main_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CREDIT DEFAULT - MLII (MAP 569)\n",
    "**Alexander Davi & Félix Chalumeau**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from tqdm import tqdm_notebook, tqdm\n",
    "\n",
    "import datetime\n",
    "\n",
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder, StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "\n",
    "### The data set\n",
    "We are provided with a dataset about credit default. It contains 5380 raws, each described by 19 features. Those features are of different types, amongst them are date values, numeric values and categorical values. \n",
    "We are going to see how to handle each of them as well as how to manage the NaN and unfeasible values in this dataset in order to be able to model it afterwards. \n",
    "\n",
    "### The problem\n",
    "This problem is a tabular one with a classification task. It is quite particular as the data seems unbalanced. We will try to resample it or we will use an appropriate model for this use case. \n",
    "\n",
    "### Our plan\n",
    "First, we are going to explore the data and infere transformations from our observations. Then we will design our pipeline. We will split the dataset in three parts: train, validation, test. The test set will be only used at the very end of our work to make sure we have not overfitted the validation set ! \n",
    "We will then try different models, choose the most promising one and tune some of its hyperparameters. \n",
    "We will finally conclude by analysing our final performance on the test set and our features importance. \n",
    "\n",
    "### Our metrics\n",
    "To measure the performance of our model, we will use accuracy and ROC AUC score, which are common metrics for classification tasks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Exploration and transformation of the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1. A first glance at the data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id_Customer</th>\n",
       "      <th>Y</th>\n",
       "      <th>Customer_Type</th>\n",
       "      <th>BirthDate</th>\n",
       "      <th>Customer_Open_Date</th>\n",
       "      <th>P_Client</th>\n",
       "      <th>Educational_Level</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Number_Of_Dependant</th>\n",
       "      <th>Years_At_Residence</th>\n",
       "      <th>Net_Annual_Income</th>\n",
       "      <th>Years_At_Business</th>\n",
       "      <th>Prod_Sub_Category</th>\n",
       "      <th>Prod_Decision_Date</th>\n",
       "      <th>Source</th>\n",
       "      <th>Type_Of_Residence</th>\n",
       "      <th>Nb_Of_Products</th>\n",
       "      <th>Prod_Closed_Date</th>\n",
       "      <th>Prod_Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>7440</td>\n",
       "      <td>0</td>\n",
       "      <td>Non Existing Client</td>\n",
       "      <td>1977-07-08</td>\n",
       "      <td>2012-02-13</td>\n",
       "      <td>NP_Client</td>\n",
       "      <td>University</td>\n",
       "      <td>Married</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>1.0</td>\n",
       "      <td>C</td>\n",
       "      <td>2012-02-14</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Owned</td>\n",
       "      <td>1</td>\n",
       "      <td>NaT</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>573</td>\n",
       "      <td>0</td>\n",
       "      <td>Existing Client</td>\n",
       "      <td>1974-06-13</td>\n",
       "      <td>2009-04-02</td>\n",
       "      <td>P_Client</td>\n",
       "      <td>University</td>\n",
       "      <td>Married</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>2.0</td>\n",
       "      <td>C</td>\n",
       "      <td>2011-06-30</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Parents</td>\n",
       "      <td>1</td>\n",
       "      <td>NaT</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>9194</td>\n",
       "      <td>0</td>\n",
       "      <td>Non Existing Client</td>\n",
       "      <td>1973-07-11</td>\n",
       "      <td>2012-03-04</td>\n",
       "      <td>NP_Client</td>\n",
       "      <td>University</td>\n",
       "      <td>Married</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10</td>\n",
       "      <td>36</td>\n",
       "      <td>1.0</td>\n",
       "      <td>C</td>\n",
       "      <td>2012-04-04</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Owned</td>\n",
       "      <td>1</td>\n",
       "      <td>NaT</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3016</td>\n",
       "      <td>1</td>\n",
       "      <td>Existing Client</td>\n",
       "      <td>1982-08-07</td>\n",
       "      <td>2011-08-25</td>\n",
       "      <td>NP_Client</td>\n",
       "      <td>University</td>\n",
       "      <td>Married</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>36</td>\n",
       "      <td>1.0</td>\n",
       "      <td>C</td>\n",
       "      <td>2011-07-09</td>\n",
       "      <td>Sales</td>\n",
       "      <td>New rent</td>\n",
       "      <td>1</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>6524</td>\n",
       "      <td>0</td>\n",
       "      <td>Non Existing Client</td>\n",
       "      <td>1953-08-18</td>\n",
       "      <td>2012-10-01</td>\n",
       "      <td>NP_Client</td>\n",
       "      <td>University</td>\n",
       "      <td>Married</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>1.0</td>\n",
       "      <td>C</td>\n",
       "      <td>2012-11-01</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Owned</td>\n",
       "      <td>1</td>\n",
       "      <td>NaT</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id_Customer  Y        Customer_Type  BirthDate Customer_Open_Date  \\\n",
       "0         7440  0  Non Existing Client 1977-07-08         2012-02-13   \n",
       "1          573  0      Existing Client 1974-06-13         2009-04-02   \n",
       "2         9194  0  Non Existing Client 1973-07-11         2012-03-04   \n",
       "3         3016  1      Existing Client 1982-08-07         2011-08-25   \n",
       "4         6524  0  Non Existing Client 1953-08-18         2012-10-01   \n",
       "\n",
       "    P_Client Educational_Level Marital_Status  Number_Of_Dependant  \\\n",
       "0  NP_Client        University        Married                  3.0   \n",
       "1   P_Client        University        Married                  0.0   \n",
       "2  NP_Client        University        Married                  2.0   \n",
       "3  NP_Client        University        Married                  3.0   \n",
       "4  NP_Client        University        Married                  2.0   \n",
       "\n",
       "   Years_At_Residence Net_Annual_Income  Years_At_Business Prod_Sub_Category  \\\n",
       "0                   1                36                1.0                 C   \n",
       "1                  12                18                2.0                 C   \n",
       "2                  10                36                1.0                 C   \n",
       "3                   3                36                1.0                 C   \n",
       "4                   1                36                1.0                 C   \n",
       "\n",
       "  Prod_Decision_Date Source Type_Of_Residence  Nb_Of_Products  \\\n",
       "0         2012-02-14  Sales             Owned               1   \n",
       "1         2011-06-30  Sales           Parents               1   \n",
       "2         2012-04-04  Sales             Owned               1   \n",
       "3         2011-07-09  Sales          New rent               1   \n",
       "4         2012-11-01  Sales             Owned               1   \n",
       "\n",
       "  Prod_Closed_Date Prod_Category  \n",
       "0              NaT             B  \n",
       "1              NaT             G  \n",
       "2              NaT             B  \n",
       "3       2012-12-31             L  \n",
       "4              NaT             D  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('data/CreditTraining.csv', parse_dates=[3, 4, 13, 17])\n",
    "data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can already delete the Id_Customer feature, which is a identification variable, but this role is already taken by our index, hence we have no reason to keep this value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data = data.drop(columns=['Id_Customer'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Another quick little fix\n",
    "\n",
    "The feature Net_Annual_Income is not considered as a float but as a string. To do so, we need to replace the ',' in '.' and then to convert the string to a float."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Net_Annual_Income'] = data['Net_Annual_Income'].str.replace(',', '.').astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2. Let's look at the target value "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 4))\n",
    "plt.hist(data['Y'], bins = 2)\n",
    "plt.title(\"Credit default\")\n",
    "plt.ylabel('Value occurence')\n",
    "plt.xlabel(\"Value\")\n",
