Predictions.ipynb

{
 "cells": [
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   "cell_type": "markdown",
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    "_uuid": "e919d1161f20999e599ba1fd66a5a45b9c82f229"
   },
   "source": [
    "# Table of content\n",
    "\n",
    "1. Introduction - Loading libraries and dataset\n",
    "2. Exploratory analysis, engineering and cleaning features - Bi-variate analysis\n",
    "3. Correlation analysis - Tri-variate analysis\n",
    "4. Predictive modelling, cross-validation, hyperparameters and ensembling\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Loading libraries and dataset\n",
    "\n",
    "#### Importing Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "_cell_guid": "14630296-b1aa-759e-bafa-b6a73f3896ed",
    "_execution_state": "idle",
    "_kg_hide-input": true,
    "_kg_hide-output": true,
    "_uuid": "2e37a274400cfeb472b6405d524325245588dd66",
    "jupyter": {
     "outputs_hidden": true
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   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.4.2.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load libraries for analysis and visualization\n",
    "import pandas as pd # collection of functions for data processing and analysis modeled after R dataframes with SQL like features\n",
    "import numpy as np  # foundational package for scientific computing\n",
    "import re           # Regular expression operations\n",
    "import matplotlib.pyplot as plt # Collection of functions for scientific and publication-ready visualization\n",
    "%matplotlib inline\n",
    "import plotly.offline as py     # Open source library for composing, editing, and sharing interactive data visualization \n",
    "from matplotlib import pyplot\n",
    "py.init_notebook_mode(connected=True)\n",
    "import plotly.graph_objs as go\n",
    "import plotly.tools as tls\n",
    "from collections import Counter\n",
    "\n",
    "# Machine learning libraries\n",
    "import xgboost as xgb  # Implementation of gradient boosted decision trees designed for speed and performance that is dominative competitive machine learning\n",
    "import seaborn as sns  # Visualization library based on matplotlib, provides interface for drawing attractive statistical graphics\n",
    "\n",
    "import sklearn         # Collection of machine learning algorithms\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC, LinearSVC\n",
    "from sklearn.ensemble import (RandomForestClassifier, AdaBoostClassifier, \n",
    "                              GradientBoostingClassifier, ExtraTreesClassifier, VotingClassifier)\n",
    "#from sklearn.cross_validation import KFold\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.linear_model import Perceptron\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import GridSearchCV, cross_val_score, StratifiedKFold, learning_curve\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score,classification_report, precision_recall_curve, confusion_matrix\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already up-to-date: prince in c:\\users\\user\\anaconda3\\lib\\site-packages (0.7.1)\n",
      "Requirement already satisfied, skipping upgrade: scipy>=1.3.0 in c:\\users\\user\\anaconda3\\lib\\site-packages (from prince) (1.5.2)\n",
      "Requirement already satisfied, skipping upgrade: scikit-learn>=0.22.1 in c:\\users\\user\\anaconda3\\lib\\site-packages (from prince) (0.24.2)\n",
      "Requirement already satisfied, skipping upgrade: matplotlib>=3.0.2 in c:\\users\\user\\anaconda3\\lib\\site-packages (from prince) (3.3.2)\n",
      "Requirement already satisfied, skipping upgrade: numpy>=1.17.1 in c:\\users\\user\\anaconda3\\lib\\site-packages (from prince) (1.19.2)\n",
      "Requirement already satisfied, skipping upgrade: pandas>=1.0.3 in c:\\users\\user\\anaconda3\\lib\\site-packages (from prince) (1.1.3)\n",
      "Requirement already satisfied, skipping upgrade: threadpoolctl>=2.0.0 in c:\\users\\user\\anaconda3\\lib\\site-packages (from scikit-learn>=0.22.1->prince) (2.1.0)\n",
      "Requirement already satisfied, skipping upgrade: joblib>=0.11 in c:\\users\\user\\anaconda3\\lib\\site-packages (from scikit-learn>=0.22.1->prince) (0.17.0)\n",
      "Requirement already satisfied, skipping upgrade: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in c:\\users\\user\\anaconda3\\lib\\site-packages (from matplotlib>=3.0.2->prince) (2.4.7)\n",
      "Requirement already satisfied, skipping upgrade: python-dateutil>=2.1 in c:\\users\\user\\anaconda3\\lib\\site-packages (from matplotlib>=3.0.2->prince) (2.8.1)\n",
      "Requirement already satisfied, skipping upgrade: kiwisolver>=1.0.1 in c:\\users\\user\\anaconda3\\lib\\site-packages (from matplotlib>=3.0.2->prince) (1.3.0)\n",
      "Requirement already satisfied, skipping upgrade: cycler>=0.10 in c:\\users\\user\\anaconda3\\lib\\site-packages (from matplotlib>=3.0.2->prince) (0.10.0)\n",
      "Requirement already satisfied, skipping upgrade: pillow>=6.2.0 in c:\\users\\user\\anaconda3\\lib\\site-packages (from matplotlib>=3.0.2->prince) (8.0.1)\n",
      "Requirement already satisfied, skipping upgrade: certifi>=2020.06.20 in c:\\users\\user\\anaconda3\\lib\\site-packages (from matplotlib>=3.0.2->prince) (2020.6.20)\n",
      "Requirement already satisfied, skipping upgrade: pytz>=2017.2 in c:\\users\\user\\anaconda3\\lib\\site-packages (from pandas>=1.0.3->prince) (2020.1)\n",
      "Requirement already satisfied, skipping upgrade: six>=1.5 in c:\\users\\user\\anaconda3\\lib\\site-packages (from python-dateutil>=2.1->matplotlib>=3.0.2->prince) (1.15.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    " pip install prince --upgrade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: mlxtend in c:\\users\\user\\anaconda3\\lib\\site-packages (0.19.0)\n",
      "Requirement already satisfied: numpy>=1.16.2 in c:\\users\\user\\anaconda3\\lib\\site-packages (from mlxtend) (1.19.2)\n",
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      "Requirement already satisfied: setuptools in c:\\users\\user\\anaconda3\\lib\\site-packages (from mlxtend) (50.3.1.post20201107)\n",
      "Requirement already satisfied: scikit-learn>=0.20.3 in c:\\users\\user\\anaconda3\\lib\\site-packages (from mlxtend) (0.24.2)\n",
      "Requirement already satisfied: pytz>=2017.2 in c:\\users\\user\\anaconda3\\lib\\site-packages (from pandas>=0.24.2->mlxtend) (2020.1)\n",
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      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\user\\anaconda3\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (8.0.1)\n",
      "Requirement already satisfied: certifi>=2020.06.20 in c:\\users\\user\\anaconda3\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (2020.6.20)\n",
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      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in c:\\users\\user\\anaconda3\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (2.4.7)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\user\\anaconda3\\lib\\site-packages (from scikit-learn>=0.20.3->mlxtend) (2.1.0)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\user\\anaconda3\\lib\\site-packages (from python-dateutil>=2.7.3->pandas>=0.24.2->mlxtend) (1.15.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install mlxtend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn import linear_model\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.decomposition import PCA,TruncatedSVD\n",
    "import prince\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import KernelPCA\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import itertools\n",
    "from sklearn import linear_model\n",
    "from sklearn import metrics\n",
    "\n",
    "#importing all the required ML packages\n",
    "from sklearn.linear_model import LogisticRegression #logistic regression\n",
    "from sklearn import svm #support vector Machine\n",
    "from sklearn.ensemble import RandomForestClassifier #Random Forest\n",
    "from sklearn.neighbors import KNeighborsClassifier #KNN\n",
    "from sklearn.naive_bayes import GaussianNB #Naive bayes\n",
    "from sklearn.tree import DecisionTreeClassifier #Decision Tree\n",
    "from sklearn.model_selection import train_test_split #training and testing data split\n",
    "from sklearn import metrics #accuracy measure\n",
    "from sklearn.metrics import confusion_matrix #for confusion matrix\n",
    "\n",
    "# data preprocessing\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from imblearn.over_sampling import SMOTENC\n",
    "from sklearn.model_selection import GridSearchCV, cross_val_score, StratifiedKFold, learning_curve\n",
    "\n",
    "from sklearn.metrics import classification_report\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(347, 189)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_excel(\"BD Kc colorectal_stage ESI.xlsx\")\n",
    "df = dataset.copy()\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "na = [\"?\",\"à compléter\", 'A compléter', 'pas de n°tel','à compléter']\n",
    "for col in df.columns:\n",
    "    for i in range(df.shape[0]):\n",
    "        if df[col][i] in na:\n",
    "            df[col][i] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "L = ['date_chir', 'D dernière onsultation', 'D contact tél', \n",
    "       'Decès date', 'statut_prog', 'Date recidive', 'Evolution recid',\n",
    "       'remarques','Unnamed: 188']\n",
    "for col in L:\n",
    "    del df[col]\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Suppression des lignes contenant Morbidité NaN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indexNames = df[df['Morbidité'].isna()].index\n",
    "df.drop(indexNames , inplace=True)\n",
    "df['Morbidité'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.reset_index(drop = True, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Suppression des features qui ne sont pas nécessaires ( avec accord)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "L = ['signif Incid Perop','signif CP', 'FistJPostOP',  'signif autres.1',\n",
    "     'PéritoJPostop', 'signif autres Manifcliniq ', 'signif autres','SténoJPostOP',\n",
    "    'FRVJPostOP','CStomiJPostOP', 'CHémorJPostOP', 'CUrinairJPostOP' ,   'CUrinairTrait',\n",
    "     'DélaisPostRCCH','DuréeSéjPostOP','DuréeSéjRéa' ,'DécJPostOP','CauseDécès']\n",
    "for col in L:\n",
    "    del df[col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(338, 162)"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remplacer les valeurs manquantes par NaN.\n",
    "\n",
    "li_8 = [\"atcd chir\",\"Localisation\",\"TDMSiège\", \"CmplcatParié\", \"Cplct Générl\"]\n",
    "li_6_8 = [\"Type histologique\"]\n",
    "\n",
    "\n",
    "for col in li_8:\n",
    "    df.loc[df[col] == 8, col] = np.nan\n",
    "\n",
    "df.loc[df[\"Décès\"] == 9, \"Décès\"] = np.nan\n",
    "df.loc[df[\"trouble transi\"] == 2, \"trouble transi\"] = np.nan\n",
    "df.loc[df[\"Type histologique\"] == 6 , \"Type histologique\"] = np.nan\n",
    "df.loc[df[\"Type histologique\"] == 8, \"Type histologique\"] = np.nan\n",
    "df.loc[df[\"ACE\"] == 'inf 0,50', \"ACE\"] = 0.5\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remplissage des valeurs manquantes ( pour les complications )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "L = ['FistEvolution', 'PéritoEvolution', 'SténoEvoluti','FRVEvolution', 'CStomiEvolut', 'CPérinEvolut', 'CUrinairEvolu' ]\n",
    "for col in L:\n",
    "    df.loc[df[col].isna() == False, col] = 1  # évolution\n",
    "    df[col] = df[col].fillna(0)  # Pas d'évolution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in df.columns[df.columns.get_loc(\"CmplcatPerOP\"):df.columns.get_loc(\"Cplct Générl\")+1]:\n",
    "    df[col] = df[col].fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Remplissage des NaN pour les features ( avant Morbidité)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fillna with mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|"
     ]
    }
   ],
   "source": [
    "numeric_features = [\"Age\",\"Hémoglobine\",\"Globule blanc\", \"Plaquette\",\"CRP\" , \"Urée\" , \"Créat\" , \"Glycémie\" , \"Albumine\" ,\n",
    "\"Préalbumine\" , \"Protidemie\" , \"ACE\" , \"CA19-9\"  , 'Histo Nbgg', 'Histo ggPosit']\n",
    "\n",
    "for col in numeric_features:\n",
    "    df[col] = df[col].fillna(df[col].mean())\n",
    "    \n",
    "    print(df[col].isnull().mean(), end='|')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Vérifier ( DécèsPostOP, Décès )."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df['DécèsPostOP'] == 1, 'Décès'] = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remplacer par les valeurs dominantes ( TNM, Stade - Localisation - Anastomose )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Anastomose'] = df['Anastomose'].fillna(2) \n",
    "df['Localisation'] = df['Localisation'].fillna(7) \n",
    "df['Stade'] = df['Stade'].fillna(2) \n",
    "df['TNM'] = df['TNM'].fillna('pT3N0') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in df.columns[df.columns.get_loc('BH'):df.columns.get_loc(\"TraitAdjAutre\")+1]:\n",
    "    df[col] = df[col].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df['HistoLRL'] == 0.9, 'HistoLRL'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in df.columns[df.columns.get_loc('HTA'):df.columns.get_loc(\"TV result\")+1]:\n",
    "    df[col] = df[col].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Décès           0.565089\n",
       "DécèsPostOP     0.002959\n",
       "ACE             0.000000\n",
       "CA19-9          0.000000\n",
       "Polype          0.000000\n",
       "                  ...   \n",
       "TraitemAdj      0.000000\n",
       "Stade           0.000000\n",
       "TNM             0.000000\n",
       "HistoPerfT/R    0.000000\n",
       "N               0.000000\n",
       "Length: 162, dtype: float64"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().mean().sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.35064650449265833\n"
     ]
    }
   ],
   "source": [
    "# how many total missing values do we have?\n",
    "total_cells = np.product(df.shape)\n",
    "missing_values_count = df.isnull().sum()\n",
    "total_missing = missing_values_count.sum()\n",
    "\n",
    "# percent of data that is missing\n",
    "percent_missing = (total_missing/total_cells) * 100\n",
    "print(percent_missing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(338, 162)"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 338 entries, 0 to 337\n",
      "Columns: 162 entries, N to Décès\n",
      "dtypes: datetime64[ns](1), float64(119), int64(12), object(30)\n",
      "memory usage: 427.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Feature selection\n",
    "data=df.copy()\n",
    "for col in numeric_features:\n",
    "    del data[col]\n",
    "del data['Date chirurg']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 338 entries, 0 to 337\n",
      "Columns: 146 entries, N to Décès\n",
      "dtypes: float64(104), int64(12), object(30)\n",
      "memory usage: 385.7+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['N',\n",
       " 'Sexe',\n",
       " 'HTA',\n",
       " 'diabète',\n",
       " 'cardiopathie',\n",
       " 'obésité',\n",
       " 'atcd med autre',\n",
       " 'signif atcd autr',\n",
       " 'atcd chir',\n",
       " 'signif atcdchir',\n",
       " 'ANTCDTs Toxique',\n",
       " 'ATCDTs Familiaux',\n",
       " 'signif atcdfamil',\n",
       " 'Rectorragie',\n",
       " 'syndro rectal',\n",
       " 'trouble transi',\n",
       " 'occlusion',\n",
       " 'dleur abdo',\n",
       " 'peritonite',\n",
       " 'sign clin autre',\n",
       " 'TR',\n",
       " 'TRProcessus',\n",
       " 'TRSténosant',\n",
       " 'TR atte sphin',\n",
       " 'TRMA',\n",
       " 'TRCIRC',\n",
       " 'TRTonus',\n",
       " 'TRFixité',\n",
       " 'TVCRV',\n",
       " 'TV result',\n",
       " 'BH',\n",
       " 'Endosc',\n",
       " 'EndoProcessus',\n",
       " 'signif endoprocess',\n",
       " 'Caratèresténo',\n",
       " 'Localisation',\n",
       " 'Endo loca mult',\n",
       " 'Type histologique',\n",
       " 'signif autre typehisto',\n",
       " 'Bilan Extension',\n",
       " 'Coloscopie',\n",
       " 'Colos result',\n",
       " 'TMSynchrone',\n",
       " 'Polype',\n",
       " 'PolypeRese',\n",
       " 'AutresLesions',\n",
       " 'Localisation.1',\n",
       " 'TDM/TAP',\n",
       " 'TDMSiège',\n",
       " 'TDM sieg mult',\n",
       " 'TDMEnvahiss',\n",
       " 'signif tdmenvah',\n",
       " 'TDMRésecab',\n",
       " 'TDMADPLO',\n",
       " 'TDMMétasta',\n",
       " 'signif tdmmeta',\n",
       " 'TDMTNM',\n",
       " 'OpérabilitéVPA',\n",
       " 'OMS',\n",
       " 'ASA',\n",
       " 'StomieDéchar',\n",
       " \"voie d'abord\",\n",
       " 'CauseLaparo',\n",
       " 'TraitemNéoad',\n",
       " 'NéoadjChimio',\n",
       " 'indicationChimio',\n",
       " 'signif indicat',\n",
       " 'NéoadjRCC',\n",
       " 'indicationRCC',\n",
       " 'NéoadjRadioS',\n",
       " \"Voie d'abord\",\n",
       " 'convers cause',\n",
       " 'PlaiePerOP',\n",
       " 'TypeRésectio',\n",
       " 'Autre typerésection+cause non résécable',\n",
       " 'Anastomose',\n",
       " 'autre typeanastomose',\n",
       " 'PieceHisto',\n",
       " 'signif typehisto',\n",
       " 'HistoLimiteRP',\n",
       " 'HistoLimiteRD',\n",
       " 'HistoLRL',\n",
       " 'HistoDégréDiff',\n",
       " 'HistoEmbol',\n",
       " 'HistoEngainPN',\n",
       " 'HistoIntéMes',\n",
       " 'HistoPerfT/R',\n",
       " 'TNM',\n",
       " 'Stade',\n",
       " 'TraitemAdj',\n",
       " 'TraitAdjChim',\n",
       " 'signif Chimio',\n",
       " 'TraitAdjRChm',\n",
       " 'TraitAdjAutre',\n",
       " 'DécèsPostOP',\n",
       " 'Morbidité',\n",
       " 'CmplcatPerOP',\n",
       " ' Incidence Perop',\n",
       " 'Compl postop',\n",
       " 'CmplcatParié',\n",
       " 'FistuleAnast',\n",
       " 'FistDiagnost',\n",
       " 'FistManifClin',\n",
       " 'FistTrait',\n",
       " 'FistEvolution',\n",
       " 'Péritonite post op',\n",
       " 'PéritoDiagnost',\n",
       " 'PéritoManifClin',\n",
       " 'PéritoRinterv',\n",
       " 'PéritoGestRéal',\n",
       " 'PéritoEvolution',\n",
       " 'SténoseAnast',\n",
       " 'SténoDiagnos',\n",
       " 'SténoManifCli',\n",
       " 'SténoRéinterv',\n",
       " 'SténoGestRéal',\n",
       " 'SténoEvoluti',\n",
       " 'FistuleRV',\n",
       " 'FRVDiagnosti',\n",
       " 'FRVManifClini',\n",
       " 'FRVTraiteme',\n",
       " 'FRVEvolution',\n",
       " 'CmplctStomie',\n",
       " 'CStomiNécros',\n",
       " 'Irritation péristomiale',\n",
       " 'DésinsStomi',\n",
       " 'AbcèPériStom',\n",
       " 'StomieAutres',\n",
       " 'CStomiTraite',\n",
       " 'CStomiEvolut',\n",
       " 'CpltHémorrag',\n",
       " 'CHémorDiagn',\n",
       " 'CHemorTransf',\n",
       " 'TransfNCG',\n",
       " 'CHémorRéint',\n",
       " 'CHémorGestR',\n",
       " 'CHemorEvolu',\n",
       " 'CpltPérinéal',\n",
       " 'CPérinéDiagnos',\n",
       " 'CPérinTraite',\n",
       " 'CPérinEvolut',\n",
       " 'CplUrinair',\n",
       " 'CUrinaiDiagn',\n",
