{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Structure of the notebook "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### I. Data cleaning :\n",
    "  #### 1. Checking data types.\n",
    "  #### 2. Treatment of  missing data.\n",
    "  #### 3. Checking the existence of duplicates.\n",
    "  #### 4. Checking the existence of constant and quasi-constant features.\n",
    "  #### 5. Verification of the data range constraint for numerical variables.\n",
    "  #### 6. Investigation of the existence of rare labels in categorical variables.\n",
    "\n",
    "### II. Exploratory data analysis\n",
    "  #### 1. Explorating of the distribution of numerical variables.\n",
    "  #### 2. Investigation of the relationship between price sensitivity and customer  churn.\n",
    "  #### 3. Further analysis of the data to get more insights.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re \n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns \n",
    "import statsmodels.api as sm\n",
    "import pylab\n",
    "from feature_engine.selection import DropDuplicateFeatures, DropConstantFeatures\n",
    "from feature_engine.encoding import RareLabelEncoder\n",
    "import scipy.stats as stats\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## I. Data  cleaning "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "client_data = pd.read_csv('client_data.csv')\n",
    "prices_data = pd.read_csv('price_data.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14606"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(client_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "193002"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(prices_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>price_date</th>\n",
       "      <th>price_off_peak_var</th>\n",
       "      <th>price_peak_var</th>\n",
       "      <th>price_mid_peak_var</th>\n",
       "      <th>price_off_peak_fix</th>\n",
       "      <th>price_peak_fix</th>\n",
       "      <th>price_mid_peak_fix</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>038af19179925da21a25619c5a24b745</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>0.151367</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.266931</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>038af19179925da21a25619c5a24b745</td>\n",
       "      <td>2015-02-01</td>\n",
       "      <td>0.151367</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.266931</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>038af19179925da21a25619c5a24b745</td>\n",
       "      <td>2015-03-01</td>\n",
       "      <td>0.151367</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.266931</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>038af19179925da21a25619c5a24b745</td>\n",
       "      <td>2015-04-01</td>\n",
       "      <td>0.149626</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.266931</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>038af19179925da21a25619c5a24b745</td>\n",
       "      <td>2015-05-01</td>\n",
       "      <td>0.149626</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.266931</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 id  price_date  price_off_peak_var  \\\n",
       "0  038af19179925da21a25619c5a24b745  2015-01-01            0.151367   \n",
       "1  038af19179925da21a25619c5a24b745  2015-02-01            0.151367   \n",
       "2  038af19179925da21a25619c5a24b745  2015-03-01            0.151367   \n",
       "3  038af19179925da21a25619c5a24b745  2015-04-01            0.149626   \n",
       "4  038af19179925da21a25619c5a24b745  2015-05-01            0.149626   \n",
       "\n",
       "   price_peak_var  price_mid_peak_var  price_off_peak_fix  price_peak_fix  \\\n",
       "0             0.0                 0.0           44.266931             0.0   \n",
       "1             0.0                 0.0           44.266931             0.0   \n",
       "2             0.0                 0.0           44.266931             0.0   \n",
       "3             0.0                 0.0           44.266931             0.0   \n",
       "4             0.0                 0.0           44.266931             0.0   \n",
       "\n",
       "   price_mid_peak_fix  \n",
       "0                 0.0  \n",
       "1                 0.0  \n",
       "2                 0.0  \n",
       "3                 0.0  \n",
       "4                 0.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>id</th>\n",
       "      <th>channel_sales</th>\n",
       "      <th>cons_12m</th>\n",
       "      <th>cons_gas_12m</th>\n",
       "      <th>cons_last_month</th>\n",
       "      <th>date_activ</th>\n",
       "      <th>date_end</th>\n",
       "      <th>date_modif_prod</th>\n",
       "      <th>date_renewal</th>\n",
       "      <th>forecast_cons_12m</th>\n",
       "      <th>...</th>\n",
       "      <th>has_gas</th>\n",
       "      <th>imp_cons</th>\n",
       "      <th>margin_gross_pow_ele</th>\n",
       "      <th>margin_net_pow_ele</th>\n",
       "      <th>nb_prod_act</th>\n",
       "      <th>net_margin</th>\n",
       "      <th>num_years_antig</th>\n",
       "      <th>origin_up</th>\n",
       "      <th>pow_max</th>\n",
       "      <th>churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24011ae4ebbe3035111d65fa7c15bc57</td>\n",
       "      <td>foosdfpfkusacimwkcsosbicdxkicaua</td>\n",
       "      <td>0</td>\n",
       "      <td>54946</td>\n",
       "      <td>0</td>\n",
       "      <td>2013-06-15</td>\n",
       "      <td>2016-06-15</td>\n",
       "      <td>2015-11-01</td>\n",
       "      <td>2015-06-23</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>t</td>\n",
       "      <td>0.00</td>\n",
       "      <td>25.44</td>\n",
       "      <td>25.44</td>\n",
       "      <td>2</td>\n",
       "      <td>678.99</td>\n",
       "      <td>3</td>\n",
       "      <td>lxidpiddsbxsbosboudacockeimpuepw</td>\n",
       "      <td>43.648</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>d29c2c54acc38ff3c0614d0a653813dd</td>\n",
       "      <td>MISSING</td>\n",
       "      <td>4660</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2009-08-21</td>\n",
       "      <td>2016-08-30</td>\n",
       "      <td>2009-08-21</td>\n",
       "      <td>2015-08-31</td>\n",
       "      <td>189.95</td>\n",
       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>0.00</td>\n",
       "      <td>16.38</td>\n",
       "      <td>16.38</td>\n",
       "      <td>1</td>\n",
       "      <td>18.89</td>\n",
       "      <td>6</td>\n",
       "      <td>kamkkxfxxuwbdslkwifmmcsiusiuosws</td>\n",
       "      <td>13.800</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>764c75f661154dac3a6c254cd082ea7d</td>\n",
       "      <td>foosdfpfkusacimwkcsosbicdxkicaua</td>\n",
       "      <td>544</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-04-16</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>2010-04-16</td>\n",
       "      <td>2015-04-17</td>\n",
       "      <td>47.96</td>\n",
       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>0.00</td>\n",
       "      <td>28.60</td>\n",
       "      <td>28.60</td>\n",
       "      <td>1</td>\n",
       "      <td>6.60</td>\n",
       "      <td>6</td>\n",
       "      <td>kamkkxfxxuwbdslkwifmmcsiusiuosws</td>\n",
       "      <td>13.856</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bba03439a292a1e166f80264c16191cb</td>\n",
       "      <td>lmkebamcaaclubfxadlmueccxoimlema</td>\n",
       "      <td>1584</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-03-30</td>\n",
       "      <td>2016-03-30</td>\n",
       "      <td>2010-03-30</td>\n",
       "      <td>2015-03-31</td>\n",
       "      <td>240.04</td>\n",
       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>0.00</td>\n",
       "      <td>30.22</td>\n",
       "      <td>30.22</td>\n",
       "      <td>1</td>\n",
       "      <td>25.46</td>\n",
       "      <td>6</td>\n",
       "      <td>kamkkxfxxuwbdslkwifmmcsiusiuosws</td>\n",
       "      <td>13.200</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>149d57cf92fc41cf94415803a877cb4b</td>\n",
       "      <td>MISSING</td>\n",
       "      <td>4425</td>\n",
       "      <td>0</td>\n",
       "      <td>526</td>\n",
       "      <td>2010-01-13</td>\n",
       "      <td>2016-03-07</td>\n",
       "      <td>2010-01-13</td>\n",
       "      <td>2015-03-09</td>\n",
       "      <td>445.75</td>\n",
       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>52.32</td>\n",
       "      <td>44.91</td>\n",
       "      <td>44.91</td>\n",
       "      <td>1</td>\n",
       "      <td>47.98</td>\n",
       "      <td>6</td>\n",
       "      <td>kamkkxfxxuwbdslkwifmmcsiusiuosws</td>\n",
       "      <td>19.800</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 id                     channel_sales  \\\n",
       "0  24011ae4ebbe3035111d65fa7c15bc57  foosdfpfkusacimwkcsosbicdxkicaua   \n",
       "1  d29c2c54acc38ff3c0614d0a653813dd                           MISSING   \n",
       "2  764c75f661154dac3a6c254cd082ea7d  foosdfpfkusacimwkcsosbicdxkicaua   \n",
       "3  bba03439a292a1e166f80264c16191cb  lmkebamcaaclubfxadlmueccxoimlema   \n",
       "4  149d57cf92fc41cf94415803a877cb4b                           MISSING   \n",
       "\n",
       "   cons_12m  cons_gas_12m  cons_last_month  date_activ    date_end  \\\n",
       "0         0         54946                0  2013-06-15  2016-06-15   \n",
       "1      4660             0                0  2009-08-21  2016-08-30   \n",
       "2       544             0                0  2010-04-16  2016-04-16   \n",
       "3      1584             0                0  2010-03-30  2016-03-30   \n",
       "4      4425             0              526  2010-01-13  2016-03-07   \n",
       "\n",
       "  date_modif_prod date_renewal  forecast_cons_12m  ...  has_gas  imp_cons  \\\n",
       "0      2015-11-01   2015-06-23               0.00  ...        t      0.00   \n",
       "1      2009-08-21   2015-08-31             189.95  ...        f      0.00   \n",
       "2      2010-04-16   2015-04-17              47.96  ...        f      0.00   \n",
       "3      2010-03-30   2015-03-31             240.04  ...        f      0.00   \n",
       "4      2010-01-13   2015-03-09             445.75  ...        f     52.32   \n",
       "\n",
       "   margin_gross_pow_ele  margin_net_pow_ele  nb_prod_act  net_margin  \\\n",
       "0                 25.44               25.44            2      678.99   \n",
       "1                 16.38               16.38            1       18.89   \n",
       "2                 28.60               28.60            1        6.60   \n",
       "3                 30.22               30.22            1       25.46   \n",
       "4                 44.91               44.91            1       47.98   \n",
       "\n",
       "  num_years_antig                         origin_up  pow_max  churn  \n",
       "0               3  lxidpiddsbxsbosboudacockeimpuepw   43.648      1  \n",
       "1               6  kamkkxfxxuwbdslkwifmmcsiusiuosws   13.800      0  \n",
       "2               6  kamkkxfxxuwbdslkwifmmcsiusiuosws   13.856      0  \n",
       "3               6  kamkkxfxxuwbdslkwifmmcsiusiuosws   13.200      0  \n",
       "4               6  kamkkxfxxuwbdslkwifmmcsiusiuosws   19.800      0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Checking data types\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 14606 entries, 0 to 14605\n",
      "Data columns (total 26 columns):\n",
      " #   Column                          Non-Null Count  Dtype  \n",
      "---  ------                          --------------  -----  \n",
      " 0   id                              14606 non-null  object \n",
      " 1   channel_sales                   14606 non-null  object \n",
      " 2   cons_12m                        14606 non-null  int64  \n",