    "# plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the **data is very unbalanced**. Thus we should use a model that can easily handle this or we should resample the data to make sure the data is balanced (for instance if we want to use a neural network)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.3. Handle the date values\n",
    "\n",
    "Here we will not treat data as time series, the timestamp is not interesting in itself, but time deltas are interesting in our use case, we are going to make those timestamps timedeltas and divise them by a time unit to convert them to floats !\n",
    "\n",
    "We have those date values:\n",
    "- BirthDate\n",
    "- Customer_Open_Date\n",
    "- Prod_Decision_Date\n",
    "- Prod_Closed_Date\n",
    "\n",
    "Here are the feature that we are going to create:\n",
    "\n",
    "\n",
    "- Cusomer_Open_Date is taken as the difference in days between the latest Customer_Open_Date and the current one. Thus, we will have a positive count of days.\n",
    "- Rather than considering the BirthDate, it seems more logical to consider the age at the time when the desision is taken (ie. Prod_Decision_Date). To do that, we will count the amount of days the customer has lived when the Decision is taken, and put it in the new variable Age_At_Decision.\n",
    "- The Prod_Decision_Date is taken as the difference in days with the latest one.\n",
    "- We will proceed in the same way with Prod_Closed_Date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "ONE_DAY = datetime.timedelta(days = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "# open date\n",
    "data['Customer_Open_Date'] = (data['Customer_Open_Date'].max() - data['Customer_Open_Date']) / ONE_DAY\n",
    "# birthdate \n",
    "data['Age_At_Decision'] = (data['Prod_Decision_Date'] - data['BirthDate']) / ONE_DAY\n",
    "data['BirthDate'] = (data['Prod_Decision_Date'].max() - data['BirthDate']) / ONE_DAY\n",
    "# prod decision date\n",
    "data['Prod_Decision_Date'] = (data['Prod_Decision_Date'].max() - data['Prod_Decision_Date']) / ONE_DAY"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As there seems to be a huge correlation between the feature Prod_Closed_Date and the credit default, the high number of NaN is not sufficient at all to delete it ! We just replace them with 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Prod_Closed_Date'] = (data['Prod_Closed_Date'].max() - data['Prod_Closed_Date']) / ONE_DAY\n",
    "data['Prod_Closed_Date'] = data['Prod_Closed_Date'].fillna(-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.4. Let's explore the continuous features\n",
    "\n",
    "We draw histograms for the continuous variables, see if we observe anything, maybe outliers..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4.1. Visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "CONTINUOUS_FEATURES = {\n",
    "    'BirthDate': 100,\n",
    "    'Customer_Open_Date': 100,\n",
    "    'Number_Of_Dependant': 20,\n",
    "    'Years_At_Residence': 60,\n",
    "    'Net_Annual_Income': 100,\n",
    "    'Years_At_Business': 100,\n",
    "    'Prod_Decision_Date': 70,\n",
    "    'Nb_Of_Products': 3,\n",
    "    'Prod_Closed_Date': 30\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x2160 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = len(CONTINUOUS_FEATURES.keys())\n",
    "Nx = N // 2 + N % 2\n",
    "\n",
    "fig, axs = plt.subplots(Nx, 2, figsize=(20, Nx * 6))\n",
    "nx, ny = 0, 0\n",
    "for i, plot_infos in enumerate(CONTINUOUS_FEATURES.items()):\n",
    "    feature, n_bins = plot_infos\n",
    "    \n",
    "    # manage plot\n",
    "    axs[nx, ny].hist(data[feature], bins = n_bins)\n",
    "    axs[nx, ny].set_title(feature)\n",
    "    \n",
    "    # generate new indices for the next subplot\n",
    "    nx += (i%2 == 1)*1\n",
    "    ny = 1 - i%2 \n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4.2 Analysis\n",
    "\n",
    "These graphs allow us to make some precious observations, which will lead us to modify slightly the data. \n",
    "\n",
    "- Concerning the number of Years_At_Business, there seems to be some strange values, around 100, which are not feasible. We will replace these values by new values.\n",
    "\n",
    "- We can observe a similar phenomenon for Customer_Open_Date, which is around 10000 days, meaning around 30 years. We will leave these values as they are feasible.\n",
    "\n",
    "- For the Years_At_Residence variable, it is interesting to realise that we have wery high peaks around 10, 20, 30 years, as if the customers gave approximate answers for this data. This means that the data is somewhat unbalanced and not smoothly dispatched. This might be a problem for some models, such as logistic regression. However, other models such as decision trees and XGBOOST, which we will use, are unbothered by such issues. We will not modify this aspect of the data. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4.3 Removing the outliers\n",
    "\n",
    "**Modify unfeasible data**:\n",
    "You cannot spend more years in a residence or working than you spend on earth... which is why we make sure that all the values are feasible, and if they are not, we replace them by feasible values ! Instead of taking the mean value, we use a approximate linear regression, saying that it is equal to 1 x Age + constant, with constant being 18 or 22."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Age'] = (data['BirthDate']) / (365)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5380/5380 [00:00<00:00, 14779.20it/s]\n"
     ]
    }
   ],
   "source": [
    "for i in tqdm(range(data.shape[0])):\n",
    "   diff_res = data['Age'][i] - data['Years_At_Residence'][i]\n",
    "   diff_bus = data['Age'][i] - data['Years_At_Business'][i]\n",
    "   if (diff_res < 0):\n",
    "       data['Years_At_Residence'][i] = max(1, data['Age'][i] - 18)\n",
    "   if (diff_bus < 10):\n",
    "       data['Years_At_Business'][i] = max(1, data['Age'][i] - 22)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.drop(columns=['Age'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4.3. Handle NaN values\n",
    "We search were the NaN are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Y                      0\n",
       "Customer_Type          0\n",
       "BirthDate              0\n",
       "Customer_Open_Date     0\n",
       "P_Client               0\n",
       "Educational_Level      0\n",
       "Marital_Status         0\n",
       "Number_Of_Dependant    2\n",
       "Years_At_Residence     0\n",
       "Net_Annual_Income      2\n",
       "Years_At_Business      2\n",
       "Prod_Sub_Category      0\n",
       "Prod_Decision_Date     0\n",
       "Source                 0\n",
       "Type_Of_Residence      0\n",
       "Nb_Of_Products         0\n",
       "Prod_Closed_Date       0\n",
       "Prod_Category          0\n",
       "Age_At_Decision        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the unfeasible data, we replaced absurd values thanks to a quick approximation of a linear regression. Here we can hardly do this, hence we simply replace the NaN values with the mean of the feature. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "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>Y</th>\n",
       "      <th>Customer_Type</th>\n",
       "      <th>BirthDate</th>\n",
       "      <th>Customer_Open_Date</th>\n",
       "      <th>P_Client</th>\n",
       "      <th>Educational_Level</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Number_Of_Dependant</th>\n",
       "      <th>Years_At_Residence</th>\n",
       "      <th>Net_Annual_Income</th>\n",
       "      <th>Years_At_Business</th>\n",
       "      <th>Prod_Sub_Category</th>\n",
       "      <th>Prod_Decision_Date</th>\n",
       "      <th>Source</th>\n",
       "      <th>Type_Of_Residence</th>\n",
       "      <th>Nb_Of_Products</th>\n",
       "      <th>Prod_Closed_Date</th>\n",
       "      <th>Prod_Category</th>\n",
       "      <th>Age_At_Decision</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>634</td>\n",
       "      <td>1</td>\n",
       "      <td>Existing Client</td>\n",
       "      <td>8187.0</td>\n",
       "      <td>806.0</td>\n",
       "      <td>P_Client</td>\n",
       "      <td>University</td>\n",
       "      <td>Single</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21</td>\n",
       "      <td>33.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>C</td>\n",
       "      <td>230.0</td>\n",
       "      <td>Branch</td>\n",
       "      <td>Owned</td>\n",
       "      <td>2</td>\n",
       "      <td>249.0</td>\n",
       "      <td>G</td>\n",
       "      <td>7957.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5045</td>\n",
       "      <td>0</td>\n",
       "      <td>Existing Client</td>\n",
       "      <td>22857.0</td>\n",
       "      <td>2999.0</td>\n",
       "      <td>NP_Client</td>\n",
       "      <td>Master/PhD</td>\n",
       "      <td>Married</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>50.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>C</td>\n",
       "      <td>222.0</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Owned</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>B</td>\n",
       "      <td>22635.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Y    Customer_Type  BirthDate  Customer_Open_Date   P_Client  \\\n",
