heartDiseaseML.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as mplot\n",
    "from matplotlib.colors import ListedColormap\n",
    "import os \n",
    "\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer\n",
    "from sklearn.datasets import load_files \n",
    "from sklearn.model_selection import train_test_split, KFold\n",
    "from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay, accuracy_score, f1_score, precision_score, recall_score,  precision_recall_fscore_support\n",
    "from sklearn.metrics import roc_auc_score, roc_curve, auc\n",
    " \n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, HistGradientBoostingClassifier, StackingClassifier, VotingClassifier, BaggingClassifier, ExtraTreesClassifier\n",
    "from sklearn.linear_model import LogisticRegression, SGDClassifier , RidgeClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis, QuadraticDiscriminantAnalysis  \n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.neighbors import KNeighborsClassifier as KNN\n",
    "from sklearn.svm import SVC\n",
    "from sklearn import svm\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.gaussian_process import GaussianProcessClassifier\n",
    "from sklearn.gaussian_process.kernels import RBF\n",
    "from sklearn.tree import DecisionTreeClassifier \n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler \n",
    "\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "import xgboost as xgb\n",
    "from catboost import CatBoostClassifier\n",
    "import lightgbm as lgb  \n",
    "\n",
    "\n",
    "import shap\n",
    "import lime\n",
    "from lime import lime_tabular\n",
    "import random\n",
    "\n",
    "from sklearn.tree import export_graphviz\n",
    "\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of fileData: (37079, 40)\n",
      "Column Headings: Index(['Gender', 'Age', 'X60-sec-pulse', 'Systolic', 'Diastolic', 'Weight',\n",
      "       'Height', 'Body-Mass-Index', 'White-Blood-Cells', 'Lymphocyte',\n",
      "       'Monocyte', 'Eosinophils', 'Basophils', 'Red-Blood-Cells', 'Hemoglobin',\n",
      "       'Platelet-count', 'Segmented-Neutrophils', 'Hematocrit', 'Albumin',\n",
      "       'ALP', 'AST', 'ALT', 'Cholesterol', 'Creatinine', 'Glucose', 'Iron',\n",
      "       'LDH', 'Phosphorus', 'Bilirubin', 'Protein', 'Uric.Acid',\n",
      "       'Triglycerides', 'Total-Cholesterol', 'HDL', 'Glycohemoglobin',\n",
      "       'Moderate-work', 'Diabetes', 'Blood-Rel-Diabetes', 'Blood-Rel-Stroke',\n",
      "       'CoronaryHeartDisease'],\n",
      "      dtype='object')\n",
      "Number of Records: 37079\n",
      "\n",
      "Number of Missing Values: 0\n",
      "Number of duplicate records removed: 0\n",
      "Shape of fileData: (37079, 40)\n",
      "Shape of fileData End: (37079, 40)\n",
      "\n",
      "\n",
      "columns of x:: 39 \n",
      "\n",
      " and features of X: Index(['Gender', 'Age', 'X60-sec-pulse', 'Systolic', 'Diastolic', 'Weight',\n",
      "       'Height', 'Body-Mass-Index', 'White-Blood-Cells', 'Lymphocyte',\n",
      "       'Monocyte', 'Eosinophils', 'Basophils', 'Red-Blood-Cells', 'Hemoglobin',\n",
      "       'Platelet-count', 'Segmented-Neutrophils', 'Hematocrit', 'Albumin',\n",
      "       'ALP', 'AST', 'ALT', 'Cholesterol', 'Creatinine', 'Glucose', 'Iron',\n",
      "       'LDH', 'Phosphorus', 'Bilirubin', 'Protein', 'Uric.Acid',\n",
      "       'Triglycerides', 'Total-Cholesterol', 'HDL', 'Glycohemoglobin',\n",
      "       'Moderate-work', 'Diabetes', 'Blood-Rel-Diabetes', 'Blood-Rel-Stroke'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "dataSetIndex = 6\n",
    "needToMakePictureOfTrees = 0\n",
    "''' ---------------------------------------------------------- '''\n",
    "dataSetFilePath = \"\"\n",
    "dataSetName = \"\"\n",
    "dataSetResultDirectory = \"./\"\n",
    "\n",
    "if(dataSetIndex == 0):\n",
    "    dataSetFilePath = \"./heartDisease/0_statLog_dataSet.csv\"\n",
    "    dataSetName = \"SateLog_DataSet\"\n",
    "elif (dataSetIndex == 1):\n",
    "    dataSetFilePath = \"./heartDisease/1_heart_statlog_cleveland_hungary_final.csv\"\n",
    "    dataSetName = \"ALL_StateLog_CleveLand_Hungary\"\n",
    "elif (dataSetIndex == 2):\n",
    "    dataSetFilePath = \"./heartDisease/2_cleveland.csv\"\n",
    "    dataSetName = \"Cleveland\"\n",
    "elif (dataSetIndex == 3):\n",
    "    dataSetFilePath = \"./heartDisease/3_framingham.csv\"\n",
    "    dataSetName = \"framingham\"\n",
    "elif (dataSetIndex == 4):\n",
    "    dataSetFilePath = \"./heartDisease/4_CardiacPrediction.xlsx\"\n",
    "    dataSetName = \"CardiacPrediction\"\n",
    "elif (dataSetIndex == 5):\n",
    "    dataSetFilePath = \"./heartDisease/5_CardiacPredictionLessDimensions.xlsx\"\n",
    "    dataSetName = \"CardiacPrediction\"\n",
    "elif (dataSetIndex == 6):\n",
    "    dataSetFilePath = \"./heartDisease/6_CardiacPredictionFewDimensions.xlsx\"\n",
    "    dataSetName = \"CardiacPrediction\"\n",
    "else:\n",
    "    dataSetFilePath = \"\"\n",
    "    dataSetName = \"\"\n",
    "\n",
    "if(dataSetIndex==4 or dataSetIndex==5 or dataSetIndex==6):\n",
    "    #fileData = pd.read_excel(dataSetFilePath, sheet_name='CoroHeartDis')\n",
    "    fileData = pd.read_excel(dataSetFilePath)\n",
    "else:\n",
    "    fileData = pd.read_csv(dataSetFilePath)\n",
    "\n",
    "print(\"Shape of fileData: {}\".format(fileData.shape))\n",
    "print(\"Column Headings: {}\".format(fileData.__dataframe__().column_names()))\n",
    "print(\"Number of Records: {}\".format(fileData.__dataframe__().num_rows()))\n",
    "\n",
    "\n",
    "missingValues = fileData.isnull().any().sum()\n",
    "print(f\"\\nNumber of Missing Values: {missingValues}\")\n",
    "\n",
    "num_rows_before = fileData.shape[0]\n",
    "# Remove duplicate records based on all columns\n",
    "fileData.drop_duplicates(inplace=True)\n",
    "# Check the number of rows after removing duplicates\n",
    "num_rows_after = fileData.shape[0]\n",
    "# Print the number of duplicate records removed\n",
    "num_duplicates_removed = num_rows_before - num_rows_after\n",
    "print(f\"Number of duplicate records removed: {num_duplicates_removed}\")\n",
    " \n",
    " # Preprocess Steps from the ChatGPT\n",
    "# 1. Handling Missing Values:\n",
    "fileData = fileData.dropna()\n",
    "print(\"Shape of fileData: {}\".format(fileData.shape))              \n",
    "#fileData.replace({'?': np.nan}).dropna().astype(float)\n",
    "#fileData = fileData.fillna(0) \n",
    "\n",
    "fileData = fileData.fillna(0) \n",
    "\n",
    "print(\"Shape of fileData End: {}\".format(fileData.shape))\n",
    "\n",
    "\n",
    "\n",
    "finalResultTable = [ ['Index', 'Method', 'Accuracy %','Recall %','Precision %','F1 Score','AUC'], ] \n",
    "'''\n",
    "dataSetResultDirectory += (\"DatasetResults_\" + dataSetName)\n",
    "dataSetResultDirectory += \"/\"\n",
    "if not os.path.isdir(dataSetResultDirectory):\n",
    "    os.makedirs(dataSetResultDirectory)\n",
    "\n",
    "dataSetName += \" {}\".format(fileData.shape)\n",
    "'''\n",
    "\n",
    "X = fileData.drop(fileData.__dataframe__().column_names()[-1], axis=1)  # Features\n",
    "Y = fileData[fileData.__dataframe__().column_names()[-1]]  # Labels\n",
    "\n",
    "columns = fileData.__dataframe__().column_names() \n",
    "totalRecords = (fileData.__dataframe__().num_rows())\n",
    "\n",
    "print(\"\\n\")\n",
    "print(\"columns of x:: {} \\n\\n and features of X: {}\".format(len(X.columns), X.columns))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 -> Gender : Unique:[1 2], ValueCount:2    19032\n",
      "1    18047\n",
      "Name: Gender, dtype: int64\n",
      "1 -> Age : Unique:[77 49 37 70 81 38 85 23 53 42 62 46 33 61 39 63 27 25 30 60 52 22 34 64\n",
      " 76 59 56 41 43 40 29 28 55 21 31 71 65 44 74 50 79 57 69 45 58 75 54 82\n",
      " 35 72 24 26 66 48 32 36 20 67 68 47 51 80 73 78 84 83], ValueCount:80    1436\n",
      "60     758\n",
      "40     698\n",
      "29     693\n",
      "23     692\n",
      "      ... \n",
      "79     246\n",
      "81     168\n",
      "82     123\n",
      "84      98\n",
      "83      94\n",
      "Name: Age, Length: 66, dtype: int64\n",
      "2 -> X60-sec-pulse : Unique:[ 68  66  64 102  72  82  74 118  78  60  76  54  86  84  80  50  70  62\n",
      "  56  92  90  58  96  48  88  94  52  46 110  44  98 114  42 104  40 108\n",
      " 100  38 106  36 134 116 112 130 126 122  34 128  32 144 120 124 220 166\n",
      " 224 136 172 160 132 140], ValueCount:70     2588\n",
      "68     2527\n",
      "72     2438\n",
      "74     2410\n",
      "66     2308\n",
      "64     2288\n",
      "76     2155\n",
      "62     2038\n",
      "78     1940\n",
      "60     1838\n",
      "80     1789\n",
      "82     1620\n",
      "58     1326\n",
      "84     1269\n",
      "86     1074\n",
      "56     1064\n",
      "88      914\n",
      "90      755\n",
      "54      747\n",
      "92      569\n",
      "52      487\n",
      "94      457\n",
      "96      417\n",
      "50      322\n",
      "98      317\n",
      "100     259\n",
      "48      196\n",
      "102     196\n",
      "104     160\n",
      "46      103\n",
      "106      89\n",
      "108      80\n",
      "44       62\n",
      "110      55\n",
      "112      46\n",
      "114      36\n",
      "42       25\n",
      "116      19\n",
      "40       17\n",
      "118      17\n",
      "120      15\n",
      "122       7\n",
      "128       7\n",
      "124       4\n",
      "38        4\n",
      "126       4\n",
      "36        4\n",
      "136       2\n",
      "140       2\n",
      "32        2\n",
      "134       2\n",
      "144       1\n",
      "34        1\n",
      "220       1\n",
      "166       1\n",
      "224       1\n",
      "172       1\n",
      "160       1\n",
      "132       1\n",
      "130       1\n",
      "Name: X60-sec-pulse, dtype: int64\n",
      "3 -> Systolic : Unique:[ 98 122 174 130 136 109 139 103 114 119 124 107 115 120 128 113 118 156\n",
      " 104 121 110 101  99 125 172 112 134 117  95 169 111  92 132 154 173 108\n",
      " 129 201 180 143 144 137 149 181 133 141 147 100  94 102 127 131 162 177\n",
      " 170 168  97 123 140 116 105 150 155 152 135 106 145  91 126 196 165 142\n",
      " 176 190 160 157 159 146 186 158 138 191 175 166  88  80 211  83  96 189\n",
      "  90 164 167 148 153 163 151 184  85 194  89  87 161 195 207 179  93 171\n",
      " 192 178 200 187 182 197  76 193 202  84  73 199 183 217 212 188 214 198\n",
      " 210 226  81  86 218 206 203  72 204 185  82 223 248   0 208  79  78 230\n",
      " 216 256 270  74 222 224 232  66 220 238], ValueCount:114    1805\n",
      "116    1708\n",
      "124    1592\n",
      "118    1570\n",
      "120    1437\n",
      "       ... \n",
      "79        1\n",
      "248       1\n",
      "223       1\n",
      "207       1\n",
      "238       1\n",
      "Name: Systolic, Length: 154, dtype: int64\n",
      "4 -> Diastolic : Unique:[ 56  83  99  66  61  69  60  71  85  77  76  65  67  82  75  72  68  80\n",
      "  73  97  78  52   0  48  64  59  57  40  70 104  94  91  74  88  93  62\n",
      "  63  58  87  55  89  81  53 100  79  90  84  49  92  42  44  50 122  38\n",
      "  39  30  86  98  24  95  10  54  96 105  51 110  46  34 102 121  45  47\n",
      " 103  35  41  26 114 108  43  18  36  29 101  37  32 109  28 106 107 112\n",
      " 116 111  20  22  25 132  17  13  31 115 120 119  27  33 113  16 118 124\n",
      "  12 128   8 126   4 130], ValueCount:74     2266\n",
      "72     2236\n",
      "70     2175\n",
      "68     2107\n",
      "76     2045\n",
      "       ... \n",
      "128       1\n",
      "8         1\n",
      "126       1\n",
      "4         1\n",
      "130       1\n",
      "Name: Diastolic, Length: 114, dtype: int64\n",
      "5 -> Weight : Unique:[ 75.4  92.5  99.2 ... 173.  178.3 146.7], ValueCount:80.90     96\n",
      "67.70     95\n",
      "77.50     94\n",
      "73.80     93\n",
      "71.90     93\n",
      "          ..\n",
      "95.34      1\n",
      "49.54      1\n",
      "93.64      1\n",
      "52.74      1\n",
      "146.70     1\n",
      "Name: Weight, Length: 1499, dtype: int64\n",
      "6 -> Height : Unique:[174.   178.3  180.   157.7  166.2  174.9  144.2  158.9  164.2  166.7\n",
      " 174.8  162.2  166.1  155.6  176.3  172.6  180.4  162.7  164.1  169.2\n",
      " 176.7  177.2  167.8  165.   159.6  159.9  172.4  157.2  181.7  171.\n",
      " 176.   174.1  169.5  175.1  179.7  154.9  164.5  171.1  181.4  172.8\n",
      " 168.6  155.2  170.9  150.5  193.5  162.3  158.2  162.8  161.3  173.7\n",
      " 159.   166.6  159.2  167.3  162.4  172.7  172.   150.4  174.7  162.1\n",
      " 156.7  163.8  175.7  171.6  163.3  157.3  154.8  153.6  164.6  165.3\n",
      " 175.4  158.8  168.3  170.1  183.4  180.1  169.3  146.9  158.   167.4\n",
      " 167.1  170.6  171.2  162.9  160.4  160.7  165.8  164.7  183.2  148.4\n",
      " 182.7  174.3  169.   146.7  160.9  158.5  158.3  177.7  165.9  164.\n",
      " 168.2  169.1  159.3  145.1  149.4  170.4  169.7  177.9  164.3  190.\n",
      " 162.   175.   168.5  190.3  161.5  144.5  149.3  165.5  164.8  175.3\n",
      " 182.9  182.4  173.3  175.5  173.8  168.   167.5  160.   176.2  177.6\n",
      " 160.8  184.1  175.9  180.9  168.9  177.4  179.4  182.5  175.8  172.3\n",
      " 172.9  166.   160.6  171.3  152.8  176.4  160.1  137.1  178.1  171.9\n",
      " 170.2  165.2  173.4  142.7  182.3  170.5  170.   161.8  170.3  181.3\n",
      " 179.8  166.3  179.1  153.8  153.3  145.9  160.5  178.7  181.8  166.4\n",
      " 163.9  158.1  173.1  159.8  161.6  166.8  161.9  172.2  171.8  166.9\n",
      " 161.1  167.   153.1  179.2  183.   157.8  174.4  158.6  183.8  148.3\n",
      " 155.9  152.7  155.8  155.   148.7  156.4  155.7  156.3  173.9  161.4\n",
      " 176.5  167.6  156.9  177.3  154.1  149.6  187.5  178.5  146.3  190.9\n",
      " 152.   144.4  165.4  169.4  156.5  184.   158.4  177.5  179.9  164.4\n",
      " 151.5  165.7  159.1  173.5  169.8  160.2  157.6  159.4  153.   154.7\n",
      " 168.1  168.8  153.9  171.7  178.8  147.9  165.6  155.3  153.5  162.5\n",
      " 168.7  180.5  180.6  174.5  167.2  146.5  150.1  183.6  169.6  183.3\n",
      " 189.1  157.   149.9  192.2  151.6  181.5  191.1  169.9  136.5  144.3\n",
      " 170.7  176.1  188.4  157.5  181.9  154.6  152.2  163.   161.7  187.3\n",
      " 149.2  184.9  172.1  180.8  186.3  179.5  178.4  165.1  157.4  153.4\n",
      " 174.2  166.5  180.2  154.   149.5  148.5  179.6  187.4  159.5  184.5\n",
      " 175.2  154.5  187.   151.4  164.9  163.4  186.6  179.   181.1  163.1\n",
      " 145.8  156.8  163.7  151.3  178.2  156.1  186.1  175.6  182.6  161.2\n",
      " 176.6  152.4  153.7  163.6  171.5  157.1  186.8  163.5  150.8  143.\n",
      " 180.3  173.2  184.6  149.1  157.9  161.   155.4  194.   173.   150.9\n",
      " 177.1  174.6  171.4  149.   163.2  153.2  145.   156.6  154.4  146.4\n",
      " 185.5  154.2  173.6  189.   189.5  144.6  154.3  150.7  185.2  177.8\n",
      " 185.7  185.3  178.   158.7  147.6  181.6  152.1  156.   150.   167.7\n",
      " 172.5  160.3  183.9  162.6  177.   167.9  181.2  184.8  178.6  155.1\n",
      " 152.9  188.2  140.4  196.8  159.7  151.1  168.4  185.9  151.7  141.1\n",
      " 150.6  184.2  136.9  146.8  149.8  191.5  138.4  180.7  186.4  147.5\n",
      " 150.2  182.   179.3  145.4  155.5  141.3  143.8  152.3  188.7  170.8\n",
      " 138.9  147.7  156.2  186.7  185.8  130.4  186.2  185.   152.6  187.9\n",
      " 189.8  151.2  183.1  149.7  184.7  182.8  152.5  151.9  182.1  187.2\n",
      " 184.4  189.2  190.8  185.1  178.9  146.6  147.1  196.   150.3  187.8\n",
      " 181.   185.6  176.8  186.   140.7  139.2  151.   176.9  145.3  191.6\n",
      " 187.6  184.3  195.3  194.3  186.5  193.4  191.7  132.5  185.4  147.4\n",
      " 136.7  199.4  147.8  187.7  148.9  183.7  183.5  148.6  140.   189.4\n",
      " 151.8  142.6  188.6  139.4  141.9  195.6  192.1  187.1  142.2  148.\n",
      " 148.1  197.5  192.5  182.2  148.2  147.2  190.5  133.9  142.   146.1\n",
      " 199.8  198.   147.3  188.3  190.2  200.1  186.9  192.4  189.9  143.2\n",
      " 194.4  144.   188.9  190.7  188.5  189.7  144.9  147.   193.3  198.6\n",
      " 188.1  142.8  145.6  198.4  188.8  198.7  191.   143.1  148.8  191.2\n",
      " 137.2  140.1  139.9  195.7  188.   189.6  195.1  146.2  193.2  196.6\n",
      " 192.9  135.7  194.9  193.7  137.4  145.5  141.8  194.8  197.7  141.\n",
      " 192.7  191.8  142.9  196.1  190.6  190.1  141.7  196.2  191.3  190.4\n",
      " 194.5  163.2  152.7  152.2  157.2  161.2  156.7  160.7  161.7  155.2\n",
      " 159.2  162.2  158.2  145.7  193.   154.2  159.7  163.7  157.7  142.4\n",
      " 147.2  155.7  156.2  145.2  151.2  146.2  201.2  162.7  150.7  153.2\n",
      " 195.5  191.4  197.1  154.7  148.7  144.8  195.9  158.7  151.7  149.2\n",
      " 191.9  148.2  160.2  144.1  194.6  149.7  143.3  142.3  203.2  147.7\n",
      " 153.7  144.7  143.7  197.6  150.2  139.   192.   198.3  192.6  194.2\n",
      " 140.9  189.3  192.8  195.8  141.5  133.7  143.6  146.7  142.1  200.7\n",
      " 193.9  167.23 140.3  140.2  204.1  196.5  196.7  143.5  143.2  146.\n",
      " 138.6  137.9  195.2  144.2  143.4  197.3  199.2  195.4  141.2  197.4\n",
      " 139.6  194.7  195.   203.8  140.8  139.8  202.7  142.5  199.6  143.9\n",
      " 135.4  201.7  193.8  199.9  197.2  139.7  135.3  204.5  139.3  200.4\n",
      " 190.54 143.7  145.7  170.92 197.   141.6  179.66 174.17 145.2  170.55\n",
      " 202.6  136.3  193.1  192.3  172.13 129.7  201.   144.7  193.6 ], ValueCount:165.5    168\n",
      "163.4    154\n",
      "167.9    153\n",
      "166.9    151\n",
      "163.0    150\n",
      "        ... \n",