       " 'CUrinairEvolu',\n",
       " 'Cplct Générl',\n",
       " 'Décès']"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>N</th>\n",
       "      <th>Sexe</th>\n",
       "      <th>HTA</th>\n",
       "      <th>diabète</th>\n",
       "      <th>cardiopathie</th>\n",
       "      <th>obésité</th>\n",
       "      <th>atcd med autre</th>\n",
       "      <th>signif atcd autr</th>\n",
       "      <th>atcd chir</th>\n",
       "      <th>signif atcdchir</th>\n",
       "      <th>...</th>\n",
       "      <th>CHemorEvolu</th>\n",
       "      <th>CpltPérinéal</th>\n",
       "      <th>CPérinéDiagnos</th>\n",
       "      <th>CPérinTraite</th>\n",
       "      <th>CPérinEvolut</th>\n",
       "      <th>CplUrinair</th>\n",
       "      <th>CUrinaiDiagn</th>\n",
       "      <th>CUrinairEvolu</th>\n",
       "      <th>Cplct Générl</th>\n",
       "      <th>Décès</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>F</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>F</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.0</td>\n",
       "      <td>M</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 146 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     N Sexe  HTA  diabète  cardiopathie  obésité  atcd med autre  \\\n",
       "0  1.0    F  0.0      0.0           0.0      0.0             0.0   \n",
       "1  2.0    M  0.0      0.0           0.0      0.0             0.0   \n",
       "2  3.0    F  1.0      0.0           0.0      0.0             0.0   \n",
       "3  4.0    M  0.0      0.0           0.0      0.0             0.0   \n",
       "4  6.0    M  1.0      0.0           0.0      0.0             0.0   \n",
       "\n",
       "  signif atcd autr  atcd chir signif atcdchir  ...  CHemorEvolu  CpltPérinéal  \\\n",
       "0                0        1.0               0  ...            0           0.0   \n",
       "1                0        1.0               0  ...            0           0.0   \n",
       "2                0        3.0               0  ...            0           0.0   \n",
       "3                0        0.0               0  ...            0           0.0   \n",
       "4                0        0.0               0  ...            0           0.0   \n",
       "\n",
       "  CPérinéDiagnos  CPérinTraite  CPérinEvolut  CplUrinair  CUrinaiDiagn  \\\n",
       "0              0             0             0         0.0             0   \n",
       "1              0             0             0         0.0             0   \n",
       "2              0             0             0         0.0             0   \n",
       "3              0             0             0         0.0             0   \n",
       "4              0             0             0         0.0             0   \n",
       "\n",
       "   CUrinairEvolu  Cplct Générl  Décès  \n",
       "0              0           0.0    NaN  \n",
       "1              0           0.0    NaN  \n",
       "2              0           0.0    NaN  \n",
       "3              0           0.0      0  \n",
       "4              0           0.0    NaN  \n",
       "\n",
       "[5 rows x 146 columns]"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering - Bi-variate statistical analysis\n",
    "\n",
    "One of the first tasks in Data Analytics is to **convert the variables into numerical/ordinal values**.\n",
    "There are multiple types of data\n",
    "\n",
    "**a) Qualitative data: discrete**\n",
    "* Nominal: no natural order between categories.\n",
    "* Categorical: Sex\n",
    "\n",
    "**b) Numeric or quantitative data**\n",
    "* Discrete: could be ordinal\n",
    "* Continuous. e.g.: age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "#On remarque que la variable sexe est textuelle, donc on accorde à F -> 1 et M->0\n",
    "data['Sexe']=np.where(data['Sexe']==\"F\",1,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.loc[data[\"TRMA\"] == -1, \"TRMA\"] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0., 15.,  7.,  6.,  5.,  3.,  8.,  2., 10.,  4.,  1.,  9.])"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['TRMA'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'pT4N1Mx': 0, 'ypT3N2a': 1, 'pT3N0': 2, 'pT3N0Mx': 3, 'ypT3N2b': 4, 'pT2N1': 5, 'ypT0N0': 6, 'ypT2N1aMx': 7, 'pT2N0': 8, 'pT2N0Mx': 9, 'pT3N0M0': 10, 'pT3N1bMx': 11, 'pT3N1M1': 12, 'pT3N1M0': 13, 'pT4mN0': 14, 'pT3N1b': 15, 'pT2N1b': 16, 'pT3N0M1a': 17, 'pT4No': 18, 'pT3N1Mx': 19, 'ypT2N1': 20, 'ypT0N0Mx': 21, 'ypT3N0': 22, 'pT4N1b': 23, 'pT4N0Mx': 24, 'ypT0N1b': 25, 'ypT3N1a': 26, 'ypT3N0Mx': 27, 'pT3N0M1': 28, 'ypT4N0': 29, 'ypT2N0': 30, 'ypT2N1M1': 31, 'ypT4bN0': 32, 'ypT3N2': 33, 'ypT0N1Mx': 34, 'pT0N0': 35, 'pT3N1': 36, 'ypT2N1b': 37, 'ypT3N1': 38, 'pT4N0': 39, 'ypT2N0Mx': 40, 'ypT4N0Mx': 41, 'ypT1N0': 42, 'ypT2N1a': 43, 'pT2N1a': 44, 'ypT4N2a': 45, 'pT3N2aMx': 46, 'ypT2N2aMx': 47, 'pT1N0': 48, 'pT3N2b': 49, 'pT4N1': 50, 'ypT4N1b': 51, 'pT3N1a': 52, 'ypT3N0;pT3N1b': 53, 'ypT3N1b': 54, 'pT4N2b': 55, 'pT3N2': 56, 'pT3N0M1b': 57, 'ypT4N2b': 58, 'pT2N2b': 59, 'pT3N2a': 60, 'ypT1N1a': 61, 'pT4N2a': 62, 'pT4N1a': 63}\n"
     ]
    }
   ],
   "source": [
    "### let's perform label encoding on TNM\n",
    "ordinal_label = {k: i for i, k in enumerate(data['TNM'].unique(), 0)}\n",
    "print(ordinal_label)\n",
    "data['TNM'] = data['TNM'].map(ordinal_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 338 entries, 0 to 337\n",
      "Columns: 146 entries, N to Décès\n",
      "dtypes: float64(104), int32(1), int64(41)\n",
      "memory usage: 384.3 KB\n"
     ]
    }
   ],
   "source": [
    "###performing label encoding on str var\n",
    "for j in data.columns:\n",
    "        if data.dtypes[j] == np.object:\n",
    "            ordinal_label2 = {k: c for c, k in enumerate(data[j].unique(), 0)}\n",
    "            data[j] = data[j].map(ordinal_label2)\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 338 entries, 0 to 337\n",
      "Columns: 146 entries, N to Décès\n",
      "dtypes: float64(104), int32(1), int64(41)\n",
      "memory usage: 384.3 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VarianceThreshold(threshold=0)"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### It will zero variance features\n",
    "from sklearn.feature_selection import VarianceThreshold\n",
    "var_thres=VarianceThreshold(threshold=0)\n",
    "var_thres.fit(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "constant_columns = [column for column in data.columns\n",
    "                    if column not in data.columns[var_thres.get_support()]]\n",
    "\n",
    "print(len(constant_columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OpérabilitéVPA\n",
      "TraitAdjAutre\n",
      "AbcèPériStom\n"
     ]
    }
   ],
   "source": [
    "for feature in constant_columns:\n",
    "     print(feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(constant_columns,axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "quant_data=pd.DataFrame(df, columns=numeric_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=pd.concat([quant_data, data], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "1c8a0f85-2ef2-4d84-bf8a-b6443e8638d2",
    "_uuid": "59d2c0cc264ee98c1b7178c12ffb733ed469be3d"
   },
   "source": [
    "##  A very first look into the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "_cell_guid": "ccbc1dd8-f6a8-418d-a156-ff42e46e3b25",
    "_uuid": "f38a7f5049a019c2af35a91d7f653ab8ba906f41",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Morbidité by obesity')"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1152 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax = plt.subplots(3,4,figsize=(20,16))\n",
    "sns.countplot('Stade',data=train,ax=ax[0,0])\n",
    "sns.countplot('Sexe',data=train,ax=ax[0,1])\n",
    "sns.boxplot(x='Stade',y='Age',data=train,ax=ax[0,2])\n",
    "sns.countplot('trouble transi',hue='Morbidité',data=train,ax=ax[0,3],palette='husl')\n",
    "sns.countplot('obésité',data=train,ax=ax[2,2])\n",
    "\n",
    "sns.countplot('Stade',hue='Morbidité',data=train,ax=ax[1,0],palette='husl')\n",
    "sns.countplot('Sexe',hue='Morbidité',data=train,ax=ax[1,1],palette='husl')\n",
    "sns.distplot(train[train['Morbidité']==0]['Age'],ax=ax[1,2],kde=False,color='r',bins=5)\n",
    "sns.distplot(train[train['Morbidité']==1]['Age'],ax=ax[1,2],kde=False,color='g',bins=5)\n",
    "sns.countplot('diabète',hue='Morbidité',data=train,ax=ax[1,3],palette='husl')\n",
    "sns.swarmplot(x='Stade',y='Age',hue='Morbidité',data=train,palette='husl',ax=ax[2,1])\n",
    "sns.countplot('obésité',hue='Morbidité',data=train,ax=ax[2,3],palette='husl')\n",
    "sns.countplot('Globule blanc',hue='Morbidité',data=train,ax=ax[2,0],palette='husl')\n",
    "\n",
    "ax[0,0].set_title('Stade du cancer')\n",
    "ax[0,1].set_title('Gender of patients')\n",
    "ax[0,2].set_title('Age Box Plot By stade')\n",
    "ax[0,3].set_title('morbidité by troule transi')\n",
    "ax[1,0].set_title('Morbidité by Stade')\n",
    "ax[1,1].set_title('Morbidité by Gender')\n",
    "ax[1,2].set_title('Morbidité by Age')\n",
    "ax[1,3].set_title('Morbidité by diabète')\n",
    "ax[2,0].set_title('Morbidité by globul blanc')\n",
    "ax[2,1].set_title('Morbidité HTA and Stade')\n",
    "ax[2,2].set_title('Total Patients by obésité')\n",
    "ax[2,3].set_title('Morbidité by obesity')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "dff1c1dd-1e59-3907-88fa-1a1d699122be",
    "_uuid": "81378834770e55c76751347588322fe32acf5737"
   },
   "source": [
    "\n",
    "\n",
    "### Detecting and correcting outliers\n",
    "Reviewing the data, there does not appear to be any aberrant or non-acceptable data inputs.\n",
    "\n",
    "There are potential outliers that we will identify :\n",
    "* It creates firset a function called detect_outliers, implementing the Tukey method\n",
    "* For each column of the dataframe, this function calculates the 25th percentile (Q1) and 75th percentile (Q3) values.\n",
    "* The  interquartile range (IQR) is a measure of statistical dispersion, being equal to the difference between the 75th and 25th percentiles, or between upper and lower quartiles.\n",
    "* Any data points outside 1.5 time the IQR (1.5 time IQR below Q1, or 1.5 time IQR above Q3), is considered an outlier.\n",
    "* The outlier_list_col column captures the indices of these outliers. All outlier data get then pulled into the outlier_indices dataframe.\n",
    "* Finally, the detect_outliers function will select only the outliers happening multiple times. This is the datadframe that will be returned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "_cell_guid": "98ca04be-4693-4eb7-b25b-cc32e65b45b9",
    "_uuid": "5abddbbf118113d33ed95f10cf73edbd4525df82",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Hémoglobine</th>\n",
       "      <th>Globule blanc</th>\n",
       "      <th>Plaquette</th>\n",
       "      <th>CRP</th>\n",
       "      <th>Urée</th>\n",
       "      <th>Créat</th>\n",
       "      <th>Glycémie</th>\n",
       "      <th>Albumine</th>\n",
       "      <th>Préalbumine</th>\n",
       "      <th>...</th>\n",
       "      <th>CHemorEvolu</th>\n",
       "      <th>CpltPérinéal</th>\n",
       "      <th>CPérinéDiagnos</th>\n",
       "      <th>CPérinTraite</th>\n",
       "      <th>CPérinEvolut</th>\n",
       "      <th>CplUrinair</th>\n",
       "      <th>CUrinaiDiagn</th>\n",
       "      <th>CUrinairEvolu</th>\n",
       "      <th>Cplct Générl</th>\n",
       "      <th>Décès</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>19.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>4620.0</td>\n",
       "      <td>434000.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>0.22</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.810000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.130000</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>55.0</td>\n",
       "      <td>13.2</td>\n",
       "      <td>14420.0</td>\n",
       "      <td>356000.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0.27</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.077961</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>41.0</td>\n",
       "      <td>12.6</td>\n",
       "      <td>17350.0</td>\n",
       "      <td>656000.000000</td>\n",
       "      <td>353.000000</td>\n",
       "      <td>0.21</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>0.259902</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>45.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>18440.0</td>\n",
       "      <td>744000.000000</td>\n",
       "      <td>312.000000</td>\n",
       "      <td>0.31</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.077961</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>0.259902</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>48.0</td>\n",
       "      <td>9.7</td>\n",
       "      <td>19220.0</td>\n",
       "      <td>566000.000000</td>\n",
       "      <td>238.000000</td>\n",
       "      <td>0.58</td>\n",
       "      <td>7.661722</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>35.601695</td>\n",
       "      <td>0.259902</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>39.0</td>\n",
       "      <td>9.9</td>\n",
       "      <td>9800.0</td>\n",
       "      <td>547000.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>0.23</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.077961</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>62.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>13240.0</td>\n",
       "      <td>505000.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>0.24</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.380000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>40.0</td>\n",
       "      <td>10.9</td>\n",
       "      <td>5540.0</td>\n",
       "      <td>399000.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>0.12</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.790000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>45.0</td>\n",
       "      <td>10.6</td>\n",
       "      <td>5140.0</td>\n",
       "      <td>314000.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>0.29</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.077961</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>75.0</td>\n",
       "      <td>14.2</td>\n",
       "      <td>11250.0</td>\n",
       "      <td>280132.835821</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.93</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>1.077961</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>0.259902</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>62.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3220.0</td>\n",
       "      <td>161000.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.31</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2.570000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>77.0</td>\n",
       "      <td>10.4</td>\n",
       "      <td>3640.0</td>\n",
       "      <td>147000.000000</td>\n",
       "      <td>26.860436</td>\n",
       "      <td>0.30</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2.090000</td>\n",
       "      <td>37.800000</td>\n",
       "      <td>0.259902</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>66.0</td>\n",
       "      <td>13.3</td>\n",
       "      <td>8900.0</td>\n",
       "      <td>463000.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.12</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.190000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>37.0</td>\n",
       "      <td>11.3</td>\n",
       "      <td>5470.0</td>\n",
       "      <td>244000.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0.33</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.570000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>0.259902</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>47.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4440.0</td>\n",
       "      <td>244000.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.44</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.610000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>57.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>7880.0</td>\n",
       "      <td>487000.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.35</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.450000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>54.0</td>\n",
       "      <td>10.7</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>261000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.33</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.630000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>41.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>5040.0</td>\n",
       "      <td>244000.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.28</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.610000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>59.0</td>\n",
       "      <td>10.3</td>\n",
       "      <td>8110.0</td>\n",
       "      <td>525000.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>0.11</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.540000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>52.0</td>\n",
       "      <td>10.3</td>\n",
       "      <td>5380.0</td>\n",
       "      <td>140000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.22</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.840000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>75.0</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5990.0</td>\n",
       "      <td>232000.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.22</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.060000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>44.0</td>\n",
       "      <td>13.2</td>\n",
       "      <td>8110.0</td>\n",
       "      <td>436000.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.16</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.077961</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>65.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>6590.0</td>\n",
       "      <td>260000.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.33</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.870000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>60.0</td>\n",
       "      <td>12.2</td>\n",
       "      <td>4870.0</td>\n",
       "      <td>376000.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.17</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.077961</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>31.0</td>\n",
       "      <td>8.9</td>\n",
       "      <td>7940.0</td>\n",
       "      <td>282000.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>0.20</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.830000</td>\n",
       "      <td>35.601695</td>\n",
       "      <td>0.259902</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>56.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>6650.0</td>\n",
       "      <td>266000.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.22</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.077961</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>48.0</td>\n",
