAeCvleG5KAhbfkgAAWLHtBH46VSVmyeTmGNJE5DZe/fooDpXq4O/lgY/mjUWEv2en1z02cQAeGBcNAHj+i0Oob27ryzKJrBjSROQWvi+swKf7zkEiAd6ZOQr9Aryuev3iW4agX4Anztc2Ytl/j/dRlUQdMaSJyOU1tRqx5MujAICHr4vDDYNCrvkeb6Ucb04fAQBYt68ExdUGu9ZI1BmGNBG5vPd2nEJJTQPC1So885uuz9gePzAYEweFwGgS8Jftp+xYIVHnGNJE5NLKdI34284zAICltw+Fj7J7i1oWpptnd/+74DyKqtiapr7FkCYil/ZO9km0tJmQGheIKcM03X7/qOgA3DjY3Jp+//vTdqiQ6MoY0kTkss5WGfB5bikA4Lkpg61LrbrriUkDAQBfHjwPXWOrzeojuhaGNBG5rJXfnYDRJOCmhFAkxwb2+HOSYwIwOMwXTa0m/Du/1IYVEl0dQ5qIXFJJdQO+PngBAJDZjclinZFIJJjdvm567d4SCILQ6/qIuoIhTUQuafWPp2ESgImDQjA8Ut3rz8sYFQlPDxlOVtRjf9FFG1RIdG0MaSJyOZV1zdax6N/dOMAmn+mn8sDtI8MBAP/KY5c39Q2GNBG5nH/uLkJLmwmjov2RGtfzsehfyxhlPr5y61EtWi453pLIXhjSRORSmtuM+HRfCQDg0ev793hGd2dS44IQ4quErrEVu05V2uxzia6EIU1ELmXL4TJUG1oQrlbhN0PDbPrZMqkE0xLNXd6bD5bZ9LOJOsOQJiKXsianGABwf0o05DLb/4i7bYQ5pL/9uRxNrUabfz7RpRjSROQyDpfqUFBSCw+ZBDNTou3yHaOjAxChVqG+uQ07T7DLm+yLIU1ELmNNThEA4NbEcIT4Ku3yHVKpBDe3by+afazCLt9BZMGQJiKXcNHQgq/aNy+ZkxZj1++aPCQUALC9sAImEzc2IfthSBORS9iQdw7NbSYMDffD6OgAu35XSlwgvBUyVNY148gFnV2/i9wbQ5qInJ7RJOCTPeZlV3PSYmy67KozSrkM18eHAGCXN9kXQ5qInN4PJypQUtMAP5UcdyZF9sl33mTp8j7OkCb7YUgTkdOzLLu6LzkKngpZn3znpMHmkD58XodyfVOffCe5H4Y0ETm14moDfmhfCvXAOPtOGLtUiK8Sie0Hd/x0qqrPvpfcC0OaiJzaJ3uKIbSfdhUb7N2n333dwGAAwC6GNNkJQ5qInFZji9F62pW9l111ZkJ7SP90qopnTJNdMKSJyGl9dfA8dI2tiAr0xI3tY8R9KTk2AAq5FOX6ZpyuNPT595PrY0gTkVMSBME6YeyB1BjIpPZddtUZlYcMY2PNa7I5Lk32wJAmIqeUX1KLoxf0UMqluC85SrQ6OC5N9sSQJiKn9H/t+3TfPjICAd4K0eqwjEvvOV0NI7cIJRtjSBOR06msa8aWw1oAwNy0WFFrGRahhq9SjrrmNhwr04taC7kehjQROZ3P9pegxWhCUpQ/EvupRa1FJpVgTPu49L6zNaLWQq6HIU1ETqXNaMLavb/s0+0IUuICAQD7ixjSZFsMaSJyKt8dq0CZrgmB3grcmhgudjkAgJRYc0jvO1vD9dJkUwxpInIqa9onjM0YGwWVR9/s030tif3UUMqlqDa04EwV10uT7TCkichpHCvTY/fpasikkj7dp/talHIZkqL8AXBcmmyLIU1ETuPDXWcBAFOHaxDp7ylyNR1Zx6UZ0mRDDGkicgpV9c348sAFAMD8CXEiV3O5se3j0vuLGdJkOwxpInIKn+wpti67Gh0dIHY5lxkV7Q+JBDhX04jKumaxyyEXwZAmIofX3GbEJ3vM+3Q7YisaAHxVHhgU6gsAKCi5KHI15CoY0kTk8L46cAFV9S0IV6swdbhG7HKuaFS0PwDzvuJEtsCQJiKHJggCPvypCAAwJy0WHjLH/bFl6YZnS5psxXH/ayciApBzphrHyvTw9JBhVop4p111haUlfahUhzajSdxiyCUwpInIob3//WkAwD1j+sHfS7zTrrpiQIgPfFVyNLYacVxbJ3Y55AIY0kTksA6eq8WPJ6sgk0rw6A39xS7nmqRSiXVTE3Z5ky0wpInIYf31+1MAgDuTIhAV6CVyNV1jGZfm5DGyBYY0ETmkk+V1+OZoOSQS4Pc3DhC7nC6ztKQPldaKWge5BoY0ETkky1j0lKEaDGxff+wMLOdbn6kyoK6pVeRqyNkxpInI4ZyracCXB81bgD4xaaDI1XRPsI8Skf6eEATg8Hmd2OWQk2NIE5HDWfXDaRhNAm4YFGJtmTqTEe01Hy5lSFPvMKSJyKFU6JuwIbcUAPCEE41FX8ryi8UhhjT1EkOaiBzK33edRYvRhOSYAOvxj85mZD9/AMCh87Wi1kHOjyFNRA6jtqHFepDGE5MGQiKRiFxRzwyPNLekz9U0osbQInI15MwY0kTkMD7eXYSGFiOGhPvhxsEhYpfTY2pPD8QFewPg5DHqHYY0ETmE+uY2fNR+kMYTkwY4bSvaIjHSMnmsVtxCyKkxpInIIazbWwxdYyv6B3vjluHhYpfTa8Mj/QAAP5fpRa6EnBlDmohE19RqxAc/ngUAPH7jAMikzt2KBoCh4eaW9M8XGNLUcwxpIhLdF3mlqKxrRoRahYykSLHLsYkh4eZd0oqqG1Df3CZyNeSsGNJEJCqjScDqnWcAAI/c0B8KuWv8WAryUULjpwIAHGeXN/WQa/xtICKntfWIFiU1DQjw8sDMsdFil2NTltY0x6WppxwipN977z3ExsZCpVIhNTUV+/btu+r1GzZsQEJCAlQqFRITE7Flyxbra62trVi0aBESExPh7e2NiIgIzJkzBxcuXOjwGTU1NZg9ezb8/Pzg7++P+fPno76+3i73R0SdEwQBf9tpPkhjTlosPBUykSuyraER5sljxxjS1EOih/Rnn32GzMxMLF26FPn5+Rg5ciSmTJmCioqKTq/fvXs3Zs2ahfnz56OgoAAZGRnIyMjAkSNHAAANDQ3Iz8/Hyy+/jPz8fGzcuBGFhYW44447OnzO7NmzcfToUWzbtg2bN2/Gzp078eijj9r9fonoF3vO1OBQqQ5KuRRz0mLELsfmOHmMeksiCIIgZgGpqakYO3Ys3n33XQCAyWRCVFQUnnrqKbzwwguXXT9jxgwYDAZs3rzZ+ty4ceOQlJSEVatWdfod+/fvR0pKCoqLixEdHY1jx45h6NCh2L9/P5KTkwEAW7duxa233orS0lJERERcs269Xg+1Wg2dTgc/P7+e3DqR23voo334vrASD46LwWsZw8Uux+bOVhkw6X++h1IuxdFXp0AuE71dRE5G1P9iWlpakJeXh/T0dOtzUqkU6enpyMnJ6fQ9OTk5Ha4HgClTplzxegDQ6XSQSCTw9/e3foa/v781oAEgPT0dUqkUe/fu7cUdEVFXHdfq8X1hJaQS4LfXx4ldjl3EBHrBSyFDc5sJZ6sMYpdDTkjUkK6qqoLRaERYWFiH58PCwqDVajt9j1ar7db1TU1NWLRoEWbNmmVt8Wq1WoSGhna4Ti6XIzAw8Iqf09zcDL1e3+FBRD1nmdE9dbgGMUHeIldjH1KpBEPCuakJ9ZxL9720trbivvvugyAIeP/993v1WVlZWVCr1dZHVFSUjaokcj9lukZ8dcA8mfOxG5zzOMquGmoJaY5LUw+IGtLBwcGQyWQoLy/v8Hx5eTk0Gk2n79FoNF263hLQxcXF2LZtW4dxY41Gc9nEtLa2NtTU1FzxexcvXgydTmd9nDt3rsv3SUQdrckpRptJQGpcIEZG+Ytdjl1ZZnizJU09IWpIKxQKjBkzBtnZ2dbnTCYTsrOzkZaW1ul70tLSOlwPANu2betwvSWgT548ie+++w5BQUGXfUZtbS3y8vKsz23fvh0mkwmpqamdfq9SqYSfn1+HBxF1X1OrEZ/tN/+SO+861xyLvtSQS1rSIs/TJSckF7uAzMxMzJ07F8nJyUhJScHKlSthMBgwb948AMCcOXMQGRmJrKwsAMCCBQswceJELF++HNOmTcP69euRm5uL1atXAzAH9D333IP8/Hxs3rwZRqPROs4cGBgIhUKBIUOGYOrUqXjkkUewatUqtLa24sknn8TMmTO7NLObiHpuy+Ey1BhaEKFWIX1I6LXf4OQGh/lCKgGqDS2orGtGaPsuZERdIXpIz5gxA5WVlViyZAm0Wi2SkpKwdetW6+SwkpISSKW/NPjHjx+PdevW4aWXXsKLL76I+Ph4bNq0CcOHm5dvnD9/Hl999RUAICkpqcN37dixAzfeeCMAYO3atXjyyScxefJkSKVSTJ8+He+88479b5jIza3JKQYA3J8a7RZLkjwVMvQP8cGpinocLdMzpKlbRF8n7ay4Tpqo+46c1+G2v+yCQibF7sU3IdhHKXZJfeLpTwvw1cELeG7KYDwxaaDY5ZATcf1fY4nIYVjGoqcM17hNQAOcPEY9x5Amoj7R1GrEpgPnAQAzkt1rCaNlGRb38KbuYkgTUZ/YekSLuqY2RPp7YvyAoGu/wYVYZnifrTKgoYVnS1PXMaSJqE98nmvu6r43uR+kUonI1fStEF8lQnyVEASgUFsndjnkRBjSRGR3pRcbsPt0NSQS4J4x/cQuRxSDwnwAACfLeSQudR1Dmojs7quD5i1Ax8UFoV+Al8jViCM+1BcAcKKcLWnqOoY0EdmdZZ/uO5Pcd7OgwZr2kK5gS5q6jiFNRHZ1XKvHcW0dFDIpbhkeLnY5ovmlu5staeo6hjQR2ZWlFX3j4BCovTxErkY8A9u7u8t0TdA3tYpcDTkLhjQR2Y0gCNbx6DuTIkWuRlxqTw9o2rcE5eQx6iqGNBHZzZHzepRebISnhww3Jbj+YRrXEt/e5c3JY9RVDGkispv/HikDAExKCIGnQiZyNeIbFMYZ3tQ9DGkisgtBELD1iPmY2CnDNCJX4xgGt4c0u7upqxjSRGQXJyvqcabKAIVMyq7uduzupu5iSBORXfz3sLkVfX18MHxV7jur+1Lx7S3pirpm6Bo4w5uujSFNRHbx7c/s6v41H6Uckf6eAIATFWxN07UxpInI5rS6Jhy9oIdEAtw0hF3dl2KXN3UHQ5qIbG5HYQUAICnKH8E+SpGrcSzWGd48DYu6gCFNRDaXfcwc0pM5Yewy8aGWljRneNO1MaSJyKaaWo346VQVAOCmhDCRq3E8loM2TnJMmrqAIU1ENpVzphqNrUaEq1UYEu4rdjkOZ2B7S7qqvgU1hhaRqyFHx5AmIpvacdzc1T0pIRQSiUTkahyPl0KOqMD2Gd6cPEbXwJAmIpv68aS5q/vGQSEiV+K4BoVadh5jSNPVMaSJyGbO1TTgbJUBMqkEaQOCxC7HYcVb9/Dm5DG6OoY0EdmMpRU9Otqfu4xdxaD2tdKFbEnTNTCkichmfjxZCQC4Pp5d3VdjWSt9ppItabo6hjQR2USb0WRdenV9fLDI1Ti2uGBvAOYZ3rUNnOFNV8aQJiKbOHReB31TG9SeHhjRz1/schyat1KOcLUKAHC60iByNeTIGNJEZBM/tY9Hjx8QBJmUS6+uZUCIeVyaXd50NQxpIrKJPWerAZhDmq5tQIi5y5staboahjQR9VpLmwl5xRcBAOP6M6S7on97S/o0W9J0FQxpIuq1Q6W1aGo1IchbYd32kq5uAEOauoAhTUS9tueMuat7XP8gbgXaRQNCzd3dJdUNaDWaRK6GHBVDmoh6bc+ZGgBAav9AkStxHho/FbwUMrSZBJTUNIhdDjkohjQR9UpLmwm5xeaQ5nh010kkEvS3TB6rYJc3dY4hTUS9YhmPDvRWIJ7j0d1iXYZVxRne1DmGNBH1yi/j0YEcj+4m6+QxtqTpChjSRNQrlvFodnV3n7W7mzO86QoY0kTUYxyP7p1flmEZIAiCyNWQI2JIE1GPcTy6d+KCvSGRALrGVtQYeNAGXY4hTUQ9xvHo3lF5yBDp7wmA24NS5xjSRNRjHI/uPe48RlfDkCaiHuF4tG3wNCy6GoY0EfXIz2V6NLWa4O/lgYEhHI/uKcv2oOzups4wpImoR3KLzK3oMdEBkPL86B7rH8zubroyhjQR9Uh+ifloyjGxASJX4twsLelzNQ1objOKXA05GoY0EXWbIAjW86PHRDOkeyPERwlflRwmASiu5kEb1BFDmoi6rfRiI8r1zZBLJRjRz1/scpya+aANbg9KnWNIE1G3Wbq6h0Wq4amQiVyN8xvA7UHpChjSRNRt7Oq2rf7B5pA+W8XubuqIIU1E3ZZb1B7SMQxpW4htD+miai7Doo4Y0kTULfXNbTiu1QMAkjmz2yZigywtaYY0dcSQJqJuOXiuFiYBiPT3RJifSuxyXEJce0u6xtACXWOryNWQI2FIE1G3sKvb9ryVcoT6KgEARWxN0yUY0kTULXntM7vZ1W1bHJemzjCkiajLTCYBBe0zu0dzZrdNxbWPS5/hHt50CYY0EXXZiYo61DW3wVshQ4LGV+xyXEpcCFvSdDmGNBF1mWV9dFK0P+Qy/viwJcsMb45J06X4t4yIuoybmNhPXPAvy7AEQRC5GnIUDGki6jJrSMcGilyJ64kJ8gIA6JvaUGNoEbkachQMaSLqksq6ZhRXN0AiAZKi/MUux+WoPGSIUJvXnXNcmiwY0kTUJZZDNQaF+kLt6SFyNa7JMnmMe3iTBUOaiLrkl65ujkfbCyeP0a8xpImoSzhpzP4unTxGBDCkiagLmtuMOFyqA8DtQO2JB23QrzGkieiajpzXocVoQrCPwjoLmWzv0g1NuAyLAIY0EXVB3iVbgUokEpGrcV1RAV6QSoCGFiMq65rFLoccAEOaiK7JEtI8VMO+FHIp+gWYeyrOsMubwJAmomsQBOGXSWMcj7Y762lYDGkCQ5qIrqGkpgFV9S1QyKQYFqEWuxyXF9c+5n+WG5oQGNJEdA2WVnRiPzVUHjKRq3F9cWxJ0yUY0kR0Vbns6u5TsVwrTZdgSBPRVeVfMrOb7M/Ski6uboDJxGVY7o4hTURXpG9qRWF5HQC2pPtKpL8n5FIJmttMKNM3iV0OiUz0kH7vvfcQGxsLlUqF1NRU7Nu376rXb9iwAQkJCVCpVEhMTMSWLVs6vL5x40bcfPPNCAoKgkQiwYEDBy77jBtvvBESiaTD4/HHH7flbRG5hIKSWgiC+RjFEF+l2OW4BblMiuhA8+SxYnZ5uz1RQ/qzzz5DZmYmli5divz8fIwcORJTpkxBRUVFp9fv3r0bs2bNwvz581FQUICMjAxkZGTgyJEj1msMBgMmTJiAZcuWXfW7H3nkEZSVlVkfb775pk3vjcgV5BXVAOB+3X0tun2Gd3ENT8Nyd6KG9IoVK/DII49g3rx5GDp0KFatWgUvLy98+OGHnV7/9ttvY+rUqXjuuecwZMgQvPbaaxg9ejTeffdd6zUPPvgglixZgvT09Kt+t5eXFzQajfXh5+dn03sjcgX7iyybmASKXIl7sZ6GxWVYbk+0kG5paUFeXl6HMJVKpUhPT0dOTk6n78nJybksfKdMmXLF669m7dq1CA4OxvDhw7F48WI0NFz9N9bm5mbo9foODyJX1mo04cC5WgDAWO401qcs3d0l1WxJuzu5WF9cVVUFo9GIsLCwDs+HhYXh+PHjnb5Hq9V2er1Wq+3Wd99///2IiYlBREQEDh06hEWLFqGwsBAbN2684nuysrLw6quvdut7iJzZzxf0aGw1wt/LAwNCfMQux61YDjEpZki7PdFCWkyPPvqo9d8TExMRHh6OyZMn4/Tp0xgwYECn71m8eDEyMzOt/1uv1yMqKsrutRKJZf8l49FSKQ/V6EsxQZZlWObTsHioifsSLaSDg4Mhk8lQXl7e4fny8nJoNJpO36PRaLp1fVelpqYCAE6dOnXFkFYqlVAqObuV3Ecux6NFExXoCYkEMLQYUW1oQbAPf/a4K9HGpBUKBcaMGYPs7GzrcyaTCdnZ2UhLS+v0PWlpaR2uB4Bt27Zd8fqusizTCg8P79XnELkKQRCsO41xPLrvKeUyRKg9AZhb0+S+RO3uzszMxNy5c5GcnIyUlBSsXLkSBoMB8+bNAwDMmTMHkZGRyMrKAgAsWLAAEydOxPLlyzFt2jSsX78eubm5WL16tfUza2pqUFJSggsXLgAACgsLAcA6i/v06dNYt24dbr31VgQFBeHQoUN45plncMMNN2DEiBF9/CdA5JiKqxtQVd8MhVyKxH48VEMM0YFeOF/biOLqBoyJYW+GuxI1pGfMmIHKykosWbIEWq0WSUlJ2Lp1q3VyWElJCaTSXxr748ePx7p16/DSSy/hxRdfRHx8PDZt2oThw4dbr/nqq6+sIQ8AM2fOBAAsXboUr7zyChQKBb777jvrLwRRUVGYPn06XnrppT66ayLHZxmPHhGphlLOQzXEEBPkhZwz1Sji5DG3JhEEgZvD9oBer4darYZOp+Maa3I5i744hM9yz+HxiQPwwi0JYpfjlt7//jSWbT2OjKQIrJw5SuxySCSibwtKRI4nt9jckuZ4tHhiuOsYgSFNRL9SXd+M05XmyUo8VEM8XCtNAEOaiH4lr31Wd3yoD/y9FCJX474sa6VrDC3QN7WKXA2JhSFNRB1YQprro8Xlo5Qj2Mf8SxK3B3VfDGki6sAys5vj0eKzHlnJkHZbDGkismpqNeLweR0AIJlrc0UXw9Ow3B5Dmois8oovotUoQOOnQlSgp9jluD3L5DF2d7svhjQRWeWcrgYApA0I4qEODuCXZVhsSbsrhjQRWe050x7S/YNEroSAS0/DYkvaXTGkiQgA0NDShoOltQDMLWkSX0z7xLEyXROaWo0iV0Ni6FFInzlzxtZ1EJHIcovM49GR/p6Iag8HElegtwI+SvMRC+e485hb6lFIDxw4EJMmTcInn3yCpqYmW9dERCLIae/qHseubochkUi485ib61FI5+fnY8SIEcjMzIRGo8Fjjz2Gffv22bo2IupDl04aI8dhCWkuw3JPPQrppKQkvP3227hw4QI+/PBDlJWVYcKECRg+fDhWrFiByspKW9dJRHZU39xmXR/NkHYslsljJezudku9mjgml8tx9913Y8OGDVi2bBlOnTqFZ599FlFRUZgzZw7KyspsVScR2dH+szUwmgREB3oh0p/rox1JDHcdc2u9Cunc3Fz8/ve/R3h4OFasWIFnn30Wp0+fxrZt23DhwgXceeedtqqTiOwoh0uvHFa0dUya3d3uSN6TN61YsQIfffQRCgsLceutt2LNmjW49dZbIZWaMz8uLg4ff/wxYmNjbVkrEdkJx6MdV2x7d3fpxUa0GU2Qy7hy1p30KKTff/99PPzww3jooYcQHh7e6TWhoaH4xz/+0aviiMj+dI2tOHqB49GOSuOngkIuRUubCRdqm6wta3IPPQrpbdu2ITo62tpythAEAefOnUN0dDQUCgXmzp1rkyKJyH72na2BSQD6B3sjzE8ldjn0K1KpBNGBXjhVUY/iGgND2s30qN9kwIABqKqquuz5mpoaxMXF9booIuo7lq7ucWxFOyzL5LEiTh5zOz0KaUEQOn2+vr4eKhV/EydyJj+dMv/CzUljjsu6DIuTx9xOt7q7MzMzAZh3wVmyZAm8vH7pdjEajdi7dy+SkpJsWiAR2Y9W14TC8jpIJMCEgcFil0NXwF3H3Fe3QrqgoACAuSV9+PBhKBQK62sKhQIjR47Es88+a9sKichudp40bzw0IlKNAG/FNa4msUQzpN1Wt0J6x44dAIB58+bh7bffhp+fn12KIqK+sfOEOaRvGBQiciV0NZZlWMU1BgiCwLO+3UiPxqQ/+ugjBjSRkzOaBOxqH49mSDu2SH9PSCVAU6sJFXXNYpdDfajLLem7774bH3/8Mfz8/HD33Xdf9dqNGzf2ujAisq8j53WobWiFr1KOpCh/scuhq1DIpYgM8MS5mkYUVzdwqZwb6XJIq9VqaxeLWq22W0FE1DcsXd3jBwbBg7tYObyYQG+cq2lEUbUBKXGBYpdDfaTLIf3RRx91+u9E5Jy2F1YAACYOChW5EuqK6CAv4BRQwsljbqVHvz43NjaioeGX/1CKi4uxcuVKfPvttzYrjIjsp6q+GQfO1QIAbkpgSDuDWJ4r7ZZ6FNJ33nkn1qxZAwCora1FSkoKli9fjjvvvBPvv/++TQskItv7vrASggAMi/CDRs3xTWcQHchzpd1Rj0I6Pz8f119/PQDgiy++gEajQXFxMdasWYN33nnHpgUSke1tP14OAJjMVrTT4IYm7qlHId3Q0ABfX18AwLfffou7774bUqkU48aNQ3FxsU0LJCLbamkzYecJ89Krm4aEiVwNdZUlpHWNrahtaBG5GuorPQrpgQMHYtOmTTh37hy++eYb3HzzzQCAiooKrp8mcnD7i2pQ39yGYB8FRkRypYaz8FLIEeKrBMDWtDvpUUgvWbIEzz77LGJjY5Gamoq0tDQA5lb1qFGjbFogEdnWtp/NXd2TBodCKuXOVc7EchpWMcel3UaPzpO+5557MGHCBJSVlWHkyJHW5ydPnoy77rrLZsURkW0JgoBvjmoBAFOGaUSuhrorOsgLucUXeRqWG+lRSAOARqOBRtPxL3lKSkqvCyIi+zlUqkOZrgleChkmxPPUK2dj3cOb3d1uo0chbTAY8MYbbyA7OxsVFRUwmUwdXj9z5oxNiiMi27K0oicNDoXKQyZyNdRdnOHtfnoU0r/97W/xww8/4MEHH0R4eDhPZCFyElvbQ/rmYZzV7YyirWPS7O52Fz0K6f/+97/4z3/+g+uuu87W9RCRnZyqqMOZSgM8ZBJM4vpop2Tp7i7XN6Op1cjeEDfQo9ndAQEBCAzkBu9EzmTzoTIAwISBwfBTeYhcDfWEv5cHfFXmthV3HnMPPQrp1157DUuWLOmwfzcROS5BEKwhfduICJGroZ6SSCTWcemiKnZ5u4MedXcvX74cp0+fRlhYGGJjY+Hh0fG38vz8fJsUR0S2UVheh1MV9VDIpPgNx6OdWkygN46c17Ml7SZ6FNIZGRk2LoOI7GnzQXMreuLgEHZ1OznO8HYvPQrppUuX2roOIrITQRDwn8OWru5wkauh3rKGNFvSbqFHY9KA+YjKv//971i8eDFqamoAmLu5z58/b7PiiKj3DpbqcLbKAJWHFJN5oIbTsxxZWcxdx9xCj1rShw4dQnp6OtRqNYqKivDII48gMDAQGzduRElJifWsaSIS38b8UgDmbUB9lD3eZJAchKUlff5iI9qMJshlPW5rkRPo0f+7mZmZeOihh3Dy5EmoVL8cGH/rrbdi586dNiuOiHqnuc2Irw5eAABMH91P5GrIFjR+KijkUrSZBFyobRK7HLKzHoX0/v378dhjj132fGRkJLRaba+LIiLb2HG8ArUNrQjzU+K6gdyr2xVIpRLrzmNF7PJ2eT0KaaVSCb1ef9nzJ06cQEhISK+LIiLb+CLPPEckY1QkZDyW0mXwyEr30aOQvuOOO/DHP/4Rra2tAMwL7EtKSrBo0SJMnz7dpgUSUc9U1zfj+8IKAOzqdjUx7duD8shK19ejkF6+fDnq6+sREhKCxsZGTJw4EQMHDoSvry9ef/11W9dIRD3w1cELaDMJSIxUY1CYr9jlkA1xrbT76NFUT7VajW3btuGnn37CwYMHUV9fj9GjRyM9Pd3W9RFRD/2rfVb39NGRIldCthbNkHYb3Q5pk8mEjz/+GBs3bkRRUREkEgni4uKg0WggCAKPrSRyAIXaOhw5r4eHTII7khjSrsYyJl1S08Cfuy6uW93dgiDgjjvuwG9/+1ucP38eiYmJGDZsGIqLi/HQQw/hrrvusledRNQNllb0pMGhCPRWiFwN2Vq/AC9IJUBjqxGVdc1il0N21K2W9Mcff4ydO3ciOzsbkyZN6vDa9u3bkZGRgTVr1mDOnDk2LZKIuq6lzWTdwORuThhzSQq5FBH+nii92IjimgaE+qmu/SZySt1qSX/66ad48cUXLwtoALjpppvwwgsvYO3atTYrjoi6b9vP5aiqb0GIrxKTh4SKXQ7ZCY+sdA/dCulDhw5h6tSpV3z9lltuwcGDB3tdFBH13Kf7SgAAM5Kj4MEtI12WZQ9vHlnp2rr1N7impgZhYVfeoD8sLAwXL17sdVFE1DPF1QbsOlUFiQSYMTZK7HLIjmI5w9stdCukjUYj5PIrD2PLZDK0tbX1uigi6plP950DAFwfH4Ko9hnA5Jp4ZKV76NbEMUEQ8NBDD0GpVHb6enMzZxkSiaWlzYQv8swhfX9KtMjVkL3xyEr30K2Qnjt37jWv4cxuInFwwph7sbSkaxtaoWtshdrTQ+SKyB66FdIfffSRveogol7ihDH34q2UI9hHiar6ZpRUNyCxn1rsksgO+DeZyAVwwph7+mVcml3eroohTeQC1rW3ojlhzL1Yj6zkDG+XxZAmcnLNbUZsyDXvMDY7lRPG3InlyEpOHnNdDGkiJ7f1iBY1hhZo/FSYnMAJY+6ER1a6PoY0kZNbu9fc1T0zJQpyThhzK5YjK7nrmOvi32giJ3aivA77ztZAJpVg5lh2dbsby5h0ma4JTa1Gkashe2BIEzmxde2t6MkJodCoeRKSuwn0VsBXaV5Je46taZfEkCZyUg0tbdZzo2ePixG5GhKDRCKxdnlzXNo1MaSJnNTmg2Woa2pDdKAXrh8YLHY5JBLrkZWc4e2SGNJETmrt3mIAwP2p0ZBKJSJXQ2KxLMPi5DHXxJAmckKHS3U4WKqDh0yCe8f0E7scEhE3NHFtoof0e++9h9jYWKhUKqSmpmLfvn1XvX7Dhg1ISEiASqVCYmIitmzZ0uH1jRs34uabb0ZQUBAkEgkOHDhw2Wc0NTXhiSeeQFBQEHx8fDB9+nSUl5fb8raI7MrSir5leDiCfDo/lY7cA5dhuTZRQ/qzzz5DZmYmli5divz8fIwcORJTpkxBRUVFp9fv3r0bs2bNwvz581FQUICMjAxkZGTgyJEj1msMBgMmTJiAZcuWXfF7n3nmGXz99dfYsGEDfvjhB1y4cAF33323ze+PyB70Ta348sAFAMADnDDm9izd3edqGtBmNIlcDdmaRBAEQawvT01NxdixY/Huu+8CAEwmE6KiovDUU0/hhRdeuOz6GTNmwGAwYPPmzdbnxo0bh6SkJKxatarDtUVFRYiLi0NBQQGSkpKsz+t0OoSEhGDdunW45557AADHjx/HkCFDkJOTg3HjxnWpdr1eD7VaDZ1OBz8/v+7eOlGPrckpwpIvjyI+1AffPnMDJBKOR7szk0lAwpKtaGkz4cfnJ3HvdhcjWku6paUFeXl5SE9P/6UYqRTp6enIycnp9D05OTkdrgeAKVOmXPH6zuTl5aG1tbXD5yQkJCA6Ovqqn9Pc3Ay9Xt/hQdTXBEHA2j3mtdGzU6MZ0ASpVIKoAE8AHJd2RaKFdFVVFYxGI8LCwjo8HxYWBq1W2+l7tFptt66/0mcoFAr4+/t363OysrKgVqutj6goHgdIfS+3+CIKy+ug8pDirtGcMEZm1oM2eGSlyxF94pizWLx4MXQ6nfVx7tw5sUsiN7R2j3nC2B0jI6D29BC5GnIUPGjDdcnF+uLg4GDIZLLLZlWXl5dDo9F0+h6NRtOt66/0GS0tLaitre3Qmr7W5yiVSiiVnEVL4qkxtGDLYXNvDyeM0aV+WYbFlrSrEa0lrVAoMGbMGGRnZ1ufM5lMyM7ORlpaWqfvSUtL63A9AGzbtu2K13dmzJgx8PDw6PA5hYWFKCkp6dbnEPW1L/LOocVoQmKkGiP6+YtdDjmQX86VZkva1YjWkgaAzMxMzJ07F8nJyUhJScHKlSthMBgwb948AMCcOXMQGRmJrKwsAMCCBQswceJELF++HNOmTcP69euRm5uL1atXWz+zpqYGJSUluHDBvESlsLAQgLkFrdFooFarMX/+fGRmZiIwMBB+fn546qmnkJaW1uWZ3UR9TRAEfLrPPMRyfypPu6KOLl0rLQgCJxS6EFFDesaMGaisrMSSJUug1WqRlJSErVu3WieHlZSUQCr9pbE/fvx4rFu3Di+99BJefPFFxMfHY9OmTRg+fLj1mq+++soa8gAwc+ZMAMDSpUvxyiuvAAD+93//F1KpFNOnT0dzczOmTJmCv/71r31wx0Q9s+9sDc5WGeCtkOGOkRFil0MOJirAC1IJ0NBiRGVdM0L9eCKaqxB1nbQz4zpp6kuZnx/AxvzzmJEchWX3jBC7HHJAN7y5AyU1DVj/6DiM6x8kdjlkI5zdTeTg9E2t2HK4DAAwI4VL/6hzccHmcemiKk4ecyUMaSIH99WBC2hqNSE+1AejovzFLocclCWkzzKkXQpDmsjBfbbfPGFsxtgoTgiiK2JIuyaGNJEDO3pBh8PnzUdS3s0dxugqYi3d3Vwr7VIY0kQO7PP2VvRvhoYh0FshcjXkyOKCLCHdAJOJ84FdBUOayEE1tRrx74LzAIAZY7k2mq4uMsATHjIJWtpMuKBrFLscshGGNJGD+uaoFvqmNkSoVZgwMFjscsjByaQSRLdvD8pxadfBkCZyUJYJY/cmR0Em5YQxura4YB8AXIblShjSRA6ouNqA3aerIZEA9yZzwhh1TVywpSXNPbxdBUOayAFtyC0FAEwYGIx+AV4iV0POIta6DKte5ErIVhjSRA6mzWjCF3nmkJ4xljuMUddZdx3jaVgugyFN5GB2nqyEVt+EAC8P/GZomNjlkBOxhHRJTQNajSaRqyFbYEgTORjLhLG7RvWDUi4TuRpyJmG+Knh6yGA0CSi9yGVYroAhTeRAKuuakX2sAgC7uqn7pFIJYoIsk8c4Lu0KGNJEDmRjfinaTAKSovwxWOMrdjnkhPqHWCaPcVzaFTCkiRyEIAjWru6ZbEVTD8UG8chKV8KQJnIQucUXcabKAC+FDLeNjBC7HHJSsTwNy6UwpIkcxPp95lb0bSPC4aOUi1wNOav+DGmXwpAmcgD6plZsOVwGgBPGqHcsLekLukY0tRpFroZ6iyFN5AC+PngBja1GDAjxxujoALHLIScW5K2Ar0oOQTCvlybnxpAmcgCfWyeMRUMi4WEa1HMSicS6qQm7vJ0fQ5pIZEcv6HCwVAcPmQR3jY4UuxxyAQxp18GQJhKZZcLYzUM1CPZRilwNuQLLMqyzlQxpZ8eQJhJRQ0sbNhWcBwDMSokWuRpyFZYNTc5w1zGnx5AmEtF/DpWhrrkNUYGeGD8gSOxyyEUMCPEBAJxmS9rpMaSJRPTpvhIA5gljUiknjJFtWEK6xtCCGkOLyNVQbzCkiURSqK1DfkktZFIJ7h3TT+xyyIV4KmSI9PcEAJyuZJe3M2NIE4nE0opOHxKKUD+VyNWQqxkQam5Nn6pgSDszhjSRCJpajfg3J4yRHQ20jEszpJ0aQ5pIBP89UgZdYysi/T1xfXyI2OWQCxoQap7hze5u58aQJhLBp+1ro2eMjYKME8bIDiwt6VMMaafGkCbqY6cq6rHvbA2kEuDeZE4YI/uwjEmXXuRBG86MIU3Uxz7bb54wdlNCKMLVniJXQ64qyFsBfy8PCAJwhuulnRZDmqgPNbcZ8UVeKQDz2mgie5FIJJdsasIub2fFkCbqQ1uPaHGxoRUaPxVuHMwJY2RfA0I4eczZMaSJ+tD/5RQDME8Yk8v414/sayDXSjs9/pQg6iNHzuuQW3wRcqkEs1PZ1U32xz28nR9DmqiPWFrRtySGc4cx6hOWlvSZynoYTYLI1VBPMKSJ+kBtQws2HTDvMDY3LUbkashd9AvwgkImRXObCRdqG8Uuh3qAIU3UBz7PPYfmNhOGhvthTEyA2OWQm5BJJYgLNk8e47i0c2JIE9mZ0STg//aYu7rnjo+BRMIdxqjvWLq8OcPbOTGkiezs+8IKnKtphNrTA3eMjBS7HHIzXIbl3BjSRHb2z0uWXXkqZCJXQ+6GR1Y6N4Y0kR2dqqjDzhOVkEiAB1I5YYz6HpdhOTeGNJEd/f3HswCA3wwJQ3SQl8jVkDuyhHSNoQU1hhaRq6HuYkgT2UllXTM2FpiXXT1yQ3+RqyF35amQIdLffJALu7ydD0OayE7+b08xWtpMGBnlj2QuuyIRWWZ4n6yoE7kS6i6GNJEdNLUa8Un7sqtHr+/PZVckqgSNLwCgUMuQdjYMaSI7+Fd+KWoMLegX4Ikpw8LELofc3OD2kD7OkHY6DGkiGzOZBPyjfcLYw9fF8bQrEt3gS1rSgsA9vJ0Jf3oQ2Vj28QqcqTLAVyXHfWOjxC6HCANCfCCTSqBrbEW5vlnscqgbGNJENrZ652kAwP2p0fBRykWuhghQecgQ274E8LhWL3I11B0MaSIb2ne2BvuLLkIhk+Lh6+LELofIKkHjBwA4Uc5xaWfCkCayoXd3nAIA3JPcD2E8M5ocCCePOSeGNJGNHC7VYeeJSsikEjx+wwCxyyHqYDCXYTklhjSRjbzX3oq+Y2QEtwAlh2NZK32yoh5tRpPI1VBXMaSJbOBkeR22HtUCAH53I1vR5HiiArzg6SFDS5sJRdUNYpdDXcSQJrKB9783z+ieMiwMg8J8Ra6G6HJSqQSDwszbg7LL23kwpIl66VxNA748eAEA8MSkgSJXQ3Rlv4xLcxmWs2BIE/XSeztOwWgScH18MEb08xe7HKIrGty+DIszvJ0HQ5qoF0qqG/BFXikAYGF6vMjVEF2dZfIY10o7D4Y0US/8ZftJtLW3osfEBIpdDtFVWbq7i2sa0NDSJnI11BUMaaIeKqoyYGPBeQDAM78ZJHI1RNcW7KNEsI8CggCcLK8XuxzqAoY0UQ/9Zbt5LHrioBCMjg4QuxyiLrGsPuAMb+fAkCbqgbNVBvy7wDwWzVY0ORNuD+pcGNJEPfCX7JMwCcBNCaFIivIXuxyiLrNMHiss5zIsZ8CQJuqmk+V12HTAPBbNGd3kbCzLsNjd7RwY0kTd9D/fFsIkADcPDeO6aHI6g8J8IJEAVfUtqKhrErscugaGNFE3FJRcxDdHyyGVAM9NGSx2OUTd5qWQo3+wNwDg6AV2eTs6hjRRFwmCgGVbjwMApo/uh3ju0U1OanikGgBw9LxO5EroWhjSRF3048kq7DlTA4VMioWc0U1ObFiEeVyaLWnHx5Am6gKTScCb35hb0Q+mxSDS31Pkioh6blhEe0uaIe3wGNJEXbDlSBmOnNfDRynH73leNDk5S0u6pKYBusZWkauhq3GIkH7vvfcQGxsLlUqF1NRU7Nu376rXb9iwAQkJCVCpVEhMTMSWLVs6vC4IApYsWYLw8HB4enoiPT0dJ0+e7HBNbGwsJBJJh8cbb7xh83sj59dqNGH5tycAAI9c3x9BPkqRKyLqHX8vhbU36Ge2ph2a6CH92WefITMzE0uXLkV+fj5GjhyJKVOmoKKiotPrd+/ejVmzZmH+/PkoKChARkYGMjIycOTIEes1b775Jt555x2sWrUKe/fuhbe3N6ZMmYKmpo7LDf74xz+irKzM+njqqafseq/knDbkluJslQFB3grMvz5O7HKIbOKXcWlOHnNkoof0ihUr8Mgjj2DevHkYOnQoVq1aBS8vL3z44YedXv/2229j6tSpeO655zBkyBC89tprGD16NN59910A5lb0ypUr8dJLL+HOO+/EiBEjsGbNGly4cAGbNm3q8Fm+vr7QaDTWh7e3t71vl5xMY4sRK78zt6KfvGkgfJRykSsisg3LDG+2pB2bqCHd0tKCvLw8pKenW5+TSqVIT09HTk5Op+/JycnpcD0ATJkyxXr92bNnodVqO1yjVquRmpp62We+8cYbCAoKwqhRo/DWW2+hre3KR7c1NzdDr9d3eJDr+2j3WVTUNaNfgCfuT40Wuxwim7G0pA9zGZZDE7VZUFVVBaPRiLCwsA7Ph4WF4fjx452+R6vVdnq9Vqu1vm557krXAMDTTz+N0aNHIzAwELt378bixYtRVlaGFStWdPq9WVlZePXVV7t3g+TUquqb8dcdpwEAmb8ZBKVcJnJFRLaT2M/ckj5dWQ9Dcxu82UvkkNz2/5XMzEzrv48YMQIKhQKPPfYYsrKyoFRePjFo8eLFHd6j1+sRFRXVJ7WSOP532wnUN7chMVKNjKRIscshsqlQXxXC1SqU6Zpw5LwOqf2DxC6JOiFqd3dwcDBkMhnKy8s7PF9eXg6NRtPpezQazVWvt/yzO58JAKmpqWhra0NRUVGnryuVSvj5+XV4kOs6UV6HT/eVAABemjYEUqlE5IqIbG9Ee2v6UCm7vB2VqCGtUCgwZswYZGdnW58zmUzIzs5GWlpap+9JS0vrcD0AbNu2zXp9XFwcNBpNh2v0ej327t17xc8EgAMHDkAqlSI0NLQ3t0Qu4vX/HINJAKYMC2MLg1yW5YCYg6W1otZBVyZ6d3dmZibmzp2L5ORkpKSkYOXKlTAYDJg3bx4AYM6cOYiMjERWVhYAYMGCBZg4cSKWL1+OadOmYf369cjNzcXq1asBABKJBAsXLsSf/vQnxMfHIy4uDi+//DIiIiKQkZEBwDz5bO/evZg0aRJ8fX2Rk5ODZ555Bg888AACAgJE+XMgx/HDiUr8cKISHjIJXrhliNjlENkNW9KOT/SQnjFjBiorK7FkyRJotVokJSVh69at1olfJSUlkEp/afCPHz8e69atw0svvYQXX3wR8fHx2LRpE4YPH2695vnnn4fBYMCjjz6K2tpaTJgwAVu3boVKpQJg7rpev349XnnlFTQ3NyMuLg7PPPNMhzFnck9tRhP+/J9jAIA5abGIC+ayPHJdIyL9AZh3HrtoaEGAt0LcgugyEkEQBLGLcEZ6vR5qtRo6nY7j0y5kTU4Rlnx5FGpPD/zw3I3w9+IPLXJtN761A0XVDfjnwymYOChE7HLoV0TfzITIUVTXN+N/vikEADx78yAGNLkFy7j0oXO1otZBnWNIE7V7c2sh9E1tGBruh/tTY8Quh6hPjIzyBwAcYEg7JIY0EYCCkov4LPccAOC1jGGQcckVuYlR0f4AgIJzteDop+NhSJPbM5oELPnyKABg+uh+GBMTKHJFRH1nWIQfFDIpagwtKKpuELsc+hWGNLm9z/afw+HzOvgq5XjhlgSxyyHqU0q5DMMjzZNf84svilwN/RpDmtxaZV0zlm017xP/zG8GIcSXZ0WT+xkTY94fIr+EIe1oGNLk1l756ih0ja0YFuGHB9M4WYzc0+hoS0jXilsIXYYhTW5r6xEt/nO4DDKpBMumj4CHjH8dyD2Nbm9JF2r1qG++8pG91Pf4U4nckq6hFS9/eQQA8NgN/TE8Ui1yRUTiCfNTIdLfEyaB66UdDUOa3NKftxxDZV0z+gd74+nJ8WKXQyQ6S2s6l5PHHApDmtzOrpNV1jXRy+4ZAZWHTOSKiMQ3NtYc0vuLakSuhC7FkCa3Ul3fjMzPDwAA5qTFYGws10QTAUBKnPnvQl7xRbQaTSJXQxYMaXIbgiDg2Q0HUVHXjIGhPlwTTXSJQaG+UHt6oKHFiKMX9GKXQ+0Y0uQ2/rHrLHYUVkIhl+Ld+0fBSyH6Sa1EDkMqlVh7lvadrRa5GrJgSJNbOFyqs25a8vJtQ5Gg4fGiRL+WGmcJaY5LOwqGNLk8fVMrnvo0H61GAVOHafBAarTYJRE5pJRLQtpk4mEbjoAhTS7NaBKw4NMCFFU3INLfE8umj4BEwhOuiDozLMIPXgoZ9E1tOK6tE7scAkOaXNybW49jR2ElVB5SrHpgDNReHmKXROSw5DIpktvHpfec4bi0I2BIk8v65+4i/G3nGQDAW/eMRGI/7ipGdC3XDQgCAPx0qkrkSghgSJOL+s+hMrzytfmM6MzfDMLtIyNErojIOVw3MBiAuSXN9dLiY0iTy/nmqBYL1hdAEIAHx8XgqZsGil0SkdMYGu6HAC8PGFqMOMh9vEXHkCaXsvVIGZ5Ym482k4A7RkbglTuGcaIYUTdIpRKMb29N72KXt+gY0uQy/m9PMX7fHtB3JkVgxX0jIZMyoIm6a0J7SHNcWnzccomcXpvRhGVbj+ODH88CAGalROO1O4dBzvOhiXrkugHmkC4oqUV9cxt8lIwKsfCnGDm1iromzPlwnzWgn0kfhD/fNZwBTdQL0UFeiA70QptJwG62pkXFn2TktL49qsXUlT9i9+lqeClkeH/2aCxIj+cYNJENTBocAgDYUVgpciXujX0Y5HTO1TTgj5t/xrafywEAQ8L98M7MJMSH+YpcGZHrmJQQin/mFOP7wgoIgsBffkXCkCan0dhixPs/nMaqH06jpc0EmVSC314fh8zfDIJSLhO7PCKXMq5/EFQeUpTpmnBcW4ch4TyURgwMaXJ4jS1GrNtXgr/9cBoVdc0AgOsGBuGV24ex9UxkJyoPGa4bEIzs4xXYUVjBkBYJQ5oclqG5DWv3FmP1zjOoqm8BAET6e+IP04bgluEadr8R2dmNCaHmkD5egd/fyE2BxMCQJodT39yGNTlF+PuPZ1FjMIdzvwBPPDlpIO4e3Q8KOec7EvUFy+SxvOKLqDG0INBbIXJF7ochTQ6joaUNa3KK8bcfTuNiQysAIDbIC09MGoiMUZHw4LIqoj7VL8ALwyL8cPSCHt/9XI77xkaJXZLbYUiT6FqNJqzJKcZfd5xCdXvLOS7YG0/dNBB3jIzgmmciEU0dpsHRC3psPaplSIuAIU2i2ne2Bi9vOoLCcvMB89GBXnh6cjwykhjORI5g6nANlm87gV0nq1DX1ApfFc9k70sMaRJFdX0z/rzlOP6VXwoACPDywLNTBuO+5Ch2axM5kPgwXwwI8cbpSgO2H6/AnUmRYpfkVvjTkPrczhOVmPr2j9aAnpUShe3/70bMTo1hQBM5oKnDNQCArUe0IlfiftiSpj5jMglYse0E3t1xCgAQH+qDZfeMwOjoAJErI6KruWV4ON7bcRrbj1ewy7uPsdlCfaKuqRW/XZNrDegHx8Xg66cmMKCJnMCwCD/0D/FGc5sJ3xwtF7sct8KQJrurqGvCjL/twfbjFVDKpfjfGSPxWsZwqDy4lSeRM5BIJMhoH4v+8sB5katxLwxpsquS6gZMf383fi7TI9hHgc8fS8Ndo/qJXRYRddOdSREAgJ9OVaGirknkatwHQ5rspqjKgJmrc3CuphExQV741+/GY2SUv9hlEVEPxAR5Y1S0P0wC8PXBMrHLcRsMabKL87WNmPXBHlzQNWFAiDc2PJaGmCBvscsiol64a5S5y3tD7jkIgiByNe6BIU02d9HQgjn/2IsyXRMGhvpg/aNpCPVTiV0WEfXSnSMjoZRLcVxbhwPnasUuxy0wpMmmGlra8PA/9+N0pQHhahXWPJyCEF+l2GURkQ2ovTwwbUQ4AODTfSUiV+MeGNJkM61GE55cV4CCklqoPT2w5uEURPh7il0WEdnQ/SnRAMzj0nVNrSJX4/oY0