      "142.7      1\n",
      "137.1      1\n",
      "140.4      1\n",
      "196.8      1\n",
      "193.6      1\n",
      "Name: Height, Length: 669, dtype: int64\n",
      "7 -> Body-Mass-Index : Unique:[24.9  29.1  30.62 ... 15.1  62.7  55.1 ], ValueCount:23.90    126\n",
      "25.90    124\n",
      "26.20    121\n",
      "25.80    121\n",
      "28.00    119\n",
      "        ... \n",
      "51.42      1\n",
      "16.92      1\n",
      "13.36      1\n",
      "41.98      1\n",
      "55.10      1\n",
      "Name: Body-Mass-Index, Length: 3366, dtype: int64\n",
      "8 -> White-Blood-Cells : Unique:[  7.6    5.9   10.2   11.6    9.1    7.4    5.6    9.4    7.8    9.5\n",
      "   8.6    6.8    6.5    4.7    4.9    4.4    7.3    6.6    6.4    7.1\n",
      "   8.     6.3    6.1    5.3    8.3    6.9    8.5    3.8    5.     9.3\n",
      "   5.1    9.9    8.2   10.6    6.     7.2    3.2    5.4    7.9    6.2\n",
      "   4.6    5.7    8.1    7.7   10.7    8.8    8.9    9.7    7.     4.3\n",
      "   9.2    5.2   10.1    5.5    7.5    8.4   13.5    9.8   15.6   11.3\n",
      "   3.4    8.7    9.6   11.9    4.2    6.7   10.9   10.    11.4    5.8\n",
      "   4.     4.1   17.3   10.8   11.8   17.     3.5   10.3   13.1   15.1\n",
      "  10.4   12.     9.     4.5    2.8    3.9   13.4   14.6   11.1   15.4\n",
      "  11.    12.3   12.5   12.9    3.7   14.7   12.2    4.8   14.9    3.6\n",
      "  13.     3.    12.8   20.7   11.7   11.2   11.5    3.3   13.2   13.3\n",
      "  12.7   12.6   17.2   13.8   18.2   10.5   14.2    2.9    2.4   12.1\n",
      "  15.2   14.1    2.5   12.4    2.7   15.9   14.5   14.    13.7   15.5\n",
      "  13.9   16.9   13.6    3.1   22.4   20.1   16.6    2.3   16.    19.5\n",
      "  14.8   15.    14.4   16.3   36.6   17.5   22.3   16.8   19.8   16.5\n",
      "   5.1    9.2    4.1    4.6    8.2    8.7   28.2   40.9   10.2   15.3\n",
      "   9.7   16.7   17.8   14.3   16.4   31.5    2.6   18.8   99.99  15.7\n",
      "  18.    20.8   15.8   16.2   62.8   25.    20.    16.9   52.2   16.1\n",
      "  19.7   55.9   19.2   42.5   18.4   18.5   19.3   22.8   39.2   18.1\n",
      "  43.9   17.1   30.3    1.5    1.6   83.2    1.7   22.    37.4   21.3\n",
      "  99.9   26.3   54.7    2.3   34.    55.7   17.9   19.9   21.4   31.4\n",
      "  18.4   17.7   18.7   18.9   26.1   16.4   21.    17.4  117.2    1.4\n",
      "  23.    42.8   19.    19.1 ], ValueCount:6.4     1112\n",
      "6.1     1081\n",
      "6.9     1064\n",
      "6.6     1058\n",
      "5.9     1048\n",
      "        ... \n",
      "22.8       1\n",
      "18.1       1\n",
      "43.9       1\n",
      "17.1       1\n",
      "19.1       1\n",
      "Name: White-Blood-Cells, Length: 224, dtype: int64\n",
      "9 -> Lymphocyte : Unique:[21.1 37.8 23.7 13.1 29.8 20.8 28.  28.1 39.1 33.   4.5 26.2 30.3 32.7\n",
      " 37.2 43.3 37.4 14.4 25.  36.4 28.3 24.4 38.1 41.2 43.9 33.6 29.6 25.1\n",
      " 36.8 27.  31.1 32.3 26.8 28.6 18.  23.5 27.5 27.3 21.2 32.1 27.2 19.1\n",
      " 22.4 22.9 21.8 23.4 38.8 29.4 32.2 25.3 24.8 26.5 26.9 23.  33.8 41.1\n",
      " 29.1 26.7 23.8 33.2 25.4 27.1 42.7 22.  40.3 30.8 31.2 32.5 37.  37.6\n",
      " 25.5 34.4 34.3 19.8 45.3 28.9 23.3 17.1 22.7 36.3 28.8 19.  28.4 40.\n",
      " 46.1 45.6 25.7 29.5 23.1 39.6 21.7 26.6 29.9 25.6 34.8 36.5 49.6 28.5\n",
      " 41.8 24.2 13.8 52.8 31.8 31.4 31.9 19.5 37.3 27.6 31.7 43.  58.3 33.7\n",
      " 14.5 44.3 45.5 35.1 27.9 16.  35.6 36.7 28.2 31.3 19.2 23.9 21.6 37.1\n",
      " 19.3 21.9 43.4 18.7 35.2 51.9 36.  24.9 20.7 25.2 24.5 30.1 17.3 31.\n",
      " 39.5 20.5 48.8 20.9 26.  39.9 16.8 22.5 33.5 15.4 21.  47.  35.4 30.5\n",
      " 21.5 38.5 44.9 14.6 27.8 15.9 18.3 35.8 42.5 40.5 32.9 34.7 43.8 15.2\n",
      " 52.2 25.9 22.2 20.3 29.2 23.2 45.2 26.1 39.8 33.9 37.5 34.2 16.2 17.\n",
      " 35.  31.6 48.6 45.8 44.7 41.4 24.1 32.6 40.9 42.3 36.1 24.  35.5 38.9\n",
      " 18.5 30.7 15.1 20.6 33.3 22.6 51.1 31.5 32.4 26.4 40.2 34.6 16.9 27.7\n",
      " 22.3 24.6 26.3 41.7 14.3 17.7 20.2 30.9 17.9 30.  25.8 23.6 17.2 36.9\n",
      " 17.5 53.4 29.  19.9 32.8 59.6 36.2 13.4 28.7 15.6 35.9 35.3 18.4 34.5\n",
      " 40.6 33.4 44.2 35.7 41.3 22.8 15.3 38.2  8.1 41.6 42.2 46.2 57.  43.6\n",
      " 39.7 19.4 20.  44.6 30.4 40.4 47.2 42.1 24.7 29.7 45.1 29.3 32.  33.1\n",
      " 34.9 43.5 16.4 18.9 44.4 37.9 22.1 12.3 49.3 39.  38.7 44.5  9.  20.4\n",
      " 13.7 18.8 64.6 13.9 13.  21.3 10.6 11.7 38.3 24.3 18.2 10.4 41.  42.8\n",
      " 16.3 41.9 51.  34.  34.1 55.  51.6 41.5 19.6 47.6 14.8 54.5 45.7 27.4\n",
      " 39.4 12.2  7.3 39.2 40.7 12.  16.6 14.2 16.7 38.6 13.6 77.9 17.8 30.2\n",
      " 40.8  7.5 57.4 49.7 16.5 18.1 12.4 12.9 42.6 37.7 52.1 19.7 11.8  5.7\n",
      " 15.5 38.4 51.8 45.  44.8 18.6 36.6 14.  15.  14.9 51.3 46.  49.5 30.6\n",
      " 47.3 38.  46.4 11.9 17.4 12.7 11.6 13.5 53.8 39.3 14.1 43.1 50.2 20.1\n",
      " 49.9 46.6 42.9 44.1 21.4 12.8 40.1 42.  49.4  9.9 13.2 11.5 56.9 45.4\n",
      " 48.5  9.4 17.6 42.4 52.  46.7 62.2 57.3 15.8 63.  16.1 56.5 48.9 67.4\n",
      " 45.9 12.5 46.5 48.1 63.5 10.9 56.3 50.4 50.9 43.2 47.1  7.9 46.3 49.\n",
      " 47.9 43.7 44.   7.7 52.7 13.3 15.7 14.7  9.8 48.4 50.3 12.1 11.2 11.\n",
      " 53.1 46.8  9.7  7.6 47.8  5.4 50.6 50.7 55.5 46.9  5.8 53.6 52.4 48.\n",
      " 53.7 48.2 47.7  6.7 10.8 47.4 50.  10.2 11.4 49.2 54.  59.5 54.3  7.1\n",
      " 56.8 56.7 61.4  9.1 65.4 54.2 55.2 58.5 54.1 56.1 55.1 64.  49.1 78.9\n",
      " 51.7 54.8 10.  56.4 50.8  4.6  8.9 59.1 58.4 11.1 47.5 51.4 53.  48.7\n",
      " 58.8 50.1  6.3  5.3 54.4 33.3 51.5 32.3 19.9 37.8 18.9 38.3 39.3 33.8\n",
      " 35.8 38.8 86.6 17.4 32.8 37.3 78.4 19.4 58.7 35.3 34.8  9.3 40.3 39.8\n",
      "  6.9 18.4 52.5 34.3 36.3 54.9 36.8  9.7 16.9 52.9 53.9  8.4  9.6 20.4\n",
      " 16.4 10.2 40.8 61.1 10.7 87.3  5.2 10.1 89.7 73.8  7.8 51.2 62.7 54.7\n",
      " 55.3  7.2 52.3 68.6 49.8 59.8 57.1  9.2 55.7 17.9 88.9 69.6 76.4 12.6\n",
      " 50.5 11.3 10.3 10.5 54.6 57.8 48.3 53.3 58.1 53.5 56.2 79.9 71.1  9.5\n",
      " 55.8 64.3 55.9  7.4 56.  58.6  4.9 77.4 62.6 60.7 77.1 57.9 68.4 75.1\n",
      "  8.7 52.6 57.7 57.5  8.5  6.8 64.8 71.3 60.2 85.5 60.9 59.4 55.4  8.\n",
      " 59.3 63.1  6.1 53.2 61.   6.5 69.9 69.2  3.5 58.  57.2 64.7 70.9 67.1\n",
      "  2.9 76.3 59.  59.9  3.6 64.6 81.8 56.6 60.3 71.8 81.6  2.7 55.6 70.\n",
      "  8.2 61.3 66.8 88.   9.2  3.4 61.2 63.3 68.7 57.6 59.2  5.  60.8 76.7\n",
      "  8.8 60.4 58.2 94.5  3.7  8.3  6.  65.8  6.6 82.5 62.4  8.2], ValueCount:28.8    304\n",
      "29.0    291\n",
      "29.5    278\n",
      "27.0    276\n",
      "26.5    269\n",
      "       ... \n",
      "60.7      1\n",
      "77.4      1\n",
      "71.1      1\n",
      "5.4       1\n",
      "8.2       1\n",
      "Name: Lymphocyte, Length: 642, dtype: int64\n",
      "10 -> Monocyte : Unique:[ 7.1  6.2  9.   3.8  5.6  6.3  9.1  7.4  8.3  5.7  6.5  7.9  5.8 11.2\n",
      "  7.2  7.7  3.4  6.4 10.7  9.6  7.3  4.8  9.3  8.  10.6  8.6  7.8 12.9\n",
      "  8.4  6.1 10.   9.4  2.9  8.1 11.   5.   6.9  6.8  5.9 15.1  9.7  4.6\n",
      "  4.7 12.2  8.2 11.4  5.5 13.5  6.6  9.8  3.7 10.2 11.9  9.5  9.9 15.6\n",
      " 10.9  7.6  8.5 10.1  5.1 13.2 11.8  6.7  7.5 10.5 10.4 12.1 10.8 12.5\n",
      "  8.8 12.4 11.5  4.1 11.3 12.3  7.   8.9 11.6  2.8  5.3  4.9 11.7 31.3\n",
      " 11.1 14.9  8.7  5.2  6.   4.3 10.3  3.9  9.2  2.5 12.  13.9  3.3 16.\n",
      "  5.4 12.8 12.7 12.6 13.4 13.3 17.2  4.2 13.1  4.4 13.  13.6 13.7 16.5\n",
      "  4.5  3.2  3.1 16.3  1.8 14.1 19.3 16.2 14.5 15.4 14.  14.4 16.1 15.5\n",
      " 15.  16.4 16.6 18.5 14.8  3.5 15.2 17.3  4.  14.7 14.6 24.6  3.6 21.9\n",
      " 17.   2.  18.3 14.2 22.8  2.6 17.7 14.3  1.  18.2  1.2  3.   2.7 23.8\n",
      " 24.9  2.2 13.8  2.4 33.3  2.3 15.3 19.1  1.6 18.  20.6 18.4 15.8  2.1\n",
      " 16.7  4.6  8.2  4.1 10.2  9.2  5.1  8.7  2.3  9.7  1.9 20.4  1.7 33.4\n",
      " 18.7 16.9 19.7  1.5 20.  19.  18.8 16.8 17.4 19.5 17.1 39.8  0.6 16.4\n",
      " 22.4 44.5 26.3 26.2 19.4 39.1  1.3 17.6  0.7  0.8 25.9 24.4 20.2 32.\n",
      " 22.3 20.7 15.9 19.2 28.4 18.6 38.8  1.4 66.9  1.1 17.5 16.9 18.9 19.4\n",
      " 17.4 38.9 23.3 29.3 18.1 26.7 20.8], ValueCount:6.9     1032\n",
      "8.1      953\n",
      "7.1      874\n",
      "7.6      855\n",
      "8.4      794\n",
      "        ... \n",
      "26.3       1\n",
      "26.2       1\n",
      "19.4       1\n",
      "39.1       1\n",
      "20.8       1\n",
      "Name: Monocyte, Length: 231, dtype: int64\n",
      "11 -> Eosinophils : Unique:[ 4.4  3.4  3.2  0.4  1.7  1.   0.6  1.5  4.9  1.6  2.5  1.3  4.7  3.1\n",
      "  2.4  2.   3.3  2.3  1.4  0.9  1.2  2.7  3.   5.2  2.2  0.8  3.5  4.3\n",
      "  4.1  7.5  6.6  2.8  1.1  6.3 14.7  3.6  1.9  8.   2.9  1.8  5.7  3.8\n",
      "  5.4  4.   3.7  8.1  5.3  4.6  7.7  6.  10.4  6.9  2.6  5.1  7.9  2.1\n",
      "  5.6  0.3  9.2  0.7  6.5  6.1  7.8  3.9  6.4  5.9  8.4  4.2 12.2  0.2\n",
      "  4.8  7.2 10.9  9.7  7.   7.6 37.3  5.8  5.5  9.4 10.5  4.5 12.   7.1\n",
      " 10.2  6.8  9.   7.3  6.7  0.5  5.  13.3  9.3 11.2  8.7 12.3 16.4 11.\n",
      " 14.4  8.2 16.7 12.5  6.2  7.4  9.8  8.6  8.8  0.1 10.1 19.6 16.8 10.3\n",
      " 12.9 11.3  9.5 22.8 22.4 11.5  9.6 17.5 10.7  8.5 30.3 11.6 15.6 11.9\n",
      "  8.9 14.2 16.1 13.1 10.6 12.1 11.4 28.2 24.3 13.9 13.7 26.7 12.4 11.1\n",
      " 17.3 15.   8.3  9.9 15.8 17.8 16.9  9.1 15.2 17.4 15.5 18.3 18.5  0.\n",
      " 34.4 11.8 14.5  2.3  4.1  5.1 14.3  4.6  8.2 10.  15.1 13.   8.7 27.7\n",
      " 10.8 14.8 12.7 13.8  9.2 13.4 10.2 11.7 13.6 14.  18.  16.5 19.8 12.6\n",
      " 32.9 27.5  9.7 27.3 29.2 20.8 19.7 26.3 17.4 21.3 15.3 14.9 17.  17.7\n",
      " 22.  25.4 24.  12.8 14.6 16.  15.9 19.  15.4 19.5 23.1 16.4 25.2 16.2\n",
      " 20.9 22.9 18.1 15.7 34.1 13.5 18.4 18.2 29.  16.6 25.3 22.7 28.3 18.4\n",
      " 36.6 32.  14.1 17.2 18.9 20. ], ValueCount:1.7     1647\n",
      "2.0     1551\n",
      "1.3     1448\n",
      "2.8     1197\n",
      "0.9     1164\n",
      "        ... \n",
      "19.7       1\n",
      "26.3       1\n",
      "17.4       1\n",
      "17.0       1\n",
      "20.0       1\n",
      "Name: Eosinophils, Length: 230, dtype: int64\n",
      "12 -> Basophils : Unique:[ 0.5  0.4  0.6  0.2  0.7  0.8  0.9  1.2  0.   0.3  1.1  1.   1.3  1.4\n",
      "  1.6  2.   0.1  1.7  2.2  1.8  1.5  3.   5.   3.1  2.5  2.9  1.9  2.8\n",
      "  3.8  2.6  3.2  2.3  5.5  3.5  5.3  3.3  4.5  6.8  4.9  2.7  2.1  2.4\n",
      "  4.1  8.5  5.2  3.4  2.3  3.6  3.9  5.6  4.   4.4  4.8 13.9  6.4  4.3\n",
      "  4.1  4.6  6.1  9.9  4.2  5.7  5.1  6.3  3.7  6.9  6.7  8.4  4.7  5.9\n",
      "  7.5  5.8], ValueCount:0.6     6551\n",
      "0.4     4825\n",
      "0.8     3745\n",
      "0.9     3498\n",
      "0.5     2922\n",
      "        ... \n",
      "9.9        1\n",
      "8.5        1\n",
      "13.9       1\n",
      "5.6        1\n",
      "5.8        1\n",
      "Name: Basophils, Length: 72, dtype: int64\n",
      "13 -> Red-Blood-Cells : Unique:[4.73 5.13 5.76 5.53 5.32 4.14 3.57 4.53 4.34 4.31 4.81 5.16 4.32 4.88\n",
      " 5.39 4.63 4.59 5.52 5.07 5.2  5.24 4.68 4.62 3.84 4.47 4.94 5.33 5.61\n",
      " 5.35 4.74 4.36 5.47 4.49 3.9  4.89 5.14 4.8  3.67 5.09 3.8  5.28 5.56\n",
      " 4.97 4.71 3.97 3.86 5.27 3.51 4.51 4.03 4.79 4.16 4.82 5.1  4.57 4.45\n",
      " 4.27 4.98 3.95 4.39 5.31 4.84 4.05 4.22 4.23 4.75 5.4  6.04 4.7  5.08\n",
      " 4.42 4.67 4.46 4.56 3.82 5.3  3.89 4.87 3.45 5.5  5.64 4.55 4.78 4.48\n",
      " 5.41 5.15 4.4  4.95 4.15 5.54 4.92 3.63 4.52 4.2  4.76 5.02 5.17 5.49\n",
      " 4.61 3.99 3.47 4.11 4.26 5.86 4.24 5.22 4.99 5.65 4.37 4.04 4.91 4.41\n",
      " 4.66 5.01 5.26 3.87 4.5  6.05 4.93 3.7  5.89 5.34 4.06 4.35 5.36 3.59\n",
      " 5.45 4.1  4.18 5.11 4.07 4.77 4.33 5.18 4.38 5.46 4.86 3.93 3.76 4.65\n",
      " 3.91 5.44 4.69 5.37 4.64 5.03 4.54 5.06 5.69 5.04 4.85 4.01 4.72 4.6\n",
      " 4.08 4.58 3.64 4.3  4.25 5.57 3.32 4.   4.21 5.05 4.96 5.77 5.78 5.43\n",
      " 3.96 5.29 5.19 3.41 3.29 4.44 3.66 3.68 4.28 5.23 4.12 6.26 4.43 4.19\n",
      " 5.51 4.83 5.21 4.9  5.81 3.72 3.98 3.88 5.79 5.42 4.09 5.82 5.63 5.12\n",
      " 3.85 5.87 5.25 2.9  3.43 3.65 4.02 3.53 5.59 5.95 5.38 5.71 3.94 5.55\n",
      " 5.48 5.8  4.17 3.81 3.73 4.29 2.92 3.74 5.75 5.74 5.6  5.72 5.66 6.07\n",
      " 3.38 3.62 5.67 5.98 3.77 4.13 3.6  3.61 5.93 5.88 5.7  3.71 5.   3.42\n",
      " 5.68 6.01 3.36 3.92 3.58 3.49 3.78 5.9  2.97 6.12 3.46 3.79 5.73 5.97\n",
      " 5.84 6.18 3.4  3.44 3.75 3.83 6.31 6.78 5.83 6.28 3.37 2.85 3.25 6.32\n",
      " 5.62 3.69 6.72 5.85 6.15 3.16 6.16 6.39 5.58 3.17 3.24 5.96 5.91 3.55\n",
      " 3.26 6.13 3.48 6.52 3.22 6.03 6.46 3.13 5.94 3.21 6.38 3.56 6.14 3.03\n",
      " 6.7  7.3  6.27 3.23 6.24 6.19 3.39 7.15 7.11 6.25 3.07 2.26 3.35 3.54\n",
      " 6.36 7.57 2.99 6.23 2.72 2.88 4.27 5.92 5.1  4.35 4.77 4.85 4.39 4.6\n",
      " 4.89 4.81 4.56 4.02 4.52 2.61 5.02 4.1  4.06 4.64 6.2  4.14 5.06 3.33\n",
      " 4.31 6.48 6.43 7.03 6.83 3.28 6.6  2.95 6.02 3.08 5.99 6.55 3.2  3.3\n",
      " 6.06 3.14 3.5  6.11 3.05 2.44 2.91 3.34 3.11 6.47 7.02 3.01 7.08 6.\n",
      " 6.08 2.83 3.09 6.91 3.12 6.64 6.53 2.56 6.44 6.73 6.22 6.21 6.97 6.67\n",
      " 3.19 6.45 3.18 2.38 2.84 3.52 2.81 2.4  6.59 2.86 6.41 2.78 3.31 2.63\n",
      " 2.65 6.34 7.05 3.   2.69 6.82 3.27 2.79 2.94 6.49 6.3  3.15 6.4  6.35\n",
      " 7.55 8.3  6.61 6.69 2.77 2.89 2.52 2.96 6.66 6.37 7.9  3.06 6.33 6.75\n",
      " 7.14 6.29 6.62], ValueCount:4.61    399\n",
      "4.72    388\n",
      "4.68    388\n",
      "4.53    385\n",
      "4.57    384\n",
      "       ... \n",
      "6.59      1\n",
      "3.08      1\n",
      "7.30      1\n",
      "3.31      1\n",
      "6.62      1\n",
      "Name: Red-Blood-Cells, Length: 423, dtype: int64\n",
      "14 -> Hemoglobin : Unique:[14.1 14.5 16.  16.8 16.6 13.3 10.9 14.7 13.4 15.8 15.3 12.4 13.6 15.\n",
      " 15.4 13.  17.5 14.8 16.5 12.5 15.9 13.8 11.6 15.1 11.3 15.2 12.3 15.6\n",
      " 16.3 17.2 14.4 12.  17.7 11.1 12.6 12.8 13.1 13.7 14.2 10.6 13.9 14.3\n",
      " 16.2 12.2 10.4 14.9 13.5 12.9 13.2 16.1 11.2 12.7 18.  11.4 10.2 16.7\n",
      " 15.7 12.1 14.6 15.5 16.4 14.  11.  17.  10.8 11.5 11.7 17.1 11.8 11.9\n",
      " 16.9  9.6 17.3 10.5 17.8 10.7 10.1  8.5 19.2 17.9 17.4 10.   9.4  8.9\n",
      "  8.8  9.3  9.9  9.7  9.1 18.2 18.3  9.8 10.3 17.6 18.5 18.6  8.2  8.\n",
      " 19.7  7.6 18.1  9.5  6.6  7.9  9.   7.8  6.9 18.4 16.9 17.9 16.4 17.4\n",
      " 19.  18.8 10.2  8.4  6.2  9.2 18.7  8.6  5.8  8.2  9.7  8.3 18.4  8.7\n",
      "  7.5  6.1 18.9  8.1 19.5 19.6  7.4  7.1  6.4  9.2  7.2  7.7  6.3  7.\n",
      "  8.7], ValueCount:13.9    1296\n",
      "14.1    1252\n",
      "15.0    1151\n",
      "15.4    1050\n",
      "13.1    1029\n",
      "        ... \n",
      "8.2        1\n",
      "7.7        1\n",
      "6.3        1\n",
      "7.0        1\n",
      "8.7        1\n",
      "Name: Hemoglobin, Length: 141, dtype: int64\n",
      "15 -> Platelet-count : Unique:[ 214.   209.   357.   228.   160.   255.   219.   220.   331.   337.\n",
      "  232.   373.   305.   197.   213.   202.   301.   334.   244.   187.\n",
      "  236.   235.   408.   322.   222.   264.   280.   180.   201.   165.\n",
      "  251.   193.   284.   161.   324.   378.   191.   375.   254.   177.\n",
      "  379.   282.   223.   252.   307.   351.   237.   234.   243.   229.\n",
      "  206.   313.   268.   124.   267.   175.   167.   272.   141.   409.\n",
      "  247.   319.   226.   292.   308.   210.   211.   274.   571.   173.\n",
      "  336.   261.   256.   277.   195.   271.   377.   329.   390.   335.\n",
      "  183.   370.   304.   184.   198.   312.   242.   300.   265.   230.\n",
      "  285.   205.   221.   295.   350.   263.   258.   208.   159.   231.\n",
      "  253.   227.   302.   269.   154.   298.   363.   374.   281.   288.\n",
      "  469.   238.   262.   257.   327.   192.   233.   190.   245.   212.\n",
      "  453.    79.   431.   294.   376.   358.   343.   348.   317.   381.\n",
      "  215.   326.   248.   155.   321.   427.   399.   270.   360.   171.\n",
      "  309.   181.   218.   310.   241.   217.   383.   278.   346.   216.\n",
      "  340.   266.   425.   132.   355.   338.   482.   225.   367.   403.\n",
      "  203.   352.    98.   536.   259.   314.   239.   224.   289.   249.\n",
      "  107.   303.   406.   296.   387.   354.   297.   290.   275.   260.\n",
      "  419.   365.   135.   194.   273.   286.   359.   330.   156.   186.\n",
      "  291.   396.   200.   178.   279.   470.   240.   131.   188.   515.\n",
      "  105.   164.   151.   333.   632.   152.   246.   328.   283.   395.\n",
      "  174.   394.   185.   250.   115.   166.   170.   182.   176.   276.\n",
      "  442.   349.   446.   204.   140.   570.   153.   402.   318.   323.\n",
      "  293.   316.   158.   311.   306.   315.   119.   344.   364.   361.\n",
      "  207.   172.   422.   196.   353.   342.   287.   339.   325.   199.\n",
      "  134.   345.    96.   397.   163.   169.   561.   401.   491.   168.\n",
      "  162.   438.   372.   189.   143.   157.   142.   362.   368.   448.\n",
      "  299.   138.   369.   139.   393.   320.   127.   144.   380.   341.\n",
      "  386.   554.    97.   179.   489.   101.   484.    74.   388.   417.\n",
      "  510.   384.   391.   414.   549.   463.   440.   146.   392.   457.\n",
      "  371.   435.   150.   416.   441.   413.   504.   639.   459.   332.\n",
      "  480.   499.   410.   113.   479.   114.   466.   121.   428.   455.\n",
      "  514.   118.   347.   129.   420.   385.   145.   464.   587.   523.\n",
      "  495.   462.   137.   433.   148.   456.   366.   467.   452.   474.\n",
      "  418.   400.   492.   437.   356.   112.   533.   505.   128.    11.\n",