       "      <td>13.2</td>\n",
       "      <td>6040.0</td>\n",
       "      <td>314000.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.29</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>64.0</td>\n",
       "      <td>11.7</td>\n",
       "      <td>5600.0</td>\n",
       "      <td>207000.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.23</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.077961</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>0.259902</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>38.0</td>\n",
       "      <td>12.9</td>\n",
       "      <td>7680.0</td>\n",
       "      <td>287000.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.18</td>\n",
       "      <td>7.661722</td>\n",
       "      <td>0.730000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>54.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6360.0</td>\n",
       "      <td>441000.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.21</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.870000</td>\n",
       "      <td>35.601695</td>\n",
       "      <td>0.259902</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>62.0</td>\n",
       "      <td>8.1</td>\n",
       "      <td>11450.0</td>\n",
       "      <td>499000.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.40</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.160000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.259902</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>67.0</td>\n",
       "      <td>12.3</td>\n",
       "      <td>6670.0</td>\n",
       "      <td>228000.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.24</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.077961</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>78.0</td>\n",
       "      <td>14.6</td>\n",
       "      <td>9010.0</td>\n",
       "      <td>178000.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.19</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.720000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>71.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>12820.0</td>\n",
       "      <td>279000.000000</td>\n",
       "      <td>37.600000</td>\n",
       "      <td>0.31</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.077961</td>\n",
       "      <td>35.601695</td>\n",
       "      <td>0.259902</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>47.0</td>\n",
       "      <td>10.9</td>\n",
       "      <td>3370.0</td>\n",
       "      <td>223000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.30</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.077961</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.259902</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>73.0</td>\n",
       "      <td>13.8</td>\n",
       "      <td>5640.0</td>\n",
       "      <td>293000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.30</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.077961</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>55.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7730.0</td>\n",
       "      <td>31500.000000</td>\n",
       "      <td>26.860436</td>\n",
       "      <td>0.15</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.820000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>0.259902</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>65.0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>3330.0</td>\n",
       "      <td>206000.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.23</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.077961</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>65.0</td>\n",
       "      <td>8.8</td>\n",
       "      <td>4230.0</td>\n",
       "      <td>256000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.21</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.690000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>69.0</td>\n",
       "      <td>11.7</td>\n",
       "      <td>4310.0</td>\n",
       "      <td>289000.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.22</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.870000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>0.259902</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>55.0</td>\n",
       "      <td>12.3</td>\n",
       "      <td>4360.0</td>\n",
       "      <td>341000.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>0.21</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.880000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>0.259902</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>43.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4680.0</td>\n",
       "      <td>166000.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.44</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.990000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0.259902</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>34.0</td>\n",
       "      <td>11.3</td>\n",
       "      <td>4050.0</td>\n",
       "      <td>264000.000000</td>\n",
       "      <td>26.860436</td>\n",
       "      <td>0.34</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.077961</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>74.0</td>\n",
       "      <td>10.7</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>254000.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.51</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.077961</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.259902</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>62.0</td>\n",
       "      <td>12.6</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>189000.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.31</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.820000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>0.259902</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>72.0</td>\n",
       "      <td>13.9</td>\n",
       "      <td>4700.0</td>\n",
       "      <td>163000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.24</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.770000</td>\n",
       "      <td>35.601695</td>\n",
       "      <td>0.259902</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>46 rows × 158 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age  Hémoglobine  Globule blanc      Plaquette         CRP  Urée  \\\n",
       "106  19.0          7.8         4620.0  434000.000000   41.000000  0.22   \n",
       "102  55.0         13.2        14420.0  356000.000000   19.000000  0.27   \n",
       "155  41.0         12.6        17350.0  656000.000000  353.000000  0.21   \n",
       "162  45.0          6.5        18440.0  744000.000000  312.000000  0.31   \n",
       "232  48.0          9.7        19220.0  566000.000000  238.000000  0.58   \n",
       "36   39.0          9.9         9800.0  547000.000000  105.000000  0.23   \n",
       "105  62.0          9.5        13240.0  505000.000000   85.000000  0.24   \n",
       "161  40.0         10.9         5540.0  399000.000000   99.000000  0.12   \n",
       "166  45.0         10.6         5140.0  314000.000000   86.000000  0.29   \n",
       "172  75.0         14.2        11250.0  280132.835821    6.000000  0.93   \n",
       "198  62.0         11.0         3220.0  161000.000000    2.000000  0.31   \n",
       "2    77.0         10.4         3640.0  147000.000000   26.860436  0.30   \n",
       "57   66.0         13.3         8900.0  463000.000000   29.000000  0.12   \n",
       "94   37.0         11.3         5470.0  244000.000000   14.000000  0.33   \n",
       "128  47.0         14.0         4440.0  244000.000000    4.000000  0.44   \n",
       "129  57.0          9.5         7880.0  487000.000000   24.000000  0.35   \n",
       "231  54.0         10.7         4500.0  261000.000000    0.000000  0.33   \n",
       "235  41.0         10.5         5040.0  244000.000000   10.000000  0.28   \n",
       "297  59.0         10.3         8110.0  525000.000000   52.000000  0.11   \n",
       "329  52.0         10.3         5380.0  140000.000000    1.000000  0.22   \n",
       "304  75.0          9.8         5990.0  232000.000000   11.000000  0.22   \n",
       "163  44.0         13.2         8110.0  436000.000000    4.000000  0.16   \n",
       "193  65.0         11.6         6590.0  260000.000000   29.000000  0.33   \n",
       "131  60.0         12.2         4870.0  376000.000000    5.000000  0.17   \n",
       "250  31.0          8.9         7940.0  282000.000000   37.000000  0.20   \n",
       "213  56.0         11.9         6650.0  266000.000000    4.000000  0.22   \n",
       "120  48.0         13.2         6040.0  314000.000000    2.000000  0.29   \n",
       "60   64.0         11.7         5600.0  207000.000000    3.000000  0.23   \n",
       "142  38.0         12.9         7680.0  287000.000000    2.000000  0.18   \n",
       "233  54.0         13.0         6360.0  441000.000000    4.000000  0.21   \n",
       "306  62.0          8.1        11450.0  499000.000000   30.000000  0.40   \n",
       "77   67.0         12.3         6670.0  228000.000000    2.000000  0.24   \n",
       "84   78.0         14.6         9010.0  178000.000000    6.000000  0.19   \n",
       "134  71.0          9.2        12820.0  279000.000000   37.600000  0.31   \n",
       "48   47.0         10.9         3370.0  223000.000000    1.000000  0.30   \n",
       "90   73.0         13.8         5640.0  293000.000000    1.000000  0.30   \n",
       "64   55.0         12.0         7730.0   31500.000000   26.860436  0.15   \n",
       "76   65.0         12.5         3330.0  206000.000000    4.000000  0.23   \n",
       "196  65.0          8.8         4230.0  256000.000000    1.000000  0.21   \n",
       "288  69.0         11.7         4310.0  289000.000000    7.000000  0.22   \n",
       "9    55.0         12.3         4360.0  341000.000000   48.000000  0.21   \n",
       "315  43.0         16.0         4680.0  166000.000000   11.000000  0.44   \n",
       "45   34.0         11.3         4050.0  264000.000000   26.860436  0.34   \n",
       "249  74.0         10.7         3800.0  254000.000000   12.000000  0.51   \n",
       "291  62.0         12.6         4000.0  189000.000000    3.000000  0.31   \n",
       "234  72.0         13.9         4700.0  163000.000000    1.000000  0.24   \n",
       "\n",
       "         Créat  Glycémie   Albumine  Préalbumine  ...  CHemorEvolu  \\\n",
       "106   5.000000  1.810000  32.000000     0.130000  ...            0   \n",
       "102   8.000000  1.077961  25.000000     0.030000  ...            0   \n",
       "155   7.000000  0.620000  34.000000     0.259902  ...            0   \n",
       "162   8.000000  1.077961  53.000000     0.259902  ...            0   \n",
       "232   7.661722  0.750000  35.601695     0.259902  ...            0   \n",
       "36    6.000000  1.077961  33.000000     0.030000  ...            0   \n",
       "105   6.000000  1.380000  22.000000     0.000000  ...            0   \n",
       "161   5.000000  0.790000  31.000000     0.130000  ...            0   \n",
       "166   8.000000  1.077961  33.000000     0.000000  ...            0   \n",
       "172  11.000000  1.077961  31.000000     0.259902  ...            0   \n",
       "198  12.000000  2.570000  39.000000    39.000000  ...            0   \n",
       "2    10.000000  2.090000  37.800000     0.259902  ...            0   \n",
       "57    6.000000  2.190000  33.000000     0.000000  ...            0   \n",
       "94    8.000000  0.570000  38.000000     0.259902  ...            0   \n",
       "128   7.000000  3.610000  36.000000     0.030000  ...            1   \n",
       "129   8.000000  1.450000  29.000000     0.030000  ...            0   \n",
       "231   7.000000  0.630000  37.000000     0.240000  ...            0   \n",
       "235   6.000000  0.610000  42.000000     0.050000  ...            0   \n",
       "297   6.000000  1.540000  35.000000     0.030000  ...            0   \n",
       "329  10.000000  1.840000  36.000000     0.020000  ...            0   \n",
       "304   6.000000  1.060000  19.000000     0.100000  ...            0   \n",
       "163   7.000000  1.077961  40.000000     0.060000  ...            0   \n",
       "193   8.000000  0.870000  39.000000     0.030000  ...            0   \n",
       "131   6.000000  1.077961  38.000000     0.000000  ...            0   \n",
       "250   6.000000  0.830000  35.601695     0.259902  ...            0   \n",
       "213   7.000000  1.077961  38.000000     0.030000  ...            0   \n",
       "120   8.000000  0.950000  33.000000     0.000000  ...            0   \n",
       "60    6.000000  1.077961  34.000000     0.259902  ...            0   \n",
       "142   7.661722  0.730000  40.000000     0.060000  ...            0   \n",
       "233   7.000000  0.870000  35.601695     0.259902  ...            0   \n",
       "306   7.000000  1.160000  30.000000     0.259902  ...            0   \n",
       "77    7.000000  1.077961  44.000000     0.170000  ...            0   \n",
       "84    7.000000  0.720000  37.000000     0.020000  ...            0   \n",
       "134  10.000000  1.077961  35.601695     0.259902  ...            0   \n",
       "48    7.000000  1.077961  40.000000     0.259902  ...            0   \n",
       "90    9.000000  1.077961  40.000000     0.090000  ...            0   \n",
       "64    6.000000  0.820000  38.000000     0.259902  ...            0   \n",
       "76    7.000000  1.077961  32.000000     0.020000  ...            0   \n",
       "196   5.000000  0.690000  26.000000     0.170000  ...            0   \n",
       "288   8.000000  0.870000  37.000000     0.259902  ...            0   \n",
       "9     7.000000  0.880000  35.000000     0.259902  ...            0   \n",
       "315   8.000000  0.990000  45.000000     0.259902  ...            0   \n",
       "45    7.000000  1.077961  39.000000     0.130000  ...            0   \n",
       "249   7.000000  1.077961  32.000000     0.259902  ...            0   \n",
       "291   8.000000  0.820000  33.000000     0.259902  ...            0   \n",
       "234   6.000000  0.770000  35.601695     0.259902  ...            0   \n",
       "\n",
       "     CpltPérinéal  CPérinéDiagnos  CPérinTraite  CPérinEvolut  CplUrinair  \\\n",
       "106           0.0               0             0             0         0.0   \n",
       "102           0.0               0             0             0         0.0   \n",
       "155           0.0               0             0             0         0.0   \n",
       "162           0.0               0             0             0         0.0   \n",
       "232           0.0               0             0             0         0.0   \n",
       "36            0.0               0             0             0         0.0   \n",
       "105           0.0               0             0             0         0.0   \n",
       "161           0.0               0             0             0         0.0   \n",
       "166           0.0               0             0             0         0.0   \n",
       "172           0.0               0             0             0         0.0   \n",
       "198           0.0               0             0             0         0.0   \n",
       "2             0.0               0             0             0         0.0   \n",
       "57            0.0               0             0             0         0.0   \n",
       "94            1.0               2             2             1         0.0   \n",
       "128           0.0               0             0             0         0.0   \n",
       "129           0.0               0             0             0         0.0   \n",
       "231           0.0               0             0             0         0.0   \n",
       "235           0.0               0             0             0         0.0   \n",
       "297           0.0               0             0             0         0.0   \n",
       "329           0.0               0             0             0         0.0   \n",
       "304           0.0               0             0             0         0.0   \n",
       "163           0.0               0             0             0         0.0   \n",
       "193           0.0               0             0             0         0.0   \n",
       "131           0.0               0             0             0         0.0   \n",
       "250           0.0               0             0             0         0.0   \n",
       "213           0.0               0             0             0         0.0   \n",
       "120           0.0               0             0             0         0.0   \n",
       "60            0.0               0             0             0         0.0   \n",
       "142           0.0               0             0             0         0.0   \n",
       "233           0.0               0             0             0         0.0   \n",
       "306           0.0               0             0             0         0.0   \n",
       "77            0.0               0             0             0         0.0   \n",
       "84            0.0               0             0             0         0.0   \n",
       "134           0.0               0             0             0         0.0   \n",
       "48            0.0               0             0             0         0.0   \n",
       "90            0.0               0             0             0         0.0   \n",
       "64            0.0               0             0             0         0.0   \n",
       "76            0.0               0             0             0         0.0   \n",
       "196           0.0               0             0             0         0.0   \n",
       "288           0.0               0             0             0         0.0   \n",
       "9             0.0               0             0             0         0.0   \n",
       "315           0.0               0             0             0         0.0   \n",
       "45            0.0               0             0             0         0.0   \n",
       "249           0.0               0             0             0         0.0   \n",
       "291           0.0               0             0             0         0.0   \n",
       "234           0.0               0             0             0         0.0   \n",
       "\n",
       "     CUrinaiDiagn  CUrinairEvolu  Cplct Générl  Décès  \n",
       "106             0              0           0.0      2  \n",
       "102             0              0           0.0      2  \n",
       "155             0              0           0.0      0  \n",
       "162             0              0           0.0      0  \n",
       "232             0              0           0.0      1  \n",
       "36              0              0           0.0      0  \n",
       "105             0              0           0.0      2  \n",
       "161             0              0           0.0      0  \n",
       "166             0              0           0.0      0  \n",
       "172             0              0           0.0      2  \n",