mQTgiBgyZdHsP14BVQeUnz40FjEh/mKXRYR2diYmAAMDPVBY6sRmwq4HMveGNJkE5/sLcGn+85BKgHenTUaY2K4SQmRK5JIJHgg1dya/uinIphMnEBmTwxp6rX9RTV49aujAIDnpyYgfWiYyBURkT3dmxwFX5UcZ6rMh26Q/TCkqVfKdI343Sf5aDMJmDYiHI/d0F/skojIzryVctzf3pr++64zIlfj2hjS1GNNrUY8/kk+quqbkaDxxVv3jIBEIhG7LCLqAw+Nj4VcKsGeMzU4VFordjkuiyFNPWKZKHbwnHkm9+oHk+Gl4KFqRO4iXO2JO0aatwp9J/ukyNW4LoY09cgne0vweW4ppBLgL7NGITrIS+ySiKiPPXnTQEglwHfHKtiathOGNHXbryeK3TAoROSKiEgM/UN8rKdjvf0dW9P2wJCmbrl0othtnChG5PaemhwPqQTIPl6B3KIasctxOQxp6rJfTxR7kxPFiNxeXLA3ZoyNAgD8cfPPXDdtYwxp6hJOFCOiK8n8zWD4KOU4VKrDv7kLmU0xpKlLPtlTbJ0o9u79nChGRL8I8VXiqZsGAgDe2Hocukbu6W0rDGm6pt2nqvDK1z8DABZNTcD18ZwoRkQdPXRdLPoHe6Oyrhl//s8xsctxGQxpuqqzVQb8bm0+jCYBdyZF4FFOFCOiTijlMrwxfQQA4LPcc/jpVJXIFbkGhjRdka6xFfP/uR+6xlaMjPLHsumcKEZEV5YSF4g5aTEAgOc2HMRFQ4vIFTk/hjR1qs1owlOfFuBMpQHhahU+eHAMVB4yscsiIgf3/NQExAV744KuCc99cRCCwNnevcGQpssIgoCXvzyKnScqofKQ4oM5yQj1U4ldFhE5AR+lHO/ePwoKuRTfHavA33byAI7eYEjTZd76phCf7iuBRAL8731JGB6pFrskInIiwyLUWHLbUADAsq3HsfWIVuSKnBdDmjr42w+n8dfvTwMAXs9IxC2J4SJXRETOaHZqNB4cFwNBABZ+VoC84otil+SUGNJktX5fCbL+exyAeamV5bxYIqLukkgkWHr7UNw4OARNrSY89OE+HDhXK3ZZTochTQCAz3PP4cV/HwYAPDaxP3534wCRKyIiZyeXSfHX2aOREheIuuY2PPiPvdh7plrsspwKQ5rwz91FeP6LQzAJwP2p0XhhaoLYJRGRi/BSyPHRQ2PNQd3Uhgf/sQ9fHuDWoV3FkHZjJpOArP8ew9L2Yycfvi4Or2cM51poIrIpb6Ucax5OwdRhGrQYTViw/gBe2/wzWtpMYpfm8CQCF7H1iF6vh1qthk6ng5+fn9jldFt9cxue23AQ/22fdflM+iA8PXkgA5qI7MZoEvDm1uPWZVkj+qnx5j0jkKBxvp+hfYUh3UPOHNLHtXr8fm0+zlQa4CGT4I27R2D6mH5il0VEbuLbo1o898Uh6Bpb4SGT4JHr++P3kwbCR8mT9X6NId1DzhjSRpOAD348gxXfnkCL0YRwtQrvzR6N0dEBYpdGRG6mXN+ElzYdwbafywEAwT5KPD6xP2amRDOsL8GQ7iFnC+ncohos+fIofi7TAwAmJ4TizXtGIMhHKXJlROSuBEHAtz+XI2vLMRRVNwAAfFVyzE6NwbzrYhHGnQ4Z0j3lLCF95LwOK787ie+OmX9b9VPJ8dJtQ3HvmH4cfyYih9DSZsIXeaX4+49ncKbKAACQSSWYMDAYt4+MwM3DwuCn8hC5SnE4xOzu9957D7GxsVCpVEhNTcW+ffuuev2GDRuQkJAAlUqFxMREbNmypcPrgiBgyZIlCA8Ph6enJ9LT03Hy5MkO19TU1GD27Nnw8/ODv78/5s+fj/r6epvfmxiaWo34z6EyzPhbDm77yy58d6wcUgkwKyUKO569EfclRzGgichhKORS3J8aje8yJ+KDOckYGxsAo0nADycq8eyGg0h+7TvM+2gf/v7jGRzX6t3q0A7RW9KfffYZ5syZg1WrViE1NRUrV67Ehg0bUFhYiNDQ0Muu3717N2644QZkZWXhtttuw7p167Bs2TLk5+dj+PDhAIBly5YhKysL//znPxEXF4eXX34Zhw8fxs8//wyVytx9csstt6CsrAx/+9vf0Nrainnz5mHs2LFYt25dl+p2tJZ0RV0T9p6pwY7jFdh2rBx1TW0AzL+N3j4iHE9Pjkf/EB+RqyQi6prTlfXYfLAMXx08j9OVhg6vBfsoMCo6AImRaiT2U2N4hBrBPgqXbHyIHtKpqakYO3Ys3n33XQCAyWRCVFQUnnrqKbzwwguXXT9jxgwYDAZs3rzZ+ty4ceOQlJSEVatWQRAERERE4P/9v/+HZ599FgCg0+kQFhaGjz/+GDNnzsSxY8cwdOhQ7N+/H8nJyQCArVu34tZbb0VpaSkiIiKuWbdYId3YYsS5iw0orm5AUZUBB0trUVBSi/O1jR2ui1CrcNfoSDwwLgbhas8+q4+IyJYEQUBheR12nqjErlPV2H+2Bo2txsuu81PJMSDUB/2DfRAd6AWNWolQPxU07Q9/Lw+nDHFRp9C1tLQgLy8Pixcvtj4nlUqRnp6OnJycTt+Tk5ODzMzMDs9NmTIFmzZtAgCcPXsWWq0W6enp1tfVajVSU1ORk5ODmTNnIicnB/7+/taABoD09HRIpVLs3bsXd911lw3v8spa2kzYmF+KplYjmtpM5n+2mv/Z3Gb+d0NzGy42tKDGYH5cbGjt9LMkEmBQqC9uHByCyUPCkBwTAKnU+f6DJCK6lEQiQYLGDwkaPzx6wwA0txlxqFSHQ6U6HC6txaHzOpytMkDf1IaCEnOjpTMKmRR+nh7w85TDV+UBP5Ucfp4e8PKQQSGXQim3/FPa4Z9SiQRSiQQyKdr/KUGAtwKTBl/e02sPooZ0VVUVjEYjwsLCOjwfFhaG48ePd/oerVbb6fVardb6uuW5q13z6650uVyOwMBA6zW/1tzcjObmZuv/1ul0AMwt6p5qajXi+U/3dvt9PioZogK8EB3ghcHhvhgR6Y9hkX7wvWRiRX19XY/rIiJyZIMD5RgcGIR7RwQBMP8sLa42oKiqAWerDCjTNaK8rgmVdS2o0DfhYkMrmgA0NQAVNvj+hHBfjHl8vA0+CfD19b1qC5+L0booKysLr7766mXPR0VFiVANcEyUbyUionMA1Its81nXGjIVNaSDg4Mhk8lQXl7e4fny8nJoNJpO36PRaK56veWf5eXlCA8P73BNUlKS9ZqKio6/T7W1taGmpuaK37t48eIO3ewmkwk1NTUICgpyynGO7tLr9YiKisK5c+ccYqJcX+A9855dlTveM+CY9+3r63vV10UNaYVCgTFjxiA7OxsZGRkAzOGXnZ2NJ598stP3pKWlITs7GwsXLrQ+t23bNqSlpQEA4uLioNFokJ2dbQ1lvV6PvXv34ne/+531M2pra5GXl4cxY8YAALZv3w6TyYTU1NROv1epVEKp7Ljxh7+/fw/v3Hn5+fk5zH/cfYX37B54z+7Dme5b9O7uzMxMzJ07F8nJyUhJScHKlSthMBgwb948AMCcOXMQGRmJrKwsAMCCBQswceJELF++HNOmTcP69euRm5uL1atXAzBPMli4cCH+9Kc/IT4+3roEKyIiwvqLwJAhQzB16lQ88sgjWLVqFVpbW/Hkk09i5syZXZrZTURE1BdED+kZM2agsrISS5YsgVarRVJSErZu3Wqd+FVSUgKp9Jc9V8aPH49169bhpZdewosvvoj4+Hhs2rTJukYaAJ5//nkYDAY8+uijqK2txYQJE7B161brGmkAWLt2LZ588klMnjwZUqkU06dPxzvvvNN3N05ERHQtAlEXNDU1CUuXLhWamprELqXP8J7dA+/ZfTjjfYu+mQkRERF1ziH27iYiIqLLMaSJiIgcFEOaiIjIQTGkiYiIHBRDmq6pu+d9O5OsrCyMHTsWvr6+CA0NRUZGBgoLCztc09TUhCeeeAJBQUHw8fHB9OnTL9v1zpm98cYb1v0FLFzxns+fP48HHngAQUFB8PT0RGJiInJzc62vC104h97ZGI1GvPzyy4iLi4OnpycGDBiA1157rcN5zM5+3zt37sTtt9+OiIgISCQS62FLFl25v5qaGsyePRt+fn7w9/fH/PnzUV9f34d3cRViTi0nx7d+/XpBoVAIH374oXD06FHhkUceEfz9/YXy8nKxS7OJKVOmCB999JFw5MgR4cCBA8Ktt94qREdHC/X19dZrHn/8cSEqKkrIzs4WcnNzhXHjxgnjx48XsWrb2bdvnxAbGyuMGDFCWLBggfV5V7vnmpoaISYmRnjooYeEvXv3CmfOnBG++eYb4dSpU9Zr3njjDUGtVgubNm0SDh48KNxxxx1CXFyc0NjYKGLlvfP6668LQUFBwubNm4WzZ88KGzZsEHx8fIS3337beo2z3/eWLVuEP/zhD8LGjRsFAMK///3vDq935f6mTp0qjBw5UtizZ4/w448/CgMHDhRmzZrVx3fSOYY0XVVKSorwxBNPWP+30WgUIiIihKysLBGrsp+KigoBgPDDDz8IgiAItbW1goeHh7BhwwbrNceOHRMACDk5OWKVaRN1dXVCfHy8sG3bNmHixInWkHbFe160aJEwYcKEK75uMpkEjUYjvPXWW9bnamtrBaVSKXz66ad9UaJdTJs2TXj44Yc7PHf33XcLs2fPFgTB9e771yHdlfv7+eefBQDC/v37rdf897//FSQSiXD+/Pk+q/1K2N1NV2Q57/vSs7mvdd63s7McQRoYGAgAyMvLQ2tra4c/g4SEBERHRzv9n8ETTzyBadOmdbg3wDXv+auvvkJycjLuvfdehIaGYtSoUfjggw+sr1/rHHpnNX78eGRnZ+PEiRMAgIMHD2LXrl245ZZbALjufVt05f5ycnLg7++P5ORk6zXp6emQSqXYu7f7RwnbmujbgpLj6sl5387MZDJh4cKFuO6666zbzGq1WigUissOU7n0fHJntH79euTn52P//v2XveaK93zmzBm8//77yMzMxIsvvoj9+/fj6aefhkKhwNy5c7t0Dr0zeuGFF6DX65GQkACZTAaj0YjXX38ds2fPBgCXvW+LrtyfVqtFaGhoh9flcjkCAwMd4s+AIU3U7oknnsCRI0ewa9cusUuxq3PnzmHBggXYtm1bh/3sXZnJZEJycjL+/Oc/AwBGjRqFI0eOYNWqVZg7d67I1dnP559/jrVr12LdunUYNmwYDhw4gIULFyIiIsKl79uVsLubrqgn5307qyeffBKbN2/Gjh070K9fP+vzGo0GLS0tqK2t7XC9M/8Z5OXloaKiAqNHj4ZcLodcLscPP/yAd955B3K5HGFhYS53z+Hh4Rg6dGiH54YMGYKSkhIAHc+hv5Qz3zMAPPfcc3jhhRcwc+ZMJCYm4sEHH8QzzzxjPVXQVe/boiv3p9FoUFFR0eH1trY21NTUOMSfAUOarujS874tLOd9W87vdnaCIODJJ5/Ev//9b2zfvh1xcXEdXh8zZgw8PDw6/BkUFhaipKTEaf8MJk+ejMOHD+PAgQPWR3JyMmbPnm39d1e75+uuu+6ypXUnTpxATEwMgI7n0FtYzqF31nsGgIaGhg6nCAKATCaDyWQC4Lr3bdGV+0tLS0NtbS3y8vKs12zfvh0mkwmpqal9XvNlxJ65Ro5t/fr1glKpFD7++GPh559/Fh599FHB399f0Gq1YpdmE7/73e8EtVotfP/990JZWZn10dDQYL3m8ccfF6Kjo4Xt27cLubm5QlpampCWliZi1bZ36exuQXC9e963b58gl8uF119/XTh58qSwdu1awcvLS/jkk0+s17zxxhuCv7+/8OWXXwqHDh0S7rzzTqdaitSZuXPnCpGRkdYlWBs3bhSCg4OF559/3nqNs993XV2dUFBQIBQUFAgAhBUrVggFBQVCcXGxIAhdu7+pU6cKo0aNEvbu3Svs2rVLiI+P5xIsch5/+ctfhOjoaEGhUAgpKSnCnj17xC7JZgB0+vjoo4+s1zQ2Ngq///3vhYCAAMHLy0u46667hLKyMvGKtoNfh7Qr3vPXX38tDB8+XFAqlUJCQoKwevXqDq+bTCbh5ZdfFsLCwgSlUilMnjxZKCwsFKla29Dr9cKCBQuE6OhoQaVSCf379xf+8Ic/CM3NzdZrnP2+d+zY0enf4blz5wqC0LX7q66uFmbNmiX4+PgIfn5+wrx584S6ujoR7uZyPKqSiIjIQXFMmoiIyEExpImIiBwUQ5qIiMhBMaSJiIgcFEOaiIjIQTGkiYiIHBRDmoiIyEExpImIiBwUQ5qIiMhBMaSJiIgcFEOaiLps69atmDBhAvz9/REUFITbbrsNp0+ftr6+e/duJCUlQaVSITk5GZs2bYJEIsGBAwes1xw5cgS33HILfHx8EBYWhgcffBBVVVUi3A2R42NIE1GXGQwGZGZmIjc3F9nZ2ZBKpbjrrrtgMpmg1+tx++23IzExEfn5+XjttdewaNGiDu+vra3FTTfdhFGjRiE3Nxdbt25FeXk57rvvPpHuiMix8YANIuqxqqoqhISE4PDhw9i1axdeeukllJaWQqVSAQD+/ve/45FHHkFBQQGSkpLwpz/9CT/++CO++eYb62eUlpYiKioKhYWFGDRokFi3QuSQ2JImoi47efIkZs2ahf79+8PPzw+xsbEAgJKSEhQWFmLEiBHWgAaAlJSUDu8/ePAgduzYAR8fH+sjISEBADp0mxORmVzsAojIedx+++2IiYnBBx98gIiICJhMJgwfPhwtLS1den99fT1uv/12LFu27LLXwsPDbV0ukdNjSBNRl1RXV6OwsBAffPABrr/+egDArl27rK8PHjwYn3zyCZqbm6FUKgEA+/fv7/AZo0ePxr/+9S/ExsZCLuePH6JrYXc3EXVJQEAAgoKCsHr1apw6dQrbt29HZmam9fX7778fJpMJjz76KI4dO4ZvvvkG//M//wMAkEgkAIAnnngCNTU1mDVrFvbv34/Tp0/jm2++wbx582A0GkW5LyJHxpAmoi6RSqVYv3498vLyMHz4cDzzzDN46623rK/7+fnh66+/xoEDB5CUlIQ//OEPWLJkCQBYx6kjIiLw008/wWg04uabb0ZiYiIWLlwIf39/SKX8cUT0a5zdTUR2s3btWsybNw86nQ6enp5il0PkdDgoREQ2s2bNGvTv3x+RkZE4ePAgFi1ahPvuu48BTdRDDGkishmtVoslS5ZAq9UiPDwc9957L15//XWxyyJyWuzuJiIiclCcqUFEROSgGNJEREQOiiFNRETkoBjSREREDoohTURE5KAY0kRERA6KIU1EROSgGNJEREQOiiFNRETkoP4/vIGrSJyl69oAAAAASUVORK5CYII=\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}}],"source":["# checking numerical features distribution\n","\n","\n","plt.figure(figsize=(20,12))\n","\n","# looping over num cols and checking its distribution\n","for col in num_cols:\n","  sns.displot(chronic_df[col],kind='kde')\n","\n","\n"]},{"cell_type":"code","execution_count":87,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Sy24epBysynk","outputId":"61b9d428-d561-42b9-9bcf-a138350ae17f","executionInfo":{"status":"ok","timestamp":1701328064886,"user_tz":-330,"elapsed":15,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["['specific_gravity',\n"," 'albumin',\n"," 'sugar',\n"," 'red_blood_cells',\n"," 'pus_cell',\n"," 'pus_cell_clumps',\n"," 'bacteria',\n"," 'hypertension',\n"," 'diabetes_mellitus',\n"," 'coronary_artery_disease',\n"," 'appetite',\n"," 'peda_edema',\n"," 'aanemia',\n"," 'class']"]},"metadata":{},"execution_count":87}],"source":["# let's see the cols in cat col list\n","cat_cols\n"]},{"cell_type":"code","execution_count":88,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":394},"id":"D9hE8grgp7a4","outputId":"a78df182-545f-4e6e-bd22-3ed52364f91d","executionInfo":{"status":"ok","timestamp":1701328078051,"user_tz":-330,"elapsed":13175,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1600x800 with 15 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checking cat features distribution\n","\n","# create the figure and axes\n","\n","fig, axes = plt.subplots(3, 5, figsize=(16,8))\n","\n","\n","axes = axes.ravel()  # flattening the array makes indexing easier\n","\n","# loop over cat cols and plot countplot\n","for col,ax in zip(cat_cols,axes):\n","  sns.countplot(data=chronic_df,x=col,ax=ax)\n","  plt.xlabel(col)\n","  fig.tight_layout()"]},{"cell_type":"code","execution_count":89,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":562},"id":"d3G8ekdXuTEe","outputId":"f335e912-c4a2-4cd4-9ff7-9f5801cb0249","executionInfo":{"status":"ok","timestamp":1701328081992,"user_tz":-330,"elapsed":3954,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}}},"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-89-76fecc122e73>:3: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n","  sns.heatmap(chronic_df.corr(),annot=True,linewidths=2,linecolor='lightgrey')\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1500x800 with 2 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\n"},"metadata":{}}],"source":["# correlated heatmap of data\n","plt.figure(figsize = (15, 8))\n","sns.heatmap(chronic_df.corr(),annot=True,linewidths=2,linecolor='lightgrey')\n","plt.show()"]},{"cell_type":"code","execution_count":90,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"NbxUMM1fGKCm","outputId":"a62dc150-cc87-48c7-a995-ee1881b0463f","executionInfo":{"status":"ok","timestamp":1701328081999,"user_tz":-330,"elapsed":83,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["red_blood_cells            152\n","red_blood_cell_count       131\n","white_blood_cell_count     106\n","potassium                   88\n","sodium                      87\n","packed_cell_volume          71\n","pus_cell                    65\n","haemoglobin                 52\n","sugar                       49\n","specific_gravity            47\n","albumin                     46\n","blood_glucose_random        44\n","blood_urea                  19\n","serum_creatinine            17\n","blood_pressure              12\n","age                          9\n","bacteria                     4\n","pus_cell_clumps              4\n","hypertension                 2\n","diabetes_mellitus            2\n","coronary_artery_disease      2\n","appetite                     1\n","peda_edema                   1\n","aanemia                      1\n","class                        0\n","dtype: int64"]},"metadata":{},"execution_count":90}],"source":["# let's check count of null values in whole df\n","chronic_df.isnull().sum().sort_values(ascending=False)"]},{"cell_type":"code","execution_count":91,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ufP3qSQ4GKE-","outputId":"7028ac52-fb33-4fb1-e93a-a13a144cea3a","executionInfo":{"status":"ok","timestamp":1701328082000,"user_tz":-330,"elapsed":63,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["red_blood_cell_count      131\n","white_blood_cell_count    106\n","potassium                  88\n","sodium                     87\n","packed_cell_volume         71\n","haemoglobin                52\n","blood_glucose_random       44\n","blood_urea                 19\n","serum_creatinine           17\n","blood_pressure             12\n","age                         9\n","dtype: int64"]},"metadata":{},"execution_count":91}],"source":["# let's check count of null values in num_cols\n","chronic_df[num_cols].isnull().sum().sort_values(ascending=False)"]},{"cell_type":"code","execution_count":92,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"u3AGTIrbGKHr","outputId":"17e02bab-afa2-4f10-e497-92ef5fff3839","executionInfo":{"status":"ok","timestamp":1701328082001,"user_tz":-330,"elapsed":52,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["red_blood_cells            152\n","pus_cell                    65\n","sugar                       49\n","specific_gravity            47\n","albumin                     46\n","pus_cell_clumps              4\n","bacteria                     4\n","hypertension                 2\n","diabetes_mellitus            2\n","coronary_artery_disease      2\n","appetite                     1\n","peda_edema                   1\n","aanemia                      1\n","class                        0\n","dtype: int64"]},"metadata":{},"execution_count":92}],"source":["# let's check count of null values in cat cols\n","chronic_df[cat_cols].isnull().sum().sort_values(ascending=False)\n"]},{"cell_type":"markdown","metadata":{"id":"DCFx44Z4aUKm"},"source":["## **`Missing Value Treatment`**"]},{"cell_type":"code","execution_count":93,"metadata":{"id":"buEJMY-zI3Gd","executionInfo":{"status":"ok","timestamp":1701328082001,"user_tz":-330,"elapsed":44,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}}},"outputs":[],"source":["# filling null values, we will use two methods, random sampling for higher null values and\n","# mean/mode sampling for lower null values\n","\n","# creating func for imputing random values\n","def random_value_imputation(feature):\n","    random_sample =chronic_df[feature].dropna().sample(chronic_df[feature].isna().sum())\n","    random_sample.index =chronic_df[chronic_df[feature].isnull()].index\n","    chronic_df.loc[chronic_df[feature].isnull(), feature] =random_sample\n","\n","# creating func for imputing most common value(modal value)\n","def impute_mode(feature):\n","    mode = chronic_df[feature].mode()[0]\n","    chronic_df[feature] =chronic_df[feature].fillna(mode)"]},{"cell_type":"code","execution_count":94,"metadata":{"id":"Z5kqNycFHZtA","executionInfo":{"status":"ok","timestamp":1701328082004,"user_tz":-330,"elapsed":46,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}}},"outputs":[],"source":["# filling num_cols null values using random sampling method\n","for col in num_cols:\n","  random_value_imputation(col)"]},{"cell_type":"code","execution_count":95,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Z04iW9wbOdnP","outputId":"aed2cf18-9452-49f8-a236-0fcc01ee339a","executionInfo":{"status":"ok","timestamp":1701328082740,"user_tz":-330,"elapsed":782,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["age                       0\n","blood_pressure            0\n","blood_glucose_random      0\n","blood_urea                0\n","serum_creatinine          0\n","sodium                    0\n","potassium                 0\n","haemoglobin               0\n","packed_cell_volume        0\n","white_blood_cell_count    0\n","red_blood_cell_count      0\n","dtype: int64"]},"metadata":{},"execution_count":95}],"source":["# let's check count of null values in num_cols again\n","chronic_df[num_cols].isnull().sum().sort_values(ascending=False)\n"]},{"cell_type":"code","execution_count":96,"metadata":{"id":"tUcLLYzHO84w","executionInfo":{"status":"ok","timestamp":1701328082757,"user_tz":-330,"elapsed":116,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}}},"outputs":[],"source":["# filling \"red_blood_cells\" and \"pus_cell\" using random sampling method and rest of cat_cols using mode imputation\n","random_value_imputation('red_blood_cells')\n","random_value_imputation('pus_cell')"]},{"cell_type":"code","execution_count":97,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"d8yhGGtwPCQg","outputId":"9e6c2ab9-f8da-4e27-a26f-275bec40d6fc","executionInfo":{"status":"ok","timestamp":1701328082758,"user_tz":-330,"elapsed":115,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["sugar                      49\n","specific_gravity           47\n","albumin                    46\n","pus_cell_clumps             4\n","bacteria                    4\n","hypertension                2\n","diabetes_mellitus           2\n","coronary_artery_disease     2\n","appetite                    1\n","peda_edema                  1\n","aanemia                     1\n","red_blood_cells             0\n","pus_cell                    0\n","class                       0\n","dtype: int64"]},"metadata":{},"execution_count":97}],"source":["# let's check count of null values in cat_cols again\n","chronic_df[cat_cols].isnull().sum().sort_values(ascending=False)\n"]},{"cell_type":"code","source":["#filling cat null values with mode treatment\n","for col in cat_cols:\n","  impute_mode(col)"],"metadata":{"id":"SLl38kJ6gzog","executionInfo":{"status":"ok","timestamp":1701328082766,"user_tz":-330,"elapsed":110,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}}},"execution_count":98,"outputs":[]},{"cell_type":"code","source":["#checking the head of dataframe\n","chronic_df.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":256},"id":"UDDp95q4hM5e","executionInfo":{"status":"ok","timestamp":1701328082767,"user_tz":-330,"elapsed":110,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}},"outputId":"e58b3c30-f36a-435e-a740-40a2eb70c6a5"},"execution_count":99,"outputs":[{"output_type":"execute_result","data":{"text/plain":["    age  blood_pressure  specific_gravity  albumin  sugar red_blood_cells  \\\n","0  48.0            80.0             1.020      1.0    0.0          normal   \n","1   7.0            50.0             1.020      4.0    0.0          normal   \n","2  62.0            80.0             1.010      2.0    3.0          normal   \n","3  48.0            70.0             1.005      4.0    0.0          normal   \n","4  51.0            80.0             1.010      2.0    0.0          normal   \n","\n","   pus_cell pus_cell_clumps    bacteria  blood_glucose_random  ...  \\\n","0    normal      notpresent  notpresent                 121.0  ...   \n","1    normal      notpresent  notpresent                 111.0  ...   \n","2    normal      notpresent  notpresent                 423.0  ...   \n","3  abnormal         present  notpresent                 117.0  ...   \n","4    normal      notpresent  notpresent                 106.0  ...   \n","\n","   packed_cell_volume  white_blood_cell_count  red_blood_cell_count  \\\n","0                44.0                  7800.0                   5.2   \n","1                38.0                  6000.0                   5.2   \n","2                31.0                  7500.0                   5.8   \n","3                32.0                  6700.0                   3.9   \n","4                35.0                  7300.0                   4.6   \n","\n","   hypertension  diabetes_mellitus  coronary_artery_disease  appetite  \\\n","0           yes                yes                       no      good   \n","1            no                 no                       no      good   \n","2            no                yes                       no      poor   \n","3           yes                 no                       no      poor   \n","4            no                 no                       no      good   \n","\n","   peda_edema aanemia class  \n","0          no      no     0  \n","1          no      no     0  \n","2          no     yes     0  \n","3         yes     yes     0  \n","4          no      no     0  \n","\n","[5 rows x 25 columns]"],"text/html":["\n","  <div id=\"df-330a0ddd-873b-42d5-9489-92baaa940138\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>age</th>\n","      <th>blood_pressure</th>\n","      <th>specific_gravity</th>\n","      <th>albumin</th>\n","      <th>sugar</th>\n","      <th>red_blood_cells</th>\n","      <th>pus_cell</th>\n","      <th>pus_cell_clumps</th>\n","      <th>bacteria</th>\n","      <th>blood_glucose_random</th>\n","      <th>...</th>\n","      <th>packed_cell_volume</th>\n","      <th>white_blood_cell_count</th>\n","      <th>red_blood_cell_count</th>\n","      <th>hypertension</th>\n","      <th>diabetes_mellitus</th>\n","      <th>coronary_artery_disease</th>\n","      <th>appetite</th>\n","      <th>peda_edema</th>\n","      <th>aanemia</th>\n","      <th>class</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>48.0</td>\n","      <td>80.0</td>\n","      <td>1.020</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>normal</td>\n","      <td>normal</td>\n","      <td>notpresent</td>\n","      <td>notpresent</td>\n","      <td>121.0</td>\n","      <td>...</td>\n","      <td>44.0</td>\n","      <td>7800.0</td>\n","      <td>5.2</td>\n","      <td>yes</td>\n","      <td>yes</td>\n","      <td>no</td>\n","      <td>good</td>\n","      <td>no</td>\n","      <td>no</td>\n","      <td>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>normal</td>\n","      <td>normal</td>\n","      <td>notpresent</td>\n","      <td>notpresent</td>\n","      <td>111.0</td>\n","      <td>...</td>\n","      <td>38.0</td>\n","      <td>6000.0</td>\n","      <td>5.2</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>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>normal</td>\n","      <td>normal</td>\n","      <td>notpresent</td>\n","      <td>notpresent</td>\n","      <td>423.0</td>\n","      <td>...