sapa_com.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sapa.com is one of the leading eCommerce platforms in Nigeria with millions of daily complete transactions. The goods and services on sapa.com cater for both the elite and the masses which makes it the first choice for almost everybody in Nigeria. Due to the COVID-19 pandemic that struck the entire world in all areas of living, the companies' daily complete transactions have dropped drastically to thousands.\n",
    "\n",
    "The CEO, Mr Echoke in his recent actions to put the platform back to the top of the eCommerce platforms chain in the country has approved the use of Artificial intelligence in User Personality Analysis. The company has contracted to build an AI System with a special focus on recommender system development to build a robust intelligent model capable of recommending products and services to Users based on their activities on sapa.com.\n",
    "\n",
    "I build a model capable of predicting users’ responses to marketing campaigns based on the features in the provided dataset by the sapa.com data engineer. The next phase of the project was highly dependent on the accuracy of my model as this was the foundation of what will constitute the features of the proposed recommender system development. \n",
    "Good luck!\n",
    "\n",
    "It was a great task \n",
    "\n",
    "DATASETS\n",
    "\n",
    "train.csv: contains the target. This is the dataset that you will use to train your model.\n",
    "test.csv- resembles train.csv but without the target-related columns. This is the dataset on which you will apply your model to.\n",
    "sample_submission.csv: shows the submission format for the project, with the ‘ID’ column mirroring that of test.csv and the ‘target’ column containing my predictions. The order of the rows does not matter, but the names of the ID must be correct.\n",
    "\n",
    "    \n",
    "VARIABLE DEFINITIONS\n",
    "\n",
    "ID: Unique identifier for each User\n",
    "\n",
    "Year_of_Birth: Year of birth\n",
    "\n",
    "Education_Level: The highest level of education attained by the User\n",
    "\n",
    "Marital_Status: Marital status\n",
    "\n",
    "Disposable_Income: Yearly User’s household disposable income\n",
    "\n",
    "No_of_Kids_in_home: total count of children in the user’s home\n",
    "\n",
    "No_of_Teen_in_home: Number of teenagers in the User's household\n",
    "\n",
    "Date_User: Date of User's enrollment with the company\n",
    "\n",
    "Recency: Number of days since User's last purchase\n",
    "\n",
    "Discounted_Purchases: Counts of purchases made by the user using coupons\n",
    "\n",
    "WebPurchases: Counts of purchases made by the user through the company’s website\n",
    "\n",
    "CatalogPurchases: Counts of purchases made by the user using a catalogue\n",
    "\n",
    "StorePurchases: Counts of purchases made by the user directly in stores\n",
    "\n",
    "Amount_on_Wines: Total amount user spent on wine and drinks within the last 3 years\n",
    "\n",
    "Amount_on_Fruits: Total amount user spent on fruity food within the last 3 years\n",
    "\n",
    "Amount_on_MeatProducts: Total amount user spent on meat products and livestock within the last 3 years\n",
    "\n",
    "Amount_on_FishProducts: Total amount user spent on fish alone within the last 3 years\n",
    "\n",
    "Amount_on_SweetProducts: Total amount user spent on sweets and chocolates within the last 3 years\n",
    "\n",
    "Amount_on_GoldProds: Total amount user spent on golden products within the last 3 years\n",
    "\n",
    "WebVisitsMonth: The number of times the user of visits to company’s website within the last 4 weeks\n",
    "\n",
    "Cmp3Accepted: 1: Offer was accepted after the third campaign, 0 otherwise\n",
    "\n",
    "Cmp4Accepted: 1: Offer was accepted after the fourth campaign, 0 otherwise\n",
    "\n",
    "Cmp5Accepted: 1: Offer was accepted after the fifth campaign, 0 otherwise\n",
    "\n",
    "Cmp1Accepted: 1: Offer was accepted after the first campaign, 0 otherwise\n",
    "\n",
    "Cmp2Accepted: 1: Offer was accepted after the second campaign, 0 otherwise\n",
    "\n",
    "Any_Complain: 1 if the user has a compliant history with the platform in the last 3 years, 0 otherwise\n",
    "\n",
    "User_Response: 1: indicates the acceptance of offer and 0 otherwise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing LIbraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "from sklearn import preprocessing\n",
    "import sklearn.model_selection as ms\n",
    "from sklearn import linear_model\n",
    "import sklearn.metrics as sklm\n",
    "\n",
    "import numpy.random as nr\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import scipy.stats as ss\n",
    "import math\n",
    "\n",
    "\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Year_of_Birth</th>\n",
       "      <th>Education_Level</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Disposable_Income</th>\n",
       "      <th>No_of_Kids_in_home</th>\n",
       "      <th>No_of_Teen_in_home</th>\n",
       "      <th>Date_Customer</th>\n",
       "      <th>Recency</th>\n",
       "      <th>Discounted_Purchases</th>\n",
       "      <th>WebPurchases</th>\n",
       "      <th>CatalogPurchases</th>\n",
       "      <th>StorePurchases</th>\n",
       "      <th>Amount_on_Wines</th>\n",
       "      <th>Amount_on_Fruits</th>\n",
       "      <th>Amount_on_MeatProducts</th>\n",
       "      <th>Amount_on_FishProducts</th>\n",
       "      <th>Amount_on_SweetProducts</th>\n",
       "      <th>Amount_on_GoldProds</th>\n",
       "      <th>WebVisitsMonth</th>\n",
       "      <th>Cmp3Accepted</th>\n",
       "      <th>Cmp4Accepted</th>\n",
       "      <th>Cmp5Accepted</th>\n",
       "      <th>Cmp1Accepted</th>\n",
       "      <th>Cmp2Accepted</th>\n",
       "      <th>Any_Complain</th>\n",
       "      <th>Response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID_4A9AR6FQ</td>\n",
       "      <td>1955</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>77504.4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-06-22</td>\n",
       "      <td>56</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>71</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID_X28T3VEK</td>\n",
       "      <td>1958</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Together</td>\n",
       "      <td>56784.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2013-01-08</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>205</td>\n",
       "      <td>22</td>\n",
       "      <td>111</td>\n",
       "      <td>50</td>\n",
       "      <td>15</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID_AWXARH57</td>\n",
       "      <td>1962</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Single</td>\n",
       "      <td>103714.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013-11-21</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>469</td>\n",
       "      <td>31</td>\n",
       "      <td>873</td>\n",
       "      <td>29</td>\n",
       "      <td>18</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_FQVZHE81</td>\n",
       "      <td>1979</td>\n",
       "      <td>2n Cycle</td>\n",
       "      <td>Single</td>\n",
       "      <td>46311.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2013-09-13</td>\n",
       "      <td>49</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>56</td>\n",
       "      <td>15</td>\n",
       "      <td>49</td>\n",
       "      <td>17</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID_QVLWGPRN</td>\n",
       "      <td>1959</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Married</td>\n",
       "      <td>87486.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014-01-21</td>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>412</td>\n",
       "      <td>117</td>\n",
       "      <td>445</td>\n",
       "      <td>181</td>\n",
       "      <td>165</td>\n",
       "      <td>203</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ID  Year_of_Birth Education_Level Marital_Status  \\\n",
       "0  ID_4A9AR6FQ           1955      Graduation       Divorced   \n",
       "1  ID_X28T3VEK           1958      Graduation       Together   \n",
       "2  ID_AWXARH57           1962      Graduation         Single   \n",
       "3  ID_FQVZHE81           1979        2n Cycle         Single   \n",
       "4  ID_QVLWGPRN           1959      Graduation        Married   \n",
       "\n",
       "   Disposable_Income  No_of_Kids_in_home  No_of_Teen_in_home Date_Customer  \\\n",
       "0            77504.4                   1                   1    2014-06-22   \n",
       "1            56784.0                   0                   1    2013-01-08   \n",
       "2           103714.8                   0                   0    2013-11-21   \n",
       "3            46311.6                   1                   0    2013-09-13   \n",
       "4            87486.0                   0                   0    2014-01-21   \n",
       "\n",
       "   Recency  Discounted_Purchases  WebPurchases  CatalogPurchases  \\\n",
       "0       56                     2             1                 1   \n",
       "1       17                     6             5                 1   \n",
       "2       17                     0             7                 4   \n",
       "3       49                     3             4                 1   \n",
       "4       59                     1             3                 7   \n",
       "\n",
       "   StorePurchases  Amount_on_Wines  Amount_on_Fruits  Amount_on_MeatProducts  \\\n",
       "0               4               71                 3                      16   \n",
       "1               8              205                22                     111   \n",
       "2               7              469                31                     873   \n",
       "3               3               56                15                      49   \n",
       "4               9              412               117                     445   \n",
       "\n",
       "   Amount_on_FishProducts  Amount_on_SweetProducts  Amount_on_GoldProds  \\\n",
       "0                       0                        6                   20   \n",
       "1                      50                       15                   19   \n",
       "2                      29                       18                   37   \n",
       "3                      17                       24                   24   \n",
       "4                     181                      165                  203   \n",
       "\n",
       "   WebVisitsMonth  Cmp3Accepted  Cmp4Accepted  Cmp5Accepted  Cmp1Accepted  \\\n",
       "0               3             0             0             0             0   \n",
       "1               6             0             0             0             0   \n",
       "2               2             0             0             0             1   \n",
       "3               8             0             0             0             0   \n",
       "4               1             0             0             0             1   \n",
       "\n",
       "   Cmp2Accepted  Any_Complain  Response  \n",
       "0             0             0         0  \n",
       "1             0             0         0  \n",
       "2             0             0         1  \n",
       "3             0             0         0  \n",
       "4             0             0         0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data= pd.read_csv('train.csv', parse_dates=['Date_Customer'])\n",
    "test_data= pd.read_csv('test.csv',parse_dates=['Date_Customer'])\n",
    "train_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1568 entries, 0 to 1567\n",
      "Data columns (total 27 columns):\n",
      " #   Column                   Non-Null Count  Dtype         \n",
      "---  ------                   --------------  -----         \n",
      " 0   ID                       1568 non-null   object        \n",
      " 1   Year_of_Birth            1568 non-null   int64         \n",
      " 2   Education_Level          1568 non-null   object        \n",
      " 3   Marital_Status           1568 non-null   object        \n",
      " 4   Disposable_Income        1552 non-null   float64       \n",
      " 5   No_of_Kids_in_home       1568 non-null   int64         \n",
      " 6   No_of_Teen_in_home       1568 non-null   int64         \n",
      " 7   Date_Customer            1568 non-null   datetime64[ns]\n",
      " 8   Recency                  1568 non-null   int64         \n",
      " 9   Discounted_Purchases     1568 non-null   int64         \n",
      " 10  WebPurchases             1568 non-null   int64         \n",
      " 11  CatalogPurchases         1568 non-null   int64         \n",
      " 12  StorePurchases           1568 non-null   int64         \n",