       "634   1  Existing Client     8187.0               806.0   P_Client   \n",
       "5045  0  Existing Client    22857.0              2999.0  NP_Client   \n",
       "\n",
       "     Educational_Level Marital_Status  Number_Of_Dependant  \\\n",
       "634         University         Single                  NaN   \n",
       "5045        Master/PhD        Married                  NaN   \n",
       "\n",
       "      Years_At_Residence  Net_Annual_Income  Years_At_Business  \\\n",
       "634                   21               33.6                1.0   \n",
       "5045                  10               50.0               10.0   \n",
       "\n",
       "     Prod_Sub_Category  Prod_Decision_Date  Source Type_Of_Residence  \\\n",
       "634                  C               230.0  Branch             Owned   \n",
       "5045                 C               222.0   Sales             Owned   \n",
       "\n",
       "      Nb_Of_Products  Prod_Closed_Date Prod_Category  Age_At_Decision  \n",
       "634                2             249.0             G           7957.0  \n",
       "5045               1              -1.0             B          22635.0  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb_dep = data['Number_Of_Dependant'].mean()\n",
    "data[data['Number_Of_Dependant'].isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Number_Of_Dependant'][634] = nb_dep\n",
    "data['Number_Of_Dependant'][5045] = nb_dep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "annual_inc = data['Net_Annual_Income'].mean()\n",
    "# data[data['Net_Annual_Income'].isna()]\n",
    "data['Net_Annual_Income'][1879] = annual_inc\n",
    "data['Net_Annual_Income'][5144] = annual_inc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Except for the Years_At_Business were we use the same trick as before !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data[data['Years_At_Business'].isna()]\n",
    "data['Years_At_Business'][1987] = (data['BirthDate'][1987] / (365)) - 22\n",
    "data['Years_At_Business'][2750] = (data['BirthDate'][2750] / (365)) - 22"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "# in case there is an issue \n",
    "data = data.fillna(value=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.5. Let's explore the discrete features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.5.1. Visualisation\n",
    "\n",
    "Let us observe how the different categorical values are spread."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Categorical_Values = ['Customer_Type', 'P_Client', 'Educational_Level', 'Marital_Status','Prod_Sub_Category',\n",
    "                     'Source', 'Type_Of_Residence', 'Prod_Category']\n",
    "\n",
    "fig, axs = plt.subplots(4, 2, figsize=(15, 15))\n",
    "for i in range(len(Categorical_Values)):\n",
    "    cat = Categorical_Values[i]\n",
    "    serie = data[cat].value_counts()\n",
    "    labels = list(serie.index.values)\n",
    "    sizes = list(serie.values)\n",
    "    \n",
    "    axs[i//2,i%2].pie(serie, labels = labels,autopct='%1.1f%%')\n",
    "    axs[i//2,i%2].set_title(cat)\n",
    "    axs[i//2,i%2].axis('equal')\n",
    "    \n",
    "plt.show()  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.5.2. Analysis - Model Selection\n",
    "\n",
    "There seems to be quite a large amount  of categories here, maby we can try to regroup some together, in order to have a simpler model.\n",
    "\n",
    "Lets take a look at the Category Marital_Status in more detail :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------------------\n",
      "Category Married\n",
      "Percent of negatives : 6.9186875891583455\n",
      "Size of category : 4206\n",
      "------------------------------------------------------\n",
      "Category Single\n",
      "Percent of negatives : 8.891013384321223\n",
      "Size of category : 1046\n",
      "------------------------------------------------------\n",
      "Category Widowed\n",
      "Percent of negatives : 6.25\n",
      "Size of category : 64\n",
      "------------------------------------------------------\n",
      "Category Separated\n",
      "Percent of negatives : 0.0\n",
      "Size of category : 1\n",
      "------------------------------------------------------\n",
      "Category Divorced\n",
      "Percent of negatives : 7.936507936507942\n",
      "Size of category : 63\n"
     ]
    }
   ],
   "source": [
    "Marital_Categories = ['Married',  'Single', 'Widowed', 'Separated', 'Divorced']\n",
    "for let in Marital_Categories:\n",
    "    print('------------------------------------------------------')\n",
    "    print('Category '+ let)\n",
    "    \n",
    "    serie = data.loc[data['Marital_Status'] == let]['Y'].value_counts()\n",
    "    \n",
    "    print('Percent of negatives : ' + str((1 - serie[0]/serie.sum())*100))\n",
    "    print('Size of category : '+ str(serie.sum()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The category 'Separated' seems of very little importance considering its size. We will regroup it with the 'Divorced' category as they are semantically similar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Marital_Status'].loc[data['Marital_Status'] == 'Separated'] = 'Separated_Or_Divorced'\n",
    "data['Marital_Status'].loc[data['Marital_Status'] == 'Divorced'] = 'Separated_Or_Divorced'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When running our model whilst testing, we found out that quite a few categories of Prod_Category turned out having an extremely weak feature importance. For this reason, we regrouped them in a class called 'Regrouping_Class', allowing us to simplify the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Prod_Category'].loc[data['Prod_Category'] == 'A'] = 'Regrouping_Class'\n",
    "data['Prod_Category'].loc[data['Prod_Category'] == 'E'] = 'Regrouping_Class'\n",
    "data['Prod_Category'].loc[data['Prod_Category'] == 'F'] = 'Regrouping_Class'\n",
    "data['Prod_Category'].loc[data['Prod_Category'] == 'G'] = 'Regrouping_Class'\n",
    "data['Prod_Category'].loc[data['Prod_Category'] == 'I'] = 'Regrouping_Class'\n",
    "data['Prod_Category'].loc[data['Prod_Category'] == 'J'] = 'Regrouping_Class'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These previous manipulations have allowed us to simplify the model. If we wonsider indicators such as BIC, these would now have a better value."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.5.3. Label Encoding \n",
    "\n",
    "\n",
    "For some variables, we will simply use label encoding. The easiest is to apply label encoding to any categorical variable with only two levels. For these, there is no need to use One-Hot encoding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LabelEncoder()"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le = LabelEncoder()\n",
    "le.fit(data['Customer_Type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Existing Client', 'Non Existing Client']"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(le.classes_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, ..., 1, 1, 1])"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le.transform(data['Customer_Type']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 columns were label encoded.\n"
     ]
    }
   ],
   "source": [
    "le = LabelEncoder()\n",
    "le_count = 0\n",
    "\n",
    "# Iterate through the columns\n",
    "for col in data:\n",
    "    if data[col].dtype == 'object':\n",
    "        # If 2 or fewer unique categories\n",
    "        if len(list(data[col].unique())) <= 2:\n",
    "            # Train on the training data\n",
    "            le.fit(data[col])\n",
    "            # Transform both training and testing data\n",
    "            data[col] = le.transform(data[col])\n",
    "            \n",
    "            \n",
    "            # Keep track of how many columns were label encoded\n",
    "            le_count += 1\n",
    "            \n",
    "print('%d columns were label encoded.' % le_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.5.4. One Hot Encoding \n",
    "\n",
    "For the other variables, there exists a pandas function that allows us to create One Hot encoding immediately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.get_dummies(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.6. A last glance at our preprocessed dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Y</th>\n",
       "      <th>Customer_Type</th>\n",
       "      <th>BirthDate</th>\n",
       "      <th>Customer_Open_Date</th>\n",
       "      <th>P_Client</th>\n",
       "      <th>Number_Of_Dependant</th>\n",
       "      <th>Years_At_Residence</th>\n",
       "      <th>Net_Annual_Income</th>\n",
       "      <th>Years_At_Business</th>\n",
       "      <th>Prod_Decision_Date</th>\n",
       "      <th>...</th>\n",
       "      <th>Type_Of_Residence_Owned</th>\n",
       "      <th>Type_Of_Residence_Parents</th>\n",
       "      <th>Prod_Category_B</th>\n",
       "      <th>Prod_Category_C</th>\n",
       "      <th>Prod_Category_D</th>\n",
       "      <th>Prod_Category_H</th>\n",
       "      <th>Prod_Category_K</th>\n",
       "      <th>Prod_Category_L</th>\n",
       "      <th>Prod_Category_M</th>\n",
       "      <th>Prod_Category_Regrouping_Class</th>\n",