</td>\n","      <td>31.0</td>\n","      <td>7500.0</td>\n","      <td>5.8</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>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>normal</td>\n","      <td>abnormal</td>\n","      <td>present</td>\n","      <td>notpresent</td>\n","      <td>117.0</td>\n","      <td>...</td>\n","      <td>32.0</td>\n","      <td>6700.0</td>\n","      <td>3.9</td>\n","      <td>yes</td>\n","      <td>no</td>\n","      <td>no</td>\n","      <td>poor</td>\n","      <td>yes</td>\n","      <td>yes</td>\n","      <td>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>normal</td>\n","      <td>normal</td>\n","      <td>notpresent</td>\n","      <td>notpresent</td>\n","      <td>106.0</td>\n","      <td>...</td>\n","      <td>35.0</td>\n","      <td>7300.0</td>\n","      <td>4.6</td>\n","      <td>no</td>\n","      <td>no</td>\n","      <td>no</td>\n","      <td>good</td>\n","      <td>no</td>\n","      <td>no</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5 rows × 25 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-330a0ddd-873b-42d5-9489-92baaa940138')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 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categories values\n","\n","peda_edema has 2 categories values\n","\n","aanemia has 2 categories values\n","\n","class has 2 categories values\n","\n"]}],"source":["# check unique values in each cat col by looping over cat cols\n","for col in cat_cols:\n","  print(f\"{col} has {chronic_df[col].nunique()} categories values\\n\")"]},{"cell_type":"code","execution_count":101,"metadata":{"id":"5JFcdCK_O0F9","executionInfo":{"status":"ok","timestamp":1701328082769,"user_tz":-330,"elapsed":88,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}}},"outputs":[],"source":["# using labelencoder and applying on cat cols\n","from sklearn.preprocessing import LabelEncoder\n","\n","le =LabelEncoder()\n","\n","for col in cat_cols[3:]:\n","    chronic_df[col] =le.fit_transform(chronic_df[col])"]},{"cell_type":"code","execution_count":102,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":256},"id":"geuRlFO8QGhw","outputId":"5914bb8c-028a-4154-eba0-8f5ea0d0cbae","executionInfo":{"status":"ok","timestamp":1701328082770,"user_tz":-330,"elapsed":88,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["    age  blood_pressure  specific_gravity  albumin  sugar  red_blood_cells  \\\n","0  48.0            80.0             1.020      1.0    0.0                1   \n","1   7.0            50.0             1.020      4.0    0.0                1   \n","2  62.0            80.0             1.010      2.0    3.0                1   \n","3  48.0            70.0             1.005      4.0    0.0                1   \n","4  51.0            80.0             1.010      2.0    0.0                1   \n","\n","   pus_cell  pus_cell_clumps  bacteria  blood_glucose_random  ...  \\\n","0         1                0         0                 121.0  ...   \n","1         1                0         0                 111.0  ...   \n","2         1                0         0                 423.0  ...   \n","3         0                1         0                 117.0  ...   \n","4         1                0         0                 106.0  ...   \n","\n","   packed_cell_volume  white_blood_cell_count  red_blood_cell_count  \\\n","0                44.0                  7800.0                   5.2   \n","1                38.0                  6000.0                   5.2   \n","2                31.0                  7500.0                   5.8   \n","3                32.0                  6700.0                   3.9   \n","4                35.0                  7300.0                   4.6   \n","\n","   hypertension  diabetes_mellitus  coronary_artery_disease  appetite  \\\n","0             1                  1                        0         0   \n","1             0                  0                        0         0   \n","2             0                  1                        0         1   \n","3             1                  0                        0         1   \n","4             0                  0                        0         0   \n","\n","   peda_edema  aanemia  class  \n","0           0        0      0  \n","1           0        0      0  \n","2           0        1      0  \n","3           1        1      0  \n","4           0        0      0  \n","\n","[5 rows x 25 columns]"],"text/html":["\n","  <div id=\"df-d755401d-06ba-4bab-a2b0-156bb47d035f\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>age</th>\n","      <th>blood_pressure</th>\n","      <th>specific_gravity</th>\n","      <th>albumin</th>\n","      <th>sugar</th>\n","      <th>red_blood_cells</th>\n","      <th>pus_cell</th>\n","      <th>pus_cell_clumps</th>\n","      <th>bacteria</th>\n","      <th>blood_glucose_random</th>\n","      <th>...</th>\n","      <th>packed_cell_volume</th>\n","      <th>white_blood_cell_count</th>\n","      <th>red_blood_cell_count</th>\n","      <th>hypertension</th>\n","      <th>diabetes_mellitus</th>\n","      <th>coronary_artery_disease</th>\n","      <th>appetite</th>\n","      <th>peda_edema</th>\n","      <th>aanemia</th>\n","      <th>class</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>48.0</td>\n","      <td>80.0</td>\n","      <td>1.020</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>121.0</td>\n","      <td>...</td>\n","      <td>44.0</td>\n","      <td>7800.0</td>\n","      <td>5.2</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>7.0</td>\n","      <td>50.0</td>\n","      <td>1.020</td>\n","      <td>4.0</td>\n","      <td>0.0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>111.0</td>\n","      <td>...</td>\n","      <td>38.0</td>\n","      <td>6000.0</td>\n","      <td>5.2</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>62.0</td>\n","      <td>80.0</td>\n","      <td>1.010</td>\n","      <td>2.0</td>\n","      <td>3.0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>423.0</td>\n","      <td>...</td>\n","      <td>31.0</td>\n","      <td>7500.0</td>\n","      <td>5.8</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>48.0</td>\n","      <td>70.0</td>\n","      <td>1.005</td>\n","      <td>4.0</td>\n","      <td>0.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>117.0</td>\n","      <td>...</td>\n","      <td>32.0</td>\n","      <td>6700.0</td>\n","      <td>3.9</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>51.0</td>\n","      <td>80.0</td>\n","      <td>1.010</td>\n","      <td>2.0</td>\n","      <td>0.0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>106.0</td>\n","      <td>...</td>\n","      <td>35.0</td>\n","      <td>7300.0</td>\n","      <td>4.6</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5 rows × 25 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d755401d-06ba-4bab-a2b0-156bb47d035f')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 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train_test_split\n","\n","X_train, X_test, y_train, y_test =train_test_split(X,y,test_size=0.3,random_state=0)\n"]},{"cell_type":"markdown","metadata":{"id":"bE2YlHFcaiJF"},"source":["## **`Model Building`**"]},{"cell_type":"code","execution_count":105,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nioveCSWaa3g","outputId":"fe773de6-8865-46cc-c745-ef235b0eaa60","executionInfo":{"status":"ok","timestamp":1701328083237,"user_tz":-330,"elapsed":548,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Training Accuracy of KNN is 0.7607142857142857\n","Test Accuracy of KNN is 0.6416666666666667 value\n","\n","Confusion Matrix :- \n","[[49 23]\n"," [20 28]] value\n","\n","classification Report :- \n","               precision    recall  f1-score   support\n","\n","           0       0.71      0.68      0.70        72\n","           1       0.55      0.58      0.57        48\n","\n","    accuracy                           0.64       120\n","   macro avg       0.63      0.63      0.63       120\n","weighted avg       0.65      0.64      0.64       120\n","\n"]}],"source":["# import KNeighborsClassifier, accuracy_score, confusion_matrix, classification_report\n","from sklearn.neighbors import KNeighborsClassifier\n","from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n","\n","knn =KNeighborsClassifier()\n","knn.fit(X_train,y_train)\n","\n","# accuracy score, confusion matrix and classification report of knn\n","\n","knn_acc =accuracy_score(y_test,knn.predict(X_test))\n","print(f\"Training Accuracy of KNN is {accuracy_score(y_train,knn.predict(X_train))}\")\n","print(f\"Test Accuracy of KNN is {knn_acc} value\\n\")\n","\n","print(f\"Confusion Matrix :- \\n{confusion_matrix(y_test,knn.predict(X_test))} value\\n\")\n","print(f\"classification Report :- \\n {classification_report(y_test,knn.predict(X_test))}\")"]},{"cell_type":"code","execution_count":106,"metadata":{"id":"a1JZEu8Ia0Bx","executionInfo":{"status":"ok","timestamp":1701328083237,"user_tz":-330,"elapsed":16,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"811b619d-18bb-452c-9eea-a938a20d765a"},"outputs":[{"output_type":"stream","name":"stdout","text":["0.9833333333333333\n","Training Accuracy of dtc is 1.0\n","Testing Accuracy of dtc is 0.9833333333333333\n","confusion Matrix :- [[72  0]\n"," [ 2 46]]\n","Classification report:-               precision    recall  f1-score   support\n","\n","           0       0.97      1.00      0.99        72\n","           1       1.00      0.96      0.98        48\n","\n","    accuracy                           0.98       120\n","   macro avg       0.99      0.98      0.98       120\n","weighted avg       0.98      0.98      0.98       120\n","\n"]}],"source":["# import DecisionTreeClassifer\n","from sklearn.tree import DecisionTreeClassifier\n","\n","dtc =DecisionTreeClassifier()\n","dtc.fit(X_train,y_train)\n","\n","# accuracy score, confusion matrix and classification report of decision tree\n","dtc_acc = accuracy_score(y_test,dtc.predict(X_test))\n","print(dtc_acc)\n","\n","print(f\"Training Accuracy of dtc is {accuracy_score(y_train,dtc.predict(X_train))}\")\n","print(f\"Testing Accuracy of dtc is {accuracy_score(y_test,dtc.predict(X_test))}\")\n","\n","print(f\"confusion Matrix :- {confusion_matrix(y_test,dtc.predict(X_test))}\")\n","\n","print(f\"Classification report:- {classification_report(y_test,dtc.predict(X_test))}\")\n"]},{"cell_type":"code","execution_count":107,"metadata":{"id":"-PgtfvfabCls","executionInfo":{"status":"ok","timestamp":1701328131748,"user_tz":-330,"elapsed":48523,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}},"colab":{"base_uri":"https://localhost:8080/","height":573},"outputId":"9df533eb-a7f9-4694-dd77-2e08b984b7e9"},"outputs":[{"output_type":"stream","name":"stdout","text":["Fitting 5 folds for each of 1200 candidates, totalling 6000 fits\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:378: FitFailedWarning: \n","1200 fits failed out of a total of 6000.