      " 13  Amount_on_Wines          1568 non-null   int64         \n",
      " 14  Amount_on_Fruits         1568 non-null   int64         \n",
      " 15  Amount_on_MeatProducts   1568 non-null   int64         \n",
      " 16  Amount_on_FishProducts   1568 non-null   int64         \n",
      " 17  Amount_on_SweetProducts  1568 non-null   int64         \n",
      " 18  Amount_on_GoldProds      1568 non-null   int64         \n",
      " 19  WebVisitsMonth           1568 non-null   int64         \n",
      " 20  Cmp3Accepted             1568 non-null   int64         \n",
      " 21  Cmp4Accepted             1568 non-null   int64         \n",
      " 22  Cmp5Accepted             1568 non-null   int64         \n",
      " 23  Cmp1Accepted             1568 non-null   int64         \n",
      " 24  Cmp2Accepted             1568 non-null   int64         \n",
      " 25  Any_Complain             1568 non-null   int64         \n",
      " 26  Response                 1568 non-null   int64         \n",
      "dtypes: datetime64[ns](1), float64(1), int64(22), object(3)\n",
      "memory usage: 330.9+ KB\n"
     ]
    }
   ],
   "source": [
    "train_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 672 entries, 0 to 671\n",
      "Data columns (total 26 columns):\n",
      " #   Column                   Non-Null Count  Dtype         \n",
      "---  ------                   --------------  -----         \n",
      " 0   ID                       672 non-null    object        \n",
      " 1   Year_of_Birth            672 non-null    int64         \n",
      " 2   Education_Level          672 non-null    object        \n",
      " 3   Marital_Status           672 non-null    object        \n",
      " 4   Disposable_Income        664 non-null    float64       \n",
      " 5   No_of_Kids_in_home       672 non-null    int64         \n",
      " 6   No_of_Teen_in_home       672 non-null    int64         \n",
      " 7   Date_Customer            672 non-null    datetime64[ns]\n",
      " 8   Recency                  672 non-null    int64         \n",
      " 9   Discounted_Purchases     672 non-null    int64         \n",
      " 10  WebPurchases             672 non-null    int64         \n",
      " 11  CatalogPurchases         672 non-null    int64         \n",
      " 12  StorePurchases           672 non-null    int64         \n",
      " 13  Amount_on_Wines          672 non-null    int64         \n",
      " 14  Amount_on_Fruits         672 non-null    int64         \n",
      " 15  Amount_on_MeatProducts   672 non-null    int64         \n",
      " 16  Amount_on_FishProducts   672 non-null    int64         \n",
      " 17  Amount_on_SweetProducts  672 non-null    int64         \n",
      " 18  Amount_on_GoldProds      672 non-null    int64         \n",
      " 19  WebVisitsMonth           672 non-null    int64         \n",
      " 20  Cmp3Accepted             672 non-null    int64         \n",
      " 21  Cmp4Accepted             672 non-null    int64         \n",
      " 22  Cmp5Accepted             672 non-null    int64         \n",
      " 23  Cmp1Accepted             672 non-null    int64         \n",
      " 24  Cmp2Accepted             672 non-null    int64         \n",
      " 25  Any_Complain             672 non-null    int64         \n",
      "dtypes: datetime64[ns](1), float64(1), int64(21), object(3)\n",
      "memory usage: 136.6+ KB\n"
     ]
    }
   ],
   "source": [
    "test_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                                 object\n",
       "Year_of_Birth                       int64\n",
       "Education_Level                    object\n",
       "Marital_Status                     object\n",
       "Disposable_Income                 float64\n",
       "No_of_Kids_in_home                  int64\n",
       "No_of_Teen_in_home                  int64\n",
       "Date_Customer              datetime64[ns]\n",
       "Recency                             int64\n",
       "Discounted_Purchases                int64\n",
       "WebPurchases                        int64\n",
       "CatalogPurchases                    int64\n",
       "StorePurchases                      int64\n",
       "Amount_on_Wines                     int64\n",
       "Amount_on_Fruits                    int64\n",
       "Amount_on_MeatProducts              int64\n",
       "Amount_on_FishProducts              int64\n",
       "Amount_on_SweetProducts             int64\n",
       "Amount_on_GoldProds                 int64\n",
       "WebVisitsMonth                      int64\n",
       "Cmp3Accepted                        int64\n",
       "Cmp4Accepted                        int64\n",
       "Cmp5Accepted                        int64\n",
       "Cmp1Accepted                        int64\n",
       "Cmp2Accepted                        int64\n",
       "Any_Complain                        int64\n",
       "Response                            int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                                 object\n",
       "Year_of_Birth                       int64\n",
       "Education_Level                    object\n",
       "Marital_Status                     object\n",
       "Disposable_Income                 float64\n",
       "No_of_Kids_in_home                  int64\n",
       "No_of_Teen_in_home                  int64\n",
       "Date_Customer              datetime64[ns]\n",
       "Recency                             int64\n",
       "Discounted_Purchases                int64\n",
       "WebPurchases                        int64\n",
       "CatalogPurchases                    int64\n",
       "StorePurchases                      int64\n",
       "Amount_on_Wines                     int64\n",
       "Amount_on_Fruits                    int64\n",
       "Amount_on_MeatProducts              int64\n",
       "Amount_on_FishProducts              int64\n",
       "Amount_on_SweetProducts             int64\n",
       "Amount_on_GoldProds                 int64\n",
       "WebVisitsMonth                      int64\n",
       "Cmp3Accepted                        int64\n",
       "Cmp4Accepted                        int64\n",
       "Cmp5Accepted                        int64\n",
       "Cmp1Accepted                        int64\n",
       "Cmp2Accepted                        int64\n",
       "Any_Complain                        int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Divorced', 'Together', 'Single', 'Married', 'Widow', 'Alone',\n",
       "       'Absurd', 'YOLO'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['Marital_Status'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Married     615\n",
       "Together    409\n",
       "Single      329\n",
       "Divorced    159\n",
       "Widow        50\n",
       "Alone         3\n",
       "YOLO          2\n",
       "Absurd        1\n",
       "Name: Marital_Status, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['Marital_Status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1329\n",
       "1     239\n",
       "Name: Response, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['Response'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25a9733a970>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.heatmap(train_data.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25a97c6feb0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ha3pfuDf0XnD3qtFPmcy/zWxbwm2pFWkEcjrh//ZgQ5njGXsCzQCeRfghVnGVmT3D3W9teGqnmtmbiCdh09Xe883sxV7hQD+Iux+drt0DxKzhg+5+UY3Y3WZ2IDG6hRgx1UX/nZm8F5ZLn2tv4MSK9scRK91PMLOPEIrt/TV93GxmuwEz0/TtQMpHbRm/MbPNAE8j6QOJYJwq3kak2VvPzO4lFoX2GGzkMWv7FWOjmzY8ZmbLeQqgSOaVXd398wX9/A34m5kdC/zFc2YfM3ueF5h9usjkaLwS7+6nAKeY2avd/ew2F4D2Hj9dZProo5WMu/9f+tt21l94sEnbgI8AnwW2pIWfLjHy+Dzx4ziL+HFXGvgZb9jeHdi8QT/PJ7wK7qCBPyzxw76fmLbdQ7PV3p2Iqf4/qJkdFMguA6yQbTVtn0D45/4B+D3h6P2EBn1sS/hSHw1s26D9euk67E/FAl2u/RLpPvgZcF36v+67XInwJvl9+jxfB1ZseM0qFzmBy9Pfwdlb7fdC8WyibqR/A7lAFWJAUOdz20WmaCW+1BMlySxKmFreS/gEf5C0SFoh82Imevy8YJQyffQxhMwm6V7+O/Aw4U/f6PecbZOaT9fMLinY7V6zuJWTn0n49L4JeIm7F/kEZ20Pcvdj6/YNvH8nYUgfZ2/1kqeZmd1F2DX/1OT8k8zdwI6EqaPRxTazfQlvhH+k87I4LX9q034b9rMk8E+PWcm6xKi6NHTUOoTODsjPJDwSHhjNJxh37EWBVzNxwfLIEfYxF5iVfY/p88z1ioVhM7vR3Z81eByvXkhrLZPaLEo8FAFud/fKkZ6Z/ZAYdAyG6Q/62Q/KrUgoHyOCo2p/D21l+uij43ldB7yOGAxuTCys/5e7v6+ur/m00dB9boSb0S6EX9w9wPE17Yvc0+pGIZWhxQXtzyEWbNrIXADMaCnzC1rasYmp5HuJKfZXsq1GplXoKONDZ++kWejsN4gR+5LA7UQwySE1MkclmYUJN7A/AXvUyPyQMf/ud2ZbRfvVi7aaPj6ZfmzbEIOBM4FjamS+TZhHFk7bQcB3J0Fm4STzrbTtT417IjUh7CUyXdzmWsn00ccQMpkdeG5u35VtrmHTiLRWmNke7v51MytMNuIV7lJJ/pvA84gf0ueAS929cOW/42p0xu1m9g3CwyBvoy3zXvg3cGMawefbl7qMEUrmUjM7f0Cm6hrcRfhGt+F7RP6EH5EbtdRg7v6Qme1DPNSOyjwTSnjM3R81s1cRCViOr2kP4bb2gJntTnh/vIdQ9p+skHmxu7/bzHYiVpR3JhZ6vl4hs5q7v6TmXPJ8P/f/YkRwxB2Eq2MZ7yE8GfYjRkYXEolvqngLYQt/P2E7vBh48yTInEAo3sy+vGfa9z8VMlea2TPd/aaaY+fd5lZKtuy8x8Mqo5Dpo4+uMjkeSusMN5rZUcTvu4l30XwmRenmTqL1AlziZCIXQBPlcSXxwVdifPjxPMJOW8XihCJ8cW6fU+4y9t20tSGz/S6StiYcSvwgrqG5cl/C3d/T8tzM2oXbZqGzr6d56OzCZrYwYWL5rLs/krnCVcmkvy8l/LP/MuAKVkRjBQLg7s/Mv7bIcLdvjcxjhCI7oardgMwfiOloY7rIEPkmZuVe/9jM6haStwDeaGb3EPdZZsYqMmPk3ebyWehK3eY6yPTRR1eZjD0JD579iQyKTyHMWo2ZljXSrFvuhV5IT7l10ss7vCJ13oDc0sQN/fcGba8lQowHbc2nVMh8mJjm/KDJ+SSZ5xPuaVe4+ycs8hW8vUy5W6SlfAtwlbufnlbIX+vuH6/o40BihDiHcAFcHfi6u29ZIfNxQkn/g/DxXQ44z92fVyFzK2H2aKJAyo5RFxhwD8U+mqW2djM7uURm7xHLXA/s7O53pddPBb5V83nWKNrv1SkkD3D348veH4VMH310lRkFk6J00/D9tcBfian7IYSnwF3Ah7zeWP1NxtJGbpDcOq7ygcWFAZlNgOOBpxMjyplEiHLV4ttqSWZz4ia/nAgF/W1J+62BU4hVTiOecm/wilyaZrYBcCrhgQBhn3y9u99SIXOlu29W9n6JzDxihvFw2jKlU/r5pwozW8jdJwQuDLRZnlgV/nd6CC/j7vdVtG+lQAZMX1lOhRXdfbuKPlbMvVyMMHus4O4frJDJj4IWI7xZflc1a+ko80Lgq4SboBH+o3u5e9Fidl5uFuFdBPBTd68cHaff4n7EKNkJk9YX3P2fo5Lpo4+2MlYSFJHR6uE+SUr3TOARQgksD9xMKN8tgGe5+8tr5K9z940tF5ZpZnMGpk8TZGi5qmhmFxELPaemXXsAu7v7tiXtZxNmjzvS63WI6W9pwnGLkOb3ZTd/UtwfrVKqFj6wv2KirbnKH7g1ZrYysfC0PvHjzvop9C5JM46PEcli8u0rvSrM7GUFfVR6FaSH1WA/ExLmm9kyyWa8wuB7SabwmlmEQGc8SjxIz676kZYc53J336JF+xnAj8qucReZ5EVxIGHPXZdQuk28Fw4iPIMyc9pOwJeqRn/ptz2P8ZFvy7v7zqOS6aOPtjJlD/WMqtlBUeORb4wlHVkIuG/gvcookdTmSsLemkUXrU2KnqqQab2qSLHfZVUS8wk+vEX76j5v3TWgW/anLBnNB9Lrp1CTjIZYCNqHCDzYivB4KE2sQswEtiFs5WsQIb5H1PTxBeBrhHfEYYTJ5KQamcOIhbPfE/b9+4ipclHb83LXbDBzVuE1I2ZBn+xwX2+U2zYmTC219/PAMdYF7hy1DHBJh88zl1xSIWoiEoe4n1vJ9NFHV5nU5klElZIdgCe1ve6TtZD2MIDHSvfvBt5rsjh2GC1zL9BtVfFPFuVQTk+vd6Xa4+E6MzuJsZHx7oQZpIq7zewDjB9N31Ml4O51uQmK+DwpGQ3wIcJ5+3NAaTIaYjp9koU/82XAZRaJY8pY3N0vNjPzeLIfbmY/Jb6vMjZz9w0t/EyPMLNjKF+ozHgNkfXpBnffy6LkUaGXgKdZU5tr5mGyqCwNVUJ+oTYbHe9SJZDMPk4y9xAPkMoFzy4yxELiZwm3uflRnF5R4iodP/97/DdjK/ll3GBmm7j71elcn0ekLB2lTB99dJIxs/8hgkh+TFyr483sSHf/Sk1f85kspbuamR2XTir7n/R61Tphd78oLQxkTssHEaPdKvYkbHP5VcVX1cjsTUTMfZq4ua9M+8rYj4jGOjCd108Yc9Gp6uMIQtFkMntVCaTV/v0IOzhEhNEXvXrRrksymux4/5dMAL8DVqto/8801f2FRU22e4lIuCr+kf4+ZJHF7M+Ee1aljEetr0ctkuT/gcgPOwEz+zORhOhK4gdzrbs3cbe70cLF8CzGK6nSB4K7v6DBcQdluoTQd/H6ycxVebONU10w4GTgGjP7Tnq9I5HdrYrnAa83s1+n16sDt2U2Ty+2bbaV6aOPrjKHAM/2VOcx2fmvJGaJjZgsm+4bqt73ilX4imP+2t1Xr3i/dUTadMXMvky4TWXXaU/g3+5e6nNp4V62GVEeaKNkr73QC1IV5mReTiwePIVYUFyGMBecU9L+uYQpYjliNL0scFQ2UiiR+UA69jbEyNuBL7v7BypkPk8EeryOCHL4O2H2mfCwSkp5k/TZNyPqfN1NUsLufmZJHycX7HYv8BCwEn/znNAEn+u6kXTRCLSLTE52JW8RKTnQ5xakAYG7V/pdd7FttpXpo48hZC4Gtnf3h9PrRYgAoRdVHWvwwFO2URNlNtD2NzXvd4lIO4VUBSC9Xp6KKC7CzHERUf3h7myr6WMdIkrsQmJK8mMaFtms2zfw/u5ExNxvifwGdxAuRFP6HQ+c46LAsi1l1qRBpY1c+yWJ2c6dxINq8P2P5v6vzTWR2h2Wtm8Q0YLHpO3nxAOkSOaStF1FzCiuI0xRj5DyP4xIZgcid8Dv0ne/WYPPs0LV1kA+n/h+JaiuatJFpo8+Op7X14jcGIene+J6Yt3iYODgJvfTlPrpWo1f5EDbwpGujUWkZRVtM5YBHvWKJ5AVJK0u2pd7r1VJ+SQzh/hSBmVKbcHWwecytVuPGFEaEc5YmZkrjYbfxMR8BYUmFmtRsntAbrOCPiZ4IgzINCpznUwW2Sg3s1/PJkwOV/nEUdT8e67N/ZfaXwi82scnij/LKyLhzOwM4COegjaSV8a73P2No5CxyAexi7vfnmySR7n7VjWf4zFCQWdue3k7rnu13/FhtEh830Wmjz6GlCnF3Y+oeh8mz6bbCSvPgm9Eft0iholIm2Fmy7v7X1P/K1B9Tf7m7ufXHHOQR929cQRT4hDgEotkOZnPZZWtOfNTvsXdP5deN0kH2DZ0+CziAXJiw/aY2amEPf7GnIwTI4YymU8Qft63DsgU+UP/lhhtfJqocfZwk/PqyOqkReLEw8TDpIr1PBcl5+43W1TPGJXMo+5+e2p3jVUnoc84nsgpfAWxiHy5Nx99tU1830Wmjz46yTRRqnVMK6XLWBb8xu/5QH7UZNh+PvB3r3HAJ5T0lWb2rfR6Z2JqPo6cre0SM/sksSiW958tss8NU3H1cuBp5Hwuaz4HdKsc0TZ0uMsDZGMi/0KbKdWOxOijScXVzYnvfifgYDP7JTE9v4pwIxw8xhOSjdZy/8/Hq3NinApcO7DwVLc+cVuy0X+deHDsQX1u4DYyg59h3Ouiz+PuB5mZEYp3T2IF/kLgBHev9KyhW1WTtjJ99NFJxsw2Bt7HxFlY4+CIqVa649xTPNyW6oXMznb3V6f/zyNGODeb2ZOJp9Z1RMmTL7n7Z8qO4+5fswh4eEE6l1d5cULzLiXlZzPm9gPja8U5JavxiavStHf+SD2ZHKoUqOUVm8fqf933e56ZvdRrQoeHfIDcTPg11pWqz9O4zLW7Zwr2U+lc1yTsnKcQnhiLDYicyFhOkPz/tbj7RywSF21JfId7ec3CE+Gpsh/hgQMxWq97cLWRGfwMjT5TulcusfB2eR2xMPoLqpPYQ/vE911k+uijq8xpxG+5TfmtcUyW98Kp7r5nnfeAmb3R3b/a4fj5SLVbPOUzNbP3ElOz16dpwhV1TyCLaJ4nMv6p9etyicpjvcFbemaY2baeqkLYWO2qrxN26nz2oy+4+3rFRwEz+zbhWpavHPECd9+xoG3eD3RJQrk9kl67D4QO21jOgSIfzkIbYM5UtDRRxeNaxivqCQUTbaz6x6qEn+7FNEj4k2zZmV13c2JB9Cri+6+aPZViZoe6+8cK9s8iZlJOg7DZJLMIMWtxGubr6CLTlDSieyVhwlmZmLl9091/01B+WyJJlAEXeH1Vk9YyffTR8bxaRSAWHmOSlO6twPbEavrWTBzRDhXOOrAYMj/hs4U7x4nufsbgeyXHOYBYgfw9Y47h3maqUHZeHT/LG4ggkI2J7PTZdZsHfNUrfEjN7AlEOsAXMpYO8O0eGat6x8wqF3OKZjXWwdXQzP5EjKKvZMxN7M52ZzuRou/SxsJmzya+myZhs1vTPl9HYxkb84EvpOhBZWYPEqPa0wkvDx+QqbrP1gL+z1O4tEX+gie6+y9HJdNHH0PIbEMEUQ0OCOoCfsbwFu47TTfGalr9i4ahmS2Pf33u/3OBA4gfwF9JLmBEGPEtNce5k4ZlYBqeV6WLWlMZYoV8Ur6bgX52IufCRfjf7ljR/m1MdLF7a00fa5Erz5O+lzVrZJYEZuZez6QkeTwNXdCAQ0fwvXQJm51N2Kez1+sAs0clw1iJqi8RawEHpO0nwKdLZL5KBEYUbXWJ768DFsm9XoTwDR+ZTB99DCHz9SR3StNrNuEYbRq33QjD/GQcN1+98wnEivr3yNWEIuy076o5ziXUVIBteV5dqgUX+RcfRJgUjAh/vZ76elfrEE/fLO/FhsD7a2Ra1fxq2z693+XGvhpYKvd6KVpm5x/2uyn5Xm5i/ANkMeorTnfJ19FF5hJylSIIm/glw1yzFvdAnQ95K5k++hhCpvL7brLNYBJx9/3MbJaZ7Z+2TtP2AuavuLv7H9z9Le7+Ss9V3HX3Szxnz0v2wkHuJqo6HGpmB2fbEOdVm2m7IXt71BF7MfFQ2QsozVmbOJFIfv4IgLvPpT4RdtH3X7X4NiOtegPz7eF1ocYLeQNPIGoAACAASURBVM6NK/1fJ7OY5/IOp/+XqJGpo+13U9T+K0TY7OFmdjjxcKgLm51tZieZ2dZpO5H6fB1dZFZh/ALaUtRUQTCzg8xsGQu+bGbXm9mLq2SAP5rZfHu8mb2SSFc6Spk++ugqc7VFXunOTKr3gkUC6zczluDktORRUJk42Mw2JyI+MreMcYUZvUU58xxFDs+/Tlubqg5V1CXYKOKXBfuyH/xLgZPdfU5e2ZWwhLtfO9CszmXuOjP7FGPhuQdQ/eO+kFjx/UJq/xYiMVEVfzSzV3gKLW54Yz9oZht5csUzs+cwlsOhK20XL87Kv7DIOXENUT02C5tt4r3wFtrn6+gi83Eigcsl6fVWxG+oir3d/Vgz246xh/vJxPdcdW6nWSTXgfCTfn1NP21l+uijq8wWwBusWbWNQia7GvBcYFN3fzC9XpJwh6rzKGgd+dXgXFovchUco1PFWWsZkWWRF2BVwh46i7BpXurVeXvPJ8Jfz/LIvfAaYB93375CZkngA0AWtXchEQn1YEl7I0qdvAjGaoR5RVkliwrCpzE26votsKenaLsSmY2JbFlZhronExUq6kZ7pdhApKG1jMZLMle5+6Yt+pxBmAU2mEyZnOyTiCQuANd4RdL31H6uRwa4Y4n76zuD16lCdilCf8xrcX6tZProo62Mdai2Mchk++l2SR0H3SK/GmPlkW9AsTtT4nuMlaxu4rjfKSKLyHH7LGLR8SGLgI/KzGTEyOhLwHpmdi+xaLl7xXnNBL7nDRN1DCiDLzSUmQm8xd03aXpjJ5ktiVLi+YTcw7pMnTXwukshzwstqjp82xuMVjx8peeY2ere0A2xrYxNTJKTuX2tYmareHVqx9kWQRFrAYdauFlW+p6a2UeJUOP70+vliarL7x+VTB99dJVx919Zy2obE/qd5JHuwcTKaj6C56teEbCQ5D5OjO5qI79anEvet7e1O1OSu7ntCMTMbqNlRJZF7bKi8yp1M8rJLknYav9BjA5Pq2h7DjHq/FvD8zqN8AJo7MdsZj/2FlUSksyl7r51S5m2eSQq3QlLZLKSSI8C/2RsallVEurHRE6IaxmfQrLswd5KJmdOKMKrrn16kGYP9/vTw33VtB5QJlOUr6SutlwrmT76GEKmdbWNQSZ1pOvunzKzSymxgVku78EA2RSpSeRXU+YHaZQp1UEsF/mWaFVxNtElIisfvbYYUZxxNgWf3yK14dsIc8T3iJHb24iCk3OIqX0Z/wRusihblP9xl9XiejJwi0XhzEYKhLAztspbC1xh7RNytx25NorGy+Pd8tx2idVvLOMdcvzmuMjdt8kd688WJWy2qZCZaWaLegqvtvBtXbSmn7YyffTRVWYfInd1ZjL9BBGIMz2ULsz/oZT9WC6mILS1zY3U1FTgHSLfmBiq26ZkdcZKwK1JUVVGZOXe2yH/2syeAhxV0vxUwj/5KuIJ/G5iUXBHd7+x4rwAvp+2pnRRICsQicvzDwynunpEl4TcbfNIHAS818weZiyZe+Go1aI23NGEmWgucIi731t1cIvirG8hKhRnJYrqinG2lsnJNk58n/pZAlgpTanzkY+VHg+En+rFNlaxeG+qTWVdZProo6tMV5Pp2AEm07xQ23mJ0d7MliUixbIb6DLgyKJpcFdTQcPzGzfV6GJELzu/NueVFrDmuvszC967Kduf7KF/IkrXN15IeDxgHUrQtzj2T4kf40+I2libuntlVRKLitaPEKPv7YFfuftBo5bJyTZOfJ+myG8nFGy+nNYDRETnZwdlBuRfQm4x1d0vaHB+rWT66KPjeXUymY47xhQr3UL7iZmdTUzL8zfQrLobfbLOzzpWnB2i3ywHAYR99lnAL919j7JzLHtd0889FMwSvCSfqo3lbIAYTS9MfZn7kynuo8pLoLCkuVd4idj4EvSVI9eczCvIjQzd/bySduPsv02u8cDDcCGijNDIZXKyE6plF+0beP+ANrbIAvklCZvmru7+ssmQ6aOPtjLWstrGIFOdZayMtQdsqUeYWeVU2TqWB68hmzZ8A3g5EzOHQUnGMEuJMQYUVXbMSmVARHFlPEqUeS/zAZ5lZg/kjr14et2kn7zNfDEitWXhgwUm2jTNbEfC3lxFXpEtRtzYg8VKB8m7rC1GXPvKdIht7a1psfa5jNm8DzKzLdz9fwuaL2Zmz2bse188/7rE1jx/Wu9RoLXJaXWRyfi3ma3t4xPfF9q2zeyF7v5j4F4zmzCQqbK3WyTieSmRkOklRB6KSm+WtjJ99NFWxqJU1Urufn7eZGpmrzCzGd7GndFHHCbYZqMkhJSwT26Re7054d9bdazW5cGT3OLk4twH3qsMvS1ov36Ha7B87v+VCU+HCccFVu7pOyksC1PR/uqW7WdQU66oQGZRIgNUXbtXELbXo4GX17SdC8zIvZ5JSagtY2V0irbCz0IovAfSNo94eGb/P1AjM6+pTE52GyLQ51LCHPdLIstcUdsj0t+TC7bCPALAtkRE3r2ELXQHYvZVdU6tZProYwiZSynIGULY39vdz20at/yhzCDlAahoU1iPiZhOz0k3zq+ImkSzao41O/29KbfvpzUyOxC1xO7J9XvOEJ95qNwLwBnAVgVttgO+MQnf0Ua5bWNiEacqJv5Vue01RBRU5cOw4BjrAne2lFke+EVNm48TC7N7p+0i4OMV7efm7z9ihF+X32CxJvumaiMeThsSATWLjvjYjxHKfK3cvrr6gK1k+uhjCJnSnAtVv5mibdLMC97AydtLbKEeq+6zkjsUHnkI6uhSHvxwYnp8adavRRLsrnTJvZCXeaYXLLC5+wVmNphIfRTkj/koEVCxS0X7vFfFo8RD8ZVVHRSYV+4jlzujROamnMxMYgZQGfVHTBOf5e6PpWOcQjysi8wFEKaoLGzWCNvuoTV9XMlEb5uifeOwljmbLeWjrtuX9i9DpCP8hbv/y8zWJWZvs8zsAnf/fUU/BxGj23lE7o6NiIIARWHAzyFyefzIoozUGcR3U0VbmT766CqzeMV7TapUjDHJT94fE1/oxURu3XOoGEkCe6S/BxdtNX09l0jysRpxI51N+NNVyVyT/uazllWOdmqON+xI9+cV7e6YhO9npRZtVyZGw8uN+jwK+lojt61Kg0xwdBu5PpkwSbwSeNLAe+vn/n8S8UO9jaiplc0Otiai5ar6OIDwKLmFcAO7qcF5XT/weiHg1pK2XwLemHv9CyKv8peJxPdV/cxJf7dLv81ZTe5hwtz3WcL3/HzgzaOW6aOPNjKErfcjJOeD3P4jiOCI5vf3sD+Qmg+0VdFW0X7f9Pewgu2DNX1NKDdetG/g/ZMII/pcoibZ8XU3as3xhlW63wdeWtBme+D8EX4vrcp2A/8D/IGwtd8HvKJBH08jAhZuJhYiV20g8wTgM8Ti28eAZVp8pl0JU9RXCa+Xe4DXjeK7JFyELiEGEHl77jlEiaeq4zTO2UyMtAdtufMIP+ePlcjckFcEjB9AVNrnScqfCBzaaVC+wfnOIBT2yZMl00cfTWSI0ezpwF3EgO7s9N2eQS4NaaO+ut6Uk7kBmzfZN/B+Uf7TSiVIOIh/hKjScF36v7ONjpaLSkkm/yNZB/h5UhxZMupT0r51Rnh95xJljSCi/y6raX8zaSGP8NSoteMSvqZvImy4hxD5Cupkfpi+g+2IB+BXW36uRiPXtt9Lbl/r5PJ0yNlcpmBL2t408HqD/PdWI3sykbToF+m3sDQVCdaTYpqR/l+EGO0XrstUHKMy6f1A26VSH51mV9k9XvH+wgX7Kmd/6f7fIW1PLXi/9j6b7NwLmxA/nqenL2kmNX6dSa6oVEqZT+/2hD1vFyJsNGMZwhOgzqWpMRYpJ2909wfNbA/ihjjWazIMVdn0zGwFz9m2LTKZ7QZkOR5uIRbR/jnCz9HKt7eLL3BH39bWMk1pe6yK++1lhDdJ3i1xgr3ZxvIyr088eL7P+IjE0qrDaW1iN2Kh50MWEYlPdvdrC9rOAbbzgYxiZrYqMTsqjZa0FrkXknvgF4lFqLcA7yVc+9YB9nP3cyuuwfxdxGj+o0XXwMw+7+5vTf9vQcyQ7iI8BPb1loEvZvZrd1+9YP8LiEjORYmZwps9legZ9p5rIj/ZfrqfJQzWZxH2wNcT085CzGxTIgR05YEvbBnKDd2/I0apr2B8Lth5RHrIon66Zhk7gVigmEWE255ERCptVXYsG1+HLcvg5MQqMz6wmOgRB35y2fHSMVulGCygbdnu1Wx8La5xr704V0MX31az8WGpM/OvB69VS4ZOMG+RR3gJoirJlwkPjgmKMJH5DXfJ2fw54l55IVGl9+9p33ML2n6SqNL8TkKBQAwGjk7vFX2O9dz9dkLhAjy1gU/wYYTNd3HCs+i57n6HRZTm2UTZrEGOAH5ADBzmf6eUVyveJPf/h4hQ9uuTz/GZ6ViDn6WsRpwR5aeKOIp4UN1ikQL1IjPb092vZvj7pFa+j9wLd5rZTI+cqyeb2ZUVzRchphQLMf6LeYC4wYuOPweYY2anecM4deKG7MKj7u4WibiPdfeTrKaYIhHjv64PkQu4gMGy4m1pW7b7kIHXTRzB/49UFj1xX+61U5xHYdl07PyNmylnp7psfR1tp3QPF+zbzCP/7Fx3PyJ5lBQGErj7hDwVaWS5lNd74zzPIxLyhnSsvyZH/qJ+vm5RnPPDxKjaCSX3QS9Pj3owUVygyCOm7LshG02nEeQdad+v0ucqYn3iO1+S8A1+yKJidpMcHstkD2Z3vzvNFovYC3gnxalWdy2RWcTdb0nH/pZFJsBvm9n/0v4+GaRevoutpIVN5SeEIv0a8XR5Bw182oA1WvRxZvp7E2GrHLc1kF+EGHU+k1wtr5K2lxHTo58TK9ozqa+R1dqm1+CcWy/YdeynsJgjMfNYuo9zmKxrBlxc0GbCvoH3M2+Xq4m8BYtS7z/8jXS9lgRuJx5Gh9T1k+6t69PrlelQ9LTsuwQ27iB/A2P23P/O7Z9Jve34lURVlddQ7af7UPrd3kTMVJdP+0t9/gkPqcKFYJL/fcH+65ho81+NyHk9b5T3WdE22SPdPdMF259QuE8hKi/U8ZCZfZKJtrOiJ3CWEOTlbU8u2ee+QNiNDFjLzPb18hHCawlb2z7ufp+ZrU7JFC5HVoetsU1vGrEz4UUAgEVFh5OJUbGZ2f1EyZfSkW9RmCmRCP4mLykPbzHX3Z1YqDgyXecneYFNswUPp2MPk2HrPDNbjvjOrydGNV+ukXmGR96O3Ynp8XuI0XzVfXMckVDliWb2EUJZlSbWbkj+uzzRIqn86USIeWWIdeLNxADlnwPfw1Ooqd/n7t8zsx8RfvG/rWj69IHXWZ28FYDCfBzEtSlc73D3tUpk/pdYY7kv1/a3ZrY1kRZ1GIpmSBNObLJHGKVhthUyFxJ5K28j7KVfAT5R0X4m8KMO53Y78F+512tT43fZoY/DirYhjznUqKdrP8QoZMvc6y2o9zn9PvAXxtxs/pz2/YJIoF4kcwJhw7wtvV6e+grCjUauxEP6HuIBeHf6/x7CTrl/i2uzKA3KvxNT/YWJdY2t0r4ms731CAWwP/D0Sfgu10334q3ECO89NJxh0mJ22FWmjz46ntfZwMvIhZC3/i6G/TJrTrBTmC1jIb1zc/vq3JrOafIjGJD5ycBrG9yX9s9jLI5+wtawryVbntsawIvS/4uTm86Tcwua5O9vcEp+RUGbCfsG3j+XiJjKXj+RsIOuQPmUMZtW593pChUVMRNaISnN5dP/KxAVJG6rOK8DOlyPJYiaciem10+jPsfDgUR05A/S/bUGNeHpSW4LIuk/hHlhrbbnW/VdDrw3ixgF39Xg+3wZURLoUsLc9mtg+1HK9NHHEDIvIpIk3UWM8Cvd0gqPMcwX2eCLnk0sjrSK+CL5uwIXpAvzbOCuGpkz00U7iZieHQccV9I2yx9wQvoxvJFwgD8POKaijyOBtxLT62WIpNHvrjmvTYnRxK9zN/jna2TeRPgO35VeP40ae+MkfX83pL9ZBNanCbehrYkZyOeJQpZVxxj0IzWSsqU84VFjmyZDjFwJT5ndCK+a1wOvr2n/TcJrJTv/xQkXwrbXtdLGT4xAzyVFKBJmj0pl2PS7LNg/g7EEMPcB3605TuvZYVuZPvroKpNruyzhOvcbIhR8Lwr8fou2ybbpPuruf2vgjjLIhy0Smb+T8PNdhhL3rxxtqiDkcwj8njGXrz8So6UytnP35+Ven2Bm11Be1QEiwioLs8SjnHphDbQcbyNyQlyTZH5hZnV5JCaDrJjj4Cr3Ybn/veYYPzWz83LHeg3wE4v8pfeXyGQ2zSfU2TTd/VjgWGuZG9a6FQxd291fa2a7pr7/YSU3t5nt4eFZMOirmlFl09+JGGhkq/e/sygaOQyDJeW3JFb3dySCX84A3uH19fL+4O535l7fTUQrjlKmjz66ypD8mfcg1qxuIEa+WxADt63r5CdF6ZrZDwjFcbOZ7Ub4Wz6NmGpVuYwB4GPJpP9G+ETW4u6n1Lea37ausm4Z/04LImcQP9BdaVCPy91/M/DbrJP5l7s/nMlYJLSuU26NsfFJ0ifgye/W3TMn9mHqcL2NmFVkSZ9PAc72GC5MOG5yP7qHGFFuk2R29JrFHnc/3tqVut+YlgVDgYct6mh5Ote1Ka8KvUT620VZPuzubmZZP7UJVaymMGf2Xaa2vyFmhWcQrlylSXEKuCX9vs8krsPOwM+yBVMvzsXbVqaPPjrJmNm3CXv7qcAO7p7VPvymmV032L6IyRrpfpUwDZxKRFb9i3CduYBweq7EIkPUQT6+NPIxXl1toHUS87SSvQ8TvSTK+tmNiFM/lviSrkj7qvhNUgaefC0PpCYhN3CZmb2XCCjYljBpFDmfdyW7OTYnrlcWybczFT64ZvZEIppoFXff3syeQZSuOalMJimPy4lVXSeqIVQp/MfM7BiP4I/bm36gDiPXLgVDDyNClZ9iURl5c8I0VcSaEP66Zratu1/Uop8zzeyLwHJm9iYiVeWJNTJtCnNu4bkoSotMZe7NSjwtxsTZ4QrE7NEp9ltuK9NHH11lPuuRAH4C7r5x0f5BJi0MOD2dP0hkZD+VsZGVe427lBWXRi6sp5Z7/3LiR/Fp4qLtRXy+wypkziJ+2LsR9trdicWXRnWpmmBmKxFKen4dJuKBUhoskUZ7+wAvTjIXuHvdj67LuV1CJGp/JL1emKgTVTiyNbPzCZex97n7rDQCv8ELarflZHYh3KMuJT7LloSf6rcqZI4gPCW+3XQkai1L3afP/iwioqxRwdAktyIROWXE2sOfStrNDwe1DqGl6WGb//4rlbZ1Kyk/zgWQMPdUugAKaDmjmsBk2nQfIWKzFyWizNpo9xmWK89uUZus7lwXd/eLzczSU/xwi4KCpUqXMKLvbGavdPdTzCwbjY/DzN7t7keVTcu9vGQ56Ue5e825D3JAslXOV7RmdlDaN0pWIX5wWXjtUlT7qq7k7mea2aEwv6RM3ajqfUTI6B9g/jT4R0Cp0iUippYkzDmZD6Z7dc6OtiPXwxu2y2pi5cn6WN0iX3RVafjWmNnehIfDYCRgFa1LyhOLZ29195+mfrcglHBVvoa1iERMazJe6ZQ+rNrK9NHHEDJd1gLGMVk23ZcQCwXnABu5+0MtD3EMcKWZZT/MnYnsU1V0SWKe1aS638w2IFZv1yxol5kDGtls8pjZOoSXxBPdfQMz25BIjfjhCrE3EKPjPG8s2DcsH2cskTfENOvwivYPppFeZmvchLC7VzHDxwdB/JlYMS/FW9Y7S7Qqde/tqkRfR/jb/jG9HqyRVxS0k+WzMCbmuqBmtrcmsIdFXoPZhNngpx7J/ctoXFI+x7xM4abGl1skna/iu4SH0LmM5RKpo61MH310lemyFjCOSTEvpBHmWzzFN3c8xjOIm9kId6lba9o/l1COyxF242WBozySWJTJ/A/h7PxMwg69FBGz/oWBdrM8cjwUHWM/dz+hoo/LiNwFX8zMI2Z2s7tvUNB2V8LUsQXxQ8tYmiin/aKyfrpiZk8i0jtChLneV9Dm7YT92oiH6QaEElqZyFlceG2S7CeJkdPpaddrCbfBuuoRjSr15tpvVbS/TLlai8rGZvYOIpLyb8Ti03fc/e+D7QZkqmZYeIP8A2nR7k3Au4jsX3XVDRqRG7nvSSz4nU5ci9cCf3X391XIXuPjPXia9NdKpo8+hpA5Czgwt4DWmiktwV6GRdjnBLyixMlkYlHSY+dBW1eyPe5QZa8zs5+5+3PzNuky+1sa2axFLAjmy8zMIxRV04Q+jTBrFm5rZkcTPq3rETbwewkb7TfLbJoD8q8mFp2y4JPv1LQfrNS7KxEwU1Z6Z2gsVTZ29/dWtFkrncsriYTpH60ZfTbp91B3/9jAvvcT12spwiXpcmKkW/lDb/qgys1sinAvDrfPZHcj/MYvZPyMotTE0lamjz6GkClaC3B3ryxbNe4Y01Tp5mtkLU4oojvcff2CtudUHavGPtOoRpSZPYfwc9zd3a9KyuoEIpfojl6QNSrZ+n6dFp/2B87yyBz1GiJ3w/ZV590HZnYCKYWguz/dwkvkQncvSiGIhffFxoQC3jRt97v7M0Z8XnMZX+9sJrFgV2VrbDxyrTjG1e6+SU2b9Yl0pXsSgTFnNj1+yfEKc0cT1SO+T0RKXe01+ZT7elCZ2ceIz34XuVSlNYq6lUwffQwhk59RGTEr3bVIN5Ux6akdu+ADq+FpOrRvSfNNiaiQ04lggjaRGHu7+7Fmth1h/92LsWz6+fOZnUZC3zGztxFTPoCXuHtZgovvEkp8fyKKaz0zu5fwQd2j6qSsY/L3DjROIZhYnAhUWTZtvyMyQk3AJhaknP8W9bZGCDNRtsC3bE3bCXbgbORa1t7GJ+KZQTxMCkcgFvlcX0eMcH9DmBg+UqcIGzLhfk3fydLED3pbIkHN7919i4rjtC3M2ckFkAjceGrFfT8KmT766CTj7peZ2bMIM+AuxO/5C9VS45mWSncQj0TGhaMvYsV6W+LJvhsxOji9oT05u+FfStRGmpNGseMbhffEb4kFru8Sq+/7A0uZGV6cXDtLvH0X8CILF7oZ3swXsij5+381kGvLI2kUmS2MrUzBgoKZfYnwZZ5HPNiuBD7lybukiI6LYRlFlXpLp/0l/X/XIj9qGW0qG99JuLB9j8i3sTrw1uxWqVkUqz3VwR1pUXdLYmFzY0LR/3SwXQGtHlTEOsbJhIcJRMrSbxKLS2XMSf3URm4NIdNHH61k0oL46wg982fiOpl3CByalkp3YKV3BjFi/GNRW4/k6D8EfmhR6mZXIpXikV4fFjrbzC4kzBeHptFF0SrmbMZ+HPOIhadrSaM2ipNrr2oFWe1zP9RSN7P0fpvk710pCrf9QEG71Um5Ywl77m8pD+EtxCKMOR+AUmqfd/fTzexSYrpswHuKFvgGjt945Jr6aBOVeGTuWEu1kGtC0czsE4RZ4Tgiu9ojBW0G6VJSvosL4BOB283sZzT3b24r00cfbWVuJx58O3gKHU4LrK2ZlkqX8aGTmW3r7LLGSdm+jFC4axI3a2FG/wH2YaxG1EMW7lATfoxenpdz8DzWz42w/0GzCgtFPJSm+Tea2VGEX2htKGhb3P00M5tNTbitu78kzQDWJ+y57wQ2MLO/EEUqqwJQXkG4AK5CjCjWILxMSm1gZnaxu29DylcxsK+MNiNXzGw1woSzOaFQLyeCVibke3X3wyv6zR9zwqJYA84a3OHuL0vHW5i4zvd6Se7hnEzlg2rg3sxo7AJoZv9FKKnB73or4kE8tEwffXSVIbxXXgdcYmY/JExM3Ur7+BCZi6bDRsTyzybKlbROeUjUVjs6bTsMeS7XF/3f4ThrEKPCZdKN8Sly2ZBGeO1ObbJv4P3VCNeiY4kFiPtr2s8BVmQsY9kLgC+VtO2UprHjZ7+IeMAulLY3AheN6vvP7VuZMI18iQhG+ArwlRL5L5CqyRLmgVsJm/m9xGLNqO7NtxPK+b+Jh839hEvgz4FZJfLnARsW7N8YOHcUMn300VUm12ZJwuPnPKLSxQlEVGfz72KUN/KwG+GkfE7ZViLzGDHlH8x5O4+aXLdEcMDFRGz73umH2Lj8dcHx8iksG5Vjp6Rkc/qxrjzJ13swX+5M4NaCdgcST/bfEJmYTiXyQcyiJpkzcF36O4exci/XlrQdTNOYbU3SNK5GmEr+QMTTnw2sVtF+QkrGon1dv//cvisJc8EuxGjp1ZSUcgduyf3/dlKaRWLdYthyPfl78+h0Xn9Jf88iEhOVlh+noiQPJSWr2sr00UdXmZK2KxAL/D9u811MN/NCVjDyVcSN9vX0eldiujgBd6+MbqqhbMW3zhZWxnwbote4HuU4lbBZZ36zhxGLdEaEQz8KHO8FZb67kmx4WUKdBxibJj1MjMgGWZMI232Ht3cKv9+iNMxPgNPM7A/E9L+IK4mMT6/xyBr2BkJJ/ZJImFTFyanNzun1HmnftiXt/2RmezAWtJEtkAxDkQ15Ca8JBMmRX0XflmR68CgNNeSpjbs33wUTXABfCLzPzMpcAKuKoS5esr+tTB99dJWZgMcC+hfT1phhFNbIcffLPCKInu3ur3X3c9OWRWlNBvkyzU1WfEdN/tf0dsLG+Fx3X9HdlycW7TbvarQvwt0/5uFd8El3X8bdl07biu4+4YHj7ge7+7c6KFwIu+pDRD7kHxImibJ6dl8k0loeb5Fz+GOE+ehvFD8M8qzs7ie7+6Np+yoxWyhjb2L0eR9hM39N2jcMRZrxPDN7aUP5+83s5Ral6jcnrhcWiYUaK4MWFLkAXlPS9mcWGc/GYWb7UL520Vamjz66yoyM6RoccRvwMne/O71eC/iBuw8Wrhu2n10JE8O4FV93P2Og3ebufoWZLeruZflTGznXF8jks1HdAGzrA1FeHUPz2QAADKJJREFUyZXrQq/IstYVM1uVsCHnE378ZITH/8TgSK9oX9o/x91npf8/B/zR0yKW1WTRsih8+FXGj1z38urFt5FiZu/1XN7atG8eYQeszYmQ3JKOI2Z5n0kPDpIf+Yvd/Z1DnNv8e7PABfBqwhxW6gKYfHq/kz5Hppg2JvzId/Li8PFWMn300VVmlExXpfsSYmRzd9q1JrCvu0/IADaCvp5MLCpA2BqLvqTZ7v4c65Cir0H/eaVbmJOh7r0h+v44sSJ7K7mMSV6T3rBlH0URV3O9ILrMzG4mzD2PmtntwJuzB0Dd57cIYf4sESzjhKniIM/ljR1o3yXDVGWi8L4o8pIo8u4o8/hIq+8rEZnZrgSuIuyctcrAzF5A5N6AsEEX5pYdRqaPPrrKjIJpqXRhvhvYeunl7VUjzCH7yaoaOHC5F+QFMLOrCTenlzKW8Hs+XuBz22V0XKXUJ0nh30Gs4o782prZfsRi21MJk0LG0kS9rwlReWb2PuIa/4nwDd7I3T25+Jzi7puP8PzmEEEAN5HzzfaK7GMWvtI/JUZH/87JlLozJrlWyXvqGHhQZyXlLyFKxeRLyp9fNjsccAHcjFA+tS6AYnimldK1lLc2/b+zu5+Ve++jXpGMpGN/nycivfIZsO5y97cNtFuJSEL+CSIx+zi8oFRQl9GxhWP6g0VvAYu5+8JNjtMUi7wQO3tNxqyOx16WcPuakLzHiyP4MrlNgCcT5pQH0751gKW8OhFJ25ytXTJMdUkUPvKcCDY+edJBxFrAKoRrWaZ0HyCqFn+25lirEfbjzQhb+4ruvlyVjBiO6aZ0S7PtT9JI7xbCtzdzDp9BuIwUOu5bRYrHgratR8d9Y2ZnE25fFzM+Imfk52YtItI6Hr/VyNW6ZZj6MHClt0gUbh2S9zQ4ZpHJpnFhTjM7kFCymxN25isIE8MVxP3fNLes6MB0cxmzkv+LXo+CO4hpbGb3ewoRY1/Gn83sOzSIYiJGDS8iXHGma/mTzAd60jCzHYjgjsYRaR35p7tPCLuu4JlEhqkXksswRXFC8owuicKhfU6EOoqS5LQpzLkm3V0AxZBMN6XrJf8XvR4FKwK3WVQbgJgGXmUpXWTB1LSxL2jyQDjDzG5rOjrumyKzyCTwYaKm2I/c/dlp8WLXSejnWIvE4U1Hrl0yTHVJ4tMlJ0IdE0KHrUUZGXcvKwsvemC6mRcym6YRPoRZmZ/JsmluVfX+4NQ079KU21fnytQ4xr9vzOweimu+lVZQ7tDHde6+cZr+P9uj2u+17l6adrFjP21ztn6TqEXXJitVp0WxnIeMMVCdwwpyInTxkrCWhTnF1DGtRro+onIkLbgO+EdSBOsQ3hLne3lWpz9a+yimtpFSfZIvGb0YcY4rjLiPNhFpw9B25FqUYcq9ogJAwaLYQWa2Rd2iWJrCl5lx5kck5mhTTj2jS0l5MQVMq5Fu31hk2NqSWGW/mlDCD7l7