      "  496.   471.   560.   488.   405.   434.   389.    59.   483.   460.\n",
      "  421.   411.    82.   104.   551.   412.   123.   520.   507.   486.\n",
      "  451.   117.   537.    81.   450.   398.   149.   133.   497.   506.\n",
      "  478.   430.   654.   445.   432.   443.   454.   130.    63.   522.\n",
      "   91.   447.   593.    70.   596.   125.   493.    92.   999.9  439.\n",
      "  501.   500.   521.    94.   111.   407.   102.    18.   415.   120.\n",
      "   47.   553.   490.   526.   468.   423.   444.   424.   147.   713.\n",
      "  461.   108.   449.   573.   531.   475.   103.    89.    67.   426.\n",
      "  511.   619.   122.   382.   126.   524.   436.   429.   136.   625.\n",
      "  508.    76.   602.   543.    57.   116.    77.    78.   109.   545.\n",
      "  975.   732.   404.   548.   481.   110.   458.    51.    35.   599.\n",
      "   45.   476.    60.   562.   552.   702.   557.   650.     4.   564.\n",
      "  494.   540.   558.    88.   535.   485.    84.   477.   613.   598.\n",
      "  530.   822.    95.   513.   534.   527.   465.   626.    66.   503.\n",
      "   41.   578.   646.    56.   589.    83.   585.    39.    93.   519.\n",
      "  556.   651.    85.    99.   667.   473.   799.   575.   516.    65.\n",
      "   87.    64.   672.   106.   100.   576.   610.    75.   539.   991.\n",
      " 1000.    86.    34.    49.   681.    22.    69.    27.    48.   569.\n",
      "  659.    80.   612.    38.    71.   579.    54.   633.    62.   601.\n",
      "  669.    14.   529.   629.    90.    53.   568.    73.    36.   777.\n",
      "  517. ], ValueCount:235.0    295\n",
      "239.0    277\n",
      "233.0    267\n",
      "220.0    267\n",
      "225.0    265\n",
      "        ... \n",
      "4.0        1\n",
      "650.0      1\n",
      "702.0      1\n",
      "552.0      1\n",
      "517.0      1\n",
      "Name: Platelet-count, Length: 551, dtype: int64\n",
      "16 -> Segmented-Neutrophils : Unique:[66.8 52.2 63.7 82.4 62.5 72.  64.6 61.6 51.6 53.1 89.1 65.1 58.6 59.3\n",
      " 46.1 47.1 49.3 73.  68.8 54.5 58.7 53.7 67.2 51.9 55.2 50.3 45.3 56.7\n",
      " 58.4 66.  64.3 51.4 57.2 59.1 74.6 61.2 57.7 61.7 66.9 58.3 62.4 70.6\n",
      " 68.3 69.5 67.7 51.3 62.7 57.5 67.1 63.9 62.2 63.4 53.3 48.4 59.2 58.8\n",
      " 50.1 74.5 50.7 60.2 62.  69.8 57.4 60.5 63.1 41.  51.8 50.8 60.4 55.\n",
      " 60.9 50.6 56.3 49.9 72.9 53.4 55.3 55.9 70.3 42.3 66.3 67.4 73.6 45.5\n",
      " 68.5 56.  43.6 34.8 54.3 59.  66.1 53.2 56.1 63.8 55.8 38.1 61.3 43.9\n",
      " 67.8 60.1 69.7 36.8 52.8 60.  59.4 65.  47.5 63.6 59.7 44.2 30.2 78.6\n",
      " 43.8 42.8 51.5 59.9 63.5 54.1 65.8 74.2 52.6 53.6 68.6 56.6 39.8 61.\n",
      " 64.9 53.9 52.  54.  70.9 72.3 65.7 31.3 48.  60.6 64.4 59.6 56.9 71.8\n",
      " 58.1 69.1 60.3 40.8 69.  64.8 54.8 73.8 61.5 41.4 64.1 55.5 65.4 69.2\n",
      " 73.9 74.4 79.2 56.8 61.8 60.8 63.2 62.8 55.1 55.4 47.9 68.4 57.6 44.6\n",
      " 79.7 44.8 73.4 52.3 61.4 41.3 69.4 56.2 70.4 62.3 67.9 66.7 48.1 52.7\n",
      " 50.5 67.6 57.9 47.6 56.5 49.4 48.6 54.9 42.2 77.4 65.6 57.8 65.3 66.2\n",
      " 70.2 72.7 55.7 58.5 50.2 54.7 65.2 58.  56.4 36.2 73.2 59.5 46.4 40.\n",
      " 47.2 47.8 46.5 43.7 48.3 65.5 69.9 63.3 78.2 68.7 68.9 61.9 54.4 45.9\n",
      " 81.3 73.1 55.6 75.9 69.3 57.3 47.4 74.3 31.  71.3 66.6 62.6 54.2 29.2\n",
      " 61.1 77.  72.1 71.  52.4 46.3 52.5 70.5 73.5 49.1 51.7 51.1 45.1 54.6\n",
      " 65.9 62.1 76.9 46.9 44.9 50.9 84.7 48.2 32.5 67.5 29.8 46.8 45.7 41.7\n",
      " 71.2 58.9 48.5 48.9 60.7 57.  67.3 46.6 72.2 45.2 51.2 39.5 48.7 33.1\n",
      " 45.6 42.7 70.  53.  72.4 49.7 66.4 72.6 38.5 49.5 71.6 57.1 51.  48.8\n",
      " 41.2 64.5 52.1 64.7 35.  85.3 43.5 70.7 27.1 42.6 45.8 71.4 76.3 78.3\n",
      " 62.9 44.3 79.4 58.2 76.4 53.5 78.4 42.4 75.1 44.7 76.1 31.1 71.9 40.6\n",
      " 67.  50.  32.8 37.3 74.1 53.8 32.9 46.7 49.  68.1 73.3 90.3 71.7 44.5\n",
      " 77.7 78.7 50.4 77.2 15.2 84.3 38.8 75.2 45.4 80.7 44.1 34.7 70.1 80.1\n",
      " 88.9 77.9 78.  43.  44.  75.3 77.5 49.2 75.  64.2 70.8 63.  89.8 79.6\n",
      " 47.7 35.8 39.2 52.9 43.2 71.5 68.2 78.8 33.3 42.  38.9 35.3 80.4 74.7\n",
      " 32.7 38.4 75.4 76.8 46.2 47.  79.3 64.  77.1 35.1 47.3 68.  39.  59.8\n",
      " 41.8 39.9 38.6 75.6 77.8 75.8 79.1 72.8 69.6 38.2 46.  44.4 37.1 37.7\n",
      " 37.4 40.9 71.1 81.9 39.3 79.9 41.6 74.9 41.9 82.2 83.4 78.1 29.9 40.1\n",
      " 38.3 43.3 22.5 49.6 77.6 32.3 33.9 28.3 49.8 66.5 32.  80.9 36.4 37.6\n",
      " 82.3 26.8 36.7 43.1 76.2 33.8 11.3 26.  40.5 29.4 30.7 37.8 74.8 87.4\n",
      " 77.3 36.1 40.3 75.5 32.2 87.9 78.9 30.  31.7 74.  39.1 80.  40.7 72.5\n",
      " 42.9 45.  81.6 81.2 30.6 33.7 33.  85.6 86.2 34.9 83.1 76.6 84.1 81.8\n",
      " 90.1 35.7 29.1 35.5 32.4 80.3 89.9 32.1 82.1 76.7 76.5 35.4 36.3 34.3\n",
      " 28.1 26.5 30.8 73.7 41.1 34.  37.5 28.6 88.  26.7 37.2 34.4 84.4 81.\n",
      " 86.  40.2 28.2 31.2 76.  27.8 81.1 79.  34.2 40.4 43.4 16.5 39.7 15.9\n",
      " 41.5 36.9 26.9 38.7 83.6 33.4 80.2 86.1 21.2 75.7 80.8 34.5 37.  84.2\n",
      " 88.1 31.9 79.5 79.8 85.4 87.3 31.6 75.6 65.6 74.6 72.1 36.  66.6 77.6\n",
      " 72.6 36.8 73.1 71.6  8.9 40.3 71.1 14.7 35.3 69.6 75.1 65.1 85.8 67.6\n",
      " 70.1 64.6 73.6 81.4 76.6 42.5 39.3 69.1 66.1 68.1 74.1 78.1 64.1 84.9\n",
      " 39.4 38.  79.6 67.1 37.9 68.6 86.3 81.1 32.8 34.3 82.9 80.6 80.1 36.5\n",
      " 38.8 85.  81.7 26.6 13.9 32.3 83.3  7.5 70.6 89.4 76.1 82.8 40.8  5.6\n",
      " 36.6 39.6 39.8 18.5 38.3 86.9 37.3 27.5 21.8 33.2 86.6 83.5 21.3 77.1\n",
      " 23.2 79.1 78.6 78.5 37.8 35.6  5.5 36.3 17.9 35.2 31.5 31.8 33.5 34.8\n",
      " 28.9 42.1 81.6 83.7 90.2  6.6 35.8 32.6  9.1 28.8 28.4 92.7  9.7 23.\n",
      " 26.3 21.9 29.3 83.  26.2 29.6 81.5 29.7 27.6 25.  84.6 34.1 17.7 19.2\n",
      " 30.4 33.3 35.9 15.4 28.5 15.1 88.3 24.5 23.6 86.4 82.6 80.5 87.6 25.6\n",
      " 21.7 31.4 10.4  6.5 23.8 25.8 90.6 24.4 83.2 33.6 24.6 29.5 85.7 21.1\n",
      " 93.6 27.2 18.8 16.3 33.8 30.1  9.   0.8 22.  87.2 14.9  1.  34.6 13.1\n",
      " 30.9 96.6 83.9 19.5 84.  89.7 82.7 27.3 80.6 86.8  8.8 21.  87.5 88.8\n",
      " 30.5 16.4 19.4 25.4 90.8 27.4 85.9 20.  25.1  3.6 92.2 88.4 83.8 22.3\n",
      " 30.3 13.5 93.2], ValueCount:61.3    246\n",
      "62.2    234\n",
      "59.3    233\n",
      "59.7    227\n",
      "61.8    223\n",
      "       ... \n",
      "30.4      1\n",
      "19.2      1\n",
      "17.7      1\n",
      "14.7      1\n",
      "93.2      1\n",
      "Name: Segmented-Neutrophils, Length: 731, dtype: int64\n",
      "17 -> Hematocrit : Unique:[41.8 43.6 48.1 50.4 40.2 33.3 41.2 42.  38.3 44.7 45.4 37.8 40.  43.8\n",
      " 42.8 45.9 44.5 37.3 49.5 49.6 47.1 38.5 45.5 40.9 35.6 45.2 48.3 50.\n",
      " 46.6 44.9 34.8 46.9 43.5 45.  35.9 40.8 45.8 34.4 47.9 36.5 48.7 47.2\n",
      " 45.1 43.1 35.8 51.6 45.7 32.6 36.1 36.3 40.3 46.8 41.3 36.7 38.7 37.5\n",
      " 39.7 40.5 39.2 41.4 31.7 47.4 44.2 40.1 40.4 42.2 40.7 48.4 42.1 39.3\n",
      " 37.7 44.6 36.9 44.1 30.9 48.6 48.8 43.9 41.1 47.  44.  38.1 45.6 39.\n",
      " 48.9 50.3 46.7 36.  39.8 43.7 41.6 42.9 42.7 43.  37.9 42.5 32.8 36.4\n",
      " 46.3 39.4 38.9 52.4 41.9 42.6 52.6 38.8 33.4 48.  33.8 43.4 43.3 41.\n",
      " 32.7 44.4 32.3 46.1 34.6 47.8 42.4 37.2 39.1 34.2 48.2 46.5 37.6 39.5\n",
      " 47.7 38.2 35.4 49.2 45.3 47.3 35.7 31.1 49.3 47.6 35.  39.9 36.8 50.1\n",
      " 32.4 34.3 46.  48.5 37.1 49.9 38.6 44.3 40.6 33.7 35.3 46.4 50.8 41.5\n",
      " 32.2 38.4 44.8 35.2 46.2 49.1 43.2 47.5 34.9 39.6 41.7 36.2 34.  31.8\n",
      " 49.4 30.  34.1 52.1 37.  35.1 51.4 32.  38.  31.  34.7 52.  51.1 42.3\n",
      " 49.7 52.5 51.7 33.  35.5 51.  28.3 31.9 51.2 50.7 37.4 51.9 27.6 34.5\n",
      " 31.5 36.6 54.9 32.5 33.6 53.6 29.8 49.  29.6 51.8 50.9 33.1 52.8 33.5\n",
      " 30.8 27.1 27.5 30.1 49.8 27.8 33.9 30.4 50.6 50.2 50.5 26.6 52.2 29.5\n",
      " 54.1 29.3 51.3 51.5 53.9 30.5 31.3 31.2 32.9 53.7 52.7 31.6 52.9 53.3\n",
      " 30.6 26.7 26.8 53.4 57.8 24.8 57.2 33.2 31.4 28.1 55.  32.1 26.5 52.3\n",
      " 27.  29.1 30.2 30.3 28.  24.  26.3 28.5 25.7 22.8 53.5 23.3 28.8 28.9\n",
      " 29.9 40.3 40.8 28.2 38.3 39.8 36.8 38.8 36.3 37.8 35.3 37.3 39.3 32.8\n",
      " 29.7 26.2 35.8 33.8 33.3 34.8 57.1 34.3 56.2 53.8 54.3 29.4 32.3 54.5\n",
      " 21.7 53.  27.7 55.5 20.1 26.1 54.2 27.3 25.5 27.9 55.2 28.4 30.7 19.7\n",
      " 23.5 53.2 28.6 21.5 26.  29.  25.  24.5 56.3 28.7 29.2 23.8 27.4 20.5\n",
      " 24.2 23.2 27.2 55.8 25.4 24.6 57.7 25.9 23.  24.4 20.6 23.4 24.3 20.8\n",
      " 55.4 54.4 54.  25.3 55.1 54.6 59.  21.8 25.1 23.9 57.5], ValueCount:42.2    451\n",
      "42.8    449\n",
      "41.3    448\n",
      "41.8    435\n",
      "39.2    413\n",
      "       ... \n",
      "24.2      1\n",
      "23.2      1\n",
      "55.8      1\n",
      "25.4      1\n",
      "57.5      1\n",
      "Name: Hematocrit, Length: 347, dtype: int64\n",
      "18 -> Albumin : Unique:[45 47 40 44 41 43 46 42 48 38 50 49 35 55 34 51 36 52 39 37 53 32 33 57\n",
      " 56 29 54 31 30 26 28 27 21 22 19 25 24 23 20], ValueCount:43    4458\n",
      "42    4401\n",
      "44    4267\n",
      "41    3820\n",
      "45    3699\n",
      "40    3049\n",
      "46    2711\n",
      "39    2161\n",
      "47    1894\n",
      "38    1478\n",
      "48    1139\n",
      "37     911\n",
      "49     627\n",
      "36     588\n",
      "50     359\n",
      "35     352\n",
      "34     231\n",
      "51     162\n",
      "33     154\n",
      "32     126\n",
      "31     115\n",
      "30      98\n",
      "52      84\n",
      "29      63\n",
      "53      35\n",
      "28      31\n",
      "27      22\n",
      "26      14\n",
      "54       8\n",
      "55       5\n",
      "25       4\n",
      "56       3\n",
      "57       2\n",
      "21       2\n",
      "24       2\n",
      "22       1\n",
      "19       1\n",
      "23       1\n",
      "20       1\n",
      "Name: Albumin, dtype: int64\n",
      "19 -> ALP : Unique:[ 62  63 103 110  31  42  50 114  53  70  83  65 130 101  76 108  84  87\n",
      " 118  94  90  45  68  91 728  79  85  98  77  82  67 120 146  69  86  57\n",
      "  74 124  75 131 104  80 631 334  71  52 111  66  61  60  95  44  72  54\n",
      "  40 117  55  81  56  58  96 197  88  59 123 107  78  97  46 125 128 148\n",
      "  93 102  73  64 100 106  49 153 133 115 112 135  39 159 119  92 139  47\n",
      "  41  51 113  99 116 140  89 105 126 121 109 164 154 127 175 132 200  43\n",
      "  23 171 141 138  36  48 136 165 208 232 179  35 122  20 151 156 134  30\n",
      " 147 158 152  37 129  28 251 160 169 355  24 174 230 172  33 271 155 236\n",
      " 137 149 224  38 143  29 187 144 142 210 328 190 150 177 170 193 182 161\n",
      " 298 199 209 145  32 231 417 203 181 213 176  34 242 167 249 514 435 283\n",
      " 188 166 194  27 635 180  19  25 163 204 189 458 185 157  12 302 178 162\n",
      " 330 264  26  16 234  22 183 324 296 218 257 246  21 325 186 223 494 206\n",
      " 282 168 241 333 320 214 259 255 248 646 239 307 245 184 198 225  18 626\n",
      " 235  15 191 287  14 173 216 633 244 349   7 729 305 265 207 237 280   9\n",
      " 192 211 336 501 196 352 326 273 220 250], ValueCount:65     814\n",
      "61     808\n",
      "63     800\n",
      "62     785\n",
      "56     773\n",
      "      ... \n",
      "494      1\n",
      "223      1\n",
      "325      1\n",
      "246      1\n",
      "250      1\n",
      "Name: ALP, Length: 262, dtype: int64\n",
      "20 -> AST : Unique:[  19   22   17   24   23   28   18   25   27   21   14   33   71   62\n",
      "   20   30   16   51   29   26   42    9   15   13   31  430   39   41\n",
      "  108   32   34   58   38   11  245   47   12   37   64   74   75   35\n",
      "   90  122   10   49   48   66   44   36  111  118   59   94  119   54\n",
      "   40   45   67   46   77   43   56  115  281   53   65   57   70   50\n",
      "   52   61  133  827   72   60  129  218  506   55   68   91   69  126\n",
      "  113   73  329  100   92   99   63  138  474   79  110   83  104    8\n",
      "  156   76   96  191   84  114  155    7  116  187   86   85  279  101\n",
      "  112  125   87  131  169  381   81   80  284  134  165  107  105  120\n",
      "   82   93   88 1672   89  213  140   78  330   95  164  117  162  141\n",
      "  146  181  172  121  106  177  159  161  400 1034   97  150  206  132\n",
      "  310  175  171  163  196  102  109  149  222  296  733  189  152  128\n",
      "  234  148  103  203  153  135  142  226  220  145  302  229  243  597\n",
      "  139  174  195  123  294  882  209  247  200   98  199  285  338  143\n",
      "  182  151  190  210  832  289], ValueCount:21     2803\n",
      "22     2684\n",
      "20     2603\n",
      "23     2551\n",
      "19     2527\n",
      "       ... \n",
      "152       1\n",
      "330       1\n",
      "234       1\n",
      "827       1\n",
      "289       1\n",
      "Name: AST, Length: 202, dtype: int64\n",
      "21 -> ALT : Unique:[  16   28   35   18   30   20   24   26   42   23   13   34   17    9\n",
      "   70  153   21   71   15   53   47   19   41   51   14   11   55   73\n",
      "  775   32   37   25   22   36   33   27   38   63   61   66    7   29\n",
      "   39   52   43   12   49   31  248   48   44    8   10   57   46  104\n",
      "   97    5   84   65   74   60   45  220  195  172   40   50  133   91\n",
      "   79   59  156   54   67   81   94   58   68  124   56  424   82   93\n",
      "  126  108   78    6  184  100 1163  147  143  399   88   69  110   72\n",
      "   96   83  129  260  127  407   98   80   87  111   90  114  191  226\n",
      "  115  163  141  106   64   99    4   95  121   89  177  123  122  241\n",
      "  118  158   86   62  139   85   75  204  145  102  244  120  131  142\n",
      "  101  109  151  250  182  179 1925  117  107  128  116   76  224  168\n",
      "  216  112  180  103  155  138   77  148 1997  135  146  105  154   92\n",
      "  132  178  176  188  149  217  130  174  167  150  140  119  819  219\n",
      "  890  194  386  293  125  393  265  171  197  157  201  199  329  290\n",
      "  225  113  136  228  175  137  152  443 1363  144  206  164  183  300\n",
      "  317  181  159  192  274  536  209  232  166  281  187  280  319  208], ValueCount:17     2094\n",
      "16     2072\n",
      "18     2046\n",
      "19     2021\n",
      "15     1975\n",
      "       ... \n",
      "399       1\n",
      "393       1\n",
      "265       1\n",
      "197       1\n",
      "208       1\n",
      "Name: ALT, Length: 224, dtype: int64\n",
      "22 -> Cholesterol : Unique:[ 5.25   7.16   3.9    7.94   4.42   4.91   4.22   3.59   5.69   3.96\n",
      "  5.46   4.76   4.78   5.51   4.71   7.37   6.15   6.52   5.56   6.26\n",
      "  8.79   6.13   5.3    5.04   6.08   3.72   4.73   4.37   6.88   6.54\n",
      "  6.49   4.27   5.22   5.48   5.15   4.53   4.32   5.02   6.67   4.01\n",
      "  4.63   3.39   5.2    4.6    5.95   4.86   5.87   7.47   5.92   4.34\n",
      "  5.72   6.28   3.13   5.9    4.89   7.55   5.35   5.28   7.45   3.8\n",
      "  7.14   7.09   4.45   4.84   6.93   3.26   3.44   5.12   4.06   5.66\n",
      "  6.36   4.94   4.03   7.34   7.89   5.4    5.84   4.4    2.56   5.64\n",
      "  5.33   4.5    3.65   4.14   5.79   5.07   3.21   4.24   3.49   6.9\n",
      "  3.34   4.16   3.88   3.83   4.47   3.54   3.93   5.43   6.05   6.83\n",
      "  3.85   4.29   5.53   5.09   4.97   8.77   6.7    6.34   4.65   3.15\n",
      "  3.78   4.81   5.74   5.77   5.59   4.68   2.9    3.98   6.41   5.97\n",
      "  5.38   7.21   4.58   5.82   6.47   4.55   6.31   4.11   6.21   7.32\n",
      "  3.1    7.11   7.68   6.62   3.41   6.75   3.67   4.19   6.85   3.75\n",
      "  6.1    2.4    7.5    5.61   7.24   6.03   7.58   6.23   8.71   5.17\n",
      "  2.97   6.44   8.07   3.47   6.78   6.18   7.78   6.59   3.03   3.52\n",
      "  4.99   6.65   3.23   4.09   6.     7.06   7.01   3.62   6.57   8.28\n",
      "  6.8    3.05   8.43   3.36   8.3    6.98   7.99   3.18   6.39   2.38\n",
      "  3.57   8.17   2.72   2.82   8.51   7.19   2.35   7.4    7.42   7.03\n",
      "  3.     8.33   7.86   7.6    7.53   2.59   3.08   3.28   7.29   2.79\n",
      "  2.92   6.96   3.7    8.02   6.72   3.31   9.21   7.27   2.87   7.73\n",
      "  8.2    7.84   8.53   2.95   7.63   2.46   2.74   9.36   2.77   7.91\n",
      "  8.84   7.71   2.48   2.33   8.04   9.34   7.76   7.65  11.9    8.61\n",
      " 10.4    2.12   8.92   8.25   7.81  10.81  10.29   2.84   2.66   8.12\n",
      "  7.96   8.15   8.56  10.03   9.     9.1    9.26   2.09   1.81   8.4\n",
      "  8.22   9.08   2.22   2.64   2.2    8.64   6.775  4.655  4.913  3.853\n",
      "  5.017  4.681  5.896  6.594  5.508  5.094  5.043  5.172  4.163  4.939\n",
      "  4.06   4.758  4.448  6.025  7.758  5.379  5.353  4.112  6.077  5.275\n",
      "  3.543  6.206  6.051  2.948  6.413  4.008  4.784  3.569  5.198  4.293\n",
      "  3.646  3.982  6.232  7.706  3.776  4.474  5.224  3.827  7.112  4.965\n",
      "  4.888  5.431  4.319  6.465  5.793  6.284  3.155  4.603  3.905  5.922\n",
      "  5.586  5.069  5.612  7.267  3.879  3.724  7.422  5.715  6.362  4.241\n",
      "  5.534  7.318  5.301  4.215  5.327  4.862  4.836 10.913  4.81   8.973\n",
      "  4.707  6.905  5.948  6.568  5.482  5.637  6.698  4.267  6.155  5.974\n",
      "  8.353  7.396  3.465  6.181  3.801  4.396  5.689  8.146  4.991  3.077\n",
      "  6.129  4.422  7.836  3.336  3.388  5.741  2.715  6.517  6.103  4.577\n",
      "  4.732  7.887  5.767  4.551  5.663  3.517  6.879  6.801  4.086  4.629\n",
      "  7.525  5.844  6.982  6.491  6.387  3.957  7.034  6.543  3.595  3.414\n",
      "  4.526  7.086  6.258  7.293  9.051  6.724  7.008  5.819  7.241  5.405\n",