       "198             0              0           0.0      0  \n",
       "2               0              0           0.0      0  \n",
       "57              0              0           0.0      0  \n",
       "94              0              0           0.0      1  \n",
       "128             0              0           0.0      2  \n",
       "129             0              0           0.0      0  \n",
       "231             0              0           0.0      0  \n",
       "235             0              0           0.0      1  \n",
       "297             0              0           0.0      0  \n",
       "329             0              0           0.0      0  \n",
       "304             0              0           0.0      2  \n",
       "163             0              0           0.0      0  \n",
       "193             0              0           0.0      0  \n",
       "131             0              0           0.0      1  \n",
       "250             0              0           0.0      0  \n",
       "213             0              0           0.0      0  \n",
       "120             0              0           0.0      1  \n",
       "60              0              0           0.0      2  \n",
       "142             0              0           1.0      1  \n",
       "233             0              0           0.0      1  \n",
       "306             0              0           0.0      1  \n",
       "77              0              0           0.0      1  \n",
       "84              0              0           0.0      2  \n",
       "134             0              0           0.0      0  \n",
       "48              0              0           0.0      0  \n",
       "90              0              0           0.0      2  \n",
       "64              0              0           0.0      1  \n",
       "76              0              0           0.0      0  \n",
       "196             0              0           0.0      0  \n",
       "288             0              0           0.0      1  \n",
       "9               0              0           0.0      0  \n",
       "315             0              0           0.0      0  \n",
       "45              0              0           0.0      1  \n",
       "249             0              0           0.0      0  \n",
       "291             0              0           0.0      0  \n",
       "234             0              0           0.0      0  \n",
       "\n",
       "[46 rows x 158 columns]"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Outlier detection \n",
    "def detect_outliers(df,n,features):\n",
    "    outlier_indices = []\n",
    "    # iterate over features(columns)\n",
    "    for col in features:\n",
    "        # 1st quartile (25%)\n",
    "        Q1 = np.percentile(df[col],25)\n",
    "        # 3rd quartile (75%)\n",
    "        Q3 = np.percentile(df[col],75)\n",
    "        # Interquartile range (IQR)\n",
    "        IQR = Q3 - Q1\n",
    "        # outlier step\n",
    "        outlier_step = 1.5 * IQR\n",
    "        # Determine a list of indices of outliers for feature col\n",
    "        outlier_list_col = df[(df[col] < Q1 - outlier_step) | (df[col] > Q3 + outlier_step )].index       \n",
    "        # append the found outlier indices for col to the list of outlier indices \n",
    "        outlier_indices.extend(outlier_list_col)\n",
    "        \n",
    "    # select observations containing more than 11 outliers\n",
    "    outlier_indices = Counter(outlier_indices)        \n",
    "    multiple_outliers = list( k for k, v in outlier_indices.items() if v > n )\n",
    "    return multiple_outliers   \n",
    "# detect outliers from Age, SibSp , Parch and Fare\n",
    "Outliers_to_drop = detect_outliers(train,11,train.columns.tolist())\n",
    "train.loc[Outliers_to_drop] # Show the outliers rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "4d70580d-8bcb-434c-8630-8bcb7aada36b",
    "_uuid": "6cf77f7bdf1d38ca620d102e75ce54ed759e7af0"
   },
   "source": [
    "** Observations**\n",
    "* The Detect_Outliers function found 46 outliers.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "_cell_guid": "f0acac5f-79ba-4c1a-aab1-f6416f963cc7",
    "_uuid": "1cb4a4a0d63f644850356d29521119025f95a257",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "# Drop outliers\n",
    "train = train.drop(Outliers_to_drop, axis = 0).reset_index(drop=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "3fa8b862-a11f-48f5-b87a-c9bd7114382a",
    "_uuid": "6c82c0056c3492ae9a4f91fac98fb40f7048b252"
   },
   "source": [
    "\n",
    "The .info function below shows how complete or incomplete the datasets are. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "_cell_guid": "3cc01b03-52b8-4c55-9dae-ed4c396d8601",
    "_uuid": "3b87698ec3ae2a12fc189a570f8871fd82053245",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 292 entries, 0 to 291\n",
      "Columns: 158 entries, Age to Décès\n",
      "dtypes: float64(116), int32(1), int64(41)\n",
      "memory usage: 359.4 KB\n",
      "________________________________________\n"
     ]
    }
   ],
   "source": [
    "train.info()\n",
    "print('_'*40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "_cell_guid": "6948be07-4fa1-456e-876f-e171390303e7",
    "_uuid": "01ad05b95eb87e1d0a179f476339f5dc232501d7",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    292.000000\n",
       "mean       0.215753\n",
       "std        0.412050\n",
       "min        0.000000\n",
       "25%        0.000000\n",
       "50%        0.000000\n",
       "75%        0.000000\n",
       "max        1.000000\n",
       "Name: Morbidité, dtype: float64"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Morbidite = train['Morbidité']\n",
    "Morbidite.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "e9814168-b7cd-d4e4-1b1d-e21c6637a663",
    "_uuid": "dd288776321804d99e4e4a7e88594c1d631e4409"
   },
   "source": [
    "## Visualising updated dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "_cell_guid": "fc426b8f-873d-6f23-4299-99f174956cca",
    "_execution_state": "idle",
    "_uuid": "1f280a1c11dc35a93b57af494938998e6d0b4544",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Hémoglobine</th>\n",
       "      <th>Globule blanc</th>\n",
       "      <th>Plaquette</th>\n",
       "      <th>CRP</th>\n",
       "      <th>Urée</th>\n",
       "      <th>Créat</th>\n",
       "      <th>Glycémie</th>\n",
       "      <th>Albumine</th>\n",
       "      <th>Préalbumine</th>\n",
       "      <th>...</th>\n",
       "      <th>CHemorEvolu</th>\n",
       "      <th>CpltPérinéal</th>\n",
       "      <th>CPérinéDiagnos</th>\n",
       "      <th>CPérinTraite</th>\n",
       "      <th>CPérinEvolut</th>\n",
       "      <th>CplUrinair</th>\n",
       "      <th>CUrinaiDiagn</th>\n",
       "      <th>CUrinairEvolu</th>\n",
       "      <th>Cplct Générl</th>\n",
       "      <th>Décès</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>8170.0</td>\n",
       "      <td>331000.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.36</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.90</td>\n",
       "      <td>35.601695</td>\n",
       "      <td>0.259902</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80.0</td>\n",
       "      <td>10.7</td>\n",
       "      <td>8680.0</td>\n",
       "      <td>197000.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.34</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>35.300000</td>\n",
       "      <td>0.230000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6890.0</td>\n",
       "      <td>157000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.52</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.02</td>\n",
       "      <td>35.601695</td>\n",
       "      <td>0.259902</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>72.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>6730.0</td>\n",
       "      <td>238000.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.31</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.75</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54.0</td>\n",
       "      <td>14.5</td>\n",
       "      <td>6230.0</td>\n",
       "      <td>183000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.41</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.02</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>0.259902</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 158 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  Hémoglobine  Globule blanc  Plaquette   CRP  Urée  Créat  Glycémie  \\\n",
       "0  60.0         11.5         8170.0   331000.0  12.0  0.36   10.0      0.90   \n",
       "1  80.0         10.7         8680.0   197000.0  16.0  0.34    9.0      2.31   \n",
       "2  50.0         16.0         6890.0   157000.0   3.0  0.52   10.0      1.02   \n",
       "3  72.0          9.5         6730.0   238000.0  10.0  0.31    7.0      2.75   \n",
       "4  54.0         14.5         6230.0   183000.0   1.0  0.41    8.0      1.02   \n",
       "\n",
       "    Albumine  Préalbumine  ...  CHemorEvolu  CpltPérinéal  CPérinéDiagnos  \\\n",
       "0  35.601695     0.259902  ...            0           0.0               0   \n",
       "1  35.300000     0.230000  ...            0           0.0               0   \n",
       "2  35.601695     0.259902  ...            0           0.0               0   \n",
       "3  33.000000     0.040000  ...            0           0.0               0   \n",
       "4  39.000000     0.259902  ...            0           0.0               0   \n",
       "\n",
       "   CPérinTraite  CPérinEvolut  CplUrinair  CUrinaiDiagn  CUrinairEvolu  \\\n",
       "0             0             0         0.0             0              0   \n",
       "1             0             0         0.0             0              0   \n",
       "2             0             0         0.0             0              0   \n",
       "3             0             0         0.0             0              0   \n",
       "4             0             0         0.0             0              0   \n",
       "\n",
       "   Cplct Générl  Décès  \n",
       "0           0.0      0  \n",
       "1           0.0      0  \n",
       "2           0.0      1  \n",
       "3           0.0      0  \n",
       "4           3.0      0  \n",
       "\n",
       "[5 rows x 158 columns]"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "009ce45c-f4db-4718-ba0c-9c3e34d0e8ee",
    "_uuid": "069695c07d229c23cb2edf99d6f5ad901b934e1d"
   },
   "source": [
    "##  Descriptive statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "_cell_guid": "522a767e-e466-4f8e-9255-039c391cded8",
    "_uuid": "9c27c628974b2556f03b8bd54fb81771bce4cf25",
    "jupyter": {
     "outputs_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Hémoglobine</th>\n",
       "      <th>Globule blanc</th>\n",
       "      <th>Plaquette</th>\n",
       "      <th>CRP</th>\n",
       "      <th>Urée</th>\n",
       "      <th>Créat</th>\n",
       "      <th>Glycémie</th>\n",
       "      <th>Albumine</th>\n",
       "      <th>Préalbumine</th>\n",
       "      <th>...</th>\n",
       "      <th>CHemorEvolu</th>\n",
       "      <th>CpltPérinéal</th>\n",
       "      <th>CPérinéDiagnos</th>\n",
       "      <th>CPérinTraite</th>\n",
       "      <th>CPérinEvolut</th>\n",
       "      <th>CplUrinair</th>\n",
       "      <th>CUrinaiDiagn</th>\n",
       "      <th>CUrinairEvolu</th>\n",
       "      <th>Cplct Générl</th>\n",
       "      <th>Décès</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>292.000000</td>\n",
       "      <td>292.000000</td>\n",
       "      <td>292.000000</td>\n",
       "      <td>292.000000</td>\n",
       "      <td>292.000000</td>\n",
       "      <td>292.000000</td>\n",
       "      <td>292.000000</td>\n",
       "      <td>292.000000</td>\n",
       "      <td>292.000000</td>\n",
       "      <td>292.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>292.0</td>\n",
       "      <td>292.000000</td>\n",
       "      <td>292.000000</td>\n",
       "      <td>292.000000</td>\n",
       "      <td>292.000000</td>\n",
       "      <td>292.000000</td>\n",
       "      <td>292.000000</td>\n",
       "      <td>292.000000</td>\n",
       "      <td>292.000000</td>\n",
       "      <td>292.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>57.618599</td>\n",
       "      <td>11.633920</td>\n",
       "      <td>6789.724649</td>\n",
       "      <td>273997.485177</td>\n",
       "      <td>24.837829</td>\n",
       "      <td>0.274130</td>\n",
       "      <td>7.716913</td>\n",
       "      <td>1.067204</td>\n",
       "      <td>35.649193</td>\n",
       "      <td>0.144893</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003425</td>\n",
       "      <td>0.003425</td>\n",
       "      <td>0.003425</td>\n",
       "      <td>0.003425</td>\n",
       "      <td>0.010274</td>\n",
       "      <td>0.020548</td>\n",
       "      <td>0.010274</td>\n",
       "      <td>0.023973</td>\n",
       "      <td>0.527397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.431315</td>\n",
       "      <td>1.966082</td>\n",
       "      <td>2664.040766</td>\n",
       "      <td>101372.204204</td>\n",
       "      <td>42.548135</td>\n",
       "      <td>0.096367</td>\n",
       "      <td>2.562807</td>\n",
       "      <td>0.329490</td>\n",
       "      <td>5.080704</td>\n",
       "      <td>0.110670</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.058521</td>\n",
       "      <td>0.058521</td>\n",
       "      <td>0.058521</td>\n",
       "      <td>0.058521</td>\n",
       "      <td>0.101012</td>\n",
       "      <td>0.218372</td>\n",
       "      <td>0.101012</td>\n",
       "      <td>0.254392</td>\n",
       "      <td>0.665241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>7.700000</td>\n",
       "      <td>2270.000000</td>\n",
       "      <td>7000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>1.530000</td>\n",
       "      <td>0.440000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>48.750000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>4750.000000</td>\n",
       "      <td>199000.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.910000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>59.000000</td>\n",
       "      <td>11.700000</td>\n",
       "      <td>6400.000000</td>\n",
       "      <td>253500.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.260000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.077961</td>\n",
       "      <td>35.601695</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>67.000000</td>\n",
       "      <td>13.125000</td>\n",
       "      <td>8420.000000</td>\n",
       "      <td>323000.000000</td>\n",
       "      <td>26.860436</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.077961</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>0.259902</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>16.700000</td>\n",
       "      <td>18340.000000</td>\n",
       "      <td>664000.000000</td>\n",
       "      <td>309.000000</td>\n",
       "      <td>0.680000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>3.770000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 158 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age  Hémoglobine  Globule blanc      Plaquette         CRP  \\\n",
       "count  292.000000   292.000000     292.000000     292.000000  292.000000   \n",
       "mean    57.618599    11.633920    6789.724649  273997.485177   24.837829   \n",
       "std     13.431315     1.966082    2664.040766  101372.204204   42.548135   \n",
       "min     21.000000     7.700000    2270.000000    7000.000000    0.000000   \n",
       "25%     48.750000    10.000000    4750.000000  199000.000000    2.000000   \n",
       "50%     59.000000    11.700000    6400.000000  253500.000000    7.000000   \n",
       "75%     67.000000    13.125000    8420.000000  323000.000000   26.860436   \n",
       "max     90.000000    16.700000   18340.000000  664000.000000  309.000000   \n",
       "\n",
       "             Urée       Créat    Glycémie    Albumine  Préalbumine  ...  \\\n",
       "count  292.000000  292.000000  292.000000  292.000000   292.000000  ...   \n",
       "mean     0.274130    7.716913    1.067204   35.649193     0.144893  ...   \n",
       "std      0.096367    2.562807    0.329490    5.080704     0.110670  ...   \n",
       "min      0.050000    1.530000    0.440000   16.000000     0.000000  ...   \n",
       "25%      0.200000    7.000000    0.910000   33.000000     0.030000  ...   \n",
       "50%      0.260000    7.000000    1.077961   35.601695     0.150000  ...   \n",
       "75%      0.330000    8.000000    1.077961   39.000000     0.259902  ...   \n",
       "max      0.680000   36.000000    3.770000   48.000000     0.340000  ...   \n",
       "\n",
       "       CHemorEvolu  CpltPérinéal  CPérinéDiagnos  CPérinTraite  CPérinEvolut  \\\n",
       "count        292.0    292.000000      292.000000    292.000000    292.000000   \n",
       "mean           0.0      0.003425        0.003425      0.003425      0.003425   \n",
       "std            0.0      0.058521        0.058521      0.058521      0.058521   \n",
       "min            0.0      0.000000        0.000000      0.000000      0.000000   \n",
       "25%            0.0      0.000000        0.000000      0.000000      0.000000   \n",
       "50%            0.0      0.000000        0.000000      0.000000      0.000000   \n",
       "75%            0.0      0.000000        0.000000      0.000000      0.000000   \n",
       "max            0.0      1.000000        1.000000      1.000000      1.000000   \n",
       "\n",
       "       CplUrinair  CUrinaiDiagn  CUrinairEvolu  Cplct Générl       Décès  \n",
       "count  292.000000    292.000000     292.000000    292.000000  292.000000  \n",
       "mean     0.010274      0.020548       0.010274      0.023973    0.527397  \n",
       "std      0.101012      0.218372       0.101012      0.254392    0.665241  \n",
       "min      0.000000      0.000000       0.000000      0.000000    0.000000  \n",
       "25%      0.000000      0.000000       0.000000      0.000000    0.000000  \n",
       "50%      0.000000      0.000000       0.000000      0.000000    0.000000  \n",
       "75%      0.000000      0.000000       0.000000      0.000000    1.000000  \n",
       "max      1.000000      3.000000       1.000000      3.000000    2.000000  \n",
       "\n",
       "[8 rows x 158 columns]"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "_cell_guid": "25f0e07e-4d62-4f12-abb8-cfcf1ba0a95f",
    "_uuid": "bb72bebe9916f26581bd773823dbe4d410787ac9",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Morbidité</th>\n",
       "      <th>Décès</th>\n",
       "      <th>Sexe</th>\n",
       "      <th>Protidemie</th>\n",
       "      <th>obésité</th>\n",
       "      <th>diabète</th>\n",
       "      <th>HTA</th>\n",
       "      <th>cardiopathie</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.462882</td>\n",
       "      <td>0.497817</td>\n",
       "      <td>66.321210</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.109170</td>\n",