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       "      <td>12</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>...</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>36.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "      <td>11077.0</td>\n",
       "      <td>467.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>514.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>64.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>...</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Y  Customer_Type  BirthDate  Customer_Open_Date  P_Client  \\\n",
       "0  0              1    12933.0               295.0         0   \n",
       "1  0              0    14054.0              1342.0         1   \n",
       "2  0              1    14391.0               275.0         0   \n",
       "3  1              0    11077.0               467.0         0   \n",
       "4  0              1    21658.0                64.0         0   \n",
       "\n",
       "   Number_Of_Dependant  Years_At_Residence  Net_Annual_Income  \\\n",
       "0                  3.0                   1               36.0   \n",
       "1                  0.0                  12               18.0   \n",
       "2                  2.0                  10               36.0   \n",
       "3                  3.0                   3               36.0   \n",
       "4                  2.0                   1               36.0   \n",
       "\n",
       "   Years_At_Business  Prod_Decision_Date  ...  Type_Of_Residence_Owned  \\\n",
       "0                1.0               294.0  ...                        1   \n",
       "1                2.0               523.0  ...                        0   \n",
       "2                1.0               244.0  ...                        1   \n",
       "3                1.0               514.0  ...                        0   \n",
       "4                1.0                33.0  ...                        1   \n",
       "\n",
       "   Type_Of_Residence_Parents  Prod_Category_B  Prod_Category_C  \\\n",
       "0                          0                1                0   \n",
       "1                          1                0                0   \n",
       "2                          0                1                0   \n",
       "3                          0                0                0   \n",
       "4                          0                0                0   \n",
       "\n",
       "   Prod_Category_D  Prod_Category_H  Prod_Category_K  Prod_Category_L  \\\n",
       "0                0                0                0                0   \n",
       "1                0                0                0                0   \n",
       "2                0                0                0                0   \n",
       "3                0                0                0                1   \n",
       "4                1                0                0                0   \n",
       "\n",
       "   Prod_Category_M  Prod_Category_Regrouping_Class  \n",
       "0                0                               0  \n",
       "1                0                               1  \n",
       "2                0                               0  \n",
       "3                0                               0  \n",
       "4                0                               0  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Data scale and split "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1. Split the data: train, validate, test\n",
    "\n",
    "In a real life situation as in a kaggle competition, we do not know the target values of the test set. To be in the same situation, we split our dataset in three parts: the train set, used to train our models, the validation set, used to estimate the quality of our model during our researches and the test set, which will only be used at the very end to see the final performance of our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_name = data.columns[1:]\n",
    "y, X = data[['Y']].values[:,0], data[features_name].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_tmp, X_test, y_tmp, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "X_train, X_val, y_train, y_val = train_test_split(X_tmp, y_tmp, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.07319198373511472, 0.06736353077816493, 0.07713754646840149)"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(y_train), np.mean(y_val), np.mean(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2. Fit the scaler on the train data\n",
    "\n",
    "To act like in a real situation, we scale on the train and not on the whole data set: when the bank create the model, it does not have access to the data that it will be doing prediction on in a few months..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler(copy=True, with_mean=True, with_std=True)"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.0000e+00, 1.1775e+04, 3.0700e+02, ..., 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00],\n",
       "       [1.0000e+00, 9.6780e+03, 5.6900e+02, ..., 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00],\n",
       "       [1.0000e+00, 2.0380e+04, 1.2200e+02, ..., 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00],\n",
       "       ...,\n",
       "       [1.0000e+00, 9.8340e+03, 4.8700e+02, ..., 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00],\n",
       "       [1.0000e+00, 1.4493e+04, 2.2700e+02, ..., 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00],\n",
       "       [0.0000e+00, 1.8087e+04, 9.3400e+03, ..., 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00]])"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "# transform train and validate \n",
    "X_train_s = scaler.transform(X_train)\n",
    "X_val_s = scaler.transform(X_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Comparison of different models \n",
    "\n",
    "Let's fit three models and compare their performances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1. Use a Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " ---------------- Model Report ----------------\n",
      "\n",
      " --- TRAIN\n",
      "Accuracy :               0.946267789718269\n",
      "ROC AUC (Train):         0.931651768615103\n",
      "\n",
      " --- VALIDATE\n",
      "Accuracy :               0.9454123112659698\n",
      "ROC AUC (Validate):      0.9195688581612058\n"
     ]
    }
   ],
   "source": [
    "model1 = LogisticRegression()\n",
    "model1.fit(X_train_s, y_train)\n",
    "\n",
    "y_train_pred = model1.predict(X_train_s)\n",
    "y_train_predprob = model1.predict_proba(X_train_s)[:,1]\n",
    "\n",
    "y_val_pred = model1.predict(X_val_s)\n",
    "y_val_predprob = model1.predict_proba(X_val_s)[:,1]\n",
    "\n",
    "# Handle the global report of the model :\n",
    "\n",
    "print(\"\\n ---------------- Model Report ----------------\")\n",
    "print(\"\\n --- TRAIN\")\n",
    "print(\"Accuracy :               {}\".format(metrics.accuracy_score(y_train, y_train_pred)))\n",
    "print(\"ROC AUC (Train):         {}\".format(metrics.roc_auc_score(y_train, y_train_predprob)))\n",
    "print(\"\\n --- VALIDATE\")\n",
    "print(\"Accuracy :               {}\".format(metrics.accuracy_score(y_val, y_val_pred)))\n",
    "print(\"ROC AUC (Validate):      {}\".format(metrics.roc_auc_score(y_val, y_val_predprob)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2. Use a Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " ---------------- Model Report ----------------\n",
      "\n",
      " --- TRAIN\n",
      "Accuracy :               0.9561428986349114\n",
      "ROC AUC (Train):         0.9563174951376142\n",
      "\n",
      " --- VALIDATE\n",
      "Accuracy :               0.9372822299651568\n",
      "ROC AUC (Validate):      0.9390110362004551\n"
     ]
    }
   ],
   "source": [
    "model2 = DecisionTreeClassifier(random_state=0, max_depth=5)\n",
    "model2.fit(X_train_s, y_train)\n",
    "\n",
    "y_train_pred = model2.predict(X_train_s)\n",
    "y_train_predprob = model2.predict_proba(X_train_s)[:,1]\n",
    "\n",
    "y_val_pred = model2.predict(X_val_s)\n",
    "y_val_predprob = model2.predict_proba(X_val_s)[:,1]\n",
    "\n",
    "# Handle the global report of the model :\n",
    "\n",
    "print(\"\\n ---------------- Model Report ----------------\")\n",
    "print(\"\\n --- TRAIN\")\n",
    "print(\"Accuracy :               {}\".format(metrics.accuracy_score(y_train, y_train_pred)))\n",
    "print(\"ROC AUC (Train):         {}\".format(metrics.roc_auc_score(y_train, y_train_predprob)))\n",
    "print(\"\\n --- VALIDATE\")\n",
    "print(\"Accuracy :               {}\".format(metrics.accuracy_score(y_val, y_val_pred)))\n",
    "print(\"ROC AUC (Validate):      {}\".format(metrics.roc_auc_score(y_val, y_val_predprob)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.3. Use a XGBoost Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " ---------------- Model Report ----------------\n",
      "\n",
      " --- TRAIN\n",
      "Accuracy :               0.9581760092942202\n",
      "ROC AUC (Train):         0.9651251287102118\n",
      "\n",
      " --- VALIDATE\n",
      "Accuracy :               0.9547038327526133\n",
      "ROC AUC (Validate):      0.9525701034912182\n"
     ]
    }