YfXetr6gSab16HgqMbPLvTpZdtPjZJmcbiQyrs0gEoWsAXzfR1zmu+3I1TpUAJikRbH5ngi5fV28JC6hQ0l50T/TaqQ7BZhHSsd9iPwGR5lZac2rtOJe5TxflNqvy+i4F2x8afEsB+0wycfzfAZ4b+b2RUz5TzGzjYmKwzuUCXak1cjVu1cAGPWiWNGop0s59cNHcC6iBxZ4pWtmmxIjsH3SvmFMHDsTCyd59iZGx59mbHTcawRTBcfk/s9y0O4yomOv6e4TPEHc/TozW3NEfeTJO/TPH7kONrLhKgBMxqJYEa29JKqCOsT0YkFXum8nfjTfcfdbLGphXTLE8YpcebqMjnuhoaLpykgyOTWlxci1cwUA75YovI4JNuguXhI2gsKcoh8WaJvuqOkSwDEZQR8t+l6WGCFmq/GXAUe6e2H1gJbHPp3IM3riwP59iOQtrx22j3S8opHru9x9jZL2O6X2mxFZvM4AvuwNq4PUnEtR0ELjnAgDbYYKHbYGJeXF1LBAKl0z+4y7v93MzqXYZarT4kPRoshkyIyKFJF2M5E+EcLlapa7v6pcqvGxe8nkZGaPESPXfXIj17vr3N4sCoXuSCjrFxLX4DvufmGVXM0x8+G5nXIiJNmRhA5bh6x3YvJZUM0Lp6a/R1e2as8Ep/UGTOVTb213f3Xu9RFVC4ltcPffA5vZ+ExO3/fRZ3LqVLsqLfCdRrixrUDY4/+XCK7oSv673JexnAizc+f0APC5muN0KafeqjCnmDoWyJFunuSIjrsXVhseaJsFOmxBrMYPHegwxSPdq4BD3P3y9Hpz4Gh333QqzmcYJmPk2uEchsqJkJOZC2ztKTFQeihcWuWaZmYn515mi6Intg3+EJPPAql0zSaWxSFu1MqyOGZ2ERHokI2U9wB2d/fOgQ5WkPi6L9LC0ymMuT79FXjjdA1bbkpu5Prasoi0Seq3cDrfIidC1r5Rcn3xn8mCqnTfQUzh3uzu96R9TyUqe/7Q3T9dIjfBab1FGPDIRsejxsyWAXD3B6b6XKYzXRbFynIieE0mtw6hw9M23FwM4ENUGP1P3Qj72ITKp0RNrdKqq0RY5h6EL+/M9P/FNX2NvNT3CK/DR4Hlcq+XBz481ec13TaGKA1PZFSzEZ9PUan3aXufaRu/TavClD2ysA/UIYP5dt2qpDpdihm2LZjYJ9u7+/3ZC48aWU2LKC5I7Esshq2X/mbb96hfFMtyIoySooXC6XyfiRwLqvdCVVKU0ve8JtChhMko9T0qZlqunJCZLQ4sOsXnNO1w92OJ1JGtF8WIWmS3WqQPHVVOhCKb4HS+z0SOBdWm26osjplNKNGTw939QxV9tU6S0xdm9m7iIXIycW57A+d4KpkkJtJhUawwfagPEbZb4iUxbe8zMZ4FUum2xcyKSl8vSeRrWNHdl+r5lEaGReXlFxEPnAt9EiouP17ouig2CeehoIf/YKR0W2JmSxMJSfYBzgSO8QJfyGFGx32RfFuzfMLrAutSnU94gcY6JArvkhOho5dE65LyYmpYUG26rUm+nwcTGclOIcqD/7VCpMh8MX90DEy50iUSi29pZssTnhnXERWRC/MJi/aJwn0geU2WE6GobS50eKX0neRDh1ep6eq7RGHOc8kV5hTTDyndBpjZJ4FXAV8CnukNSpa7+/y0ibnR8V5EmOoxZXI9U5RP+IapPqlpzNCLYu7+XTMrC+cdJnS4bWFOMUXIvNCAlFTlX0TUWv6CGRV5TgtGx8fWjI57JSnYtxK5fvfxSG95k7s/c4pPbVrSZVGsJCfCVl4Rat0xdLh1SXkxNWik2wB3b+3P3GV0PAWMOp/w45qOHgf5ChlZToTSOmypnzbl1DO6lJQXU4BGupNE19GxmL70lSi8i5eEmd0ObOgtSsqLqUEj3Umiy+i4LyYrn/DjnTaLYrk2XXIibEz7cupziPptyio2zZHSXTCZrHzCCxQ1i2IZJxOZ6XZOr/dI+6oy03Upp966pLyYGmReWMBpk094QafjoliXzHSty6lbh5LyYmrQSHcBpCifsJnV5hMW7RfF6JYT4fC2J+bdS8qLntFIdwGkaz5h0Z7JzolgLQtziqlHSncBJPnnbjuY3jKZGi70KSofNN3pK1F4Gy+JroU5xdQh88KCSWk+YTOryie8oNN6UaxLToSWXhKdCnOKqUMj3QWQotSATd5b0Om4KDaHyIlwE7mcCG0DLeoyi02HwpyiGRrpLpjMMrOiemhGlKYRxXRZFGudE6FLOXWfnJLyYhLQSFeIhnRZFOuSE0Hl1B/fSOkKMYmY2ceInAh3kcuJ4D2WhhfTCyldIRrSZVGsS04ElVN/fCObrhDN6ZIovEtOhC6hw+I/BI10hWiImV3j7s9rKXMpsCHQOCdCFy8J8Z+DRrpCNOdYMzuMdonCD8v9Pz8nQk0/Kqf+OEZKV4jmtE4U3jEnwt6El8SnGfOS2HuoMxfTBpkXhGhIm0Ux5UQQZWikK0Rz2iyK3U7kRNghlxPhHU06UTn1xzdSukI0p02i8GFyIqic+uMYmReEaEiXROFdciJ08ZIQ/zlI6QrRgoJFsW83LZeey4nw2qqINJVTf3wjpStEDX0viil0+PGNlK4QNfSdKFzl1B/fTNsy4UJMI14N3Ecsip1oZtswuYnCMy8J8ThEI10hGtJXovAuocPiPwcpXSE60HRRrOOxVU79cYyUrhDTkGG8JMT0RsERQkwTSrwkzN1fMKUnJkaKRrpCTBNUTn3BQN4LQkwf+vaSEFOARrpCTDNUTv3xjZSuENOYyfSSEFODlK4QQvSIbLpCCNEjUrpCCNEjUrpCCNEjUrpCCNEj/w9g9qeMekj51QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(test_data.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25a97cc6c40>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train_data['Response'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def numeric_distribution_plot(df):\n",
    "    \"\"\"\n",
    "    Description : Gives distribution plot for all the numeric features\n",
    "    in the dataframe passed\n",
    "    \n",
    "    Argument : Dataframe\n",
    "    \"\"\"\n",
    "    for col in df.columns:\n",
    "        if df[col].dtype != 'object':\n",
    "            print(df[col].describe())\n",
    "            plt.figure(figsize=(12,5))\n",
    "            plt.title(\"Distribution of \"+col)\n",
    "            ax = sns.distplot(df[col].dropna())\n",
    "            plt.tight_layout()\n",
    "            plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    1568.000000\n",
      "mean     1970.073342\n",
      "std        11.920781\n",
      "min      1900.000000\n",
      "25%      1960.000000\n",
      "50%      1971.000000\n",
      "75%      1979.000000\n",
      "max      1997.000000\n",
      "Name: Year_of_Birth, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count      1552.000000\n",
      "mean      62381.186598\n",
      "std       32089.169563\n",
      "min        2076.000000\n",
      "25%       41612.400000\n",
      "50%       60964.200000\n",
      "75%       81493.200000\n",
      "max      799999.200000\n",
      "Name: Disposable_Income, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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97spzjnjsvpFx/dsvn9GC+pTe/hsnqLWpTu6uA4cmtPPgmLb3j2jjswf1+J7hWdUEAAAAxFm5m1y8XdL3Z9ppZldLulqSVq2i0cFc6RsOGlyU2qL9SM0qzlnVodse7dU//fRJLZphTa2hzKS+eNfTcklXvSQIV1JwL9eilgYtamnQmSva9HjvkLbsGTq2NwMAAAAkSNmaXJjZKxQErPfPdIy7X+/uG9x9Q2dnZ7leGlMUWrTP5h6sggtOXKSaGtMNdz2t/9q2T5nJ3PP2j03kdONdz+jQeE5ve8kaLZ7hNWvMdFJ3Wlv3jnA/FgAAACpWWQKWmZ0p6QuSLnP3/eU4J45f3/C4GutqlG6Y/QBla2Od3nLBarU21em2R3v1sR88oVsf7tG+4XFN5vL60t3Pqn94XFe+eJVWdDQf8Vzrl6Q1NpnTQzsHZl0XAAAAEEez/gRuZqsk3SLpze6+dfYlYbb6h8fVlW6ccf2qY7WuK611XWntPjimXz61T/c9c0B3b9+vjuY6HRyd1BvPXal1XemSzmOS7tjSpxet7ihLbQAAAECclNKm/auSfiVpvZntMrO3m9k1ZnZNeMiHJS2S9Hkze8jMNs5hvShB39DsWrTPZHlHk96wYaXed/F6XXRKl8xMv3vWMp21orRuhU31Ka1a1Kyfbekre20AAABAHJTSRfCKo+x/h6R3lK0izMroeFaHJnKz7iB4JOnGOl10crcuOrn7mJ+7vjutHz22V31DmZKbcAAAAABJUbYmF4iHwx0E5zBgzcb6JcFUwju29EdcCQAAAFB+BKwK81zAiufo0JLWRi1pbWSaIAAAACoSAavC9A9nVJcytTXXRV3KtMxMrzi5U794cp8mc/moywEAAADKioBVYfqGx9WZblBNmToIzoUL13dpZDyrjc8cjLoUAAAAoKwIWBWmL2zRHmcvfcFi1aVMdzBNEAAAABWGgFVBxidzGhybnJMW7eXU0lCr809YpNufIGABAACgshCwKkj/SLw7CBa7cH2nnuwb0c4Do1GXAgAAAJQNAauCFDoIxn0ES5JecXKXJOmOrbRrBwAAQOUgYFWQvqFxpcy0aEH8A9baxQu0amGz7mCaIAAAACoIAauC9A9ntKilXqma+HYQLDAzvWJ9p+56ap8yk7moywEAAADKgoBVQYIOgvEfvSp4xcldykzmdff2/VGXAgAAAJQFAatCZCZzOnBoQp0xb9Fe7MVrF6mxrkZ3bOE+LAAAAFQGAlaFeGb/IbmS0UGwoLEupZecuFi3P9End4+6HAAAAGDWCFgV4sm9I5KkrtbkBCwpWHR4x4FR9YcdEAEAAIAkI2BViG19IzJJi1uSFbBOWZKWJG0NAyIAAACQZASsCrGtb0QdC+pVl0rWr3RddyFgDUdcCQAAADB7yfo0jhlt6xtJ1P1XBYtb6tXRXKcn+whYAAAASD4CVgXI5vLavi+ZAcvMtK47zRRBAAAAVAQCVgXYcWBUkzlPVIv2Yuu6WrR17zCdBAEAAJB4BKwKsK0v7CCYwBEsSTqpO63hTFZ7h+gkCAAAgGQjYFWAHQdGJUmLWuojruT4rOtukUSjCwAAACRfbdQFYPZ6BjJqqkupqS4VdSkluemeHc97PDKelSTdfN9O7To4NuPz3nT+qjmtCwAAAJgtRrAqQO/gmJa1N8rMoi7luLQ01Kq5PqW+oUzUpQAAAACzQsCqAD0DY1rW3hR1GbPS3dqovQQsAAAAJBwBqwL0DGa0tC2ZHQQLutIN6hsep5MgAAAAEo2AlXAT2bz2jYxraVvyR7DGs3kNjk1GXQoAAABw3AhYCbd3KCN3aXnCpwh2tQYt5vuGadUOAACA5CJgJVzPQNB1b2l7sqcIdoeLJHMfFgAAAJKMgJVwPYNhwEr4FMEFDbVa0FCrPhYbBgAAQIIRsBKuZyAY8VmW8BEsSepON2jvMCNYAAAASC4CVsL1Do6pralOzfXJXzO6q7WRToIAAABINAJWwvUOZBK/BlZBd2uDJrJ5DdBJEAAAAAlFwEq43QNjWpbwNbAKusJGF300ugAAAEBCEbASrncwk/gOggXdYav2vTS6AAAAQEIdNWCZ2Q1m1mdmm2bYb2b2GTPbZmaPmNk55S8T0xmdyGpwbDLxHQQLmutrlW6oVR+NLgAAAJBQpYxg3SjpkiPsv1TSuvDrakn/PPuyUIpCB8GkLzJcrKu1gREsAAAAJNZRA5a7/1zSgSMccpmkf/fA3ZLazWxpuQrEzHoPr4FVGVMEpUInwYzydBIEAABAApXjHqzlknYWPd4Vbvs1Zna1mW00s439/f1leOnq1jMQBKxK6SIoSd3pRk3mXAOjdBIEAABA8pQjYNk026YdfnD36919g7tv6OzsLMNLV7eegYzMpO7WyhnBKjS6oJMgAAAAkqgcAWuXpJVFj1dI6inDeXEUvYNj6mxpUH1t5TSDLLRq3zvMfVgAAABInnJ8Mr9V0lvCboIvljTo7r1lOC+OImjRXjnTAyWpqT6l1sZaRrAAAACQSLVHO8DMvirpQkmLzWyXpL+WVCdJ7n6dpNskvUbSNkmjkq6aq2LxfLsHxrS+Ox11GWXX1dqovbRqBwAAQAIdNWC5+xVH2e+S3lm2ilASd1fvQEYXntQVdSll151u0L3PHFLeXTU23S1+AAAAQDxVzs07VWZwbFJjkzkta6+cBhcFXa1BJ8GDhyaiLgUAAAA4JgSshCosMlxJLdoLutNhJ0EaXQAAACBhCFgJVYmLDBd0hW3n99LoAgAAAAlDwEqoSlxkuKCxLqW2pjpGsAAAAJA4BKyE6hnMqLbGtLilIepS5kRXuoERLAAAACQOASuhegfGtKStUamayuyy15lu0P6RCQVNKgEAAIBkIGAlVM9gRsvaKm96YEFbU50mcnllJvNRlwIAAACUjICVUD0DY1pagS3aC9qa6iRJA2O0agcAAEByELASKJ937R3KaGkFj2C1hwFrcGwy4koAAACA0hGwEmjfyLgmc67llTyC1VwviYAFAACAZCFgJVDPYNBdr5JHsNKNtaoxaWCUgAUAAIDkIGAlUG+4BlYl34NVY6bWxjpGsAAAAJAoBKwE2l1YZLiCR7CkoNEFAQsAAABJQsBKoN7BjBrratTeXBd1KXOqrZmABQAAgGQhYCVQ7+CYlrU3yawyFxkuKIxg5VlsGAAAAAlBwEqgnoHKXmS4oK2pTrm869B4NupSAAAAgJIQsBKod3BMS9sqt8FFQXsTrdoBAACQLASshJnI5tU3PK6l7VUwghXeY0ardgAAACQFASth9g5l5K6KXmS4oK0pCFiMYAEAACApCFgJ01sFiwwXLKhPqbbGCFgAAABIDAJWwvQOhmtgVcEIlpmxFhYAAAAShYCVMIVFhqthBEtisWEAAAAkCwErYXoHMmprqtOChtqoS5kX7Sw2DAAAgAQhYCVMtbRoL2hrqtPQ2KRyeRYbBgAAQPwRsBKmZyCjZVXQor2graleLmk4wygWAAAA4o+AlTDVOIIl0aodAAAAyUDASpCxiZwOjk5W1whWYbFhAhYAAAASgICVID1V1KK9oL0wgjVKwAIAAED8VUcruoS56Z4d025/qn9EkrR595DGJqY/ptI01qXUUFvDFEEAAAAkAiNYCTKcyUqSWhqrKxezFhYAAACSgoCVICNhJ710Q13Elcwv1sICAABAUhCwEmRkPKtUjamxrrp+bW1NdRoYnYi6DAAAAOCoquuTesKNjGfV0lArM4u6lHnV1lSnQxM5ZSZzUZcCAAAAHFFJAcvMLjGzLWa2zcyunWZ/m5l918weNrPNZnZV+UtFIWBVm7ameknSnsFMxJUAAAAAR3bUgGVmKUmfk3SppFMlXWFmp0457J2SHnP3syRdKOkTZlZf5lqr3kimWgNWcM9ZoU09AAAAEFeljGCdJ2mbu2939wlJN0u6bMoxLiltwdy1FkkHJGXLWik0PJ6tug6C0nNrYfUOMIIFAACAeCslYC2XtLPo8a5wW7HPSjpFUo+kRyX9qbvnp57IzK42s41mtrG/v/84S65OeXcdGs8qXY0jWM1hwGIECwAAADFXSsCarqOCT3l8saSHJC2TdLakz5pZ6689yf16d9/g7hs6OzuPudhqNjaRU96rbw0sSapL1ai5PqUe7sECAABAzJUSsHZJWln0eIWCkapiV0m6xQPbJD0t6eTylAgpaHAhqSrvwZKCaYI9A4xgAQAAIN5KCVj3SVpnZieEjSsul3TrlGN2SLpIksysW9J6SdvLWWi1G85Ud8Bqa6rjHiwAAADE3lE/rbt71szeJemHklKSbnD3zWZ2Tbj/OkkfkXSjmT2qYErh+9193xzWXXWqfQSrrblOm3uGoi4DAAAAOKKSPq27+22Sbpuy7bqi73skvbq8paFYIWClG+siriQabU31Gs5kq3YtMAAAACRDSQsNI3ojmaxSNabGuur8lbUdbtXOfVgAAACIr+r8tJ5AhZGbYKmx6tN+eLFh7sMCAABAfBGwEmJkfLKqp8YdXguLESwAAADEGAErIUYy1X3vUWtjncxEq3YAAADEGgErIUbGs1W5yHBBqsbUlW5giiAAAABijYCVAHl3uudJWtrWpN5BRrAAAAAQXwSsBBibyCnv1bsGVsGy9kYWGwYAAECsEbAS4PAiw1U8RVCSlrU1qWdwTO4edSkAAADAtAhYCXB4keEqH8Fa2t6kzGReA6OTUZcCAAAATIuAlQAjmXAEq8oD1rK2RklSD/dhAQAAIKYIWAnAFMHA0vYmSVIP92EBAAAgpghYCTCcySplpqa6VNSlRKowgkUnQQAAAMQVASsBCmtgmVnUpURqcUuD6lLGCBYAAABii4CVACPjk1V//5Uk1dSYulsbGcECAABAbBGwEoBFhp+zrL2JtbAAAAAQWwSsBBjJELAKulsb1TdMwAIAAEA8EbBiLu9++B4sSJ0tDeobHo+6DAAAAGBaBKyYy0zklHfWwCroam3Q6ETucOt6AAAAIE4IWDE3zBpYz9OVbpAk9Q0xTRAAAADxQ8CKucOLDDOCJUnqSgdrYTFNEAAAAHFEwIq5kQwBq1hXaziCRcACAABADBGwYq4wgpVmiqAkpggCAAAg3ghYMTcynlXKTE11qahLiYW2pjrV19aonxEsAAAAxBABK+ZGMlktaEjJzKIuJRbMTJ0tDQQsAAAAxBIBK+aGxyfpIDhFVytrYQEAACCeCFgxNzKeVbqhLuoyYqUr3aC+Ye7BAgAAQPwQsGJuJJOlg+AUnWlGsAAAABBPBKwYc3cdGs8xRXCKrnSjBkYnNZ7NRV0KAAAA8DwErBgbm8wp584I1hSFVu00ugAAAEDcELBibJhFhqfFYsMAAACIKwJWjBUWGWaK4PN1pRslSX1DBCwAAADECwErxg4HLEawnue5KYJ0EgQAAEC8ELBibCScIpgmYD3PopYG1Rj3YAEAACB+SgpYZnaJmW0xs21mdu0Mx1xoZg+Z2WYzu7O8ZVankfGsakxqrE9FXUqspGpMi1po1Q4AAID4OerQiJmlJH1O0qsk7ZJ0n5nd6u6PFR3TLunzki5x9x1m1jVXBVeTwhpYNWZRlxI7XayFBQAAgBgqZQTrPEnb3H27u09IulnSZVOOeZOkW9x9hyS5e195y6xOI+NZGlzMIFhsmHuwAAAAEC+lBKzlknYWPd4Vbit2kqQOM7vDzO43s7eUq8BqNjKepcHFDLrSDXQRBAAAQOyU8ul9uvlpPs15XiTpIklNkn5lZne7+9bnncjsaklXS9KqVauOvdoqMzKeVXdrY9RlxFJXulH7RsaVy7tSNUyhBAAAQDyUMoK1S9LKoscrJPVMc8wP3P2Qu++T9HNJZ009kbtf7+4b3H1DZ2fn8dZcFdz98D1Y+HVdrQ3Ku7T/EKNYAAAAiI9SAtZ9ktaZ2QlmVi/pckm3TjnmO5JeZma1ZtYs6XxJj5e31OoyNplTzl1p7sGaVmEtLKYJAgAAIE6O+und3bNm9i5JP5SUknSDu282s2vC/de5++Nm9gNJj0jKS/qCu2+ay8IrXWENLEawpteZDqZO9o8QsAAAABAfJX16d/fbJN02Zdt1Ux7/o6R/LF9p1W1kPAxYjGBNqzCC1c8IFm9oVPcAAB0LSURBVAAAAGKkpIWGMf+GxxnBOpLOwhRBWrUDAAAgRghYMVWYIpgmYE2rsS6ltqY6FhsGAABArBCwYmpkPKsakxrrU1GXElushQUAAIC4IWDFVGGR4RpjjaeZdKYbmCIIAACAWCFgxRRrYB1dV7qBKYIAAACIFQJWTI2MZ+kgeBRdrY3qGx6Xu0ddCgAAACCJgBVbwRTBuqjLiLWudIMmsnkNjWWjLgUAAACQRMCKJXc/fA8WZlZo1d4/wn1YAAAAiAcCVgxlJvPK5Z0pgkfRlW6UJDoJAgAAIDYIWDE0lJmUJKUJWEfU1VpYbJiABQAAgHggYMXQ0FgQsFobuQfrSLrShYDFFEEAAADEAwErhgojWG1NBKwjaWmoVVNdiimCAAAAiA0CVgwNHh7BYorgkZiZulpZCwsAAADxQcCKoaGxrBbUp1Sb4tdzNJ0tDUwRBAAAQGzwCT6GBscm1cr0wJIwggUAAIA4IWDF0FBmkgYXJepKN6qfe7AAAAAQEwSsGBoam6TBRYk60w0aHs9qbCIXdSkAAAAAAStuxrM5HZrIqbWJBhelKLRq72eaIAAAAGKAgBUzhZbjjGCVpqu1URJrYQEAACAeCFgx0zsYBAXuwSrNc4sNM4IFAACA6BGwYmbPUBiwGMEqyeGANcQIFgAAAKJHwIqZPYNjkpgiWKqO5nrV1hgjWAAAAIgFAlbM7BkcV32qRg21/GpKUVNjWtzCWlgAAACIBz7Fx8zeoYxam+pkZlGXkhgsNgwAAIC4IGDFTO/gGC3aj1FXuoE27QAAAIgFAlbM7B0aVxsdBI9JZ7pR/bRpBwAAQAwQsGIkn/fDUwRRuq50g/YfmlA2l4+6FAAAAFQ5AlaM7Ds0rmzeCVjHqKu1Qe7SvpGJqEsBAABAlSNgxcjeweA+IqYIHpuudKMkqY9pggAAAIgYAStGesM1sGhycWyeW2yYRhcAAACIFgErRvYOBSMwTBE8Nl2tYcCikyAAAAAiRsCKkd7BjGprTC0NjGAdi8UthYDFFEEAAABEi0/yMbJnKKOudINqWGR4Wjfds2PGfc31Kf1y2351pac/5k3nr5qrsgAAAIDDGMGKkb1DGS1pa4y6jERqb6rT4Nhk1GUAAACgypUUsMzsEjPbYmbbzOzaIxx3rpnlzOz3yldi9egdJGAdr/bmeh0cpU07AAAAonXUgGVmKUmfk3SppFMlXWFmp85w3Mcl/bDcRVYDd9eewYyWtDZFXUoiLVwQBCx3j7oUAAAAVLFSRrDOk7TN3be7+4SkmyVdNs1x75b0LUl9ZayvagyPZzU6kdOStoaoS0mk9uY6TeZchyZyUZcCAACAKlZKwFouaWfR413htsPMbLmk/ybpuiOdyMyuNrONZraxv7//WGutaHsHgw543a1METweC5vrJUkHDzFNEAAAANEpJWBN19Ju6jysT0l6v7sfcfjA3a939w3uvqGzs7PUGqvCnnANrKVtTBE8Hu0LwoDFfVgAAACIUClt2ndJWln0eIWkninHbJB0swXtxRdLeo2ZZd3922Wpsgr0hiNYS1obta1vJOJqkqejOVicmREsAAAARKmUgHWfpHVmdoKk3ZIul/Sm4gPc/YTC92Z2o6TvEa6OTWGKYFcr92Adj4balJrrUzo4Sqt2AAAAROeoAcvds2b2LgXdAVOSbnD3zWZ2Tbj/iPddoTS9QxktXFCvxrpU1KUkVqGTIAAAABCVUkaw5O63SbptyrZpg5W7v232ZVWfvYMZGlzMUntzvfYMjkVdBgAAAKpYSQsNY+71Dma0lEWGZ2Vhc50Ojk4qz1pYAAAAiAgBKyb2DjGCNVvtzfXK5V0jmWzUpQAAAKBKEbBiYDyb0/5DE4xgzdJCWrUDAAAgYgSsGOgbGpcUtGjH8WsvtGonYAEAACAiBKwYKCwy3M0I1qx0NBdGsGjVDgAAgGgQsGKgsMgwUwRnpy5Vo3RDLYsNAwAAIDIErBgoLDJMk4vZ62AtLAAAAESIgBUDe4Yyaq5PqbWxpGXJcATtYat2AAAAIAoErBjYM5jRktZGmVnUpSTewuZ6DYxOsBYWAAAAIkHAioE9rIFVNh3N9cq7NDTGKBYAAADmHwErBvYMZmhwUSYdC+gkCAAAgOgQsCKWz7v2DmVo0V4mHYW1sOgkCAAAgAgQsCK2/9CEsnlnBKtM2prrZGKxYQAAAESDgBWxPbRoL6vamhq1NtURsAAAABAJAlbE9gwFAWsJAatsOmjVDgAAgIgQsCK2Z3BMkpgiWEYdzfXcgwUAAIBIELAitmcoo1SNaVFLQ9SlVIyOBfUaHJtULs9aWAAAAJhfBKyI7RkcV3e6QakaFhkul47mOrmkQdbCAgAAwDwjYEVsz9AYLdrLrL25sBYW0wQBAAAwvwhYEdszmKHBRZktLAQs7sMCAADAPCNgRcjdtWcwQ4v2MmttqlONMYIFAACA+UfAitDugTEdmsjpBV0tUZdSUVI1prYmWrUDAABg/hGwIrRp96Ak6fTlbRFXUnnaadUOAACACBCwIrRp95BSNaaTl6SjLqXiLGyuZ4ogAAAA5h0BK0Kbega1rqtFjXWpqEupOO0L6jSUySqby0ddCgAAAKoIASsi7q5Nuwd12jKmB86FQifBAe7DAgAAwDwiYEWkb3hc+0YmdMby1qhLqUishQUAAIAoELAiQoOLubVwQRCwDhCwAAAAMI8IWBHZtHtIZtIpSxnBmgvpxlqlzJgiCAAAgHlFwIrIpp5BrV28QAsaaqMupSLVmKm9uU4HaNUOAACAeUTAisjm3YNMD5xjHc31GmCKIAAAAOYRASsC+0fG1TOY0el0EJxTHQvqdIApggAAAJhHBKwIbOoZkiSdRgfBOdXRXK9D41lNZFkLCwAAAPOjpIBlZpeY2RYz22Zm106z/0ozeyT8+qWZnVX+UitHoYMga2DNrQ5atQMAAGCeHTVgmVlK0uckXSrpVElXmNmpUw57WtLL3f1MSR+RdH25C60km3sGtXpRs9qa6qIupaJ1NAc/X+7DAgAAwHwpZQTrPEnb3H27u09IulnSZcUHuPsv3f1g+PBuSSvKW2Zl2bR7iPuv5kHH4bWwuA8LAAAA86OUgLVc0s6ix7vCbTN5u6TvT7fDzK42s41mtrG/v7/0KivI4OikdhwY5f6redDSUKuG2hr1DWWiLgUAAABVopSAZdNs82kPNHuFgoD1/un2u/v17r7B3Td0dnaWXmUF2dwb3H/FCNbcMzOt7GjWzoOjUZcCAACAKlFKwNolaWXR4xWSeqYeZGZnSvqCpMvcfX95yqs8m3eHHQSXMYI1H1YubNKewYxGJ7JRlwIAAIAqUErAuk/SOjM7wczqJV0u6dbiA8xslaRbJL3Z3beWv8zKsalnUMvaGrWopSHqUqrCyoXNyrv06K7BqEsBAABAFThqwHL3rKR3SfqhpMclfd3dN5vZNWZ2TXjYhyUtkvR5M3vIzDbOWcUJt2n3oE5bzvTA+bKqo1mS9MCOgYgrAQAAQDWoLeUgd79N0m1Ttl1X9P07JL2jvKVVnpHxrLbvO6TfPetIPUJQTs0NtVq0oF4P7jh49IMBAACAWSppoWGUx+O9Q3KXTqeD4LxatbBZD+wYkPu0vVkAAACAsiFgzaNNu4P7gM5giuC8WrmwWftGxrXr4FjUpQAAAKDCEbDm0abdQ+pMN6irtTHqUqrKqoWF+7CYJggAAIC5RcCaR5t7BnU67dnnXXdro5rrU3qQRhcAAACYYwSseZKZzOnJvhGdzvTAeZeqMZ25ok0P7iRgAQAAYG4RsObJE3uGlcu7TltGwIrCC1d16LGeQWUmc1GXAgAAgApGwJonhQYXdBCMxgtXtmsy59rcw4LDAAAAmDslrYOF43PTPTsOf/+dh3arqS6lO7f0y8wirKo6vXBVhyTpgWcH9KLVCyOuBgAAAJWKEax5suvgmJa3NxGuItKZbtDKhU16cCedBAEAADB3CFjzYP/IuHoHM1rX3RJ1KVXtnFUdeuBZGl0AAABg7hCw5sFDuwZkks5c0R51KVXthSvbtWcoo95BFhwGAADA3CBgzTF318M7B7Vm8QK1NdVFXU5VO2f1c/dhAQAAAHOBgDXHegYy2jcyrrNXMnoVtZOXtKqhtkYP7uA+LAAAAMwNAtYce2jnQaVqTKez/lXk6mtrdOaKNj1AwAIAAMAcIWDNoby7Htk9qPXdaTXVp6IuBwratW/qGdJ4lgWHAQAAUH4ErDm0vf+QhjNZncX0wNh44cp2TWTzeqxnKOpSAAAAUIEIWHPo4Z0Daqit0clL0lGXglCh0cWDO2h0AQAAgPIjYM2RzGROm3oGddqyNtWl+DHHRXdro5a1NerBnQQsAAAAlB+f/OfIz57o03g2r7NW0twibl64ukMPPEujCwAAAJQfAWuOfPuh3WppqNWJnS1Rl4IpXriyXbsHxtQ3lIm6FAAAAFQYAtYcGByb1M+e6NeZK9pUYxZ1OZiicB/Wr7bvj7gSAAAAVBoC1hz4waZeTeTyLC4cU2cub9MJixfo8z97Srm8R10OAAAAKggBaw5856EerVnUrOXtTVGXgmnUpmr0nledpC17h3Xrw7ujLgcAAAAVhIBVZnsGM/rV9v267OzlMqYHxtZvn7FUpy1r1Sd/vFUT2XzU5QAAAKBCELDK7LsP98hduuzsZVGXgiOoqTH9r4vXa+eBMX3tvh1RlwMAAIAKQcAqo8HRSX3hv7br7JXtWkv3wNh7+UmdOu+Ehfr0T7dpdCIbdTkAAACoAASsMvrwrZu0f2RCH7ns9KhLQQnMTO+/ZL32jYzri3c9E3U5AAAAqAAErDL5z0d69Z2HevTu31qnM1awuHBSvGj1Qr3ylC79y51PaXB0MupyAAAAkHAErDLoG8roQ99+VGetaNMfv+LEqMvBMXrvxes1PJ7VP9/5VNSlAAAAIOEIWLPk7rr2lkc1OpHTJ37/bNWl+JEmzclLWnXZWct04y+f1t6hTNTlAAAAIMFIA7P0tft26vYn+nTtpSfrBV00tkiqP3/VScrmXJ/56ZNRlwIAAIAEI2DNwo79o/rI9x7TS05cpLdesCbqcjALqxct0BXnrdLX7tupL9/9rCZzrI0FAACAY1cbdQFJlcu73vuNh1Vjpn98w1mqqWFR4Ti76Z6jr3W1cmGzlnc06UPf3qRP/nirXnVKt/7h9Wck4nc79f3l3bVj/6ge3T2oXQdHVVNjqq0x1dbUKFVjqk2Z2prq9N5Xr9eZK9pYFBsAAKBMCFjH4fHeIX3uZ9t07zMH9Ik3nKXl7U1Rl4QyaGmo1dUvW6ste4f1o8179bWNO/XI7kG97+L1unB9Z+xDSN5dOw8EoWrT7kENZbKqrTGtXNgskzSZc2Ums8rm88rlXZt7hnTZ5+7S6kXN+p0zl+l3zlqm9UvSUb8NAACARCspYJnZJZI+LSkl6Qvu/rEp+y3c/xpJo5Le5u4PlLnWSOXyrp88vldfvOtp3b39gJrqUvrjC0/U689ZHnVpKCMz08lLWnVSd1qP7BrU3dv366ob79NZK9r0otULdVJ3i9Z1t+gFXWm1NdVFXa72DGb0q+379K0Hdmlb34gGxyZVW2M6qTutM5a36eQlaTXUpaZ97thETummWn334R59/o5t+uzPtml9d1qvOrVbG9Z06JzVHWptjP49AgAAJIm5+5EPMEtJ2irpVZJ2SbpP0hXu/ljRMa+R9G4FAet8SZ929/OPdN4NGzb4xo0bZ1f9HMlM5jQ0NqmBsUkNjE7qkV0D+rdfPaOdB8a0rK1Rb3nJGl1+7kq1N9cf8TylTEtDvP3ei1boaxt36hsbd+rJvSMam8wd3rektVEnLF6grtYGdbY0qDPdoMXhnwsX1Ku5PqWWhlo1N9SquS51XFMN3V1jkzkNjAbX4sDYhPqGxnXvMwd091P7tX3fIUlSY12N1i5u0WnLWnXK0lY1zhCqpnrT+askSftGxvX9R3v13Yd7df+Og8rlXWbS+u60Nqzp0LlrFmr1ogVKN9Yq3Vir1sY6NdTWxH5UDwAAzJ67ayKX13g2r9HxnIYykxoamwz/zGooM6mxiZwmsnlN5vIaz+U1mXVN5vJqqK1RS2OtWhqCzxAtDXVqCT9PpBtqD+9bUF+biNsyipnZ/e6+4de2lxCwLpD0N+5+cfj4A5Lk7v9QdMy/SLrD3b8aPt4i6UJ3753pvHELWHdu7df7vvmwBscmlZn89QYH567p0FUvPUGvPrVbtSW2YidgVZa8uwZGJ9U3lNHe4XH1DWW0/9CERsazGs5MajJ35P+WmutTqq997top/iukOKgUvnNJI5msJqZpuNHSUKvzTlioC9Yu0gUnLtJDOwdUcxxhpxCwih0az+rhnQO675mD2vjsAT3w7EEdmsj92nF1KVO6sU614V+GZpLpue8L7+V5782m/ClTpWS0o/xVGnuuhL8BJf93IFXGe0i6o30uSoKkv4MK+BVUxN+pubw0ns1pbCKnbP7Y3k9tjampLqXalGkim5/2c8R0FtSnlKqx8KtGqRopZaaaGtMtf/wSdaUbj+etzJmZAlYpUwSXS9pZ9HiXglGqox2zXNLzApaZXS3p6vDhSBjEEuFZSd8s/fDFkvbNVS2AJG2W9MVZnuPKmXdxDSPpuIaRdFzDSLqyXsPd15brTGW1erqNpQSs6f59eWqMLeUYufv1kq4v4TUTzcw2TpdmgaTgGkbScQ0j6biGkXTVfA2XMtdtl6SVRY9XSOo5jmMAAAAAoKKVErDuk7TOzE4ws3pJl0u6dcoxt0p6iwVeLGnwSPdfAQAAAEAlOuoUQXfPmtm7JP1QQZv2G9x9s5ldE+6/TtJtCjoIblPQpv2quSs5ESp+GiQqHtcwko5rGEnHNYykq9pr+KhdBAEAAAAApSmt3zgAAAAA4KgIWAAAAABQJgSsMjKzS8xsi5ltM7N4dutHxTKzlWb2MzN73Mw2m9mfhtsXmtmPzezJ8M+Ooud8ILxet5jZxUXbX2Rmj4b7PmPhasFm1mBmXwu332Nma4qe89bwNZ40s7fO3ztHpTGzlJk9aGbfCx9zDSNRzKzdzL5pZk+EfydfwHWMJDGzPw8/S2wys6+aWSPX8DFwd77K8KWgAchTktZKqpf0sKRTo66Lr+r5krRU0jnh92lJWyWdKul/S7o23H6tpI+H358aXqcNkk4Ir99UuO9eSRcoWOPu+5IuDbf/saTrwu8vl/S18PuFkraHf3aE33dE/TPhK5lfkt4j6SZJ3wsfcw3zlagvSf8m6R3h9/WS2rmO+UrKl6Tlkp6W1BQ+/rqkt3ENl/7FCFb5nCdpm7tvd/cJSTdLuizimlBF3L3X3R8Ivx+W9LiCvyQvU/A/e4V/vi78/jJJN7v7uLs/raAL6HlmtlRSq7v/yoO/7f59ynMK5/qmpIvCf426WNKP3f2Aux+U9GNJl8zh20WFMrMVkn5b0heKNnMNIzHMrFXSb0r6V0ly9wl3HxDXMZKlVlKTmdVKalawvi3XcIkIWOWzXNLOose7wm3AvAuH2l8o6R5J3R6uSxf+2RUeNtM1uzz8fur25z3H3bOSBiUtOsK5gGP1KUnvk5Qv2sY1jCRZK6lf0hfDqa5fMLMF4jpGQrj7bkn/R9IOSb0K1rf9kbiGS0bAKh+bZhs98DHvzKxF0rck/Zm7Dx3p0Gm2+RG2H+9zgJKY2Wsl9bn7/aU+ZZptXMOIWq2kcyT9s7u/UNIhBdOpZsJ1jFgJ7626TMF0v2WSFpjZHxzpKdNsq+prmIBVPrskrSx6vELBcCowb8ysTkG4+oq73xJu3hsO0yv8sy/cPtM1uyv8fur25z0nnDbQJunAEc4FHIuXSvpdM3tGwTTr3zKzL4trGMmyS9Iud78nfPxNBYGL6xhJ8UpJT7t7v7tPSrpF0kvENVwyAlb53CdpnZmdYGb1Cm7YuzXimlBFwrnL/yrpcXf/ZNGuWyUVuvC8VdJ3irZfHnbyOUHSOkn3hsP+w2b24vCcb5nynMK5fk/S7eG86h9KerWZdYT/8vXqcBtQMnf/gLuvcPc1Cv4Ovd3d/0Bcw0gQd98jaaeZrQ83XSTpMXEdIzl2SHqxmTWH195FCu7r5houUW3UBVQKd8+a2bsUXAQpSTe4++aIy0J1eamkN0t61MweCrd9UNLHJH3dzN6u4C/NN0iSu282s68r+B9/VtI73T0XPu9/SrpRUpOCrj/fD7f/q6Qvmdk2Bf/SdHl4rgNm9hEF/9AgSX/n7gfm6o2i6nANI2neLekr4T+4bpd0lYJ/1OY6Ruy5+z1m9k1JDyi4Jh+UdL2kFnENl8SCsAgAAAAAmC2mCAIAAABAmRCwAAAAAKBMCFgAAAAAUCYELAAAAAAoEwIWAAAAAJQJAQsAAAAAyoSABQCYkZnlzOwhM9tsZg+b2XvMrCbct8HMPhNhbTea2e9Ns/1CM/vecZzvuJ4HAEAxFhoGABzJmLufLUlm1iXpJkltkv7a3TdK2hhlcQAAxA0jWACAkrh7n6SrJb3LAodHfMzs5eFI10Nm9qCZpcP9Pzez/zCzx8zsuqLRryvM7FEz22RmHw+3pcJRqU3hvj8Pt/8PM7svHEH7lpk1F5X1SjP7hZltNbPXTq3ZzBaY2Q3h8x80s8tKea9m9jfh8+4ws+1m9idF+95iZo+E9Xwp3LbazH4abv+pma0Kt99oZv9sZj8Lz/Py8LyPm9mNRed8tZn9ysweMLNvmFnLsf12AABxQcACAJTM3bcr+H9H15Rd75X0znC062WSxsLt50n6C0lnSDpR0uvNbJmkj0v6LUlnSzrXzF4Xfr/c3U939zMkfTE8xy3ufq67nyXpcUlvL3rdNZJeLum3JV1nZo1T6vpLSbe7+7mSXiHpH81sQYlv92RJF4fv4a/NrM7MTgvP+VthPX8aHvtZSf/u7mdK+oqk4qmTHeF7/XNJ35X0fyWdJukMMzvbzBZL+pCkV7r7OQpGBd9TYo0AgJghYAEAjpVNs+0uSZ8MR3ra3T0bbr/X3be7e07SVyX9hqRzJd3h7v3hcV+R9JuStktaa2b/ZGaXSBoKz3F6OEr1qKQrFYSTgq+7e97dnwyff/KUul4t6Voze0jSHZIaJa0q8X3+p7uPu/s+SX2SuhUEpW+G2+TuB8JjL1AwfVKSvhS+z4LvurtLelTSXnd/1N3zkjYrCIgvlnSqpLvCOt8qaXWJNQIAYoZ7sAAAJTOztZJyCgLHKYXt7v4xM/tPSa+RdLeZvbKwa8opXNMHNLn7QTM7S8Go0Tsl/b6kP5R0o6TXufvDZvY2SRdOOd/U8z+vZEn/3d23lPL+phgv+j6n4P+ZNs1rTKf4mMJ58lPOmQ/PmZP0Y3e/4jhqBADEDCNYAICSmFmnpOskfTYckSned2I4MvNxBVPcCiNJ55nZCeG9V2+U9F+S7pH0cjNbbGYpSVdIujOcKlfj7t+S9FeSzgnPkZbUa2Z1Ckawir3BzGrM7ERJayVNDVI/lPRuM7OwzhfO8sfwU0m/b2aLwvMtDLf/UtLl4fdXhu+zVHdLeqmZvSA8Z7OZnTTLOgEAEWEECwBwJE3htLU6SVkF098+Oc1xf2Zmr1AwGvOYpO8rmDb3K0kfU3AP1s8l/Ye7583sA5J+pmBE6DZ3/044evXFQiMMSR8I//wrBaHsWQXT7NJFr7tF0p0Kpu9d4+6ZMEsVfETSpyQ9EoasZyT9WjOMUrn7ZjP7qIJAmJP0oKS3SfoTSTeY2f+S1C/pqmM4Z384MvdVM2sIN39I0tbjrRMAEB2b8o+QAACUhdn/384d21AIgFAUhc3srN3l7+soWNhYm5cYv+dMQHsDoZeq+s3M7aABgLdxIggAABBigwXAp3T3Wueb+Kt9ZrYn5gHgvwgsAACAECeCAAAAIQILAAAgRGABAACECCwAAICQA5jW5vLSDhxPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    1568.000000\n",
      "mean        0.460459\n",
      "std         0.540361\n",
      "min         0.000000\n",
      "25%         0.000000\n",
      "50%         0.000000\n",
      "75%         1.000000\n",
      "max         2.000000\n",
      "Name: No_of_Kids_in_home, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    1568.000000\n",
      "mean        0.497449\n",
      "std         0.544151\n",
      "min         0.000000\n",
      "25%         0.000000\n",
      "50%         0.000000\n",
      "75%         1.000000\n",
      "max         2.000000\n",
      "Name: No_of_Teen_in_home, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count                    1568\n",
      "unique                    619\n",
      "top       2014-12-05 00:00:00\n",
      "freq                       10\n",
      "first     2012-01-08 00:00:00\n",
      "last      2014-12-05 00:00:00\n",
      "Name: Date_Customer, dtype: object\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    1568.000000\n",
      "mean       55.408801\n",
      "std        28.788037\n",
      "min         7.000000\n",
      "25%        31.000000\n",
      "50%        56.000000\n",
      "75%        80.000000\n",
      "max       106.000000\n",
      "Name: Recency, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    1568.000000\n",
      "mean        2.292730\n",
      "std         1.937544\n",
      "min         0.000000\n",
      "25%         1.000000\n",
      "50%         2.000000\n",
      "75%         3.000000\n",
      "max        15.000000\n",
      "Name: Discounted_Purchases, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    1568.000000\n",
      "mean        4.001276\n",
      "std         2.773748\n",
      "min         0.000000\n",
      "25%         2.000000\n",
      "50%         3.000000\n",
      "75%         6.000000\n",
      "max        27.000000\n",
      "Name: WebPurchases, dtype: float64\n"
     ]
    },
    {
     "data": {
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HCFgAAAAA4BECFgAAAAB4hIAFAAAAAB4hYAEAAACARwhYAAAAAOARAhYAAAAAeISABQAAAAAeIWABAAAAgEcIWAAAAADgEQIWAAAAAHiEgAUAAAAAHiFgAQAAAIBHCFgAAAAA4BECFgAAAAB4JMXvAoAj9Yacduw9KDPTkLSghqQF5ZyTmfldGgAAAHBMBCzEhX2d3dq4q0Mbdu1TTdM+dXaH3jP/P/6yXgVDUlWUna5zxxTqogklmlFVoJQgjbAAAACIHwQs+CbknF7fvFsrG/Zo256DkqScjBRNHpGn6pIcBU060NWrA129qioaoj37u9W454AeWLRVP3t1i/IyU3XBacW6aEKJzh9XrLzMVJ+fEQAAAAY7AhZ80RMK6bEV27SyYY/KCzL1wYklOq0kR6V5GX12Bbx+VuU79zsO9ejVjc16YV2TXtrQpD+u3K70lICunlGhm88brYqhQ07lUwEAAADeQcDCKdfZ3avfLKnT5ub9umRiuPWpP+dXZaenaN6UUs2bUqrekNPKhj363bIGPby0XvPfqNeHp5bq1gvGaPzw3AF8FgAAAMD7EbBwSrUf7NavXt+qXe2d+tj0cp1ZVXBS2wsGTGdWFejMqgLdefE4/eK1Wv1mSb2eWLldHxg/TF/8wFidUXly+wAAAABiRcDCKdPU3qkHFm3Vge5efeackRpXkuPp9ofnZej/XT5Rt104Vr9+vU6/XLhFH71nkT57zkiNLMxSWgoDYgAAAGBgEbBwStTt3q9fv16nYMD0+bmjVZafOWD7yh+SpjsuqtZNc0bpv57doAcWbVXBkFR9dHq5xhRnD9h+AQAAAP6ljwHX1RPS/DfqNSQtqFvPHzOg4SpaVnqKvvWRSXr0lnMUMNMvXtuiJ1Zu06Hu3lOyfwAAAAw+BCwMuFc3NWtfZ48+fma5hmalnfL9nzVqqL74gWrNGVukpVtaddeLm1Tb3HHK6wAAAEDyI2BhQO092K0Fm5o1uSxPVYVZvtWRlhLQZVNKdct5o5USDOj+hVu0sqHNt3oAAACQnGIKWGZ2qZltMLMaM/t6H/PHm9nrZnbIzP7uiHlbzWy1ma00s2VeFY7E8PzbuxRy0qWThvtdiiSpsjBLXzh/jKoKs/ToskYt2Ngs55zfZQEAACBJHDdgmVlQ0t2S5kmaKOk6M5t4xGKtku6Q9N9H2cyFzrnTnXMzTqZYJJbtew7qzfo2nTum0JeugUeTmRbUjeeO1JSyPD2zdqf+vHqHQoQsAAAAeCCWFqyzJNU452qdc12SHpZ0ZfQCzrkm59xSSd0DUCMSkHNOT6/eocy0oC4YN8zvct4nJRjQNTMrNHtMoRZt3q1Hljaopzfkd1kAAABIcLEErDJJDVGPGyPTYuUkPWdmy83s5v4Uh8S1fuc+1bbs10UTSpSZFvS7nD4FzHTZlFLNmzxcq7ft1S8XbdXBLkYYBAAAwImLJWBZH9P6059qtnNuusJdDG8zs/P63InZzWa2zMyWNTc392PziDe9Iae/rNmh4ux0nTVyqN/lHJOZaW51sa6eUaH63Qf04OKt6qYlCwAAACcoloDVKKki6nG5pO2x7sA5tz1y2yTpcYW7HPa13H3OuRnOuRnFxcWxbh5xaMmW3Wrp6NK8KcMVDPSVz+PP6RX5+sSMcm3dfUC/W9bAOVkAAAA4IbEErKWSqs1slJmlSbpW0pOxbNzMssws5/B9SZdIWnOixSL+Hezq1YvrmjSmOEunleT4XU6/TC3P12WTh2vN9nY9vXoHowsCAACg31KOt4BzrsfMbpf0rKSgpPudc2vN7NbI/HvNbLikZZJyJYXM7E6FRxwskvS4mR3e13zn3DMD81QQD16raVZnd68um1KqyHFPKHOqi7X3YLcWbt6tvMxUza2mNRUAAACxO27AkiTn3NOSnj5i2r1R93cq3HXwSO2Spp1MgUgczjm92bBH1SXZKs3L9LucEzZvSqn2dvboL2t2KjczVdPK8/0uCQAAAAkipgsNA7FoaD2gPQe6NTXBA0nATJ84s1wjC7P0++WNqm3u8LskAAAAJAgCFjzzVuNepQRME0tz/S7lpKUGA/r02VUqzErTQ0vqtKu90++SAAAAkAAIWPBEyDmt3rZXpw3PUUZqfF73qr8y04K64dyRCgYCmr+kXoe6uUYWAAAAjo2ABU/UNu9Xx6GehO8eeKT8IWm6dmaFWjoO6bE3tzGyIAAAAI6JgAVPrGrco7SUgMYPT6yh2WMxpjhbl0wartXb9mrR5t1+lwMAAIA4RsDCSesJhbR2e7smluYqNZicb6nzqos0oTRXf1mzQ3W79/tdDgAAAOJUTMO0A8eyaVeHDnb3alp5nt+lDBiLjCx490s1+u0b9brtwrHKyUg9Zfufv6T+pNa/flalR5UAAADgWJKzuQGn1KrGPcpMDWrMsGy/SxlQGalBXT+rUge7e/XI0gb1hjgfCwAAAO9FwMJJ6eoJad2OfZpclqeUQPK/nUrzMnXl6WWqbdmvF9bt8rscAAAAxJnk/0aMAbV+Z7u6ekNJ3T3wSNMrC3TWyKF6ZWOz1u9s97scAAAAxBECFk7Kqsa9yslI0ciiLL9LOaUun1qq0rwM/X55o9oPdvtdDgAAAOIEAQsn7GBXrzbs2qepZXkKmPldzimVGgzompkV6u4N6dFlDQpxfSwAAACIgIWT8PaOdvWGXNJdXDhWw3Iy9JFpI1Tbsl8vb2j2uxwAAADEAQIWTtiqxj0qGJKq8oJMv0vxzfTKAk0rz9Nf1+/S1haujwUAADDYEbBwQjoO9Whzc4emlufLBln3wGhmpitPL1P+kDQ9sqxBB7p6/C4JAAAAPiJg4YSs296ukJOmDqLRA48mIzWoa2dWaF9ntx5bsU2O87EAAAAGLQIWTsjmlg7lZKRoeG6G36XEhfKCIfrQpOF6e0e7lmxp9bscAAAA+ISAhX5zzqm2eb/GFGcP6u6BR5o9tkjjSrL19Ood2rH3oN/lAAAAwAcpfheAxNO075A6DvVo9CC79tXxBMz08TMr9KMXN+nhNxp024VjlZYSH//DmL+k/qTWv35WpUeVAAAAJLf4+PaHhFIbGS1vdHG2z5XEn+z0FH1iRoVaOg7pqVXb/S4HAAAApxgBC/1W29yh/MxUFQxJ9buUuDR2WLbOH1esZXVteqtxj9/lAAAA4BQiYKFfQpHzr0Zz/tUxXTShRJVDh+iJN7epdX+X3+UAAADgFCFgoV92tXfqYHevRhdz/tWxBAOma2ZUyEx6eGm9unpCfpcEAACAU4CAhX6pbY6cf8UAF8dVkJWmj55Rrsa2g/r+cxv8LgcAAACnAAEL/VLb3KHCrDTlD