      " 3   cons_gas_12m                    14606 non-null  int64  \n",
      " 4   cons_last_month                 14606 non-null  int64  \n",
      " 5   date_activ                      14606 non-null  object \n",
      " 6   date_end                        14606 non-null  object \n",
      " 7   date_modif_prod                 14606 non-null  object \n",
      " 8   date_renewal                    14606 non-null  object \n",
      " 9   forecast_cons_12m               14606 non-null  float64\n",
      " 10  forecast_cons_year              14606 non-null  int64  \n",
      " 11  forecast_discount_energy        14606 non-null  float64\n",
      " 12  forecast_meter_rent_12m         14606 non-null  float64\n",
      " 13  forecast_price_energy_off_peak  14606 non-null  float64\n",
      " 14  forecast_price_energy_peak      14606 non-null  float64\n",
      " 15  forecast_price_pow_off_peak     14606 non-null  float64\n",
      " 16  has_gas                         14606 non-null  object \n",
      " 17  imp_cons                        14606 non-null  float64\n",
      " 18  margin_gross_pow_ele            14606 non-null  float64\n",
      " 19  margin_net_pow_ele              14606 non-null  float64\n",
      " 20  nb_prod_act                     14606 non-null  int64  \n",
      " 21  net_margin                      14606 non-null  float64\n",
      " 22  num_years_antig                 14606 non-null  int64  \n",
      " 23  origin_up                       14606 non-null  object \n",
      " 24  pow_max                         14606 non-null  float64\n",
      " 25  churn                           14606 non-null  int64  \n",
      "dtypes: float64(11), int64(7), object(8)\n",
      "memory usage: 2.9+ MB\n"
     ]
    }
   ],
   "source": [
    "# Checking for data types\n",
    "client_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 193002 entries, 0 to 193001\n",
      "Data columns (total 8 columns):\n",
      " #   Column              Non-Null Count   Dtype  \n",
      "---  ------              --------------   -----  \n",
      " 0   id                  193002 non-null  object \n",
      " 1   price_date          193002 non-null  object \n",
      " 2   price_off_peak_var  193002 non-null  float64\n",
      " 3   price_peak_var      193002 non-null  float64\n",
      " 4   price_mid_peak_var  193002 non-null  float64\n",
      " 5   price_off_peak_fix  193002 non-null  float64\n",
      " 6   price_peak_fix      193002 non-null  float64\n",
      " 7   price_mid_peak_fix  193002 non-null  float64\n",
      "dtypes: float64(6), object(2)\n",
      "memory usage: 11.8+ MB\n"
     ]
    }
   ],
   "source": [
    "prices_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transforming the type of columns containing dates.\n",
    "\n",
    "# A function to change the datatype     \n",
    "def to_date(df, column):\n",
    "    \"\"\" This function takes as inputs the dataframe and the column and\n",
    "        convert the type of the column to  datetime  \"\"\"\n",
    "    \n",
    "    df[column] = pd.to_datetime(df[column])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting date columns from the clients data set\n",
    "date_cols_client = []\n",
    "for col in client_data.columns:\n",
    "    pattern = re.findall(r'date_\\w+|\\w+_date', col)\n",
    "    if len(pattern) > 0:\n",
    "        date_cols_client.append(pattern[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting date columns from the prices data set\n",
    "date_cols_prices = []\n",
    "for col in prices_data.columns:\n",
    "    pattern = re.findall(r'date_\\w+|\\w+_date', col)\n",
    "    if len(pattern) > 0:\n",
    "        date_cols_prices.append(pattern[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Iterating through the date columns in the clients dataset  \n",
    "for col in date_cols_client:\n",
    "    to_date(client_data, col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Iterating through the date columns in the prices dataset  \n",
    "for col in date_cols_prices:\n",
    "    to_date(prices_data, col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 14606 entries, 0 to 14605\n",
      "Data columns (total 26 columns):\n",
      " #   Column                          Non-Null Count  Dtype         \n",
      "---  ------                          --------------  -----         \n",
      " 0   id                              14606 non-null  object        \n",
      " 1   channel_sales                   14606 non-null  object        \n",
      " 2   cons_12m                        14606 non-null  int64         \n",
      " 3   cons_gas_12m                    14606 non-null  int64         \n",
      " 4   cons_last_month                 14606 non-null  int64         \n",
      " 5   date_activ                      14606 non-null  datetime64[ns]\n",
      " 6   date_end                        14606 non-null  datetime64[ns]\n",
      " 7   date_modif_prod                 14606 non-null  datetime64[ns]\n",
      " 8   date_renewal                    14606 non-null  datetime64[ns]\n",
      " 9   forecast_cons_12m               14606 non-null  float64       \n",
      " 10  forecast_cons_year              14606 non-null  int64         \n",
      " 11  forecast_discount_energy        14606 non-null  float64       \n",
      " 12  forecast_meter_rent_12m         14606 non-null  float64       \n",
      " 13  forecast_price_energy_off_peak  14606 non-null  float64       \n",
      " 14  forecast_price_energy_peak      14606 non-null  float64       \n",
      " 15  forecast_price_pow_off_peak     14606 non-null  float64       \n",
      " 16  has_gas                         14606 non-null  object        \n",
      " 17  imp_cons                        14606 non-null  float64       \n",
      " 18  margin_gross_pow_ele            14606 non-null  float64       \n",
      " 19  margin_net_pow_ele              14606 non-null  float64       \n",
      " 20  nb_prod_act                     14606 non-null  int64         \n",
      " 21  net_margin                      14606 non-null  float64       \n",
      " 22  num_years_antig                 14606 non-null  int64         \n",
      " 23  origin_up                       14606 non-null  object        \n",
      " 24  pow_max                         14606 non-null  float64       \n",
      " 25  churn                           14606 non-null  int64         \n",
      "dtypes: datetime64[ns](4), float64(11), int64(7), object(4)\n",
      "memory usage: 2.9+ MB\n"
     ]
    }
   ],
   "source": [
    "client_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 193002 entries, 0 to 193001\n",
      "Data columns (total 8 columns):\n",
      " #   Column              Non-Null Count   Dtype         \n",
      "---  ------              --------------   -----         \n",
      " 0   id                  193002 non-null  object        \n",
      " 1   price_date          193002 non-null  datetime64[ns]\n",
      " 2   price_off_peak_var  193002 non-null  float64       \n",
      " 3   price_peak_var      193002 non-null  float64       \n",
      " 4   price_mid_peak_var  193002 non-null  float64       \n",
      " 5   price_off_peak_fix  193002 non-null  float64       \n",
      " 6   price_peak_fix      193002 non-null  float64       \n",
      " 7   price_mid_peak_fix  193002 non-null  float64       \n",
      "dtypes: datetime64[ns](1), float64(6), object(1)\n",
      "memory usage: 11.8+ MB\n"
     ]
    }
   ],
   "source": [
    "prices_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Treatement of  missing data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a function to get the unique values of a colum\n",
    "def get_unique(df, column):\n",
    "    return df[column].unique()\n",
    "\n",
    "# A function used to verify the values used for missing values\n",
    "def miss_val(df, column, value):\n",
    "    \"\"\" \n",
    "    A function to get the indices of the rows with missing values\n",
    "    \n",
    "        Inputs:\n",
    "               - The dataframe object.\n",
    "               - The name of the column.\n",
    "               - The value to verify.\n",
    "        Outputs:\n",
    "               - list containing the indices of the rows with missing values\n",
    "    \"\"\"\n",
    "    unique_vls = df[column].unique()\n",
    "    null_rows = []\n",
    "    for i in range(len(unique_vls)):\n",
    "        if str(unique_vls[i]) == str(value):\n",
    "            # returns the index of the missing column\n",
    "            null_rows.append(df.loc[df[column]==unique_vls[i], df.columns].index)\n",
    "    return null_rows\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The used value  for column 'channel_sales' is 'MISSING'\n",
      "The used value  for column 'origin_up' is 'MISSING'\n"
     ]
    }
   ],
   "source": [
    "# Checking the following values with the clients dataset\n",
    "vals = ['', '.', '?', np.nan, ' ', 'MISSING', 'missing', 'Missing', '!']\n",
    "\n",
    "for column in client_data.columns :\n",
    "    for j in vals:\n",
    "        if len(miss_val(client_data, column, j))> 0:\n",
    "            print(\"The used value  for column '{0}' is '{1}'\".format(column, j))\n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Checking the following values with the prices dataset\n",
    "vals = ['', '.', '?', np.nan, ' ', 'MISSING', 'missing', 'Missing', '!']\n",
    "\n",
    "for column in prices_data.columns :\n",
    "    for j in vals:\n",
    "        if len(miss_val(prices_data, column, j))> 0:\n",
    "            print(\"The used value  for column '{0}' is '{1}'\".format(column, j))\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replacing the term \"MISSING\" with np.nan\n",
    "client_data.loc[client_data['channel_sales']==\"MISSING\", 'channel_sales'] = np.nan\n",
    "client_data.loc[client_data['origin_up']==\"MISSING\", 'origin_up'] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The used value  for column 'channel_sales' is 'nan'\n",
      "The used value  for column 'origin_up' is 'nan'\n"
     ]
    }
   ],
   "source": [
    "# Checking the following values with the clients dataset\n",
    "vals = ['', '.', '?', np.nan, ' ', 'MISSING', 'missing', 'Missing', '!']\n",
    "\n",
    "for column in client_data.columns :\n",
    "    for j in vals:\n",
    "        if len(miss_val(client_data, column, j))> 0:\n",
    "            print(\"The used value  for column '{0}' is '{1}'\".format(column, j))\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that we have now \"nan\" as a representing value for misssing values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                 0.000000\n",
       "channel_sales                     25.503218\n",
       "cons_12m                           0.000000\n",
       "cons_gas_12m                       0.000000\n",
       "cons_last_month                    0.000000\n",
       "date_activ                         0.000000\n",
       "date_end                           0.000000\n",
       "date_modif_prod                    0.000000\n",
       "date_renewal                       0.000000\n",
       "forecast_cons_12m                  0.000000\n",
       "forecast_cons_year                 0.000000\n",
       "forecast_discount_energy           0.000000\n",
       "forecast_meter_rent_12m            0.000000\n",
       "forecast_price_energy_off_peak     0.000000\n",
       "forecast_price_energy_peak         0.000000\n",
       "forecast_price_pow_off_peak        0.000000\n",
       "has_gas                            0.000000\n",
       "imp_cons                           0.000000\n",
       "margin_gross_pow_ele               0.000000\n",
       "margin_net_pow_ele                 0.000000\n",
       "nb_prod_act                        0.000000\n",
       "net_margin                         0.000000\n",