       "      <td>0.126638</td>\n",
       "      <td>0.048035</td>\n",
       "      <td>57.845550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.761905</td>\n",
       "      <td>0.539683</td>\n",
       "      <td>69.197279</td>\n",
       "      <td>0.031746</td>\n",
       "      <td>0.063492</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>0.079365</td>\n",
       "      <td>56.793651</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Morbidité     Décès      Sexe  Protidemie   obésité   diabète       HTA  \\\n",
       "0        0.0  0.462882  0.497817   66.321210  0.000000  0.109170  0.126638   \n",
       "1        1.0  0.761905  0.539683   69.197279  0.031746  0.063492  0.095238   \n",
       "\n",
       "   cardiopathie        Age  \n",
       "0      0.048035  57.845550  \n",
       "1      0.079365  56.793651  "
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[['Décès', 'Sexe', 'Protidemie', 'obésité', 'diabète', 'HTA', 'cardiopathie', 'Age', 'Morbidité']].groupby(['Morbidité'], as_index=False).mean().sort_values(by='Age' ,ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "6a0bde77-40d6-4b59-8e3c-7a08b219bd30",
    "_uuid": "991534cba51f0387c1cd8a8d5457c7ccd7bafd4b"
   },
   "source": [
    "**Initial observations from the descriptive statistics:**\n",
    "* 76% of those who had \"morbidité=1\" died, a real tragedy :-(\n",
    "* Also, the higher the \"Protidemie\", the higher the probability to have \"morbidité=1\". Similarly, having obesity increase the probability of getting complications.\n",
    "* Women (1) have a higher probability to get complications than men (0)\n",
    "* older people slightly have chance than younger\n",
    "* having cardipathie increase the probability to get complications.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "d51a4163-10c2-443e-bec2-0ec3b2a93b40",
    "_uuid": "d3d1a1236728a132b0bc4e02f85da6e1a7ce2942"
   },
   "source": [
    "# Correlation analysis - Multi-variate analysis\n",
    "This section summarizes  bivariate analysis asthe simplest forms of quantitative (statistical) analysis.\n",
    "It involves the analysis of one or two features, and their relative impact of \"Morbidité\". \n",
    "This is a useful frist step of our anblaysis in order to determine the empirical relationship between all features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "2ae4e483-0ae8-422d-abe5-71330366e0a3",
    "_uuid": "58a067cb3eb5edbccb6f9f20fab06c26c1d02da0"
   },
   "source": [
    "##  Correlation analysis with histograms and pivot-tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "_cell_guid": "3e32fa2e-496f-47ab-a8a0-0a7277d0473f",
    "_uuid": "4a7d5a8d1e764a7f5a7cea45ca373488717382da",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1296x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (axis1,axis2) = plt.subplots(1,2,figsize=(18,6))\n",
    "sns.barplot(x=\"Localisation\", y=\"Morbidité\", hue=\"Sexe\", data=train, ax = axis1);\n",
    "sns.barplot(x=\"Stade\", y=\"Morbidité\", hue=\"occlusion\", data=train, ax = axis2);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "25e95e2f-5be3-4afe-b23f-47bc8fe80620",
    "_uuid": "de57c4821ae253395ad26821f6eaacfcde79801c"
   },
   "source": [
    "**Observations\n",
    ":**\n",
    "* 0 or blue represent men; 1 or orange represent women. Gender and localisation seem to have a stronger influence of the complication's rate.\n",
    "* We start to find on which \"localisation\" patients have a high probability to get complications during their operation : localisation=7.\n",
    "* What is statistically interesting is that only men (Age Category = 0) have \"localisation=2\" and all those who have \"cancer stade=0\" got \"occlusion=0\". \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "_cell_guid": "1ad428d8-c429-40e1-bf63-f1322ab0378f",
    "_uuid": "97d135ceed4cbb2a5df1ae118dff34feb90d3930",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_077b636c_2603_11ec_a9f3_14f6d8044c0erow0_col0,#T_077b636c_2603_11ec_a9f3_14f6d8044c0erow0_col1,#T_077b636c_2603_11ec_a9f3_14f6d8044c0erow0_col2,#T_077b636c_2603_11ec_a9f3_14f6d8044c0erow1_col0,#T_077b636c_2603_11ec_a9f3_14f6d8044c0erow1_col1,#T_077b636c_2603_11ec_a9f3_14f6d8044c0erow1_col2,#T_077b636c_2603_11ec_a9f3_14f6d8044c0erow2_col0,#T_077b636c_2603_11ec_a9f3_14f6d8044c0erow2_col1,#T_077b636c_2603_11ec_a9f3_14f6d8044c0erow2_col2,#T_077b636c_2603_11ec_a9f3_14f6d8044c0erow3_col0,#T_077b636c_2603_11ec_a9f3_14f6d8044c0erow3_col1,#T_077b636c_2603_11ec_a9f3_14f6d8044c0erow3_col2,#T_077b636c_2603_11ec_a9f3_14f6d8044c0erow4_col0,#T_077b636c_2603_11ec_a9f3_14f6d8044c0erow4_col2,#T_077b636c_2603_11ec_a9f3_14f6d8044c0erow7_col0,#T_077b636c_2603_11ec_a9f3_14f6d8044c0erow7_col1,#T_077b636c_2603_11ec_a9f3_14f6d8044c0erow7_col2{\n",
       "            color:  red;\n",
       "        }#T_077b636c_2603_11ec_a9f3_14f6d8044c0erow4_col1,#T_077b636c_2603_11ec_a9f3_14f6d8044c0erow5_col0,#T_077b636c_2603_11ec_a9f3_14f6d8044c0erow5_col1,#T_077b636c_2603_11ec_a9f3_14f6d8044c0erow5_col2,#T_077b636c_2603_11ec_a9f3_14f6d8044c0erow6_col0,#T_077b636c_2603_11ec_a9f3_14f6d8044c0erow6_col1,#T_077b636c_2603_11ec_a9f3_14f6d8044c0erow6_col2{\n",
       "            color:  black;\n",
       "        }</style><table id=\"T_077b636c_2603_11ec_a9f3_14f6d8044c0e\" ><thead>    <tr>        <th class=\"blank\" ></th>        <th class=\"index_name level0\" >Sexe</th>        <th class=\"col_heading level0 col0\" >0</th>        <th class=\"col_heading level0 col1\" >1</th>        <th class=\"col_heading level0 col2\" >All</th>    </tr>    <tr>        <th class=\"index_name level0\" >obésité</th>        <th class=\"index_name level1\" >Stade</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_077b636c_2603_11ec_a9f3_14f6d8044c0elevel0_row0\" class=\"row_heading level0 row0\" rowspan=5>0.0</th>\n",
       "                        <th id=\"T_077b636c_2603_11ec_a9f3_14f6d8044c0elevel1_row0\" class=\"row_heading level1 row0\" >0</th>\n",
       "                        <td id=\"T_077b636c_2603_11ec_a9f3_14f6d8044c0erow0_col0\" class=\"data row0 col0\" >0.166667</td>\n",
       "                        <td id=\"T_077b636c_2603_11ec_a9f3_14f6d8044c0erow0_col1\" class=\"data row0 col1\" >0.384615</td>\n",
       "                        <td id=\"T_077b636c_2603_11ec_a9f3_14f6d8044c0erow0_col2\" class=\"data row0 col2\" >0.280000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <th id=\"T_077b636c_2603_11ec_a9f3_14f6d8044c0elevel1_row1\" class=\"row_heading level1 row1\" >1</th>\n",
       "                        <td id=\"T_077b636c_2603_11ec_a9f3_14f6d8044c0erow1_col0\" class=\"data row1 col0\" >0.074074</td>\n",
       "                        <td id=\"T_077b636c_2603_11ec_a9f3_14f6d8044c0erow1_col1\" class=\"data row1 col1\" >0.259259</td>\n",
       "                        <td id=\"T_077b636c_2603_11ec_a9f3_14f6d8044c0erow1_col2\" class=\"data row1 col2\" >0.166667</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <th id=\"T_077b636c_2603_11ec_a9f3_14f6d8044c0elevel1_row2\" class=\"row_heading level1 row2\" >2</th>\n",
       "                        <td id=\"T_077b636c_2603_11ec_a9f3_14f6d8044c0erow2_col0\" class=\"data row2 col0\" >0.295082</td>\n",
       "                        <td id=\"T_077b636c_2603_11ec_a9f3_14f6d8044c0erow2_col1\" class=\"data row2 col1\" >0.200000</td>\n",
       "                        <td id=\"T_077b636c_2603_11ec_a9f3_14f6d8044c0erow2_col2\" class=\"data row2 col2\" >0.250000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <th id=\"T_077b636c_2603_11ec_a9f3_14f6d8044c0elevel1_row3\" class=\"row_heading level1 row3\" >3</th>\n",
       "                        <td id=\"T_077b636c_2603_11ec_a9f3_14f6d8044c0erow3_col0\" class=\"data row3 col0\" >0.157895</td>\n",
       "                        <td id=\"T_077b636c_2603_11ec_a9f3_14f6d8044c0erow3_col1\" class=\"data row3 col1\" >0.192308</td>\n",
       "                        <td id=\"T_077b636c_2603_11ec_a9f3_14f6d8044c0erow3_col2\" class=\"data row3 col2\" >0.177778</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <th id=\"T_077b636c_2603_11ec_a9f3_14f6d8044c0elevel1_row4\" class=\"row_heading level1 row4\" >4</th>\n",
       "                        <td id=\"T_077b636c_2603_11ec_a9f3_14f6d8044c0erow4_col0\" class=\"data row4 col0\" >0.000000</td>\n",
       "                        <td id=\"T_077b636c_2603_11ec_a9f3_14f6d8044c0erow4_col1\" class=\"data row4 col1\" >nan</td>\n",
       "                        <td id=\"T_077b636c_2603_11ec_a9f3_14f6d8044c0erow4_col2\" class=\"data row4 col2\" >0.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_077b636c_2603_11ec_a9f3_14f6d8044c0elevel0_row5\" class=\"row_heading level0 row5\" rowspan=2>1.0</th>\n",
       "                        <th id=\"T_077b636c_2603_11ec_a9f3_14f6d8044c0elevel1_row5\" class=\"row_heading level1 row5\" >0</th>\n",
       "                        <td id=\"T_077b636c_2603_11ec_a9f3_14f6d8044c0erow5_col0\" class=\"data row5 col0\" >nan</td>\n",
       "                        <td id=\"T_077b636c_2603_11ec_a9f3_14f6d8044c0erow5_col1\" class=\"data row5 col1\" >1.000000</td>\n",
       "                        <td id=\"T_077b636c_2603_11ec_a9f3_14f6d8044c0erow5_col2\" class=\"data row5 col2\" >1.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <th id=\"T_077b636c_2603_11ec_a9f3_14f6d8044c0elevel1_row6\" class=\"row_heading level1 row6\" >2</th>\n",
       "                        <td id=\"T_077b636c_2603_11ec_a9f3_14f6d8044c0erow6_col0\" class=\"data row6 col0\" >1.000000</td>\n",
       "                        <td id=\"T_077b636c_2603_11ec_a9f3_14f6d8044c0erow6_col1\" class=\"data row6 col1\" >nan</td>\n",
       "                        <td id=\"T_077b636c_2603_11ec_a9f3_14f6d8044c0erow6_col2\" class=\"data row6 col2\" >1.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_077b636c_2603_11ec_a9f3_14f6d8044c0elevel0_row7\" class=\"row_heading level0 row7\" >All</th>\n",
       "                        <th id=\"T_077b636c_2603_11ec_a9f3_14f6d8044c0elevel1_row7\" class=\"row_heading level1 row7\" ></th>\n",
       "                        <td id=\"T_077b636c_2603_11ec_a9f3_14f6d8044c0erow7_col0\" class=\"data row7 col0\" >0.201389</td>\n",
       "                        <td id=\"T_077b636c_2603_11ec_a9f3_14f6d8044c0erow7_col1\" class=\"data row7 col1\" >0.229730</td>\n",
       "                        <td id=\"T_077b636c_2603_11ec_a9f3_14f6d8044c0erow7_col2\" class=\"data row7 col2\" >0.215753</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1388c7a1d90>"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_pivot = pd.pivot_table(train, values= 'Morbidité',index=['obésité', 'Stade'],columns='Sexe',aggfunc=np.mean, margins=True)\n",
    "def color_negative_red(val):\n",
    "    # Takes a scalar and returns a string with the css property 'color: red' if below 0.4, black otherwise.\n",
    "    color = 'red' if val < 0.4 else 'black'\n",
    "    return 'color: %s' % color\n",
    "train_pivot = train_pivot.style.applymap(color_negative_red)\n",
    "train_pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "_cell_guid": "f6f28a5b-fee9-4f70-bfb1-7f0f026de045",
    "_uuid": "224a3f391434aa77da825c3984324be2c0b17335",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 872x720 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "grid = sns.FacetGrid(train, col='Morbidité', row='Stade', size=2, aspect=3)\n",
    "grid.map(plt.hist, 'Age', alpha=.50, bins=8)\n",
    "grid.add_legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 338 entries, 0 to 337\n",
      "Columns: 158 entries, Age to Décès\n",
      "dtypes: float64(116), int32(1), int64(41)\n",
      "memory usage: 416.0 KB\n"
     ]
    }
   ],
   "source": [
    "allData= pd.concat([quant_data, data], axis=1)\n",
    "allData.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "_cell_guid": "18bd73d2-b436-4500-af02-59274fa97358",
    "_uuid": "4ccf0eb696b2ae00be51777dee3f75f32cd19ad2",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "possible_features1=[]\n",
    "for col in allData.columns[allData.columns.get_loc('Age'):allData.columns.get_loc(\"DécèsPostOP\")]:\n",
    "    possible_features1.append(col)\n",
    "X_Train, x_test, y_Train, y_test=train_test_split(allData[possible_features1],\n",
    "                                              allData['Morbidité'],test_size=0.2,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "smote_nc=SMOTENC(categorical_features=[i for i in range (15,107)],random_state=42) #specifying categorical column numbers\n",
    "# New training dataset with smote applied to it\n",
    "X_train, Y_train = smote_nc.fit_resample(X_Train, y_Train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "3eb2dad3-31d7-4367-a0ab-881729c6e8e8",
    "_uuid": "5336a714f678d54e161172ef22827489d1a86a83"
   },
   "source": [
    "# 4. Predictive modelling, cross-validation, hyperparameters and ensembling\n",
    "\n",
    "* 4.1. Logistic Regression\n",
    "* 4.2. Support Vector Machines (supervised)\n",
    "* 4.3. k-Nearest Neighbors algorithm (k-NN)\n",
    "* 4.4. Naive Bayes classifier\n",
    "* 4.5. Perceptron\n",
    "* 4.6 Linear SVC\n",
    "* 4.7 Stochastic Gradient Descent\n",
    "* 4.8. Decision tree\n",
    "* 4.9 Random Forrest\n",
    "* 4.10 Model summary\n",
    "* 4.11. Model cross-validation with K-Fold\n",
    "* 4.12 Hyperparameter tuning & learning curves for selected classifiers\n",
    "* 4.13 Selecting and combining the best classifiers\n",
    "* 4.14 Ensembling\n",
    "* 4.15. Summary of most important features\n",
    "\n",
    "## 4.1. Logistic Regression\n",
    "Logistic regression measures the relationship between the categorical dependent feature (in our case morbidité) and the other independent features.\n",
    "It estimates probabilities using a cumulative logistic distribution:\n",
    "* The first value shows the accuracy of this model\n",
    "* The table after this shows the importance of each feature according this classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "_cell_guid": "d64bab2c-7654-4d0e-a2d3-77103e80e50f",
    "_uuid": "1e8259699d46e14e005b9cb104b4d68848a6d8de",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "57.35"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg = LogisticRegression()\n",
    "logreg.fit(X_train, Y_train)\n",
    "Y_pred1 = logreg.predict(x_test)\n",
    "acc_log = round(logreg.score(x_test, y_test) * 100, 2)\n",
    "acc_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "_uuid": "dc82a40522b6352f8119d239e9018cd43dadf303",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.76      0.55      0.64        47\n",
      "         1.0       0.38      0.62      0.47        21\n",
      "\n",
      "    accuracy                           0.57        68\n",
      "   macro avg       0.57      0.59      0.56        68\n",
      "weighted avg       0.65      0.57      0.59        68\n",
      "\n",
      "             Predicted: NO  Predicted: YES\n",
      "Actual: NO              26              21\n",
      "Actual: YES              8              13\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "print(classification_report(y_test, Y_pred1))\n",
    "cm = pd.DataFrame(confusion_matrix(y_test, Y_pred1), ['Actual: NO', 'Actual: YES'], ['Predicted: NO', 'Predicted: YES'])\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "537434f6-8d86-4bab-9828-a92909c14fe9",
    "_uuid": "2e3d6369eb83be933e874845ee87610be012a1cb"
   },
   "source": [
    "\n",
    "## 4.2. Support Vector Machines (supervised)\n",
    "Given a set of training samples, each sample is marked as belonging to one or the other of two categories.\n",
    "\n",
    "The SVM training algorithm builds a model that assigns new test samples to one category or the other, making it a non-probabilistic binary linear classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "_cell_guid": "0f01682c-4a1d-414c-8905-76897a32ce13",
    "_uuid": "79bdd20f5eb3ef2685384aeea7fe6c9140442163",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41.18"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc=SVC()\n",
    "svc.fit(X_train, Y_train)\n",
    "Y_pred2 = svc.predict(x_test)\n",
    "acc_svc = round(svc.score(x_test, y_test) * 100, 2)\n",
    "acc_svc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "_uuid": "014fe936f5dd3734248ba1a81f5a3216a26de9c3",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.68      0.28      0.39        47\n",
      "         1.0       0.31      0.71      0.43        21\n",
      "\n",
      "    accuracy                           0.41        68\n",
      "   macro avg       0.50      0.50      0.41        68\n",
      "weighted avg       0.57      0.41      0.40        68\n",
      "\n",
      "             Predicted: NO  Predicted: YES\n",
      "Actual: NO              13              34\n",
      "Actual: YES              6              15\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, Y_pred2))\n",
    "cm = pd.DataFrame(confusion_matrix(y_test, Y_pred2), ['Actual: NO', 'Actual: YES'], ['Predicted: NO', 'Predicted: YES'])\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "a9c6eaa4-bad7-4c48-9d6b-3e02e655a183",
    "_uuid": "be87e04341b24917d87761256e67304c19412cc6"
   },
   "source": [
    "## 4.3. k-Nearest Neighbors algorithm (k-NN)\n",
    "This is a non-parametric method used for classification and regression.\n",
    "A sample is classified by a majority vote of its neighbors, with the sample being assigned to the class most common among its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of that single nearest neighbor. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "_cell_guid": "3e21e1c1-abb6-4c00-a6aa-ce293e0d9ca4",
    "_uuid": "0baf00ea77739487b08035892793c9983c6141d8",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "54.41"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(algorithm='auto', leaf_size=26, metric='minkowski', \n",
    "                           metric_params=None, n_jobs=1, n_neighbors=10, p=2, \n",
    "                           weights='uniform')\n",
    "knn.fit(X_train, Y_train)\n",
    "knn_predictions = knn.predict(x_test)\n",
    "acc_knn = round(knn.score(x_test, y_test) * 100, 2)\n",
    "\n",
    "test_morbidité = pd.Series(knn_predictions, name=\"Morbidité\")\n",
    "\n",
    "acc_knn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "_uuid": "0d13d4c4159b159181c15eefd24317023f21e8f6",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.69      0.62      0.65        47\n",
      "         1.0       0.31      0.38      0.34        21\n",
      "\n",
      "    accuracy                           0.54        68\n",
      "   macro avg       0.50      0.50      0.50        68\n",
      "weighted avg       0.57      0.54      0.56        68\n",
      "\n",
      "             Predicted: NO  Predicted: YES\n",
      "Actual: NO              29              18\n",
      "Actual: YES             13               8\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, knn_predictions))\n",
    "cm = pd.DataFrame(confusion_matrix(y_test, knn_predictions), ['Actual: NO', 'Actual: YES'], ['Predicted: NO', 'Predicted: YES'])\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "_cell_guid": "e6802255-8522-4de4-b519-6775a2eb31ac",