0nzu5SEMLksT2eNHKqfLqjVKxub/S4HAAAAA4yAhZiFnNOW3fvpHthPl08t1WklOfrqoyvVtK/T73IAAAAwgAhYiNn2PQfV2R3S6CKGZ++P1GBAP7r+DHUc6tFXHnlLoZDzuyQAAAAMEAIWYnb4/KtRtGD127iSHP3zhyfptZoW3btgs9/lAAAAYIAQsBCz2pYOFeekKzeD61+diGtnVujyKaX6/nMbtbyuze9yAAAAMAAIWIhJb8hpa8sBRg88CWamf//oFJXmZeiO376pvQe7/S4JAAAAHiNgISbb2g6oqzek0cWcf3Uy8jJT9cPrztDO9k7942Or5RznYwEAACQTAhZiUtvC9a+8Mr2yQH93yWn68+odenhpg9/lAAAAwEMELMSktnm/hudmKCs9xe9SksIt543W3OoifevJtdq4a5/f5QAAAMAjBCwc16GeXm3l+leeCgRM3796mnIyUnTrg8vV3sn5WAAAAMmAgIXjWlm/Rz0hx/WvPDYsJ0M/vn666lsP6CuPrOT6WAAAAEmAgIXjer12t0zSKM6/8tzZowv1jcsn6IV1TbrrxU1+lwMAAICTRMDCcb2+ebdG5GcqMy3odylJ6bPnjtTHppfrrhc36bm1O/0uBwAAACeBgIVj6uzu1Zv1exg9cACZmf7tbyZranmevvLoW6pp6vC7JAAAAJwgAhaO6c36PerqDWkUA1wMqIzUoO791JnKSA3o5l8vY9ALAACABEXAwjGtqG+TJFUNJWANtBH5mbo7MujFlx9m0AsAAIBERMDCMa2oa9OY4izOvzpFZo0u1DevmKgX1zfpu8+s97scAAAA9BNXjcVROef0ZsMeXTR+mN+lDCqfOadKNU0dum9BrYqy03TzeWP8LgkAAAAxImDhqLbuPqDW/V06s6pA9FY7dcxM3/rIJLUe6NK/P71eQ7PS/S4JAAAAMaKLII5qeV34/KvpVQU+VzL4BAOmH1w9TbPHFuprf1il9Tva/S4JAAAAMSBg4ahW1LcpJyNFY4uz/S5lUEpPCeqnn56hiaW5mv9Gvep27/e7JAAAABwHAQtHtaKuTWdUFigQML9LGbSy01P0wI0zlT8kVb96fat2tnf6XRIAAACOgYCFPu3r7NaGXfs0vTLf71IGvcLsdN147iilBQN6YOEWtXQc8rskAAAAHEVMAcvMLjWzDWZWY2Zf72P+eDN73cwOmdnf9WddxKe3GvbKOWl6JedfxYOCrDTdcO4o9YScfrqgVtv3HPS7JAAAAPThuKMImllQ0t2SPiipUdJSM3vSOfd21GKtku6QdNUJrIs4tKK+TWbS6bRgxY3heRm65bwxun/hFv3s1Vp9+pwqjS5KjPPj5i+pP6n1r59V6VElAAAAAyuWFqyzJNU452qdc12SHpZ0ZfQCzrkm59xSSd39XRfxaUV9m8YNy1FuRqrfpSBKcU66bjlvtHIzU/XAwq1ax+iCAAAAcSWWgFUmqSHqcWNkWixiXtfMbjazZWa2rLm5OcbNYyCEQk4r6to0vYrWq3iUPyRNt8wdreF5GfrNkjqtiAynDwAAAP/FErD6GkIu1svOxryuc+4+59wM59yM4uLiGDePgVDb0qH2zh6dwflXcWtIeopumjNKo4uz9fsVjXp1U7Oc42rQAAAAfoslYDVKqoh6XC5pe4zbP5l14ZMVdXskSWdygeG4lp4S1GfOrtLksjz9Zc1O/W55o7p6Qn6XBQAAMKjFErCWSqo2s1FmlibpWklPxrj9k1kXPllR36b8IakaXZTldyk4jpRgQNfOrNDFE4bprYY9+snLNdrFtbIAAAB8c9yA5ZzrkXS7pGclrZP0qHNurZndama3SpKZDTezRklfkfQNM2s0s9yjrTtQTwbeWF7XpjMq8mXGBYYTQcBMHxhfohtnj9L+rl795OUarWzgvCwAAAA/HHeYdklyzj0t6ekjpt0bdX+nwt3/YloX8WvvwW5taurQR6aN8LsU9NPYYdn64oVj9fDSej26rFFbWw7o8qmlSg1yPXEAAIBThW9eeI+VDZx/lchyM1N105zROq+6WG9sbdU9L2/WtjYuSgwAAHCqELDwHsvr2hQwaVoFQ7QnqmDAdOnk4frMOVXa39Wjn7xco7+s3sEAGAAAAKcAAQvv8WZ9m04bnqus9Jh6jyKOjR+eqzsvGqcZIwv0ak2LfvjXTapp6vC7LAAAgKRGwMI7QiGnlfV7NL2S1qtkkZkW1N+cUa7PzRklk3T/wi36w4pGHejq8bs0AACApETAwjs2NXVo36Eezr9KQqOLs3XHRdU6f1yx3qxv0w+e36ilW1sV4uLEAAAAniJg4R3L68JDe0+vJGAlo9RgQB+aNFy3XThWxdnpevzNbbr3lc1qaD3gd2kAAABJg4CFd6yob9PQrDRVFQ7xuxQMoNK8TN183mhdPaNcew92655XNusPKxrVcYhugwAAACeLkQzwjhX1bZpeWcAFhgcBM9PpFQWaMDxXf93QpIU1LVq7fa8unlCis0cXKsB7AAAA4ITQggVJUtv+LtU279f0Kga4GEzSU4OaN7lUd1xUrfL8IXpq1Q7d8/JmNbbRbRAAAOBEELAgSXqzgfOvBrNhORm6cfZIXTOzQu0Hu3XPy5v15Fvb1dnd63dpAAAACYUugpAkrajbo2DANLU8z+9S4BMz07TyfJ1WkqPn3t6lJbW7tXbbXl02tVTXnVVB11EAAIAY0IIFSeHzryaW5mpIGpl7sMtIDeoj00boCxeMUU5mih5Z2qCbH1yulo5DfpcGAMLTdpoAAB5jSURBVAAQ9whYUE9vSCsbuMAw3qu8YIj+zwVjNW/ycL2yoVmX/u8CvfD2Lr/LAgAAiGsELGjDrn060NWr6VxgGEcImGludbGe/OJsFedk6HO/Xqav/2EVQ7oDAAAcBQELWlG/RxIDXODoxg/P1RO3natbzx+jR5Y16LK7XtXyula/ywIAAIg7BCzozbo2Feekq7wg0+9SEMfSU4L6+rzxeuTmcxRyTp+493Xd8/JmOef8Lg0AACBuELCg5fVtml6ZzyhxiMlZo4bqmTvP02VTSvW9Z9br1oeWq72z2++yAAAA4gIBa5Br6Tikut0H6B6IfslOT9GPrjtD37xiol5Y16Qrf7xQG3bu87ssAAAA3xGwBrk3I+dfnckAF+gnM9NNc0Zp/udmaV9nj666e6H+uHKb32UBAAD4ioA1yC2va1Nq0DS5jAsM48TMGl2oP98xR5NG5OpLD6/Uv/xprXp6Q36XBQAA4AsC1iC3or5NE0fkKSM16HcpSGAluRn67c1n64ZzR+qXC7fq879exlDuAABgUCJgDWLdvSGtauQCw/BGajCgb31kkr5z1WQt2NSiT9z7unbsPeh3WQAAAKcUAWsQW79jnzq7Q5x/BU996uwq3X/DTDW0HtBVdy/Umm17/S4JAADglCFgDWKHLxTLCILw2vnjivX7L5yjoJmu/unrWr+j3e+SAAAATgkC1iC2on6PhudmaEQ+FxiG98YPz9UTt83WmOJsPbi4Totrd/tdEgAAwIAjYA1iK+rbNL2K868wcIblZuiRW87W+OE5evKt7frr+l1yzvldFgAAwIAhYA1STe2damw7SPdADLghaSm6flaVzqjI1wvrmvT06h2ELAAAkLRS/C4A/lhR3yZJms4AFzgFggHTx84sV0ZaUAs371Znd0hXnVGmYMD8Lg0AAMBTBKxBakX9HqUFA5o0ItfvUjBIBMx0xZRSZaYG9df1Ters6dU1MyqUEqQhHQAAJA++2QxSK+raNKU8T+kpXGAYp46Z6eIJJbp8SqnWbm/Xg4vr1NUT8rssAAAAzxCwBqGunpBWbdvLBYbhm9lji/Sx6WWqaerQLxdt0aGeXr9LAgAA8AQBaxBau32vunpCDHABX51ZNVTXzKxQQ+sBPbBoKyELAAAkBQLWILSifo8kBriA/6aW5+vqGYQsAACQPAhYg9CK+jaV5WeqJDfD71IAQhYAAEgqjCI4yDjntGxrq84aVeh3KcA7ppaHzwd8dFmDHli0VTecO/I9A7DMX1J/0vu4flblSW8DAADgeGjBGmQ2N3doV/shnTuGgIX48r6WrG5asgAAQOIhYA0yC2t2S5JmjynyuRLg/d4Tsl4nZAEAgMRDwBpkFta0qLwgU5WFQ/wuBegTIQsAACQyAtYg0htyWly7m9YrxD1CFgAASFQErEFkzba9au/s0bljOf8K8W9qeb6umVlJyAIAAAmFgDWILNzcIkk6lxYsJIgpZXmELAAAkFAIWIPIoprdOq0kR8U56X6XAsTsPSGL0QUBAECcI2ANEp3dvVq6tZXugUhI74SsNkIWAACIbwSsQWJFfZsO9YQY4AIJi5AFAAASAQFrkFhUs1vBgGnW6KF+lwKcMEIWAACIdwSsQWLh5hZNLc9TTkaq36UAJ4WQBQAA4llMAcvMLjWzDWZWY2Zf72O+mdkPI/NXmdn0qHlbzWy1ma00s2VeFo/Y7Ovs1qrGvXQPRNIgZAEAgHh13IBlZkFJd0uaJ2mipOvMbOIRi82TVB35uVnSPUfMv9A5d7pzbsbJl4z+WlLbqt6QY4ALJJXokPXLRVt1sIuQBQAA/BdLC9ZZkmqcc7XOuS5JD0u68ohlrpT0axe2WFK+mZV6XCtO0MLNLUpPCWh6ZYHfpQCeOhyytrUd1H2vbtbeg91+lwQAAAa5WAJWmaSGqMeNkWmxLuMkPWdmy83s5qPtxMxuNrNlZrasubk5hrIQq0U1uzVz5FBlpAb9LgXw3JSyPH323JFqO9Cte1/ZrKb2Tr9LAgAAg1gsAcv6mOb6scxs59x0hbsR3mZm5/W1E+fcfc65Gc65GcXFxTGUhVg07zukDbv20T0QSW3ssGx9fu5o9YacfrqgVvW79/tdEgAAGKRiCViNkiqiHpdL2h7rMs65w7dNkh5XuMshTpFFm1skiQEukPTK8jN16/ljNCQtqF8s3KJ1O9r9LgkAAAxCKTEss1RStZmNkrRN0rWSrj9imScl3W5mD0uaJWmvc26HmWVJCjjn9kXuXyLp296Vj+NZVLNbuRkpmlyW53cpwIAbmpWmW84fo18t2qqHFtfpqtPLNHOUN9d+m7+k/qTWv35WpSd1AACA+HbcgOWc6zGz2yU9Kyko6X7n3FozuzUy/15JT0u6TFKNpAOSboysXiLpcTM7vK/5zrlnPH8WOKqFm1t09uhCBQN99eIEkk92eoo+N3eU5i+p1+Mrt2lne6cum8KYOwAA4NSIpQVLzrmnFQ5R0dPujbrvJN3Wx3q1kqadZI04QfW7D6ix7aA+P3e036UAp1R6SlCfOWeknlmzQws379bO9k7NmzJcRdnpfpcGAACSXEwXGkZiWnj4/CsGuMAgFAyYLp86Qp84s1wNrQf0kR+9ptWNe/0uCwAAJDkCVhJ7dVOzhuWka0xxtt+lAL45o7JAt5w/Rmamj9+7SI+taPS7JAAAkMQIWEnqQFePXlrfrEsmlShyDhwwaJXlZ+rJ22frjMp8feXRt/TPf1yjzu5ev8sCAABJiICVpF5c16SD3b26YuoIv0sB4kJhdroevGmWbpozSr96vU5X/nih1u9kKHcAAOAtAlaSemrVdg3LSdfMkd4MUQ0kg9RgQN+8YqJ+eeNM7d7fpY/8eKF+uXCLwuP0AAAAnDwCVhLa19mtlzY067IppQzPDvThwtOG6Zk752ru2CL9y5/e1g2/XKqmfZ1+lwUAAJIAASsJvbBul7p6QvrwNK79AxxNUXa6fv7ZGfrXqyZrce1uzfvfV/XMmp1+lwUAABIcASsJPfXWDpXmZeiMigK/SwHimpnp02dX6akvztHwvAzd+tBy3frgcu1qpzULAACcGAJWktl7oFsLNjXr8imlCtA9EIhJdUmOnrhttr526Xi9tKFJF//gFc1fUq9QiHOzAABA/xCwksyzb+9Ud6/TFdMYPRDoj9RgQF+4YIyevfM8TR6Rp398fLWu/dlibW7u8Ls0AACQQAhYSeapVTtUMTRT08rz/C4FSEgji7I0//Oz9J8fm6r1O9o1765X9YPnNqirJ+R3aQAAIAGk+F0AvNO6v0sLa1r0+bmjubgwcBLMTFfPrNAF44v1nafW6Yd/rVF+ZqrmTSnV5BG5fL4AAMBREbCSyDNrdqo35HTF1PePHjh/Sb0PFQGJbVhOhn543Rn65KxKfenhlfrtG/UaXZylD08doZLcDL/LAwAAcYgugknkqVXbNaooS5NG5PpdCpBUZo0u1G0XjtWHp43Qjj2d+tFfN+nPq7brYFev36UBAIA4QwtWkmjed0iLa3frtgvH0n0JGADBgOmc0YWaWpan59/epUWbd2tF/R5dNGGYZo0q5KLeAABAEi1YSeMva3Yo5KQrpjJ6IDCQstJTdNUZZbr9A2M1Ij9DT63aobte3Kh1O9rlHMO6AwAw2BGwksRTq3aoeli2Thue43cpwKBQmpepv509Sp85p0om04OL6/Tz17Zo256DfpcGAAB8RMBKArvaO7V0ayutV8ApZmYaPzxXd1xUrY9MG6Fd7Z26+6UaPby0Xrs7DvldHgAA8AHnYCWBP67cJuekK6a9f/RAAAMvGDCdPbpQ08rz9eqmZi3c3KI12/Zq5sihunD8MOVmpJ70SJ7Xz6r0qFoAADCQCFgJ7lBPr37x2hbNGjVUY4qz/S4HGNQy04K6ZNJwnT2mUC+tb9LSra1aUd+mc8cU6bzqYmWmBf0uEQAADDACVoL7w/Jt2tV+SP/9iWl+lwIgIjcjVVeeXqY5Y4v0wrpdemVjs5Zs2a2zRxdq9pgiZaXzqxcAgGTFX/kE1tMb0r2vbNa08jzNGVvkdzkAjlCYna5rZlZqbvVBvbShSa9saNbCmhadNXKo5lYXKzcz1e8SAQCAxwhYCeypVTtU33pA37j8TK59BcSxEfmZ+uSsKu1q79QrG5v1eu1uLd7SqjMrCzS3ukiF2ekDXgPngAEAcGoQsBJUKOR090s1GleSrYsnlPhdDoAYlORm6OoZFbp4Qole2dis5fVtemNrq6qHZWvWqKE6bXguFywGACDBEbAS1HNv79Kmpg7dde3pCvCFDEgoQ7PS9DdnlOmi8cO0tK5VS7e06qEl9crLTNWMkQWaWTWU7oMAACQoAlYCci7celVVOESXT2FodiBR5Wam6qLxJbpg3DBt2NmuxVta9eK6Jr20vknVw3I0tTxPE0pzlZHK6IMAACQKAlYCWrCpRau37dV3PzpFKUGuFQ0kumDANHFEniaOyFNLxyEt29qqVY179bvl+5QSMI0ryVFuZoouGl/CUO8AAMQ5AlYCuvuvNSrNy9BHp5f7XQoAjxVlp+vSyaW6ZNJwNbQe0Kpte7Wmca9un/+mMlODOm9ckS6eUKIPjB92SgbHAAAA/UPASjBvbGnVG1tb9c8fnqi0FFqvgGQVMFNVYZaqCrN0+ZRSjSnO1tOrd+iFdbv07NpdMpPOrCzQRRNK9MGJwzSmOJvRRAEAiAMErATz45dqVJiVpmtnMmQyMFgEzHTOmEKdM6ZQ375yktZub9cL63bphXW79L1n1ut7z6zXyMIhunhCiS6eWKIZVQV0HwYAwCcErASyvK5VCzY26+8/dBrnYQCDlJlpclmeJpfl6c6Lx2nH3oN6cV2TXli3S79+vU4/f22L8jJTdeFpxfrQpOG6cPwwBskAAOAUImAliH2d3brzkZUqy8/UZ86p8rscAHGiNC9Tnzq7Sp86u0odh3r02qZmPf92k/66fpeeWLlduRkpunzqCOVmpKhy6BC6EQIAMMAIWAnim0+s0ba2g3r0lnOUk8H1cQC8X3Z6ii6dXKpLJ5eqN+S0aHOLHluxTU+8uU0Hu3s1NCtNp1fk68zKAhVkpfldLgAASYmAlQAeW9GoJ1Zu15cvHqcZI4f6XQ6ABBAMmOZWF2tudbG+c1WP/vnJtXqzvk0vrQ9fZ2tyWZ7mVhepvGCI36UCAJBUCFhxbmvLfn3ziTU6a+RQ3f6BsX6XAyABZaWnaHplgaZXFmjPgS4trt2tJVtatXrbXo0szNLc6iKdNjxHAboPAgBw0ghYcayrJ6QvPfymggHT/1x7uoIBvvwAODn5Q9J06eRSXXDaMC2ra9OimhY9uLhORdnpuuC0Yp1ekU/QAgDgJBCw4tgPnt+otxr36p5PTldZfqbf5QBIIhmpQc0ZW6RzRhdqzba9WrCpWb9f3qgFG5t1ycQSTSjNZUAMAABOAAErTi2sadFPF2zWdWdVaN6UUr/LAZCkggHTtIp8TS3P05rt7Xr+7Z16aEm9Kgoydcmk4RpTnO13iQAAJBQCVhyqbe7Qlx9ZqTHF2fqnKyb5XQ6AQcDMNKUsTxNLc/VmfZteXN+kX7y2RWOHZeuSiSV+lwcAQMIgYMWZN7a06uYHlyloph9ffwYXFAZwSgUDphkjh2paRb6WbGnVyxua9JOXN2tLy3599ZLTNHYYLVoAABwLASuO/HHlNv3971apfGimHrjhLFUWMnwyAH+kBgOaM7ZIM6oK9FpNixZsbNaza3fq42eW60sXj+O8UAAAjiLgdwGQnHO6+6UafenhlTq9Ml+PfeFcwhWAuJCRGtTFE0q04B8u1I2zR+mJldt14X+9rG//6W3tau/0uzwAAOIOLVg+6+4N6RuPr9Ejyxp05ekj9J8fn6r0FLoFAogvhdnp+uYVE3XTnFG664VN+tXrW/XQ4jp97Mwy3XzeGI0qyvK7RAAA4gIBy0erG/fqX//8tt7Y0qo7PjBWX/7gOIZFBhDXRuRn6nsfn6rbLhyrn71aq0eWNeiRpQ2aN6VUXzh/jCaX5fldIgAAviJg+WDNtr363xc26YV1u5SXmar//sQ0ffzMcr/LAoCYVRYO0b9eNVl3XFStXy7cogdfr9OfV+3Q7LGF+viZ5bpk4nBlpfMnBgAw+PDX7xRat6Nd//vCRj27dpdyM1L01Q+O0w2zRyonI9Xv0gDghBTnpOsfLh2vWy8Yo98srtdvltTpy4+8pczUNfrgxBJddcYIza0uVmqQU34BAIMDAWsAOee0cVeHXt3UrFc2NuvVTS3KSU/Rly6q1t/OGaW8TIIVgOSQm5GqL1wwRreeP1rL69r0xMptemrVDj351nYNzUrTByeUaOaooZpRVaCqwiF0hwYAJK2YApaZXSrpLklBST93zn33iPkWmX+ZpAOSbnDOrYhl3WTS2d2rxraDWrNtrxZsatZrm1rUtO+QJGlMcZbu+MBY3TRntPKGEKwAJCez8HW0Zowcqn+6YpIWbGzWEyu36Zm1O/XIsgZJUlF2umZUFWjGyAJNLM1VxdAhKs3LUAqtXACAJHDcgGVmQUl3S/qgpEZJS83sSefc21GLzZNUHfmZJekeSbNiXDeuHerp1Y49neo41KP2zm51dPZoX2ePOg71qHV/lxraDqih9YAaWg9qZ9SQxQVDUjV7bJHOqy7WnOoijeCaMQAGmbSUgC6eWKKLJ5YoFHKqae7Qsq1tWra1VUvrWvXM2p3vLBsMmEbkZ6hy6BBVFAxRYXaacjNSlZeZqtzM8G1ORorSUgJKCZhSAgGlBN+9TY3cBgOm1GBAwQAtZPCfcy6GZWLYjkf7kqSQk0LORX6k3pCTc069Ife+eaFQ+P6R88LrKDL9vcv3uj7mhRSZ7tQbenc7khQwi/yE/0ETsMi0wOHHUdMO3w9EL//ufOtzuaPPtyO3HXj3/rvLvrtuXy3vh1+79z3vyHMO34+81u/cj6wTcuruderuDaknFFJXj1NPKKTu3tC703udunpDWlSzW72hkHoj+zj8E72tw3X0Nb8oO13dvSE5SSmR35OHf4emBk0pwfDt4d+lqcHw79rUlIDSggGlRd2mHn6cElBa0CK3QaUevh+9TkpAKYFA1HF972se/ToHA/bOa+wir6dT+P3iXHh+ovxuj6UF6yxJNc65Wkkys4clXSkpOiRdKenXLvzpXmxm+WZWKmlkDOvGtdWNe/Xxe1/vc56ZNDw3QxVDh2hOdVH4i8HQTFUPy9HE0lwFEuRNAAADLRAwjSvJ0biSHF0/q1KS1NTeqZrmjnf+SVXfekANbQf0wromtR3oUm8oti+MfTGTgpE/3n3OVx8zjrrs0fcR87aPsfzxxPYF/NR9kY91wVNdU0xhJqbtxLhDDDrRYeBwOPVb0MKhIxCQgoGAgvZuEHnnx0y9Iae0lIDMpO5epwNdPeqJBLye3ndDXU8oHOrCoc+9Mz0e/MdHp+i6syr9LiMmsQSsMkkNUY8bFW6lOt4yZTGuK0kys5sl3Rx52GFmG2KozXdbJS32u4jYFElq8buIE/VJvws4SQNc/4Af20R//aWEfQ7vHFu/6/d7/0kooX8n45g4tsmLY+uj678nXT9wmz/RY1vV18RYAlZf/3M7MsoebZlY1g1PdO4+SffFUA9OgJktc87N8LsOeI9jm7w4tsmLY5u8OLbJi2ObvLw+trEErEZJFVGPyyVtj3GZtBjWBQAAAICkEMuQTUslVZvZKDNLk3StpCePWOZJSZ+xsLMl7XXO7YhxXQAAAABICsdtwXLO9ZjZ7ZKeVXio9fudc2vN7NbI/HslPa3wEO01Cg/TfuOx1h2QZ4Ljoftl8uLYJi+ObfLi2CYvjm3y4tgmL0+PrcU6rCcAAAAA4Ni4qiMAAAAAeISABQAAAAAeIWAlOTO71Mw2mFmNmX3d73rgHTPbamarzWylmS3zux6cHDO738yazGxN1LShZva8mW2K3Bb4WSNOzFGO7bfMbFvk87vSzC7zs0b0n5lVmNlLZrbOzNaa2Zci0/ncJrhjHFs+twnOzDLM7A0zeytybP8lMt3Tzy3nYCUxMwtK2ijpgwoPpb9U0nXOubd9LQyeMLOtkmY457joYRIws/MkdUj6tXNucmTaf0pqdc59N/IPkgLn3Nf8rBP9d5Rj+y1JHc65//azNpw4MyuVVOqcW2FmOZKWS7pK0g3ic5vQjnFsrxaf24RmZiYpyznXYWapkl6T9CVJH5WHn1tasJLbWZJqnHO1zrkuSQ9LutLnmgD0wTm3QFLrEZOvlPSryP1fKfwHHgnmKMcWCc45t8M5tyJyf5+kdZLKxOc24R3j2CLBubCOyMPUyI+Tx59bAlZyK5PUEPW4UfyCSCZO0nNmttzMbva7GAyIksg1BRW5HeZzPfDW7Wa2KtKFkG5kCczMRko6Q9IS8blNKkccW4nPbcIzs6CZrZTUJOl555znn1sCVnKzPqbRJzR5zHbOTZc0T9JtkW5IABLDPZLGSDpd0g5J3/e3HJwoM8uW9AdJdzrn2v2uB97p49jyuU0Czrle59zpksolnWVmk73eBwEruTVKqoh6XC5pu0+1wGPOue2R2yZJjyvcJRTJZVfkXIDD5wQ0+VwPPOKc2xX5Ix+S9DPx+U1IkXM4/iDpN865xyKT+dwmgb6OLZ/b5OKc2yPpZUmXyuPPLQEruS2VVG1mo8wsTdK1kp70uSZ4wMyyIifeysyyJF0iac2x10ICelLSZyP3Pyvpjz7WAg8d/kMe8Tfi85twIifL/0LSOufcD6Jm8blNcEc7tnxuE5+ZFZtZfuR+pqSLJa2Xx59bRhFMcpEhRP9XUlDS/c65f/O5JHjAzEYr3GolSSmS5nNsE5uZ/VbSBZKKJO2S9M+SnpD0qKRKSfWSPuGcY7CEBHOUY3uBwt2MnKStkm453P8ficHM5kh6VdJqSaHI5H9U+FwdPrcJ7BjH9jrxuU1oZjZV4UEsggo3ND3qnPu2mRXKw88tAQsAAAAAPEIXQQAAAADwCAELAAAAADxCwAIAAAAAjxCwAAAAAMAjBCwAAAAA8AgBCwDgOTP7HzO7M+rxs2b286jH3zezrxxl3ZfNbEYf028ws2YzW2lmb5vZ5z2qdauZFXmxLQAACFgAgIGwSNK5kmRmAYWvATUpav65khaewHYfcc6drvB1pP7dzEpiWcnMgiewL+D/t3M/IVZWYRzHv79GjQjSggIxsoRIyYoMDE0sSGpRIJU0UC6ihOyPi6hNVKuoKKKoIIwiNMhwkYuiRUppgVqGZeP0B/pjFBVYGNOfhTTytLjnMrepcRBmKOX72bzvPe85zzm8m8vDc94jSUfMBEuSNBm20xIsOonVIPBbkpOTHA/MA0jyTpLdrcI1s2f8yiQ7kgwmWTg6eFXtB74CZidZl2RF91mS39v1siRbk2wA9ibpS/J4kr1JBpKs6Qm5JsmH7dncNn5hW8NH7XpOaz83ya5WSRtIcnZrX9nT/lybr6+tb7DFvmtiXq8k6f9qyn+9AEnSsaeqfkgynOQMOonWTmAWsAgYAj4DngSWV9VPSfqBh4CbW4gTq2pxkqXAi8D83vhJ5gBzgC/HWcpCYH5V7UtyG3AWcGFVDSc5paffz1W1IMntwD3AKuBzYGnruwx4GLgOWA08VVUvJ5kG9CWZB/QDl1TVn0meBW4EPgFmVdX8tu4ZR/IeJUlHHxMsSdJk6VaxFgNP0EmwFtNJsL4HrgC2JAHoA37sGfsKQFW9m+SknsSkP8kS4CBwa1UdaOPHsquq9rX7ZcDaqhpusQ/09NvUrruBa9v9dGB9q1AVMLW17wTuS3I6sKmqvkhyOXAR8EFbzwnAfuB1YE6SZ4A3gM2HW6wk6ehngiVJmizd77DOo7NF8DvgbuBX4G06lZ1FY4ytMX5vrKo7Rz0bpm15Tye7mdbz7I+e+/xL3K6D7XqIkf/GB4GtVXVNkjOBbQBVtSHJ+8BVwJtJVrXY66vq3tGBk1wAXAncAVzPSJVOknQM8hssSdJk2Q5cDRyoqkOtYjSDzjbBjcCpSRYBJJmapPcQjP7WvgQYqqqhw8zzDZ3qEcByRipNo20GVieZ0mKfMka/rul0Km0AN3Ub2/bEr6vqaeA14HzgLWBFktO6sZPMbqcTHldVrwIPAAvGmVOSdJQzwZIkTZa9dE4PfG9U21A7pGIF8GiSj4E9jByKAfBLkh3AWuCWceZ5Hrg0yS7gYv5eter1AvAtMNDmvGGcuI8BjyTZTmcLY1c/MJhkDzAXeKmqPgXuBzYnGQC2ADPpbIvc1vquA/5R4ZIkHVtSNdZuCUmSJEnSkbCCJUmSJEkTxARLkiRJkiaICZYkSZIkTRATLEmSJEmaICZYkiRJkjRBTLAkSZIkaYKYYEmSJEnSBPkL5lnockm6//AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    1568.000000\n",