\n","The score on these train-test partitions for these parameters will be set to nan.\n","If these failures are not expected, you can try to debug them by setting error_score='raise'.\n","\n","Below are more details about the failures:\n","--------------------------------------------------------------------------------\n","1200 fits failed with the following error:\n","Traceback (most recent call last):\n","  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 686, in _fit_and_score\n","    estimator.fit(X_train, y_train, **fit_params)\n","  File \"/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py\", line 889, in fit\n","    super().fit(\n","  File \"/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py\", line 177, in fit\n","    self._validate_params()\n","  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 600, in _validate_params\n","    validate_parameter_constraints(\n","  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 97, in validate_parameter_constraints\n","    raise InvalidParameterError(\n","sklearn.utils._param_validation.InvalidParameterError: The 'min_samples_split' parameter of DecisionTreeClassifier must be an int in the range [2, inf) or a float in the range (0.0, 1.0]. Got 1 instead.\n","\n","  warnings.warn(some_fits_failed_message, FitFailedWarning)\n","/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_search.py:952: UserWarning: One or more of the test scores are non-finite: [       nan        nan 0.90357143 ... 0.82857143 0.925      0.88928571]\n","  warnings.warn(\n"]},{"output_type":"execute_result","data":{"text/plain":["GridSearchCV(cv=5, estimator=DecisionTreeClassifier(), n_jobs=-1,\n","             param_grid={'criterion': ['gini', 'entropy'],\n","                         'max_depth': [3, 5, 7, 10],\n","                         'max_features': ['auto', 'sqrt', 'log2'],\n","                         'min_samples_leaf': [1, 2, 3, 5, 7],\n","                         'min_samples_split': [1, 2, 3, 5, 7],\n","                         'splitter': ['best', 'random']},\n","             verbose=1)"],"text/html":["<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=DecisionTreeClassifier(), n_jobs=-1,\n","             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n","                         &#x27;max_depth&#x27;: [3, 5, 7, 10],\n","                         &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;, &#x27;log2&#x27;],\n","                         &#x27;min_samples_leaf&#x27;: [1, 2, 3, 5, 7],\n","                         &#x27;min_samples_split&#x27;: [1, 2, 3, 5, 7],\n","                         &#x27;splitter&#x27;: [&#x27;best&#x27;, &#x27;random&#x27;]},\n","             verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=DecisionTreeClassifier(), n_jobs=-1,\n","             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n","                         &#x27;max_depth&#x27;: [3, 5, 7, 10],\n","                         &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;, &#x27;log2&#x27;],\n","                         &#x27;min_samples_leaf&#x27;: [1, 2, 3, 5, 7],\n","                         &#x27;min_samples_split&#x27;: [1, 2, 3, 5, 7],\n","                         &#x27;splitter&#x27;: [&#x27;best&#x27;, &#x27;random&#x27;]},\n","             verbose=1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"]},"metadata":{},"execution_count":107}],"source":["# hyper parameter tuning of decision tree , import GridSearchCV\n","\n","from sklearn.model_selection import GridSearchCV\n","\n","grid_param= {\n","    'criterion' : ['gini', 'entropy'],\n","    'max_depth' : [3, 5, 7, 10],\n","    'splitter' : ['best', 'random'],\n","    'min_samples_leaf' : [1, 2, 3, 5, 7],\n","    'min_samples_split' : [1, 2, 3, 5, 7],\n","    'max_features' : ['auto', 'sqrt', 'log2']\n","}\n","\n","\n","# Apply gridsearchcv with cv = 5, n_jobs = -1, verbose = 1\n","grid_search_dtc = GridSearchCV(dtc,grid_param,cv=5,n_jobs=-1,verbose=1)\n","grid_search_dtc.fit(X_train,y_train)"]},{"cell_type":"code","execution_count":108,"metadata":{"id":"0BMgFN20bEjo","executionInfo":{"status":"ok","timestamp":1701328131749,"user_tz":-330,"elapsed":178,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"d456eb54-0553-4994-e017-33b862ffb10c"},"outputs":[{"output_type":"stream","name":"stdout","text":["{'criterion': 'entropy', 'max_depth': 5, 'max_features': 'log2', 'min_samples_leaf': 2, 'min_samples_split': 3, 'splitter': 'best'}\n","0.9857142857142858\n"]}],"source":["# print best parameters and best score in grid search dtc\n","print(grid_search_dtc.best_params_)\n","print(grid_search_dtc.best_score_)"]},{"cell_type":"code","execution_count":109,"metadata":{"id":"HrRF1_U3bkjx","executionInfo":{"status":"ok","timestamp":1701328131751,"user_tz":-330,"elapsed":169,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"8aa8fd55-2cc4-4abf-aa6b-ddc408a3045b"},"outputs":[{"output_type":"stream","name":"stdout","text":["Training Accuracy of Decision Tree classifier is 0.9785714285714285\n","Test Accuracy of Decision Tree classifier is 0.975 \n","\n","Confusion Matrix : \n","[[70  2]\n"," [ 1 47]] \n","\n","Classification report:-               precision    recall  f1-score   support\n","\n","           0       0.99      0.97      0.98        72\n","           1       0.96      0.98      0.97        48\n","\n","    accuracy                           0.97       120\n","   macro avg       0.97      0.98      0.97       120\n","weighted avg       0.98      0.97      0.98       120\n","\n"]}],"source":["# storing best estimator\n","dtc =grid_search_dtc.best_estimator_\n","\n","# accuracy score, confusion matrix and classification report of decision tree\n","\n","dtc_acc =accuracy_score(y_test,dtc.predict(X_test))\n","print(f\"Training Accuracy of Decision Tree classifier is {accuracy_score(y_train,dtc.predict(X_train))}\")\n","print(f\"Test Accuracy of Decision Tree classifier is {dtc_acc} \\n\")\n","\n","print(f\"Confusion Matrix : \\n{confusion_matrix(y_test,dtc.predict(X_test))} \\n\")\n","print(f\"Classification report:- {classification_report(y_test,dtc.predict(X_test))}\")\n"]},{"cell_type":"code","execution_count":110,"metadata":{"id":"mBNtVkhmbt9V","executionInfo":{"status":"ok","timestamp":1701328131752,"user_tz":-330,"elapsed":163,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"be2514b8-152f-42f3-f692-87a1f1d7b967"},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n","  warn(\n"]},{"output_type":"stream","name":"stdout","text":["Training Accuracy of rfc is 1.0\n","Test Accuracy of rfc is 0.9916666666666667 \n","\n","Confusion Matrix :- \n"," [[72  0]\n"," [ 1 47]]\n","\n","classification report :- \n","               precision    recall  f1-score   support\n","\n","           0       0.99      1.00      0.99        72\n","           1       1.00      0.98      0.99        48\n","\n","    accuracy                           0.99       120\n","   macro avg       0.99      0.99      0.99       120\n","weighted avg       0.99      0.99      0.99       120\n","\n"]}],"source":["# import RandomForestClassifier\n","\n","from sklearn.ensemble import RandomForestClassifier\n","\n","rd_clf =RandomForestClassifier(criterion=\"entropy\",max_depth=11,max_features='auto',min_samples_split=3,n_estimators=130)\n","rd_clf.fit(X_train,y_train)\n","\n","\n","# accuracy score, confusion matrix and classification report of random forest\n","\n","rf_acc = accuracy_score(y_test,rd_clf.predict(X_test))\n","print(f\"Training Accuracy of rfc is {accuracy_score(y_train,rd_clf.predict(X_train))}\")\n","print(f\"Test Accuracy of rfc is {rf_acc} \\n\")\n","\n","print(f\"Confusion Matrix :- \\n {confusion_matrix(y_test,rd_clf.predict(X_test))}\\n\")\n","print(f\"classification report :- \\n {classification_report(y_test,rd_clf.predict(X_test))}\")\n"]},{"cell_type":"code","execution_count":111,"metadata":{"id":"LJv9sgk5fWZZ","executionInfo":{"status":"ok","timestamp":1701328131753,"user_tz":-330,"elapsed":155,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"53652d40-5f8a-41d2-ec13-769d49c601ef"},"outputs":[{"output_type":"stream","name":"stdout","text":["Training Accuracy of adc is 1.0\n","Test Accuracy of adc is 1.0 \n","\n","confusion Matrix :- \n"," [[72  0]\n"," [ 0 48]]\n","classification Report :- \n","               precision    recall  f1-score   support\n","\n","           0       1.00      1.00      1.00        72\n","           1       1.00      1.00      1.00        48\n","\n","    accuracy                           1.00       120\n","   macro avg       1.00      1.00      1.00       120\n","weighted avg       1.00      1.00      1.00       120\n","\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n","  warnings.warn(\n"]}],"source":["# import AdaBoostClassifier\n","from sklearn.ensemble import AdaBoostClassifier\n","\n","ada = AdaBoostClassifier(base_estimator=dtc)\n","ada.fit(X_train,y_train)\n","\n","# accuracy score, confusion matrix and classification report of ada boost\n","\n","ada_acc =accuracy_score(y_test,ada.predict(X_test))\n","\n","print(f\"Training Accuracy of adc is {accuracy_score(y_train,ada.predict(X_train))}\")\n","print(f\"Test Accuracy of adc is {ada_acc} \\n\")\n","\n","print(f\"confusion Matrix :- \\n {confusion_matrix(y_test,ada.predict(X_test))}\")\n","print(f\"classification Report :- \\n {classification_report(y_test,ada.predict(X_test))}\")\n"]},{"cell_type":"code","execution_count":112,"metadata":{"id":"Bj8a3Atvfb9k","executionInfo":{"status":"ok","timestamp":1701328131754,"user_tz":-330,"elapsed":150,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"0a768b2c-f0bd-4521-9c2f-6edfe6925cf5"},"outputs":[{"output_type":"stream","name":"stdout","text":["Training Accuracy of gb is 1.0\n","Test Accuracy of adc is 0.9916666666666667 \n","\n","confusion Matrix :- \n"," [[72  0]\n"," [ 1 47]]\n","classification Report :- \n","               precision    recall  f1-score   support\n","\n","           0       0.99      1.00      0.99        72\n","           1       1.00      0.98      0.99        48\n","\n","    accuracy                           0.99       120\n","   macro avg       0.99      0.99      0.99       120\n","weighted avg       0.99      0.99      0.99       120\n","\n"]}],"source":["# import GradientBoostingClassifier\n","from sklearn.ensemble