    "_uuid": "5700969e1be9c7b9a13bc80f57f500b41f9f7589",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[40, 0.696969696969697]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1388cf379d0>]"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Selecting the right n_neighbors for the k-NN classifier\n",
    "x_trainknn, x_testknn, y_trainknn, y_testknn = train_test_split(X_train,Y_train,test_size = .33, random_state = 0)\n",
    "nn_scores = []\n",
    "best_prediction = [-1,-1]\n",
    "for i in range(1,100):\n",
    "    knn = KNeighborsClassifier(n_neighbors=i, weights='distance', metric='minkowski', p =2)\n",
    "    knn.fit(x_trainknn, y_trainknn)\n",
    "    score = accuracy_score(y_testknn, knn.predict(x_testknn))\n",
    "    #print i, score\n",
    "    if score > best_prediction[1]:\n",
    "        best_prediction = [i, score]\n",
    "    nn_scores.append(score)\n",
    "print (best_prediction)\n",
    "plt.plot(range(1,100),nn_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "08739336-522e-4b96-a51d-b32ca035937b",
    "_uuid": "691639deb7d9cc3a5200a38ccf45c45204b694a1"
   },
   "source": [
    "## 4.4. Naive Bayes classifier\n",
    "This is a family of simple probabilistic classifiers based on applying Bayes' theorem with strong (naive) independence assumptions between the features. Naive Bayes classifiers are highly scalable, requiring a number of parameters linear in the number of features in a learning problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "_cell_guid": "4d363cdb-9f1d-4e43-9750-38b82971f223",
    "_uuid": "f8338165241ba93eb873903e0bf02fe603c5bfb3",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "63.24"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gaussian = GaussianNB(var_smoothing=1e-12)\n",
    "gaussian.fit(X_train, Y_train)\n",
    "Y_pred3 = gaussian.predict(x_test)\n",
    "acc_gaussian = round(gaussian.score(x_test, y_test) * 100, 2)\n",
    "acc_gaussian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "_uuid": "2dab386c7fc9e86f8c3677732d570030ad6dd203",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.81      0.62      0.70        47\n",
      "         1.0       0.44      0.67      0.53        21\n",
      "\n",
      "    accuracy                           0.63        68\n",
      "   macro avg       0.62      0.64      0.61        68\n",
      "weighted avg       0.69      0.63      0.65        68\n",
      "\n",
      "             Predicted: NO  Predicted: YES\n",
      "Actual: NO              29              18\n",
      "Actual: YES              7              14\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, Y_pred3))\n",
    "cm = pd.DataFrame(confusion_matrix(y_test, Y_pred3), ['Actual: NO', 'Actual: YES'], ['Predicted: NO', 'Predicted: YES'])\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "940ba7a6-109d-447c-8f0f-35d95d751ee7",
    "_uuid": "e32fab307ef58b8e00bcf2f956ed50094a4bb5c9"
   },
   "source": [
    "## 4.5. Perceptron\n",
    "This is an algorithm for supervised learning of binary classifiers: like the other classifiers before, it decides whether an input, represented by a vector of numbers, belongs to some specific class or not. It is a type of linear classifier, i.e. a classification algorithm that makes its predictions based on a linear predictor function combining a set of weights with the feature vector. The algorithm allows for online learning, in that it processes elements in the training set one at a time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "_cell_guid": "e1548c66-6c08-4f0a-a8a4-79962ede6cb1",
    "_uuid": "74a727ebe8bd1a1ee7ea25840754e94af455ee2c",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "69.12"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perceptron = Perceptron()\n",
    "perceptron.fit(X_train, Y_train)\n",
    "Y_pred4 = perceptron.predict(x_test)\n",
    "acc_perceptron = round(perceptron.score(x_test, y_test) * 100, 2)\n",
    "acc_perceptron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "_uuid": "0775ea4e23a8921457ac1605127640c945941ff1",
    "jupyter": {
     "outputs_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.69      1.00      0.82        47\n",
      "         1.0       0.00      0.00      0.00        21\n",
      "\n",
      "    accuracy                           0.69        68\n",
      "   macro avg       0.35      0.50      0.41        68\n",
      "weighted avg       0.48      0.69      0.56        68\n",
      "\n",
      "             Predicted: NO  Predicted: YES\n",
      "Actual: NO              47               0\n",
      "Actual: YES             21               0\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, Y_pred4))\n",
    "cm = pd.DataFrame(confusion_matrix(y_test, Y_pred4), ['Actual: NO', 'Actual: YES'], ['Predicted: NO', 'Predicted: YES'])\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "b9a23f92-0bbf-48c4-9ab6-15dbfe5116a7",
    "_uuid": "2a9436f61d14962f8516df1fc1a18b1a3a72c135"
   },
   "source": [
    "## 4.6. Linear SVC\n",
    "This is another implementation of Support Vector Classification (similar to 4.2.) for the case of a linear kernel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "_cell_guid": "60611a3f-fd51-4229-acaf-c133ca2e9847",
    "_uuid": "17e366e5f4eb069cbfdbad90453b6e76f05acdef",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30.88"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_svc = LinearSVC()\n",
    "linear_svc.fit(X_train, Y_train)\n",
    "Y_pred5 = linear_svc.predict(x_test)\n",
    "acc_linear_svc = round(linear_svc.score(x_test, y_test) * 100, 2)\n",
    "acc_linear_svc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "_uuid": "c03ad12b9cd51cb3db1b352dbbbb8984fbd1e438",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.00      0.00      0.00        47\n",
      "         1.0       0.31      1.00      0.47        21\n",
      "\n",
      "    accuracy                           0.31        68\n",
      "   macro avg       0.15      0.50      0.24        68\n",
      "weighted avg       0.10      0.31      0.15        68\n",
      "\n",
      "             Predicted: NO  Predicted: YES\n",
      "Actual: NO               0              47\n",
      "Actual: YES              0              21\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, Y_pred5))\n",
    "cm = pd.DataFrame(confusion_matrix(y_test, Y_pred5), ['Actual: NO', 'Actual: YES'], ['Predicted: NO', 'Predicted: YES'])\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "244579fa-56f2-4bb6-8b1f-22f4ff061da9",
    "_uuid": "5fce77fff72d983e6e079a9a4a878f200c8249c5"
   },
   "source": [
    "## 4.7. Stochastic Gradient Descent (sgd)\n",
    "This is a stochastic approximation of the gradient descent optimization and iterative method for minimizing an objective function that is written as a sum of differentiable functions. In other words, SGD tries to find minima or maxima by iteration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "_cell_guid": "2b46b161-4da8-4652-a250-6173618b8f10",
    "_uuid": "a6ec7ea6c3b3a749ac38e9d54aa3571b14952322",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "69.12"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd = SGDClassifier()\n",
    "sgd.fit(X_train, Y_train)\n",
    "Y_pred6 = sgd.predict(x_test)\n",
    "acc_sgd = round(sgd.score(x_test, y_test) * 100, 2)\n",
    "acc_sgd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "_uuid": "fd97601e09aaa5e728a0694330da63d0184e19f6",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.69      1.00      0.82        47\n",
      "         1.0       0.00      0.00      0.00        21\n",
      "\n",
      "    accuracy                           0.69        68\n",
      "   macro avg       0.35      0.50      0.41        68\n",
      "weighted avg       0.48      0.69      0.56        68\n",
      "\n",
      "             Predicted: NO  Predicted: YES\n",
      "Actual: NO              47               0\n",
      "Actual: YES             21               0\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, Y_pred6))\n",
    "cm = pd.DataFrame(confusion_matrix(y_test, Y_pred6), ['Actual: NO', 'Actual: YES'], ['Predicted: NO', 'Predicted: YES'])\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "56d4b334-9d9b-461a-bcc8-e249647222a2",
    "_uuid": "e5ff90c38f7a875bdc8cc78e3a56204759d6d088"
   },
   "source": [
    "## 4.8. Decision tree\n",
    "This predictive model  maps features (tree branches) to conclusions about the target value (tree leaves).\n",
    "\n",
    "The target features  take a finite set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "_cell_guid": "ff611104-8d18-40ea-a4d9-1cc6ae190312",
    "_uuid": "94fa5a802bfd7692d3f9fb3f10a77840bedc2807",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "51.47"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decision_tree = DecisionTreeClassifier()\n",
    "decision_tree.fit(X_train, Y_train)\n",
    "Y_pred7 = decision_tree.predict(x_test)\n",
    "acc_decision_tree = round(decision_tree.score(x_test, y_test) * 100, 2)\n",
    "acc_decision_tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "_uuid": "8fa762a1915848f9593403816de0c344aed158aa",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.64      0.68      0.66        47\n",
      "         1.0       0.17      0.14      0.15        21\n",
      "\n",
      "    accuracy                           0.51        68\n",
      "   macro avg       0.40      0.41      0.41        68\n",
      "weighted avg       0.49      0.51      0.50        68\n",
      "\n",
      "             Predicted: NO  Predicted: YES\n",
      "Actual: NO              32              15\n",
      "Actual: YES             18               3\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, Y_pred7))\n",
    "cm = pd.DataFrame(confusion_matrix(y_test, Y_pred7), ['Actual: NO', 'Actual: YES'], ['Predicted: NO', 'Predicted: YES'])\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "d2250de2-014f-44fe-8d1e-6802dc4256c9",
    "_uuid": "d2fb348dae0ae80f8d92a7cb352dc476c0df834e"
   },
   "source": [
    "## 4.9. Random Forests\n",
    "This is one of the most popular classfier.\n",
    "Random forests or random decision forests are an ensemble learning method for classification, regression and other tasks, that operate by constructing a multitude of decision trees (n_estimators=100) at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "_cell_guid": "a23b35cd-e90b-4414-b19a-aab033fc5197",
    "_uuid": "c97d4e9bb57a147d8871ac55ede16c92f94f8937",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "66.18"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_forest = RandomForestClassifier(n_estimators=100)\n",
    "random_forest.fit(X_train, Y_train)\n",
    "random_forest_predictions = random_forest.predict(x_test)\n",
    "acc_random_forest = round(random_forest.score(x_test, y_test) * 100, 2)\n",
    "\n",
    "\n",
    "# Preparing data for Submission 2\n",
    "test_morbidité = pd.Series(random_forest_predictions, name=\"Morbidité\")\n",
    "\n",
    "\n",
    "acc_random_forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "_uuid": "3fb840a5153d3230b04053d5b20e9d7cf2afadc4",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.69      0.91      0.79        47\n",
      "         1.0       0.33      0.10      0.15        21\n",
      "\n",
      "    accuracy                           0.66        68\n",
      "   macro avg       0.51      0.51      0.47        68\n",
      "weighted avg       0.58      0.66      0.59        68\n",
      "\n",
      "             Predicted: NO  Predicted: YES\n",
      "Actual: NO              43               4\n",
      "Actual: YES             19               2\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, random_forest_predictions))\n",
    "cm = pd.DataFrame(confusion_matrix(y_test, random_forest_predictions), ['Actual: NO', 'Actual: YES'], ['Predicted: NO', 'Predicted: YES'])\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "af2231ef-5396-4dcc-9622-b134f75adcf3",
    "_uuid": "a6c47798f6078f3af1e46714dd9ea2f3afd89662"
   },
   "source": [
    "## 4.10. Model summary\n",
    "I found that the picture illustrates the various model better than words.\n",
    "This should be taken with a grain of salt, as the intuition conveyed by these two-dimensional examples does not necessarily carry over to real datasets.\n",
    "The reality os that the algorithms work with many dimensions (11 in our case).\n",
    "\n",
    "But it shows how each classifier algorithm partitions the same data in different ways.\n",
    "The three rows represent the three different data set on the right.\n",
    "The plots show training points in solid colors and testing points semi-transparent. The lower right shows the classification accuracy on the test set.\n",
    "\n",
    "For instance, the visualization helps understand how RandomForest uses multiple Decision Trees, the linear SVC, or Nearest Neighbors grouping sample by their relative distance to each others.\n",
    "\n",
    "![image](http://scikit-learn.org/0.15/_images/plot_classifier_comparison_0011.png)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "_cell_guid": "9235ce64-cec0-496f-a906-9d9fb11ac6eb",
    "_uuid": "03ca3ec943ac68754945b897831809945cc49ba1",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PwG62d6CUeXiFpBcAR1HW8d2aMkfgqOWKPCIilst4Ev8/UxL0PwFvAa5h4QldfbkYGf+/Rr0Z2AuYWffPBPZetpAjIuIvsdTEXxdc/zFwEzAVmAbcMJ4Pr+UdrgLmAhfZ/gmwme059bPnAJsu5r1HSJotafa8efPGc7iIiBiHxfbxS3omsD9wAHAXpV8e2y8b74fX+j5TJG0InC1p3OWcbZ8MnAwwdepUL+XlERExTktq8f+S0rrf0/aLbH8KmL88B6mjgr4HvAK4Q9JkgHo/d3k+MyIils+SEv9rgT8Al0j6vKRplAlc4yJpUm3pI2ktYHfKl8m5wPT6sunAOcsRd0RELKfFdvXYPpvSPbMO5QLsO4DNJJ0EnG37wqV89mRgZh3+uRpwpu1vS7qcMkrocOBWYJ8V8O+IiIhxGk/JhgeBrwBfqWPw96EMwVxi4rd9DbBjn/13UbqQIiKiBcu05q7tu21/zvZugwooIiIGa3kWW4+IiJVYEn9ERMck8UdEdEwSf0RExyTxR0R0TBJ/RETHJPFHRHRMEn9ERMck8UdEdEwSf0RExyTxR0R0TBJ/RETHJPFHRHRMEn9ERMck8UdEdEwSf0RExyTxR0R0TBJ/RETHJPFHRHRMEn9ERMcMLPFLeoqkSyTdIOl6SW+v+zeWdJGkG+v9RoOKISIiFjXIFv/jwLts/zXwAuAtkrYDjgJm2d4amFW3IyKiIQNL/Lbn2L6yPn4AuAF4ErAXMLO+bCaw96BiiIiIRTXSxy9pK2BH4CfAZrbnQPlyADZdzHuOkDRb0ux58+Y1EWZERCcMPPFLWhc4C5hh+/7xvs/2yban2p46adKkwQUYEdExA038ktagJP2v2P5m3X2HpMn1+cnA3EHGEBERCxvkqB4BpwA32P5Yz1PnAtPr4+nAOYOKISIiFrX6AD97V+D1wLWSrqr73gccB5wp6XDgVmCfAcYQERFjDCzx274M0GKenjao40ZExJJl5m5ERMck8UdEdEwSf0RExyTxR0R0TBJ/RETHJPFHRHRMEn9ERMck8UdEdEwSf0RExyTxR0R0TBJ/RETHJPFHRHRMEn9ERMck8UdEdEwSf0RExyTxR0R0TBJ/RETHJPFHRHRMEn9ERMck8UdEdEwSf0RExwws8Us6VdJcSdf17NtY0kWSbqz3Gw3q+BER0d8gW/xfBF4xZt9RwCzbWwOz6nZERDRoYInf9veBu8fs3guYWR/PBPYe1PEjIqK/pvv4N7M9B6Deb7q4F0o6QtJsSbPnzZvXWIAREau6ob24a/tk21NtT500aVLb4URErDKaTvx3SJoMUO/nNnz8iIjOazrxnwtMr4+nA+c0fPyIiM4b5HDO04HLgW0k3S7pcOA4YA9JNwJ71O2IiGjQ6oP6YNsHLOapaYM6ZkRELN3QXtyNiIjBSOKPiOiYJP6IiI5J4o+I6Jgk/oiIjknij4jomCT+iIiOSeKPiOiYJP6IiI5J4o+I6Jgk/oiIjknij4jomCT+iIiOSeKPiOiYJP6IiI5J4o+I6Jgk/oiIjknij4jomCT+iIiOSeKPiOiYJP6IiI5pJfFLeoWkX0n6taSj2oghIqKrGk/8kiYAnwZeCWwHHCBpu6bjiIjoqjZa/DsDv7Z9k+1Hga8Be7UQR0REJ8l2sweU/gF4he031O3XA8+3/dYxrzsCOKJubgP8qtFAYRPgzoaPuTjDEsuwxAHDE8uwxAGJpZ9hiQPaiWVL25PG7ly94SAA1GffIt8+tk8GTh58OP1Jmm17alvH7zUssQxLHDA8sQxLHJBYhjkOGK5Y2ujquR14Ss/2k4HftxBHREQntZH4fwZsLempktYE9gfObSGOiIhOaryrx/bjkt4KXABMAE61fX3TcYxDa91MfQxLLMMSBwxPLMMSBySWfoYlDhiiWBq/uBsREe3KzN2IiI5J4o+I6Jgk/oiVjKSJkhYZmy1pU0kT24gpFiXp7ePZ14Yk/oiVz38CL+6zfw/g4w3HsoCkSf2+kBqO4ZmSZkm6rm5vL+n9LYUzvc++Q5oOop9c3K0k/T3wEWBTyiQzAba9foMx7ARsYvv8MftfA/zO9hUNxvJ/l/C0bX+oqVhGSHoSsCU9o9Fsf7+FOJ4JvKdPLLs1dPxf2O5b30rS9baf1UQc9XgCjgHeSvmbWQ14HPiU7Q82FUdPPJdS/m8+Z3vHuu86289uMIYDgNcBLwJ+0PPU+sDjtndvKpbFaWPm7rA6HtjT9g0txnAC/VsEv6AMBWsksVQP9tm3NvAG4IlAo4lf0keA/Sg/i/l1t4HGEz/wdeCzwOd7YmlSv9nvI5o+i58B7ArsZPu3AJKeBpwk6R22mz4DWdv2T8v30QKPNxzDj4A5lBINH+3Z/wBwTcOx9JXEP+qOlpM+wBNt3zx2p+1fS3pik4HYXvALK2k94O3AYZSieh9d3PsGaG9gG9t/auHYYz1u+6QWjz9X0s62f9q7s54xzms4loOBPWwvqEFj+yZJBwEX0nzX052Snk4tA1Nrg81pMgDbtwC3SNodeNj2n+tZ4rbAtU3GsjhJ/KNmSzoD+BawILnY/maDMay1hOfWaSyKStLGwDuBA4GZwHNt39N0HNVNwBr0/N+06DxJbwbOZuHflbsbOv57gDMlfREY6f6bSknC+zcUw4g1epP+CNvzJK3RcCwAb6GcHW8r6XfAb4GDWogDytnoiyVtBMwCZlPOWg9sKZ4FkvhHrQ88BLy8Z5+BJhP/dyX9O/B+91x8kfQB4OIG40DSCcDfU/6InmP7j00ev4+HgKskzWLhZPu2FmIZuWj3np59Bp7WxMFrV8bOlCR3SN19PaXK7dwmYujx6HI+NxC2bwJ2l7QOsJrtB5qOoYdsPyTpcMo1j+Ml/bzFeBbIxd0hUn9ZTwF2Aq6qu3egtBTe0GTylfRnSoJ9nIWrpzZ+0bvG02+EBLZnNhnHMJD0buBrtm8fgljm0/96kICJthtt9UvaDPgwsLntV9ZFnnaxfUqTcdRYfg68mdLddbjt6yVda/s5TccyVhJ/JenJwKcoF6oMXAa8vY0/rnpxbGRkxvW1FRNDonZh/BPwkrrre5RRJI81dPyPA/9A6cY4HTjT9l1NHHvYSTofOA34F9s7SFod+HkbyVbS3wDvAn5o+yP173pGS2epC0niryRdBHwV+K+66yDgQNt7NBjDL4AvA2fY/k1Tx10ZSNoa+A/Kcp0LJinZbqR7ZUwsX6Bcbxg523g9MH9kcaGGYhDli2d/ygp2V1O+BM5us3ujDrmdUDd/b7vRETWSfmZ7J0k/7xnOeZXtKU3GMSamdWz3OytqTSZwjZpk+zTbj9fbF4GmJ6McAKwHXCjpJ5JmSNq84RgAkPSApPvr/QM92w9Janp4HJRW3EmUrqeXAV9i9Eu6aTvZnm774no7lNI91xgXl9r+J8r6Fp8A3gHc0WQcko4eM+fjcuA7lBE97+n/roF6sI6AGxnV8wLgvhbiQNIutTF3Q93eQdJn2ohlrCT+UXdKOkjShHo7CGj09Nn21baPtv10yvDJLYEfS7pY0hsbjmU92+vX+/WAzYF/B/4AfLLJWKq1bM+inKXeYvtYmp3X0Gt+HTIILOiaa2M8P5KeA3wQ+DTlYur7Gg5hHxYe3ntX7VZ5FvB3DccCZRTaucDTJf2Q0kA4soU4oHwZ/y01j9i+mtHuwVZlVM+ow4ATKRdiTJmEcVhbwdj+MSXpn1NjOpEyYahRkjakTNI5mNIVtlNL/cmPSFoNuLGu5/A7yizrNrwbuETSTZSLmFsChzZ18NrttT/lDHE+ZW7Fy9u6FjSmG+OTdd98SUsanrzCSZoA/E29bUP5v/lVU9de+rF925jJZK00EMZK4q9s3wq8pu04YMFEnAOA1wI3U4ZUfr3hGDahXJjaDzgV2NF2K6fM1QzKzOG3UWYN70b/WigDVZPLDsDWjCaXXzY8sewCSn/+frbbnhC0rqQ1RpJr7SJF0hMoQ6QbU79s9qqzhYdhcafbJL0QsMpqg2+jdvu0rfMXdyW9t46v/RT9F31v7Aq8pA8D+wL3UlpxrQ3Zk/QgZRboaZSp5gux/bHGgxoSki6x/bK24xhR+7RfAtzaZD2neuwPA38FvNX2Q3XfOpSupzm2j244nn8HNgDOoGeYqe0rm4yjxrIJ5Qxod0oD4ULKSMHWR2ClxT/6DTy71SiKKcBhroXHJB0s6bXALcCxDc4MhVI3aOSLcL0xzzXWWpD0CdszJJ3X77i22zhL+5GkE2kpuUj6NnCU7eskTQaupPz+Pl3SybY/0UQc1b9Srv3cKumWum8LynyUf20qCEkX2n458MK6q7dAnGn4elA9M/yE7dZn6fbT+RZ/P7UveV3b9zd83CuB3W3fLekllFb/kZQvhL+2/Q8NxvLkxZ1tSNrT9nkNxfE821fUMdGLsH1pE3GMiemS/qE0Vp1zQQVOSe8DtrV9sEpNpR/a3r6JOOrxdwJup5ylPgN4KbAn8EsabKz0Dt8cFpIuoBR+bHwG89KkxV9J+irwJsrFlyuADSR9zPYJDYaxWs8fyn7AybbPAs6SdFWDcQDMkvS3HlM0TtKhwPuBRhL/SNdFb4KvtU+eYrutSoeHj72QWkf2NKX3YuU06kV/2w/UGddN+hylsfJw/X85mtHGysmUiWZN2ECltHpfbrbm1oibgR9KOpeFzwxb7yZN4h+1ne37JR0I/Dfwz5QvgCYT/+qSVq+TXqYBR/Q+12AcUMaEXyTpVbZvhDJmm1JnvG/re5AkfY9y8X11SjmLeZIutf3OpmMBvgE8d8y+rwPPa+j4t0k6ktLSfi7wPwB1FE3ThdEmDEljZQPg1fQvWd10za0Rv6+31Vi0u7RVSfyj1qhT8fcGTrT9mKSm+8FOBy6VdCfwMHURB0nPoOFJKLb/W9KfgPMl7U2pw78T8BK3U6Fzg/rF/AbgNNvHSGq0xS9pW8r49LGty/XpmU3cgMMpfdi7U0b23Fv3v4ByMb5JE4aksXKL7daGX/dj+wOwoKy53X6hwwWS+Ed9jnJqdjXwfUlbAo328dv+d5Xqk5OBCz16AWY1WpiEYnuWpEMotWh+BEyz/UjTcVSr1wuZ+wL/0lIM21BalRtS+rFHPAA0NsHOpQLnm/rsvwTod/1hkIalsbKkxWlaIenZlNnlG9ftO4GDbbc+1DQXd5egpyXTOZIeoJwiC3gCpV95Pu1V59yHMkrkMttvrn3qJ9h+bZNx1Fh2sX1508cdVrUswkhj5cG675mUARJNjXR6tu3rmjjWeEn6EaVY3CV1+6XAh22/cEnva0ISfyXp7YyOWf8CsCNlyNyFrQYWQ6d+6XyS0rViSn2ad7Q1czaGk6Srbe+wtH1tSK2eUYfV4ZsvpxRnOxQ4rt2QYoSk4yWtL2kNSbMk3VnrKbXhq8CZlFbu5pQLu6c3GUCtJ/WOJo8Zy+wmSf8qaat6ez+llHbrkvhHjfQRvopy8fBqhrDfsMNeXr+YX00ZzfJM2qn+COVM+b88Wsn1yzQ4qQ1KeQJKOeYYXodRGpHfrLdNaLCm05Lk4u6oKyRdCDwVOLpeiW96THQs3sgwxVcBp9dJbm3FcomkoygT7EwZxvgdlTWKm1x794dtziAeRpJ2BY6lFM5bndFrUo3Ns5D097a/afseSce0NApuidLHX9XZulOAm2zfW+ufPKnFSULRQ9JxlKG2DwM7U0bWfNv281uIZUmn640lmbZnEA8jSb+kzEG5gp5KmE3Wx5F0pe3njn08TJL4K5Xm44HA02x/UNIWwF/Z/mnLoUVVZ4be71KFcW1gfdt/aDuuGB6SftJGY2BMDAvKRwxjKQlIV0+vz1C6dnajTI55ADiLhldWiv4kHdzzuPepL7UQy9qUBT+2sH2ESn38bWx/u4VY/o4yqax3OcoPLv4dq7xLJJ1A6VNfUCq74e6vtSTtSLmGOrE+XvBLOwxdcUn8o55v+7mSfg5Q++fWbDuoWKD3C3giZZbolbSQ+CnDfq9gtBLk7ZSRPY0mfkmfpaxR8DLKEOR/ALp+hjrS2p/as6/p6pxzgJF6PH/oedxGLH0l8Y96rJZSHVmrcxK5uDs0bC80c1nSBrS35u7Tbe8n6QCAWqCsjSvNL7S9vaRrbH9A0kdppybN0PAQrJMwDDEsTYZzjvpP4GxgU5XFHC4DPtxuSLEED1FWwWrDo7Ug2kgj4en0dCs06OF6/5CkzSmzq5/aQhxDQ9IGkj4maXa9fbQ2EqJHWvwsGNHzW+C9lC4EAXvbHopl0gK08EIsqwHbUSZRteEYSkXMp0j6CrArcEgLcXxbZU3kEyjdXqZ0+XTZqcB1lJpOAK+ndM0ttmRzF2VUTyXpctu7tB1H9KeFF2J5nFKNsZVlKWs8T6SUbBDwY9t3thVLjecJwES3uy5y6yRdZXvK0vZ1XVr8oy5UWebwm8634dBxCyttLY6k/wNcbPs7dXtDSXvb/lbDcawNvIsyuuiNkraQ9OI2RhcNkYclvcj2ZbBgQtfDS3nPwEjaHtiKnlzb0qIwC0mLv6rVKNehtCYfoaUqlNFfrQD5KeCvgTWBCcCDbfz/LKZV2fh4bUlnUEYXHWz72fW6w+Vdbt1KmgLMpCzMIuBu4JBagqXpWE4FtgeuZ3SgiIdh3YC0+CvbQ7VCTiziRGB/yrDJqcDBlDVe29BvUEQbf0vDMrpoaNi+CthB0vp1u9E1NcZ4ge3tWjz+YiXxV5L6Tau+j9KX3Mma/MPG9q8lTagFyk6r9c7bMFvSx4BPUy6oHklpeTdtWEYXtU7SQba/LOmdY/YDra1ze7mk7Wz/ooVjL1ES/6jPUNYvvbZuP4eyGtcTJb0pdflb91CdUHe1pOMpk2TWaSmWIymLwpxRty+kLEDftGEZXTQMRn4XhunMfSYl+f+B8oU80n28fbthpY9/AUlfAz40siyapO0oZX8/RLngO6XF8DpPZSnMOyj9+++grHN7ku1fNxzHBOAC27s3edzFGbbRRTFK0q8ppT2upWcyqO1bWguqSot/1La9a2Ha/oWkHW3f1PFu01ZJ2gt4su1P1+1LgU0ZXfmq0cRfC8Q9JGmDIRk6ORG4h/K3vJ0kbH+/5ZhaU88G/40ykud/gB2AGXXNhKbdavvcFo67VEn8o34l6SRKjXUoNdb/t46Pfqy9sDrvvZSLuiOeADwPWJcyMecbLcT0CHCtpItYuA7+25oMQtJHKL+nC40aATqb+CkL9ry3Drm9HdiHsgB9G4n/l5K+CpzHwgXjWh/OmcQ/6hDgzcAMymnzZcC7KUl/6GtvrMLWtH1bz/ZldaGTuyW11cf/nXpr296UqqCdvKC7GMO0YM9alIT/8p59ZgjqKaWPv0cdIbGF7V+1HUsUkn5tu++wTUm/sf30pmOqx279d0XS+cA+tv/YVgzDZpgW7BlmKdJWSXoNcBWlXxBJUyQNZf9cx/xE0hvH7pT0j7RUgljSngzH78pDwFWSPifpP0duLcQxNGwfBewCTLX9GKUrrpW1iSU9WdLZkuZKukPSWZKe3EYsY6XFX0m6glIn+3s9q+dcMwxDr7pM0qbAtyinzCMLWDyP0te/t+07Woip3+/Ktbaf03Ac0/vttz2zyTiGgaTdbF8sqW8xtjb61es1oK8yWj78IOBA23s0HctY6eMf9bjt+zKCZ7jYngu8UNJulJWmAL5j++IWw+r3u9J4C6qLCX4J/ga4GNizz3Nt9atPsn1az/YXJc1oIY5FJPGPuk7S64AJKkvpvQ1oa2ZojFETfZvJvlervyuSzrS9r6Rr6fOF08WzVNvH1PtD246lx52SDgJOr9sHAI0t+r4k6eqpaqXDf6FcgRel//ZDGTERY435XQG4APg32480dPzJtufUSW2LGIYJQm2R9GHgeNv31u2NgHfZbnxmtaQtKDWmdqF8Qf8IePsw/P8k8S+GpG0pvzCLXFiMbpI0EXgTpTjctcApqeM0XPpVSZV0pe1+tbg6q/NdPbVe9v8DNqcsvXgipW7P84GPthhaDJ+ZlHkdPwBeSSkRPaPpIGoJ8X4ttpQSL91vTxg5U6/Dbp/QZACSPsUSrvk0PdGvn84nfuDzwEmU6f+voIwc+Srl6nsjp+6x0thuZOSOpFNoaThpSogv0ZeBWZJOoyTfwyhf2E2aXe93pSwROlLMbx/aqeK6iM539YxdVEPSbcBWtfRvxAJjuwzShTCcJL0C2J1yBnSh7QtaiuMSSgmJx+r2GjWe1isBpMUPEyXtSPklAfgjsP3Igha2r1zsO6NrdpA0srCHgLXqdrpYhssNlCG335W0tqT1bD/QQhybU8pE31231637WpcWf/lWXhzb3q2xYCLiL1JneR8BbGz76XW47WdtT2shlkOBYylF4qDMNTh2GOZfdD7xR8SqQ9JVlBo9P2lzVnVPPH9FGShCjekPbcQxVmr1RMSq5E+2Hx3ZkLQ6Lcyq7jEBmEdZM+GZkl7SYiwLpI8/IlYll0p6H+X6yx6UUuvntRHIMK+XkK6eiFhlSFoNOJzRGfgXAF9wC4lO0q+A7Ydx9n8Sf1VX7Ll4ZDk9SRsCL7X9rTbjiohlI2kSgO15LccxtOslJPFXY8fz132LTP+OiOFTh18fA7yV0tIXMB/4lO0PthTTWZQ1f2ex8NKLmbk7RPpd6M7PJ2LlMIMyU3Yn278FkPQ04CRJ77D98RZiOrfehk5a/JWkU4F7gU9TLsAcCWxk+5AWw4qIcZD0c2AP23eO2T+JMls2Z+49Mpxz1JHAo5S6Gl8HHgHe0mpEETFea4xN+rCgn3+NPq8fOElbS/qGpF9Iumnk1kYsY6Uro7L9IHBU23FExHJ5dDmfG6TTKNcdPg68DDiU0dIwrep8V4+kT9ieIek8+q9m9JoWwoqIZSBpPmVh9UWeAibabrzVL+kK28/rnTks6Qe2X9x0LGOlxT+6EPL/azWKiFhutie0HUMfj9R5BTdKeivwO2DTlmMC0seP7ZH62FNsX9p7A6a0GFpErNxmAGtT1mR+HvB64OA2AxrR+a6eEf1qq2ccf0SsKLVu0H62v9J2LJ3v6pF0APA64KmSesfcrg/c1U5UEbGykrQ+ZUTgkyjj+C+q2+8GrgZaT/ydb/FL2hJ4KvAfLDyq5wHgmiymHRHLQtI5lGqclwPTgI2ANYG3276qxdAW6HziHyFpHeBh23+W9ExgW+D8kWXTIiLGY8wongnAncAWLa0C1lfnL+72+D5lGcYnUWprHAp8sdWIImJltKCxWNfu/u0wJX1IH38v2X5I0uGUwk7H12ngERHLYujXZk7iHyVJuwAHUup5Q34+EbGMhnROwULS1TNqBnA0cLbt62tlvyUtxB4RsVLKxd2IiI7pfFdGavVERNd0PvGTWj0R0THp6omI6Ji0+CtJ17JoV899wGzg32ynfENErBKS+EedT1mc+at1e3/KuNv7KBO59mwnrIiIFStdPZWkH9retd++3inYEREru4zjH7WupOePbEjaGVi3bqZQW0SsMtLVM+oNwKmS1qV08dwPHF6Lt/1Hq5FFRKxA6eoZQ9IGlJ/LvW3HEhExCOnqqSRtIOljlMqc35X00folEBGxSkniH3UqZfGVfevtfuC0ViOKiBiAdPVUkq6yPWVp+yIiVnZp8Y96WNKLRjYk7Qo83GI8EREDkRZ/JWkH4EvASL/+PcB029e0F1VExIqXxD+GpPUBbN8vaYbtT7QcUkTECpXEvwSSbrW9RdtxRESsSOnjXzK1HUBExIqWxL9kOR2KiFVO50s2SHqA/glewFoNhxMRMXDp44+I6Jh09UREdEwSf0RExyTxR0R0TBJ/RETHJPFHRHTM/wcOyJtDiDoPwwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "objects = ('Logistic Regression', 'SVC', 'KNN', 'Gaussian', 'Perceptron', 'linear SVC', 'SGD', 'Decision Tree', 'Random Forest')\n",
    "x_pos = np.arange(len(objects))\n",
    "accuracies1 = [acc_log, acc_svc, acc_knn, acc_gaussian, acc_perceptron, acc_linear_svc, acc_sgd, acc_decision_tree, acc_random_forest]\n",
    "    \n",
    "plt.bar(x_pos, accuracies1, align='center', alpha=0.5, color='r')\n",
    "plt.xticks(x_pos, objects, rotation='vertical')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title('Classifier Outcome')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "04d20d05-98ed-45f6-84d9-aa3e70e9d2af",
    "_uuid": "9fa35c1dda5fe275477b1aac2a23b18ca29a0bbb"
   },
   "source": [
    "**Observations**\n",
    "* The above models (classifiers) were applied to a split training and x_test datasets.\n",
    "* This results in some classifiers (Decision_tree and Random_Forest) over-fitting the model to the training data. \n",
    "* This happens when the classifiers use many input features (to include noise in each feature) on the complete dataset, and ends up “memorizing the noise” instead of finding the signal.\n",
    "* This overfit model will then make predictions based on that noise. It performs unusually well on its training data, but will not necessarilyimprove the prediction quality with new data from the test dataset.\n",
    "* In the next section, we will cross-validate the models using sample data against each others. We will this by using StratifiedKFold to train and test the models on sample data from the overall dataset.\n",
    "Stratified K-Folds is a cross validation iterator. It provides train/test indices to split data in train test sets. This cross-validation object is a variation of KFold, which returns stratified folds. The folds are made by preserving the percentage of samples for each class."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "6f4eb75d-3718-4bf9-9bda-ca53fe8f3826",
    "_uuid": "230e8659fb39cf8fdc6e0d531bf9a98c9674d84d"
   },
   "source": [
    "## 4.11. Model cross-validation with K-Fold\n",
    "\n",
    "The fitting process applied above optimizes the model parameters to make the model fit the training data as well as possible.\n",
    "Cross-validation is a way to predict the fit of a model to a hypothetical validation set when an explicit validation set is not available.\n",
    "In simple words, it allows to test how well the model performs on new data.\n",
    "In our case, cross-validation will also be applied to compare the performances of different predictive modeling procedures. \n",
    "![Cross-validation process:](https://image.slidesharecdn.com/kagglesharingmarkpeng20151216finalpresented-151216161621/95/general-tips-for-participating-kaggle-competitions-13-638.jpg?cb=1452565877)\n",
    "### Cross-validation scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "_cell_guid": "51a3d12e-6709-4c99-8499-e9065de25d21",
    "_uuid": "cd1a76ef5d8a7093e3d05ef0f244591e9ddafe8c",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cross validate model with Kfold stratified cross validation\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "kfold = StratifiedKFold(n_splits=10)\n",
    "# Modeling step Test differents algorithms \n",
    "random_state = 0\n",
    "\n",
    "classifiers = []\n",
    "classifiers.append(LogisticRegression(random_state = random_state))\n",
    "classifiers.append(SVC(random_state=random_state))\n",
    "classifiers.append(KNeighborsClassifier(n_neighbors=40, weights='distance', metric='minkowski', p =2))\n",
    "classifiers.append(GaussianNB())\n",
    "classifiers.append(Perceptron(random_state=random_state))\n",
    "classifiers.append(LinearSVC(random_state=random_state))\n",
    "classifiers.append(SGDClassifier(random_state=random_state))\n",
    "classifiers.append(DecisionTreeClassifier(random_state = random_state))\n",
    "classifiers.append(RandomForestClassifier(random_state = random_state))\n",
    "\n",
    "cv_results = []\n",
    "for classifier in classifiers :\n",
    "    cv_results.append(cross_val_score(classifier, X_train, y = Y_train, scoring = \"accuracy\", cv = kfold, n_jobs=4))\n",
    "\n",
    "cv_means = []\n",
    "cv_std = []\n",
    "for cv_result in cv_results:\n",
    "    cv_means.append(cv_result.mean())\n",
    "    cv_std.append(cv_result.std())\n",
    "\n",
    "cv_res = pd.DataFrame({\"CrossValMeans\":cv_means,\"CrossValerrors\": cv_std,\"Algorithm\":['Logistic Regression',  'KNN', 'Gaussian',\n",
    "    'Perceptron', 'linear SVC', 'SGD', 'Decision Tree','SVMC', 'Random Forest']})\n",
    "\n",
    "g = sns.barplot(\"CrossValMeans\",\"Algorithm\",data = cv_res, palette=\"Set3\",orient = \"h\",**{'xerr':cv_std})\n",
    "g.set_xlabel(\"Mean Accuracy\")\n",
    "g = g.set_title(\"Cross validation scores\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CrossValMeans</th>\n",
       "      <th>CrossValerrors</th>\n",
       "      <th>Algorithm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.598013</td>\n",
       "      <td>0.044359</td>\n",
       "      <td>Logistic Regression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.502436</td>\n",
       "      <td>0.069303</td>\n",
       "      <td>KNN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.648141</td>\n",
       "      <td>0.057549</td>\n",
       "      <td>Gaussian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.575321</td>\n",
       "      <td>0.063923</td>\n",
       "      <td>Perceptron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.005734</td>\n",
       "      <td>linear SVC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.492628</td>\n",
       "      <td>0.025933</td>\n",
       "      <td>SGD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.497500</td>\n",
       "      <td>0.009441</td>\n",
       "      <td>Decision Tree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.663782</td>\n",
       "      <td>0.125730</td>\n",
       "      <td>SVMC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.829615</td>\n",
       "      <td>0.152181</td>\n",
       "      <td>Random Forest</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CrossValMeans  CrossValerrors            Algorithm\n",
       "0       0.598013        0.044359  Logistic Regression\n",
       "1       0.502436        0.069303                  KNN\n",
       "2       0.648141        0.057549             Gaussian\n",
       "3       0.575321        0.063923           Perceptron\n",
       "4       0.500000        0.005734           linear SVC\n",
       "5       0.492628        0.025933                  SGD\n",
       "6       0.497500        0.009441        Decision Tree\n",
       "7       0.663782        0.125730                 SVMC\n",
       "8       0.829615        0.152181        Random Forest"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "056a8fdb-8d59-452a-82b1-86e37e62ea5b",
    "_uuid": "4d268142781fde8a736992af97a30e606094c7a5"
   },
   "source": [
    "## 4.12 Hyperparameter tuning & learning curves for selected classifiers\n",
    "\n",
    "**1. Adaboost** is used in conjunction with many other types of learning algorithms to improve performance. The output of the other learning algorithms ('weak learners') is combined into a weighted sum that represents the final output of the boosted classifier. AdaBoost is adaptive in the sense that subsequent weak learners are tweaked in favor of those instances misclassified by previous classifiers. AdaBoost is sensitive to noisy data and outliers.\n",
    "\n",
    "**2. ExtraTrees** implements a meta estimator that fits a number of randomized decision trees (a.k.a. extra-trees) on various sub-samples of the dataset and use averaging to improve the predictive accuracy and control over-fitting.\n",
    "\n",
    "**3. RandomForest** operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees. Random decision forests correct for decision trees' habit of overfitting to their training set.\n",
    "\n",
    "**4. GradientBoost** produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees. It builds the model in a stage-wise fashion like other boosting methods do, and it generalizes them by allowing optimization of an arbitrary differentiable loss function.\n",
    "\n",
    "**5. KNN .** a supervised machine learning algorithm (as opposed to an unsupervised machine learning algorithm) is one that relies on labeled input data to learn a function that produces an appropriate output when given new unlabeled data.\n",
    "\n",
    "All descripotion adapted from Wikipedia."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adaboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "_cell_guid": "b0263595-bfd7-498c-8161-e76fbc2bdd28",
    "_uuid": "1de7046e7704ee29b4827b3bde077af98bedb4df",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 112 candidates, totalling 1120 fits\n",
      "AdaBoostClassifier(algorithm='SAMME',\n",
      "                   base_estimator=DecisionTreeClassifier(criterion='entropy'),\n",
      "                   learning_rate=0.0001, n_estimators=1, random_state=7)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7394736842105263"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adaboost\n",
    "DTC = DecisionTreeClassifier()\n",
    "adaDTC = AdaBoostClassifier(DTC, random_state=7)\n",
    "ada_param_grid = {\"base_estimator__criterion\" : [\"gini\", \"entropy\"],\n",
    "              \"base_estimator__splitter\" :   [\"best\", \"random\"],\n",
    "              \"algorithm\" : [\"SAMME\",\"SAMME.R\"],\n",
    "              \"n_estimators\" :[1,2],\n",
    "              \"learning_rate\":  [0.0001, 0.001, 0.01, 0.1, 0.2, 0.3,1.5]}\n",
    "gsadaDTC = GridSearchCV(adaDTC,param_grid = ada_param_grid, cv=kfold, scoring=\"recall\", n_jobs= 4, verbose = 1)\n",
    "gsadaDTC.fit(X_train,Y_train)\n",
    "adaDTC_best = gsadaDTC.best_estimator_\n",
    "print(adaDTC_best)\n",
    "gsadaDTC.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "from 0.48 to 0.73"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.72      0.70      0.71        47\n",
      "         1.0       0.36      0.38      0.37        21\n",
      "\n",
      "    accuracy                           0.60        68\n",
      "   macro avg       0.54      0.54      0.54        68\n",
      "weighted avg       0.61      0.60      0.61        68\n",
      "\n"
     ]
    }
   ],
   "source": [
    "ada=AdaBoostClassifier(algorithm='SAMME',\n",
    "                   base_estimator=DecisionTreeClassifier(criterion='entropy'),\n",
    "                   learning_rate=0.0001, n_estimators=1, random_state=7)\n",
    "ada.fit(X_train, Y_train)\n",
    "print(classification_report(y_test, ada.predict(x_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extra trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "_cell_guid": "b45f6aa5-d003-460e-b051-c412409e946f",
    "_uuid": "1f6162dde2e422d192470675a6e7fa5f8bc3fba4",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 54 candidates, totalling 540 fits\n",
      "ExtraTreesClassifier(max_features=7, n_estimators=600)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8020512820512821"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ExtraTrees \n",
    "ExtC = ExtraTreesClassifier()\n",
    "ex_param_grid = {\"max_depth\": [None],\n",
    "              \"max_features\": [1, 3, 7],\n",
    "              \"min_samples_split\": [2, 3, 7],\n",
    "              \"min_samples_leaf\": [1, 3, 7],\n",
    "              \"bootstrap\": [False],\n",
    "              \"n_estimators\" :[300,600],\n",
    "              \"criterion\": [\"gini\"]}\n",
    "gsExtC = GridSearchCV(ExtC,param_grid = ex_param_grid, cv=kfold, scoring=\"accuracy\", n_jobs= 4, verbose = 1)\n",
    "gsExtC.fit(X_train,Y_train)\n",
    "ExtC_best = gsExtC.best_estimator_\n",
    "print(ExtC_best)\n",
    "gsExtC.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.80      0.79      0.80        47\n",
      "         1.0       0.55      0.57      0.56        21\n",
      "\n",
      "    accuracy                           0.72        68\n",
      "   macro avg       0.67      0.68      0.68        68\n",
      "weighted avg       0.72      0.72      0.72        68\n",
      "\n"
     ]
    }
   ],
   "source": [
    "Ext=ExtraTreesClassifier(max_features=3, min_samples_leaf=10, n_estimators=300)\n",
    "Ext.fit(X_train, Y_train)\n",
    "print(classification_report(y_test, Ext.predict(x_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GBC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "_cell_guid": "29ca52ff-55d8-4948-aaac-baf1b4be4bf2",
    "_uuid": "d0e880fe0a20338bd6ae7bfd756c192b2424a0a9",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 72 candidates, totalling 720 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.74"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Gradient boosting tunning\n",
    "GBC = GradientBoostingClassifier()\n",
    "gb_param_grid = {'loss' : [\"deviance\"],\n",
    "              'n_estimators' : [100,200,300],\n",
    "              'learning_rate': [0.1, 0.05, 0.01],\n",
    "              'max_depth': [4, 8],\n",
    "              'min_samples_leaf': [100,150],\n",
    "              'max_features': [0.3, 0.1] }\n",
    "gsGBC = GridSearchCV(GBC,param_grid = gb_param_grid, cv=kfold, scoring=\"recall\", n_jobs= 4, verbose = 1)\n",
    "gsGBC.fit(X_train,Y_train)\n",
    "GBC_best = gsGBC.best_estimator_\n",
    "gsGBC.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GradientBoostingClassifier(max_depth=4, max_features=0.1, min_samples_leaf=100,\n",
      "                           n_estimators=300)\n"
     ]
    }
   ],
   "source": [
    "print(GBC_best)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.75      0.81      0.78        47\n",
      "         1.0       0.47      0.38      0.42        21\n",
      "\n",
      "    accuracy                           0.68        68\n",
      "   macro avg       0.61      0.59      0.60        68\n",
      "weighted avg       0.66      0.68      0.67        68\n",
      "\n"
     ]
    }
   ],
   "source": [
    "gbc=GradientBoostingClassifier(max_depth=4, max_features=0.1, min_samples_leaf=100,\n",
    "                           n_estimators=300)\n",
    "gbc.fit(X_train, Y_train)\n",
    "print(classification_report(y_test, gbc.predict(x_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {
    "_cell_guid": "f841ae86-1e02-4626-b1e3-f3087cfee304",
    "_uuid": "29f6e95093b5d3d5f54a6042ffe9a1ef2245af2a",
    "jupyter": {
     "outputs_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 54 candidates, totalling 540 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7943475873821875"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random Forest\n",
    "rf_param_grid = {\"max_depth\": [None],\n",
    "              \"max_features\": [1, 3, 7],\n",
    "              \"min_samples_split\": [2, 3, 7],\n",
    "              \"min_samples_leaf\": [1, 3, 7],\n",
    "              \"bootstrap\": [False],\n",
    "              \"n_estimators\" :[300,600],\n",
    "              \"criterion\": [\"gini\"]}\n",