       "num_years_antig                    0.000000\n",
       "origin_up                          0.438176\n",
       "pow_max                            0.000000\n",
       "churn                              0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the percentages of missing data in each column\n",
    "client_data.isna().mean()*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the feature \"channel_sales\" has more than 25% of its rows without information. So we will keep this feature apart.\n",
    "For the second feature with missing data, namely \"orgin_up\" we will drop the rows with missing data and keep them in a separate data frame.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Keeping channel_sales apart\n",
    "df_channel_sales = client_data.pop('channel_sales')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Keeping the rows with missing data apart.\n",
    "df_na = client_data[client_data.isna().any(axis=1)]\n",
    "client_data = client_data[~client_data.isna().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                0.0\n",
       "cons_12m                          0.0\n",
       "cons_gas_12m                      0.0\n",
       "cons_last_month                   0.0\n",
       "date_activ                        0.0\n",
       "date_end                          0.0\n",
       "date_modif_prod                   0.0\n",
       "date_renewal                      0.0\n",
       "forecast_cons_12m                 0.0\n",
       "forecast_cons_year                0.0\n",
       "forecast_discount_energy          0.0\n",
       "forecast_meter_rent_12m           0.0\n",
       "forecast_price_energy_off_peak    0.0\n",
       "forecast_price_energy_peak        0.0\n",
       "forecast_price_pow_off_peak       0.0\n",
       "has_gas                           0.0\n",
       "imp_cons                          0.0\n",
       "margin_gross_pow_ele              0.0\n",
       "margin_net_pow_ele                0.0\n",
       "nb_prod_act                       0.0\n",
       "net_margin                        0.0\n",
       "num_years_antig                   0.0\n",
       "origin_up                         0.0\n",
       "pow_max                           0.0\n",
       "churn                             0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client_data.isna().mean()*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Checking the existence of duplicates "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>id</th>\n",
       "      <th>cons_12m</th>\n",
       "      <th>cons_gas_12m</th>\n",
       "      <th>cons_last_month</th>\n",
       "      <th>date_activ</th>\n",
       "      <th>date_end</th>\n",
       "      <th>date_modif_prod</th>\n",
       "      <th>date_renewal</th>\n",
       "      <th>forecast_cons_12m</th>\n",
       "      <th>forecast_cons_year</th>\n",
       "      <th>...</th>\n",
       "      <th>has_gas</th>\n",
       "      <th>imp_cons</th>\n",
       "      <th>margin_gross_pow_ele</th>\n",
       "      <th>margin_net_pow_ele</th>\n",
       "      <th>nb_prod_act</th>\n",
       "      <th>net_margin</th>\n",
       "      <th>num_years_antig</th>\n",
       "      <th>origin_up</th>\n",
       "      <th>pow_max</th>\n",
       "      <th>churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [id, cons_12m, cons_gas_12m, cons_last_month, date_activ, date_end, date_modif_prod, date_renewal, forecast_cons_12m, forecast_cons_year, forecast_discount_energy, forecast_meter_rent_12m, forecast_price_energy_off_peak, forecast_price_energy_peak, forecast_price_pow_off_peak, has_gas, imp_cons, margin_gross_pow_ele, margin_net_pow_ele, nb_prod_act, net_margin, num_years_antig, origin_up, pow_max, churn]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 25 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the duplicated columns for the client data set\n",
    "dup = client_data.duplicated()\n",
    "client_data[dup]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>id</th>\n",
       "      <th>price_date</th>\n",
       "      <th>price_off_peak_var</th>\n",
       "      <th>price_peak_var</th>\n",
       "      <th>price_mid_peak_var</th>\n",
       "      <th>price_off_peak_fix</th>\n",
       "      <th>price_peak_fix</th>\n",
       "      <th>price_mid_peak_fix</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [id, price_date, price_off_peak_var, price_peak_var, price_mid_peak_var, price_off_peak_fix, price_peak_fix, price_mid_peak_fix]\n",
       "Index: []"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the duplicated columns for the client data set\n",
    "dup = prices_data.duplicated()\n",
    "prices_data[dup]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So there is no duplicated columns in both datasets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DropDuplicateFeatures()"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the existence of duplicated features for the client dataset\n",
    "sel_1_clt = DropDuplicateFeatures()\n",
    "sel_1_clt.fit(client_data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "set()"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sel_1_clt.features_to_drop_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we got an empty set which means that there is no duplicated features in the client dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DropDuplicateFeatures()"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the existence of duplicated features in the prices dataset\n",
    "sel_1_pr = DropDuplicateFeatures()\n",
    "sel_1_pr.fit(prices_data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "set()"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sel_1_pr.features_to_drop_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The same thing for the prices dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. Checking the existence of constant and quasi-constant features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Constant and quasi-constant features contain almost no information that the model can use to discriminate between data points. So the best thing to do when have this kind of features is to drop them. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DropConstantFeatures(tol=0.98)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the existence of constant and quasi-constant features in the clients dataset\n",
    "# Variables showing the same value in a percentage of observations greater than \n",
    "# \"tol\" will be considered as constant/quasi-constant and dropped.\n",
    "sel_2_clt = DropConstantFeatures(tol=0.98)\n",
    "sel_2_clt.fit(client_data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sel_2_clt.features_to_drop_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we got an empty list which means that there is no constant/quasi-constant features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DropConstantFeatures(tol=0.98)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the existence of constant and quasi-constant features in the clients dataset\n",
    "\n",
    "sel_2_pr = DropConstantFeatures(tol=0.98)\n",
    "sel_2_pr.fit(prices_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sel_2_pr.features_to_drop_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We got an empty list which means that there is no constant/quasi-constant features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  5. Verification of the data range constraint for numerical variables\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting the categorical features in the clients dataset\n",
    "cat_ftr_clt = [col for col in client_data.columns if client_data[col].dtype == 'O']\n",
    "\n",
    "# Getting the categorical features in the clients dataset\n",
    "num_ftr_clt = [col for col in client_data.columns if col not in cat_ftr_clt + date_cols_client]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# Checking the number of features \n",
    "print((len(cat_ftr_clt) + len(num_ftr_clt) + len(date_cols_client)) == len(client_data.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting the categorical features in the clients dataset\n",
    "num_ftr_pr = [col for col in prices_data.columns if prices_data[col].dtype == 'float64']\n",
    "\n",
    "# Getting the categorical features in the clients dataset\n",
    "cat_ftr_pr = [col for col in prices_data.columns if prices_data[col].dtype not in ['datetime64','float64']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Investigating the existence of negative values in the clients dataset\n",
    "\n",
    "for col in num_ftr_clt:\n",
    "    if client_data[col].min() < 0:\n",
    "        print(\"Feature {} has negative values\".format(col))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Investigating the existence of negative values in the prices dataset\n",
    "\n",
    "for col in num_ftr_pr:\n",
    "    if prices_data[col].min() < 0:\n",
    "        print(\"Feature {} has negative values\".format(col))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So there is no negative values in both datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6. Investigation of the existence of rare labels in categorical variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rare labels can cause the following issues :\n",
    "    \n",
    "   - The data points with rare categories are so few that the algorithm does not get any information from them, which turns them into a source of noise that can make the model overfit the data.\n",
    "    \n",
    "   - Rare labels may appear only in the test set which will make it hard for the model to evaluate them. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For feature 'has_gas' the percentage of datapoints with category 't' is: \n",
      "18.12 %\n",
      "-------------------------------------------------------------------\n",
      "For feature 'has_gas' the percentage of datapoints with category 'f' is: \n",
      "81.88 %\n",
      "-------------------------------------------------------------------\n",
      "For feature 'origin_up' the percentage of datapoints with category 'lxidpiddsbxsbosboudacockeimpuepw' is: \n",
      "48.8 %\n",
      "-------------------------------------------------------------------\n",
      "For feature 'origin_up' the percentage of datapoints with category 'kamkkxfxxuwbdslkwifmmcsiusiuosws' is: \n",
      "29.53 %\n",
      "-------------------------------------------------------------------\n",
      "For feature 'origin_up' the percentage of datapoints with category 'ldkssxwpmemidmecebumciepifcamkci' is: \n",
      "21.65 %\n",
      "-------------------------------------------------------------------\n",
      "For feature 'origin_up' the percentage of datapoints with category 'usapbepcfoloekilkwsdiboslwaxobdp' is: \n",
      "0.01 %\n",
      "-------------------------------------------------------------------\n",
      "For feature 'origin_up' the percentage of datapoints with category 'ewxeelcelemmiwuafmddpobolfuxioce' is: \n",
      "0.01 %\n",
      "-------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "for col in cat_ftr_clt:\n",
    "    if col != 'id':\n",
    "        for cat in client_data[col].unique():\n",
    "            perct = round(len(client_data[client_data[col] == cat])/len(client_data) * 100, 2)\n",
    "            print(\"For feature '{}' the percentage of datapoints with category '{}' is: \\n{} %\".format(col,cat,perct))\n",
    "            print(\"-------------------------------------------------------------------\")\n",
    "          "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RareLabelEncoder(n_categories=4, variables=['origin_up'])"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grouping rare categories (present in less than 5% of the data points) in a new\n",
    "# category called \"rare\"\n",