      "mean        2.665816\n",
      "std         3.043493\n",
      "min         0.000000\n",
      "25%         0.000000\n",
      "50%         2.000000\n",
      "75%         4.000000\n",
      "max        28.000000\n",
      "Name: CatalogPurchases, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    1568.000000\n",
      "mean        5.703444\n",
      "std         3.260553\n",
      "min         0.000000\n",
      "25%         3.000000\n",
      "50%         5.000000\n",
      "75%         8.000000\n",
      "max        13.000000\n",
      "Name: StorePurchases, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    1568.000000\n",
      "mean      303.190051\n",
      "std       340.141396\n",
      "min         5.000000\n",
      "25%        27.000000\n",
      "50%       167.000000\n",
      "75%       499.500000\n",
      "max      1498.000000\n",
      "Name: Amount_on_Wines, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    1568.000000\n",
      "mean       29.091199\n",
      "std        39.565637\n",
      "min         3.000000\n",
      "25%         4.000000\n",
      "50%        11.000000\n",
      "75%        36.000000\n",
      "max       202.000000\n",
      "Name: Amount_on_Fruits, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    1568.000000\n",
      "mean      168.376276\n",
      "std       233.419980\n",
      "min         0.000000\n",
      "25%        15.000000\n",
      "50%        64.000000\n",
      "75%       232.000000\n",
      "max      1725.000000\n",
      "Name: Amount_on_MeatProducts, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    1568.000000\n",
      "mean       37.166454\n",
      "std        53.994951\n",
      "min         0.000000\n",
      "25%         3.000000\n",
      "50%        12.000000\n",
      "75%        49.250000\n",
      "max       259.000000\n",
      "Name: Amount_on_FishProducts, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    1568.000000\n",
      "mean       26.800383\n",
      "std        40.857657\n",
      "min         0.000000\n",
      "25%         1.000000\n",
      "50%         8.000000\n",
      "75%        33.000000\n",
      "max       263.000000\n",
      "Name: Amount_on_SweetProducts, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    1568.000000\n",
      "mean       43.739158\n",
      "std        51.591557\n",
      "min         0.000000\n",
      "25%         8.000000\n",
      "50%        25.000000\n",
      "75%        56.000000\n",
      "max       362.000000\n",
      "Name: Amount_on_GoldProds, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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SsGZI2pf0vlJvDkkDtjGzOZLOlbQuafFtwZDC1WY2obcPN7ObzWyjmW2srq5OoVwAAAAASI9UApb1sswH08bM8iX9XNJfuntDsPgOSadJWiypStK3e/twd7/L3Ze6+9LS0tIUygUAAACA9EglYFVKKkt6P1PSgVTbmFmGEuHqAXf/j64G7v6Gu3e6e1zSD5UYiggAAAAAo1YqAWuDpPlmVm5mmZKukbSmR5s1km4IZhO8UFK9u1eZmUm6W9J2d/9O8gZmNi3p7QckbT3powAAAACAEWDAWQTdvcPMbpP0mKSopNXuvs3MbgnW3ylpraSrJO2U1CTpxmDzt0q6XtIWM3s5WPY37r5W0jfNbLESQwl3S/qz0I4KAAAAANJgwIAlSUEgWttj2Z1Jr13Srb1s96x6vz5L7n79oCoFAAAAgBEupRsNAwAAAAAGRsACAAAAgJAQsAAAAAAgJAQsAAAAAAgJAQsAAAAAQkLAAgAAAICQELAAAAAAICQELAAAAAAICQELAAAAAEJCwAIAAACAkBCwAAAAACAkBCwAAAAACAkBCwAAAABCQsACAAAAgJAQsAAAAAAgJAQsAAAAAAgJAQsAAAAAQkLAAgAAAICQELAAAAAAICQELAAAAAAICQELAAAAAEJCwAIAAACAkBCwAAAAACAkBCwAAAAACAkBCwAAAABCQsACAAAAgJAQsAAAAAAgJAQsAAAAAAgJAQsAAAAAQkLAAgAAAICQELAAAAAAICQELAAAAAAICQELAAAAAEJCwAIAAACAkBCwAAAAACAkKQUsM1tuZjvMbKeZ3d7LejOz7wXrN5vZkmB5mZk9YWbbzWybmX06aZsSM/utmb0WPE8I77AAAAAAYPgNGLDMLCrp+5JWSFoo6VozW9ij2QpJ84PHzZLuCJZ3SPqMuy+QdKGkW5O2vV3S4+4+X9LjwXsAAAAAGLVS6cFaJmmnu+9y9zZJD0la2aPNSkn3ecKLkorNbJq7V7n7S5Lk7kclbZc0I2mbe4PX90p6/ykeCwAAAACkVSoBa4akfUnvK3U8JKXcxszmSDpX0rpg0RR3r5Kk4Hlybx9uZjeb2UYz21hdXZ1CuQAAAACQHqkELOtlmQ+mjZnlS/q5pL9094bUy5Pc/S53X+ruS0tLSwezKQAAAAAMq1QCVqWksqT3MyUdSLWNmWUoEa4ecPf/SGrzhplNC9pMk3RocKUDAAAAwMiSSsDaIGm+mZWbWaakaySt6dFmjaQbgtkEL5RU7+5VZmaS7pa03d2/08s2q4LXqyT94qSPAgAAAABGgNhADdy9w8xuk/SYpKik1e6+zcxuCdbfKWmtpKsk7ZTUJOnGYPO3Srpe0hYzezlY9jfuvlbSNyQ9bGY3Sdor6erwDgsAAAAAht+AAUuSgkC0tseyO5Neu6Rbe9nuWfV+fZbc/bCkKwZTLAAAAACMZCndaBgAAAAAMDACFgAAAACEhIAFAAAAACEhYAEAAABASAhYAAAAABASAhYAAAAAhISABQAAAAAhIWABAAAAQEgIWAAAAAAQEgIWAAAAAISEgAUAAAAAISFgAQAAAEBICFgAAAAAEBICFgAAAACEJJbuAjD67Dh4VKufrVBbZ1xf/+DZys6IprskAAAAYEQgYCEl7q6d1cd0w+r1evrVamVnRNTSHtex1g794CNLlBGlMxQAAAAgYKFfcXe9vLdOz+6s0cGGFpUWZOmz7zpdH7lgtta8ckB/t2abPvezzfr21ecoErF0lwsAAACkFQEL/Xr+9cNau6VKUwqz9MdLZuprH1ykrFhiSOCqi+foaEu7vvWbV5WXFdVXVi6SGSELAAAA4xcBC33qjLue31mjORPz9IlLy2Vm3eGqy62Xz9PRlg7969O7VJidoc8tPzNN1QIAAADpR8BCn7ZXNaiuuV3vecu0PnumzEy3rzhTDS0d+sGTr6sgO0OfvOy0Ya4UAAAAGBkIWOjTC7sOqzg3QwumFfbbzsz09+9fpGOtHfp/v/6DJuVn6uqlZcNUJQAAADByMPUbenWgrlkVNY26aO5ERVK4rioaMX3nw+doWXmJvrZ2u461dgxDlQAAAMDIQsBCr154/bAyoqals0tS3iYjGtHfXLVAR5radc9zFUNYHQAAADAyMUQQb3KstUOvVNZpyewJysk8cVKLB9ftHXD7M6cW6F+e2KmcjNgJ2193wazQawUAAABGEnqw8CYbdteqI+66aO7Ek9r+ygVT1NIe13Ov14RcGQAAADCyEbBwgs64a92uw5o3OV9TCrNPah/Ti3N01vRCPbezRk1ciwUAAIBxhICFE2w9UK+Glg5dfNrJ9V51uXLBFLV1xPXMTnqxAAAAMH4QsHCCF14/rIl5mTp9SsEp7WdKYbbOnlmk51+vYUZBAAAAjBsELHSrPNKkvbVNuui01KZmH8gVZ05RR6fr6VerQ6gOAAAAGPkIWOj2/OuHlRWLaMmsCaHsr7QgS+fOKtaLuw6robk9lH0CAAAAIxkBC5ISU7NvqazXktkTlJ0RHXiDFL3jzCmKu+tJerEAAAAwDhCwIEl6vfqYOt11bllxqPstycvUebMnaMPuWu2vaw513wAAAMBIQ8CCJKmiulFZsYimFeWEvu/Lz5gsufTDp3eFvm8AAABgJCFgQZK0q6ZRcybmKRo59ckteirOzdRbZhbp4Y37VM+1WAAAABjDCFhQQ0u7ao61qnxS3pB9xlvnTVJTW6d+umHvkH0GAAAAkG4ELGh3TaMkaW7p0AWs6cU5umjuRN3z3G61d8aH7HMAAACAdEopYJnZcjPbYWY7zez2XtabmX0vWL/ZzJYkrVttZofMbGuPbb5kZvvN7OXgcdWpHw5Oxq6aobv+KtmfXlquA/UtenTrwSH9HAAAACBdBgxYZhaV9H1JKyQtlHStmS3s0WyFpPnB42ZJdyStu0fS8j52/4/uvjh4rB1k7QhJRfXQXX+V7PIzJmvupDzd/cwuufuQfhYAAACQDqn0YC2TtNPdd7l7m6SHJK3s0WalpPs84UVJxWY2TZLc/WlJtWEWjfAcbWlX9RBff9UlEjHdeEm5Xqms16Y9R4b88wAAAIDhlkrAmiFpX9L7ymDZYNv05rZgSOFqM5vQWwMzu9nMNprZxupqblYbtophuP4q2R8vmaHi3Az96JmKYfk8AAAAYDilErB6GzfWc3xXKm16ukPSaZIWS6qS9O3eGrn7Xe6+1N2XlpaWDlQrBmm4rr/qkpsZ03XLZumx3x/UnsONw/KZAAAAwHBJJWBVSipLej9T0oGTaHMCd3/D3TvdPS7ph0oMRcQwq6hp1OyJuUN+/VWyVRfPUSxi+rfndg/bZwIAAADDIZWAtUHSfDMrN7NMSddIWtOjzRpJNwSzCV4oqd7dq/rbadc1WoEPSNraV1sMjaMt7ao+2qq5k/KH9XOnFGbrj94yXf/OjYcBAAAwxgwYsNy9Q9Jtkh6TtF3Sw+6+zcxuMbNbgmZrJe2StFOJ3qhPdW1vZj+R9IKkM8ys0sxuClZ908y2mNlmSZdL+quwDgqp6br+ajgmuOjp45eUq5EbDwMAAGCMiaXSKJhCfW2PZXcmvXZJt/ax7bV9LL8+9TIxFCpqGpUZi2h68fBcf5Vs0YwiXTi3RPc8t1s3vrVcGVHueQ0AAIDRj2+149iumkbNGebrr5L96SVzdaC+RWu39DuaFAAAABg1CFjj1LHWDlUfbVX5MF9/lewdZ07WvMn5uvMpbjwMAACAsYGANU513/8qDddfdYlETDe/ba62VzXoqVe5xxkAAABGPwLWOLWr+ljarr9K9v7FMzS1MFt3PvV6WusAAAAAwkDAGqcq0nz9VZfMWEQ3XVKuF3fV6uV9dWmtBQAAADhVBKxxqOZYqw4dbVX5xPQND0x27QWzVJgd051P0osFAACA0Y2ANQ6t21UrSSovTd8EF8nys2K6/qLZeuz3B/V69bF0lwMAAACcNALWOLSu4rAyoxHNSPP1V8k+dnHiXlg/emZXuksBAAAAThoBaxx6cddhzR4B118lKy3I0tXnzdTPN+3XoYaWdJcDAAAAnBQC1jjT0NKuV984ptkTc9Ndypvc/La56ojHtfq53ekuBQAAADgpBKxxZtv+BknSjOKRF7BmT8zTirOn6YEX96ihpT3d5QAAAACDRsAaZ7bur5ckzZgwcq6/SvbJt5+mo60denDd3nSXAgAAAAwaAWuc2bK/XtOLspWfFUt3Kb1aNKNIl8ybpLufrVBzW2e6ywEAAAAGhYA1zmzZX69FM4rSXUa/Pn3lfFUfbdU9z+9OdykAAADAoIzMbgwMiYaWdlXUNOqD585Iy+cPZtjfGVMK9N3HX1VmNKKbLi0fwqoAAACA8NCDNY50TXCxaObI7sGSpHedNUWt7XE99Wp1uksBAAAAUkbAGke6Jrg4e4QPEZSkaUU5OqesWM+/XqOD9dwXCwAAAKMDAWsc2RxMcDEpPyvdpaTkygVT5C599/HX0l0KAAAAkBIC1jiydRRMcJGsJC9Ty8pL9PDGfXq9+li6ywEAAAAGRMAaJ7omuHjLKLj+KtnlZ05WViyib/9mR7pLAQAAAAZEwBonuie4GEU9WJKUnxXTn146V2u3HNQr++rSXQ4AAADQLwLWOLFlfyKcjIYJLnr6xKXlKsnL1Dcf+0O6SwEAAAD6RcAaJ7bsb9D0omxNHCUTXCQryM7QbZfP03M7D+uZ15i2HQAAACMXAWuc2Lq/XmePsuuvkn3kwlmaUZyjr/5qu9o74+kuBwAAAOgVAWsc6JrgYjQOD+ySFYvqi+9dqD8cPKofPVOR7nIAAACAXhGwxoGuGwyPtgkuelq+aKrefdYU/dN/v6rdNY3pLgcAAAB4EwLWONAVsEZzD1aX/7tykTKjEf3tf22Ru6e7HAAAAOAEBKxxYMv+Bs0ozhmVE1z0NKUwW59fcaae23lYP9tUme5yAAAAgBMQsMaBLZV1WjSjMN1lhOa6ZbO0dPYEfXXtdtUca013OQAAAEA3AtYY19DSrt2Hm8bE8MAukYjp6x88W42tHfrKI79PdzkAAABANwLWGNd9/dXM4jRXEq75Uwr0qcvm6RcvH9ATOw6luxwAAABAEgFrzBtLE1z09KnLT9O8yfn6wn9uVWNrR7rLAQAAAAhYY93mynrNKM5RSV5muksJXVYsqq9/8Gztr2vW1x/dnu5yAAAAAALWWLd1f/2YmuCip/PnlOgTl5brxy/u1a82V6W7HAAAAIxzBKwxrL45McHFW8bY9Vc9fW75mVpcVqzP/3wzNyAGAABAWqUUsMxsuZntMLOdZnZ7L+vNzL4XrN9sZkuS1q02s0NmtrXHNiVm9lszey14nnDqh4Nk24LrrxaNweuvkmVEI/qX685VxKTbfvKSWjs6010SAAAAxqkBA5aZRSV9X9IKSQslXWtmC3s0WyFpfvC4WdIdSevukbS8l13fLulxd58v6fHgPUK0ZQxPcNHTzAm5+tbV52jr/gZ97VdcjwUAAID0SKUHa5mkne6+y93bJD0kaWWPNisl3ecJL0oqNrNpkuTuT0uq7WW/KyXdG7y+V9L7T+YA0Lct+8fuBBe9eddZU3XTJeW694U9WruF67EAAAAw/FIJWDMk7Ut6XxksG2ybnqa4e5UkBc+TU6gFg7B1f/246L1K9vnlZ+qcsmJ9/mebtfdwU7rLAQAAwDgTS6GN9bLMT6LNSTGzm5UYdqhZs2aFsctxoWuCi6uXlqW7lGGVGYvoX649V+/53jO69cGXdPV5MxWLDn4ul+su4FwDAADA4KXyzbNSUvK39JmSDpxEm57e6BpGGDwf6q2Ru9/l7kvdfWlpaWkK5UIaPxNc9KasJFf/cPU52rK/Xr94+YDcQ8n6AAAAwIBS6cHaIGm+mZVL2i/pGknX9WizRtJtZvaQpAsk1XcN/+vHGkmrJH0jeP7FYApH/8bSBBcPrtt7UttdfsZkPbHjkCYXZunS+YRzAAAADL0Be7DcvUPSbZIek7Rd0sPuvs3MbjGzW4JmayXtkrRT0g8lfaprezP7iaQXJJ1hZpVmdlOw6huS3mlmr0l6Z/AeIRlvE1z05ooFk3XW9EL9eutB/aGqId3lAAAAYBxIpQdL7r5WiRCVvJROitIAACAASURBVOzOpNcu6dY+tr22j+WHJV2RcqUYlC3jcIKLniJmuvq8Mt3V+Loe2rhPt7z9NE0tzE53WQAAABjDBn/1P0a8+uZ27TncpLNnju+AJSUmvbj+ojnKikZ0/wu7day1I90lAQAAYAwjYI1B28bQ9VdhKMrJ0EcvnK2jLR16cN0edcTj6S4JAAAAYxQBawwaSxNchKWsJFd/vGSmdh9uYmZBAAAADJmUrsHC6LI5mOBiwjie4KI355QV69DRFj2xo1ql+Vl62+nMLAgAAIBwEbDGoK376/UWrr/q1RULpqjmWJse23ZQJXmZ4/I+YQAAABg6DBEcY+qbEhNcEBx6FzHTh86bqZkTcvTvm/ap8khTuksCAADAGELAGmO2HuD6q4FkRBMzC+ZnxXT/C3tU19SW7pIAAAAwRhCwxhgmuEhNflZMN1w0R+3xuO59Ybda2jvTXRIAAADGAALWGLNlf71mTmCCi1RMKczWdctmq/poqx7asFedcWYWBAAAwKkhYI0xW/fX03s1CPMm52vlOTP06hvH9Mhmpm8HAADAqSFgjSFMcHFyzi8v0aXzJ2ldRa2ef/1wussBAADAKMY07WNI1wQXTNE+eO8+a6pqG9u0dkuVShheCQAAgJNED9YY0jXBxaLpBKzBipjp6vPKNL04Rz/dsE9bg58lAAAAMBgErDGECS5OTWYsousvmq2czKhuuneDDta3pLskAAAAjDIErDFkSyUTXJyqwuwM3XDRbDW2duqmezeosbUj3SUBAABgFCFgjRH1Te3aW9uks7n+6pRNK8rRP193rrZXNejTD/2O6dsBAACQMgLWGNE1wQU9WOG4/IzJ+tL7ztJ/bz+kr63dnu5yAAAAMEowi+AYsbmSCS7CdsNFc7SrulF3P1uheZPzde2yWekuCQAAACMcPVhjxNb99SorYYKLsH3hPQv09tNL9cX/2qrnX69JdzkAAAAY4QhYY8SW/UxwMRRi0Yj++bpzVT4pT5/88UuqqGlMd0kAAAAYwQhYY0DXBBeLCFhDojA7Q3evOl/RiOmmezaovqk93SUBAABghCJgjQFdNximB2vozJqYqzs/ep72HWnSrQ++pPbOeLpLAgAAwAhEwBoDNu+vk8QEF0NtWXmJvvaBs/Xszhp9+Zfb5M707QAAADgRswiOARt3H9Hc0jwmuBgGVy8t087qY/rXp3Zp/uQCrbp4TrpLAgAAwAhCwBrl4nHXxt21uursaekuZUx5cN3ePteVTcjVgqkF+tKabaqoadTpUwpOWH/dBUznDgAAMF4xRHCU2/HGUTW0dGhZeUm6Sxk3Imb68PllmlqUrZ+s36tDDS3pLgkAAAAjBAFrlNuwu1aSdP4cAtZwyopFdf2Fs5URjei+F/eosbUj3SUBAABgBCBgjXLrK2o1rShbMyfkpLuUcac4N1MfvXC2Gprb9cC6veqIM7MgAADAeEfAGsXcXesranX+nBKZWbrLGZdmleTqg0tmavfhRv3i5QPMLAgAADDOEbBGsb21TTp0tJXrr9JscVmxLj9jsjbtOaJnd9akuxwAAACkEQFrFFtfkbj+ioCVflcsmKxF0wv1660H9eiWqnSXAwAAgDQhYI1iG3bXqjg3Q/NK89NdyrgXMdOHzitTWUmuPv3Qy3px1+F0lwQAAIA0IGCNYusrarV0dokiEa6/GgkyYxHdcOFszZqYq0/cu1HbqxrSXRIAAACGGQFrlDp0tEW7DzfpAoYHjii5WTHd+/FlysuKadXq9dpX25TukgAAADCMCFij1IaKI5Kk8wlYI86M4hzd+/Flamnv1KrV61Xb2JbukgAAADBMCFij1IbdtcrJiOqs6YXpLgW9OGNqgX606nxV1jXr4/dsUFMbNyIGAAAYD1IKWGa23Mx2mNlOM7u9l/VmZt8L1m82syUDbWtmXzKz/Wb2cvC4KpxDGh/WVdRqyexiZUTJyCPVsvIS/fO152pzZZ0++eOX1NrRme6SAAAAMMQG/HZuZlFJ35e0QtJCSdea2cIezVZImh88bpZ0R4rb/qO7Lw4ea0/1YMaL+uZ2/eFgg5bNmZjuUjCAd581VV/9wNl66tVq/dn9m9TSTsgCAAAYy1Lp/lgmaae773L3NkkPSVrZo81KSfd5wouSis1sWorbYpBe2nNE7tL55RPSXQpScO2yWfr6B8/Wkzuq9Yn7NhKyAAAAxrBUAtYMSfuS3lcGy1JpM9C2twVDClebWa9pwcxuNrONZraxuro6hXLHvvW7a5URNZ1bRsAaLa5dNkvf/NBb9OzOGq7JAgAAGMNiKbTp7SZLnmKb/ra9Q9JXgvdfkfRtSR9/U2P3uyTdJUlLly7t+bnj0vqKWi2aUaSczGi6S0EvHly3t891H1oyUz/bVKn3fO9Z3XDRbGXFTvwdXnfBrKEuDwAAAEMolR6sSkllSe9nSjqQYps+t3X3N9y9093jkn6oxHBCDKClvVObK+u0jOnZR6VzZ03Qh5eWac/hRt3z/G61MlwQAABgTEklYG2QNN/Mys0sU9I1ktb0aLNG0g3BbIIXSqp396r+tg2u0eryAUlbT/FYxoWX99WpvdO1bA4Ba7Q6p6xYf3L+LO2rbdKPnq1QfXN7uksCAABASAYcIujuHWZ2m6THJEUlrXb3bWZ2S7D+TklrJV0laaekJkk39rdtsOtvmtliJYYI7pb0Z2Ee2Fi1oaJWZtLS2QSs0ezsGUXKiMzWQxv36QdP7NRHL5ytspLcdJcFAACAU2Tuo+eypqVLl/rGjRvTXUZaXX/3OlUfbdWv//Jtp7Sf/q4TwvA52NCi+1/YraMtHfrAuTP0D1efk+6SAAAAkAIz2+TuS3su5y61o0hHZ1wv7TnC9VdjyNTCbH3qsnkqK8nVv2+q1Ncf3a7O+Oj5owcAAABORMAaRbYdaFBjW6fO5/qrMSUvK6aPv7Vcy8pL9K9P7dIn7tuohhauywIAABiNCFijyGPbDioaMb113qR0l4KQRSOm9y+eoa+sPEtPvVqtd//j03riD4fSXRYAAAAGiYA1Sri7frWlShefNlEleZnpLgdD5PqL5uhnt1yk/KyYbrxng/76py+rrqkt3WUBAAAgRQSsUWLr/gbtOdyk975l2sCNMaqdO2uCHvmLS/QX75inNa8c0JXfeVqPbqlKd1kAAABIAQFrlHhkywHFIqZ3nzU13aVgGGTFovrrd52hNbddoqlFWfrkAy/pkz/epD2HG9NdGgAAAPpBwBoF3F2/2lylS+ZPUnEuwwPHk4XTC/Vfn3qrPr/8TP3PHw7p8m89qb/66cvaeehouksDAABALwhYo8DmynpVHmnWe85meOB4FItG9MnLTtMzn7tcN11Srl9vPah3/uPTuvWBl/T7Aw3pLg8AAABJYukuAAP71ZYqZURN71rI8MDxbHJhtv72PQv1ycvmafWzFbr3+d361ZYqXX5GqT64ZKauWDBZuZn8Jw0AAJBOfBsb4bqGB146v1RFuRnpLgcjQElepj777jP0ibfN1T3P7dYD6/boiR2/U05GVFcunKI/ess0vf2MUmXFoukuFQAAYNwhYI1wv9tXp/11zfrrd56e7lIwwhTlZOjTV87Xbe+Ypw27a/XLVw7o0a0H9ctXDqggK6bLzpysC8pLdEF5ieZNzpeZpbtkAACAMY+ANcL9anOVMqMRvfOsKekuBSNUNGK6cO5EXTh3or70vrP0/OuH9cgrB/TUq9X65SsHJCV6vc6fM0HLyidqcVmxzpxaoLysxH/+D67be1Kfe90Fs0I7BgAAgLGCgDWCxeOutVuq9LbTS1WYzfBADCwjGtHbTy/V208vlbtrb22T1lXUan3weGzbG5IkM6l8Yp4WTC9Ue0dc04pyNK04m/MMAADgFBGwRrDf7TuiqvoWfX75mekuBSPcQL1