      "  4.189  3.181  6.336  3.439  8.508  2.974  6.672  6.646  9.439  5.146\n",
      "  4.034  7.551  3.491  3.698  5.456  6.853  6.439  3.258  3.931  9.335\n",
      "  7.991  4.138  4.344  6.956  7.655  3.362  2.793  8.482  8.767  3.284\n",
      "  7.189  8.275  3.129  3.672  8.663  2.638  7.448  6.749  7.215  7.474\n",
      "  6.827  7.603  3.207  9.258  7.344  7.939  2.224  8.017  8.068  7.629\n",
      "  7.137  7.861  8.611  7.499  8.198  9.154  7.913  2.845  8.818  8.456\n",
      "  3.233  7.577  8.715  2.767  8.379  7.163  2.896  2.741  2.922 11.146\n",
      "  8.223  8.896  2.483  8.327  2.172  9.31   3.051  7.965 10.473  8.534\n",
      "  7.732  8.792  2.534  9.413  9.232  8.948  2.586  9.465  8.249  8.12\n",
      "  8.405  3.026  8.999  2.664  2.302  8.094  7.784 12.387  3.103  2.457\n",
      "  8.689  8.741  2.405  9.568  8.637  8.301  2.819  2.25   2.276  8.042\n",
      "  9.103  9.025  9.801  9.723 10.111  8.922  2.508  2.379 11.559  8.172\n",
      "  2.095  9.077 10.318 11.43  10.085  2.612  2.689 10.37  10.68   9.361\n",
      "  2.353  2.327  2.431  9.542  8.586  9.93   9.853  1.914  1.603  9.827\n",
      "  0.155  3.284  5.353  4.396  4.991  4.965  4.422  4.215  4.241  4.888\n",
      "  6.387  3.465  5.534  3.207  4.707  5.896  4.784  3.776  3.155  6.646\n",
      "  3.957  5.172  3.388  4.603  5.637  6.465  6.103  3.517  6.749  6.129\n",
      "  3.336  5.715  4.526  4.138  5.741  3.646  5.922  6.672  6.491  5.379\n",
      "  7.603  3.905  6.284  4.629  5.146  7.241  3.026  7.991  7.965  2.638\n",
      "  7.499  7.784  2.896  7.215  4.034  8.042  7.137  6.879  6.568  2.534\n",
      "  7.422  2.146  8.792  6.853  7.034  7.629  9.18   2.457  2.069  8.87\n",
      "  8.767  8.379  8.405  7.396  7.887  9.672  7.318 11.818  2.715  2.276\n",
      "  9.258  8.818  2.767  1.862  7.861 14.611  8.353  2.586  8.017  9.129\n",
      "  9.698  8.094  8.844  9.465 11.249  8.456  9.62   9.646  2.017  8.715\n",
      "  8.508], ValueCount:5.250    366\n",
      "5.120    362\n",
      "4.500    354\n",
      "4.370    333\n",
      "4.836    313\n",
      "        ... \n",
      "9.335      1\n",
      "8.663      1\n",
      "7.860      1\n",
      "0.155      1\n",
      "8.508      1\n",
      "Name: Cholesterol, Length: 631, dtype: int64\n",
      "23 -> Creatinine : Unique:[  61.9    70.7    88.4    53.     79.6    26.5    44.2    35.4   238.7\n",
      "   97.2   106.1   123.8   114.9    17.7   168.    769.1   141.4   185.6\n",
      "  892.8   221.    132.6   742.6   618.8   371.3   176.8   884.    203.3\n",
      "  194.5   733.7  1043.1   150.3   212.2   406.6   468.5   680.7   159.1\n",
      "  565.8   875.2   901.7   689.5   751.4    70.72   79.56   61.88  114.92\n",
      "   53.04   97.24  106.08  123.76   35.36   26.52  733.72  203.32  892.84\n",
      "  141.44  150.28  663.    159.12  185.64  875.16  167.96  238.68  194.48\n",
      "  212.16  601.12  388.96  344.76  265.2   804.44  274.04  282.88  371.28\n",
      " 1140.36  645.32  256.36  318.24  954.72  327.08  229.84  830.96 1140.36\n",
      "  795.6   247.52  433.16  760.24  450.84  291.72 1573.52  353.6   565.76\n",
      "  309.4   380.12  583.44   45.97   67.18   63.65  120.22   54.81   81.33\n",
      "  115.8    72.49  638.25   90.17   84.86  107.85   85.75   91.94  110.5\n",
      "   78.68   99.01   69.84   68.07   62.76   58.34   76.02   37.13   60.11\n",
      "  100.78   68.95   47.74   57.46   53.92   66.3    76.91   49.5   121.99\n",
      "   55.69   86.63   92.82   99.89  111.38   74.26   93.7    96.36   80.44\n",
      "  112.27  169.73   91.05  213.93  106.96  125.53   61.     50.39   71.6\n",
      "   65.42   83.1    95.47   56.58   52.16   75.14  113.15   94.59  143.21\n",
      "  116.69   73.37   87.52   77.79  103.43   48.62   64.53  160.89   89.28\n",
      "  104.31   83.98   82.21   59.23   43.32   46.85   29.17  134.37  129.95\n",
      "  682.45  114.04  129.06   51.27  109.62   98.12  246.64  149.4    40.66\n",
      "  108.73  152.05  105.2   140.56  101.66  196.25  160.    147.63   45.08\n",
      "  491.5   174.15  135.25   38.9   139.67  102.54   38.01  131.72  126.41\n",
      "  156.47  117.57  152.93  122.88  136.14  127.3   235.14  124.64  168.84\n",
      "  119.34  146.74  328.85   36.24  118.46   39.78  148.51  121.11  130.83\n",
      "  138.79  205.09  144.09  208.62   42.43  172.38  735.49  187.41   41.55\n",
      "  178.57  190.94  222.77  227.19  173.26  249.29   28.29   32.71  434.93\n",
      "  151.16  142.32  240.45  137.02  807.09  632.06  560.46  514.49  144.98\n",
      "  179.45  128.18   30.06  245.75  133.48  492.39  137.9   165.31  829.19\n",
      "   34.48  186.52  192.71  195.36  182.99  158.24  164.42  199.78  572.83\n",
      "  145.86  154.7   163.54  182.1   479.13  153.82  170.61  197.13  244.87\n",
      "  752.28  175.03  551.62  521.56  213.04  162.66  298.79  175.92  237.8\n",
      "  210.39  671.84  528.63  181.22   33.59  278.46  177.68  289.95  300.56\n",
      "  206.86  970.63  155.58  167.08  546.31  253.71  305.86  243.98  647.97\n",
      "  659.46  248.4   556.04  840.68  818.58  180.34  198.02  454.38  557.8\n",
      "  220.12  191.83  211.28  375.7   189.18  161.77  218.35  480.01  330.62\n",
      "  525.98  377.47  263.43  204.2   171.5    71.6    68.07  200.67  136.14\n",
      "   72.49  221.88   40.66  358.02  384.54  129.95  542.78 1470.98  304.98\n",
      "  183.87  261.66   34.48  154.7   157.35  473.82  617.92  591.4   320.01\n",
      "  215.7   133.48  228.07  307.63  160.89  139.67  198.9   556.92  193.6\n",
      "  456.14  343.88  342.11  202.44  394.26  483.55  496.81  713.39  266.97\n",
      "   31.82  144.98  540.12  258.13  216.58  455.26  449.96  372.16  205.97\n",
      "  448.19  903.45  946.76  312.05  827.42  284.65  184.76  914.94  272.27\n",
      "  475.59  233.38  302.33  236.03  257.24  207.74], ValueCount:70.72     2509\n",
      "79.56     2369\n",
      "88.40     2110\n",
      "61.88     2041\n",
      "97.24     1292\n",
      "          ... \n",
      "179.45       1\n",
      "492.39       1\n",
      "583.44       1\n",
      "829.19       1\n",
      "207.74       1\n",
      "Name: Creatinine, Length: 384, dtype: int64\n",
      "24 -> Glucose : Unique:[ 4.33   5.273  4.163  7.882  6.384  5.162  4.774  4.718  7.549  5.607\n",
      "  4.996  4.108  5.551  8.549  4.885  5.051  5.218  4.552  5.329  4.663\n",
      "  5.107  4.829  6.772  5.829  6.217  5.884  5.662  3.442  5.44  13.1\n",
      "  4.607  5.995  4.274  6.828  4.385  8.493 12.601  4.441  5.773  4.052\n",
      "  6.717  4.94   6.55   4.219  5.94   5.495  6.273  4.496  6.439  6.162\n",
      "  3.83  11.046  7.716  7.66   7.827  6.495  5.384 19.762  8.882  6.328\n",
      "  3.775  3.22   3.997  6.106  9.992  7.494  6.661 17.319  8.937  5.718\n",
      "  3.886  6.606 20.539  8.382 16.986 11.38   3.497 12.268  7.161  9.936\n",
      "  7.05   7.216 10.824 15.876  6.051  8.771  7.383  9.159  3.664  8.327\n",
      "  8.271 13.989 16.32   3.941 24.313 12.046  3.719  6.939  7.105 13.655\n",
      "  8.715  3.608 11.158  6.994 12.212  7.327 11.491 22.648 12.157 20.483\n",
      " 10.658 28.199  6.883  9.714  7.438 11.213 12.823 13.433 11.102 12.989\n",
      "  7.993 13.156 18.041 15.765 24.924 10.269 18.929 15.543  7.771  7.938\n",
      " 21.815 18.596 12.323 10.547  8.16   8.049 17.597 13.6   25.091  9.437\n",
      "  9.326 13.045 17.208  8.438  2.609  9.215  9.381 11.435 18.207 14.211\n",
      " 10.991 14.821 11.879 21.593  7.605 10.769 24.369 22.704 12.379  9.603\n",
      " 10.38  11.602  8.215 16.486  7.272 14.544  9.104  8.66   9.27  10.713\n",
      " 17.93  10.047  3.164  3.386  9.881  9.492 11.824 11.269 17.708 13.211\n",
      " 21.482 26.645  3.553  2.776 12.49  12.712  9.048 12.767 12.434 10.935\n",
      "  9.77   3.053 30.031 10.214 19.54  14.155 19.651  8.826 13.544 13.322\n",
      " 10.602  8.104 10.158 11.713 11.324 13.822  2.387 16.653  2.998 19.151\n",
      " 13.878 25.757 10.436 14.322  1.665 16.597  9.825  3.331 28.921 12.878\n",
      " 14.71  11.768  1.887 14.433  9.659 16.098 28.754 21.205  9.548  2.054\n",
      " 20.983 10.103 15.709 17.486 23.37  18.096 14.988 15.654 11.935 18.429\n",
      " 16.931  8.993 15.154  3.275 18.152 16.875 15.432 14.488 11.546 12.101\n",
      "  8.604 19.206  5.88   4.66   5.05   5.11   4.83   4.72   4.39   5.55\n",
      "  5.61   4.88   4.77   4.55   4.44   5.22   6.22   5.72   5.83   4.61\n",
      "  5.5    5.38   5.    20.93   5.33  14.21   5.16   7.11   6.49   4.22\n",
      "  5.27   6.27   4.5    6.44  14.38  15.93   6.94   3.72   5.77  12.38\n",
      " 21.2    6.05   4.27  11.49   5.66   6.83   7.44  10.32   6.61   4.16\n",
      "  6.11   6.     6.16   6.72   6.38  10.44  12.82   8.94   7.49   7.83\n",
      "  8.72   6.77  19.87  10.88   8.38   3.77   2.55   8.33   4.05   4.\n",
      " 11.05   7.77   8.49   3.5   18.04   9.83   3.94   6.88   7.55   7.38\n",
      "  2.61   9.55   9.66   7.33   4.11   7.22   8.05  10.55   7.72  15.49\n",
      "  3.89   6.33   6.99  12.55   3.39   8.6    3.61  11.27  18.76  14.65\n",
      " 16.54   7.88   7.27   7.16   6.66   8.27  13.32   7.99  13.27  11.77\n",
      "  3.     9.38   8.99   3.55   3.33   9.33   7.94  10.82  13.66  16.82\n",
      "  3.66   3.05   9.88  12.1   10.27  11.66   8.1    3.44   2.94  13.43\n",
      "  9.44  12.43  10.05  15.38  28.42  11.88   9.16   8.55  10.71   3.16\n",
      " 15.88  11.93  10.1    8.83  10.66  20.76  12.99   8.22  13.16   2.89\n",
      " 10.49  13.71  22.09  26.98  15.04   3.28   3.11  13.04   7.6   14.1\n",
      " 11.32  16.1   12.05  15.76  10.16  11.82   9.21   9.99  11.71   8.88\n",
      " 14.43  17.54  11.16  22.37   9.1   16.65  10.77  10.6    9.49  21.76\n",
      " 15.99   9.71   9.05  13.38  15.43  19.21  17.82   9.94  14.82  10.21\n",
      " 12.32  18.93   8.44  11.6   15.71  10.94  17.87  11.44  16.6    8.77\n",
      " 17.15  12.16  15.21  19.65  10.99  17.99  20.87  15.1   13.54   2.78\n",
      "  2.83   2.72  16.15  20.37  17.04  16.26   2.39  14.49  13.49  15.15\n",
      "  2.33  20.32  12.66  14.16  19.43  16.04   9.6    2.44  14.04  12.88\n",
      " 15.6   17.65  12.71  13.99  13.77  23.76  17.1   14.77  13.21  14.6\n",
      " 11.1   13.82  13.93  22.54  19.04  18.32  15.65  21.43  20.43  15.54\n",
      " 24.2    1.83  12.21  14.93   2.22  16.76  24.76  23.48  11.21  18.1\n",
      " 16.93  17.21   2.11  14.32  14.54  16.88  19.98  18.15  11.55  14.88\n",
      " 21.26  18.48  12.77  16.99  12.27  12.6   22.48  11.99  16.21  22.26\n",
      " 20.48  29.81  17.76  22.15  17.49  17.71  15.27  18.21   2.66  20.09\n",
      " 19.82  14.99  17.37   2.28  19.1    2.5   25.81  18.87  16.49   1.89\n",
      " 18.82  20.71  19.48  25.42  16.43  18.37  15.82  12.93  18.43  20.82\n",
      " 19.15  18.6   18.26  18.65  26.76  21.98  18.71  19.93   1.94  15.32\n",
      " 34.25   1.72  25.98  29.2   27.7   33.58  20.04  27.59  21.7   30.75\n",
      "  4.27   4.77   4.39  23.15  24.54  26.81  16.38  21.87  31.53  17.32\n",
      " 32.03  21.82  21.59  16.65  14.27  27.64  19.26  23.7   24.37  28.92\n",
      " 19.71  16.15  27.42  17.15  19.65  20.65  17.65  19.37  16.99   1.05\n",
      " 25.26  23.04  25.7   26.87  21.04  25.59  20.26  26.53  22.87  19.15\n",
      " 13.88  17.99  16.49  22.81  18.98  33.86  17.26  24.09 ], ValueCount:4.940     1307\n",
      "5.000     1061\n",
      "4.830     1049\n",
      "4.880      981\n",
      "5.050      960\n",
      "          ... \n",
      "18.152       1\n",
      "3.275        1\n",
      "18.150       1\n",
      "15.154       1\n",
      "24.090       1\n",
      "Name: Glucose, Length: 628, dtype: int64\n",
      "25 -> Iron : Unique:[11.28 24.54 12.18 11.82 16.66  8.06 20.95 17.91 13.07  6.09 15.76 17.01\n",
      " 15.04  7.52 12.36 20.42  8.78 25.79 20.06 15.94 23.82  6.98 15.58 17.37\n",
      " 36.36 20.78 14.51 21.49 20.24 10.03 29.73 13.79 28.12 16.12 13.43 10.21\n",
      " 37.61 18.09  9.13 22.21 18.27  9.85 18.81 19.16  8.42 13.25  4.84 31.16\n",
      " 10.75  7.7  15.22 27.58 17.55 23.64 26.33  3.76 18.98 29.37 22.57 21.67\n",
      " 20.6  13.61 18.63 31.7  14.87 11.46 12.54 14.15 29.01 19.34 12.9  22.03\n",
      " 16.84 22.75 12.   10.39 15.4   5.55 14.33  7.34  8.24 22.92 30.63  7.16\n",
      " 35.82 16.48 11.64  3.22 28.3   9.49 28.48 26.51 33.67  9.67 13.97 12.72\n",
      "  6.27 11.1  23.46 25.25 16.3  17.73 23.1  19.88 25.61 30.27 21.85 39.4\n",
      " 24.18  8.96  6.45 35.46  5.19 10.93 21.13 10.57 17.19 29.55 18.45  5.37\n",
      "  3.58  7.88 19.7   4.66 24.   31.52 33.31  9.31 21.31 25.07  8.6  19.52\n",
      "  5.91 40.48 27.04 30.09 31.88 14.69 23.28 35.1  26.87  4.3  32.24  6.63\n",
      " 31.34 22.39  6.81 32.78 29.19  5.73  3.4  26.69 24.72 28.66 25.97  4.12\n",
      " 27.22 30.98 35.64 27.76  2.51 25.43 35.28 46.92 40.3  46.39  5.01 24.36\n",
      " 34.92 24.89 41.01 26.15 41.37  4.48  3.04 28.84  3.94 38.69 36.72 32.06\n",
      " 33.13 44.6  32.42 32.6  34.39 27.94 50.51  2.33  2.15 36.   40.66 34.57\n",
      " 37.79  2.87 37.97 32.95 27.4  34.21 30.81  2.69 30.45 46.57 63.94 43.16\n",
      " 29.91 56.6   1.43 37.43 59.64 36.54 34.03 39.76 34.75 45.49 37.07 46.21\n",
      "  1.61 36.18 54.27 42.09 47.1  43.7  48.18  1.97 44.06 44.95 40.12 33.85\n",
      " 38.86 33.49 38.33  1.79  1.25 47.28 41.73 53.19 36.89 46.03 41.55 11.3\n",
      " 15.9  17.7  16.5  10.9  29.2  29.9  12.7  17.4   7.    1.4  22.4  11.6\n",
      " 20.4  15.2  21.3  21.9  10.7  12.2   8.8   7.2  18.8  24.2  17.9  13.1\n",
      " 14.3  30.3  15.   14.1   9.    6.1  17.   17.2  13.4   8.1   8.4  26.7\n",
      " 15.6  24.7  11.8  16.8  17.6  20.2  26.9   6.6  35.8  10.2   9.7  34.2\n",
      " 19.2  15.8  13.8  13.3  16.1  18.6  21.   20.1   9.9   7.5   5.6  18.3\n",
      " 31.9   9.1  19.   12.4   9.5  27.2  26.3   0.9   6.8  20.8  14.9  16.7\n",
      " 14.7  21.1   4.7  26.5  25.4   3.8  13.6  26.    3.9   2.9  32.1  11.5\n",
      " 10.4  22.2  18.1  23.3  14.   29.4  19.3  12.5  10.   39.8   7.3  27.8\n",
      "  5.4  21.5  19.9   8.2  24.4  36.7  22.9  32.4  14.5  25.8  22.7   5.2\n",
      "  5.9   5.7   6.4  10.6  33.8  49.8  22.6  27.   29.7  27.6  21.7   9.3\n",
      " 19.5   1.8  25.1  18.4  25.3  23.5  24.9   4.5  38.7  26.1  23.8  30.6\n",
      "  7.9  23.6  34.9   5.   30.1   2.1  33.   37.3   6.3  29.6  35.3   3.2\n",
      " 32.8  29.   28.7   2.5   3.6  25.6  31.   22.   39.    2.3   4.1  38.3\n",
      "  4.8  28.5   2.   28.1  43.3  42.1  34.6  27.9  28.8  35.5  24.5  30.8\n",
      " 31.3  31.5  39.9  38.   42.6  31.2  46.7  40.7  36.9  30.4  36.5  34.\n",
      "  3.   40.5  33.1  38.1  40.1  35.6  32.2  37.6  37.1  36.4  41.7  37.4\n",
      " 34.4   1.3  38.9  43.2  44.8  45.3  53.9   2.7  39.2  33.5  38.5  53.2\n",
      " 53.7  33.3   1.6  68.4  41.2  56.4  42.8  33.7  34.7  42.3  44.4  41.6\n",
      " 42.4  39.6  44.1  36.2  50.   47.6  51.4  52.7  45.8  46.9  52.8  65.\n",
      " 61.4  46.   43.5  47.5  45.5  40.8  41.   45.7  40.3  41.4  37.8  47.8\n",
      " 43.    1.1  48.5  46.2  46.4  41.9  54.1  20.4  17.4  17.9  18.4  37.8\n",
      " 19.9  10.2  32.8   8.2   9.7   4.1  33.8   2.3  49.3  50.7  38.3  35.3\n",
      " 99.8  58.2  37.3  35.8  46.6  44.2  51.2  33.3  39.8 ], ValueCount:12.00    490\n",
      "12.90    457\n",
      "12.20    428\n",
      "11.10    426\n",
      "15.40    405\n",
      "        ... \n",
      "51.40      1\n",
      "56.60      1\n",
      "37.43      1\n",
      "33.85      1\n",
      "39.80      1\n",
      "Name: Iron, Length: 513, dtype: int64\n",
      "26 -> LDH : Unique:[ 140  133  131  181  150  121  193  119  135  151  130  143  179  168\n",
      "  145  161  203  144  165  152  139  169  149  167  155  206  184  185\n",
      "  159  129  146  164  157  170  158  125  112   86  138  120  117  182\n",
      "  174  118  180  136  223  137  148  142  147  192  191  166  197  220\n",
      "  115  134  116  173  127  183   97  160  128  111  122  153  154  222\n",
      "  141  172  123  189  178  156  126  124  113  176  171  190  352  132\n",
      "  228  186  108   75  163  254  248  208  195  177  207  162  230  110\n",
      "  243  106  212  229  194  219  187  109  204  198   84  199  114   94\n",
      "  103  201  104   88  209  102  258  215  245  202  214  253  213   87\n",
      "  107  205  175  100  289  200  217  239   90  235  105  310  196  236\n",
      "  210   98   76  232  216   79  218  255   93  211  238  225  227  287\n",
      "  277  224  441  270  226  259  371  221  231  360   96  244   85  252\n",
      "  292   95   63  359  188   67  341   91  234   65   45  101  409  295\n",
      "  251  467   83  309  302  250   99   71  305   89  246  284   81   61\n",
      "  578  290  375  257   74   92   78  399  297   52  346  247  249   77\n",
      "  265   80   68  329  470   69   64  319   51   66   73   70   60  587\n",
      "   82   48  263  272  350  269   57   58 1180   56   40   44   42  317\n",
      "  279   59  293  285  241  965  256   47  237  666   39   43  268  336\n",
      "   72  326   54   53  242   62   35  378  262  337  264  267  233  661\n",
      "  260   50  266  364  312  485   37  518  273  344 1539  322  274  318\n",
      "  476    4  311  301 1092  673  261  345  271  286  296  276   32   55\n",
      "  240  343  356  308  303  577  552  294  333  339   46  510  361  369\n",
      "  618  278  455  275], ValueCount:121     690\n",
      "120     672\n",
      "122     660\n",
      "123     638\n",
      "119     633\n",
      "       ... \n",
      "42        1\n",
      "44        1\n",
      "40        1\n",
      "1180      1\n",
      "275       1\n",
      "Name: LDH, Length: 312, dtype: int64\n",
      "27 -> Phosphorus : Unique:[1.066 1.033 1.13  0.904 1.195 1.227 1.098 0.84  1.453 1.356 0.936 1.001\n",