    "enc = RareLabelEncoder(variables=['origin_up'],n_categories=4, tol=0.05)\n",
    "enc.fit(client_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['lxidpiddsbxsbosboudacockeimpuepw',\n",
       "       'kamkkxfxxuwbdslkwifmmcsiusiuosws',\n",
       "       'ldkssxwpmemidmecebumciepifcamkci', 'Rare'], dtype=object)"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client_data = enc.transform(client_data)\n",
    "client_data['origin_up'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### II. Exploratory data analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Exploring  the distribution of numerical variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "client_data[num_ftr_clt].hist(figsize=(20,20))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plots to assess normality \n",
    "# plot the histograms to have a quick look at the variable distribution\n",
    "# histogram and Q-Q plots\n",
    "\n",
    "def diagnostic_plots(df, column):\n",
    "    \n",
    "    # function to plot a histogram and a Q-Q plot\n",
    "    # side by side, for a certain variable\n",
    "    \n",
    "    plt.figure(figsize=(15,6))\n",
    "    plt.subplot(1, 2, 1)\n",
    "    df[column].hist(bins=30)\n",
    "\n",
    "    plt.subplot(1, 2, 2)\n",
    "    stats.probplot(df[column], dist=\"norm\", plot=plt)\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plots for feature cons_12m\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plots for feature cons_gas_12m\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plots for feature cons_last_month\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plots for feature forecast_cons_12m\n"
     ]
    },
    {
     "data": {
      "image/png": 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MiJ9ExLE1xzmz9F8bEWfWtL8iIu4o+3whIqIZn1OS1L/Zs/eufaQZ8iRJqs/5wHcy8wXAS4G7gXOB6zNzIXB9WQd4I7Cw/KwALgSIiEOB84DjgeOA87qCYenzOzX7nTwKn0mStBdWrYKpU3u2TZ1atTeDIU+SpGGKiIOB1wAXAWTmM5m5DVgGXFK6XQKcVpaXAZdm5UbgkIg4EngDcG1mbs3MR4FrgZPLtoMy88bMTODSmmNJksaI5cth9WqYMwciqtfVq5sz6Qo4u6YkSfWYBzwM/H1EvBS4FfgQMD0z7y99HgCml+UZwKaa/TeXtoHaN/fR3kNErKAaGWR2s64NkqQ2t3x580Jdb47kSZI0fJOAY4ELM/PlwBN0X5oJQBmBy5EsIjNXZ+bizFx8xBFHjORbSZLGAUOeJEnDtxnYnJk3lfUrqELfg+VSS8rrQ2X7FmBWzf4zS9tA7TP7aJckqV+GPEmShikzHwA2RcQxpWkpcBdwFdA1Q+aZwJVl+SrgjDLL5gnAY+WyzmuAkyJiWplw5STgmrLt8Yg4ocyqeUbNsSRJ6pP35EmSVJ8PAh0RMRlYB7yP6h9RL4+Is4ANwDtK36uBU4BOYEfpS2ZujYiPAzeXfh/LzK1l+WzgK8AU4NvlR5KkfhnyJEmqQ2beDizuY9PSPvomcE4/x7kYuLiP9luAF9VXpSSpnXi5piRJkiS1EEOeJEmSJLUQQ54kSZIktRBDniRJkiS1EEOeJEmSJLUQQ54kSZIktRBDniRJkiS1EEOeJEmSJLUQQ54kSZIktZC6Ql5EfDgi7oyIn0bE1yJiv4iYFxE3RURnRHw9IiaXvvuW9c6yfW7NcT5a2u+JiDfU+ZkkSZIkqW0NO+RFxAzg94HFmfkiYCJwOvAp4POZuQB4FDir7HIW8Ghp/3zpR0QsKvu9EDgZuCAiJg63LkmSJElqZ/VerjkJmBIRk4CpwP3A64EryvZLgNPK8rKyTtm+NCKitF+WmU9n5n1AJ3BcnXVJkiRJUluaNNwdM3NLRHwW2Ag8CXwXuBXYlpk7S7fNwIyyPAPYVPbdGRGPAYeV9htrDl27Tw8RsQJYATB9+nTWrFkz3PIBmD4FPvLinYN3LOp9v1a2fft2z08DeT4bx3PZWJ5PSZLGvmGHvIiYRjUKNw/YBvwj1eWWIyYzVwOrARYvXpxLliyp63hf7LiSz90x9FOwfnl979fK1qxZQ73/PdTN89k4nsvG8nxKkjT21XO55onAfZn5cGY+C3wDeDVwSLl8E2AmsKUsbwFmAZTtBwOP1Lb3sY8kSZIkaS/UE/I2AidExNRyb91S4C7ge8DbSp8zgSvL8lVlnbL9hszM0n56mX1zHrAQ+GEddUmSJElS26rnnrybIuIK4DZgJ/AjqkspvwVcFhGfKG0XlV0uAv4hIjqBrVQzapKZd0bE5VQBcSdwTmbuGm5dkiRJktTOhh3yADLzPOC8Xs3r6GN2zMx8Cnh7P8dZBayqpxZJkiRJUv2PUJAkSZIkjSGGPEmSJElqIYY8SZIkSWohhjxJkiRJaiGGPEmSJElqIYY8SZIkSWohhjxJkiRJaiGGPEmSJElqIYY8SZIkSWohhjxJkiRJaiGGPEmSJElqIYY8SZJqRMSEiDio2XVIkjRchjxJUtuLiP8bEQdFxP7AT4G7IuKPml2XJEnDYciTJAkWZebjwGnAt4F5wHuaWpEkScNkyJMkCfaJiH2oQt5VmfkskM0tSZKk4THkSZIEfwesB/YHvh8Rc4DHm1qRJEnDNKnZBUiS1GyZ+QXgCzVNGyLidc2qR5KkejiSJ0lqexExPSIuiohvl/VFwJlNLkuSpGEx5EmSBF8BrgGOKuv/BfxBs4qRJKkehjxJkuDwzLwceA4gM3cCu5pbkiRJw2PIkyQJnoiIwygzakbECcBjzS1JkqThceIVSZLgD4GrgPkR8QPgCOBtzS1JkqThMeRJktpeZt4WEa8FjgECuKc8K0+SpHHHkCdJansRcUavpmMjgsy8tCkFSZJUB0OeJEnwazXL+wFLgdsAQ54kadwx5EmS2l5mfrB2PSIOAS5rTjWSJNXH2TUlSdrTE8C8ZhchSdJwOJInSWp7EfEvlMcnUP0D6CLg8uZVJEnS8BnyJEmCz9Ys7wQ2ZObmZhUjSVI9DHmSpLaXmf/a7BokSWoUQ54kqW1FxC/pvkyzxyYgM/OgUS5JkqS6GfIkSW0rMw9sdg2SJDWaIU+SpCIinkf1nDwAMnNjE8uRJGlYfISCJKntRcSbI2ItcB/wr8B64NtNLUqSpGEy5EmSBB8HTgD+KzPnAUuBG5tbkiRJw2PIkyQJns3MR4AJETEhM78HLG52UZIkDYf35EmSBNsi4gDg+0BHRDwEPNHkmiRJGhZH8iRJbSsi3h4R+wHLgB3Ah4HvAPcCv9nM2iRJGi5H8iRJ7ex/AH8LXAN8DbgmMy9pbkmSJNXHkTxJUtvKzLcAC4DrgA8CmyPiSxHx2uZWJknS8BnyJEltLTMfz8xLMvONwIuAHwFfiIhNQ9k/IiZGxI8i4ptlfV5E3BQRnRHx9YiYXNr3LeudZfvcmmN8tLTfExFvqGk/ubR1RsS5jfzckqTWZciTJAmIiGnAW4F3AocCVwxx1w8Bd9esfwr4fGYuAB4FzirtZwGPlvbPl35ExCLgdOCFwMnABSU4TqS6lPSNwCLgXaWvJEkDMuRJktpWRBwQEe+JiKuBu6gem/BxYHZmfngI+88E3gR8uawH8Hq6A+IlwGlleVlZp2xfWvovAy7LzKcz8z6gEziu/HRm5rrMfAa4rPSVJGlATrwiSWpn66lm07yAatKVZ/dy/78G/hg4sKwfBmzLzJ1lfTMwoyzPADYBZObOiHis9J9Bzwev1+6zqVf78X0VERErgBUAs2fP3suPIElqNYY8SVI7m5WZTw5nx4g4FXgoM2+NiCUNrWovZeZqYDXA4sWLs5m1SJKar67LNSPikIi4IiJ+FhF3R8QrI+LQiLg2ItaW12mlb0TEF8rN4z+JiGNrjnNm6b82Is6s90NJkjQUww14xauBN0fEeqpLKV8PnA8cEhFd/4g6E9hSlrcAswDK9oOBR2rbe+3TX7skSQOq956884HvZOYLgJdS3Xh+LnB9Zi4Eri/rUN04vrD8rAAuBIiIQ4HzqC5BOQ44rysYSpI0VmXmRzNzZmbOpZo45YbMXA58D3hb6XYmcGVZvqqsU7bfkJlZ2k8vs2/Oo/p78ofAzcDCMlvn5PIeV43CR5MkjXPDDnkRcTDwGuAigMx8JjO30fPG8t43nF+alRup/qXzSOANwLWZuTUzHwWupZpdTJKk8ehPgD+MiE6qe+4uKu0XAYeV9j+k/CNoZt4JXE418ct3gHMyc1e5r+8DVA9qvxu4vPSVJGlA9dyTNw94GPj7iHgpcCvVNNLTM/P+0ucBYHpZ3n3DedF1Y3l/7XuovbF8+vTprFmzpo7yYfoU+MiLdw7esaj3/VrZ9u3bPT8N5PlsHM9lY7Xa+YyIfwH6vYctM988lONk5hpgTVleR3VlSu8+TwFv72f/VcCqPtqvBq4eSg2SJHWpJ+RNAo4FPpiZN0XE+XRfmglAZmZENOwG8N43li9ZsqSu432x40o+d8fQT8H65fW9Xytbs2YN9f73UDfPZ+N4LhurBc/nZ8vrW4HnA18t6+8CHmxKRZIk1amekLcZ2JyZN5X1K6hC3oMRcWRm3l8ux3yobB/oxvIlvdrX1FGXJElDkpn/ChARn8vMxTWb/iUibmlSWZIk1WXY9+Rl5gPApog4pjQtpbqfoPbG8t43nJ9RZtk8AXisXNZ5DXBSREwrE66cVNokSRot+0fE0V0rZQKU/ZtYjyRJw1bvc/I+CHSUWb/WAe+jCo6XR8RZwAbgHaXv1cApQCewo/QlM7dGxMepZhED+Fhmbq2zLkmS9saHgTURsQ4IYA7wu80tSZKk4akr5GXm7cDiPjYt7aNvAuf0c5yLgYvrqUWSpOHKzO9ExELgBaXpZ5n5dDNrkiRpuOp9Tp4kSeNeREwF/gj4QGb+GJgdEac2uSxJkobFkCdJEvw98AzwyrK+BfhE88qRJI1VHR0wdy5MmFC9dnQ0u6I9GfIkSYL5mflp4FmAzNxBdW+eJEm7g10EvOc9sGEDZFavK1aMvaBnyJMkCZ6JiCmUB6NHxHzAe/IkSXR0VEFuw4ZqPXs9BXzHDli5cvTrGki9s2tKktQKzgO+A8yKiA7g1cB7m1qRJGlMWLmyCnID2bhxdGoZKkOeJKmtRcQEYBrwVuAEqss0P5SZv2hqYZKkMWEoAW727JGvY294uaYkqa1l5nPAH2fmI5n5rcz8pgFPktRlsAA3dSqsWjU6tQyVIU+SJLguIv6/iJgVEYd2/TS7KElS861aVQW5WlGm5pozB1avhuXLR7+ugXi5piRJ8M7yek5NWwJHN6EWSdIY0hXgVq6sLt2cPbsKfmMt2NUy5EmS2l5mzmt2DZKksWv58rEd6nrzck1JUtuLiKkR8WcRsbqsL4yIU5tdlyRJw2HIkyQJ/h54BnhVWd8CfKJ55UiSNHyGPEmSYH5mfhp4FiAzd1A9SkGSpHHHkCdJEjwTEVOoJlshIuYDTze3JElSM3V0wNy5MGFC9drR0eyKhs6JVyRJgvOA7wCzIqIDeDXw3qZWJElqmo4OWLECduyo1jdsqNZhfEzA4kieJKntZea1wFupgt3XgMWZuaaZNUmSRl/X6N27390d8Lrs2FE9RmE8cCRPktS2IuLYXk33l9fZETE7M28b7ZokSc3Re/SuLxs3jl499TDkSZLa2efK637AYuDHVBOuvAS4BXhlk+qSJI2ylSsHDnhQPQh9PPByTUlS28rM12Xm66hG8I7NzMWZ+Qrg5VSPUZAktYGOjuq+u4FMnQqrVo1OPfUy5EmSBMdk5h1dK5n5U+BXm1iPJGmUdF2mOZA5c2D16vEx6Qp4uaYkSQB3RMSXga+W9eXAT5pYjyRplAx0mebUqeMr3HUx5EmSVM2q+X7gQ2X9+8CFTatGkjRqBppMZTwGPDDkSZLaXERMBL5d7s37fLPrkSSNrtmz+74fb86c8RnwwHvyJEltLjN3Ac9FxMHNrkWSNPpWraouy6w1niZZ6YsjeZIkwXaq+/KuBZ7oaszM329eSZKk0dA1WrdyZXXp5uzZVcAbr6N4YMiTJAngG+VHktRmOjpaK+CBIU+SJICvAwvKcmdmPtXMYiRJo6Pr8Qlds2tu2ND9OIXxHPS8J0+S1LYiYlJEfBrYDFwCXApsiohPR8Q+za1OkjTS+np8wo4dVft4ZsiTJLWzzwCHAvMy8xWZeSwwHzgE+GwzC5MkjayOjr5n1YSBH6swHhjyJEnt7FTgdzLzl10Nmfk41TPzTmlaVZKkEdV1mWZ/Zs8evVpGgiFPktTOMjOzj8ZdwB7tkqTW0Ndlml3G++MTwJAnSWpvd0XEGb0bI+LdwM+aUI8kaRQMdDnm6tXje9IVcHZNSVJ7Owf4RkT8FnBraVsMTAHe0rSqJEkjavbsvu/HmzNn/Ac8cCRPktTGMnNLZh4PfAxYX34+lpnHZeaWZtYmSRo5p5wCET3bWuEyzS6O5EmS2l5m3gDc0Ow6JEkjq6MDPvQheOSRnu0RcOaZrTGKB4Y8SZIkSW3g7LPhS1+CPafbqtquvnr0axophjxJkiRJLe3ss+HCCwfuM96fjVfLe/IkSZIktayOjmoEbzDj/dl4tQx5kiRJklrWypV9X6JZq5UmXQFDniRJkqQWNthlmIcd1hrPxqvlPXmSJEmSWlJHRzVzZn8jee9/P1xwwejWNBocyZMkSZLUcjo64H3vg+ee63t7qwY8MORJkiRJakErV8Kzz/a97bDDWjfggSFPkiRJUgsa6F68rVtHr45mMORJkiRJajkDPRKhlR6X0Je6Q15ETIyIH0XEN8v6vIi4KSI6I+LrETG5tO9b1jvL9rk1x/hoab8nIt5Qb02SJEmS2tuqVbDPPnu2T57cWo9L6EsjRvI+BNxds/4p4POZuQB4FDirtJ8FPFraP1/6ERGLgNOBFwInAxdExMQG1CVJkiSpTf3gB7BzZ8+2ww6Diy9urccl9KWukBcRM4E3AV8u6wG8HriidLkEOK0sLyvrlO1LS/9lwGWZ+XRm3gd0AsfVU5ckSZKk9nX22XDhhXs+OuEd72j9gAf1Pyfvr4E/Bg4s64cB2zKzKzNvBmaU5RnAJoDM3BkRj5X+M4Aba45Zu08PEbECWAEwffp01qxZU1fx06fAR168c/CORb3v18q2b9/u+Wkgz2fjeC4by/MpSRoPVq/uv72VZ9XsMuyQFxGnAg9l5q0RsaRhFQ0gM1cDqwEWL16cS5bU97Zf7LiSz90x9FOwfnl979fK1qxZQ73/PdTN89k4nsvG8nxKksaDXbv2rr3V1DOS92rgzRFxCrAfcBBwPnBIREwqo3kzgS2l/xZgFrA5IiYBBwOP1LR3qd1HkiRJkoaso6P/bRPbZOaPYd+Tl5kfzcyZmTmXauKUGzJzOfA94G2l25nAlWX5qrJO2X5DZmZpP73MvjkPWAj8cLh1SZIkSWpfH/pQ/9tWrBi9Opqp3nvy+vInwGUR8QngR8BFpf0i4B8iohPYShUMycw7I+Jy4C5gJ3BOZrbJQKokSZKkRjnxRHjkkf63t8P9eNCgh6Fn5prMPLUsr8vM4zJzQWa+PTOfLu1PlfUFZfu6mv1XZeb8zDwmM7/diJokSRppETErIr4XEXdFxJ0R8aHSfmhEXBsRa8vrtNIeEfGF8mzYn0TEsTXHOrP0XxsRZ9a0vyIi7ij7fKHMTC1J6uXEE+H66/vfPmfO6NXSbA0JeZIktamdwEcycxFwAnBOef7rucD1mbkQuL6sA7yR6raEhVSzRV8IVSgEzgOOp3qM0HldwbD0+Z2a/U4ehc8lSeNKR8fAAQ9a/wHotQx5kiQNU2ben5m3leVfAndTPQao9tmwvZ8Ze2lWbqSarOxI4A3AtZm5NTMfBa4FTi7bDsrMG8t97JfWHEuSVKxcOfD2ww5rj+fjdTHkSZLUABExF3g5cBMwPTPvL5seAKaX5d3PjC26ng07UPvmPtolSTU2bBh4+/nnj04dY4UhT5KkOkXEAcA/AX+QmY/XbisjcDnC778iIm6JiFsefvjhkXwrSRpzOjpgoLuVly5tr1E8MORJklSXiNiHKuB1ZOY3SvOD5VJLyutDpb2/Z8MO1D6zj/YeMnN1Zi7OzMVHHHFE/R9KksaRlSsh+/mntKVL4brrRreescCQJ0nSMJWZLi8C7s7Mv6rZVPts2N7PjD2jzLJ5AvBYuazzGuCkiJhWJlw5CbimbHs8Ik4o73VGzbEkSQx8qWY7BjwYmefkSZLULl4NvAe4IyJuL21/CnwSuDwizgI2AO8o264GTgE6gR3A+wAyc2tEfBy4ufT7WGZuLctnA18BpgDfLj+SJOCFL+x/28SJo1fHWGPIkyRpmDLz34H+7gRZ2kf/BM7p51gXAxf30X4L8KI6ypSklnTiiXDXXf1v37Vr9GoZa7xcU5IkSdK4MpTn4rXTw897M+RJkiRJGjc6OuDd7x68Xzs9/Lw3Q54kSZKkcaGjA844Y/B+7fjYhFqGPEmSJEnjwsqV8NxzA/dZtKh9Z9XsYsiTJEmSNC4M9LgEgMmT4c47R6eWscyQJ0mSJGlcGOyxCBfvMUdxezLkSZIkSRoXBnosQrvfh1fLkCdJkiRpXJgwQHpp9/vwahnyJEmSJI15J544+KQrqhjyJEmSJI1pZ5898MPP2/nB530x5EmSJEka0/7u7wbe3s4PPu+LIU+SJEnSmNXRMfhlmk640pMhT5IkSdKY9Xu/N/D2ww4bnTrGE0OeJEmSpDHpxBNh+/aB+5x//ujUMp4Y8iRJkiSNOYNNtgLVKJ6Xau7JkCdJkiRpzLnwwsH7OIrXN0OeJEmSpDFl6tTB++y/v6N4/THkSZIkSRozpk2DJ58cvN9gj1VoZ4Y8SZIkSWPCC18I27YN3m/pUkfxBmLIkyRJktR0J54Id901eL+lS+G660a+nvHMkCdJkiSpqTo6Bp9JE2DRIgPeUExqdgGSJEmS2teJJw4t4E2ZAnfeOfL1tAJDniRJkqSmeOELh3aJJsCOHSNbSyvxck1JkiRJo+7ss4ce8N7//pGtpdU4kidJkiRpVO3NCN6iRXDBBSNbT6txJE+SJEnSqDj7bIgYesA76ijvwxsOR/IkSZIkjagZM+DnP9+7ffbZB7ZsGZl6Wp0jeZIkSZIa7sQTq1G7iL0PeFOmwDPPjExd7cCRPEmSJEkNMXkyPPtsfcc45BB49NGGlNO2HMmTJEmSNGy1I3b1BrwJEwx4jeBIniRJkqS9FtHY4+2zTwteovnII9DZCWvXVq+1y7ffDrNmjcjbGvIkSZIkDaoRl2L2Z+lSuO66kTn2iMqsglxfIa6zs+ewZATMng0LF8I73jGiZRnyJEmSJPWr0SN2tRYtGgePSMiEhx/uf0Tusce6+06YAHPmwIIFcPrpVaBbsKB6nTcP9t13VEo25EmSJEnqYSSDHYzBkbtMeOih/kfkHn+8u++ECTB3bhXeTjihO8QtWFC1j1KQG4ghT5IkSdKIB7umz5qZCQ88sGeI63rdvr2778SJ1cjbggXwqld1h7iuIDd5ctM+xlAY8iRJkqQ2NdLB7v3vhwsuGNn36CET7r+//xG5J57o7jtpUneQe81rukPcwoXVJZf77DOKhTeWIU+SJElqI9OmwbZtI3f8CRNg166ROz7PPVc9Xb2vEbl774UdO7r77rMPHH10Fd6WLOk5IjdnThX0WtCwP1VEzAIuBaYDCazOzPMj4lDg68BcYD3wjsx8NCICOB84BdgBvDczbyvHOhP4s3LoT2TmJcOtayTNPfdbQ+67/pNvGsFKJEmSpKEb6RE7qAbRGua552DLlr5H5O69F558srvv5MndQe7EE3uOyM2a1bJBbiD1fOKdwEcy87aIOBC4NSKuBd4LXJ+Zn4yIc4FzgT8B3ggsLD/HAxcCx5dQeB6wmCos3hoRV2Wmj0GUJEmShunss+HCC0fu+FOm9Bw022vPPQebNvU/Ivf00919J0+G+fOr4HbSST0nO5k1q7qHTrsNO+Rl5v3A/WX5lxFxNzADWAYsKd0uAdZQhbxlwKWZmcCNEXFIRBxZ+l6bmVsBSlA8GfjacGuTJEmS2tWYus9u167uINd7opN163oGuf326w5yp5zSc0RuxgyD3F5oyNhlRMwFXg7cBEwvARDgAarLOaEKgJtqdttc2vprlyRJkjQEIx3sBhy127kTNm7se6KTdevgmWe6++63XxXcXvACOPXUniNyM2ZUN/SpbnWHvIg4APgn4A8y8/Go+YZlZkZEw67OjYgVwAqA6dOns2bNmrqON30KfOTFOxtQ2Z7qrW282b59e9t95pHk+Wwcz2VjeT4laewY1fvsdu6Eezf0PSJ3333w7LPdO02dWoW2RYtg2bKeI3JHHmmQGwV1hbyI2Icq4HVk5jdK84MRcWRm3l8ux3yotG8BZtXsPrO0baH78s6u9jV9vV9mrgZWAyxevDiXLFnSV7ch+2LHlXzujpG5EXP98iUjctyxas2aNdT730PdPJ+N47lsLM+nJDXXSAa7STzLHDbQeXVNiDulszvI7awZHNl//yq4veQl8Na39hyRO/LI0Umg6lc9s2sGcBFwd2b+Vc2mq4AzgU+W1ytr2j8QEZdRTbzyWAmC1wB/ERHTSr+TgI8Oty5JkiSplTQyL+3DM8xlPQvoZCFrWUAnC+jkjfPXwvr11T10p5TOBxxQBbeXvQze9raejx94/vMNcmNYPcNYrwbeA9wREbeXtj+lCneXR8RZwAbgHWXb1VRfmU6qRyi8DyAzt0bEx4GbS7+PdU3CIkmSJLWberPTPjzDPO7bHeJqw9wcNjCJmofYHXhgFd4WLobTT+85Ive85xnkxql6Ztf8d6C//+pL++ifwDn9HOti4OLh1iJJkiSNZ3ubpfblKeZx3x4hbiFrmc1GJvLc7r7bOJi1LOSHHMf8P/sfPUfkjjjCINeC2u/JgJIkSVKTDSVX7ctTHM26PkfkZrORCXTPb/goh7CWhfwnr+RSzii9FrCWhTzCYWQGvzaCn0djiyFPkiRJGmH9hbr9eJL53NvniNxMNvcIco9wKGtZyL/z33YHuK4wt5XD9jh2NmyOe403hjxJkiRpBHQFuyns4EXcu0eIW0Ans9jcY5+HOZxOFvCvvLZHiOtkAY9y6KDvabATGPIkSZKk+j3xBC89oGeA+15ZnsHPe3R9iCPoZAE38PoeI3L3Mp9tTOvnDfpnsFNvhjxJkiRpKH75S7j3Xli7lo++o2egO4r7+XFN1wd5HmtZyLX8xh4jco9zcF1lvP/9cMEF9X0UtTZDniRJktTl8cerh3+Xn+/8zVr2v78Kc8/nwd3d/hK4n+fTyQK+w8k9Jjq5l/n8koMaWpajddobhjxJkiS1l8ceq0Lc2rXdgW7tWh78j06m81CPri/hSNaykG/xpj0mO3mCA0asxKOOgi1bRuzwanGGPEmSJLWebdv2CHF0dvLwf67lCH7Ro+tmZpQA9+Y9RuR2sP+olexonRrFkCdJkqTxaevWPUbkbvxqdY/c4TzSo+smZpZRuLfsMdnJk0xtSvmGOo0UQ54kSZLGpswqyK1dy7tf2XOik4Ws5VAe3d31OYJNzOIJFvBP/PfdIW4tC1nH0TzFlCZ+kG4GO40GQ54kSZJGRd8PBE8O5xd9Pgx8AZ1MYxsAX6UKchuZzVoW8nXe2WNEbh1H8zT7jeKnGdyUKbBjR7OrUDsy5EmSJKmHvsNYPZIjeJhX9RHiFtDJITy2u+cuJrCBOXSygK/xrh4jcvcxj2fYt9HFNYyjdBorDHmSJI1xEXEycD4wEfhyZn6yySVpBDQ+WI22ZDoP7hHiul4P4pe7e+5iAuuZSycLuJETeozIrWfumA5yYJjT2GfIkyRpDIuIicDfAr8BbAZujoirMvOukXi/jg5YuRI2boTZs2HVKli+fGh9atsPPbTqu3Vr38uPPFKFGn9ZHm+S5/NAn5dVLqCTA9m+u+dOJnIf8+hkAT/g1T0ePbCeuTzL5CZ+jqHzO6rxyJAnSdLYdhzQmZnrACLiMmAZ0PCQ19EBK1Z030O0YUO1Dt1Br78+P/gBXHJJd/sjNRMb9rfsL89jVXIUP+93RG5/um8ye5ZJu4Pc93lNj8cPbGAOO9mniZ9j6PwuqtUY8iRJGttmAJtq1jcDx4/EG61cueckETt2VO1dIa+/PqtXw65dI1GVRkLwHEfx835H5Kby5O6+z7AP6ziaThbwPV7XY0RuA3PYNQ5+nTTEqd2M/f8rJUnSgCJiBbACYPbs2cM+zsaNg7f318eAN/YEzzGTzX2OyM3nXqbw1O6+TzN5d5C7jhN7jMhtYtaYDHIGN6l/Y+//WEmSVGsLMKtmfWZp2y0zVwOrARYvXjzsX31nz64uv+yrfbA+Eyca9JphArt2B7nel1XO51724+ndfZ9mMvcyn7Us5Bre0GOyk03M4jkmNuUzGNakxjPkSZI0tt0MLIyIeVTh7nTgf4zEG61a1fN+O4CpU6v2wfqceWbPe/LUOBPYxWw29nlZ5XzuZV+e2d33SfbbHeSu5pQeI3JbmDHsIPf+98MFFzTqE0kaaYY8SZLGsMzcGREfAK6heoTCxZl550i8V+19d/3NrjlQn1e/2tk1h2siO3cHudoRuVOP6YR16+DZZ7s777cfLFgAC18AC04tywthwQKmzJjBiyZM4EXN+yiSxgBDniRJY1xmXg1cPRrvtXz5no9MGGqfoezb1nburK51XbsWOju7Xzs74b77ega5qVOr8LbghXDaaWW5hLkjj4QJE5r2MSSNfYY8SZKkRnn2WVi/fs8Qt3Zt1b5zZ3ff/fevgttLXgJvfWuPETmOPLIVno4uqUkMeZIkSXvjmWeqwNbXiNz69T1noDnggCq4vfzl8Pa3d4e4BQvg+c83yEkaEYY8SZKk3p5+urqEsq8RuQ0b4LnnuvseeGAV3hYvhtNP7zki97znGeQkjTpDniRJak9PPVUFub5G5DZu7BnkDj64Cm7HH1/deFg7InfEEQY5SWOKIU+SJLWuJ5+sZqfsa0Ru06ae03seckgV3l75SjjjjJ6TnRx2mEFO0rhhyJMkSePbjh1VkOsd4jo7YfPmnkHu0EOr4Pbrv97zssoFC6ogJ0ktwJAnSZLGvieegHvv7XtEbsuWnn0PP7wKba99bc8Qt2BB94P6JKmFGfIkSdLYsH17FeT6GpH7+c979j3iiCq0LV3ac0Ru/nyYNq059UvSGGHIkyRJo+eXv9wzwHUtP/BAz77Pe14V3n7jN/YckTv44ObUL0njgCFvhMw991t71X/9J980QpVIkjTKHn+87xDX2QkPPtiz7/OfX4W2N76x50Qn8+fDQQc1p35JGucMeZIkae899ljfIW7tWnj44Z59jzyyCm5vetOek50ccEBz6pekFmbIkyRJfXv00f5H5H7xi559Z8yoQtuyZXuOyO2/f3Pql6Q2ZciTJEndfvxj+N3frcLc1q09t82cWQW3t7xlz8lOpk5tTr2SpD0Y8iRJUreDDqpG3t72tu4Qt3AhHH00TJnS7OokSUNgyJMkSd3mzYPrr292FZKkOkxodgGSJEmSpMYx5EmSJElSCzHkSZIkSVILMeRJkiRJUgsx5EmSJElSCzHkSZIkSVILMeRJkiRJUgsx5EmSJElSC/Fh6GPE3HO/NeS+6z/5phGsRJIkSdJ4NmZG8iLi5Ii4JyI6I+LcZtcjSZIkSePRmAh5ETER+FvgjcAi4F0Rsai5VUmSJEnS+DMmQh5wHNCZmesy8xngMmBZk2uSJEmSpHFnrNyTNwPYVLO+GTi+SbWMeXtz/x54D58kSZLUTsZKyBuSiFgBrCir2yPinjoPeTjwizqPMebFp0blbdriXI4iz2fjeC4bqxnnc84ov9+4duutt/4iIjbs5W7j8f+T8Vaz9Y6s8VYvjL+arXfkDafmPv+OHCshbwswq2Z9ZmnrITNXA6sb9aYRcUtmLm7U8dqZ57KxPJ+N47lsLM/n2JeZR+ztPuPxv+t4q9l6R9Z4qxfGX83WO/IaWfNYuSfvZmBhRMyLiMnA6cBVTa5JkiRJksadMTGSl5k7I+IDwDXARODizLyzyWVJkiRJ0rgzJkIeQGZeDVw9ym/bsEs/5blsMM9n43guG8vz2ZrG43/X8Vaz9Y6s8VYvjL+arXfkNe62tMxs1LEkSZIkSU02Vu7JkyRJkiQ1QFuGvIg4OSLuiYjOiDi32fWMFRExKyK+FxF3RcSdEfGh0n5oRFwbEWvL67TSHhHxhXIefxIRx9Yc68zSf21EnFnT/oqIuKPs84WIiNH/pKMrIiZGxI8i4ptlfV5E3FTOwdfLZENExL5lvbNsn1tzjI+W9nsi4g017W3zXY6IQyLiioj4WUTcHRGv9Ls5fBHx4fL/+U8j4msRsZ/fTQFExEciIiPi8GbXMpCI+Hj5//v2iPhuRBzV7JoGExGfKX+G/SQi/l9EHNLsmgYSEW8vf048FxFjdpbC8fbnTURcHBEPRcRPm13LUPT3++FYVf4++2FE/LjU++fNrmkoev++WLfMbKsfqold7gWOBiYDPwYWNbuusfADHAkcW5YPBP4LWAR8Gji3tJ8LfKosnwJ8GwjgBOCm0n4osK68TivL08q2H5a+UfZ9Y7M/9yic1z8E/i/wzbJ+OXB6Wf4S8P6yfDbwpbJ8OvD1sryofE/3BeaV7+/EdvsuA5cAv12WJwOH+N0c9rmcAdwHTKn5Tr7X76Y/VI8zugbYABze7HoGqfWgmuXf7/qOjuUf4CRgUln+VNefWWP1B/hV4BhgDbC42fX0U+O4+/MGeA1wLPDTZtcyxHr7/P2w2XUNUG8AB5TlfYCbgBOaXdcQ6u7x+2K9P+04kncc0JmZ6zLzGeAyYFmTaxoTMvP+zLytLP8SuJvql8FlVL9gU15PK8vLgEuzciNwSEQcCbwBuDYzt2bmo8C1wMll20GZeWNW3+ZLa47VkiJiJvAm4MtlPYDXA1eULr3PZ9d5vgJYWvovAy7LzKcz8z6gk+p73Dbf5Yg4mOovxYsAMvOZzNyG3816TAKmRMQkYCpwP343BZ8H/hgY8zfsZ+bjNav7Mz5q/m5m7iyrN1I9F3jMysy7M/OeZtcxiHH3501mfh/Y2uw6hmqA3w/HpPJ3//ayuk/5GdN/PvT+fbER2jHkzQA21axvZgx/UZulXI71cqp//ZiemfeXTQ8A08tyf+dyoPbNfbS3sr+m+oXpubJ+GLCt5i/52nOw+7yV7Y+V/nt7nlvRPOBh4O/LpQxfjoj98bs5LJm5BfgssJEq3D0G3IrfzbYWEcuALZn542bXMlQRsSoiNgHLgf/V7Hr20m9RXTWg+vjnzSjq9fvhmFUufbwdeIjqH3fHdL3s+fti3dox5GkQEXEA8E/AH/T6l1LKKMeY/teQsSIiTgUeysxbm11LC5hEdWnLhZn5cuAJqsszd/O7OXTl3sVlVOH5KKpRkJObWpRGRURcV+7D7P2zDPhTxlhQGqReMnNlZs4COoAPNLfaymA1lz4rgZ1UdTfVUOqVYODfD8eazNyVmS+jGi0/LiJe1OSS+jVSvy+OmefkjaItVPccdJlZ2gRExD5U/wN3ZOY3SvODEXFkZt5fLmt7qLT3dy63AEt6ta8p7TP76N+qXg28OSJOAfYDDgLOp7p0cFIZEak9B13nc3O5hO5g4BEG/s62y3d5M7C55l/irqAKeX43h+dE4L7MfBggIr5B9X31u9niMvPEvtoj4sVUof/H1ZW4zARui4jjMvOBUSyxh/7q7UMH1bN2zxvBcoZksJoj4r3AqcDS8o9TTbUX53is8ve6UdDP74djXmZui4jvUf1D5lid6GaP3xcj4quZ+e56DtqOI3k3AwujmkVuMtUkAlc1uaYxodxjcxFwd2b+Vc2mq4CuWQjPBK6saT8jKicAj5VL564BToqIaWXE4CTgmrLt8Yg4obzXGTXHajmZ+dHMnJmZc6m+Zzdk5nLge8DbSrfe57PrPL+t9M/SfnpUMxzOAxZSTRLSNt/l8kvmpog4pjQtBe7C7+ZwbQROiIip5fN2nU+/m20qM+/IzOdl5tzyZ9ZmqokWmhbwBhMRC2tWlwE/a1YtQxURJ1NdkvXmzNzR7HpahH/ejLABfj8ckyLiiCgz10bEFOA3GMN/PvTz+2JdAa/rwG33QzXz3n9Rzca0stn1jJUf4L9RXe72E+D28nMK1b031wNrgeuAQ0v/AP62nMc7qJl5i+peg87y876a9sVU/5JyL/A3QDT7c4/SuV1C9+yaR1P9ItwJ/COwb2nfr6x3lu1H1+y/spyze6iZ9bGdvsvAy4Bbyvfzn6lmx/S7Ofzz+edUf+n9FPgHqhky/W760/Xfbz1jf3bNfyrf358A/wLMaHZNQ6i5k+r+sa6/Y8f0jKDAW6gC/9PAg1T/KNb0uvqoc1z9eQN8jep+6GfL+T2r2TUNUm+fvx82u64B6n0J8KNS70+B/9Xsmvai9iU0aHbNKAeUJEmSJLWAdrxcU5IkSZJaliFPkiRJklqIIU+SJEmSWoghT5IkSZJaiCFPkiRJklqIIU+SJKnNRMRhEXF7+XkgIraU5W0Rcdco13JaRCyqWf9YROz1Q9ojYm5ENO2B1xHxp73W/6O8NrUutSdDniRJUpvJzEcy82WZ+TLgS8Dny/LLgOca/X4RMWmAzacBu0NeZv6vzLyu0TWMgh4hLzNf1axCJEOeJEmSak2MiP8TEXdGxHcjYgpARMyPiO9ExK0R8W8R8YLSPjciboiIn0TE9RExu7R/JSK+FBE3AZ/ua/+IeBXwZuAzZSRxftnvbeUYvxYR/xERP46IH0bEgeX9/i0ibis/A4apqPxNRNwTEddFxNU1x18fEYeX5cURsaYsHxcR/xkRPyrvf0xpf29EfKN8jrUR8enS/klgSvkMHaVtex+1TIyIz0TEzeV8/W5pPzIivl/2/2lE/Hqd/w3V5gb6VxVJkiS1n4XAuzLzdyLicuC/A18FVgO/l5lrI+J44ALg9cAXgUsy85KI+C3gC1SjcwAzgVdl5q6IuL73/pn5+oi4CvhmZl4BEBGU18nA14F3ZubNEXEQ8CTwEPAbmflURCwEvgYsHuDzvAU4hmq0cDpwF3DxIOfgZ8CvZ+bOcunoX5TzANVo58uBp4F7IuKLmXluRHygjIYO5Czgscz8tYjYF/hBRHwXeCtwTWauioiJwNRBjiMNyJAnSZKkWvdl5u1l+VZgbkQcALwK+MeuEAbsW15fSRVSAP4B+HTNsf6xBLyB9u/PMcD9mXkzQGY+DhAR+wN/ExEvA3YBvzLIcV4DfC0zdwE/j4gbBukPcDBwSQmRCexTs+36zHys1HIXMAfYNIRjApwEvKRrJLG8z0LgZuDiiNgH+Oea8y8NiyFPkiRJtZ6uWd4FTKG6xWfbEEaqenuivA53/758GHgQeGk57lN1HGsn3bcv7VfT/nHge5n5loiYC6yp2db7/OzN79MBfDAzr9ljQ8RrgDcBX4mIv8rMS/fiuFIP3pMnSZKkAZVRtPsi4u2w+z63l5bN/wGcXpaXA/+2l/v/Ejiwj7e9BzgyIn6t7HNgmcDlYKoRvueA9wATByn/+8A7y/1wRwKvq9m2HnhFWf7vNe0HA1vK8nsHOX6XZ8tI3ECuAd7f1S8ifiUi9o+IOcCDmfl/gC8Dxw7xPaU+GfIkSZI0FMuBsyLix8CdwLLS/kHgfRHxE6rQ9aG93P8y4I/KJCfzuzpn5jPAO4Evln2upRptuwA4s7S9gO7Rwv78P2At1b14lwL/WbPtz4HzI+IWqlG5Lp8G/jIifsTQR+pWAz/pmnilH18uddwW1WMV/q4cfwnw4/J+7wTOH+J7Sn2KzGx2DZIkSdKoiIivUDPRi9SKHMmTJEmSpBbiSJ4kSZIktRBH8iRJkiSphRjyJEmSJKmFGPIkSZIkqYUY8iRJkiSphRjyJEmSJKmFGPIkSZIkqYX8/27Ru09T5BQ/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plots for feature forecast_cons_year\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plots for feature forecast_discount_energy\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plots for feature forecast_meter_rent_12m\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plots for feature forecast_price_energy_off_peak\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plots for feature forecast_price_energy_peak\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plots for feature forecast_price_pow_off_peak\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plots for feature imp_cons\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plots for feature margin_gross_pow_ele\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plots for feature margin_net_pow_ele\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plots for feature nb_prod_act\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plots for feature net_margin\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plots for feature num_years_antig\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plots for feature pow_max\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plots for feature churn\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for col in num_ftr_clt:\n",
    "    print('Plots for feature {}'.format(col))\n",
    "    diagnostic_plots(client_data, col)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Investigation of the relationship between price sensitivity and customer churn\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Price sensistivity is a concept that helps us understand how the willingness to buy of a customer is affected by the price of a product or a service.\n",
    "To quantify this concept we will use the elasticity of demand that can computed using the following formula:\n",
    "\n",
    "    Price elasticity of demand = % change in quantity demanded / % change in price\n",
    "\n",
    "- To compute % change in quantity we will use the variables \"cons_12m\" and \"forecast_cons_12m\".\n",
    "- To compute % change in price we will use the average of the two variables \"forecast_price_energy_off_peak\" and \"forecast_price_energy_peak\" and the average of the variables \"price_off_peak_var\" and \"price_peak_var\".\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting the previous year average price for each customer using the price dataset\n",
    "gr_df_pr =prices_data.groupby(['id'])[prices_data.columns].mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>id</th>\n",
       "      <th>cons_12m</th>\n",
       "      <th>cons_gas_12m</th>\n",
       "      <th>cons_last_month</th>\n",
       "      <th>date_activ</th>\n",
       "      <th>date_end</th>\n",
       "      <th>date_modif_prod</th>\n",
       "      <th>date_renewal</th>\n",
       "      <th>forecast_cons_12m</th>\n",
       "      <th>forecast_cons_year</th>\n",
       "      <th>...</th>\n",
       "      <th>num_years_antig</th>\n",
       "      <th>origin_up</th>\n",
       "      <th>pow_max</th>\n",
       "      <th>churn</th>\n",
       "      <th>price_off_peak_var</th>\n",
       "      <th>price_peak_var</th>\n",
       "      <th>price_mid_peak_var</th>\n",
       "      <th>price_off_peak_fix</th>\n",
       "      <th>price_peak_fix</th>\n",
       "      <th>price_mid_peak_fix</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24011ae4ebbe3035111d65fa7c15bc57</td>\n",
       "      <td>0</td>\n",
       "      <td>54946</td>\n",
       "      <td>0</td>\n",
       "      <td>2013-06-15</td>\n",
       "      <td>2016-06-15</td>\n",
       "      <td>2015-11-01</td>\n",
       "      <td>2015-06-23</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>lxidpiddsbxsbosboudacockeimpuepw</td>\n",
       "      <td>43.648</td>\n",
       "      <td>1</td>\n",
       "      <td>0.124787</td>\n",
       "      <td>0.100749</td>\n",
       "      <td>0.066530</td>\n",
       "      <td>40.942265</td>\n",
       "      <td>22.352010</td>\n",
       "      <td>14.901340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>d29c2c54acc38ff3c0614d0a653813dd</td>\n",
       "      <td>4660</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2009-08-21</td>\n",
       "      <td>2016-08-30</td>\n",
       "      <td>2009-08-21</td>\n",
       "      <td>2015-08-31</td>\n",
       "      <td>189.95</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>kamkkxfxxuwbdslkwifmmcsiusiuosws</td>\n",
       "      <td>13.800</td>\n",
       "      <td>0</td>\n",
       "      <td>0.149609</td>\n",
       "      <td>0.007124</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>44.311375</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>764c75f661154dac3a6c254cd082ea7d</td>\n",
       "      <td>544</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-04-16</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>2010-04-16</td>\n",
       "      <td>2015-04-17</td>\n",