   ],
   "source": [
    "# use xgboost \n",
    "model3 = xgb.XGBClassifier(n_estimators = 50)\n",
    "\n",
    "model3.fit(X_train_s, y_train)\n",
    "\n",
    "# Predict training and validation set:\n",
    "y_train_pred = model3.predict(X_train_s)\n",
    "y_train_predprob = model3.predict_proba(X_train_s)[:,1]\n",
    "\n",
    "y_val_pred = model3.predict(X_val_s)\n",
    "y_val_predprob = model3.predict_proba(X_val_s)[:,1]\n",
    "\n",
    "# Handle the global report of the model :\n",
    "\n",
    "print(\"\\n ---------------- Model Report ----------------\")\n",
    "print(\"\\n --- TRAIN\")\n",
    "print(\"Accuracy :               {}\".format(metrics.accuracy_score(y_train, y_train_pred)))\n",
    "print(\"ROC AUC (Train):         {}\".format(metrics.roc_auc_score(y_train, y_train_predprob)))\n",
    "print(\"\\n --- VALIDATE\")\n",
    "print(\"Accuracy :               {}\".format(metrics.accuracy_score(y_val, y_val_pred)))\n",
    "print(\"ROC AUC (Validate):      {}\".format(metrics.roc_auc_score(y_val, y_val_predprob)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can observe that we get the best ROC AUC Score while using the **XGBoost Classifier**, hence we will use it as a final model. We still observe some overfitting as the results on the train set are consistently better than on the validation set, which is why it might be interesting to do some **parameter tuning**. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Tuning of the model \n",
    "XGBoost is known as a very consistent model. Many of its hyperparameters do not need a lot of tuning. Nevertheless, the number of estimators is the one that can have a big influence on the final performance of the model. Let's tune it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "40ea8a3f26124884a0489d38777d1344",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=20), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "N_ESTIMATORS_RANGE = [10, 20, 30, 35, 40, 45, 50, 55, 60, 65, 70, 80, 90, 100, 110, 120, 130, 140, 150, 200]\n",
    "train_scores = []\n",
    "validate_scores = []\n",
    "\n",
    "for n_estim in tqdm_notebook(N_ESTIMATORS_RANGE):\n",
    "    model = xgb.XGBClassifier(n_estimators = n_estim)\n",
    "    model.fit(X_train_s, y_train)\n",
    "\n",
    "    # Predict training and validation set:\n",
    "    y_train_predprob = model.predict_proba(X_train_s)[:,1]\n",
    "    y_val_predprob = model.predict_proba(X_val_s)[:,1]\n",
    "\n",
    "    train_scores.append(metrics.roc_auc_score(y_train, y_train_predprob))\n",
    "    validate_scores.append(metrics.roc_auc_score(y_val, y_val_predprob))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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axvbe5K5nviZM8upaOnB/436uPdjCrJKckT+BoxTLxG8SsCvqfQVwRr9t1gFXALcBlwM5ZlYYLv9XM/seEAGWcmjCKCIiIiIiJ7jubqehtZOaprZDauRqmto5ED3fHCR0tU3ttHR0Dbiv5CQjP5JGYVYa+VmpzC3NpSArjfysYFlB1LRx3WqmF2eP8NkeG/OB0tfjsWOzK4EL3f2G8P11wGJ3/0zUNhOB24FpwBMESeB8d68zs38GrgT2A1XA8+5+W79j3AjcCFBSUnLa8uXLY3IucmQaGxvJzh5dP4SxTOWRWFQeiUXlkVhUHolF5ZFYTpTy6Ox2Gtqdxg5oaHfq253G9mBZQ0f4Gi6rb4fGDqd7kHQmLRlyUo3cNCM7zchJM3LSgmU5aW+cMlMgyWxYcSZqeSxdunSNuy8aaF0sE7+3ADe7+4Xh+y8DuPutg2yfDWx098kDrPsVcK+7PzzY8RYtWuSrV68+LrHLsVm5ciVLliyJdxgSUnkkFpVHYlF5JBaVR2JReSSW0Vge7k5Te9eATSp7mlpGN6msbWqnobVz0P2Ni6RSENa+5UfSKMwOat/65tMpiKRRkJ1GQSSNzLTkmJ1bopaHmQ2a+MWyqecqYKaZTQN2A1cD1/QLrAiodfdu4MvAneHyZGCcu9eY2cnAycBfYhiriIiIiIgMoavbOdivk5OeJpU9yVt0k8ra5nbaO7sH3FdactIhzSin5Ed6m1EO1LRyXGYqKckaie5YxCzxc/dOM/s08AiQDNzp7uvN7BZgtbs/CCwBbjUzJ2jq+anw46nAkxZUtdYD14YdvYiIiIiIyHHQ2tF1SBL3xkSujQNNHb3Pzx0cpJMTgJz0FAqyg9q3CXkZzJ+Ye0ji1jMVZqWTn5VKdnoKNsxmlXJ8xLT/0bBp5sP9ln0tav5+4P4BPtdK0LOniIiIiIgchrtT39oZJnBt1DZ1BE0r+9fIRc03tw/cyUmS0Vf7FkljdmlO8D4SJnDZ6b3zhdlpjIukkp4Su2aVcnyMnoEnREREREROEB1d3X1jxzW28/yeTnY+u33QZ+QONLXTOUgvJxmpSRRmpfc2ozypOHvA2rie5+dyM1JJSlJt3FijxE9EREREJIbcneb2riGbVPbU0B1o7qCmsY36gTo5WbceCDs5CWvcphZEOGXKuEGbVBZmpce0kxMZPZT4iYiIiIgcge5u52BLx5BNKvtPbYN0cpKabIf0TDkpP0JBJDXooTK7r3nltg3ruGDJ2eRH1MmJHB0lfiIiIiJyQmvrDGrjeppRRs/3NKmsjerN8mBz+6Bjx2Wnp/Q2qSzJzWDuhH6dnITDDRSG2+QMs5OTtl1JFOekH+czlxOJEj8RERERGTPcnYa2Tmob+5pT9jat7B1qoI3a5rDGrrGdpiE6OcmP9CVtM8dn940jN0DTynGRVDJS1axSEpMSPxERERFJWJ1d3Rxo7giTt2B4gaGaVh5obqeja+DquPSUpGB8uHCw72mFEQqy0ikMhyHo6aUyPxIkd3mZ6uRExg4lfiIiIiIyYprbO9/QwcmAA4GH83UtHYPuKy8ztbfGbUpBhIWTx/U1o4xuUhk+PxdJ062vnLj07RcRERGRo9Ld7dS1dPRrRjl4k8ra5nZaOwbu5CQlyQ5pOjkvagDw6KaVPb1V5kfSSFUnJyLDpsRPRERERIC+QcD31rWyvrqLurW73zDod/9mlYN1cpKVltzbpLI4O53ZJbmHNKPs6QClp+nlcDs5EZGjo8RPRERE5ATQ1e1UN7axt66VPXWt7Kvve91b18re8LWlI6qjk9VrAbB+nZxML87m9GmHNqOMHpIgP5KmTk5EEowSPxEREZFRrrWj65Dkrfc1nN9X30pVQxtd/arnUpON8TkZTMjLYN7EXJbNGc+EvAxKcjOo3Poqbz9nMQVZ6eRlppKsTk5ERjUlfiIiIiIJyj14hm5Pv2TukNq6+lYONr+xA5Sc9BRK8jIozc1gxowiSnMzKA3fl+YFU0EkbdBeK1ceeI0Z43NifYoiMkKU+ImIiIjEQWdXN/sb24IEbqDauvC1rfPQzlDMoCg7ndLcDCbnRzi9vIDSsJaup7auNC+D7HTd5olIH/2LICIiIhIjLe1d7KxtZntNEztqmni9upkdNU3sqGlmT13LGzpGSUtOoiQvnQm5mZw8eRwXzEunNC8zrKUL5sfnpKs3SxE5Ykr8RERERI5BY1tnbzK3vaaJHdU9iV4ze+tbD9m2ICuNssIIi6cVMDk/k9K8vlq6CXmZ5EdS1bOliMSEEj8RERGRw6hr6WBnmNhtr25ie01Qc7e9ppnqxrZDti3KTqe8MMLZM4ooL4xQXpRFeWEWUwsj5GWmxukMROREp8RPRERETnjuzsHmjt6auv6vtU3th2xfmptBWWGEt88ZT1lRhPLCLMoKI5QVZunZOhFJSPqXSURERE4I7k5NU3tQUxc+a/d6T81ddRP1rZ2925rBxLxMygojXDi/lGlFQVJXXpjF1IIImWkao05ERhclfiIiIjJmuDtVDW1srx645q6xrS+5SzKYnB+hrDDCpadMoqwwqLkrL4owOT+iAchFZExR4iciIiKjSle3s6eu5ZCEbkfvazMtHV2926YkGVMKguTu9PICygsjlIXP3E0al0lainrHFJETgxI/ERERSTgdXd3sPtASldj1dKbSxK7aFtq7+sa2S0tJoqwgaIrZ06FKWfjM3aRxmaRo6AMRESV+IiIiEh+tHV1UHGhmezj8wbMb2rhz2/PsqGmi4kALXVGD3EXSkikrzGJWSQ7nzSvp7UylvDCL0twMkpI0BIKIyFCU+ImIiEjMNLd3RtXWNR/SLLOyrgWPGsA8MwVmlrZz8uRxvPvkiUFiVxQkeMXZ6RrfTkTkGCjxExERkWNS39rRO2j5ztrmQzpWqWo4dIy7wqgBzHtq7Hpe1z7/NEuXnhOnsxARGduU+ImIiMiw7G9oY9PeBjburWfj3ga27m8ccIy7ktx0ygqzeNus4t4au54BzHMzBh/AXDV6IiKxo8RPREREDtHa0cXmfY29Cd7GvfVs2ttAdWNfgleck86M4mwunF/a25lKeVGEqQURImm6vRARSTT6l1lEROQE1d3t7D7Ywqt7ggRv094GXt1bz/bqJnr6VclITWJWSQ7L5oxndmkuc0tzmF2aQ2F2enyDFxGRI6LET0RE5ARQ19LR20zz1T0NbApr8Zra+8a8KyuMMKc0h3edPLE3wSsrzCJZPWaKiIx6SvxERETGkI6ubrbtb+prprknSPAq61p7t8nLTGVOaQ5XLprC7NIc5pTmMKskh6x03RaIiIxV+hdeRERkFHJ39tW39SZ4m/Y28Oqeerbub6SjK2inmZpsTC/OZvG0AuZMyGV2aQ5zS3MpydXQCCIiJxolfiIiIgmuub2TTWFytzGqV82DzR2920zIy2BOaQ5LZo9n7oQc5pTmMq0oi7SUpDhGLiIiiUKJn4iISIJwdyoOtLBhTz2vhtOmvQ3sqG3uHeg8Ky2Z2aU5vGPBBOZOyGF2SZDk5UUGHyZBREQkpomfmV0E3AYkAz9z92/1W18G3AkUA7XAte5eEa77NnAxkAQ8Cvy9e89/eyIiIqNbW2cwZMKGPfVsqKzvTfYaWjsBSDIoL8xi/sQ83nPqZOaUBgne5PxMktTZioiIHKGYJX5mlgz8CDgfqABWmdmD7r4harPvAPe4+91mtgy4FbjOzM4CzgZODrd7CngbsDJW8YqIiMRKbVM7r/ZL8LZUNdIZjpkQSUtm7oRcLj1lIvMm5DFvYi6zS3LITEuOc+QiIjJWxLLGbzGwxd23AZjZcuBSIDrxmwfcFM6vAB4I5x3IANIAA1KBfTGMVURE5Jh1dzs7apvDBK+OV/c0sKGynr31fT1qluZmMG9iLufNLWHuhFzmTcylrCCiWjwREYmpWCZ+k4BdUe8rgDP6bbMOuIKgOejlQI6ZFbr7s2a2AthDkPjd7u6vxjBWERGRI9LS3sXGvX01eBsqgw5XmsNx8ZKTjBnF2bxleiHzwgRv7oRcCrLS4hy5iIiciCxWj82Z2ZXAhe5+Q/j+OmCxu38mapuJwO3ANOAJgiRwPsEzf7cBV4WbPgr8o7s/0e8YNwI3ApSUlJy2fPnymJyLHJnGxkays7PjHYaEVB6JReWRWIZTHu5OXZuzs6E7mOq72dXQzd4mp+d/0MwUmJqTxNTcJKbkJDE1J4mJ2UmkJasW70jo95FYVB6JReWRWBK1PJYuXbrG3RcNtC6WNX4VwJSo95OByugN3L0SeA+AmWUDV7h7XZjQPefujeG6PwFnEiSH0Z+/A7gDYNGiRb5kyZLYnIkckZUrV6KySBwqj8Si8kgs/cujs6ub16ub3tDhSnVje+82k8ZlsqAsl/eFtXjzJgQdrmhcvGOn30diUXkkFpVHYhmN5RHLxG8VMNPMpgG7gauBa6I3MLMioNbdu4EvE/TwCbAT+KiZ3UrQ1PNtwA9iGKuIiJxg6lo6eO1AFzue2R401QyHTmjr7AYgLTmJmSXZLJ09vvdZvLkaNkFEREapmCV+7t5pZp8GHiEYzuFOd19vZrcAq939QWAJcKuZOUFt3qfCj98PLANeJujo5c/u/odYxSoiImOPu1PT1M6OmmZ21DSxPXzteX+gd/Dz9YyLpDJvQi7XnVnW+yze9OJsDX4uIiJjRkzH8XP3h4GH+y37WtT8/QRJXv/PdQEfi2VsIiIy+nV3O/saWgdJ7pppbOvs3dYMJuZlUlYY4aIFEygvjNC873WuvvBsSnMz1FRTRETGtJgmfiIiIseqs6ubyoOt7KgNE7vq4HVnbZDg9TTNBEhJMqYURCgrjLCoLJ+ywizKiyJMLchiSkEm6SmHjou3cuUuJuRljvQpiYiIjDglfiIiEndtnV3sqm1hZ20T26v7au921jazq7a5d6BzgPSUJMoKI5QVZvG2WcVMLcyivDBCeWEWE/IySElW80wREZH+lPiJiMiIaG7v7G2C2ZfYBYleZV0L0aMLZaenUF4UYd6EXN6xoJTywiymhsnd+Jx0DXYuIiJyhJT4iYjIceXubNzbwNNbqtm4t4GdNc1sr2miqqHtkO0KstIoK4xwenk+ZYWTe5tklhdGKMhK0zN3IiIix5ESPxEROWZV9a08taWaJzdX89SWavaHSV5JbjplhVksmV1MWWEWZWGt3dTCCLkZGhZBRERkpCjxExGRI9bS3sXfXq/hqTDR27i3AYDCrDTOmVnEOTOKOHdmMaV5GXGOVERERECJn4iIDEN3t7NhTz1Pbq7myc37Wb39AO1d3aSlJLG4vIB/esckzp1ZxNzSXD1/JyIikoCU+ImIyID21LWEiV41T2+pprapHYA5pTl86OxyzplRxOJpBWSkJh9mTyIiIhJvSvxERASAprZOnttW0/uc3paqRgCKc9JZMruYc2cWcfaMIsbnqPmmiIjIaKPET0TkBNXV7by8u46nNu/nic3VvLjzAB1dTkZqEmdMK+Tq06dw7sxiZpVkq4dNERGRUU6Jn4jICWRXbXPY++Z+nt5SQ11LBwALJuVyw7knce6MIk4ty1fzTRERkTFGiZ+IyBhW39rBs1uD3jef3Lyf7TXNAEzIy+DC+SWcM7OYs6cXUpidHudIRUREJJaU+ImIjCGdXd2sqzjY2ynL2l0H6ep2ImnJvOWkQq4/q5xzZxYzvThLzTdFREROIEr8RERGMXdnR00zT27ez5Obq3l2aw0NbZ2YwcmTx/GJt03n3JlFvHlqPmkpSfEOV0REROJEiZ+IyCjT3N7J01tqWLGpiide20/FgRYAJudn8q6FEzh3ZjFnTS9kXCQtzpGKiIhIolDiJyIyCuyqbeaxjVU8trGKZ7fV0N7ZTVZaMmfNKOJjbz2Jc2cWU1YYUfNNERERGZASPxGRBNTR1c3q7QdYsSlI9nrG1DupKIvrzixj2ZzxnF5eoOabIiIiMixK/EREEkR1YxsrN+1nxcYqnti8n4bWTlKTjTOmFXLN4qksmzOe8qKseIcpIiIio5ASPxGROOnudtZX1gdNODdV8VLFQdxhfE4671wwgaVzxnPOzCKy0/VPtYiIiBwb3U2IiIyglk7nz6/sZcXGKlZsqpLOsDMAACAASURBVKKqoQ0zWDh5HDedN4tlc8Yzb0IuSUl6Vk9ERESOHyV+IiIx9np1E49trGLFxiqe3dpMl68hJz2Ft84qZtmc8bxtdjFFGkBdREREYkiJn4jIcdbe2c3zr9cGyd6mKl6vbgJgxvhszi9L5frzT2NReT6pyeqYRUREREaGEj8RkeOgqqGVlRv389jGKp7aUk1jWydpKUm85aRCPnRWOcvmjGdKQYSVK1fylumF8Q5XRERETjBK/EREjkJ3t/PS7rreJpwv764DoDQ3g3cvnMiyOeM5e0YhkTT9MysiIiLxpzsSEZFhqm/t4MnXqnlsYxWPv1ZFdWM7SQZvnprPFy+czdLZ45k7IUeDqIuIiEjCUeInIjIId2fr/iZWbAwGUV+1vZbObic3I4Uls8cHHbPMKiY/Ky3eoYqIiIgMSYmfiEg/VfWt3P9CBfevrmBb2DHL7JIcbjj3JJbNGc+pU8eRoo5ZREREZBRR4iciAnR2dbNy036Wr9rFik1VdHU7i6cV8OFzprF0djGT8yPxDlFERETkqCnxE5ET2o6aJn6zehe/XV1BVUMbRdnpfPTck3jfosmcVJwd7/BEREREjgslfiJywmnt6OKR9XtZ/vwunt1WQ5LB0tnjed/pU1g2Z7zG1xMREZExR4mfiJwwNlTWc9+qnTywtpK6lg6mFGTyhQtm8d7TplCalxHv8ERERERiRomfiIxp9a0d/GFdJfet2sVLFXWkJSdx0YJSrj59CmeeVEhSkoZeEBERkbEvpomfmV0E3AYkAz9z92/1W18G3AkUA7XAte5eYWZLge9HbToHuNrdH4hlvCIyNrg7q3ccYPnzu3jo5UpaO7qZU5rDze+ex2VvnsS4iIZfEBERkRNLzBI/M0sGfgScD1QAq8zsQXffELXZd4B73P1uM1sG3Apc5+4rgFPC/RQAW4C/xCpWERkb9je08bsXKrhv9S627W8iOz2F95w6masWTeHkyXkaWF1EREROWLGs8VsMbHH3bQBmthy4FIhO/OYBN4XzK4CBavTeC/zJ3ZtjGKuIjFK7apv5y4Z9PLJ+L6u319LtsKgsn0+8dzoXnzyBSJpatIuIiIjE8o5oErAr6n0FcEa/bdYBVxA0B70cyDGzQnevidrmauB7MYxTREYRd2fj3gYeWb+Xv6zfx4Y99QDMKc3h00tncMkpE5kxPifOUYqIiIgkFnP32OzY7ErgQne/IXx/HbDY3T8Ttc1E4HZgGvAEQRI4393rwvUTgJeAie7eMcAxbgRuBCgpKTlt+fLlMTkXOTKNjY1kZ2v8s0QxFsqj253NB7p5oaqTF/Z1sb/FMWDGuCROLUnh1PHJlGSNjiEYxkJ5jCUqj8Si8kgsKo/EovJILIlaHkuXLl3j7osGWhfLGr8KYErU+8lAZfQG7l4JvAfAzLKBK3qSvtD7gP8ZKOkLP38HcAfAokWLfMmSJccteDl6K1euRGWROEZrebR2dPHM1moeeWUff311HzVN7aQlJ3H2jCIumF/KeXNLKM5Jj3eYR2y0lsdYpfJILCqPxKLySCwqj8QyGssjlonfKmCmmU0DdhM02bwmegMzKwJq3b0b+DJBD5/R3h8uF5ETQH1rBys2VvGX9ftYuamKpvYustNTWDpnPBfMK2HJ7GJyMlLjHaaIiIjIqDOsxC8cdmGmu//VzDKBFHdvGOoz7t5pZp8GHiEYzuFOd19vZrcAq939QWAJcKuZOUFTz09FHbOcoMbw8SM+KxEZNfbVt/Jo2DnLc9tq6OhyirLTueSUSVw4v4S3TC8kPSU53mGKiIiIjGqHTfzM7KMEz9EVANMJmmz+BHj74T7r7g8DD/db9rWo+fuB+wf57HaCDmJEZIxp7+zmT6/s4d7ndrBq+wEAygsj/N3Z07hgfglvnpKvgdVFREREjqPh1Ph9imBohr8BuPtmMxsf06hEZEyqamjlV3/byS//tpP9DW1MK8riH86fxQXzS5lVkq1x9kRERERiZDiJX5u7t/fckJlZChCbrkBFZExau+sgdz39Og+9vIeOLmfJ7GI+dFY5b51ZrJo9ERERkREwnMTvcTP7CpBpZucDnwT+ENuwRGS0a+/s5uGX9/Dfz2xn3a6DZKen8IEzyvjgW8o4qTjxuj8WERERGcuGk/j9E/AR4GXgYwTP7P0slkGJyOhVVd/KL8PmnNWNbZxUnMXXL5nPFadNJjs9lh0Ji4iIiMhghrwLM7Nk4G53vxb46ciEJCKj0Ys7D3DXM9t5OGzOuWzOeK4/q5xzZxSpOaeIiIhInA2Z+Ll7l5kVm1mau7ePVFAiMjq0dXbx0Et7uPuZ7ayrqCMnPYXrzizng28po7woK97hiYiIiEhoOO2utgNPm9mDQFPPQnf/XqyCEpHEtq++lV8+t4NfPb+T6sZ2phdn8W+XzufyU9WcU0RERCQRDecOrTKckoCc2IYjIonK3Vmz4wD3PLuDh1/eQ5c7y2aP50Nnl3POjCINxSAiIiKSwA6b+Ln71wHMLCd4640xj0pEEoK7s3bXQR56aQ9/emUvuw+2kJORwvVnBc05ywrVnFNERERkNDhs4mdmC4BfAAXh+2rgg+6+PsaxiUgc9CR7D7+8h4dfDpK9tOQkzp1ZxOfPn8VFC0rJUnNOERERkVFlOHdvdwCfd/cVAGa2hKCHz7NiGJeIjKCBkr3UZOOtM4v5/PmzOG9eCXmZqfEOU0RERESO0nASv6yepA/A3Veamdp3iYxySvZEREREThzDSfy2mdm/EDT3BLgWeD12IYlIrLg76yrqeOilykOSvXOV7ImIiIiMacNJ/P4O+Drwu/D9E8CHYxaRiBxX0TV7D72055Bk76bzZ3G+kj0RERGRMW84vXoeAD47ArGIyHHi7mzYU8+Dayu5//kWah55mtRk45wZRUGyN7eEvIiSPREREZETxXB69XwUuNLdD4bv84Hl7n5hrIMTkSOzq7aZ36/dzQNrK9lS1UhKkjGvMIl/etd8LphXqmRPRERE5AQ1nKaeRT1JHwQ1gGY2PoYxicgRqG1q56GXKnlgbSVrdhwA4PTyfL5x2QIuftME1q16hiWLpsQ5ShERERGJp+Ekft1mNtXddwKYWRngsQ1LRIbS0t7FXzbs5fdrK3nitf10djuzSrL54oWzuWThRKYUROIdooiIiIgkkOEkfv8MPGVmj4fv3wrcGLuQRGQgnV3dPLWlmt+vreSR9Xtpbu9iQl4GHzlnGpeeMom5E3Iws3iHKSIiIiIJaDidu/zZzE4FzgwX3eTu1bENS0Sgr0fO36+t5I8vVVLd2E5uRgqXnjKRSxZO4oxpBSQlKdkTERERkaENmviFTToPunudu1ebWRNwGTDLzG539/YRi1LkBLNtfyMPrK3kwbW72V7TTFpKEufNHc+lp0xiyexi0lOS4x2iiIiIiIwiQ9X4/Qa4HKgzs1OA3wK3AguBHwM3xD48kRNHS3sX963aye9e3M1LFXWYwVnTC/nk0hlctKCU3Az1yCkiIiIiR2eoxC/T3SvD+WuBO939u2aWBKyNfWgiJ4aOrm5+u7qC2/73NfbVt7FgUi5fvXgu7144kZLcjHiHJyIiIiJjwFCJX/SDQ8uALwO4e7c6kBA5dt3dzkMv7+G7f9nE9ppmFpXl88P3n8riaQXxDk1ERERExpihEr/HzOw3wB4gH3gMwMwmAHq+T+QouTtPbK7m23/eyPrKemaX5PDz6xexbM549copIiIiIjExVOL3OeAqYAJwjrt3hMtLCYZ4EJEj9MLOA3z7zxt5blstk/Mz+f5VC7lk4SSS1TOniIiIiMTQoImfuzuwfIDlL8Y0IpEx6LV9DfznI5t4dMM+irLTuOXS+Vx9+lTSUpLiHZqIiIiInACGM4C7iBylXbXN/OCvm/ndixVkp6XwhQtm8eGzp5GVrp+eiIiIiIwc3X2KxEB1Yxs/WrGFXz63Eww+eu5JfOJt08nPSot3aCIiIiJyAhpqAPdioNjdN/RbPh+ocvf9sQ5OZLRpaO3gp0++zs+f3EZrZzfvWzSZz759JhPyMuMdmoiIiIicwIaq8fsh8F8DLJ9M0LnLNTGJSGQUau3o4t7ndvCjFVs40NzBxW+awOcvmMX04ux4hyYiIiIiMmTi9yZ3f7z/Qnd/xMy+G8OYREaNzq5ufvfibn7w6GtU1rVy7swivnjhbE6ePC7eoYmIiIiI9Boq8Us9ynW9zOwi4DYgGfiZu3+r3/oy4E6gGKgFrnX3inDdVOBnwBTAgXe6+/bhHFdkJKyvrONzy9eyuaqRhVPG8Z0rF3LWjKJ4hyUiIiIi8gZDJX6bzeyd7v5w9EIzewew7XA7NrNk4EfA+UAFsMrMHuz3zOB3gHvc/W4zWwbcClwXrrsH+Ka7P2pm2UD3sM9KJIbcneWrdvGvD64nP5LKT649lQvnl2rwdRERERFJWEMlfjcBfzSz9wFrwmWLgLcA7xrGvhcDW9x9G4CZLQcuBaITv3nhcQBWAA+E284DUtz9UQB3bxzW2YjEWHN7J1/9n1f43Yu7OXdmET+46hQKs9PjHZaIiIiIyJAGHT3a3V8D3gQ8DpSH0+PAyeG6w5kE7Ip6XxEui7YOuCKcvxzIMbNCYBZw0Mx+Z2Yvmtl/hjWIInGzeV8Dl97+NP+zdjc3nTeLuz68WEmfiIiIiIwK5u6x2bHZlcCF7n5D+P46YLG7fyZqm4nA7cA04AmCJHA+QfPQnwNvBnYC9wEPu/vP+x3jRuBGgJKSktOWL18ek3ORI9PY2Eh29tjqzfKZyk7uWt9GRjJ8fGEG8wpHz98hxmJ5jGYqj8Si8kgsKo/EovJILCqPxJKo5bF06dI17r5ooHVDjePXQNCpSg8HqgmaZP6ju9cc5rgVBB2z9JgMVEZv4O6VwHvC42UDV7h7nZlVAC9GNRN9ADiTIBmM/vwdwB0AixYt8iVLlhwmJBkJK1euZKyURWtHF1//wwZ+/dJOFpcX8MNr3kxJbka8wzoiY6k8xgKVR2JReSQWlUdiUXkkFpVHYhmN5TFo4ufuOf2XmVk+8CHgJ8CVh9n3KmCmmU0DdgNX02/sPzMrAmrdvRv4MkEPnz2fzTez4nCg+GXA6uGckMjxsqOmiU/c+wIb9tTz8bdN5wsXzCIledDW0SIiIiIiCeuI7mLd/YC7fx+YPoxtO4FPA48ArwK/cff1ZnaLmV0SbrYE2GRmrwElwDfDz3YBXwD+18xeBgz46ZHEKnIs/vzKHt71f55i98EWfn79Iv7pHXOU9ImIiIjIqDVUr54DMrPU4X4uHAri4X7LvhY1fz9w/yCffRQ4+UjjEzkW7Z3dfOtPG7nz6ddZODmP2685lSkFkXiHJSIiIiJyTIZ6xu89AyzOB65ikGRNZDTbfbCFT//qBV7ceZAPnVXOV945l7QU1fKJiIiIyOg3VM3du/u9d6AGuM3dH4pdSCIjb8WmKm66by2dXc6PP3Aq73zThHiHJCIiIiJy3AzVucuHB1tnZqe7+6rYhCQycjq7uvn+X1/jRyu2MndCLj/+wKlMK8qKd1giIiIiIsfVsJ/xM7N5BD1zvh+oAwYcH0JktKiqb+Wzy1/kuW21XH36FG6+ZD4ZqaNnfD4RERERkeEaMvEzszKCRO/9QCdQBixy9+2xD00kdp7ZWs1nf72WprZOvnvlQq44bXK8QxIRERERiZmhOnd5BsgDlgPvdffNZva6kj4Zzbq7nR+v3ML3Hn2NaUVZ/OqjZzCr5A1DVoqIiIiIjClD1fjtByYTjK9XDGwm6OBFZFSqa+7g7+97kZWb9nPpKRP598vfRFb6EY9oIiIiIiIy6gzVuculZpYHXAF83cxmAOPMbLG7Pz9iEYocB69XN/GRu1exq7aZb1y2gA+cMRUzi3dYIiIiIiIjYsjqDnevA+4E7jSz8QRj+P3AzKa4+5SRCFDkWD27tYaP37uGJINf3nAmi6cVxDskEREREZERNex2bu5eBfwQ+GHY6YtIwlv+/E6++sArlBdlcef1pzO1MBLvkERERERERtxRPeDk7juOdyAix1NXt3Prw6/ys6de562zirn9mjeTm5Ea77BEREREROJCPVvImNPY1snf//pF/ndjFR86q5yvXjyXlOSkeIclIiIiIhI3SvxkTKk40MwNd69mc1Uj/3bZAq47U62SRURERESGGsfv28A2d/9Jv+U3AaXu/o+xDk7kSKzZcYCP/WI1bZ3d3PXh0zl3ZnG8QxIRERERSQhDtX97F3DHAMtvAy6OTTgiR+eBF3fz/p8+R1Z6Cv/zybOV9ImIiIiIRBmqqae7e/cAC7tNA6BJgujudn7w19f4P49t4YxpBfzk2tPIz0qLd1giIiIiIgllqMSv2cxmuvvm6IVmNhNoiW1YIofX0t7FF367jode3sNVi6bwb5ctIC1FnbiIiIiIiPQ3VOL3NeBPZvYNYE24bBHwZeBzsQ5MZCj76lv56D2reXl3Hf/8zrnccO40VBEtIiIiIjKwQRM/d/+TmV0GfBH4TLh4PXCFu788EsGJDOSV3XXccPdq6ls7uOO6RZw/ryTeIYmIiIiIJLQhh3Nw91eA680sO3jrTSMTlsjA/vzKXm66by35kVTu//hZzJuYG++QREREREQS3pAPRJnZJ81sJ7AD2GlmO8zskyMTmkgfd+fHK7fw8XvXMLs0hwc+fbaSPhERERGRYRpqHL+vAmcBS9x9W7jsJOA2Mytw92+MUIxygmvr7OLLv3uZ372wm0sWTuTb7z2ZjNTkeIclIiIiIjJqDNXU8zpgobu39ixw921m9j5gHaDET2KuprGNj/1iDat3HOCm82bx2bfPUCcuIiIiIiJH6HDP+LUOsKzFzN4wvp/I8VbT2MZlP36aqvo2br/mzbzr5InxDklEREREZFQa6hm/CjN7e/+FZrYM2BO7kEQC//nIJvYcbOVXHz1TSd/R6mgF93hHISIiIiJxNlSN32eB35vZUwTj+DlwOnA2cOkIxCYnsHW7DnLf6l3ccM40TivLj3c4iaurA+p2wYEdcHBH1Ov2YL65mnOSI7BlLhTNhqKZUDwbimZB/jRIHrLSX0RERETGiKHG8VtvZguAa4D5gAFPAB8bqAmoyPHS3e187cH1FGWn89m3z4x3OPHlDo37giTuwPZ+yd0OqK8Aj2p5nZQCeVMgvwzmXAx5k9m36UUmpTXCthWw7ldR26ZCwUlQPCtIBItmB/OFMyE9e8RPVURERERiZzjP+N0ZvczMks3sA+7+y5hGJies+9dUsG7XQb73voXkZKTGO5zYc4eGvVC1AfZvhNrX+5K8gzuhs9/fWbJLg8Ru6pmQXx7MjysLXnMmvqEWb7OvZNKSJcGb1jqo3gLVm6D6Ndj/GlS9ChsfBu/q+1Du5KjawZlhbeEsyB4P6lxHREREZNQZajiHXOBTwCTg98Bfw/dfBNYCSvzkuKtr6eA//ryRRWX5XL6wFNb+GrKKYeKbIasw3uEdu5aDQXK3b32QcFVtCKaWA33bpOdB/tQg6Zp5QZDc9SR246ZCaubRHz8jDyafFkzROtuhdluQDFZvgurNsH8TvPAL6GiK+vw4mLwIys+B8rfChIVqLioiIiIyCgx1x/YL4ADwLPBR4EtAGnCpu68dgdjkBPT9R1+jtrmduy9ZjL1wFzz0D30rx00NEsCJp4avpwSJTCLqaA2SqJ7Ebt+GINGrr+jbJi0Hxs+FeZfC+HnB/Ph5kFU08vGmpMH4OcEUzR3qd/fVDu5/FXY+B3+9OViflg1T3xImgucqERQRERFJUEPdoZ3k7m8CMLOfAdXAVHdvGJHI5ITz6p567nl2Ox84YyoL8trgnluCZOJtX4LKF2H3C8Hrht/3fahwRpgEhgnhhJMhLWvkgu7uCppl9tbgha81W/uaTialBrV3ZWcFyV3J/OA1b0riN5s0g7zJwTR9Wd/yxirY8TS8/iRsfwr++q/B8rQcKOtJBM+BUiWCIiIiIolgqDuyjp4Zd+8ys9eV9EmsuDv/+uB68jJT+cIFs+GRz0JHM1z8vaDDkWlv7du4uRYqwyRw94uw/Wl4+bfBOkuC4jmHJoMl8yE1Y3iBdHcFzS6bqqG5GpprwvmavmVN1UEMPfPdPT8VC5pllsyPqsWbB4XTIXmMPauYPR7mXx5MECSC25/qmzb/JVjemwieGyaCJysRFBEREYmDoe7AFppZfThvQGb43gB399zD7dzMLgJuA5KBn7n7t/qtLyPoPKYYqAWudfeKcF0X8HK46U53v2T4pyWjzR9e2sPzr9fyzcsXMK5qFaz7NZzz+SDp6y9SADPOC6YeDXuDRLBneu0RWBs+hpqUEiRgk06F0jdBV2dUAlcNTTV9SV5zLcHIJQNIzwueM4wUBjVgExdCpCjo/GT83CDhHMnaxkSSPR4WvCeYABr2wY4BEsH03KimoecETUOTkuMXt4iIiMgJYqjhHI7pbszMkoEfAecDFcAqM3vQ3TdEbfYd4B53vzscGP5W4LpwXYu7n3IsMcjo0NTWyTcf2sCCSblcfeoEuOMqyJsKb/3i8HeSUwqz3xFMEDybVlcRJoJh7eD6/4E1dwXrLQkyC4Ln6SJFQdLWM59VFCR3Pa+R8DUl7bif+5iVUwILrggmCBLB7U9GJYKPBMvTc2Hy6TDlDJhyOkxaBBmH/ZuSiIiIiByhWLa5WgxscfdtAGa2nGDg9+jEbx5wUzi/AngghvFIgvrhY1vYV9/Gjz9wGsnP/yToQOTqX0Na5Oh3agbjpgTTvLCyuKejkpRMyBynmqaRlFMCb3pvMEFQQ7v9qSAZ3PU8rLyVoKbVgtrZKYvD6YxgrMGRfhayrRFqtwbPavYOpxHG0BuLHTp/yDoGWWcUV62HDXUDrhvqc4OuyyoOmhOn5xzNmYqIiMgJIpaJ3yRgV9T7CuCMftusA64gaA56OZBjZoXuXgNkmNlqoBP4lrsrKRyDtu1v5OdPbeO9p03mtHFNcO+3YNY7YM47j//BejoqkfjLKT00EWytg4rVULEqSARf+X+w5r+DdZFCmLw4qBGcvDhosns8mtR2dQZjJdZsCabqzeH8VmioPPb9D2I+HPrnr+Mla3yQABZOh4Ko14KTju2PKCIiIjImmPsgzzMd647NrgQudPcbwvfXAYvd/TNR20wEbgemAU8QJIHz3b3OzCa6e6WZnQQ8Brzd3bf2O8aNwI0AJSUlpy1fvjwm5yJHprGxkezs7MNu5+58d00bWw928a1zI5y1+T8oqF3DqtNvpzWzZAQiPTEMtzwSincTaa4gr24jufUbya3fRFZzMBSGk0Rj9jTq8uZQnzub+tw5tGYMMrC8O2ntB8ls2U2kuTJ8DeYzWveSFDVofUdKNs2RSbRkTqI5MpGWzIk0RybSlRyh77nP4NWcNywDD5cPtA4s/Le2ubmJSCQ6EfPeddHLhrsurb02PLc9ZLZUEmmuJK3j4CGfaEsr7D2nlswJ4fwEWjIn4EljrOOhIzQqfx9jmMojsag8EovKI7EkanksXbp0jbsvGmhdLGv8KoApUe8nA4f8Gd3dK4H3AJhZNnCFu9dFrcPdt5nZSuDNwNZ+n78DuANg0aJFvmTJklichxyhlStXMpyy+Mv6vbxSvYZ/edc8Li3ZCs8+C8v+hTPfelXsgzyBDLc8El5zLVSsxnb9jZyK58mpWAm7HwrWZZcEzwpOXgSdbVE1eFuhPaoz4uT0oCas/FQonBkMB1I4A4pmkhopIA+I9ciQK1eu5PRYl0drPdRuC5urbiO9divpNVvIr1kFe2qjNgybRBcMUFOYXzb2eqMdwJj5fYwRKo/EovJILCqPxDIayyOWid8qYKaZTQN2A1cD10RvYGZFQK27dwNfJujhEzPLB5rdvS3c5mzg2zGMVUZYa0cX//bQBmaVZPPB00vgJ5cHN+BnfebwH5YTU6QAZl0QTBA01azaABXPB81Ddz0PG/9IbzJTOCN4TrBwZtgEckYwdmJSUlxPY0Rk5MLEU4Kpv5YDULOt7xnGmi3B/Eu/hba6vu0sOUj+DkkKTwpex03VM7IiIiKjTMwSP3fvNLNPA48QDOdwp7uvN7NbgNXu/v/bu+/4uKo77+Ofn7otyXKT5W7L3caY5kIAgw2ETihenpBNSLIJIdkE8mSzYRNSCCEFksBuwgaehAABNoDXIRQDpgVsbLrBuGDL3Qb3JmwjN7Xf88e58oxkybhoiqTv+/U6r5k5c+6dc+fojuY359xzpgITgFvMzAlDPb8VbT4c+JOZ1QIZhGv8EnFVjKTIn15ZyZryPTz8tXFkv3EHfLQKrnoCsnJTXTVpKTKzoMeokMZcHfJ2l0N2u5Ckce06Qe+TQornHpY02bYiCgqXx+5/8DpU7YqVzcwJa1buDwoHxILDDr3aRnAtIiLSwiR0JWV3nwZMa5B3Y9z9R4FHG9nudeDYRNZNUmdN+W7umrGcC0f14JSOO+Dh/wzT/g+cmOqqSUvXvnOqa9BymYUlTPK7Qt8G83C5Q8Wm+j2E21aE4aQrp8fNfApk5YVAsC4Y7HF8WLOxoFtyj0dERETqSWjgJ9KYXzyziAwzfnT+MHj6C6H34JxfprpaItIUszATa2F36H9q/edqa8MsqPt7CFeG261LYenzUFsVyhUPg/7joXQ89DsN8rsk/zhERETaMAV+klQzl27h+YWbuP7cofTc8CKseAnOuxU69Eh11UTkSGRkhGVSinrDgAn1n6uphg1zYdXMsG7j3Idh9p/Dc92OCUFg//HQ7xT11oqIiCSYAj9JmsrqWm56aiH9u7Tn6nHF8McboORYGPO1VFdNRBIhMyvMtNp7NIz/LtRUwbo5sHpWSO8+AG/9ETDoPhL6nx6GhfY7Bdp1THXtRUREWhUFfpI0f3ltFSu37OIvXx5D7qu3wc51cMX94cuhiLR+mdnh+sG+4+D074WlN9a9G3oDV82E2ffARhg2AQAAIABJREFUm3eCZUD3UVGP4OnQ9+QwU6mIiIgcMX3jlqTYtHMvd7y0jLOGdWNip60w5S444aow3b6ItE1ZuaF3r98pcMZ/QNVeWDs7BIKrZ8Fbf4LX/zssLdHz+Ng1gn1Ohtz0WzRXREQknSnwk6S4ZVoZVTXOjRcNh6lXQG4hnP2zVFdLRNJJdl4I7ErHAzdA5e6wTuPqV2HVLHjjD/Da7yAjC3qeGPUInhYCwZz2qa69iIhIWlPgJwn39qpynpi7nuvOHES/tU/Bh6/DxXdoVj8RObic9mHCmAETwuPKXbDmrRAErp4Fr/4OZt0OGdnhOsL+dYHgWK3lKCIi0oACP0mo6ppabnzyfXoW5fHNcV3hTz+G3mPCME8RkcORkw8DzwwJYN/H8OFbsHpmCAZn3QYzfwOZueFzpq5HsPeYMKxURESkDVPgJwn18Nsfsnjjx9z1+RNp9+qvYE85XPh4mAJeRORo5BbC4LNDAti7Az58M1o+YhbMuBXwsKh8n7GxWUN7nQRZOSmtuoiISLIp8JOE2Vaxj9ueX8Kpg7pwfucN8Pd7YdzXoceoVFdNRFqjvCIYcm5IAHu2wwevhyBw1SyY/ouQn90e+oyD0vF03VIJK2ohpyDk5+SH+zn5YbioWeqOR0REpBkp8JOEue2FJeyurOGmC4dhT10KBd1g4g9TXS0RaSvadYRhF4QEsLscPngtdo3gSzczEmBhUzuwKAisCwjjgsKcfMjObzz/gFQXSLYP99XbKCIiKaDATxJi3prtTJ69hq+eWsrgtY/C+vdg0r3hF3kRkVRo3xmGXxwSwK5tvPPyk4weNRyqdoXJY/aniuh2d9z9KH93OexYW79cTeWh1yMju0Gw2L6RwDHqgSzsDsXDQsrvqh5IERE5Ygr8pNnVunPj1IV0yc/lO58qgrtvhtLTYeSkVFdNRCQmvwsVhQOg36eOfl81VU0EjrsaBJXxQWSDoHLneqjaXb+s18Zeo13nEAB2iwLB4qHhtqBEAaGIiHwiBX7S7F5bV828Nbu5/YrjKJj58/BF5oLb9cVERFqvzOwwtLRdx+bbpzt8vAG2LIbNi8PtliXw/t/DRDZ18jrWDwSLh0K34VDYQ5+7IiKynwI/aVY79lQxZWklJ/XrxGWdV8NTj8D4f4fiIamumohIy2IGHXqGVLeEBYSAsGJTLBCsuy17CuY8ECuX2yEKBodC8fBYUFjUWwGhiEgbpMBPmtV/vbiUikr42UVDyJh6ERT1hfHfS3W1RERaD7Nw7V9h99ji9hACwl1bo0AwLihc+jy899dYuZyC+r2DddcQFvXRUjsiIq2YAj9pNos37uR/3vyACX2yGPnhw7ClDK58JExcICIiiWUGBcUhlY6v/9yubbB1CWwuiwWEy1+CuQ/FymS3h65D6g8XLR4KHftBRmZyj0VERJqdAj9pFu7OT59cSGFeFp/vuz0snDzk/Ng06iIikjr5XSD/FOh3Sv38PR/VHy66ZXFY6mL+5FiZrDzoOjgaLhrXQ9ipP2Tqa4SISEuhT2xpFk/N38Bbq8r55WUjOW7218NMdOffmupqiYjIwbTrBH1PDine3h2wZWncsNHF8OEbsGBKrExmLnQeAF0Ghtv99weGiWU0bFREJK0o8JOjtmtfNb96poyRvTpwZaelZG59A878Sfg1WEREWp68IugzJqR4+z6GrUtD7+DmMti2PKRlL0LNvli5rHbQubRBQDhAQaGISAop8JOj9ofpy9m4cy93fXYEmU+fz+52vWh/ynWprpaIiDS33ELodVJI8WprwjqE5Stg2wooXxnS4QaF8esWiohIs1LgJ0dl5ZYK7pm1kkkn9ubENQ/AR6tYetzNHJ+Vm+qqiYhIsmRkQsc+IQ2YUP+52hrYuS4KBOOCwq3LYNkLUFO5v+j4jBwoG3Tg0NHOA9RTKCJylBT4yRFzd3721CLysjL54ck58MB/wshJbO90XKqrJiIi6SIjEzr2DWnAhPrP1QWFUUC4ft4r9Glf2WhQWK+nML6XUEGhiMghUeAnR+wfZZt5ZekWfnzBMLq8cj1k5sA5v4Q5S1JdNRERaQnig8KBE1mxayB9JkwIz9ULCldA+apwv8mgcEAIDOODwuKhUNAtJYcmIpJuFPjJEdlbVcPNTy9kcLcCvtx5Abz8Epx3K3ToASjwExGRo9QgKKyntgZ2rI2Gja6AbXXDR5ceGBQWlED3Y6H7qNht5wHqIRSRNkeBnxyRu2euZE35HiZ/aSRZz/4rlBwLY76W6mqJiEhbkJEJnfqF1GRQuAI2L4aNC0JaeQfUVocy2fnQfWQsGOwxKqxTmJ2X/GMREUkSBX5y2NaU7+bO6cu58NgenLzmnjAU54r7tZCviIikXr2g8MxYfvW+sB7hhvlRMDgf5k2G2X8Oz1tmGBoaHwyWjIT2nVNzHCIizUzf1OWw/fKZMjLMuHEc8PBdcMJV0GdsqqslIiLStKxc6HFcSHVqa2H76vrB4KpXYP7kWJmiPvWDwe7HhjyzpB+CiMjRUOAnh2XWsi08t3Aj158zhJKZ3w5rOp39s1RXS0RE5PBlZMSWjjjm0lh+xZYQBNYFgxsXwJJpgIfn8zrGrhesCwa7DoHM7JQchojIoVDgJ4essrqWm6YupF+X9lzT8W348HW4+A7I75LqqomIiDSfgmIYdFZIdSp3waZFUSAYBYPv3AvVe8PzmbnQbXgUCEbBYMlIyC1IzTGIiDSgwE8O2f2vr2LFll08+M+DyX7uaug9JgzzFBERae1y8qHPmJDq1FTDtuVRz+C8cFv2NMx5MCpgoTexR9yMot1HQWFJSg5BRNo2BX5ySDbv3Mvv/7GMs4Z14/Q1f4I95XDh45oOW0RE2q7MLOg2LKRRV4Q8d9i5Pm6Y6HxYNwcWPh7bLr9b/WCwx3HQqVT/U0UkoRIa+JnZecDvgUzgHne/tcHz/YD7gGKgHPiCu6+Ne74DUAY87u7XJrKucnC3PLuYqhrn52Oq4G/3wrhvhH9aIiIiEmMGRb1CGnpeLH/Pdti0MDZMdMN8WDkjtsRETmFsiYm64aLFwyArJyWHISKtT8ICPzPLBO4EPg2sBWab2VR3XxRX7DbgQXd/wMzOBG4B4scO/hx4JVF1lEMze3U5j7+3jusmlNLztW9CQTeYeEOqqyUiItJytOsI/U8NqU71PthcFoLBDVHv4Ht/hbd3heczsuOuGzwutsSErhsUkSOQyB6/scByd18JYGaTgUuA+MBvBPBv0f3pwBN1T5jZSUAJ8BwwOoH1lIOoqXVufHIhPYvyuK7oVVj/Hky6F/KKUl01ERGRli0rF3oeH1Kd2hooXwkb5sUCwiXPhoAQAIMuA+v3DPY4DvK7puQQRKTlSGTg1wtYE/d4LTCuQZl5wCTCcNDLgEIz6wJ8BNxO6P07C0mZh9/6gLINO/nz5f3IeflfoPR0GDkp1dUSERFpnTIyoevgkI79p5C3/7rBuJ7Bde/Awsdi2xX2jFtrMAoKO/bTeoMisp+5e2J2bHYFcK67Xx09vgoY6+7XxZXpCfwBKAVmEoLAYwgBX3t3/42ZfRkY3dg1fmZ2DXANQElJyUmTJ09uWESOwseVzg9m7aZvYQb3Ff6Jks2zeGf079md3/ug21VUVFBQoGEo6ULtkV7UHulF7ZFe1B6HJ6uqgoKKlRRUrKKgYiWFH6+k/e61GLUAVGXlU1FQSkXBgP1pd/veeEbmIe1f7ZFe1B7pJV3bY+LEie+6e6OjJRPZ47cW6BP3uDewPr6Au68HLgcwswJgkrvvMLNPAePN7JtAAZBjZhXu/oMG298N3A0wevRonzBhQqKOpU264bEF7KtZw92fzqLHk9Nh/L8z9qwvfOJ2M2bMQG2RPtQe6UXtkV7UHulF7dEMqvZE6w3OI3vDfDptnE+njS/E1hvMyoNuI+oPE+02AnLaH7ArtUd6UXukl5bYHokM/GYDg82sFFgHXAn8c3wBM+sKlLt7LXADYYZP3P3zcWW+TOjxqxf0SWLNX7udybM/5OpP9aHP69dAUV8Y/71UV0tEREQOJrsd9D4ppDo11bBtWWyY6IZ5YXmJd+8Pz1sGdB3S4LpBzdwt0tokLPBz92ozuxZ4nrCcw33uvtDMbgbecfepwATgFjNzwlDPbyWqPnLoaqMJXbrk5/LvRS/BnDK48pFGfw0UERGRNJeZFWYH7TYcjvtsyHOH7R/Wv25w9auwYMr+zU7OLYZ1J0TbjgjLSxQPDcGliLQ4CV3Hz92nAdMa5N0Yd/9R4NFP2Mf9wP0JqJ404e9z1jJ3zXbuuqgbea/8FoacD8MuSHW1REREpLmYQad+IQ2/OJa/a+v+GUV3zH+ZvJ3rw3qDNZXRdhlhsfm6YLAuoOwyCDKzU3IoInJoEhr4ScuzY08Vv35uMSf27cj56+4Ar4Xzb011tURERCQZ8rvCoLNg0FmUVZ9AyYQJUFMVlpjYXBalReF2ybTwPQHCmoNdB4cgsHh4LCDs1D/MVCoiKafAT+r53T+Wsm1XJY+evRt7biqc+ZPwoS0iIiJtU2Z2GOJZPBSOuTSWX7U3XDsYHwyunQ3v/z1WJqtd2K4uEKzrJezQS0tNiCSZAj/Zb8nGj3nwjQ/44ugS+r91NXQZDKdc98kbioiISNuTnRfWDux+bP38fRWwZUksGNy8KAwXnfdIrExuh3DNYL0hoyOgoDiphyDSlijwEwDcnZ9OfZ/CvCx+UPg8fLQKvvgkZOWmumoiIiLSkuQWHDizKMDuctiyOC4gXAxlU2HOA7Ey7bvUv3awblKZdh2TewwirZACPwHg6fkbeHNlOb/7dAfavf57GDkJBkxIdbVERESktWjfGfqdElIdd6jYHAsGt0TXEc59GCorYuUKex44XLR4KOTkJ/84RFooBX7Crn3V/GpaGcf0KOSS9f8FmTlwzi9TXS0RERFp7cygsCSkgRNj+e6wY03chDLRkNG3X4WafXUbh1lJ44eKFg8Lk8xoxJLIART4CXdOX86GHXv56ykbsekvwXm3Qoceqa6WiIiItFVm0LFvSEPOjeXX1kD5qljPYF1P4bIXoLY62jYzLC+xv3dwWLjtVBrWNBRpo/TX38at2rqLP89ayZXHd2Lgu9dDybEw5muprpaIiIjIgTIyoeugkOLXH6yuhG3L464fLAvrES56EvBQJjMXiofELTcR9RQW9YGMjJQcjkgyKfBrw9ydnz21kNysTH5S8DTsXAdX3K9fw0RERKRlycqBkhEhxavcDVuX1O8d/OB1WDAlVianIJphdFiDGUZLtOSEtCr6ht9Gbf54Lz99ciEzlmzh9jOyyZ/9Jzjxi9BnbKqrJiIiItI8ctpDzxNCird3R5hVNH5SmaXPw3t/jZVp16n+YvR1QWH7zsk9BpFmosCvjXF3/vbuWn7x9CL2Vtdy/TlDuHz1dZBbCGfdlOrqiYiIiCReXhH0HRdSvIotcdcPRmnBo7BvR6xMQcmB6w8WDw3fpUTSmAK/NuTDbbv54eMLeHX5Vsb278wtk45l4LqnYOYbcPEdkN8l1VUUERERSZ2C4pBKT4/lucPO9fWXm9i8CN75C1TviZUr6tugd3AYdB0C2e2SfxwijVDg15LU1kD1Pqjee+BtTWXj+dV7qa3ayzsrNvL2sg1MsCp+NLSAYV1zsFcfhKXPQe8xcMJVqT46ERERkfRjBkW9Qhp8diy/tha2r64/ZHRzGax4GWqrom0zoPOABusPDocuAyEzOyWHI22XAr9E2rEWFj8TBWCV+wOxRoO3mn1NB3XV0XN1HyKHKQMYC4zNAM/MxTbmwdZcyMoL0yRffIdmsxIRERE5HBlRUNd5AAy7IJZfUwXlK+OCweh28TPgtdG22aE3sNvw+pPKdOyv72SSMAr8Eql8FTz7H7HHlgFZ7cKioll5YQaqrLy4x7lhfHj846y8MP1ww7wDbuv2FfIqLZsH397EPW+uIye3PddfeBwXndAP04eJiIiISOJkZodr/oqHwjGXxfKr9sLWpfWDwbVvw/uPxspkt4+2bTChTIeeyT8OaXUU+CVSn3Fw/cq4AC45b/ecDz/i+4/OZ9nmCi49fjg3XnwMnfNzkvLaIiIiItKI7DzoMSqkePs+hi1LDhwuOu/hWJncIk7I7QEfn1x/Upn8rsk9BmnRFPglUlYOZCVvwpRd+6q57YUl3P/6arp3yOMvXx7DxGHdkvb6IiIiInKYcguh9+iQ4u0uj/UOblmML30DFj4B794fK5NfHK1BOKL+pDJ5RUk9BGkZFPi1ErOWbeGGxxaw9qM9XHVyP/7jvKEU5umiYREREZEWqX1n6H9qSMDc/BlMOOMMqNgU9Q7GTSoz9yGorIht26HXgesPdh0a1jWUNkuBXwu3fXclv3imjEffXcuArvlM+fqnGFuqhUVFREREWh0zKOwe0sAzY/m1tbBjDWxZXH9SmVWzwgSCYWPo1D+udzBKXQaHUWrS6inwa8GeXbCBnzy5kI92V/LNCQP59lmDycvOTHW1RERERCSZMjKgU7+Qhpwby6+pho9WHzjD6NLnwGuibbOgy6C4xeijoaOdSyFD3ytbEwV+LdDmnXv5yZPv8/zCTYzs1YEHvjKGY3pqLLeIiIiIxMnMgq6DQhrxmVh+9T7Ytrx+MLh+briGEA9lsvKiJSdG1F9yoqhP6HmUFkeBXwvi7kx5Zw2/eKaMyupafnD+MK4+rZSsTC3RICIiIiKHKCsXSo4JKV7lrmiG0dikMqyeBfMnx8rkFIZAsOGkMgXdFBCmOQV+LcQH23Zxw2MLeH3FNsaVdubWSaMo7Zqf6mqJiIiISGuRkw+9Tgwp3p7tcdcPRrdLpsF7/xMr065z/WsH64aNttfcE+lCgV+aq6l17nt1Fbe/uISsjAx+edlIPjemLxkZ+kVFRERERJKgXUfoe3JI8Sq2wJay+kNG50+BfTtjZQq6159dtNuIsEh9bkFyj0EU+KWzxRt38v1H5zNv7Q7OGtaNX1w2kh5F7VJdLRERERERKCgOqfT0WJ477FxXf7mJzYvgnfugek+sXMe+DdYfjGYYzc5L/nG0EQr80tC+6hrufHk5d81YQVG7bO743AlcPKoHpnHTIiIiIpLOzKCod0iDz47l19bA9g/q9w5uLoPlL0FtVbRtBnQeWH8x+m4jQl6mwpajpXcwzbz7QTnf//sClm+u4LITevGTi0bQOV9rq4iIiIhIC5aRCZ0HhDTswlh+TRVsW9FgyYlFsPhp8NpQJjMnmmF0eP1JZTr2C0tZyCFR4Jcmdu2r5rfPL+GBN1bTo0Mef/mXMUwc2i3V1RIRERERSZzM7Khnb1j9/Ko9sHVprGdwcxl8+BYs+FusTHb7KBBsMKlMYQ/NMNoIBX5p4JWlW/jhYwtYv2MPXzy5H9efN4yCXDWNiIiIiLRR2e2gx3Ehxdu7M1pyYlFsptHl/4C5D8XK5BVB8fADJ5XJ75LcY0gzii5S6KNdlfz8mUU8NmcdA4vz+dvXP8Xo/pryVkRERESkUXkdoM+YkOLt2hY3w2iUFj4O7/4lVia/W/3F6OuWnMjrkNxjSBEFfing7jyzYAM3TV3I9t1VXDtxENeeOYi87MxUV01EREREpOXJ7wL5p0H/02J57lCxqcH1g2Uw53+galesXIfe9YeKdhselpzIbl2z6SvwS7KNO/by4yfe5x9lmzi2VxEPfmUcI3q2jV8ZRERERESSxgwKu4c08MxYfm0t7Fhz4Ayjq2ZCzb66jaFzaVzv4PAwfLTLIMhqmRMvJjTwM7PzgN8DmcA97n5rg+f7AfcBxUA58AV3XxvlPxZtlw38t7v/MZF1TTR3Z/LsNfzqmTIqa2q54fxhfPW0UrIyNRORiIiIiEjSZGRAp34hDT0vll9TDR+tatBDuBiWPAteE22bBV0GM4IucFxp2EcLkbDAz8wygTuBTwNrgdlmNtXdF8UVuw140N0fMLMzgVuAq4ANwCnuvs/MCoD3o23XJ6q+ibR66y5ueGwBb6zcxskDOnPr5aPo3zU/1dUSEREREZE6mVnQdXBIIy6J5Vfvg63LQjAYXUdY+MEcyGpZi80nssdvLLDc3VcCmNlk4BIgPvAbAfxbdH868ASAu1fGlckFWmS3WHVNLfe9torbX1hKTmYGt1x+LJ8d3YeMDE0vKyIiIiLSImTlQveRIUXemjGDCYUlKazU4UtkQNULWBP3eG2UF28eMCm6fxlQaGZdAMysj5nNj/bx65bY2/fS4s38atpixg8u5sXvnsHnxvZV0CciIiIiIkln7p6YHZtdAZzr7ldHj68Cxrr7dXFlegJ/AEqBmYQg8Bh339GgzBPAxe6+qcFrXANcA1BSUnLS5MmTE3IsR8rdKSuvZXjnDKwNLSJZUVFBQUFBqqshEbVHelF7pBe1R3pRe6QXtUd6UXukl3Rtj4kTJ77r7qMbey6RQz3XAn3iHvcG6vXaRb14lwNE1/JNig/66sqY2UJgPPBog+fuBu4GGD16tE+YMKGZD+HoTUx1BVJgxowZpGNbtFVqj/Si9kgvao/0ovZIL2qP9KL2SC8tsT0SOdRzNjDYzErNLAe4EpgaX8DMuppZXR1uIMzwiZn1NrN20f1OwKnAkgTWVUREREREpNVKWODn7tXAtcDzQBkwxd0XmtnNZvaZqNgEYImZLQVKgF9G+cOBt8xsHvAKcJu7L0hUXUVERERERFqzhK7j5+7TgGkN8m6Mu/8oDYZvRvkvAqMSWTcREREREZG2okUukyAiIiIiIiKHToGfiIiIiIhIK6fAT0REREREpJVT4CciIiIiItLKKfATERERERFp5RT4iYiIiIiItHIK/ERERERERFo5c/dU16FZmNkW4INU10MA6ApsTXUlZD+1R3pRe6QXtUd6UXukF7VHelF7pJd0bY9+7l7c2BOtJvCT9GFm77j76FTXQwK1R3pRe6QXtUd6UXukF7VHelF7pJeW2B4a6ikiIiIiItLKKfATERERERFp5RT4SSLcneoKSD1qj/Si9kgvao/0ovZIL2qP9KL2SC8trj10jZ+IiIiIiEgrpx4/ERERERGRVk6BnxwxM+tjZtPNrMzMFprZ/43ybzKzdWY2N0oXpLqubYWZrTazBdH7/k6U19nMXjSzZdFtp1TXsy0ws6Fx58BcM9tpZt/R+ZFcZnafmW02s/fj8ho9Jyy4w8yWm9l8MzsxdTVvnZpoj9+a2eLoPX/czDpG+f3NbE/cufLH1NW8dWqiPZr8jDKzG6LzY4mZnZuaWrdeTbTH/8a1xWozmxvl6/xIsIN8z22x/0M01FOOmJn1AHq4+xwzKwTeBS4F/g9Q4e63pbSCbZCZrQZGu/vWuLzfAOXufquZ/QDo5O7fT1Ud2yIzywTWAeOAf0HnR9KY2elABfCgu4+M8ho9J6IvuNcBFxDa6vfuPi5VdW+NmmiPc4CX3b3azH4NELVHf+DpunLS/Jpoj5to5DPKzEYAjwBjgZ7AP4Ah7l6T1Eq3Yo21R4Pnbwd2uPvNOj8S7yDfc79MC/0foh4/OWLuvsHd50T3PwbKgF6prZU04hLggej+A4QPLUmus4AV7v5BqivS1rj7TKC8QXZT58QlhC9c7u5vAh2jf/zSTBprD3d/wd2ro4dvAr2TXrE2qonzoymXAJPdfZ+7rwKWE4JAaSYHaw8zM8IP648ktVJt2EG+57bY/yEK/KRZRL88nQC8FWVdG3Vz36ehhUnlwAtm9q6ZXRPllbj7BggfYkC3lNWu7bqS+v+sdX6kVlPnRC9gTVy5tejHrGT7CvBs3ONSM3vPzF4xs/GpqlQb1NhnlM6P1BoPbHL3ZXF5Oj+SpMH33Bb7P0SBnxw1MysA/g58x913Av8PGAgcD2wAbk9h9dqaU939ROB84FvRsBFJITPLAT4D/C3K0vmRvqyRPF0PkSRm9iOgGngoytoA9HX3E4DvAg+bWYdU1a8NaeozSudHan2O+j8g6vxIkka+5zZZtJG8tDpHFPjJUTGzbMLJ8JC7Pwbg7pvcvcbda4E/o6EgSePu66PbzcDjhPd+U91Qg+h2c+pq2CadD8xx902g8yNNNHVOrAX6xJXrDaxPct3aJDP7EnAR8HmPJh+IhhRui+6/C6wAhqSulm3DQT6jdH6kiJllAZcD/1uXp/MjORr7nksL/h+iwE+OWDTe/F6gzN3/My4/fjzzZcD7DbeV5mdm+dHFx5hZPnAO4b2fCnwpKvYl4MnU1LDNqvcrrc6PtNDUOTEV+GI0M9vJhEkUNqSigm2JmZ0HfB/4jLvvjssvjiZGwswGAIOBlampZdtxkM+oqcCVZpZrZqWE9ng72fVro84GFrv72roMnR+J19T3XFrw/5CsVFdAWrRTgauABXXTCwM/BD5nZscTurdXA19PTfXanBLg8fA5RRbwsLs/Z2azgSlm9lXgQ+CKFNaxTTGz9sCnqX8O/EbnR/KY2SPABKCrma0FfgrcSuPnxDTCbGzLgd2EGVilGTXRHjcAucCL0efXm+7+DeB04GYzqwZqgG+4+6FORCKHoIn2mNDYZ5S7LzSzKcAiwpDcb2lGz+bVWHu4+70ceJ046PxIhqa+57bY/yFazkFERERERKSV01BPERERERGRVk6Bn4iIiIiISCunwE9ERERERKSVU+AnIiIiIiLSyinwExERERERaeUU+ImIyFExMzez2+Mef8/Mbmqmfd9vZv/UHPv6hNe5wszKzGz6Ue7nO9EyHnWPp5lZx2ao3/FmdsHR7idZktVuIiJy6BT4iYjI0doHXG5mXVNdkXh1ixsfoq8C33T3iUf5st8B9gd+7n6Bu28/yn0CHE9YH+qQmZnW6hURkf0U+ImIyNGqBu4G/q3hEw17fsysIrqdYGavmNkUM1tqZrea2efN7G0zW2BmA+N2c7aZzYrKXRRtn2lmvzWz2WY238y+Hrff6Wb2MLCgkfp8Ltr/+2YTqijqAAAEmUlEQVT26yjvRuA04I9m9ttGtrk+7nV+FuXlm9kzZjYv2tdnzezbQE9gel3PoZmtNrOuZtbfzBab2T1R+YfM7Gwze83MlpnZ2Kj8WDN73czei26HmlkOcDPwWTObG71WZzN7IqrTm2Y2Ktr+JjO728xeAB40s2Oi93RuVHZwI8dXEXf/n8zs/uj+FVFd55nZzE94383M/mBmi8zsGaBbI38nIiKSQvo1UEREmsOdwHwz+81hbHMcMBwoB1YC97j7WDP7v8B1hN4zgP7AGcBAQlA1CPgisMPdx5hZLvBaFOwAjAVGuvuq+Bczs57Ar4GTgI+AF8zsUne/2czOBL7n7u802OYcYHC0TwOmmtnpQDGw3t0vjMoVufsOM/suMNHdtzZyvIOAK4BrgNnAPxMCzs8APwQuBRYDp7t7tZmdDfzK3SdFwelod782er3/Bt5z90ujuj9I6BUkOr7T3H1PVO737v5QFEAeTi/ojcC57r4ubrjqV2n8fT8BGAocC5QAi4D7DuO1REQkwRT4iYjIUXP3nWb2IPBtYM8hbjbb3TcAmNkKoC5wWwDED7mc4u61wDIzWwkMA84BRsX1JhYRArRK4O2GQV9kDDDD3bdEr/kQcDrwxEHqeE6U3oseF0SvMwu4Leo1fNrdZx3C8a5y9wXRay8EXnJ3N7MFhOC27jgeiHrmHMhuYl+nAZMA3P1lM+tiZkXRc1Pdva4N3gB+ZGa9gcfcfdkh1LPOa8D9ZjYFeCzKa+p9Px14xN1rgPVm9vJhvI6IiCSBhnqKiEhz+R2hRyg/Lq+a6H+NmRmQE/fcvrj7tXGPa6n/w6Q3eB0n9L5d5+7HR6nU3esCx11N1M8O9UAabHNL3OsMcvd73X0poWdtAXBL1CP3SQ7leH8OTHf3kcDFQN5B6tVQ3fu0//jd/WFCj+Ie4Pmod7Cp7Yh/PXf/BvBjoA8w18y6cPD3vWE7iYhIGlHgJyIizcLdy4EphOCvzmpCgARwCU33YB3MFWaWEV33NwBYAjwP/KuZZQOY2RAzyz/YToC3gDOia+4ygc8Br3zCNs8DXzGzguh1eplZt2jY6G53/ytwG3BiVP5joPAIjrFOEbAuuv/luPyG+50JfD6q0wRgq7vvbLgzMxsArHT3O4CpwKhGXnOTmQ03swzgsrhtB7r7W+5+I7CVEAA29b7PBK6MrgHsQf0eWxERSQMa6ikiIs3pduDauMd/Bp40s7eBl2i6N+5glhACtBLgG+6+18zuIQyPnBP1JG4hXCPXJHffYGY3ANMJPVfT3P3JT9jmBTMbDrwRXoYK4AuE6/V+a2a1QBXwr9EmdwPPmtmGI5wh9DeEoZ7fBeKHS04HfmBmc4FbgJuAv5jZfGA38KUm9vdZ4AtmVgVsJEwS09APgKeBNcD7hOGsRMc3mPBevQTMA+bT+Pv+OHAmoQd0KZ8cUIuISJKZu0ZmiIiIiIiItGYa6ikiIiIiItLKKfATERERERFp5RT4iYiIiIiItHIK/ERERERERFo5BX4iIiIiIiKtnAI/ERERERGRVk6Bn4iIiIiISCunwE9ERERERKSV+/+OsSrPZsKeBgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 5))\n",
    "\n",
    "plt.plot(N_ESTIMATORS_RANGE, train_scores)\n",
    "plt.plot(N_ESTIMATORS_RANGE, validate_scores)\n",
    "\n",
    "plt.title(\"Choice of the best number of estimators for our final model\")\n",
    "plt.ylabel('ROC AUC Score')\n",
    "plt.xlabel(\"Number of estimators used\")\n",
    "\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We were previously working with n_estimators = 50. This plot shows that we could reduce overfitting with a choice of **n_estimators = 30**. Hence we now run our new model, which is the final one ! To be even more precise, we could use cross validation but here this quick approach provides us an acceptable result. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's train our final model with the new hyper parameter. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " ---------------- Model Report ----------------\n",
      "\n",
      " --- TRAIN\n",
      "Accuracy :               0.9535288992158002\n",
      "ROC AUC (Train):         0.9529927922281417\n",
      "\n",
      " --- VALIDATE\n",
      "Accuracy :               0.9547038327526133\n",
      "ROC AUC (Validate):      0.9516897839996565\n",
      "\n",
      "\n",
      "\n",
      " \t\t\t----------------------------- Feature Importances -----------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# use xgboost\n",
    "final_model = xgb.XGBClassifier(n_estimators = 30)\n",
    "\n",
    "final_model.fit(X_train_s, y_train)\n",
    "\n",
    "# Predict training and validation set:\n",
    "y_train_pred = final_model.predict(X_train_s)\n",
    "y_train_predprob = final_model.predict_proba(X_train_s)[:,1]\n",
    "\n",
    "y_val_pred = final_model.predict(X_val_s)\n",
    "y_val_predprob = final_model.predict_proba(X_val_s)[:,1]\n",
    "\n",
    "# Handle the global report of the model :\n",
    "\n",
    "print(\"\\n ---------------- Model Report ----------------\")\n",
    "print(\"\\n --- TRAIN\")\n",
    "print(\"Accuracy :               {}\".format(metrics.accuracy_score(y_train, y_train_pred)))\n",
    "print(\"ROC AUC (Train):         {}\".format(metrics.roc_auc_score(y_train, y_train_predprob)))\n",
    "print(\"\\n --- VALIDATE\")\n",
    "print(\"Accuracy :               {}\".format(metrics.accuracy_score(y_val, y_val_pred)))\n",
    "print(\"ROC AUC (Validate):      {}\".format(metrics.roc_auc_score(y_val, y_val_predprob)))\n",
    "\n",
    "print(\"\\n\\n\\n \\t\\t\\t----------------------------- Feature Importances -----------------------------\")\n",
    "\n",
    "feat_imp = pd.Series(final_model.feature_importances_, index=features_name).sort_values(ascending=False)\n",
    "feat_imp.plot(kind='bar', figsize=(15, 6))\n",
    "plt.ylabel('Feature Importance Score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Conclusion "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To conclude, we will use our Test Set to make sure that our models are efficient classifiers. Our most effective model seems to be the XGBOOST model. Let's see how it performs on the Test Set, now that we have tuned the hyperparameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Scaling the test set\n",
    "X_test_s = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " ---------------- Final Results ----------------\n",
      "\n",
      " --- TEST\n",
      "Accuracy :               0.9451672862453532\n",
      "ROC AUC (Test):         0.9304650626675888\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = final_model.predict(X_test_s)\n",
    "y_test_predprob = final_model.predict_proba(X_test_s)[:,1]\n",
    "\n",
    "print(\"\\n ---------------- Final Results ----------------\")\n",
    "print(\"\\n --- TEST\")\n",
    "print(\"Accuracy :               {}\".format(metrics.accuracy_score(y_test, y_test_pred)))\n",
    "print(\"ROC AUC (Test):         {}\".format(metrics.roc_auc_score(y_test, y_test_predprob)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the ROC curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpr, tpr, threshold = metrics.roc_curve(y_test, y_test_predprob)\n",
    "roc_auc = metrics.auc(fpr, tpr)\n",
    "\n",
    "fig = plt.figure(figsize=(15, 5))\n",
    "\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0, 1])\n",
    "plt.ylim([0, 1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The way this ROC curve looks suggests an efficient model, meaning that the distribution curves of True Positives and True Negatives barely overlap. Even if we have a little decrease in AUC ROC Score in comparison to what we had on the validation set, it is still a very good result and this overfit is still quite reasonable thanks to our work on hyperparameter tuning and our effort to reduce the complexity of the model. \n",
    "\n",
    "This notebook thus provide a quite **simple but efficient way to build a model to predict credit default.** \n",
    "\n",
    "Thanks for reading. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}