      " 0.872 0.969 1.647 1.518 0.775 1.162 1.259 1.292 0.743 1.324 0.807 1.421\n",
      " 1.388 0.71  0.646 1.711 0.678 1.55  1.582 1.485 1.937 1.679 0.549 0.614\n",
      " 2.325 0.581 2.099 2.357 2.163 1.16  1.36  0.9   1.29  1.23  1.03  0.81\n",
      " 1.19  1.45  1.26  1.07  1.49  0.94  1.1   1.32  1.39  1.    0.87  1.52\n",
      " 1.42  1.58  1.68  0.65  1.61  0.97  1.65  1.78  1.74  0.77  1.71  0.68\n",
      " 0.74  1.87  2.39  1.84  1.91  1.94  2.    2.29  0.58  1.81  1.615 1.033\n",
      " 1.162 1.388 1.13  1.259 1.001 1.776 1.808 0.969 2.002 0.743 1.744 1.841\n",
      " 2.196 1.905 1.873 1.97  2.131 0.614 0.484 2.163 2.422 2.034 2.615 0.517\n",
      " 0.581 2.034 2.002 1.15  2.067 2.648], ValueCount:1.195    2432\n",
      "1.227    2337\n",
      "1.098    2151\n",
      "1.292    2017\n",
      "1.066    1893\n",
      "         ... \n",
      "1.870       1\n",
      "1.780       1\n",
      "0.650       1\n",
      "2.357       1\n",
      "2.648       1\n",
      "Name: Phosphorus, Length: 114, dtype: int64\n",
      "28 -> Bilirubin : Unique:[1.2000e+01 8.6000e+00 6.8000e+00 1.0300e+01 5.1000e+00 1.8800e+01\n",
      " 1.3700e+01 1.7100e+01 2.9100e+01 2.5700e+01 1.5400e+01 2.0500e+01\n",
      " 2.3900e+01 3.4000e+00 2.2200e+01 3.0800e+01 3.2500e+01 2.7400e+01\n",
      " 3.9300e+01 3.4200e+01 4.2800e+01 3.7600e+01 6.3300e+01 4.4500e+01\n",
      " 1.7000e+00 3.5900e+01 1.0260e+01 1.1970e+01 8.5500e+00 1.8810e+01\n",
      " 6.8400e+00 1.3680e+01 1.5390e+01 2.0520e+01 5.6430e+01 2.2230e+01\n",
      " 5.1300e+00 3.2490e+01 2.5650e+01 2.3940e+01 2.7360e+01 3.4200e+00\n",
      " 1.7100e+00 3.7620e+01 2.9070e+01 3.0780e+01 4.6170e+01 3.5910e+01\n",
      " 3.9330e+01 4.2750e+01 4.7880e+01 4.4460e+01 1.8810e+01 4.1040e+01\n",
      " 3.5910e+01 3.7620e+01 5.1300e+01 5.4720e+01 6.1560e+01 2.2401e+02\n",
      " 4.9590e+01 5.3010e+01 1.2483e+02 7.0110e+01 7.5240e+01 6.6690e+01\n",
      " 1.2141e+02 5.8100e+00 7.1800e+00 1.4540e+01 1.7000e-01 3.4000e-01\n",
      " 0.0000e+00 1.3700e+00 6.8000e-01 1.2000e+00 5.1000e-01 8.6000e-01\n",
      " 1.5400e+00 1.0300e+00], ValueCount:10.26     6387\n",
      "11.97     5242\n",
      "8.55      4921\n",
      "13.68     4077\n",
      "6.84      3066\n",
      "          ... \n",
      "5.81         1\n",
      "121.41       1\n",
      "66.69        1\n",
      "75.24        1\n",
      "44.50        1\n",
      "Name: Bilirubin, Length: 80, dtype: int64\n",
      "29 -> Protein : Unique:[ 72.   73.   66.   79.   75.   76.   74.   78.   77.   80.   71.   82.\n",
      "  81.   83.   68.   65.   86.   85.   84.   70.   69.   64.   63.   67.\n",
      "  87.   91.   90.   88.   60.   62.   95.   92.   89.  100.   57.   59.\n",
      "  61.   94.   96.   93.   58.   98.   55.   56.   54.   97.   47.   52.\n",
      "  53.   99.   49.   51.  104.  101.  113.  102.   53.8], ValueCount:72.0     3280\n",
      "71.0     3092\n",
      "73.0     2975\n",
      "70.0     2894\n",
      "74.0     2732\n",
      "69.0     2541\n",
      "75.0     2508\n",
      "68.0     2191\n",
      "76.0     2145\n",
      "67.0     1738\n",
      "77.0     1694\n",
      "66.0     1405\n",
      "78.0     1339\n",
      "65.0     1018\n",
      "79.0      997\n",
      "64.0      738\n",
      "80.0      698\n",
      "81.0      553\n",
      "63.0      512\n",
      "82.0      374\n",
      "62.0      313\n",
      "83.0      243\n",
      "61.0      207\n",
      "84.0      156\n",
      "85.0      118\n",
      "60.0      113\n",
      "59.0       92\n",
      "86.0       76\n",
      "58.0       66\n",
      "87.0       59\n",
      "57.0       32\n",
      "88.0       28\n",
      "89.0       25\n",
      "56.0       25\n",
      "55.0       19\n",
      "90.0       12\n",
      "91.0       10\n",
      "53.0        9\n",
      "54.0        9\n",
      "92.0        7\n",
      "93.0        7\n",
      "96.0        4\n",
      "94.0        3\n",
      "52.0        3\n",
      "95.0        3\n",
      "101.0       3\n",
      "100.0       2\n",
      "47.0        2\n",
      "104.0       1\n",
      "102.0       1\n",
      "113.0       1\n",
      "98.0        1\n",
      "51.0        1\n",
      "49.0        1\n",
      "99.0        1\n",
      "97.0        1\n",
      "53.8        1\n",
      "Name: Protein, dtype: int64\n",
      "30 -> Uric.Acid : Unique:[ 362.8  404.5  339.   410.4  368.8  226.   261.7  160.6  309.3  463.9\n",
      "  458.   285.5  333.1  267.7  487.7  446.1  249.8  350.9  541.3  380.7\n",
      "  481.8  303.3  386.6  208.2  582.9  237.9  523.4  315.2  190.3  178.4\n",
      "  273.6  243.9  255.8  297.4  392.6  232.   553.2  291.5  469.9  166.5\n",
      "  184.4  279.6  428.3  327.1  345.   452.   356.9  321.2  202.2  220.1\n",
      "  440.2  172.5  398.5  214.1  624.5  422.3  511.5  374.7  154.6  148.7\n",
      "  196.3  416.4  434.2  499.6  571.   654.3  695.9  505.6  547.2  124.9\n",
      "  517.5  130.9  475.8  588.9  142.8  535.3  725.7  136.8  666.2  559.1\n",
      "  618.6  594.8  529.4  493.7  642.4  119.   797.   672.1  648.3  577.\n",
      "  107.1  814.9  113.   565.1   95.2   89.2  606.7  600.7  636.4   83.3\n",
      "  612.6  826.8   23.8  297.4  535.3  148.7  594.8  541.3  630.5  648.3\n",
      "  101.1  690.   701.9   71.4 1046.8   29.7  654.3  684.   779.2  737.6\n",
      "  678.1   47.6  791.1  707.8   41.6   65.4  773.2  660.2 1070.6], ValueCount:315.2     1083\n",
      "309.3     1067\n",
      "273.6     1025\n",
      "291.5     1011\n",
      "279.6     1007\n",
      "          ... \n",
      "684.0        1\n",
      "29.7         1\n",
      "71.4         1\n",
      "826.8        1\n",
      "1070.6       1\n",
      "Name: Uric.Acid, Length: 129, dtype: int64\n",
      "31 -> Triglycerides : Unique:[ 1.298  3.85   1.581  3.635  0.756  0.474  1.242  0.847  2.077  1.919\n",
      "  1.061  1.095  0.948  1.355  2.281  1.027  1.524  2.145  0.835  2.156\n",
      "  3.206  1.682  6.909  2.269  0.723  1.818  2.981  0.644  2.021  1.773\n",
      "  3.398  1.852  2.1    0.881  0.689  2.631  3.319  0.824  3.071  0.61\n",
      "  1.456  1.073  2.089 13.819  1.739  8.129  1.264  1.49   1.05   0.768\n",
      "  0.802  1.761  0.971  1.106  1.276  0.914  0.926  1.942  1.513  2.653\n",
      "  0.994  1.694  1.547  0.903  1.377  1.31   0.542  2.043  1.129  1.603\n",
      "  2.235  1.366  0.96   1.445  1.152  1.535  2.371  0.937  1.174  1.434\n",
      "  2.619  1.637  0.677  0.745  2.303  1.118  1.66   0.553  0.779  1.219\n",
      "  2.856  1.976  1.389  1.468  1.592  1.84   1.569  1.321  0.858  1.197\n",
      "  1.185  1.897  0.632  1.084  1.716  0.982  1.502  1.231  4.177  1.874\n",
      "  1.648  1.964  1.14   0.734  0.282  2.179  1.806  2.123  1.558  2.45\n",
      "  1.016  3.353  0.497  0.711  1.671  0.7    5.622  4.313  3.443  0.452\n",
      "  4.708  1.953  0.519  3.139  3.172  0.813  1.987  0.666  0.621  0.316\n",
      "  0.384  1.287  2.01   1.727  0.463  2.427  2.066  4.2    1.344  1.332\n",
      "  2.743  1.411  1.784  2.902  2.416  0.587  1.863  1.479  1.4    1.614\n",
      "  2.337  0.565  2.348  4.019  0.576  1.795  2.8    3.771  2.811  1.626\n",
      "  2.19   0.361  3.466  3.94   3.15   4.595  3.184  2.032  0.869  0.892\n",
      "  2.676  1.253  2.134  7.779  2.168  3.477  3.455  1.885  3.048  2.552\n",
      "  2.642  2.54   3.014  0.598  1.705  4.426  3.816  2.506  3.613  1.423\n",
      "  2.314  1.908  1.163  1.039  6.887  6.345  2.755  0.79   0.44   1.998\n",
      "  3.161  2.732  2.89   2.924  2.913  2.766  0.531  2.992  2.574  2.247\n",
      "  5.126  3.669  2.055  2.823  4.392  6.672  4.347  4.697  5.871  2.868\n",
      "  4.155  4.403  0.395  5.001  1.75   0.508  3.805  2.879  9.766  5.532\n",
      "  6.108  2.292  2.585  2.382  1.931  1.208  1.829  3.906  4.64   4.076\n",
      "  3.387  3.489  2.529  2.213  4.877  0.485  2.687  2.484  3.037  5.86\n",
      "  2.71   3.884  2.935  1.005  2.845  2.111  2.461  3.76   5.25   2.258\n",
      "  2.698  6.492  4.471  0.655  4.663  5.701  3.003  3.308  3.726  4.166\n",
      "  0.373  2.224  2.777  4.11   2.597  2.439  4.561  4.855  3.432  4.279\n",
      "  4.866  2.405  2.473  3.376  2.958  7.564  3.872  3.602  3.342  5.103\n",
      "  4.121  4.505  3.579  6.119  2.664  4.798  4.606  3.127  3.06   3.026\n",
      "  0.339  3.997 11.595  4.968  3.918  2.608  7.384  6.13   2.518  3.703\n",
      "  2.721  3.421  2.834  3.556  0.294  2.202  3.195  0.406  3.522  4.945\n",
      "  0.418  3.647  4.414  8.479  4.064  5.905  0.429  3.974  2.393  3.274\n",
      "  3.116  7.745  6.097  3.218  6.977  5.713  4.832  6.198  4.132  3.511\n",
      "  5.43   3.24   2.789  5.092  5.272  6.469  3.929  7.982  3.692  2.326\n",
      "  8.242  8.411  4.787  6.243  4.098  4.358  5.182  5.216  3.093  4.493\n",
      "  7.418  4.053  2.969  3.839  3.952  3.658 10.116  4.031  2.36   3.714\n",
      "  6.074  5.058  4.245  4.189  4.922  6.684  3.861  4.143  3.963  3.105\n",
      "  6.627  4.9    3.41   3.082  3.263  5.442  3.545  5.306  2.563  3.782\n",
      "  9.326  4.572  4.776  4.821  3.5    8.693 11.2    2.495  5.961  5.939\n",
      "  5.329  6.187  4.042  2.947  3.737  6.864  5.069 10.342  4.685 10.94\n",
      "  6.548  0.305 11.572  8.163  4.211  5.284  6.334  6.176  6.83   6.379\n",
      "  3.297  5.476 13.221  5.34   7.35   6.966  4.753  0.327  3.985 15.162\n",
      " 12.306  3.827  5.611  3.252  3.364  4.448  8.806  4.437  9.122  0.35\n",
      "  5.137  3.229  4.843  9.653  5.826  6.051  7.508  3.793  5.239  6.458\n",
      "  5.193 11.188  5.148  3.331  5.51   5.747  5.656  5.893  6.401  7.09\n",
      "  4.268  3.285  3.624  5.668  5.758  3.748  5.035  7.079  5.487  7.124\n",
      "  6.029  4.81   5.419  4.674 11.29   4.482  5.498  7.406  5.318  3.681\n",
      " 12.159  5.634  8.377  4.539  5.114  4.008  4.381 12.363  7.192  3.895\n",
      "  0.271  4.651  4.584 11.561  4.629  7.993  4.29   5.171  4.222  3.568\n",
      "  4.516  5.047  3.534  6.718  4.55   8.343  4.234  5.792  4.527 12.069\n",
      " 10.353  6.322  6.255  8.084  4.742  0.847  0.948  0.982  7.643  1.535\n",
      "  2.574  0.858  1.626  1.908  0.418  1.513  2.066  0.723  0.734  1.253\n",
      "  1.174  3.658  1.039  2.642  0.994  9.292  0.565  1.005  1.061  1.287\n",
      "  0.835  2.134  6.808  2.292  0.971  0.869  3.534  2.913  2.303  4.143\n",
      "  2.687  1.795  3.703  2.393  0.361  0.587  0.689  1.027  3.477  2.506\n",
      "  3.432  4.087  5.78   3.014  1.648  2.247  2.235  2.405  1.152  2.969\n",
      "  1.942  4.301  0.824  1.761  1.547  2.021  0.813  0.474  2.348  2.529\n",
      "  3.139  3.59   0.598  0.711  0.576  2.981  3.545  3.037  3.602  2.743\n",
      "  1.773  0.429  4.64   0.26   1.682  1.434  2.518  0.305  1.14   7.948\n",
      "  3.985  2.01   4.561 15.501  0.485  3.308  2.811  4.177  4.098  8.23\n",
      "  2.416  5.464  6.571  3.997  3.127  7.034  6.006  2.461  3.872  4.268\n",
      "  3.929  4.324  4.132  4.358  4.81   2.179  4.719  3.082  3.489  2.473\n",
      "  9.393  5.058  0.226  4.764  5.882  4.651  3.364  0.294  5.419  5.453\n",
      "  5.543  5.148  5.724  4.414  3.522  4.369  4.031 11.584  2.631  5.114\n",
      "  3.026  2.868  3.884  5.645  5.566  4.934  5.227  3.421  7.688  3.252\n",
      "  4.313 27.152  5.521  4.042  3.635  6.797  3.376  5.927  4.629  2.755\n",
      "  3.195  5.103 16.529  8.456  2.924  4.99   4.505  8.253  4.256  6.989\n",
      "  2.856  5.6    6.616  7.632  7.259  3.771  7.474  5.092  4.279  9.642\n",
      "  8.829  8.58   6.955  7.18   7.282  5.239  4.539  4.956  6.356  4.053\n",
      " 11.166  5.284  8.772  6.063  5.205 14.767  4.911  5.261  6.345  4.787\n",
      "  7.621  4.584  3.816  4.889  9.224  6.537  5.916  6.898  4.877 12.916\n",
      "  5.047  7.056  6.593  5.701 11.674  5.408  5.363  3.647  7.609  5.081\n",
      "  6.763  7.835  5.137  5.374  6.65   4.618  8.738  0.282  5.611  7.045\n",
      " 10.466  5.193  6.277  5.848  4.821 17.262  5.16   6.368  6.413  6.672\n",
      "  5.013  7.406  7.587 15.219 11.098  4.076  5.318  4.731  6.909  7.079\n",
      "  5.656  8.309  6.085  4.46   5.351  6.232  9.845  6.921  7.44   4.335\n",
      "  6.627  6.311  4.866  6.943  6.266  7.542  4.922  9.371  7.158  9.009\n",
      "  7.497  5.577 10.082 10.816 14.575  6.48  11.064  8.422  8.964  5.024\n",
      "  5.679  8.614  8.987 10.827  9.427 12.588  7.429  7.711  4.595  6.153\n",
      "  6.706  6.029  8.287  8.569  5.769  3.263 11.855  7.124  5.589 16.235\n",
      "  6.21   6.503  7.79  11.143  6.142  9.89   4.403  6.729  6.074  6.887\n",
      "  5.995  5.837  4.776  9.506  5.329  6.864  8.434 17.07   7.813  6.018\n",
      " 23.709 10.206  8.806  6.932  6.255  7.316  7.937  7.88   7.147  6.514\n",
      "  5.814  0.237  5.295  8.817  5.984 10.059  9.134  9.495  8.027 11.956\n",
      "  7.068  6.526  6.164  8.558  6.3    7.203  8.321  9.924  9.664  8.005\n",
      "  9.314  9.732  4.832 10.737  9.698  8.411  0.102  5.622  9.461  7.101\n",
      "  6.221  8.84   7.734  6.842  8.626 15.095  8.151  6.853  8.219  8.14\n",
      "  6.751  9.111  6.774  0.248  7.903  6.04   7.169  7.519  8.637  6.74\n",
      "  0.181 11.911  5.792  6.785 13.661  6.559  5.735  5.972  6.401  9.517\n",
      "  8.942  5.95   4.979  9.834  7.226  6.695  0.135  8.276  5.69   5.555\n",
      " 10.093  5.397 13.051  4.685  7.248  5.385 11.042  6.763  7.237 11.211\n",
      "  6.164  7.451  7.53   4.369  7.688 10.263  5.295  9.427  9.54   5.566\n",
      "  8.524  8.727  7.7    6.018  5.803  7.971  8.626  4.911  8.897  7.293\n",
      "  5.645 10.026  6.639 10.511  6.311  8.682 33.712  4.087  5.837  0.215\n",
      "  6.876  6.063  5.374  9.664 10.884  0.203  7.926  6.808 13.063  7.768\n",
      " 11.516  6.661  6.435  5.882 10.296  8.716  7.169  6.797  4.731  8.321\n",
      " 34.559 14.146  0.237  7.892 18.12   5.521  7.395  9.755  7.959  9.563\n",
      " 10.703  7.858  7.587 11.967  7.542  4.324 10.15   6.571  9.472 10.274], ValueCount:0.881     286\n",
      "1.129     279\n",
      "0.926     277\n",
      "0.960     272\n",
      "0.914     266\n",
      "         ... \n",
      "23.709      1\n",
      "10.206      1\n",
      "8.806       1\n",
      "6.932       1\n",
      "10.274      1\n",
      "Name: Triglycerides, Length: 1000, dtype: int64\n",
      "32 -> Total-Cholesterol : Unique:[ 5.56  7.21  4.03  8.12  4.5   5.15  4.24  3.75  5.66  4.11  5.59  4.89\n",
      "  5.04  5.53  4.84  7.37  6.28  6.88  6.54  8.9   6.49  5.4   5.3   6.31\n",
      "  3.8   4.73  4.6   3.7   7.03  7.14  6.57  4.32  5.46  5.9   5.12  4.81\n",
      "  5.35  6.21  6.67  4.09  3.49  6.52  4.86  5.07  5.95  7.84  5.38  7.4\n",
      "  4.68  5.02  6.13  5.51  4.53  4.76  4.58  3.21  6.1   7.94  7.78  5.77\n",
      "  3.93  7.53  6.    4.45  7.27  4.19  4.63  4.4   5.79  3.47  6.34  4.01\n",
      "  5.69  6.36  7.45  5.25  7.42  8.07  5.72  6.08  4.42  2.61  5.84  5.09\n",
      "  4.47  3.78  4.29  6.05  4.78  5.64  3.36  4.34  3.67  4.65  3.59  4.16\n",
      "  3.96  5.97  6.72  6.18  4.99  4.97  3.88  5.82  5.17  6.41  4.55  3.9\n",
      "  5.92  5.28  9.52  7.06  6.62  3.23  4.27  4.91  5.87  4.71  8.28  4.94\n",
      "  3.1   5.22  6.03  4.06  5.33  7.58  6.75  3.98  7.11  6.65  5.61  6.47\n",
      "  7.55  6.85  6.39  7.73  3.26  4.37  5.48  6.9   7.32  7.01  6.15  2.48\n",
      "  6.26  7.76  7.29  4.14  6.8   3.52  8.79  3.    6.23  6.44  8.38  7.47\n",
      "  5.2   3.83  7.09  6.78  5.43  3.72  7.34  4.22  3.13  3.62  3.34  5.74\n",
      "  3.65  7.71  6.59  3.57  8.15  8.46  6.98  3.08  8.69  3.41  8.53  6.7\n",
      "  3.44  2.92  3.39  3.85  8.59  6.83  2.38  8.22  3.54  2.82  7.16  2.97\n",
      "  8.43  2.51  7.63  8.51  7.86  7.68  7.6   2.72 10.24  2.9   6.96  6.93\n",
      "  7.19  8.33  3.28  7.24  8.95  8.04  3.18  9.31  2.95  3.03  3.15  7.65\n",
      "  7.89  2.84  7.96  2.77  3.05  2.56  2.79  8.48  9.46  9.57  8.17  8.25\n",
      "  8.09  3.31  2.4   2.87  7.5   8.02  7.91  9.41  8.4  11.51  7.81  8.61\n",
      " 10.37  2.07  8.2   9.23 10.96 10.42  2.66  8.35 11.27 10.03  9.15  9.03\n",
      "  9.05  8.66  9.08  8.71  8.64  1.94  8.92  2.25  2.2   9.   11.02  8.3\n",
      "  8.82  2.22  7.99  2.74  2.64 11.4  10.19  2.53  8.77  2.15  9.62 11.84\n",
      "  2.46  8.97  8.56  2.12  9.28  2.43  2.17 11.15  8.84 10.65 11.17  9.59\n",
      "  2.59  8.87  2.69 10.5   9.13 10.63  2.33  9.34  9.9  10.29  2.3   1.53\n",
      "  8.74 10.06  2.35  9.36  9.83 11.97  1.78 13.58 14.09 11.2   1.99  9.26\n",
      "  2.09], ValueCount:5.09     400\n",
      "5.02     398\n",
      "4.86     392\n",
      "4.76     391\n",
      "4.97     382\n",
      "        ... \n",
      "11.15      1\n",
      "10.65      1\n",
      "11.17      1\n",
      "9.59       1\n",
      "2.09       1\n",
      "Name: Total-Cholesterol, Length: 313, dtype: int64\n",
      "33 -> HDL : Unique:[1.39 1.08 0.98 1.28 1.04 1.49 1.41 1.1  2.72 1.42 1.21 1.06 0.93 1.7\n",
      " 1.2  1.5  1.07 1.47 0.89 1.55 1.26 1.14 0.72 1.31 1.11 0.91 1.   0.8\n",
      " 0.96 2.17 0.87 1.02 1.23 1.15 1.17 0.94 0.74 0.65 1.86 1.16 1.67 1.37\n",
      " 1.91 1.22 1.87 1.65 1.71 1.03 1.96 1.84 1.54 0.68 1.34 1.88 1.01 1.8\n",
      " 0.85 0.86 2.3  1.92 1.36 1.09 2.07 0.82 0.6  2.53 0.81 1.44 1.85 2.41\n",
      " 1.68 0.75 1.48 0.84 1.45 1.52 1.18 1.19 0.61 1.82 2.21 2.38 0.64 1.25\n",
      " 0.69 1.69 0.97 2.56 1.74 0.78 0.79 1.53 1.78 0.95 1.56 2.04 1.76 1.94\n",
      " 1.24 1.95 1.6  0.67 1.73 1.29 1.59 1.98 1.72 2.42 2.02 1.12 0.83 0.71\n",
      " 0.66 1.9  1.46 2.27 1.58 2.   1.64 2.11 2.84 2.36 1.05 1.33 2.28 1.38\n",
      " 1.61 2.13 1.99 1.3  2.12 2.16 1.57 2.34 1.66 0.99 2.24 1.83 2.06 2.61\n",
      " 0.52 1.62 2.23 0.76 2.03 2.2  2.95 1.63 1.89 2.29 2.1  2.83 1.93 1.77\n",
      " 2.94 2.5  1.13 2.33 0.59 2.43 0.9  2.78 2.44 0.57 0.73 1.75 2.35 3.\n",
      " 0.56 1.27 1.51 0.5  0.49 2.62 2.68 2.08 0.63 2.18 0.21 1.81 2.89 2.46\n",