       "      <td>47.96</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>kamkkxfxxuwbdslkwifmmcsiusiuosws</td>\n",
       "      <td>13.856</td>\n",
       "      <td>0</td>\n",
       "      <td>0.170512</td>\n",
       "      <td>0.088421</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>44.385450</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bba03439a292a1e166f80264c16191cb</td>\n",
       "      <td>1584</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-03-30</td>\n",
       "      <td>2016-03-30</td>\n",
       "      <td>2010-03-30</td>\n",
       "      <td>2015-03-31</td>\n",
       "      <td>240.04</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>kamkkxfxxuwbdslkwifmmcsiusiuosws</td>\n",
       "      <td>13.200</td>\n",
       "      <td>0</td>\n",
       "      <td>0.151210</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>44.400265</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>149d57cf92fc41cf94415803a877cb4b</td>\n",
       "      <td>4425</td>\n",
       "      <td>0</td>\n",
       "      <td>526</td>\n",
       "      <td>2010-01-13</td>\n",
       "      <td>2016-03-07</td>\n",
       "      <td>2010-01-13</td>\n",
       "      <td>2015-03-09</td>\n",
       "      <td>445.75</td>\n",
       "      <td>526</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>kamkkxfxxuwbdslkwifmmcsiusiuosws</td>\n",
       "      <td>19.800</td>\n",
       "      <td>0</td>\n",
       "      <td>0.124174</td>\n",
       "      <td>0.103638</td>\n",
       "      <td>0.072865</td>\n",
       "      <td>40.688156</td>\n",
       "      <td>24.412893</td>\n",
       "      <td>16.275263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14537</th>\n",
       "      <td>18463073fb097fc0ac5d3e040f356987</td>\n",
       "      <td>32270</td>\n",
       "      <td>47940</td>\n",
       "      <td>0</td>\n",
       "      <td>2012-05-24</td>\n",
       "      <td>2016-05-08</td>\n",
       "      <td>2015-05-08</td>\n",
       "      <td>2014-05-26</td>\n",
       "      <td>4648.01</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>lxidpiddsbxsbosboudacockeimpuepw</td>\n",
       "      <td>15.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.144124</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>44.370635</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14538</th>\n",
       "      <td>d0a6f71671571ed83b2645d23af6de00</td>\n",
       "      <td>7223</td>\n",
       "      <td>0</td>\n",
       "      <td>181</td>\n",
       "      <td>2012-08-27</td>\n",
       "      <td>2016-08-27</td>\n",
       "      <td>2012-08-27</td>\n",
       "      <td>2015-08-28</td>\n",
       "      <td>631.69</td>\n",
       "      <td>181</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>lxidpiddsbxsbosboudacockeimpuepw</td>\n",
       "      <td>6.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.106799</td>\n",
       "      <td>0.095406</td>\n",
       "      <td>0.070817</td>\n",
       "      <td>59.015674</td>\n",
       "      <td>36.393379</td>\n",
       "      <td>8.345418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14539</th>\n",
       "      <td>10e6828ddd62cbcf687cb74928c4c2d2</td>\n",
       "      <td>1844</td>\n",
       "      <td>0</td>\n",
       "      <td>179</td>\n",
       "      <td>2012-02-08</td>\n",
       "      <td>2016-02-07</td>\n",
       "      <td>2012-02-08</td>\n",
       "      <td>2015-02-09</td>\n",
       "      <td>190.39</td>\n",
       "      <td>179</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>lxidpiddsbxsbosboudacockeimpuepw</td>\n",
       "      <td>15.935</td>\n",
       "      <td>1</td>\n",
       "      <td>0.124338</td>\n",
       "      <td>0.103794</td>\n",
       "      <td>0.073160</td>\n",
       "      <td>40.701732</td>\n",
       "      <td>24.421038</td>\n",
       "      <td>16.280694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14540</th>\n",
       "      <td>1cf20fd6206d7678d5bcafd28c53b4db</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2012-08-30</td>\n",
       "      <td>2016-08-30</td>\n",
       "      <td>2012-08-30</td>\n",
       "      <td>2015-08-31</td>\n",
       "      <td>19.34</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>lxidpiddsbxsbosboudacockeimpuepw</td>\n",
       "      <td>11.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.149609</td>\n",
       "      <td>0.007124</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>44.311375</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14541</th>\n",
       "      <td>563dde550fd624d7352f3de77c0cdfcd</td>\n",
       "      <td>8730</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2009-12-18</td>\n",
       "      <td>2016-12-17</td>\n",
       "      <td>2009-12-18</td>\n",
       "      <td>2015-12-21</td>\n",
       "      <td>762.41</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>ldkssxwpmemidmecebumciepifcamkci</td>\n",
       "      <td>10.392</td>\n",
       "      <td>0</td>\n",
       "      <td>0.168662</td>\n",
       "      <td>0.087344</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>44.266930</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14542 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     id  cons_12m  cons_gas_12m  \\\n",
       "0      24011ae4ebbe3035111d65fa7c15bc57         0         54946   \n",
       "1      d29c2c54acc38ff3c0614d0a653813dd      4660             0   \n",
       "2      764c75f661154dac3a6c254cd082ea7d       544             0   \n",
       "3      bba03439a292a1e166f80264c16191cb      1584             0   \n",
       "4      149d57cf92fc41cf94415803a877cb4b      4425             0   \n",
       "...                                 ...       ...           ...   \n",
       "14537  18463073fb097fc0ac5d3e040f356987     32270         47940   \n",
       "14538  d0a6f71671571ed83b2645d23af6de00      7223             0   \n",
       "14539  10e6828ddd62cbcf687cb74928c4c2d2      1844             0   \n",
       "14540  1cf20fd6206d7678d5bcafd28c53b4db       131             0   \n",
       "14541  563dde550fd624d7352f3de77c0cdfcd      8730             0   \n",
       "\n",
       "       cons_last_month date_activ   date_end date_modif_prod date_renewal  \\\n",
       "0                    0 2013-06-15 2016-06-15      2015-11-01   2015-06-23   \n",
       "1                    0 2009-08-21 2016-08-30      2009-08-21   2015-08-31   \n",
       "2                    0 2010-04-16 2016-04-16      2010-04-16   2015-04-17   \n",
       "3                    0 2010-03-30 2016-03-30      2010-03-30   2015-03-31   \n",
       "4                  526 2010-01-13 2016-03-07      2010-01-13   2015-03-09   \n",
       "...                ...        ...        ...             ...          ...   \n",
       "14537                0 2012-05-24 2016-05-08      2015-05-08   2014-05-26   \n",
       "14538              181 2012-08-27 2016-08-27      2012-08-27   2015-08-28   \n",
       "14539              179 2012-02-08 2016-02-07      2012-02-08   2015-02-09   \n",
       "14540                0 2012-08-30 2016-08-30      2012-08-30   2015-08-31   \n",
       "14541                0 2009-12-18 2016-12-17      2009-12-18   2015-12-21   \n",
       "\n",
       "       forecast_cons_12m  forecast_cons_year  ...  num_years_antig  \\\n",
       "0                   0.00                   0  ...                3   \n",
       "1                 189.95                   0  ...                6   \n",
       "2                  47.96                   0  ...                6   \n",
       "3                 240.04                   0  ...                6   \n",
       "4                 445.75                 526  ...                6   \n",
       "...                  ...                 ...  ...              ...   \n",
       "14537            4648.01                   0  ...                4   \n",
       "14538             631.69                 181  ...                3   \n",
       "14539             190.39                 179  ...                4   \n",
       "14540              19.34                   0  ...                3   \n",
       "14541             762.41                   0  ...                6   \n",
       "\n",
       "                              origin_up  pow_max  churn  price_off_peak_var  \\\n",
       "0      lxidpiddsbxsbosboudacockeimpuepw   43.648      1            0.124787   \n",
       "1      kamkkxfxxuwbdslkwifmmcsiusiuosws   13.800      0            0.149609   \n",
       "2      kamkkxfxxuwbdslkwifmmcsiusiuosws   13.856      0            0.170512   \n",
       "3      kamkkxfxxuwbdslkwifmmcsiusiuosws   13.200      0            0.151210   \n",
       "4      kamkkxfxxuwbdslkwifmmcsiusiuosws   19.800      0            0.124174   \n",
       "...                                 ...      ...    ...                 ...   \n",
       "14537  lxidpiddsbxsbosboudacockeimpuepw   15.000      0            0.144124   \n",
       "14538  lxidpiddsbxsbosboudacockeimpuepw    6.000      1            0.106799   \n",
       "14539  lxidpiddsbxsbosboudacockeimpuepw   15.935      1            0.124338   \n",
       "14540  lxidpiddsbxsbosboudacockeimpuepw   11.000      0            0.149609   \n",
       "14541  ldkssxwpmemidmecebumciepifcamkci   10.392      0            0.168662   \n",
       "\n",
       "      price_peak_var  price_mid_peak_var  price_off_peak_fix  price_peak_fix  \\\n",
       "0           0.100749            0.066530           40.942265       22.352010   \n",
       "1           0.007124            0.000000           44.311375        0.000000   \n",
       "2           0.088421            0.000000           44.385450        0.000000   \n",
       "3           0.000000            0.000000           44.400265        0.000000   \n",
       "4           0.103638            0.072865           40.688156       24.412893   \n",
       "...              ...                 ...                 ...             ...   \n",
       "14537       0.000000            0.000000           44.370635        0.000000   \n",
       "14538       0.095406            0.070817           59.015674       36.393379   \n",
       "14539       0.103794            0.073160           40.701732       24.421038   \n",
       "14540       0.007124            0.000000           44.311375        0.000000   \n",
       "14541       0.087344            0.000000           44.266930        0.000000   \n",
       "\n",
       "       price_mid_peak_fix  \n",
       "0               14.901340  \n",
       "1                0.000000  \n",
       "2                0.000000  \n",
       "3                0.000000  \n",
       "4               16.275263  \n",
       "...                   ...  \n",
       "14537            0.000000  \n",
       "14538            8.345418  \n",
       "14539           16.280694  \n",
       "14540            0.000000  \n",
       "14541            0.000000  \n",
       "\n",
       "[14542 rows x 31 columns]"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merging the two dataframes \n",
    "new_df = client_data.merge(gr_df_pr, how='left', left_on='id', right_on='id')\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Averaging forcasted peak and off-peak prices.\n",
    "new_df[\"forcasted_price\"] = (new_df[\"forecast_price_energy_off_peak\"] + new_df[\"forecast_price_energy_peak\"])/2\n",
    "\n",
    "# Averaging previous peak and off-peak prices.\n",
    "new_df[\"previous_price\"] = new_df[\"price_off_peak_var\"] + new_df[\"price_off_peak_fix\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Computing the percentage change in quantity demanded\n",
    "prct_qte = ((new_df['forecast_cons_12m'] - new_df['cons_12m'])/new_df['cons_12m']) * 100\n",
    "\n",
    "# Computing the percentage change in price\n",
    "prct_prc = ((new_df[\"forcasted_price\"] - new_df[\"previous_price\"])/new_df[\"previous_price\"]) * 100\n",