    "gsrandom_forest = GridSearchCV(random_forest,param_grid = rf_param_grid, cv=kfold, scoring=\"f1\", n_jobs= 4, verbose = 1)\n",
    "gsrandom_forest.fit(X_train,Y_train)\n",
    "# Best score\n",
    "random_forest_best = gsrandom_forest.best_estimator_\n",
    "gsrandom_forest.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier(bootstrap=False, max_features=7, n_estimators=600)\n"
     ]
    }
   ],
   "source": [
    "print(random_forest_best)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.70      0.91      0.80        47\n",
      "         1.0       0.43      0.14      0.21        21\n",
      "\n",
      "    accuracy                           0.68        68\n",
      "   macro avg       0.57      0.53      0.51        68\n",
      "weighted avg       0.62      0.68      0.62        68\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rdf=RandomForestClassifier(bootstrap=False, max_features=7, n_estimators=600)\n",
    "rdf.fit(X_train, Y_train)\n",
    "print(classification_report(y_test, rdf.predict(x_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "from plotly.subplots import make_subplots\n",
    "import plotly.graph_objs as go\n",
    "import pprint\n",
    "from scipy import stats\n",
    "from IPython.display import display\n",
    "def plot_grid_search(clf):\n",
    "    \"\"\"Plot as many graphs as parameters are in the grid search results.\n",
    "\n",
    "    Each graph has the values of each parameter in the X axis and the Score in the Y axis.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    clf: estimator object result of a GridSearchCV\n",
    "        This object contains all the information of the cross validated results for all the parameters combinations.\n",
    "    \"\"\"\n",
    "    # Convert the cross validated results in a DataFrame ordered by `rank_test_score` and `mean_fit_time`.\n",
    "    # As it is frequent to have more than one combination with the same max score,\n",
    "    # the one with the least mean fit time SHALL appear first.\n",
    "    cv_results = pd.DataFrame(clf.cv_results_).sort_values(by=['rank_test_score', 'mean_fit_time'])\n",
    "\n",
    "    # Get parameters\n",
    "    parameters=cv_results['params'][0].keys()\n",
    "\n",
    "    # Calculate the number of rows and columns necessary\n",
    "    rows = -(-len(parameters) // 2)\n",
    "    columns = min(len(parameters), 2)\n",
    "    # Create the subplot\n",
    "    fig = make_subplots(rows=rows, cols=columns)\n",
    "    # Initialize row and column indexes\n",
    "    row = 1\n",
    "    column = 1\n",
    "\n",
    "    # For each of the parameters\n",
    "    for parameter in parameters:\n",
    "\n",
    "        # As all the graphs have the same traces, and by default all traces are shown in the legend,\n",
    "        # the description appears multiple times. Then, only show legend of the first graph.\n",
    "        if row == 1 and column == 1:\n",
    "            show_legend = True\n",
    "        else:\n",
    "            show_legend = False\n",
    "\n",
    "        # Mean test score\n",
    "        mean_test_score = cv_results[cv_results['rank_test_score'] != 1]\n",
    "        fig.add_trace(go.Scatter(\n",
    "            name='Mean test score',\n",
    "            x=mean_test_score['param_' + parameter],\n",
    "            y=mean_test_score['mean_test_score'],\n",
    "            mode='markers',\n",
    "            marker=dict(size=mean_test_score['mean_fit_time'],\n",
    "                        color='SteelBlue',\n",
    "                        sizeref=2. * cv_results['mean_fit_time'].max() / (40. ** 2),\n",
    "                        sizemin=4,\n",
    "                        sizemode='area'),\n",
    "            text=mean_test_score['params'].apply(\n",
    "                lambda x: pprint.pformat(x, width=-1).replace('{', '').replace('}', '').replace('\\n', '<br />')),\n",
    "            showlegend=show_legend),\n",
    "            row=row,\n",
    "            col=column)\n",
    "\n",
    "        # Best estimators\n",
    "        rank_1 = cv_results[cv_results['rank_test_score'] == 1]\n",
    "        fig.add_trace(go.Scatter(\n",
    "            name='Best estimators',\n",
    "            x=rank_1['param_' + parameter],\n",
    "            y=rank_1['mean_test_score'],\n",
    "            mode='markers',\n",
    "            marker=dict(size=rank_1['mean_fit_time'],\n",
    "                        color='Crimson',\n",
    "                        sizeref=2. * cv_results['mean_fit_time'].max() / (40. ** 2),\n",
    "                        sizemin=4,\n",
    "                        sizemode='area'),\n",
    "            text=rank_1['params'].apply(str),\n",
    "            showlegend=show_legend),\n",
    "            row=row,\n",
    "            col=column)\n",
    "\n",
    "        fig.update_xaxes(title_text=parameter, row=row, col=column)\n",
    "        fig.update_yaxes(title_text='Score', row=row, col=column)\n",
    "\n",
    "        # Check the linearity of the series\n",
    "        # Only for numeric series\n",
    "        if pd.to_numeric(cv_results['param_' + parameter], errors='coerce').notnull().all():\n",
    "            x_values = cv_results['param_' + parameter].sort_values().unique().tolist()\n",
    "            r = stats.linregress(x_values, range(0, len(x_values))).rvalue\n",
    "            # If not so linear, then represent the data as logarithmic\n",
    "            if r < 0.86:\n",
    "                fig.update_xaxes(type='log', row=row, col=column)\n",
    "\n",
    "        # Increment the row and column indexes\n",
    "        column += 1\n",
    "        if column > columns:\n",
    "            column = 1\n",
    "            row += 1\n",
    "\n",
    "            # Show first the best estimators\n",
    "    fig.update_layout(legend=dict(traceorder='reversed'),\n",
    "                      width=columns * 360 + 100,\n",
    "                      height=rows * 360,\n",
    "                      title='Best score: {:.6f} with {}'.format(cv_results['mean_test_score'].iloc[0],\n",
    "                                                                str(cv_results['params'].iloc[0]).replace('{',\n",
    "                                                                                                          '').replace(\n",
    "                                                                    '}', '')),\n",
    "                      hovermode='closest',\n",
    "                      template='none')\n",
    "    fig.show()\n",
    "\n",
    "\n",
    "def table_grid_search(clf, all_columns=False, all_ranks=False, save=True):\n",
    "    \"\"\"Show tables with the grid search results.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    clf: estimator object result of a GridSearchCV\n",
    "        This object contains all the information of the cross validated results for all the parameters combinations.\n",
    "\n",
    "    all_columns: boolean, default: False\n",
    "        If true all columns are returned. If false, the following columns are dropped:\n",
    "\n",
    "        - params. As each parameter has a column with the value.\n",
    "        - std_*. Standard deviations.\n",
    "        - split*. Split scores.\n",
    "\n",
    "    all_ranks: boolean, default: False\n",
    "        If true all ranks are returned. If false, only the rows with rank equal to 1 are returned.\n",
    "\n",
    "    save: boolean, default: True\n",
    "        If true, results are saved to a CSV file.\n",
    "    \"\"\"\n",
    "    # Convert the cross validated results in a DataFrame ordered by `rank_test_score` and `mean_fit_time`.\n",
    "    # As it is frequent to have more than one combination with the same max score,\n",
    "    # the one with the least mean fit time SHALL appear first.\n",
    "    cv_results = pd.DataFrame(clf.cv_results_).sort_values(by=['rank_test_score', 'mean_fit_time'])\n",
    "\n",
    "    # Reorder\n",
    "    columns = cv_results.columns.tolist()\n",
    "    # rank_test_score first, mean_test_score second and std_test_score third\n",
    "    columns = columns[-1:] + columns[-3:-1] + columns[:-3]\n",
    "    cv_results = cv_results[columns]\n",
    "\n",
    "    if save:\n",
    "        cv_results.to_csv('--'.join(cv_results['params'][0].keys()) + '.csv', index=True, index_label='Id')\n",
    "\n",
    "    # Unless all_columns are True, drop not wanted columns: params, std_* split*\n",
    "    if not all_columns:\n",
    "        cv_results.drop('params', axis='columns', inplace=True)\n",
    "        cv_results.drop(list(cv_results.filter(regex='^std_.*')), axis='columns', inplace=True)\n",
    "        cv_results.drop(list(cv_results.filter(regex='^split.*')), axis='columns', inplace=True)\n",
    "\n",
    "    # Unless all_ranks are True, filter out those rows which have rank equal to one\n",
    "    if not all_ranks:\n",
    "        cv_results = cv_results[cv_results['rank_test_score'] == 1]\n",
    "        cv_results.drop('rank_test_score', axis = 'columns', inplace = True)        \n",
    "        cv_results = cv_results.style.hide_index()\n",
    "\n",
    "    display(cv_results)"
   ]
  },
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      "Fitting 10 folds for each of 14 candidates, totalling 140 fits\n"
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       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_1445b004_260d_11ec_b86a_14f6d8044c0e\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >mean_test_score</th>        <th class=\"col_heading level0 col1\" >mean_fit_time</th>        <th class=\"col_heading level0 col2\" >mean_score_time</th>        <th class=\"col_heading level0 col3\" >param_var_smoothing</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_1445b004_260d_11ec_b86a_14f6d8044c0erow0_col0\" class=\"data row0 col0\" >0.716751</td>\n",
       "                        <td id=\"T_1445b004_260d_11ec_b86a_14f6d8044c0erow0_col1\" class=\"data row0 col1\" >0.001846</td>\n",
       "                        <td id=\"T_1445b004_260d_11ec_b86a_14f6d8044c0erow0_col2\" class=\"data row0 col2\" >0.004056</td>\n",
       "                        <td id=\"T_1445b004_260d_11ec_b86a_14f6d8044c0erow0_col3\" class=\"data row0 col3\" >0.000000</td>\n",
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   "source": [
    "parameters = {\n",
    "    'var_smoothing': [1e-2, 1e-3, 1e-4, 1e-5, 1e-6, 1e-7, 1e-8, 1e-9, 1e-10, 1e-11, 1e-12, 1e-13, 1e-14, 1e-15]\n",
    "}\n",
    "clf = GridSearchCV(gaussian, parameters, cv=kfold, scoring=\"f1\", n_jobs=4, verbose=1)\n",
    "clf.fit(X_train, Y_train)\n",
    "plot_grid_search(clf)\n",
    "table_grid_search(clf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.84      0.55      0.67        47\n",
      "         1.0       0.43      0.76      0.55        21\n",
      "\n",
      "    accuracy                           0.62        68\n",
      "   macro avg       0.64      0.66      0.61        68\n",
      "weighted avg       0.71      0.62      0.63        68\n",
      "\n",
      "             Predicted: NO  Predicted: YES\n",
      "Actual: NO              26              21\n",
      "Actual: YES              5              16\n"
     ]
    }
   ],
   "source": [
    "gaussian = GaussianNB(var_smoothing=1e-11)\n",
    "gaussian.fit(X_train, Y_train)\n",
    "Y_pred3 = gaussian.predict(x_test)\n",
    "acc_gaussian = round(gaussian.score(x_test, y_test) * 100, 2)\n",
    "acc_gaussian\n",
    "\n",
    "print(classification_report(y_test, Y_pred3))\n",
    "cm = pd.DataFrame(confusion_matrix(y_test, Y_pred3), ['Actual: NO', 'Actual: YES'], ['Predicted: NO', 'Predicted: YES'])\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Learning curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "_cell_guid": "eb168d27-5841-4b06-a320-9309c980468e",
    "_uuid": "81070e0011002d63d313a322bd38636d23f48580",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\n",
    "        n_jobs=-1, train_sizes=np.linspace(.1, 1.0, 5)):\n",
    "    \"\"\"Generate a simple plot of the test and training learning curve\"\"\"\n",
    "    plt.figure()\n",
    "    plt.title(title)\n",
    "    if ylim is not None:\n",
    "        plt.ylim(*ylim)\n",
    "    plt.xlabel(\"Training examples\")\n",
    "    plt.ylabel(\"Score\")\n",
    "    train_sizes, train_scores, test_scores = learning_curve(\n",
    "        estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n",
    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
    "    train_scores_std = np.std(train_scores, axis=1)\n",
    "    test_scores_mean = np.mean(test_scores, axis=1)\n",
    "    test_scores_std = np.std(test_scores, axis=1)\n",
    "    plt.grid()\n",
    "\n",
    "    plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
    "                     train_scores_mean + train_scores_std, alpha=0.1,\n",
    "                     color=\"r\")\n",
    "    plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
    "                     test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n",
    "    plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n",
    "             label=\"Training score\")\n",
    "    plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n",
    "             label=\"Cross-validation score\")\n",
    "    plt.legend(loc=\"best\")\n",
    "    return plt\n",
    "g = plot_learning_curve(gsadaDTC.best_estimator_,\"AdaBoost learning curves\",X_train,Y_train,cv=kfold)\n",
    "g = plot_learning_curve(gsExtC.best_estimator_,\"ExtC ExtraTrees learning curves\",X_train,Y_train,cv=kfold)\n",
    "g = plot_learning_curve(gsGBC.best_estimator_,\"GBC Gradient Boost learning curves\",X_train,Y_train,cv=kfold)\n",
    "g = plot_learning_curve(gsrandom_forest.best_estimator_,\"RandomForest learning curves\",X_train,Y_train,cv=kfold)\n",
    "g = plot_learning_curve(gaussian,\"Naive Bayes curves\",X_train,Y_train,cv=kfold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "fc320444-1097-4bd7-995b-908065b188e9",
    "_uuid": "ce75e068abae919bc0c8e8e0d88353d85290cd84"
   },
   "source": [
    "**Observations to fine-tune our models**\n",
    "\n",
    "First, let's compare their best score after fine-tuning their parameters:\n",
    "1. Adaboost: 73\n",
    "2. ExtraTrees: 79\n",
    "3. RandomForest: 79\n",
    "4. GradientBoost: 73\n",
    "5. Naive Bayes : 71\n",
    "it appears that random forest and extra trees classifiers are doing the best job on the Train data. This is good because we want to keep the model as close to the training data as possible. But not too close!\n",
    "The two major sources of error are bias and variance; as we reduce these two, then we could build more accurate models:\n",
    "\n",
    "* **Bias**: The less biased a method, the greater its ability to fit data well.\n",
    "* **Variance**: with a lower bias comes typically a higher the variance. And therefore the risk that the model will not adapt accurately to new test data.\n",
    "This is the case here with other models except GBC: high score but cross-validation is very distant.\n",
    "\n",
    "The reverse also holds: the greater the bias, the lower the variance. A high-bias method builds simplistic models that generally don't fit well training data. \n",
    "We can see the red and green curves from ExtraTrees, RandomForest are pretty far.\n",
    "**This points to a lower variance, i.e. a stronger ability to apply the model to new data.**\n",
    "\n",
    "I used the above graphs to optimize the parameters for Adaboost, ExtraTrees, RandomForest, GradientBoost.\n",
    "This resulted in a significant improvement of the prediction accuracy on the test data (score).\n",
    "\n",
    "\n",
    "## 4.13 Selecting and combining the best classifiers\n",
    "So, how do we achieve the best trade-off beween bias and variance?\n",
    "1. We will first compare in the next section the classifiers; results between themselves and applied to the same test data.\n",
    "2. Then \"ensemble\" them together with an automatic function callled *voting*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "_cell_guid": "c9f5ad35-050a-4bb1-a3b6-bb88042b2e82",
    "_uuid": "26ccf158bbddbf74058fd2bddb6f54e5afc657c6",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_morbidité_AdaDTC = pd.Series(adaDTC_best.predict(x_test), name=\"AdaDTC\")\n",
    "test_morbidité_ExtC = pd.Series(ExtC_best.predict(x_test), name=\"ExtC\")\n",
    "test_morbidité_GBC = pd.Series(GBC_best.predict(x_test), name=\"GBC\")\n",
    "test_morbidité_random_forest = pd.Series(random_forest_best.predict(x_test), name=\"random_forest\")\n",
    "test_morbidité_gaussian = pd.Series(gaussian.predict(x_test), name=\"gaussian\")\n",
    "\n",
    "\n",
    "# Concatenate all classifier results\n",
    "ensemble_results = pd.concat([test_morbidité_AdaDTC, test_morbidité_ExtC, test_morbidité_GBC,test_morbidité_random_forest, test_morbidité_gaussian],axis=1)\n",
    "g= sns.heatmap(ensemble_results.corr(),annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "bed2c50a-063f-45e7-b4c7-523f4a5af86d",
    "_uuid": "7f758f0ee9d19608100c393c5b7d0800346ddf05"
   },
   "source": [
    "**Observations:**\n",
    "* As indicated before, AdaDTC has the lowest correlations when compared to other predictors. This indicates that it predicts differently than the others when it comes to the test data.\n",
    "* We will therefore 'ensemble' the remaining three predictors.\n",
    "\n",
    "##  Ensembling\n",
    "This is the final step, pulling it together with an amazing 'Voting' function from sklearn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {
    "_cell_guid": "d2cc701c-b37f-4745-939a-8062a41f66d7",
    "_uuid": "6b36c683ce6f17cbf9803ac508cb3dabafedf059",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7539 (+/- 0.1045) [soft voting]\n"
     ]
    }
   ],
   "source": [
    "VotingPredictor = VotingClassifier(estimators=[('ExtC', ExtC_best), ('GBC', GBC_best),\n",
    "('random_forest', random_forest_best), ('Naive Bayes', gaussian)], voting='soft',weights=(1.5,1,1,1.3), n_jobs=4)\n",
    "VotingPredictor = VotingPredictor.fit(X_train, Y_train)\n",
    "VotingPredictor_predictions = VotingPredictor.predict(x_test)\n",
    "\n",
    "scores = cross_val_score(VotingPredictor, X_train, Y_train, cv=kfold, scoring='accuracy')\n",
    "print(\"Accuracy: %0.4f (+/- %0.4f) [%s]\" % (scores.mean(), scores.std(), 'soft voting'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.83      0.85      0.84        47\n",
      "         1.0       0.65      0.62      0.63        21\n",
      "\n",
      "    accuracy                           0.78        68\n",
      "   macro avg       0.74      0.74      0.74        68\n",
      "weighted avg       0.78      0.78      0.78        68\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, VotingPredictor_predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(confusion_matrix(y_test,VotingPredictor_predictions),annot=True,fmt='2.0f')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Black boxes are wrong"
   ]
  }
 ],
 "metadata": {
  "_change_revision": 0,
  "_is_fork": false,
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}