import GradientBoostingClassifier\n","\n","gb =GradientBoostingClassifier()\n","gb.fit(X_train,y_train)\n","\n","# accuracy score, confusion matrix and classification report of gradient boosting classifier\n","\n","gb_acc =accuracy_score(y_test,gb.predict(X_test))\n","\n","print(f\"Training Accuracy of gb is {accuracy_score(y_train,gb.predict(X_train))}\")\n","print(f\"Test Accuracy of adc is {gb_acc} \\n\")\n","\n","print(f\"confusion Matrix :- \\n {confusion_matrix(y_test,gb.predict(X_test))}\")\n","print(f\"classification Report :- \\n {classification_report(y_test,gb.predict(X_test))}\")\n"]},{"cell_type":"code","execution_count":120,"metadata":{"id":"XQjU5X-zfhFC","executionInfo":{"status":"ok","timestamp":1701328586028,"user_tz":-330,"elapsed":29,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"b194bc69-ed5d-4540-fe62-d129db463962"},"outputs":[{"output_type":"stream","name":"stdout","text":["Training Accuracy of gb is 1.0\n","Test Accuracy of adc is 0.9916666666666667 \n","\n","confusion Matrix :- \n"," [[72  0]\n"," [ 1 47]]\n","classification Report :- \n","               precision    recall  f1-score   support\n","\n","           0       0.99      1.00      0.99        72\n","           1       1.00      0.98      0.99        48\n","\n","    accuracy                           0.99       120\n","   macro avg       0.99      0.99      0.99       120\n","weighted avg       0.99      0.99      0.99       120\n","\n"]}],"source":["# using max_depth = 4, subsample = 0.90, max_features = 0.75, n_estimators = 200\n","sgb =GradientBoostingClassifier(max_depth=4,subsample=0.90,max_features=0.75,n_estimators=200)\n","sgb.fit(X_train,y_train)\n","\n","# accuracy score, confusion matrix and classification report of stochastic gradient boosting classifier\n","sgb_acc =accuracy_score(y_test,sgb.predict(X_test))\n","\n","print(f\"Training Accuracy of gb is {accuracy_score(y_train,gb.predict(X_train))}\")\n","print(f\"Test Accuracy of adc is {gb_acc} \\n\")\n","\n","print(f\"confusion Matrix :- \\n {confusion_matrix(y_test,gb.predict(X_test))}\")\n","print(f\"classification Report :- \\n {classification_report(y_test,gb.predict(X_test))}\")\n"]},{"cell_type":"code","execution_count":114,"metadata":{"id":"KO7z2GwQfm-k","executionInfo":{"status":"ok","timestamp":1701328131755,"user_tz":-330,"elapsed":132,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"fc8a2336-7f01-4687-801f-4696d045716c"},"outputs":[{"output_type":"stream","name":"stdout","text":["Training Accuracy of xgboost is 1.0\n","Test Accuracy of xgboost is 1.0 \n","\n","confusion Matrix :- \n"," [[72  0]\n"," [ 0 48]]\n","classification Report :- \n","               precision    recall  f1-score   support\n","\n","           0       1.00      1.00      1.00        72\n","           1       1.00      1.00      1.00        48\n","\n","    accuracy                           1.00       120\n","   macro avg       1.00      1.00      1.00       120\n","weighted avg       1.00      1.00      1.00       120\n","\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/xgboost/core.py:160: UserWarning: [07:08:44] WARNING: /workspace/src/learner.cc:742: \n","Parameters: { \"objectve\" } are not used.\n","\n","  warnings.warn(smsg, UserWarning)\n"]}],"source":["# import XGBClassifier\n","from xgboost import XGBClassifier\n","\n","xgb =XGBClassifier(objectve='binary:logistic',learning_rate=0.5,max_depth=5,n_estimators=150)\n","xgb.fit(X_train,y_train)\n","\n","# accuracy score, confusion matrix and classification report of xgboost\n","\n","xgb_acc =accuracy_score(y_test,xgb.predict(X_test))\n","\n","print(f\"Training Accuracy of xgboost is {accuracy_score(y_train,xgb.predict(X_train))}\")\n","print(f\"Test Accuracy of xgboost is {xgb_acc} \\n\")\n","\n","print(f\"confusion Matrix :- \\n {confusion_matrix(y_test,xgb.predict(X_test))}\")\n","print(f\"classification Report :- \\n {classification_report(y_test,xgb.predict(X_test))}\")\n"]},{"cell_type":"code","execution_count":115,"metadata":{"id":"clmWYLjlf6aL","executionInfo":{"status":"ok","timestamp":1701328131756,"user_tz":-330,"elapsed":120,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"6b3c4f8d-f9d3-4391-e5f8-019c0cf37939"},"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: catboost in /usr/local/lib/python3.10/dist-packages (1.2.2)\n","Requirement already satisfied: graphviz in /usr/local/lib/python3.10/dist-packages (from catboost) (0.20.1)\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from catboost) (3.7.1)\n","Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.10/dist-packages (from catboost) (1.23.5)\n","Requirement already satisfied: pandas>=0.24 in /usr/local/lib/python3.10/dist-packages (from catboost) (1.5.3)\n","Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from catboost) (1.11.3)\n","Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from catboost) (5.15.0)\n","Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from catboost) (1.16.0)\n","Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24->catboost) (2.8.2)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24->catboost) (2023.3.post1)\n","Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (1.2.0)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (0.12.1)\n","Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (4.44.3)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (1.4.5)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (23.2)\n","Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (9.4.0)\n","Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (3.1.1)\n","Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly->catboost) (8.2.3)\n"]}],"source":["pip install catboost"]},{"cell_type":"code","execution_count":116,"metadata":{"id":"ZJRNARFNfy8j","executionInfo":{"status":"ok","timestamp":1701328131756,"user_tz":-330,"elapsed":103,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"f87ef16d-695c-4eb6-dcd6-52778d39837a"},"outputs":[{"output_type":"stream","name":"stdout","text":["Learning rate set to 0.408198\n","0:\tlearn: 0.3332160\ttotal: 2.49ms\tremaining: 22.4ms\n","1:\tlearn: 0.1537928\ttotal: 4.3ms\tremaining: 17.2ms\n","2:\tlearn: 0.1077036\ttotal: 6.18ms\tremaining: 14.4ms\n","3:\tlearn: 0.0706281\ttotal: 8.17ms\tremaining: 12.3ms\n","4:\tlearn: 0.0513567\ttotal: 10.2ms\tremaining: 10.2ms\n","5:\tlearn: 0.0406723\ttotal: 12.2ms\tremaining: 8.14ms\n","6:\tlearn: 0.0310861\ttotal: 14.2ms\tremaining: 6.07ms\n","7:\tlearn: 0.0268624\ttotal: 16.2ms\tremaining: 4.04ms\n","8:\tlearn: 0.0221479\ttotal: 18.1ms\tremaining: 2.01ms\n","9:\tlearn: 0.0207930\ttotal: 20ms\tremaining: 0us\n","Training Accuracy of catboost is 1.0\n","Test Accuracy of catboost is 0.9916666666666667 \n","\n","confusion Matrix :- \n"," [[72  0]\n"," [ 1 47]]\n","classification Report :- \n","               precision    recall  f1-score   support\n","\n","           0       0.99      1.00      0.99        72\n","           1       1.00      0.98      0.99        48\n","\n","    accuracy                           0.99       120\n","   macro avg       0.99      0.99      0.99       120\n","weighted avg       0.99      0.99      0.99       120\n","\n"]}],"source":["# import CatBoostClassifier\n","from catboost import CatBoostClassifier\n","\n","cat =CatBoostClassifier(iterations=10)\n","cat.fit(X_train,y_train)\n","\n","# accuracy score, confusion matrix and classification report of cat boost\n","\n","cat_acc = accuracy_score(y_test,cat.predict(X_test))\n","\n","print(f\"Training Accuracy of catboost is {accuracy_score(y_train,cat.predict(X_train))}\")\n","print(f\"Test Accuracy of catboost is {cat_acc} \\n\")\n","\n","print(f\"confusion Matrix :- \\n {confusion_matrix(y_test,cat.predict(X_test))}\")\n","print(f\"classification Report :- \\n {classification_report(y_test,cat.predict(X_test))}\")\n"]},{"cell_type":"code","execution_count":117,"metadata":{"id":"0QBEvlIzgHlp","executionInfo":{"status":"ok","timestamp":1701328131756,"user_tz":-330,"elapsed":94,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"c6bd9073-21e1-42ff-ae5e-2e183755a905"},"outputs":[{"output_type":"stream","name":"stdout","text":["Training Accuracy of xgboost is 1.0\n","Test Accuracy of xgboost is 0.9916666666666667 \n","\n","confusion Matrix :- \n"," [[72  0]\n"," [ 1 47]]\n","classification Report :- \n","               precision    recall  f1-score   support\n","\n","           0       0.99      1.00      0.99        72\n","           1       1.00      0.98      0.99        48\n","\n","    accuracy                           0.99       120\n","   macro avg       0.99      0.99      0.99       120\n","weighted avg       0.99      0.99      0.99       120\n","\n"]}],"source":["# import ExtraTreesClassifier\n","\n","from sklearn.ensemble import ExtraTreesClassifier\n","\n","etc =ExtraTreesClassifier()\n","etc.fit(X_train,y_train)\n","\n","# accuracy score, confusion matrix and classification report of extra trees classifier\n","\n","etc_acc =accuracy_score(y_test,etc.predict(X_test))\n","\n","print(f\"Training Accuracy of xgboost is {accuracy_score(y_train,etc.predict(X_train))}\")\n","print(f\"Test Accuracy of xgboost is {etc_acc} \\n\")\n","\n","print(f\"confusion Matrix :- \\n {confusion_matrix(y_test,etc.predict(X_test))}\")\n","print(f\"classification Report :- \\n {classification_report(y_test,etc.predict(X_test))}\")\n","\n"]},{"cell_type":"code","execution_count":118,"metadata":{"id":"UmjtTA_-gNSp","executionInfo":{"status":"ok","timestamp":1701328131757,"user_tz":-330,"elapsed":88,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"cc00e56e-ebb4-41b7-df0c-a93a1243ab0e"},"outputs":[{"output_type":"stream","name":"stdout","text":["[LightGBM] [Info] Number of positive: 102, number of negative: 178\n","[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000176 seconds.\n","You can set `force_row_wise=true` to remove the overhead.\n","And if memory is not enough, you can set `force_col_wise=true`.\n","[LightGBM] [Info] Total Bins 515\n","[LightGBM] [Info] Number of data points in the train set: 280, number of used features: 23\n","[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.364286 -> initscore=-0.556811\n","[LightGBM] [Info] Start training from score -0.556811\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n","Training Accuracy of lightbgm is 1.0\n","Test Accuracy of lightbgm is 1.0 \n","\n","confusion Matrix :- \n"," [[72  0]\n"," [ 0 48]] \n","\n","classification Report :- \n","               precision    recall  f1-score   support\n","\n","           0       1.00      1.00      1.00        72\n","           1       1.00      1.00      1.00        48\n","\n","    accuracy                           1.00       120\n","   macro avg       1.00      1.00      1.00       120\n","weighted avg       1.00      1.00      1.00       120\n","\n"]}],"source":["# import LGBMClassifier\n","from lightgbm import LGBMClassifier\n","\n","lgbm =LGBMClassifier(learning_rate=1)\n","lgbm.fit(X_train,y_train)\n","\n","# accuracy score, confusion matrix and classification report of lgbm classifier\n","\n","lgbm_acc =accuracy_score(y_test,lgbm.predict(X_test))\n","\n","print(f\"Training Accuracy of lightbgm is {accuracy_score(y_train,lgbm.predict(X_train))}\")\n","print(f\"Test Accuracy of lightbgm is {lgbm_acc} \\n\")\n","\n","print(f\"confusion Matrix :- \\n {confusion_matrix(y_test,lgbm.predict(X_test))} \\n\")\n","print(f\"classification Report :- \\n {classification_report(y_test,lgbm.predict(X_test))}\")\n"]},{"cell_type":"code","execution_count":123,"metadata":{"id":"4wNh2OlrgS4d","executionInfo":{"status":"ok","timestamp":1701328970434,"user_tz":-330,"elapsed":441,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}},"colab":{"base_uri":"https://localhost:8080/","height":332},"outputId":"2ded2d53-5a56-490c-d6e5-c87f13431390"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                          Model     Score\n","3          Ada Boost Classifier  1.000000\n","6                       XgBoost  1.000000\n","2      Random Forest Classifier  0.991667\n","4  Gradient Boosting Classifier  0.991667\n","5  Stochastic Gradient Boosting  0.991667\n","7                     Cat Boost  0.991667\n","8         Extra Tree Classifier  0.991667\n","1      Decision Tree Classifier  0.975000\n","0                           KNN  0.641667"],"text/html":["\n","  <div id=\"df-025f8b86-83c3-4d45-b50c-70cd1f113b2b\" class=\"colab-df-container\">\n","    <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>Model</th>\n","      <th>Score</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>3</th>\n","      <td>Ada Boost Classifier</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>XgBoost</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Random Forest Classifier</td>\n","      <td>0.991667</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Gradient Boosting Classifier</td>\n","      <td>0.991667</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>Stochastic Gradient Boosting</td>\n","      <td>0.991667</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>Cat Boost</td>\n","      <td>0.991667</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>Extra Tree Classifier</td>\n","      <td>0.991667</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Decision Tree Classifier</td>\n","      <td>0.975000</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>KNN</td>\n","      <td>0.641667</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-025f8b86-83c3-4d45-b50c-70cd1f113b2b')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" 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Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","<div id=\"df-c71b7df5-68db-49aa-b652-15f2578394e8\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c71b7df5-68db-49aa-b652-15f2578394e8')\"\n","            title=\"Suggest charts\"\n","            style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","  </button>\n","\n","<style>\n","  .colab-df-quickchart {\n","      --bg-color: #E8F0FE;\n","      --fill-color: #1967D2;\n","      --hover-bg-color: #E2EBFA;\n","      --hover-fill-color: #174EA6;\n","      --disabled-fill-color: #AAA;\n","      --disabled-bg-color: #DDD;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","      --bg-color: #3B4455;\n","      --fill-color: #D2E3FC;\n","      --hover-bg-color: #434B5C;\n","      --hover-fill-color: #FFFFFF;\n","      --disabled-bg-color: #3B4455;\n","      --disabled-fill-color: #666;\n","  }\n","\n","  .colab-df-quickchart {\n","    background-color: var(--bg-color);\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: var(--fill-color);\n","    height: 32px;\n","    padding: 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: var(--hover-bg-color);\n","    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: var(--button-hover-fill-color);\n","  }\n","\n","  .colab-df-quickchart-complete:disabled,\n","  .colab-df-quickchart-complete:disabled:hover {\n","    background-color: var(--disabled-bg-color);\n","    fill: var(--disabled-fill-color);\n","    box-shadow: none;\n","  }\n","\n","  .colab-df-spinner {\n","    border: 2px solid var(--fill-color);\n","    border-color: transparent;\n","    border-bottom-color: var(--fill-color);\n","    animation:\n","      spin 1s steps(1) infinite;\n","  }\n","\n","  @keyframes spin {\n","    0% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","      border-left-color: var(--fill-color);\n","    }\n","    20% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    30% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","      border-right-color: var(--fill-color);\n","    }\n","    40% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    60% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","    }\n","    80% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-bottom-color: var(--fill-color);\n","    }\n","    90% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","    }\n","  }\n","</style>\n","\n","  <script>\n","    async function quickchart(key) {\n","      const quickchartButtonEl =\n","        document.querySelector('#' + key + ' button');\n","      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n","      quickchartButtonEl.classList.add('colab-df-spinner');\n","      try {\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      } catch (error) {\n","        console.error('Error during call to suggestCharts:', error);\n","      }\n","      quickchartButtonEl.classList.remove('colab-df-spinner');\n","      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n","    }\n","    (() => {\n","      let quickchartButtonEl =\n","        document.querySelector('#df-c71b7df5-68db-49aa-b652-15f2578394e8 button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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NAYAK","userId":"05998776148464585121"}},"colab":{"base_uri":"https://localhost:8080/","height":332},"outputId":"b60ddfde-6ea9-4b32-ee1c-5e1cddcc427a"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                          Model     Score\n","3          Ada Boost Classifier  1.000000\n","6                       XgBoost  1.000000\n","2      Random Forest Classifier  0.991667\n","4  Gradient Boosting Classifier  0.991667\n","5  Stochastic Gradient Boosting  0.991667\n","7                     Cat Boost  0.991667\n","8         Extra Tree Classifier  0.991667\n","1      Decision Tree Classifier  0.975000\n","0                           KNN  0.641667"],"text/html":["\n","  <div id=\"df-add63512-9af3-4377-ae92-ba54cc335e60\" class=\"colab-df-container\">\n","    <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","    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    <th>8</th>\n","      <td>Extra Tree Classifier</td>\n","      <td>0.991667</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Decision Tree Classifier</td>\n","      <td>0.975000</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>KNN</td>\n","      <td>0.641667</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-add63512-9af3-4377-ae92-ba54cc335e60')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 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Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","<div id=\"df-c4711ebe-7c78-467c-8ed0-ebfb74680ad5\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c4711ebe-7c78-467c-8ed0-ebfb74680ad5')\"\n","            title=\"Suggest charts\"\n","            style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","  </button>\n","\n","<style>\n","  .colab-df-quickchart {\n","      --bg-color: #E8F0FE;\n","      --fill-color: #1967D2;\n","      --hover-bg-color: #E2EBFA;\n","      --hover-fill-color: #174EA6;\n","      --disabled-fill-color: #AAA;\n","      --disabled-bg-color: #DDD;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","      --bg-color: #3B4455;\n","      --fill-color: #D2E3FC;\n","      --hover-bg-color: #434B5C;\n","      --hover-fill-color: #FFFFFF;\n","      --disabled-bg-color: #3B4455;\n","      --disabled-fill-color: #666;\n","  }\n","\n","  .colab-df-quickchart {\n","    background-color: var(--bg-color);\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: var(--fill-color);\n","    height: 32px;\n","    padding: 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: var(--hover-bg-color);\n","    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: var(--button-hover-fill-color);\n","  }\n","\n","  .colab-df-quickchart-complete:disabled,\n","  .colab-df-quickchart-complete:disabled:hover {\n","    background-color: var(--disabled-bg-color);\n","    fill: var(--disabled-fill-color);\n","    box-shadow: none;\n","  }\n","\n","  .colab-df-spinner {\n","    border: 2px solid var(--fill-color);\n","    border-color: transparent;\n","    border-bottom-color: var(--fill-color);\n","    animation:\n","      spin 1s steps(1) infinite;\n","  }\n","\n","  @keyframes spin {\n","    0% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","      border-left-color: var(--fill-color);\n","    }\n","    20% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    30% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","      border-right-color: var(--fill-color);\n","    }\n","    40% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    60% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","    }\n","    80% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-bottom-color: var(--fill-color);\n","    }\n","    90% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","    }\n","  }\n","</style>\n","\n","  <script>\n","    async function quickchart(key) {\n","      const quickchartButtonEl =\n","        document.querySelector('#' + key + ' button');\n","      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n","      quickchartButtonEl.classList.add('colab-df-spinner');\n","      try {\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      } catch (error) {\n","        console.error('Error during call to suggestCharts:', error);\n","      }\n","      quickchartButtonEl.classList.remove('colab-df-spinner');\n","      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n","    }\n","    (() => {\n","      let quickchartButtonEl =\n","        document.querySelector('#df-c4711ebe-7c78-467c-8ed0-ebfb74680ad5 button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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