      " 1.97 0.55 2.67 2.25 2.22 2.47 0.7  2.05 2.4  2.14 2.45 2.15 0.77 0.53\n",
      " 2.9  2.73 3.25 2.01 2.26 0.92 2.55 2.19 2.39 2.6  0.48 3.12 0.31 1.32\n",
      " 1.4  2.51 0.88 2.77 2.79 0.62 2.09 2.87 2.64 2.92 2.69 4.14 2.59 2.66\n",
      " 2.82 2.48 3.03 3.05 3.67 3.21 2.74 0.54 3.08 3.18 2.97 3.15 3.65 3.9\n",
      " 4.86 0.44 3.78 3.1  3.59 3.93 0.41 0.18 3.75 3.83 0.28 3.41 3.34 3.31\n",
      " 3.49 0.39 0.34 0.47 3.44 3.28 4.63 3.36 3.72 3.13 3.88 0.36 3.57 4.03\n",
      " 3.23 3.26 0.26 4.09 4.47 5.84 3.54 3.52 0.16 3.85], ValueCount:1.14    1072\n",
      "1.09    1025\n",
      "1.22    1023\n",
      "1.19    1004\n",
      "1.03    1000\n",
      "        ... \n",
      "4.14       1\n",
      "2.41       1\n",
      "3.90       1\n",
      "4.86       1\n",
      "3.85       1\n",
      "Name: HDL, Length: 276, dtype: int64\n",
      "34 -> Glycohemoglobin : Unique:[ 4.7  5.5  5.2  7.6  5.8  4.6  5.3  5.1  6.2  4.8  5.4  6.7  4.9  5.\n",
      "  5.7  8.6  5.6  6.1  6.   5.9  4.5  4.4  6.4  8.2  8.8  7.3  8.5  6.5\n",
      " 12.5  7.1 11.4  6.3  8.1 12.   8.3  7.9  7.  10.8  7.4  7.7  7.8  7.5\n",
      "  9.6  7.2  9.5  9.4 11.8 14.3  9.2  8.4  4.3  8.7  8.  12.4  6.9 10.5\n",
      "  6.6  9.1  8.9 13.  11.   9.3 14.6  9.  12.9 11.1  6.8 12.7 10.3 13.5\n",
      " 11.2 10.2 11.6 14.  10.1  3.8 10.9 13.1 11.5  4.1  4.  11.3 10.7  9.8\n",
      " 10.6 13.2 12.3 14.7 12.1  9.7  4.2 10.  16.1  3.7 10.4 11.9 13.9  3.6\n",
      " 14.1 12.8  3.3 18.8 14.2 12.6 11.7 18.  13.7  9.9  3.9 12.2 13.3 15.2\n",
      "  2.  15.5 13.6 13.4 16.4 15.9 16.9 16.5 17.8 14.4 17.5  3.5 15.  17.\n",
      " 15.6], ValueCount:5.4     3488\n",
      "5.3     3361\n",
      "5.5     3282\n",
      "5.2     3195\n",
      "5.6     2762\n",
      "        ... \n",
      "18.8       1\n",
      "3.3        1\n",
      "3.6        1\n",
      "14.6       1\n",
      "15.6       1\n",
      "Name: Glycohemoglobin, Length: 127, dtype: int64\n",
      "35 -> Moderate-work : Unique:[3 1 2], ValueCount:2    21423\n",
      "1    15265\n",
      "3      391\n",
      "Name: Moderate-work, dtype: int64\n",
      "36 -> Diabetes : Unique:[2 1 3], ValueCount:2    32227\n",
      "1     4144\n",
      "3      708\n",
      "Name: Diabetes, dtype: int64\n",
      "37 -> Blood-Rel-Diabetes : Unique:[2 1], ValueCount:2    20375\n",
      "1    16704\n",
      "Name: Blood-Rel-Diabetes, dtype: int64\n",
      "38 -> Blood-Rel-Stroke : Unique:[2 1], ValueCount:2    29521\n",
      "1     7558\n",
      "Name: Blood-Rel-Stroke, dtype: int64\n",
      "39 -> CoronaryHeartDisease : Unique:[0 1], ValueCount:0    35571\n",
      "1     1508\n",
      "Name: CoronaryHeartDisease, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "\n",
    "for colli in range(len(fileData.__dataframe__().column_names())):\n",
    "    unique_values = fileData[fileData.__dataframe__().column_names()[colli]].unique()\n",
    "    value_counts = fileData[fileData.__dataframe__().column_names()[colli]].value_counts()\n",
    "    if(len(unique_values)< 10):\n",
    "        print(\"\\n\\n{} -> {} : Unique:{}, ValueCount:{}\".format(colli, fileData.__dataframe__().column_names()[colli], unique_values, value_counts))\n",
    "    else:\n",
    "        print(\"\\n\\n{} -> {} : Unique:{}, ValueCount:{}\".format(colli, fileData.__dataframe__().column_names()[colli], len(unique_values), value_counts))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "finalResultTable = [ ['Index', 'Method', 'Accuracy %','Recall %','Precision %','F1 Score', 'AUC'], ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "columnsForGraph = []\n",
    "columnsForGraph.clear()\n",
    "tableDataRow = []\n",
    "for column in columns:\n",
    "    singleColumnCount = fileData[column].value_counts()\n",
    "    if(len(singleColumnCount) < 3):\n",
    "        #print('Column Name:{} -> total records:{}'.format(column, totalRecords ) )\n",
    "        #print('Number of classes:', len(singleColumnCount))\n",
    "        #print('Class distribution:')\n",
    "        #print(singleColumnCount)\n",
    "        #print(\"np Array: {}\".format(np.array(singleColumnCount)))\n",
    "        #print(\"index: 0: {} -> {} %\".format(np.array(singleColumnCount)[0], (np.array(singleColumnCount)[0] /totalRecords) * 100))\n",
    "        #print(\"index: 1: {} -> {} %\".format(np.array(singleColumnCount)[1], ( np.array(singleColumnCount)[1] /totalRecords) * 100))  \n",
    "        #print('---------------------------------------------------------------')\n",
    "        columnsForGraph.append(column)\n",
    " \n",
    "tableDataRow = [ ['Index', 'Column Name', 'Total Classes','Class A Records','Class B Records'], ]\n",
    "\n",
    "indexx = 1\n",
    "for column in columnsForGraph:\n",
    "    singleColumnCount = fileData[column].value_counts()\n",
    "    singleRowInTable = [] \n",
    "    singleRowInTable.append(indexx)\n",
    "    singleRowInTable.append(column)\n",
    "    singleRowInTable.append(len(singleColumnCount))\n",
    "    cellDataString = \"{} -> {:.2f}%\".format(np.array(singleColumnCount)[0], (np.array(singleColumnCount)[0] /totalRecords) * 100)\n",
    "    singleRowInTable.append((cellDataString)) \n",
    "    cellDataString = \"{} -> {:.2f}%\".format(np.array(singleColumnCount)[1], (np.array(singleColumnCount)[1] /totalRecords) * 100)\n",
    "    singleRowInTable.append((cellDataString)) \n",
    "    indexx += 1\n",
    "    tableDataRow.append(singleRowInTable) \n",
    "\n",
    " \n",
    "# Determine the number of rows in the table (excluding the header)\n",
    "num_rows = len(tableDataRow) + 1\n",
    "# Calculate the desired figure size based on the number of rows\n",
    "fig_width = 6  # Set the desired width of the figure\n",
    "fig_height = num_rows * 0.5  # Adjust the scaling factor to control the height\n",
    "\n",
    "fig, ax = mplot.subplots(figsize=(fig_width, fig_height)) \n",
    "table = mplot.table(cellText=tableDataRow, loc='center') \n",
    "\n",
    "table.auto_set_column_width(col=list(range(len(tableDataRow[0]))))\n",
    "\n",
    "\n",
    "\n",
    "table.auto_set_font_size(False)\n",
    "table.set_fontsize(12) \n",
    "table.scale(2.0, 2.0) \n",
    "\n",
    "dataSetString = \"Dataset:  {}, Total Records: {}, No. Features: {}\".format(dataSetName, totalRecords, fileData.__dataframe__().num_columns())\n",
    "target =\"Target Column Name: {} , No of Classes: {}\".format(columns[-1], len(fileData[columns[-1]].value_counts()))\n",
    "distributionOfTargetClassA =\"Class A Records: {} , {:.2f} %\".format(np.array(fileData[columns[-1]].value_counts())[0], (np.array(fileData[columns[-1]].value_counts())[0] /totalRecords) * 100)\n",
    "distributionOfTargetClassB =\"Class B Records: {} , {:.2f} %\".format(np.array(fileData[columns[-1]].value_counts())[1], (np.array(fileData[columns[-1]].value_counts())[1] /totalRecords) * 100)\n",
    "\n",
    "fig.text(-0.1, +0.10,  dataSetString, horizontalalignment='left', wrap=False , fontsize=12 )  \n",
    "fig.text(-0.1, +0.02,  target, horizontalalignment='left', wrap=False  , fontsize=12 )   \n",
    "fig.text(-0.1, -0.06,  distributionOfTargetClassA, horizontalalignment='left', wrap=False , fontsize=12  )   \n",
    "fig.text(-0.1, -0.14,  distributionOfTargetClassB, horizontalalignment='left', wrap=False  , fontsize=12 )   \n",
    "\n",
    "remarks = \"You need to distribute the target class in equal number of records in training-set.\"\n",
    "#fig.text(-0.2, -0.15,  remarks, horizontalalignment='left', wrap=True ,fontsize=12, fontweight='bold' )   \n",
    " \n",
    "mplot.axis('off')\n",
    "mplot.title(f'Exploring Dataset - {dataSetName}' ,fontsize=16, fontweight='bold') \n",
    "picturePath = \"{}0.DataSet_analysis_{}.png\".format(dataSetResultDirectory, dataSetName)\n",
    "mplot.savefig(picturePath,  dpi=300, bbox_inches='tight')\n",
    "#mplot.savefig(picturePath,  dpi=300 )\n",
    "mplot.show()\n",
    "mplot.close()\n",
    "\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def makeCorrelationPic(correlationMatrix, numberOfTopFeatures, targetColumnName):     \n",
    "    correlation_values = correlationMatrix.abs()\n",
    "    sorted_correlation = correlation_values.unstack().sort_values(ascending=False)\n",
    "    sorted_correlation = sorted_correlation[sorted_correlation != 1.0]\n",
    "\n",
    "    num_features = numberOfTopFeatures  # Number of top features to display\n",
    "    top_features = sorted_correlation.head(num_features)\n",
    "    print(\"Top\", num_features, \"features based on correlation:\")\n",
    "    print(top_features)\n",
    " \n",
    "    top_features = correlationMatrix.abs().nlargest(numberOfTopFeatures, targetColumnName)[targetColumnName].index\n",
    "    top_correlation_matrix = correlationMatrix.loc[top_features, top_features]\n",
    "\n",
    "    mplot.figure(figsize=(10, 8))\n",
    "    sns.heatmap(top_correlation_matrix, annot=True, cmap='coolwarm')\n",
    "    # Set the title of the plot\n",
    "    mplot.title('Correlation Heatmap ({})'.format(dataSetName)  ,fontsize=16, fontweight='bold')\n",
    "    \n",
    "    picturePath = \"{}0.1_Correlation_Matrix_DateSetName_{}.png\".format(dataSetResultDirectory, dataSetName)\n",
    "    \n",
    "    mplot.savefig(picturePath,  dpi=300, bbox_inches='tight')\n",
    "    mplot.show()\n",
    "    mplot.close()\n",
    "def plot_classification_report(title, dataSetName, y_tru, y_prd, figsize=(6, 6), ax=None):\n",
    "    #mplot.figure(figsize=figsize)\n",
    "    xticks = ['precision', 'recall', 'f1-score', 'support']\n",
    "    yticks = ['Healthy', 'Heart Disease']\n",
    "    rep = np.array( precision_recall_fscore_support(y_tru, y_prd) ).T\n",
    "    rep[0][0] *= 100.0\n",
    "    rep[0][1] *= 100.0\n",
    "    rep[0][2] *= 100.0\n",
    "    rep[1][0] *= 100.0\n",
    "    rep[1][1] *= 100.0\n",
    "    rep[1][2] *= 100.0\n",
    "    \n",
    "    ax = sns.heatmap(rep, annot=True, cmap='Blues', cbar=False, xticklabels=xticks, yticklabels=yticks)\n",
    "    ax.set_title(\"Classification Report {} Model\\n\\n\".format(title) ,fontsize=16, fontweight='bold')\n",
    "    ax.set_xlabel('\\nDataset:{}'.format(dataSetName))\n",
    "    ax.xaxis.set_ticklabels(xticks)\n",
    "    ax.set_ylabel('Classes')\n",
    "    ax.yaxis.set_ticklabels(yticks)\n",
    "    \n",
    "    picturePath = \"{}ClassificationReport_{}_{}.png\".format(dataSetResultDirectory, title, dataSetName) \n",
    "    mplot.savefig(picturePath, dpi=300, bbox_inches='tight')\n",
    "    #os.startfile(picturePath)\n",
    "    mplot.close()\n",
    "def makeConfusionMatrixPic(fileID, method, dataSet, classifierObj , X_test, y_test, predicted_Y):\n",
    "    display = ConfusionMatrixDisplay.from_predictions(y_test,predicted_Y, display_labels=['Healthy', \"Heart Disease\"], cmap=mplot.cm.Blues) #, normalize=\"true\"\n",
    "    \n",
    "    display.ax_.set_title(\"Confusion Matrix ({} Model)\".format(method) ,fontsize=16, fontweight='bold')\n",
    "    display.ax_.set_xlabel('\\nPredicted Values')\n",
    "    display.ax_.set_ylabel('Actual Values ') \n",
    " \n",
    "    accuracyValue = (accuracy_score(y_test, predicted_Y)*100.0) \n",
    "    recallValue = (recall_score(y_test, predicted_Y, average='weighted') * 100.0)\n",
    "    precisionValue = (precision_score(y_test, predicted_Y) * 100.0) \n",
    "    f1Score = (f1_score(y_test, predicted_Y, average='weighted') * 100.0)\n",
    "\n",
    " \n",
    "    singleRowInTable = [] \n",
    "    singleRowInTable.append(fileID)\n",
    "    singleRowInTable.append(method)\n",
    "    singleRowInTable.append(\"{:.2f}\".format(accuracyValue) )\n",
    "    singleRowInTable.append(\"{:.2f}\".format(recallValue) )\n",
    "    singleRowInTable.append(\"{:.2f}\".format(precisionValue) )\n",
    "    singleRowInTable.append(\"{:.2f}\".format(f1Score) )\n",
    "    singleRowInTable.append(\"-\")\n",
    "\n",
    "    finalResultTable.append((singleRowInTable) )\n",
    "\n",
    "\n",
    "    accuracyString =\"Accuracy : {:.2f} %\".format( accuracyValue) \n",
    "    recallString =  'Recall : {:.2f} %'.format(recallValue)\n",
    "    precisionString = 'Precision : {:.2f} %'.format(precisionValue) \n",
    "    precisionString = 'Precision : {:.2f} %'.format(precisionValue) \n",
    "    f1String = 'F1 Score : {:.2f} %'.format(f1Score) \n",
    "    dataSetString = \"Dataset: {}\".format(dataSet)\n",
    "    \n",
    "    numberOfTrainingRecords = \"No of Training Records: {}  {:.2f} %\".format(len(X_train), ((len(X_train)/totalRecords) * 100.0))\n",
    "    numberOfTestingRecords = \"No of Testing Records: {}  {:.2f} %\".format(len(X_test), ((len(X_test)/totalRecords) * 100.0))\n",
    "\n",
    "\n",
    "    display.figure_.text(0.010, -0.05,  accuracyString, horizontalalignment='left', wrap=False , fontsize=12 )  \n",
    "    display.figure_.text(0.010, -0.09,  recallString, horizontalalignment='left', wrap=False , fontsize=12 )      \n",
    "    display.figure_.text(0.010, -0.13,  precisionString, horizontalalignment='left', wrap=False , fontsize=12 ) \n",
    "    display.figure_.text(0.010, -0.17,  f1String, horizontalalignment='left', wrap=False , fontsize=12 ) \n",
    "    display.figure_.text(0.010, -0.21,  dataSetString, horizontalalignment='left', wrap=False , fontsize=12 ) \n",
    "    display.figure_.text(0.010, -0.25,  numberOfTrainingRecords, horizontalalignment='left', wrap=False , fontsize=12 ) \n",
    "    display.figure_.text(0.010, -0.29,  numberOfTestingRecords, horizontalalignment='left', wrap=False , fontsize=12 ) \n",
    "    \n",
    "    '''\n",
    "    if(classifierObj.n_features_in_ > 10):\n",
    "        featureListString = 'Total Features: {}'.format(classifierObj.n_features_in_) \n",
    "    else:\n",
    "        featureListString = 'Features: {}'.format(classifierObj.feature_names_in_) \n",
    "    display.figure_.text(0.010, -0.28,  featureListString, horizontalalignment='left', wrap=False , fontsize=12 ) \n",
    "    '''\n",
    "    \n",
    "    picturePath = \"{}{}.Confusion_Matrix_{}_{}.png\".format(dataSetResultDirectory, fileID, method, dataSetName)\n",
    "    mplot.savefig(picturePath,  dpi=300, bbox_inches='tight')\n",
    "    #print(\"{} Confusion Matrix saved:: path: {}\".format(method, picturePath))\n",
    "    #os.startfile(picturePath)\n",
    "    mplot.show()\n",
    "    mplot.close()\n",
    "\n",
    "\n",
    "def makeConfusionMatrixPicWithProbibality(fileID, method, dataSet, classifierObj , X_test, y_test, predicted_Y):\n",
    "    display = ConfusionMatrixDisplay.from_predictions(y_test,predicted_Y, display_labels=['Healthy', \"Heart Disease\"], cmap=mplot.cm.Blues) #, normalize=\"true\"\n",
    "    \n",
    "    display.ax_.set_title(\"Confusion Matrix ({} Model)\".format(method) ,fontsize=16, fontweight='bold')\n",
    "    display.ax_.set_xlabel('\\nPredicted Values')\n",
    "    display.ax_.set_ylabel('Actual Values ') \n",
    "\n",
    "    predicted_Proba = classifierObj.predict_proba(X_test)[:, 1]\n",
    "    fpr, tpr, thresholds = roc_curve(y_test, predicted_Proba)\n",
    "    roc_auc = auc(fpr, tpr) \n",
    "\n",
    "    accuracyValue = (accuracy_score(y_test, predicted_Y)*100.0) \n",
    "    recallValue = (recall_score(y_test, predicted_Y, average='weighted') * 100.0)\n",
    "    precisionValue = (precision_score(y_test, predicted_Y) * 100.0) \n",
    "    f1Score = (f1_score(y_test, predicted_Y, average='weighted') * 100.0)\n",
    "\n",
    " \n",
    "    singleRowInTable = [] \n",
    "    singleRowInTable.append(fileID)\n",
    "    singleRowInTable.append(method)\n",
    "    singleRowInTable.append(\"{:.2f}\".format(accuracyValue) )\n",
    "    singleRowInTable.append(\"{:.2f}\".format(recallValue) )\n",
    "    singleRowInTable.append(\"{:.2f}\".format(precisionValue) )\n",
    "    singleRowInTable.append(\"{:.2f}\".format(f1Score) )\n",
    "    singleRowInTable.append(\"{:.2f}\".format(roc_auc) )\n",
    "\n",
    "    finalResultTable.append((singleRowInTable) )\n",
    "\n",
    "\n",
    "    accuracyString =\"Accuracy : {:.2f} %\".format( accuracyValue) \n",
    "    recallString =  'Recall : {:.2f} %'.format(recallValue)\n",
    "    precisionString = 'Precision : {:.2f} %'.format(precisionValue) \n",
    "    precisionString = 'Precision : {:.2f} %'.format(precisionValue) \n",
    "    f1String = 'F1 Score : {:.2f} %'.format(f1Score) \n",
    "    dataSetString = \"Dataset: {}\".format(dataSet)\n",
    "    \n",
    "    numberOfTrainingRecords = \"No of Training Records: {}  {:.2f} %\".format(len(X_train), ((len(X_train)/totalRecords) * 100.0))\n",
    "    numberOfTestingRecords = \"No of Testing Records: {}  {:.2f} %\".format(len(X_test), ((len(X_test)/totalRecords) * 100.0))\n",
    "\n",
    "\n",
    "    display.figure_.text(0.010, -0.05,  accuracyString, horizontalalignment='left', wrap=False , fontsize=12 )  \n",
    "    display.figure_.text(0.010, -0.09,  recallString, horizontalalignment='left', wrap=False , fontsize=12 )      \n",
    "    display.figure_.text(0.010, -0.13,  precisionString, horizontalalignment='left', wrap=False , fontsize=12 ) \n",
    "    display.figure_.text(0.010, -0.17,  f1String, horizontalalignment='left', wrap=False , fontsize=12 ) \n",
    "    display.figure_.text(0.010, -0.21,  dataSetString, horizontalalignment='left', wrap=False , fontsize=12 ) \n",
    "    display.figure_.text(0.010, -0.25,  numberOfTrainingRecords, horizontalalignment='left', wrap=False , fontsize=12 ) \n",
    "    display.figure_.text(0.010, -0.29,  numberOfTestingRecords, horizontalalignment='left', wrap=False , fontsize=12 ) \n",
    "    \n",
    "    '''\n",
    "    if(classifierObj.n_features_in_ > 10):\n",