    "\n",
    "# Computing the price elasticity of demand\n",
    "new_df['price_sens'] = prct_qte/prct_prc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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l+RWWEpOAJPWV1DrsOIqDpKRLGfLuhwDMnDWPCsdWoFq1KpnKVKtWhXLlyzFz1jwAhrz7IR07tkvff/CQ4QAMHjI8fX1G11xzBUOHfVKIZxGeRg3PpEL5cmGHUWgaN27I99+vYtWqn9i/fz/Dh4+mQ4c2mcp06NCGd9/9CIARI8bRosWFAOzatZuvv57Dnj17Dzrujh2/AVCqVCkSEhJKxI2OGaXVyw8/rE6vl6SktpnKJCW15Z13Iv+2RowYR8uWGetlNnv37inyuA+XpVqeX2EpEQlIUryZPW5mk8KOpTioWaMaa35al768ds16ataodlCZtWvWZ1umapXKbNiwEYANGzZStUrlTPuWKVOaS9u2YMTHB7razIzx495n5ozx3HTjdQV+Tq7g1KhRjTUZ/9+vXU/NmtWyKRP5HUpJSWH79h1UqnRcrsceNWowq1fP47ffdjJiRMnqis14zhCplxo1qh6yTH7qpVjyLrjcSTpR0nJJ70paJulDSUdLWiXpOUnzgK6SBkrqEuzTWNLXkhZKmiWpnKR4Sf+UNFvSIkm35PCZ1SVNlbRA0mJJFwXr20qaLmmepOGSjgnWr5L0VLD+G0n1g/WXBMdYIGm+pIP+rJbUW9IcSXNSU3cWSh0eqax/yXbo0Javp8/J1P12ScsradK0HR2SunHbbb24qHnTIo7SFQcdO/bgpJMac9RRibRo0SzscFwOLDXvr7CEnoACpwH/NrPfAduBtKuDm83sXDMbmlZQUiIwDLjHzM4GWgO7gRuBX82sMdAYuFnSSYf4vD8CE8ysIXA2sEBSZeAxoLWZnQvMAe7PsM+mYP1/gD7Buj7AHcFxLgriyMTM+ptZIzNrFBdXNl+VktFtt/ZMHyCwfsPP1KpdI31bzVrVWbtuQ6bya9dtoGat6tmW+XnjpvQuu2rVqrDxl82Z9r3m6o4Hdb+tC/b95ZfNjBw5nsaNGx72ubjCtW7dBmpl/H9fszpr127Ipkzkdyg+Pp7y5cuxefPWPB1/7969jB792UHdV8VdxnOGSL2sW/fzIcvkt16KHW8B5dlPZvZV8P4doHnwflg2ZU8D1pvZbAAz225myUBboIekBcBMoBJQL5v9AWYD10t6EjjTzHYA5wMNgK+CY/QE6mTYZ0Twcy5wYvD+K6CfpLuBY4M4CsV/Xh+UPkBg1KgJdL+uCwBNm5zL9l+3p3eppdmwYSM7tu+gaZNzAeh+XRdGj54AwJjRn9Gje1cAenTvmr4eoHz5clx80fmMGnVg3dFHl+GYY8qmv2/T+hKWLFlRWKfqjtCcOQs55ZSTqFOnNgkJCXTtmsTYsRMzlRk7dhLXXdcZgKuuuowpU77O8Zhlyx6d/kdLfHw87du3YsWK7wvnBApJWr2ceOKBehkzJnO9jBkzkW7dIv+2rrrqMr78Mud6Kc4sOe+vvJDUTtIKSd9JeuQQZa6WtFTSEknv5XbM4nIfUNarYGnL+emzEnCXmU3IraCZTZV0MXA5MFBSP2ArMNHMrj3EbmlXZVMI6s3MnpU0FriMSOK61MyW5yPmwzJu/Oe0a9eKFcu+Ytfu3dx004GG2pzZn9GoceQv0zvv+hMDBrxImdKl+XTCF4z/dDIAz/3zNYa+9zrX97qW1avX8Ic/HhiO3OmK9kycNJVduw405qpWPZ4Phw8AoFSpeIYO/YQJn31Z2KdZaB584llmz1/Etm3b+X2nbtx+Y3c6J10adlgFJiUlhfvue5zRowcTHx/PoEEfsGzZ//jLX+5n3rxFjB07iYEDh/HWWy+yePEUtm7dRvfud6bvv3z5NMqVK0diYgJJSW3p0KE7W7Zs5cMP3yQxMZG4uDimTp3Of//7TohnmX8pKSnce+9fGD16SFAvw1i27Fsef/x+5s79hrFjJwb18i+WLJnKli3b6NHjQL2sWPFVhnq5lA4durF8+f9CPKOcFWTXmqR44DWgDbAGmC1plJktzVCmHvAocKGZbZVUJfujZThu2CNZJJ0I/AA0M7Ppkt4ElgF3AY3MbFNQbiAwBhgFLAeuMbPZwXWX3cANRBJBVzPbL+lUYK2ZHZTEJNUB1phZiqQ7gVOAZ4i0blqZ2XeSygI1zexbSavSYpHUCHjezFpIOtnMvg+O+SHwjpl9cqhzLZVYs2QNGyoku9f9X9ghFBvla7cMO4RiI+zvouJmz57VOpL9N/7+kjxXaJXPp+T4WZIuAJ40s0uD5UcBzOzvGcr8A/jWzN7M6+cWly64FcAdkpYBxxG5zpItM9sHXAO8ImkhMBEoDbwJLAXmSVoMvMGhW3gtgIWS5gfHesnMfgF6Ae9LWgRMB+rnEve9wSCGRcB+YHweztU55wpdfgYhZBwsFbx6ZzlcTeCnDMtrgnUZnQqcKukrSTMkHXx/RxbFpQU0xsyyv50/ingLKMJbQAd4C+iAsL+LipsjbQH93KJFniu06pdf5tYC6gK0M7ObguXuQFMzuzNDmTFE/hC/GqgFTCVyjX3boY5bXFpAzjnnClABD8NeC9TOsFwrWJfRGmCUme03sx+Abzn0QDCgGAxCMLNVQKG0fiSdCQzJsnqvmflNLM65qJaafEQNqKxmA/WCW1vWAn8gcjtLRp8A1wJvB7e1nAqszOmgoSegwmRm3wANw47DOeeKmlnBJSAzSw4GbE0A4oG3zGyJpL7AHDMbFWxrK2kpkdHCD5rZ5kMfNcoTkHPOxaqCfsKBmY0DxmVZ93iG90bk5v37ySNPQM45F4UstUC74AqFJyDnnItCJWFQoScg55yLQt4Ccs45F4rUFE9AzjnnQuAtIOecc6EoyGHYhcUTkHPORaEwJ5rLK09AzjkXhVK9BeSccy4MqSnF/1GfnoCccy4K+X1AzjnnQuGj4JxzzoXCrwE555wLhQ/Dds45Fwq/BuSccy4UKak+Cs4551wIvAXkMjk64aiwQygWytduGXYIxcb2n74IO4Riw3bvCDuEqOKDEJxzzoXCByE455wLhbeAnHPOhaIEXALyBOScc9HIR8E555wLRQmYjcETkHPORSPDrwE555wLQWoJuAjkCcg556JQqreAnHPOhSHFE5Bzzrkw+DUg55xzofBRcM4550LhCcg551wovAvOOedcKFKLf/7xBOScc9HIR8E555wLhV8Dcs45F4pUFf8WUPF/XKpzzrl8s3y88kJSO0krJH0n6ZEcynWWZJIa5XZMT0DOOReFUvPxyo2keOA1oD3QALhWUoNsypUD7gFm5iVGT0DOOReFkqU8v/KgCfCdma00s33AUOCKbMr9FXgO2JOXg3oCcs65KJSfLjhJvSXNyfDqneVwNYGfMiyvCdalk3QuUNvMxuY1Rh+E4JxzUSg/9wGZWX+g/+F+lqQ4oB/QKz/7eQuohGvd5mLmzp/EgkWTue+BWw/anpiYyNuDXmbBoslM/nIEJ5wQ+aOlZavmTJk2kumzxjNl2kguvuSC9H3Gjn+PufMnMW36GKZNH0Pl4ysV2fkciTZtLmHhwsksXjyFPn1uO2h7YmIiQ4a8yuLFU5g69RNOOKEWABUrHsunnw7ll1+W8uKLfTPtM3LkIGbOHM/cuRN5+eVniIuLrn8yj/2tHxdf/gc6dTv4dycaTZu1gKRe93BZj7t48/1PDtq+7udfuOnBvlx1cx+uv/9JNvyyOX1bv/7v0OnG++l4w338/dW3MCveE+4U5DUgYC1QO8NyrWBdmnLAGcCXklYB5wOjchuIEF3/mmJMXFwcL/R7is5XXk/j8y6lS9ckTqt/SqYyPXpezbZt22l4Vitee/UtnvrrwwBs3ryFa7rczAVN2nNr7wfp/+YLmfa76Yb7aH5BB5pf0IFNGf4RFldxcXH8619/5YorenLOOa3p2rUj9evXy1SmV69r2Lr1V8444xJeeWUAzzwTGcizZ89e+vZ9nkcffeag43brdgdNm7bnvPPacPzxlejc+fIiOZ+i0umyNrze7+mwwygSKSmpPPPKAP79tz8xcsCLjP/iK77/cU2mMs+/MYSkNhcz4r/Pc2v3Lrw04D0AFixZwfwlK/io//N8/N8XWLzie+YsXBrGaeRZAY+Cmw3Uk3SSpETgD8Co9M8y+9XMKpvZiWZ2IjAD6Ghmc3I6aK4JSNLdkpZJejdvcR45Sb0k1cjnPidKWlxAnz9O0rEFcazC1KjR2axc+SOrVv3E/v37+ejDMVzeoU2mMpd3aM37734EwCcfj6dFi2YALFq4lA0bNgKwbOm3lCldmsTExKI9gQLUuHFDvv9+VXpdDB8+mg5Z6qJDhza8G9TFiBHjaNHiQgB27drN11/PYc+evQcdd8eO3wAoVaoUCQkJxf6v3vxq1PBMKpQvF3YYReKbFd9xQo1q1K5RlYSEUrRv0YwvvpqdqczKH9fQtOEZADRpeDpffB18f0rs3beP/cnJ7Nu/n+SUFCodV6GoTyFfUpX3V27MLBm4E5gALAM+MLMlkvpK6ni4MealBXQ70MbMrsutoKSCuqbUC8hXAioIiogzs8vMbFtRf35+Va9RjTVr1qcvr1u7nhrVq2YpUzW9TEpKCtu376BipeMylbmiU3sWLFzCvn370tf9+41/MG36GB56+M5CPIOCUyNLXaxdu56aNatlU2YdcKAuKmWpi+yMGjWY1avn8dtvOxkxYlzBBu6KzMZNW6hW5UB3ctXjK/Hz5i2Zypxatw6Tps0C4PNps9i5azfbft1Bwwan0qTh6bS6ujetru7NhY3Opm6dWkUaf34l5+OVF2Y2zsxONbOTzeyZYN3jZjYqm7Itcmv9QC4JSNLrQF1gvKQHJH0iaZGkGZLOCso8KWmIpK+AIZKqSvpY0sLg1Swo94mkuZKWpI2wkBQvaaCkxZK+kXSfpC5AI+BdSQsklZF0nqQpwf4TJFUP9j8v7XOAO3I5l16SRkr6UtL/JD0RrD8xuLlqMLAYqC1plaTKwfYewTkvlDQkWHe8pI8kzQ5eF+ZW0cVV/d/Vo+9fH+Leu/6cvu6mG+7jgibtadfmGppd2Jhr/3hliBGGr2PHHpx0UmOOOioxvQXpolOfW7ozZ9FSut7yEHMWLaVK5YrExcexeu0GVv64lklDX+fzYW8wc/5i5n6zLOxwc2TK+yssOSYgM7sVWAe0BE4E5pvZWcCfgMEZijYAWpvZtcDLwBQzOxs4F1gSlLnBzM4jklzullQJaAjUNLMzzOxM4G0z+xCYA1xnZg2JJOhXgC7B/m8BaZ31bwN3BZ+VF02AzsBZQNcMF8jqAf82s9PN7Me0wpJOBx4DWgWfcU+w6SXgRTNrHBzvzUN9YMbhjfuSt+cxzLxZv24DtWpVT1+uUbM669b/nKXMz+ll4uPjKV++HFs2b42Ur1GN995/nd439+GHH1Yf2Cc4xm+/7eSDD0Zx3nl5rd7wrMtSFzVrVmft2g3ZlIk0rNPqYnNQF7nZu3cvo0d/RlJS24IL2hWpKpUrsmHjgeuZP/+ymaqVKh5U5l9P9mH4G//g7huuBaD8MWX5fNoszmpQj6PLlOboMqVp3uQcFi79tkjjz68CHoRQKPIzCKE5MATAzCYDlSSVD7aNMrPdwftWwH+Ccilm9muw/u6gpTKDyGiKesBKoK6kVyS1A7L7hj6NyOiKiZIWEEkItYJrNMea2dSg3JA8nMNEM9scxDoiOCeAH81sRjblWwHDzWxTcD5p7fXWwKtBPKOA8pKOye4Dzay/mTUys0aJpcpnV+SwzZ27iLonn0idOrVISEigc5cOjBs7KVOZcWM/59rrOgPQ6cr2TJkyHYAKFcoxfMQAnnj8H8ycMTe9fHx8fHoXXalSpWjXrhVLi/k/NIA5cxZyyiknUadObRISEujaNYmxYydmKjN27CSuC+riqqsuY8qUr3M8ZtmyR1OtWhUgUi/t27dixYrvC+cEXKE747ST+XHtetas38j+/cmM//JrWjTLPEhr66/bSU2NfCW/+f7HXNmuJQDVq1RmzsJlJKeksD85mbmLllL3hJoHfUZxUhISUEFds9mZ00ZJLYh8aV9gZrskfQmUNrOtks4GLgVuBa4Gbsi6O7DEzC7ItPLwBglkvYKctpxj/NmIA843szzd7VtYUlJSePCBJ/l45CDi4+MYMng4y5f9jz8/di/z5n3D+HGfM3jQMPq/2Y8FiyazdeuvXN/zbgB639KDunXr8PCjd/Hwo3cB0KljT3bt3MXHIweSkJBAfFwcX375FQPfHhrmaeZJSkoK9933OKNHDyY+Pp5Bgz5g2bL/8Ze/3M+8eYsYO3YSAwcO4623XmTx4ils3bqN7t0PXN9avnwa5cqVIzExgaSktnTo0J0tW7by4YdvkpiYSFxcHFOnTue//30nxLMseA8+8Syz5y9i27bt/L5TN26/sTudky4NO6xCUSo+nj/ddQO3PvIMKampXNmuJaecWJtXBw7j9FNPpmWzRsxeuJSXBryHEOed9Tv+fNeNALS5+HxmLljMVTf3QcCFjRvS4oJcH3UWqpIwXEa5jeoJxnQ3Ah4HfjGzvwYJ5UUzO0fSk8BvZvZ8UH4oMMPM/hU8P+gYoAVwk5klSaoPLADaEbnmss/Mtks6A3jHzBpKGg30M7MvgiF/S4HuZjZdUgJwajACYxFwu5lNk/QccLmZnXGI8+gF/I1Ia2o3kWcV3QBsAsZk3C/DOVcFPiaSODdLqmhmWyS9R6Q78p9B+YZmtiC3yi5ftm5J+J0odPtTU8IOodjY/tMXYYdQbNjuHWGHUKwk1j77iK7OvHRCtzx/39yz+p1QrgTlpwvuSeC84Ev/WaDnIcrdA7SU9A0wl8j1oU+BUpKWBfumdXfVJHLj0gLgHeDRYP1A4PVgfTzQBXgu6MJbAKRdCb4eeC0ol5cKnAV8BCwCPsptlIaZLSFyvWlK8Nn9gk13A42CwQlLibTenHOu2CjoUXCFIdcWULQIWkCNzCy0ccXeAorwFtAB3gI6wFtAmR1pC+j5fLSA+oTUAvJnwTnnXBTKz7PgwhJ1CUjSpUQeB57RD2Z2JZGuPeeci3o+JXcIzGwCkcdFOOdczCoJ/f1Rl4Ccc85BcglIQZ6AnHMuChX/9OMJyDnnopJfA3LOORcKHwXnnHMuFKkloBPOE5BzzkWh4p9+PAE551xU8lFwzjnnQlH8048nIOeci0o+Cs4551wofBCCc865UBT/9OMJyDnnopJ3wTnnnAtFSgloA3kCcs65KOTXgJxzzoWi+KcfT0DOOReVvAXknHMuFD4IwblsmBX/v8yKiu3eEXYIxYbKlAs7hKjigxCcc86FwjwBOeecC4N3wTnnnAtFagno6vYE5JxzUaj4px9PQM45F5V8GLZzzrlQlIRRcHFhB+Ccc67gpWJ5fuWFpHaSVkj6TtIj2Wy/X9JSSYskfS6pTm7H9ATknHNRyPLxX24kxQOvAe2BBsC1khpkKTYfaGRmZwEfAv/I7biegJxzLgql5uOVB02A78xspZntA4YCV2QsYGZfmNmuYHEGUCu3g3oCcs65KGRmeX5J6i1pToZX7yyHqwn8lGF5TbDuUG4ExucWow9CcM65KJSfUXBm1h/oXxCfK6kb0Ai4JLeynoCccy4KFfAouLVA7QzLtYJ1mUhqDfwZuMTM9uZ2UE9AzjkXhQr4PqDZQD1JJxFJPH8A/pixgKRzgDeAdma2MS8H9QTknHNRqCCfOm9myZLuBCYA8cBbZrZEUl9gjpmNAv4JHAMMlwSw2sw65nRcT0DOOReFCvphpGY2DhiXZd3jGd63zu8xPQE551wU8ukYnHPOhSLFiv+EDJ6AnHMuCvnDSJ1zzoXCu+Ccc86Fwiekc845F4rin348ATnnXFQqCdeA/GGkJVzrNhczd/4kFiyazH0P3HrQ9sTERN4e9DILFk1m8pcjOOGEyPMDW7ZqzpRpI5k+azxTpo3k4ksuSN+nS9ckps8az9czxzHik7epWOm4IjufgtKmzSUsWvQFS5ZMpU+f2w/anpiYyJAhr7FkyVSmTh1JnTqRB/dWrHgsEyYMZdOmZbz4Yt+iDrtQTJu1gKRe93BZj7t48/1PDtq+7udfuOnBvlx1cx+uv/9JNvyyOX1bv/7v0OnG++l4w338/dW3CvTmxuLmsb/14+LL/0Cnbgf/OyqJUiw1z6+weAIqweLi4nih31N0vvJ6Gp93KV26JnFa/VMylenR82q2bdtOw7Na8dqrb/HUXx8GYPPmLVzT5WYuaNKeW3s/SP83XwAgPj6e5/75Fy5v/0eaNb2MxYuXc8stPYr83I5EXFwcL730NFdc0ZOGDX/P1Vd3pH79epnK9Op1Ddu2/crpp1/MK6+8ydNPPwrAnj17eeqpF3jkkWfCCL3ApaSk8swrA/j33/7EyAEvMv6Lr/j+xzWZyjz/xhCS2lzMiP8+z63du/DSgPcAWLBkBfOXrOCj/s/z8X9fYPGK75mzcGkYp1EkOl3Whtf7PR12GAWmoCekKwzFJgFJGifp2LDjSCPp67BjyE2jRmezcuWPrFr1E/v37+ejD8dweYc2mcpc3qE177/7EQCffDyeFi2aAbBo4VI2bIg8rmnZ0m8pU7o0iYmJSEKIskeXAaBcuXKsX/9zEZ7VkWvcuCHff7+KH35Yzf79+xk+fDRJSW0zlUlKass773wIwIgR42jZ8kIAdu3azddfz2bv3j1FHndh+GbFd5xQoxq1a1QlIaEU7Vs044uvZmcqs/LHNTRteAYATRqezhdfz4lskNi7bx/7k5PZt38/ySkpVDquQlGfQpFp1PBMKpQvF3YYBaYgJ6QrLIWSgILZ8/LFzC4zs22FEE6+SCoFYGbNwo4lN9VrVGPNmvXpy+vWrqdG9apZylRNL5OSksL27TsO6lK7olN7Fixcwr59+0hOTua+ex9n+qzxfPv9DOrXP4XBgz4o/JMpQDVqVGPNmnXpy2vXrqdGjaqHLJNWL5VKYFdjbjZu2kK1KpXSl6seX4mfN2/JVObUunWYNG0WAJ9Pm8XOXbvZ9usOGjY4lSYNT6fV1b1pdXVvLmx0NnXr5DrHmCsm8jMfUFjynYAknShpuaR3JS2T9KGkoyWtkvScpHlAV0ltJU2XNE/ScEnHBHOKD89wrBaSxgTvV0mqHLy/X9Li4HVvhs9dnGHfPpKeDN7fnWEu8qE5xP6kpCFBXP+TdHOGOP5P0ihgabDutwz7PSzpG0kLJT0brDtZ0qeS5gb71s9vXRYH9X9Xj75/fYh77/ozAKVKleLGm6/jomZJnHry+SxZvJwH+twWcpSuMPW5pTtzFi2l6y0PMWfRUqpUrkhcfByr125g5Y9rmTT0dT4f9gYz5y9m7jfLwg7X5VFJ6II73FFwpwE3mtlXkt4C0q7ybjazc4NEMgJobWY7JT0M3A/8DegvqayZ7QSuITK1azpJ5wHXA00BATMlTQG25hDPI8BJZrY3D914ZwHnA2WB+ZLGBuvPBc4wsx+yxNOeyNSzTc1sl6SKwab+wK1m9j9JTYF/A62yflgws2BvgKMSK5FYqnwu4eXd+nUbqFWrevpyjZrVWZelu2z9up+pVas669ZtID4+nvLly7Flc6Qqa9Soxnvvv07vm/vwww+rATjrrMg072nLI0aM5f4HSlYCWrduA7Vq1UhfrlmzOuvW/ZxtmbVrD9TL5s05/YqVTFUqV2TDxgODCn7+ZTNVK1U8qMy/nuwDwK7de5j4fzMpf0xZPhr7OWc1qMfRZUoD0LzJOSxc+i3nnfm7ojsBd9hKwqN4DrcL7icz+yp4/w7QPHg/LPh5PtAA+ErSAqAnUMfMkoFPgaSgq+tyYGSWYzcHPjaznWb2G5FEdlEu8SwC3g1m4kvOpexIM9ttZpuAL4jMdQ4wK2vyCbQG3k6b69zMtkg6BmhG5LHjC4jMgVE9m30xs/5m1sjMGhVk8gGYO3cRdU8+kTp1apGQkEDnLh0YN3ZSpjLjxn7Otdd1BqDTle2ZMmU6ABUqlGP4iAE88fg/mDljbnr5des2UL/+KVSqHPmSatWqOStWfFegcRe2OXMWcsopJ3HiibVJSEiga9ckxoyZmKnMmDET6datCwBXXXUZX35Z7C/5HZYzTjuZH9euZ836jezfn8z4L7+mRbNGmcps/XU7qamRL6s33/+YK9u1BKB6lcrMWbiM5JQU9icnM3fRUuqekNMszK44KQnXgA63BZQ14rTlncFPARPN7Nps9h0K3AlsITKPxI48fmYymRNm6QzvLwcuBpKAP0s6M0h2hxN7XsQB28ysYT72KXApKSk8+MCTfDxyEPHxcQwZPJzly/7Hnx+7l3nzvmH8uM8ZPGgY/d/sx4JFk9m69Veu73k3AL1v6UHdunV4+NG7ePjRuwDo1LEnGzZs5Nm/vcynE4ayPzmZn1av5bZbHgzzNPMtJSWFe+/9C6NHDyE+Pp5Bg4axbNm3PP74/cyd+w1jx05k4MBhvPXWv1iyZCpbtmyjR4870/dfseIrypUrR2JiAklJl9KhQzeWL/9fiGd0+ErFx/Onu27g1keeISU1lSvbteSUE2vz6sBhnH7qybRs1ojZC5fy0oD3EOK8s37Hn++6EYA2F5/PzAWLuermPgi4sHFDWlzQKOcPLMEefOJZZs9fxLZt2/l9p27cfmN3OiddGnZYh60kPAlB+b0AJelE4AegmZlNl/QmsAy4C2hkZpskHQ/MBVqZ2XeSygI1zezbYIDC90Rm2BtuZh8Ex11FZB7xE4CBRFpRAmYC3YHFwHoi3X+/AVOItKb6AieY2SpJCcCPQIPsBjQE14w6kaELLnh/KtDHzDpkKPubmR0jqR3wOJHuxF2SKgatoK+BF81suCKzL51lZgtzqrvyZesW/9+IIrAvJbdGauzY/r/RYYdQbKhM9IxAKwgJlevqSPY/vWrTPH/fLPl55hF91uE63C64FcAdkpYBxwH/ybjRzH4BegHvS1oETAfqB9tSgDFA++AnWfadRyQBzSKSfN40s/lmtp9IspkFTASWB7vEA+9I+oZIQnk5l9F0i4h0vc0A/mpm63Ioi5l9CowC5gTdbX2CTdcBN0paCCwhcp3IOeeKhVSzPL/CcrgtoDFmdkahRFSIghbQb2b2fBif7y2gCG8BHeAtoAO8BZTZkbaA6ldpnOfvm+UbZ4fSAvJnwTnnXBQqCaPg8p2AzGwVUKxbP5KuB+7JsvorM7sjjHicc66olYRBCFHZAjKzt4G3w47DOefC4hPSOeecC4VFYxecc8654q8kzAfkCcg556JQSZi7yROQc85FoagcBeecc67481FwzjnnQuGj4JxzzoXCrwE555wLhY+Cc845F4qUVB+E4JxzLgTeBeeccy4U3gXnnHMuFN4Ccs45F4qScB/Q4c6I6pxzrhizfPyXF5LaSVoh6TtJj2Sz/ShJw4LtM4PJS3PkCcg556JQSmpqnl+5kRQPvAa0BxoA10pqkKXYjcBWMzsFeBF4LrfjegJyzrkoVMAtoCbAd2a20sz2AUOBK7KUuQIYFLz/EPi9pByn+vYE5JxzUcjM8vzKg5rATxmW1wTrsi1jZsnAr0ClnA7qCcg556JQfhKQpN6S5mR49S6KGH0UXBHavnNljs3RoiCpt5n1DzuO4sDr4gCviwOipS7271ub3++bnM55LVA7w3KtYF12ZdZIKgVUADbn9IHeAoo9RfKXTQnhdXGA18UBXhcHmw3Uk3SSpETgD8CoLGVGAT2D912AyZZL/563gJxzzuXIzJIl3QlMAOKBt8xsiaS+wBwzGwUMAIZI+g7YQiRJ5cgTkHPOuVyZ2ThgXJZ1j2d4vwfomp9jehdc7CnxfdsFyOviAK+LA7wuiohKwvOCnHPORR9vATnnnAuFJyDnnHOh8ATknHMuFJ6AnItxko4OOwYXmzwBxQBJp0r6r6TPJE1Oe4UdV1gklZUUF7w/VVJHSQlhx1XUJDWTtBRYHiyfLenfIYcVmuB34XNJi4PlsyQ9FnZc0cxHwcUASQuB14G5QEraejObG1pQIZI0F7gIOA74ishd3vvM7LpQAytikmYSuWN9lJmdE6xbbGZnhBtZOCRNAR4E3vD6KBp+I2psSDaz/4QdRDEiM9sl6Ubg32b2D0kLwg4qDGb2U5Yn5qccqmwMONrMZmWpj+SwgokF3gUXG0ZLul1SdUkV015hBxUiSboAuA4YG6yLDzGesPwkqRlgkhIk9QGWhR1UiDZJOhkiE+RI6gKsDzek6OZdcDFA0g/ZrDYzq1vkwRQDki4G+gBfmdlzkuoC95rZ3SGHVqQkVQZeAloDAj4D7jGzHJ9gHK2C34P+QDNgK/AD0M3MVoUZVzTzBBTlgovtXc1sWNixOFcSSCoLxJnZjrBjiXbeBRflzCyVyIVVFwhGO/WP9VGBkgZJOjbD8nGS3goxpFBJ+pukY81sp5ntCOrj6bDjimbeAooBkp4FNgHDgJ1p681sS2hBhchHBUZImp822iundbHiEPUxz8zODSumaOej4GLDNcHPOzKsMyAmrwHhowLTxEk6zsy2AgQDU2L5OyFe0lFmthdAUhngqJBjimqx/MsWM8zspLBjKGZGS7od+BjYm7YyBluELwDTJQ0nMgihC/BMuCGF6l3gc0lvB8vXA4NCjCfqeRdcDJDUI7v1Zja4qGMpDnxU4AGSGgCtgsXJZrY0zHjCJqk98PtgcaKZTQgznmjnCSgGSHolw2JpIv/A5plZl5BCciGSVN7Mth/qXrAYbAm6kHgCikHByKehZtYu7FjCEDx8837gBDPrLakecJqZjQk5tCIhaYyZdQhaghm/AEQMtgQlTTOz5pJ2kH19lA8ptKjnCSgGBQ/eXGxmp4UdSxgkDSMyAq6HmZ0RJKSvzaxhuJEVHUWeN1PbzFaHHYuLXT4IIQZIGs2Bv+zigAbAB+FFFLqTzewaSdcCBM+FU247RRMzM0ljgTPDjqU4kBQPLDGz+mHHEks8AcWG5zO8TwZ+NLM1YQVTDOwLhtimPfPrZDKMhosh8yQ1NrPZYQcSNjNLkbRC0gneKiw63gXnYo6kNsBjRFqCnwEXAr3M7Msw4ypqkpYDpwA/ErlBOe2ax1mhBhYSSVOBc4BZZL5hu2NoQUU5T0AxQNJVwHNAFSJfMjF/cVVSJeB8InUxw8w2hRxSkZNUJ7v1ZvZjUcdSHEi6JLv1ZjalqGOJFZ6AYoCk74AkM4vlR+2nk3QhsMDMdkrqBpwLvBSLX7ySzgWaE+mO/MrM5oUcUqgkVQOaEKmP2Wa2IeSQopo/jDQ2/OzJJ5P/ALsknU1kOPb3QMzdlCvpcSJ3+lcCKgNvx/IU1JJuItL9dhWRp0LMkHRDuFFFN28BRbGg6w3gEqAa8AmZHz0zIoSwQpf2gMngC3itmQ2IxYdOSloBnG1me4LlMkRahrE6PH8F0CxtPqSgm/brWK2PouCj4KJbUvDTgF1A2wzbDIjJBATskPQo0A24OJgzKSHkmMKwjsiTMfYEy0cBa8MLJ3SbgYxzAO0I1rlC4i2gGCBpEJGZLrcFy8cBL5hZTHYvBP38fyTSx/9/kk4AWsTas/EkfQI0BiYS+YOkDZEuqDUAMThD7GAi90WNJFIfVwCLghdm1i+86KKTJ6AY4PO+5I+k6WZ2QdhxFDZJPXPabmYx9SRoSU/ktN3MniqqWGKFd8HFBp/3JX9Khx1AUcgtwUj6yMw6F1U8YcstwUh6xczuKqp4YoF/CcWGjPO+AHQltud9yY13C0TE1ENJ8+DCsAOINp6AYoCZDZY0hwPzvlwV6/O+uDzxROwKlSegGBEkHE86eRNTDyZ1Lix+I6qLSZLqSGodvC8jqVyGzd1DCqu48UScmddHAfME5GKOpJuBD4E3glW1iNykC4CZLQ4hrOLo4bADCIukOElZn5X4UijBRDEfhu1ijqQFRJ73NTNtKLqkb8wsJubGkfQNOVzfieGnYb8H3AqkALOB8kSeEfjPUAOLYn4NyMWivWa2L20OOkmliK0L7h2Cn3cEP4cEP68LIZbipIGZbZd0HTAeeITIzLmegAqJJyAXi6ZI+hNQJpgb6HZgdMgxFZm0p35LapPlZuRHJM0j8sUbixKC6eo7Aa+a2X5JsfSHSZHza0AuFj0C/AJ8A9wCjCMyQV2sUTA1RdpCM2L7O+ENYBVQFpgazJe0PdSIopxfA3IxR1JZYI+ZpQTL8cBRZrYr3MiKlqTzgLeACsGqbcANsT4nUEaSSplZcthxRCtPQC7mSJoBtDaz34LlY4DPzKxZuJGFQ1IFADP7NexYwhRMz3EQM+tb1LHEilhubrvYVTot+QAE748OMZ5QSKoqaQAw1Mx+ldRA0o1hxxWinRleKUB74MQwA4p2PgjBxaKdks5N62oKuqJ2hxxTGAYCbwN/Dpa/BYYBA8IKKExm9kLGZUnPAxNCCicmeAJyseheYLikdUTubq8GXBNqROGobGYfBJPzYWbJklLCDqoYOZrITcqukHgCcjHHzGZLqg+kTbW8wsz2hxlTSHYG004bgKTzgZi9DpTlBt144HjAr/8UIh+E4GKGpFZmNlnSVdltN7OYmqJc0rnAK8AZwGIiX7hdzWxhqIGFJBh2nSYZ+NlHwBUubwG5WHIJMBlIymabATGVgIAlROrkNCJdkSuI4YFJZvajpLOBi4JVUwmm43aFw1tALqZIigO6mNkHYccSNknzzOzc3NbFCkn3ADdz4A+RK4H+ZvZKeFFFN09ALuZImmNmjcKOIyySqgE1gXeAP3JgmoHywOtmVj+s2MIkaRFwgZntDJbLAtNj9eGsRcG74FwsmiSpD5EhxzvTVprZlvBCKlKXAr2IjPB6gQMJaAfwp5BiKg5E5P6fNCn4HECFyltALuZI+oFsnn5tZnVDCCc0kjqb2Udhx1FcSLof6Al8HKzqBAw0s3+FFVO0i9kLji6mNQBeAxYCC4iMBDs9zIBCUktSeUW8KWmepLZhBxUWM+sHXA9sCV7Xe/IpXN4CcjFH0gdEnnL8brDqj0AFM7s6vKiKnqSFZna2pEuJTMT2GDAk1gYhSCofzANUMbvtMdQ1W+T8GpCLRWeYWYMMy19IWhpaNOFJu75xGTDYzJYobZa+2PIekUn65pK5a1bBckx1zRYlT0AuFs2TdL6ZzQCQ1BSYE3JMYZgr6TPgJOBRSeWA1JBjKnJm1iH4eVLYscQa74JzMUfSMiI3X64OVp1A5CbMZMBiZdhtcE9UQ2ClmW0LHstT08xi8uZLSaOA94GRsTY3VFg8AbmYk+WRKwdJm7I6Fkg6DqgHlE5bZ2ZTw4soPJIuIfJQ2suB2cBQYIyZ7Qk1sCjmCci5GCXpJuAeIvcDLQDOJ3LjZasw4wpbMENuKyJPRWhnZuVDDilq+TBs52LXPUBj4EczawmcQ2Ra7pglqQzQmciowMbAoHAjim4+CMG52LXHzPZIQtJRZrZc0mm57xadguH5TYBPgVeBKWYWc4MyipInIOdi1xpJxwKfABMlbQVi5vpXNgYA15qZT8pXRPwakHMu7QJ8BeBTM9sXdjxhkHQ0cD9wgpn1llQPOM3MxoQcWtTya0DOxShJ5wf3/mBmU4AviVwHilVvA/uAZsHyWuDp8MKJfp6AnItd/wF+y7D8W7AuVp1sZv8A9gME9wLF4pMhiownIOdilyxDH3xwwT2WrwvvC0bBGYCkk4G94YYU3TwBORe7Vkq6W1JC8LoHWBl2UCF6gsgIuNqS3gU+Bx4KN6To5oMQnItRkqoALxO56dKIfOHea2YbQw0sBGlTtROpg/OJdL3NMLNNoQYW5TwBOReDgrv9B5vZdWHHUlzE+lTtYfAuOOdiUHCvSx1JiWHHUoxMktRHUm1JFdNeYQcVzbwF5FyMkjQY+B0wCtiZtj6YGTTm+FTtRS+WR7w4F+u+D15xQLmQYykOGgC3A82JJKL/A14PNaIo5y0g52KcpGMAzOy33MpGM5+qveh5AnIuRkk6AxgCpF3n2AT0MLMl4UUVHklLs0zVnu06V3B8EIJzsas/cL+Z1TGzOsADwH9DjilM8ySdn7YQw1O1FxlvATkXoyQtNLOzc1sXK3yq9qLngxCci10rJf2FSDccQDdi+0kI7cIOINZ4AnIuxkgaYmbdiYzyOhEYEWyaCtwQVlxhM7NYngspFJ6AnIs950mqAfQEWhJ57ExaX7w//dkVGU9AzsWe14k886wumS+ypyUiv/HSFQkfhOBcjJL0HzO7Lew4XOzyBOSccy4Ufh+Qc865UHgCcs45FwpPQM4550LhCcg551woPAE555wLxf8DN+hMPKQfQesAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the correlation matrix \n",
    "sns.heatmap(new_df[['churn', 'price_sens', 'forcasted_price','previous_price']].corr(), annot=True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that there is almost no correlation between price sensitivity - and other price related variables - and customer churn."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Further analysis of the data to get more insights "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "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>previous_price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>churn</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       previous_price\n",
       "churn                \n",
       "0               0.109\n",
       "1               0.111"
      ]
     },
     "execution_count": 329,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the median price for clients who did churn and for those who did not\n",
    "\n",
    "round_func = lambda x : round(x, 3)\n",
    "churn_price = pd.DataFrame(new_df.groupby(['churn'])['previous_price'].median()).apply(round_func)\n",
    "churn_price\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "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>churn</th>\n",
       "      <th>forcasted_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.105</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   churn  forcasted_price\n",
       "0      0            0.103\n",
       "1      1            0.105"
      ]
     },
     "execution_count": 330,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the median of the forcasted price for clients who did churn and for those who did not\n",
    "\n",
    "churn_forc_price = pd.DataFrame(new_df.groupby(['churn'])['forcasted_price'].median()).apply(round_func).reset_index()\n",
    "churn_forc_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "churn_viz = churn_forc_price.merge(churn_price, how='left', on='churn').set_index('churn')\n",
    "churn_viz.plot(kind='bar', figsize=(10,10))\n",
    "plt.title('Median price comparison')\n",
    "plt.xlabel('Churn')\n",
    "plt.ylabel('Average price')\n",
    "plt.xticks(ticks =np.arange(2), labels=['current customers','shurned customers'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the median price values for customers who did churn and for those who did not, are almost the same. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "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>cons_last_month</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>churn</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17093.442599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7216.081979</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       cons_last_month\n",
       "churn                 \n",
       "0         17093.442599\n",
       "1          7216.081979"
      ]
     },
     "execution_count": 325,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the last month consumption of electricity for clients who did churn and for those who did not\n",
    "churn_last_month = pd.DataFrame(new_df.groupby(['churn'])['cons_last_month'].mean())\n",
    "churn_last_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Code source: https://www.geeksforgeeks.org/how-to-annotate-bars-in-barplot-with-matplotlib-in-python/\n",
    "\n",
    "# Defining the plot size\n",
    "plt.figure(figsize=(8, 8))\n",
    " \n",
    "# Defining the values for x-axis, y-axis\n",
    "# and from which dataframe the values are to be picked\n",
    "plots = sns.barplot(x=\"churn\", y=\"cons_last_month\", data=churn_last_month.reset_index())\n",
    " \n",
    "# Iterrating over the bars one-by-one\n",
    "for bar in plots.patches:\n",
    "   \n",
    "  # Using Matplotlib's annotate function and\n",
    "  # passing the coordinates where the annotation shall be done\n",
    "  # x-coordinate: bar.get_x() + bar.get_width() / 2\n",
    "  # y-coordinate: bar.get_height()\n",
    "  # free space to be left to make graph pleasing: (0, 8)\n",
    "  # ha and va stand for the horizontal and vertical alignment\n",
    "    plots.annotate(format(bar.get_height(), '.2f'),\n",
    "                   (bar.get_x() + bar.get_width() / 2,\n",
    "                    bar.get_height()), ha='center', va='center',\n",
    "                   size=15, xytext=(0, 8),\n",
    "                   textcoords='offset points')\n",
    "\n",
    "plt.xlabel(\"Churn\", size=14)\n",
    " \n",
    "# Setting the label for y-axis\n",
    "plt.ylabel(\"Last month consumption\", size=14)\n",
    " \n",
    "# Setting the title for the graph\n",
    "plt.title(\"Last month consumption and churn\")\n",
    "\n",
    "plt.xticks(np.arange(2), labels=['False', 'True'])\n",
    " \n",
    "# Finally showing the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On average a churned customer consumes in total  42 % of what a not churning customer consumes in the last month before leaving.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {
    "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>churn</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>has_gas</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>1199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <td>216</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         churn\n",
       "has_gas       \n",
       "f         1199\n",
       "t          216"
      ]
     },
     "execution_count": 323,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The number of churned customers who were gaz consumers\n",
    "churn_has_gas = pd.DataFrame(new_df.groupby(['has_gas'])['churn'].sum())\n",
    "churn_has_gas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Defining the plot size\n",
    "plt.figure(figsize=(8, 8))\n",
    " \n",
    "# Defining the values for x-axis, y-axis\n",
    "# and from which dataframe the values are to be picked\n",
    "plots = sns.barplot(x=\"has_gas\", y=\"churn\", data=churn_has_gas.reset_index())\n",
    " \n",
    "# Iterrating over the bars one-by-one\n",
    "for bar in plots.patches:\n",
    "   \n",
    "  # Using Matplotlib's annotate function and\n",
    "  # passing the coordinates where the annotation shall be done\n",
    "  # x-coordinate: bar.get_x() + bar.get_width() / 2\n",
    "  # y-coordinate: bar.get_height()\n",
    "  # free space to be left to make graph pleasing: (0, 8)\n",
    "  # ha and va stand for the horizontal and vertical alignment\n",
    "    plots.annotate(format(bar.get_height(), '.2f'),\n",
    "                   (bar.get_x() + bar.get_width() / 2,\n",
    "                    bar.get_height()), ha='center', va='center',\n",
    "                   size=15, xytext=(0, 8),\n",
    "                   textcoords='offset points')\n",
    "\n",
    "plt.xlabel(\"Gaz customer\", size=14)\n",
    " \n",
    "# Setting the label for y-axis\n",
    "plt.ylabel(\"Number of churned customers\", size=14)\n",
    " \n",
    "# Setting the title for the graph\n",
    "plt.title(\"Customer churn and Gaz\")\n",
    "\n",
    "plt.xticks(np.arange(2), labels=['False', 'True'])\n",
    " \n",
    "# Finally showing the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that 85% of churned customer were not gas buyers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df.to_csv('clean_data_after_eda.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}