    "        featureListString = 'Total Features: {}'.format(classifierObj.n_features_in_) \n",
    "    else:\n",
    "        featureListString = 'Features: {}'.format(classifierObj.feature_names_in_) \n",
    "    display.figure_.text(0.010, -0.28,  featureListString, horizontalalignment='left', wrap=False , fontsize=12 ) \n",
    "    '''\n",
    "    \n",
    "    picturePath = \"{}{}.Confusion_Matrix_{}_{}.png\".format(dataSetResultDirectory, fileID, method, dataSetName)\n",
    "    mplot.savefig(picturePath,  dpi=300, bbox_inches='tight')\n",
    "    #print(\"{} Confusion Matrix saved:: path: {}\".format(method, picturePath))\n",
    "    #os.startfile(picturePath)\n",
    "    mplot.show()\n",
    "    mplot.close()\n",
    "    \n",
    " \n",
    "    mplot.figure(figsize=(8, 6))\n",
    "    mplot.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC Curve (AUC = {roc_auc:.2f})')\n",
    "    mplot.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "    mplot.xlim([0.0, 1.0])\n",
    "    mplot.ylim([0.0, 1.05])\n",
    "    mplot.xlabel('False Positive Rate (FPR)')\n",
    "    mplot.ylabel('True Positive Rate (TPR)')\n",
    "    mplot.title('(ROC) Curve {} ({})'.format(method, dataSet))\n",
    "    mplot.legend(loc='lower right')\n",
    "    picturePath = \"{}{}.ROC_{}_{}.png\".format(dataSetResultDirectory, fileID, method, dataSetName)\n",
    "    mplot.savefig(picturePath,  dpi=300, bbox_inches='tight')\n",
    "    mplot.show()\n",
    "    mplot.close()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def makeLIMEreport(method, classifierObj):\n",
    "    class_names =  ['Healthy', 'Heart Disease']\n",
    "    listOfFeatures =  classifierObj.feature_names_in_\n",
    "    print(\"\\n\\n\\n Obj Features: {}, len: {} \\n\\n\".format(listOfFeatures, len(listOfFeatures)))\n",
    "    #explainer = lime_tabular.LimeTabularExplainer(np.array(X_train) , mode='regression', feature_names=listOfFeatures, verbose=True, class_names=class_names )\n",
    "    explainer = lime_tabular.LimeTabularExplainer(np.array(X_train) , mode='classification', feature_names=listOfFeatures, verbose=True, class_names=class_names )\n",
    "\n",
    "    num_instances = int(len(X_test)/4)\n",
    "    print(\"num_instances: {}\".format(num_instances))\n",
    "    # Generate explanations for each testing instance\n",
    "    explanations = []\n",
    "    for i in range(num_instances):\n",
    "        instan = instance[i]\n",
    "        explanation = explainer.explain_instance(instan, classifierObj.predict_proba, num_features=len(instan))\n",
    "        explanations.append(explanation)\n",
    "\n",
    "    #Extract the feature importance values from the explanations\n",
    "    feature_importances = np.mean([exp.as_map()[1] for exp in explanations], axis=0)\n",
    "    averageF = []\n",
    "    for singleFeature in feature_importances:\n",
    "        averageF.append(singleFeature[0])\n",
    "     \n",
    "    print(\"\\n\\nfeature_list:{}\".format(listOfFeatures))\n",
    "    print(\"averageF:{} \\n\\n\".format(averageF))\n",
    "\n",
    "\n",
    "    fig, ax = mplot.subplots()\n",
    "    ax.barh(listOfFeatures, averageF )\n",
    "    ax.set_ylabel('Feature List')\n",
    "    ax.set_xlabel('Average Importance')\n",
    "    ax.set_title(\"XAI LIME ({} Model)\".format(method) ,fontsize=16, fontweight='bold')\n",
    "     \n",
    "    dataSetString = \"Dataset:  {}\".format(dataSetName)\n",
    "    testingDatasetString =\"length of Testing Set: {}\".format(len(X_test))\n",
    "    explainerModelString = \"LIME Explainer Model: {}\".format(explainer.mode)\n",
    "    ax.figure.text(0.020, -0.05,  dataSetString, horizontalalignment='left', wrap=False )  \n",
    "    ax.figure.text(0.020, -0.09,  testingDatasetString, horizontalalignment='left', wrap=False )   \n",
    "    ax.figure.text(0.020, -0.13,  explainerModelString, horizontalalignment='left', wrap=False )   \n",
    " \n",
    "\n",
    "    picturePath = \"{}XAI_LIME_{}_{}.png\".format(dataSetResultDirectory, method, dataSetName)\n",
    "    mplot.savefig(picturePath,  dpi=300, bbox_inches='tight')\n",
    "    #mplot.savefig(picturePath,  dpi=300) \n",
    "    #os.startfile(picturePath)\n",
    "    mplot.show()\n",
    "    mplot.close()\n",
    "def makeSHAPreport(method, model):\n",
    "    shap_explainer = shap.Explainer(model, X_train) \n",
    "    shap_values = shap_explainer.shap_values(X_test)  \n",
    "    shap.summary_plot(shap_values, X_test, show=False)\n",
    "    ax = mplot.gca() \n",
    "    ax.set_title(\"XAI SHAP Explainer ({} Model)\".format(method) ,fontsize=16, fontweight='bold')     \n",
    "\n",
    "    dataSetString = \"Dataset:  {}\".format(dataSetName)\n",
    "    testingDatasetString =\"length of Testing Set: {}\".format(len(X_test))\n",
    "    shapTypeString =\"SHAP Type: {}\".format(repr(shap_explainer)) \n",
    "    ax.figure.text(0.020, -0.05,  dataSetString, horizontalalignment='left', wrap=False )  \n",
    "    ax.figure.text(0.020, -0.09,  testingDatasetString, horizontalalignment='left', wrap=False )   \n",
    "    ax.figure.text(0.020, -0.13,  shapTypeString, horizontalalignment='left', wrap=False )   \n",
    "\n",
    "    picturePath = \"{}XAI_SHAP_Explainer_{}_{}.png\".format(dataSetResultDirectory, method, dataSetName)\n",
    "    mplot.savefig(picturePath,  dpi=300, bbox_inches='tight')\n",
    "    #mplot.savefig(picturePath,  dpi=300) \n",
    "    mplot.show()\n",
    "    #os.startfile(picturePath)\n",
    "    mplot.close()\n",
    "def makeSHAP_KERNELreport(method, model):\n",
    "    shap_explainer = shap.KernelExplainer(model.predict_proba, X_train) \n",
    "    testingShape = X_test[0:10]\n",
    "    shap_values = shap_explainer.shap_values(testingShape)  \n",
    "    shap.summary_plot(shap_values, X_test, show=False)\n",
    "    ax = mplot.gca() \n",
    "    ax.set_title(\"XAI SHAP KernelExplainer ({} Model)\".format(method) ,fontsize=16, fontweight='bold')     \n",
    "\n",
    "    dataSetString = \"Dataset:  {}\".format(dataSetName)\n",
    "    testingDatasetString =\"length of Testing Set: {}\".format(len(testingShape))\n",
    "    shapTypeString =\"SHAP Type: {}\".format(repr(shap_explainer)) \n",
    "    ax.figure.text(0.020, -0.05,  dataSetString, horizontalalignment='left', wrap=False )  \n",
    "    ax.figure.text(0.020, -0.09,  testingDatasetString, horizontalalignment='left', wrap=False )   \n",
    "    ax.figure.text(0.020, -0.13,  shapTypeString, horizontalalignment='left', wrap=False )   \n",
    "\n",
    "    picturePath = \"{}XAI_SHAP_KernelExplainer_{}_{}.png\".format(dataSetResultDirectory, method, dataSetName)\n",
    "    mplot.savefig(picturePath,  dpi=300, bbox_inches='tight')\n",
    "    #mplot.savefig(picturePath,  dpi=300) \n",
    "    mplot.show()\n",
    "    #os.startfile(picturePath)\n",
    "    mplot.close()\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the correlation matrix \n",
    "correlation_matrix = fileData.corr()\n",
    "#makeCorrelationPic(correlation_matrix, 15, 'CoronaryHeartDisease') \n",
    "\n",
    "makeCorrelationPic(correlation_matrix, 15, fileData.__dataframe__().column_names()[-1] ) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "column_names = X.columns\n",
    "\n",
    "#print(\"columns of x:: {} \\n and features of X: {} \\n\\n\".format(len(column_names), column_names))\n",
    "\n",
    "# Apply PCA\n",
    "pca = PCA(n_components=6)\n",
    "pca.fit(X, Y)\n",
    "X_pca = pca.fit_transform(X)\n",
    "\n",
    "# Get the selected column indices\n",
    "selected_columns_indices = pca.components_ #.argsort()[:-1][:pca.n_components_]\n",
    "\n",
    "# Get the selected column names\n",
    "#selected_columns = column_names[selected_columns_indices]\n",
    "\n",
    "pd2 = pd.DataFrame(X_pca, columns=[\"col1\", \"col2\", \"col3\", \"col4\", \"col5\", \"col6\" ])\n",
    "#pd3 = pd.DataFrame(Y, columns=[fileData.__dataframe__().column_names()[-1]]) \n",
    "pd2[fileData.__dataframe__().column_names()[-1]] = Y \n",
    "\n",
    "#pd2 = pd2.add(pd3)\n",
    "#pd2 = pd2 + pd3\n",
    "\n",
    "pd2.info()\n",
    "\n",
    "# Print the selected column names\n",
    "print(f' len: {len(selected_columns_indices)} ')\n",
    "print(f' len: {len(selected_columns_indices[0])} ')\n",
    "#print(selected_columns)\n",
    "\n",
    "# Print the selected column names\n",
    "#print(f'\\n\\n len: {len(selected_columns[5])} -> col: {selected_columns[5]} \\n') \n",
    "\n",
    "\n",
    "X = pd2.drop(pd2.__dataframe__().column_names()[-1], axis=1)  # Features\n",
    "Y = pd2[pd2.__dataframe__().column_names()[-1]]  # Labels\n",
    "\n",
    "columns = pd2.__dataframe__().column_names() \n",
    "totalRecords = (pd2.__dataframe__().num_rows())\n",
    "\n",
    "\n",
    "\n",
    "finalResultTable = [ ['Index', 'Method', 'Accuracy %','Recall %','Precision %','F1 Score', 'AUC'], ] \n",
    "\n",
    "dataSetResultDirectory += (\"DatasetResults_\" + dataSetName)\n",
    "dataSetResultDirectory += \"/\"\n",
    "if not os.path.isdir(dataSetResultDirectory):\n",
    "    os.makedirs(dataSetResultDirectory)\n",
    "\n",
    "\n",
    "dataSetName += \" {}\".format(pd2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#print(\"columns of x:: {} \\n\\n and features of X: {}\".format(len(X.columns), X.columns))\n",
    "\n",
    "print(\"***************************************\") \n",
    "\n",
    "print(\"Shape of fileData: {} , target Len:{}\".format(fileData.shape, len(Y)))\n",
    "print(\"X: {} , Y:{}\".format(X.shape, Y.shape))\n",
    "#print(\"\\n\\nX: head:: \\n{}\".format(X.head()))\n",
    "#print(\"\\n\\nY: head::\\n {}\".format(Y.head()))\n",
    "\n",
    "# Split dataset into training set and test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.3, stratify=Y, random_state=42)\n",
    " \n",
    "\n",
    "print(\"Target Column Name:: {} \\n\".format(fileData.__dataframe__().column_names()[-1]))\n",
    "\n",
    "\n",
    "rus = RandomUnderSampler(random_state=42)\n",
    "smote = SMOTE(random_state=42)\n",
    "\n",
    "# Undersample the majority class\n",
    "X_train, y_train = rus.fit_resample(X_train, y_train)\n",
    "X_test, y_test = rus.fit_resample(X_test, y_test)\n",
    "'''\n",
    "\n",
    "# Oversample the minority class using SMOTE\n",
    "X_train, y_train = smote.fit_resample(X_train, y_train)\n",
    "#X_test, y_test = rus.fit_resample(X_test, y_test)\n",
    "X_test, y_test = smote.fit_resample(X_test, y_test)\n",
    "\n",
    "'''\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "\n",
    "\n",
    "print(\"\\n X Train: Shape:: {}\".format(X_train.shape))\n",
    "print(\" X Test: Shape:: {}\".format(X_test.shape)) \n",
    "print(\"\\n X Train: Shape::\\n {}\".format(X_train.shape))\n",
    "print(\"\\n X Train: head::\\n {}\".format(X_train.columns))\n",
    "print(\"\\n X Test: head:: \\n{}\".format(X_test.columns))\n",
    "print(\"\\n Y Train: shape::\\n {}\".format(y_train.shape)) \n",
    "print(\"\\n Y Test: shape::\\n {}\".format(y_test.shape))  \n",
    "#print(\"\\n X Train: Info::\\n {}\".format(X_train.info())) \n",
    "\n",
    "\n",
    "instance = np.array(X_test)  # Example: explaining the first instance in the dataset\n",
    " \n",
    "dataSetResultDirectory = \"./\"\n",
    "dataSetResultDirectory += (\"DatasetResults_\" + dataSetName)\n",
    "dataSetResultDirectory += \"/\"\n",
    "if not os.path.isdir(dataSetResultDirectory):\n",
    "    os.makedirs(dataSetResultDirectory)\n",
    "\n",
    "dataSetName += \" {}\".format(fileData.shape)\n",
    " \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "positiveClass =  0\n",
    "negativeClass = 0\n",
    "for i in y_train:\n",
    "    if(i == 0):\n",
    "        negativeClass += 1\n",
    "    if(i == 1):\n",
    "        positiveClass += 1\n",
    "print(\"Train DataSet Positive Class Records:: {}\".format(positiveClass)) \n",
    "print(\"Train DataSet Negative Class Records:: {}\".format(negativeClass)) \n",
    "print(\"Train DataSet Total Records:: {}\".format(positiveClass + negativeClass)) \n",
    "\n",
    "print(\"\\n\\n\") \n",
    "\n",
    "\n",
    "positiveClass =  0\n",
    "negativeClass = 0\n",
    "for i in y_test:\n",
    "    if(i == 0):\n",
    "        negativeClass += 1\n",
    "    if(i == 1):\n",
    "        positiveClass += 1\n",
    "print(\"Test DataSet Positive Class Records:: {}\".format(positiveClass)) \n",
    "print(\"Test DataSet Negative Class Records:: {}\".format(negativeClass)) \n",
    "print(\"Test DataSet Total Records:: {}\".format(positiveClass + negativeClass)) \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#1.     Logistic Regression\n",
    "classifierLR = LogisticRegression(solver='lbfgs', max_iter=100, C=0.20,  class_weight=\"Mapping\",  random_state=20, fit_intercept=True, warm_start= True)\n",
    "#classifierLR = LogisticRegression()\n",
    "classifierLR.fit(X_train_scaled, y_train)\n",
    "predicted_LR = classifierLR.predict(X_test_scaled)\n",
    "print('Accuracy of logistic regression classifier on test set: {:.4f}'.format(classifierLR.score(X_test_scaled, y_test)))\n",
    "methodName = \"LogisticRegression\" \n",
    "#plot_classification_report(methodName, dataSetName, y_test, predicted_LR)  \n",
    "makeConfusionMatrixPicWithProbibality(1, methodName, dataSetName, classifierLR , X_test_scaled, y_test, predicted_LR)\n",
    "#makeSHAPreport(methodName, classifierLR)\n",
    "#makeSHAP_KERNELreport(methodName, classifierLR)\n",
    "#makeLIMEreport(methodName, classifierLR)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2.     Decision Trees\n",
    "classifierDT = DecisionTreeClassifier(criterion=\"gini\", splitter=\"best\", max_depth=8 , max_features=\"log2\",  random_state=18) \n",
    "classifierDT.fit(X_train_scaled, y_train) \n",
    "predicted_DT = classifierDT.predict(X_test_scaled) \n",
    "\n",
    "methodName = \"DecisionTree(Scaled data)\" \n",
    "#plot_classification_report(methodName, dataSetName, y_test, predicted_DT)  \n",
    "makeConfusionMatrixPicWithProbibality(2, methodName, dataSetName, classifierDT , X_test, y_test, predicted_DT)\n",
    "#makeSHAPreport(methodName, classifierDT)\n",
    "#makeSHAP_KERNELreport(methodName, classifierDT)\n",
    "#makeLIMEreport(methodName, classifierDT)\n",
    "\n",
    "# Calculate the accuracy of the classifier\n",
    "accuracy = accuracy_score(y_test, predicted_DT)\n",
    "print(\"Accuracy:\", accuracy)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#3.     Random Forests\n",
    "classifierRF = RandomForestClassifier(n_estimators=100, criterion=\"log_loss\", max_depth=8, max_features=15,n_jobs=-1, oob_score=True, bootstrap=True, random_state = 18)\n",
    "classifierRF.fit(X_train, y_train)\n",
    "y_PredictionRF = classifierRF.predict(X_test)\n",
    "\n",
    "methodName = \"RF\" \n",
    "#plot_classification_report(methodName, dataSetName, y_test, y_PredictionRF)  \n",
    "makeConfusionMatrixPicWithProbibality(3, methodName, dataSetName, classifierRF , X_test, y_test, y_PredictionRF)\n",
    "#makeSHAPreport(methodName, classifierRF)\n",
    "#makeSHAP_KERNELreport(methodName, classifierRF)\n",
    "#makeLIMEreport(methodName, classifierRF)\n",
    "  \n",
    "'''\n",
    "confusionM_rf = confusion_matrix(y_test, y_PredictionRF)\n",
    "print(\" Random Forest Accuracy:\", accuracy_score(y_test, y_PredictionRF))\n",
    "print(\" Random Forest ConfusionM: \", confusionM_rf)\n",
    "print(\" Random Forest  length of estimators:{}\".format(len(classifierRF.estimators_)))\n",
    "\n",
    "\n",
    "folderPath = r'./RandomForestTrees_{}/'.format(dataSetName)\n",
    "print(\"FolderPath:{}\".format(folderPath))\n",
    "if not os.path.isdir(folderPath):\n",
    "    os.makedirs(folderPath)\n",
    "\n",
    "#for indx in range(len(rfClassifier.estimators_)):\n",
    "for indx in range(5):\n",
    "    tree = classifierRF.estimators_[indx]\n",
    "    dot_data = export_graphviz(tree, out_file=None, feature_names=classifierRF.feature_names_in_, rounded=True, precision=1)\n",
    "    graph = pydotplus.graph_from_dot_data(dot_data)\n",
    "    picturePath = \"{}tree_{}.png\".format(folderPath, indx)\n",
    "    graph.write_png(picturePath)\n",
    "    print(\"Random Forest Tree Picture Generated:: path:\", picturePath)\n",
    "    #os.startfile(picturePath)\n",
    "'''\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#4.     Support Vector Machines (SVM)\n",
    "#classifierSVM = SVC(C=33, kernel=\"poly\") \n",
    "classifierSVM = SVC() \n",
    "classifierSVM.fit(X_train, y_train)\n",
    "y_PredictionSVM = classifierSVM.predict(X_test)\n",
    "\n",
    "methodName = \"SVM\"\n",
    "#plot_classification_report(methodName, dataSetName, y_test, y_PredictionSVM)  \n",
    "makeConfusionMatrixPic(4, methodName, dataSetName, classifierSVM , X_test, y_test, y_PredictionSVM)\n",
    "#makeSHAPreport(methodName, classifierSVM)\n",
    "#makeSHAP_KERNELreport(methodName, classifierSVM)\n",
    "#makeLIMEreport(methodName, classifierSVM)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#5.     K-Nearest Neighbors (KNN)\n",
    "classifierKNN = KNN(n_neighbors=30, weights='uniform', algorithm='ball_tree', metric='manhattan' )\n",
    "classifierKNN.fit(X_train, y_train)\n",
    "y_PredictionKNN = classifierKNN.predict(X_test)\n",
    "\n",
    "methodName = \"KNN\"\n",
    "#plot_classification_report(methodName, dataSetName, y_test, y_PredictionKNN)  \n",
    "makeConfusionMatrixPicWithProbibality(5, methodName, dataSetName, classifierKNN , X_test, y_test, y_PredictionKNN)\n",
    "#makeSHAPreport(methodName, classifierKNN)\n",
    "#makeSHAP_KERNELreport(methodName, classifierKNN)\n",
    "#makeLIMEreport(methodName, classifierKNN)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#6.     Naive Bayes (with appropriate modifications for numerical data, such as Gaussian Naive Bayes)\n",
    "classifierNB = GaussianNB(var_smoothing=0.00001 )\n",
    "classifierNB.fit(X_train, y_train)\n",
    "predicted_NB = classifierNB.predict(X_test)\n",
    "\n",
    "methodName = \"NB\" \n",
    "#plot_classification_report(methodName, dataSetName, y_test, predicted_NB)  \n",
    "makeConfusionMatrixPicWithProbibality(6, methodName, dataSetName, classifierNB , X_test, y_test, predicted_NB)\n",
    "#makeSHAPreport(methodName, classifierNB)\n",
    "#makeSHAP_KERNELreport(methodName, classifierNB)\n",
    "#makeLIMEreport(methodName, classifierNB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#7.     Neural Networks (e.g., Multilayer Perceptron)\n",
    "#scaler = StandardScaler()\n",
    "#X_train_scaled = scaler.fit_transform(X_train)\n",
    "#X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "mlp = MLPClassifier(hidden_layer_sizes=(10, 10), max_iter=1000)  # Adjust the hidden_layer_sizes and max_iter as desired\n",
    "mlp.fit(X_train_scaled, y_train)\n",
    "\n",
    "y_pred = mlp.predict(X_test_scaled)\n",
    "\n",
    "accuracy = mlp.score(X_test_scaled, y_test)\n",
    "print(\"Accuracy:\", accuracy)\n",
    "\n",
    "methodName = \"Multilayer Perceptron\" \n",
    "#plot_classification_report(methodName, dataSetName, y_test, predicted_AdaBoost)  \n",
    "makeConfusionMatrixPicWithProbibality(7, methodName, dataSetName, mlp , X_test_scaled, y_test, y_pred)\n",
    "#makeSHAPreport(methodName, classifierAdaBoost)\n",
    "#makeSHAP_KERNELreport(methodName, classifierAdaBoost)\n",
    "#makeLIMEreport(methodName, classifierAdaBoost)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#8.     AdaBoost \n",
    "classifierAdaBoost = AdaBoostClassifier(base_estimator=DecisionTreeClassifier(), random_state=42) \n",
    "classifierAdaBoost.fit(X_train, y_train) \n",
    "predicted_AdaBoost = classifierAdaBoost.predict(X_test)\n",
    "\n",
    "\n",
    "methodName = \"AdaBoost\" \n",
    "#plot_classification_report(methodName, dataSetName, y_test, predicted_AdaBoost)  \n",
    "makeConfusionMatrixPicWithProbibality(8, methodName, dataSetName, classifierAdaBoost , X_test, y_test, predicted_AdaBoost)\n",
    "#makeSHAPreport(methodName, classifierAdaBoost)\n",
    "#makeSHAP_KERNELreport(methodName, classifierAdaBoost)\n",
    "#makeLIMEreport(methodName, classifierAdaBoost)\n",
    " \n",
    "accuracy = accuracy_score(y_test, predicted_AdaBoost)\n",
    "print(\"Accuracy:\", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#9.     Gaussian Processes\n",
    "# Create a Gaussian Process classifier with RBF kernel\n",
    "#kernel = 1.0 * RBF(1.0)\n",
    "#classifierGPC = GaussianProcessClassifier(kernel=kernel, random_state=42) \n",
    "classifierGPC = GaussianProcessClassifier() \n",
    "classifierGPC.fit(X_train_scaled, y_train) \n",
    "predicted_GPC = classifierGPC.predict(X_test_scaled)\n",
    "\n",
    "methodName = \"GaussianProcessClassifier\" \n",
    "#plot_classification_report(methodName, dataSetName, y_test, predicted_GPC)  \n",
    "makeConfusionMatrixPic(9, methodName, dataSetName, classifierGPC , X_test_scaled, y_test, predicted_GPC)\n",
    "#makeSHAPreport(methodName, classifierGPC)\n",
    "#makeSHAP_KERNELreport(methodName, classifierGPC)\n",
    "#makeLIMEreport(methodName, classifierGPC)\n",
    "\n",
    "# Calculate the accuracy of the classifier\n",
    "accuracy = accuracy_score(y_test, predicted_GPC)\n",
    "print(\"Accuracy:\", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#10.     Extreme Gradient Boosting (XGBoost)\n",
    "classifierXGB = xgb.XGBClassifier() \n",
    "classifierXGB.fit(X_train, y_train) \n",
    "predicted_XGB = classifierXGB.predict(X_test)\n",
    "\n",
    "methodName = \"XGBClassifier\" \n",
    "#plot_classification_report(methodName, dataSetName, y_test, predicted_XGB)  \n",
    "makeConfusionMatrixPicWithProbibality(10, methodName, dataSetName, classifierXGB , X_test, y_test, predicted_XGB)\n",
    "#makeSHAPreport(methodName, classifierXGB)\n",
    "#makeSHAP_KERNELreport(methodName, classifierXGB)\n",
    "#makeLIMEreport(methodName, classifierXGB)\n",
    "\n",
    "# Calculate the accuracy of the classifier\n",
    "accuracy = accuracy_score(y_test, predicted_XGB)\n",
    "print(\"Accuracy:\", accuracy)\n",
    "\n",
    "'''\n",
    "selector = SelectFromModel(classifierXGB, threshold=0.01)  # Adjust the threshold as needed\n",
    "selector.fit(X_train, y_train)\n",
    "\n",
    "# Get selected features\n",
    "selected_features = X_train.columns[selector.get_support()]\n",
    "\n",
    "print(\"Shape of X_Test: {}\".format(X_test.shape))\n",
    "print(\"X_Test Columns: {}\".format(X_test.columns))\n",
    "print(\"------------------------\")\n",
    "print(\"Shape of Features: {}\".format(selected_features.shape))\n",
    "print(selected_features)\n",
    "'''\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#11.     CatBoost\n",
    "classifierCatBoost = CatBoostClassifier() \n",
    "classifierCatBoost.fit(X_train, y_train) \n",
    "predicted_CatBoost = classifierCatBoost.predict(X_test)\n",
    "\n",
    "methodName = \"CatBoostClassifier\" \n",
    "#plot_classification_report(methodName, dataSetName, y_test, predicted_CatBoost)  \n",
    "makeConfusionMatrixPicWithProbibality(11, methodName, dataSetName, classifierCatBoost , X_test, y_test, predicted_CatBoost)\n",
    "#makeSHAPreport(methodName, classifierCatBoost)\n",
    "#makeSHAP_KERNELreport(methodName, classifierCatBoost)\n",
    "#makeLIMEreport(methodName, classifierCatBoost)\n",
    "\n",
    "# Calculate accuracy\n",
    "accuracy = accuracy_score(y_test, predicted_CatBoost)\n",
    "print('Accuracy:', accuracy)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#12.     LightGBM\n",
    "classifierLGB = lgb.LGBMClassifier() \n",
    "classifierLGB.fit(X_train, y_train) \n",
    "predicted_LGB = classifierLGB.predict(X_test)\n",
    " \n",
    "methodName = \"LightGBM\" \n",
    "#plot_classification_report(methodName, dataSetName, y_test, predicted_LGB)  \n",
    "makeConfusionMatrixPicWithProbibality(12, methodName, dataSetName, classifierLGB , X_test, y_test, predicted_LGB)\n",
    "#makeSHAPreport(methodName, classifierLGB)\n",
    "#makeSHAP_KERNELreport(methodName, classifierLGB)\n",
    "#makeLIMEreport(methodName, classifierLGB)\n",
    "\n",
    "# Calculate the accuracy of the classifier\n",
    "accuracy = accuracy_score(y_test, predicted_LGB)\n",
    "print(\"Accuracy:\", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#13.     Histogram-Based Gradient Boosting (HistGradientBoosting)\n",
    "classifierHGB = HistGradientBoostingClassifier() \n",
    "classifierHGB.fit(X_train, y_train) \n",
    "predicted_HGB = classifierHGB.predict(X_test)\n",
    "\n",
    "methodName = \"HistGradientBoostingClassifier\" \n",
    "#plot_classification_report(methodName, dataSetName, y_test, predicted_HGB)  \n",
    "makeConfusionMatrixPicWithProbibality(13, methodName, dataSetName, classifierHGB , X_test, y_test, predicted_HGB)\n",
    "#makeSHAPreport(methodName, classifierHGB)\n",
    "#makeSHAP_KERNELreport(methodName, classifierHGB)\n",
    "#makeLIMEreport(methodName, classifierHGB)\n",
    "\n",
    "# Calculate the accuracy of the classifier\n",
    "accuracy = accuracy_score(y_test, predicted_HGB)\n",
    "print(\"Accuracy:\", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#14      Stacking: \n",
    "clf1 = RandomForestClassifier()\n",
    "clf2 = KNN()\n",
    "# Define the meta-classifier\n",
    "meta_clf = LogisticRegression()\n",
    "# Create the stacking classifier\n",
    "classifierStacking = StackingClassifier(estimators=[('rf', clf1), ('knn', clf2)], final_estimator=meta_clf)\n",
    "classifierStacking.fit(X_train, y_train) \n",
    "predicted_Stacking = classifierStacking.predict(X_test)\n",
    "\n",
    "methodName = \"Stacking(RF,KNN,LR)\" \n",
    "#plot_classification_report(methodName, dataSetName, y_test, predicted_Stacking)  \n",
    "makeConfusionMatrixPicWithProbibality(14, methodName, dataSetName, classifierStacking , X_test, y_test, predicted_Stacking)\n",
    "#makeSHAPreport(methodName, classifierStacking)\n",
    "#makeSHAP_KERNELreport(methodName, classifierStacking)\n",
    "#makeLIMEreport(methodName, classifierStacking)\n",
    "\n",
    "\n",
    "# Calculate the accuracy of the classifier\n",
    "accuracy = accuracy_score(y_test, predicted_Stacking)\n",
    "print(\"Accuracy:\", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#15      Voting: \n",
    "# Define the individual classifiers\n",
    "clf1 = RandomForestClassifier()\n",
    "clf2 = KNN()\n",
    "clf3 = LogisticRegression()\n",
    "\n",
    "# Create the voting classifier\n",
    "classifierVoting = VotingClassifier(estimators=[('rf', clf1), ('knn', clf2), ('lr', clf3)], voting='hard')\n",
    "# Train the voting classifier\n",
    "classifierVoting.fit(X_train, y_train)\n",
    "# Make predictions on the test set\n",
    "predicted_Voting = classifierVoting.predict(X_test)\n",
    "\n",
    "methodName = \"Voting(RF,KNN,LR)\" \n",
    "#plot_classification_report(methodName, dataSetName, y_test, predicted_Voting)  \n",
    "makeConfusionMatrixPic(15, methodName, dataSetName, classifierVoting , X_test, y_test, predicted_Voting)\n",
    "#makeSHAPreport(methodName, classifierVoting)\n",
    "#makeSHAP_KERNELreport(methodName, classifierVoting)\n",
    "#makeLIMEreport(methodName, classifierVoting)\n",
    "\n",
    "# Calculate the accuracy of the classifier\n",
    "accuracy = accuracy_score(y_test, predicted_Voting)\n",
    "print(\"Accuracy:\", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#16      Bagging:\n",
    "# Create a base classifier\n",
    "base_clf = DecisionTreeClassifier() \n",
    "classifierBagging = BaggingClassifier(base_estimator=base_clf, n_estimators=10) \n",
    "classifierBagging.fit(X_train, y_train) \n",
    "predicted_Bagging = classifierBagging.predict(X_test)\n",
    "\n",
    "\n",
    "methodName = \"Bagging(DT)\" \n",
    "#plot_classification_report(methodName, dataSetName, y_test, predicted_Bagging)  \n",
    "makeConfusionMatrixPicWithProbibality(16, methodName, dataSetName, classifierBagging , X_test, y_test, predicted_Bagging)\n",
    "#makeSHAPreport(methodName, classifierBagging)\n",
    "#makeSHAP_KERNELreport(methodName, classifierBagging)\n",
    "#makeLIMEreport(methodName, classifierBagging)\n",
    "\n",
    "# Calculate the accuracy of the classifier\n",
    "accuracy = accuracy_score(y_test, predicted_Bagging)\n",
    "print(\"Accuracy:\", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#17      Extra Trees:  \n",
    "classifierExtraTree = ExtraTreesClassifier(n_estimators=100) \n",
    "classifierExtraTree.fit(X_train, y_train) \n",
    "predicted_ExtraTree = classifierExtraTree.predict(X_test)\n",
    "\n",
    "methodName = \"ExtraTree\" \n",
    "#plot_classification_report(methodName, dataSetName, y_test, predicted_ExtraTree)  \n",
    "makeConfusionMatrixPicWithProbibality(17, methodName, dataSetName, classifierExtraTree , X_test, y_test, predicted_ExtraTree)\n",
    "#makeSHAPreport(methodName, classifierExtraTree)\n",
    "#makeSHAP_KERNELreport(methodName, classifierExtraTree)\n",
    "#makeLIMEreport(methodName, classifierExtraTree)\n",
    "\n",
    "# Calculate the accuracy of the classifier\n",
    "accuracy = accuracy_score(y_test, predicted_ExtraTree)\n",
    "print(\"Accuracy:\", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#18      Ridge Classifier: \n",
    "classifierRidge = RidgeClassifier() \n",
    "classifierRidge.fit(X_train, y_train) \n",
    "predicted_Ridge = classifierRidge.predict(X_test)\n",
    " \n",
    "methodName = \"RidgeClassifier\" \n",
    "#plot_classification_report(methodName, dataSetName, y_test, predicted_Ridge)  \n",
    "makeConfusionMatrixPic(18, methodName, dataSetName, classifierRidge , X_test, y_test, predicted_Ridge)\n",
    "#makeSHAPreport(methodName, classifierRidge)\n",
    "#makeSHAP_KERNELreport(methodName, classifierRidge)\n",
    "#makeLIMEreport(methodName, classifierRidge)\n",
    "\n",
    "\n",
    "# Calculate the accuracy of the classifier\n",
    "accuracy = accuracy_score(y_test, predicted_Ridge)\n",
    "print(\"Accuracy:\", accuracy)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Determine the number of rows in the table (excluding the header)\n",
    "num_rows = len(finalResultTable) - 1\n",
    "print(\"total Rows: {} -> Cols: {}\".format(len(finalResultTable), len(finalResultTable[0])))\n",
    "# Calculate the desired figure size based on the number of rows\n",
    "fig_width = 6  # Set the desired width of the figure\n",
    "fig_height = num_rows * 0.5  # Adjust the scaling factor to control the height\n",
    "\n",
    "fig, ax = mplot.subplots(figsize=(fig_width, fig_height)) \n",
    "table = mplot.table(cellText=finalResultTable, loc='center') \n",
    "\n",
    "table.auto_set_column_width(col=list(range(len(finalResultTable[0]))))\n",
    "table.auto_set_font_size(False)\n",
    "table.set_fontsize(12) \n",
    "table.scale(2.0, 2.0) \n",
    "\n",
    "dataSetString = \"Dataset:  {}, Total Records: {}, No. Features: {}\".format(dataSetName, X.__dataframe__().num_rows(), X.__dataframe__().num_columns())\n",
    "target =\"Target Column Name: {} , No of Classes: {}\".format(columns[-1], len(fileData[columns[-1]].value_counts()))\n",
    "distributionOfTargetClassA = \"No of Training Records: {}  {:.2f} %\".format(len(X_train), ((len(X_train)/totalRecords) * 100.0))\n",
    " \n",
    "positiveClass =  0\n",
    "negativeClass = 0\n",
    "for i in y_train:\n",
    "    if(i == 0):\n",
    "        negativeClass += 1\n",
    "    if(i == 1):\n",
    "        positiveClass += 1\n",
    "classBrecords = \"Healthy Persons in training data: {}       {:.1f} %\".format(negativeClass, ((positiveClass/len(y_train)) * 100.0))\n",
    "classArecords = \"Non-Healthy Persons in training data: {}       {:.1f} %\".format(positiveClass, ((positiveClass/len(y_train)) * 100.0))\n",
    "\n",
    "\n",
    "\n",
    "positiveClassTest =  0\n",
    "negativeClassTest = 0\n",
    "for i in y_test:\n",
    "    if(i == 0):\n",
    "        negativeClassTest += 1\n",
    "    if(i == 1):\n",
    "        positiveClassTest += 1\n",
    "classBrecordsTest = \"Healthy Persons in test data: {}       {:.1f} %\".format(negativeClassTest, ((negativeClassTest/len(y_test)) * 100.0))\n",
    "classArecordsTest = \"Non-Healthy Persons in test data: {}       {:.1f} %\".format(positiveClassTest, ((positiveClassTest/len(y_test)) * 100.0))\n",
    "\n",
    "\n",
    "\n",
    "distributionOfTargetClassB = \"No of Testing Records: {}  {:.2f} %\".format(len(X_test), ((len(X_test)/totalRecords) * 100.0))\n",
    "\n",
    "fig.text(-0.1, +0.10,  dataSetString, horizontalalignment='left', wrap=False , fontsize=12 )  \n",
    "fig.text(-0.1, +0.05,  target, horizontalalignment='left', wrap=False  , fontsize=12 )   \n",
    "fig.text(-0.1, 0.00,  distributionOfTargetClassA, horizontalalignment='left', wrap=False , fontsize=12  )   \n",
    "fig.text(-0.1, -0.03,  classBrecords, horizontalalignment='left', wrap=False , fontsize=12  )   \n",
    "fig.text(-0.1, -0.06,  classArecords, horizontalalignment='left', wrap=False , fontsize=12  )   \n",
    "\n",
    "fig.text(-0.1, -0.12,  distributionOfTargetClassB, horizontalalignment='left', wrap=False  , fontsize=12 )  \n",
    "fig.text(-0.1, -0.15,  classBrecordsTest, horizontalalignment='left', wrap=False , fontsize=12  )   \n",
    "fig.text(-0.1, -0.17,  classArecordsTest, horizontalalignment='left', wrap=False , fontsize=12  )   \n",
    " \n",
    "mplot.axis('off')\n",
    "mplot.title(f'Final Result Table ({dataSetName})' , fontsize=16, fontweight='bold') \n",
    "picturePath = \"{}99.Final_Result_Table_{}.png\".format(dataSetResultDirectory, dataSetName)\n",
    "mplot.savefig(picturePath,  dpi=300, bbox_inches='tight')\n",
    "#mplot.savefig(picturePath,  dpi=300)\n",
    "mplot.show()\n",
    "mplot.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    " \n",
    "#fpr, tpr, thresholds = roc_curve(y_test, classifierLR.predict_proba(X_test)[:,1])\n",
    "mplot.figure()\n",
    "#mplot.plot(fpr, tpr, label=\"(area = {:.2f})\".format(logit_roc_auc))\n",
    "\n",
    "fprLR, tprLR, threshLR = roc_curve(y_test, classifierLR.predict_proba(X_test)[:,1])\n",
    "#fprSVM, tprSVM, threshSVM = roc_curve(y_test, classifierSVM.predict_proba(X_test)[:,1])\n",
    "fprKNN, tprKNN, threshKNN = roc_curve(y_test, classifierKNN.predict_proba(X_test)[:,1])\n",
    "fprNB, tprNB, threshNB = roc_curve(y_test, classifierNB.predict_proba(X_test)[:,1])\n",
    " \n",
    "    \n",
    "# plotting    \n",
    "mplot.plot(fprLR, tprLR, linestyle='--',color='green', label='Class 1 vs Rest')\n",
    "#mplot.plot(fprSVM, tprSVM, linestyle='--',color='orange', label='Class 0 vs Rest')\n",
    "mplot.plot(fprKNN, tprKNN, linestyle='--',color='blue', label='Class 2 vs Rest')\n",
    "mplot.plot(fprNB, tprNB, linestyle='--',color='red', label='Class 2 vs Rest')\n",
    "mplot.title('Multiclass ROC curve' ,fontsize=16, fontweight='bold')\n",
    "mplot.xlabel('False Positive Rate')\n",
    "mplot.ylabel('True Positive rate')\n",
    "mplot.legend(loc='best')\n",
    "mplot.savefig('Multiclass ROC',dpi=300);    \n",
    "mplot.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "roundValue = 0.0\n",
    "for colName in fileData.__dataframe__().column_names():\n",
    "    fileColData = fileData[colName].astype(\"int\")\n",
    "    roundValue = round(fileData[colName].mean(), 2)\n",
    "\n",
    "    print(\"Column Name: {} -> MeanValue:{}\".format(colName, roundValue))"
   ]
  }
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