QS2ZN0JJZE1Tf3K4Ddc2qqm9WVX2Lnt9ZoyNN7d3t8rNimlaU3R24phVla1J+liIMLwQAAEgJAWsEe2RzlTJjEV2xYHK6S8EwOdnheqkqyslQUU6GFkwr7F7W3Napgw0tidBVl3h+bmeNOt0lSRlR09TCbE0rztG0omxNL8rRlMLsIa0TAABgtCJgjVBdwwMvO71UBQzbwhDKyYyqfFKeyifldS/riMdVfbS1O3AdqG/R5so6ra+IS5JM0v0v7tbC6UVaOK1QZ00v1MLphZqUn5WmowAAABgZCFgj1Ka9R/RGQ6vee870dJeCcSgWiSSGCRblSJogKXHLgLqm9u7AFY2YXtpzpHsiDUmaXJClhdMLtXBaYffznIl5ikQYYggAAMYHAtYI9cgrB5QVi+iKMxkeiJHBzDQhL1MT8jK1cHpR9yyCdU1t+n1Vg35/oKH7+dnXatQRTwwxzM2MasG0E0PXGVMLlJ3BfboAAMDYQ8Aagdo741q79aDecebk7qm0gZGqODdTF582SRefNql7WUt7p3YeOnZC6PrP3+3X/S/ukSRFTJpbmq/ySXmaMzFXsyfmac7EPM2emKvpxTmKDkGP16lc38aU9AAAIFV8ex+BfrJ+r6qPturD55eluxTgpGRnRLVoRpEWzSjqXhaPu/YdaeoOXdurjmp3TaOeerVabR3x7naxiGlKYWIGw2nFOZpelK2pwcyG04sTzxPzMhl2CAAARiQC1gjT0NKuf/rv13TR3Im67PTSdJcD9OlUeoQ+864zul/H466DDS3ac7hJew43am9tkw7Wt+hAfbM2V9bpsW0tJwQwScqMRjSlKCsRuk4IYomZDmcU56g4N0PG9PIAAGCYEbBGmDuffF21jW36m6sW8OUQY1Z/4WzmhFzNnJDb/d7d1djWqfrmdtU3tWv+lHxV1R+fVn7jniN6Y0uV2jv9hP0U5WRobmmeTivN19zSPO0/0qzSgizu6wUAAIYUAWsEOVDXrLufrdD7F0/X2TOLBt4AGAfMTPlZMeVnxTSjOKfX66HicVdN4/Fp5ffXtaii5pheP9SoZ16r1s82VXa3zYgmhiBO776Zco6mFmYrMxYZzsMCAABjFAFrBPnWb3bIJX323WcM2BbAcZGIaXJBtiYXZOucsuI3rT/a0q47nnxdhxpaj9/Xa3+d1u8+fl+viflZwY2Uj99UmXvQAQCAwSJgjRBb99frP3+3Xze/be4Jw6MAnOhkr/06PvQw6b5eze3dvV5V9S2qPNKkLfvru7cpyIppWnG29h1p6p5mfs7EvJRnOWTmQgAAxh8C1gjg7vr6o9tVnJOhT102L93lAOOCmWlCbqYm5GZq4fTC7uXNbZ2qamgOglcifP3omV3d13jlZEQ1qyRX04uzNb04R9OLczSjOEdTi7JVmJ2hguyYCrITQxoBAMD4wzeAEeDJV6v13M7D+rs/WqiiHIYkAemUkxnV3En5mjspv3vZh86bmbivV1WDtlc1aF9tkw7UN+uVynrVNrb1ua+MqCkzGlE0YopETFE7/hyNmCKmE9YlliWe11ccViwaUUbUlBGNKC8rEdwKsmIq6A5yGcoPlhdmZyg/O3ZK9xCjxw0AgFNHwEqzjs64vr52u+ZMzNVHLpid7nIA9CIzFtHC6YUn9HR1aWrr0IG6Fh2sb9HRlnYdbe3QsZYOHWvt0PqKWrV1xhWPuzrjrk73xGtX8Bws73S1eVxxd8XjUmfc1dDSro5OV3tnXO2dcR1r7XjTTIm9ycuMqiA7Q0U5GSrMiQXPwftgefKyopwMTcjL0ITczKH40QEAMO4QsNLsZ5sq9eobx/SDjyxhFjNgFMrNjGne5HzNm5z/pnWn0iPUm/bOuFraO9XaHldLR6da2oP3Sa/nTMpTQ3O7GlraVd/crv11LdpedVQNzYnw15/sjIjyMmPKy0o8inJiKsrJTHrOUGF2TLEo/1YBANAXAlYavbjrsL7yyO913uwJWrFoarrLATDCZUQjyohGVJB9ctt3xl2tHZ1qbutUc3vwaOtUU1unGts61NjaqcbWDjW1dai2sVUVNcfU0h5/037ys2LdvV9dj7ysqKYHsy9OKcxWxhgNYScbmhlCCQDjBwErTZ7ccUh/dv8mlZXk6vvXLeGmwgCGXDRiys2MKTcz9X/6WzuCmzw3t6uhuV11wXN9c7tqjrVqVxDCfr3tYPc2EZMmF2RrWnG2JhdkaWJ+liblZWpSQZYm5mVpYn5m973N8oLn7IwI/w4CAMYEAlYa/Hprlf78J7/T6VMKdN/Hl2lifla6SwLQj7CH+o0mWbGoJhdENbmfbrOW9k5dOn+SDtS36EBds6rqmrtfV9Q0auPuI6ptapP3cwmZKXGtW6R7IhB1T/5hpjdt65IKsmPdy11+/HXPtsECT1rn8hPauk7cNisWSYTAYEbIrtkhq+pauicYSX4eqz12AIDBI2ANs//8XaU++++bdc7MIv3bjcuYNRDAqJedEdX8KQWaP6WgzzYdnXEdaWrX4cZWHT7WpmOtHWps7dCTO6rV2hFXa0en2jvi3ROAxIMJQOLuiruU3LnV9bJ8Ul7ifbDSklZa8MLsxG3MpF3VjSfUdnzfxz+kMx5XS3tcdU1teqOhpfsat6a2DsV7CYoFWTFNyMtUSV5i6v+SvOOPgmz+VwsA4wn/6g+jB9bt0Rf+a6sumjtRP7xhqfK4Tw6AcSIWjai0IEulBSf22Pd2jddQW1w24aS3jbursTUxS+TRlg4dbWlXfXOH6praVNvYpt2HG/XKvjolZ7BoxLT6uQqVTcjVrJLEo6z7OUcF2fyhDQDGEr7hD4PXq4/p7mcr9OC6vbrizMn6/keWKDsjmu6yACA042UYZcQsGBaYoWlFvbfpiMdV39Su2iB0HWlsU352THtrm/TS3iM62nLibI4TcjM0qyRXU4uyVVqQpckFiefS/CxNKshSYXZiqGJBVkao16q5uzq6biEQP/66Ix7XzzftD24bkOhBjHtiCGYkGLoZscR93LqGdEZ0fPk1y8oUiyTu5zaYWrkPG4CxgoA1ROJx11OvVeue53brqVerlRmN6IaLZuuL713IWH0AGMNikYgm5medcH1tcgCob2rX3tom7TvSpL21ice+2iZV1DRqfUWtjjS197nvaMSUlxlVXlZMsagpI5K4kXU0Yt3/b2nvjHcHpvbOuDo6E6GpvdPV0RlXR1KYGgpf+uW27tcRS/w8umpM1GmKRSKKRU2xiCkWjSgWMR1t6XjTzbdjEVNGLKLMYAbNzFji5tvZGVHlZESVmxlVTmZMu6qPqTg3k9sIABgRzPu76rirkdlySd+VFJX0I3f/Ro/1Fqy/SlKTpI+5+0v9bWtmJZJ+KmmOpN2SPuzuR/qrY+nSpb5x48ZBHN7wau+Ma3dNo57bWaP7XtijXTWNKi3I0vUXzta1y2a9aWhMOo2XvzYDwGjTEdxY+lhw0+olsyd038C6a3hiY2tHUlBKhKfOuMvdFYsmQkg0ElFlbVN3WOk5eUhXj1O0Z69UxBSRKRIJeqiCtlKi16vrhthd18fF/fi1cvG46+yZRYonhbiuG2p3dAa1xoOg1+lqT6p/7+Gm7rZdN+JOvtl2W+fx4+xPQXZMxbkZKs7JVHFu4jYCxbmJm2kX52ZqQvC6KHiekJu4CXfXMQJAqsxsk7sv7bl8wB4sM4tK+r6kd0qqlLTBzNa4+++Tmq2QND94XCDpDkkXDLDt7ZIed/dvmNntwfvPn8pBDqdjrR169rUavfrG0e5HRU2j2jsT//CfU1as716zWCsWTeMGwgCAlMWiERUHYUCS3n/ujJPeVzr+mHayw/VSrbUz7mpt71RT0n3czp1VrLqmNtU1t6uuKXEbga73lUeau1/39TfliElFOYnAVZAdU05mVLmZwXNGosew63XXukTvWbS7dy0WPd47lxGE3K6wmxH00sUiEVkkMZ1KV3g1S0y0EjHrXp5YdmLg654N05NnvTw+Q2bXusT7E2fJ7G+d97Of5IsJ+6u7K5xzq4X08OAPHN3Pwayq3jW8V8eH+XrwnLxcwTnVNVFQb+di7+doYkKhrt/9UJ8D7j5qzrFUhgguk7TT3XdJkpk9JGmlpOSAtVLSfZ74L/RFMys2s2lK9E71te1KSZcF298r6UmNooBVe6xNt/x4kySprCRHp08u0BULpuj0KflaOK1IZ0ztezYtAABSNdpGHAx1vdGIKTcrptykiaKa2jqVOcAtBeLuwUyQXY8ONbV1hbSO7uXN7Yl7v7V1JHrNup7bOxK9aujf8dAVfDGXTrhmT3bie0v6ct4V1rr205eT/TX0N2proF3295nez9b9b9e1vkdA6gpDOh6MugJR17quntyReEYGv+YTA1rS+66gZj2CWSRpKtjOePz4daHBMOd3Lpyif73+TZ1FI1IqAWuGpH1J7yuV6KUaqM2MAbad4u5VkuTuVWY2ubcPN7ObJd0cvD1mZjtSqHlY7ZH0bLqLGLxJkmrSXQRwCjiHMdpxDmO04xzGsLlL0l03hL7bUz2HZ/e2MJWA1dvfEHoG5r7apLJtv9z9LiV+pgiRmW3sbcwoMFpwDmO04xzGaMc5jNFuqM7hVC4OqpRUlvR+pqQDKbbpb9s3gmGECp4PpV42AAAAAIw8qQSsDZLmm1m5mWVKukbSmh5t1ki6wRIulFQfDP/rb9s1klYFr1dJ+sUpHgsAAAAApNWAQwTdvcPMbpP0mBJTra92921mdkuw/k5Ja5WYon2nEtO039jftsGuvyHpYTO7SdJeSVeHemQYCMMuMdpxDmO04xzGaMc5jNFuSM7hlO6DBQAAAAAYGDdoAgAAAICQELAAAAAAICQErHHGzJab2Q4z22lmt6e7HqA3ZrbazA6Z2dakZSVm9lszey14npC07n8H5/QOM3t3eqoGjjOzMjN7wsy2m9k2M/t0sJzzGKOCmWWb2XozeyU4h78cLOccxqhiZlEz+52ZPRK8H/JzmIA1jphZVNL3Ja2QtFDStWa2ML1VAb26R9LyHstul/S4u8+X9HjwXsE5fI2ks4JtfhCc60A6dUj6jLsvkHShpFuDc5XzGKNFq6R3uPs5khZLWh7MFM05jNHm05K2J70f8nOYgDW+LJO00913uXubpIckrUxzTcCbuPvTkmp7LF4p6d7g9b2S3p+0/CF3b3X3CiVmM102LIUCfXD3Knd/KXh9VIn/uc8Q5zFGCU84FrzNCB4uzmGMImY2U9J7JP0oafGQn8MErPFlhqR9Se8rg2XAaDAluL+egufJwXLOa4xoZjZH0rmS1onzGKNIMLTqZUmHJP3W3TmHMdr8k6TPSYonLRvyc5iANb5YL8uYpx+jHec1Riwzy5f0c0l/6e4N/TXtZRnnMdLK3TvdfbGkmZKWmdmifppzDmNEMbP3Sjrk7ptS3aSXZSd1DhOwxpdKSWVJ72dKOpCmWoDBesPMpklS8HwoWM55jRHJzDKUCFcPuPt/BIs5jzHquHudpCeVuC6FcxijxVslvc/MditxWcw7zOzHGoZzmIA1vmyQNN/Mys0sU4kL+dakuSYgVWskrQper5L0i6Tl15hZlpmVS5ovaf3/b+9+Q/Ws6ziOvz9tVusPDFyUYrkyUrLlskxi2WYZWFBaGLZ00JCCsNwEn5gPsgfWBkNBDCJtaGXZNCJQl5FtC9NtlW0ep8sQKzQfCD0pKW3btwfX77TLc+5z77bdZ4cD79eTc92/67p+3991c3Hu8z3f6/e752B80v8kCfBd4PGqur63y/tY80KSNyRZ3LYXAecB+/Ee1jxRVVdX1UlVtZTub95fVdWlHIN7eOFRjVzzSlUdSPJl4D5gAbC5qvbN8bCkaZL8CFgFLEnyNPA1YAOwJcllwF+BzwBU1b4kW4DH6FZuu7yqDs7JwKXDVgBrgIk2hwXgq3gfa/44AbitraL2CmBLVd2d5CG8hzW/zfrv4VT5eKwkSZIkjYOPCEqSJEnSmJhgSZIkSdKYmGBJkiRJ0piYYEmSJEnSmJhgSZIkSdKYmGBJkiRJ0piYYEmSBkryqSSV5LQ5HMP6JK85RrEuTfJIkn1J9ia5ZfKLVoecsz3J+wa0fz7JTW372iTPJNmT5NEknzyKMa5Kcvf/e74kafaZYEmSZrIaeAD47ByOYT0w6wlWkvOBK4GPVdXpwJnAg8AbxxTihqpaTveFlpuTvOTzN8nCMcWRJM0xEyxJ0jRJXgesAC6jJViterIjyZYkTyTZkOSSJLuTTCQ5pR13cpL7WzXo/iRvae23JrmoF+OfvX63J7kryf4kt6dzBXAisC3JtiFjXd3iP5pkY7//JNe1atTOJMOSpWuAq6rqGYCqOlhVm6vqj62vjyT5Q4uzOcmrBoxjbXtfdrT3bpqqehw4ACxp1/yNdvy6mWIkOb+9Lw8An+7FW9mqYnvaea8fcn2SpGPEBEuSNMiFwM+r6gng70nObO1nAOuAZcAa4B1V9X7gFuAr7ZibgO9V1buB24EbR4j3Hrpq1TuBtwErqupG4G/AuVV17qCTkpwIbAQ+DCwHzkpyYdv9WmBnVZ0B/Br4wpD4pwMPzxDj1cCtwMVVtQxYCHxpyjEnAF+nS6w+2q5jUF9nA4eA51rT4qpaCXxrUIwW+2bgE8A5wJt63V0FXN4qY+cA/xpyfZKkY8QES5I0yGrgjrZ9R3sN8NuqeraqXgCeBH7R2ieApW37A8AP2/b3gQ+OEG93VT1dVYeAPb2+juQsYHtVPVdVB+gSug+1fS8Ck/OVfj9qn0mWtarQk0kuBk4FnmrJJsBtvRiTzu6N40Xgx1P2X5lkD7CJLomq1j553EwxTmvtf2rn/KDX52+A61ulb3G7fknSHPOZb0nSSyQ5nq4i9K4kBSwACrgXeKF36KHe60PM/JkymUwcoP1jL0mAV/aO6fd7cEhf04Y7ZN9/eonMkfrcRzfvaltVTQDL2yIVi44Qo6+G7LuhqjYNaH++/RwWY2C/VbUhyT3Ax4GdSc6rqv2jDVWSNFusYEmSprqI7hG/k6tqtH6cxAAAAV5JREFUaVW9GXiK0SpR0C0OMbkwxiV0C2UA/Bl4b9u+ADhuhL7+AQybW7QLWJlkSZIFdJW2HSOOs++bwKYkJ/XaFrWf+4GlSd7eXq8ZEGMXsCrJ8UmOo1vM4uWYKcZ+4K2T89s4XEkkySlVNVFVG4Hf0VW7JElzzARLkjTVauCnU9p+AnxuxPOvANYmeYQuUVjX2m+mS4Z20z1S9/wM5/d9B9g60yIXVfUscDWwDdgLPFxVPxtxnP1+7qWbK7Y1yWNJHqSret1XVf8G1gJ3Jpmgq9Z9e8A4rgUeAn7JDPO5hsQfGKO1fxG4py1y8Zfeaevbwh576eZfbX2Zly1JmgU5/PSEJEmSJOloWMGSJEmSpDFxkQtJ0ryQZBcw9fun1rRFKUbt4xqmz4+6s6quO9rxSZIEPiIoSZIkSWPjI4KSJEmSNCYmWJIkSZI0JiZYkiRJkjQmJliSJEmSNCb/BWq+8E+EoSD3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    1568.000000\n",
      "mean        5.326531\n",
      "std         2.468023\n",
      "min         0.000000\n",
      "25%         3.000000\n",
      "50%         6.000000\n",
      "75%         7.000000\n",
      "max        20.000000\n",
      "Name: WebVisitsMonth, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    1568.000000\n",
      "mean        0.072704\n",
      "std         0.259733\n",
      "min         0.000000\n",
      "25%         0.000000\n",
      "50%         0.000000\n",
      "75%         0.000000\n",
      "max         1.000000\n",
      "Name: Cmp3Accepted, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    1568.000000\n",
      "mean        0.072704\n",
      "std         0.259733\n",
      "min         0.000000\n",
      "25%         0.000000\n",
      "50%         0.000000\n",
      "75%         0.000000\n",
      "max         1.000000\n",
      "Name: Cmp4Accepted, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    1568.000000\n",
      "mean        0.080357\n",
      "std         0.271932\n",
      "min         0.000000\n",
      "25%         0.000000\n",
      "50%         0.000000\n",
      "75%         0.000000\n",
      "max         1.000000\n",
      "Name: Cmp5Accepted, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    1568.000000\n",
      "mean        0.064413\n",
      "std         0.245566\n",
      "min         0.000000\n",
      "25%         0.000000\n",
      "50%         0.000000\n",
      "75%         0.000000\n",
      "max         1.000000\n",
      "Name: Cmp1Accepted, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    1568.000000\n",
      "mean        0.012755\n",
      "std         0.112252\n",
      "min         0.000000\n",
      "25%         0.000000\n",
      "50%         0.000000\n",
      "75%         0.000000\n",
      "max         1.000000\n",
      "Name: Cmp2Accepted, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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V3RcludcK229I8qD1KAoAAGAR7dQ9WAAAAOyYgAUAADCIgAUAADCIgAUAADCIgAUAADCIgAUAADCIgAUAADCIgAUAADCIgAUAADCIgAUAADCIgAUAADCIgAUAADCIgAUAADCIgAUAADCIgAUAADCIgAUAADCIgAUAADCIgAUAADCIgAUAADCIgAUAADCIgAUAADCIgAUAADCIgAUAADCIgAUAADCIgAUAADCIgAUAADCIgAUAADDIqgGrqg6vqndW1SVVdXFVPX3afmpVfbqqLpgeJ65/uQAAABvXvnO0uSnJM7v7Q1V1hyTnV9XZ077f7e7fXr/yAAAAFseqAau7r0lyzfT8xqq6JMmh610YAADAotmpe7CqanOSeyX5wLTpqVV1UVW9vKoOGFwbAADAQpk7YFXV7ZO8IckzuvvzSV6U5G5Jjs1shuuFOzju5KraWlVbt23bNqBkAACAjWmugFVV+2UWrl7V3W9Mku6+trtv7u5bkrwkyXErHdvdp3f3lu7esmnTplF1AwAAbDjzrCJYSV6W5JLu/p0l2w9Z0uzRST4yvjwAAIDFMc8qgvdN8sQkH66qC6Ztz0nyuKo6Nkkn+WSSn12XCgEAABbEPKsIvjdJrbDrrePLAQAAWFw7tYogAAAAOyZgAQAADCJgAQAADCJgAQAADCJgAQAADCJgAQAADCJgAQAADCJgAQAADCJgAQAADCJgAQAADCJgAQAADCJgAQAADCJgAQAADCJgAQAADCJgAQAADCJgAQAADCJgAQAADCJgAQAADCJgAQAADCJgAQAADCJgAQAADCJgAQAADCJgAQAADCJgAQAADCJgAQAADCJgAQAADCJgAQAADLJqwKqqw6vqnVV1SVVdXFVPn7YfWFVnV9Unpp8HrH+5AAAAG9c8M1g3JXlmd98jyX2S/EJVHZ3klCTndPdRSc6ZXgMAAOy1Vg1Y3X1Nd39oen5jkkuSHJrkkUnOnJqdmeRR61UkAADAItipe7CqanOSeyX5QJKDu/uaZBbCktx5dHEAAACLZO6AVVW3T/KGJM/o7s/vxHEnV9XWqtq6bdu2XakRAABgIcwVsKpqv8zC1au6+43T5mur6pBp/yFJrlvp2O4+vbu3dPeWTZs2jagZAABgQ5pnFcFK8rIkl3T37yzZdVaSk6bnJyV58/jyAAAAFse+c7S5b5InJvlwVV0wbXtOktOSvK6qnpLkiiSPWZ8SAQAAFsOqAau735ukdrD7QWPLAQAAWFw7tYogAAAAOyZgAQAADCJgAQAADCJgAQAADCJgAQAADCJgAQAADCJgAQAADCJgAQAADCJgAQAADCJgAQAADCJgAQAADCJgAQAADCJgAQAADCJgAQAADCJgAQAADCJgAQAADCJgAQAADCJgAQAADCJgAQAADCJgAQAADCJgAQAADCJgAQAADCJgAQAADCJgAQAADCJgAQAADCJgAQAADCJgAQAADLJqwKqql1fVdVX1kSXbTq2qT1fVBdPjxPUtEwAAYOObZwbrjCTHr7D9d7v72Onx1rFlAQAALJ5VA1Z3vzvJZ3ZDLQAAAAttLfdgPbWqLpouITxgWEUAAAALalcD1ouS3C3JsUmuSfLCHTWsqpOramtVbd22bdsung4AAGDj26WA1d3XdvfN3X1LkpckOe7rtD29u7d095ZNmzbtap0AAAAb3i4FrKo6ZMnLRyf5yI7aAgAA7C32Xa1BVb0myf2THFRVVyX5tST3r6pjk3SSTyb52XWsEQAAYCGsGrC6+3ErbH7ZOtQCAACw0NayiiAAAABLCFgAAACDCFgAAACDCFgAAACDCFgAAACDCFgAAACDCFgAAACDCFgAAACDCFgAAACDCFgAAACDCFgAAACDCFgAAACDCFgAAACDCFgAAACDCFgAAACDCFgAAACDCFgAAACDCFgAAACDCFgAAACDCFgAAACDCFgAAACDCFgAAACDCFgAAACDCFgAAACDCFgAAACDCFgAAACDCFgAAACDCFgAAACDrBqwqurlVXVdVX1kybYDq+rsqvrE9POA9S0TAABg45tnBuuMJMcv23ZKknO6+6gk50yvAQAA9mqrBqzufneSzyzb/MgkZ07Pz0zyqMF1AQAALJxdvQfr4O6+Jkmmn3feUcOqOrmqtlbV1m3btu3i6QAAADa+dV/kortP7+4t3b1l06ZN6306AACAPWZXA9a1VXVIkkw/rxtXEgAAwGLa1YB1VpKTpucnJXnzmHIAAAAW1zzLtL8myfuSfEdVXVVVT0lyWpKHVNUnkjxkeg0AALBX23e1Bt39uB3setDgWgAAABbaui9yAQAAsLcQsAAAAAYRsAAAAAYRsAAAAAYRsAAAAAYRsAAAAAYRsAAAAAYRsAAAAAYRsAAAAAYRsAAAAAYRsAAAAAYRsAAAAAYRsAAAAAYRsAAAAAYRsAAAAAYRsAAAAAYRsAAAAAYRsAAAAAYRsAAAAAYRsAAAAAYRsAAAAAYRsAAAAAYRsAAAAAYRsAAAAAYRsAAAAAYRsAAAAAYRsAAAAAbZdy0HV9Unk9yY5OYkN3X3lhFFAQAALKI1BazJA7r7+gH9AAAALDSXCAIAAAyy1oDVSd5RVedX1ckrNaiqk6tqa1Vt3bZt2xpPBwAAsHGtNWDdt7u/N8kJSX6hqn5weYPuPr27t3T3lk2bNq3xdAAAABvXmgJWd189/bwuyZuSHDeiKAAAgEW0ywGrqr6pqu6w/XmShyb5yKjCAAAAFs1aVhE8OMmbqmp7P6/u7rcPqQoAAGAB7XLA6u7LkhwzsBYAAICFZpl2AACAQQQsAACAQQQsAACAQQQsAACAQQQsAACAQQQsAACAQQQsAACAQQQsAACAQQQsAACAQQQsAACAQQQsAACAQQQsAACAQQQsAACAQQQsAACAQQQsAACAQQQsAACAQQQsAACAQQQsAACAQQQsAACAQQQsAACAQQQsAACAQQQsAACAQQQsAACAQQQsAACAQQQsAACAQQQsAACAQfbd0wUAAAC3Lq/+wBXD+nr8vY8Y1tfusKYZrKo6vqo+XlWXVtUpo4oCAABYRLscsKpqnyR/lOSEJEcneVxVHT2qMAAAgEWzlhms45Jc2t2Xdfe/J3ltkkeOKQsAAGDxrOUerEOTXLnk9VVJ7r28UVWdnOTk6eUXqurjazjnejgoyfWjOnvCqI5YdEPHFcSYYn0YV6wH44qhnrBxx9SRK21cS8CqFbb112zoPj3J6Ws4z7qqqq3dvWVP18Gti3HFaMYU68G4Yj0YV4y2aGNqLZcIXpXk8CWvD0ty9drKAQAAWFxrCVgfTHJUVd21qr4xyWOTnDWmLAAAgMWzy5cIdvdNVfXUJH+dZJ8kL+/ui4dVtvts2MsXWWjGFaMZU6wH44r1YFwx2kKNqer+mtumAAAA2AVr+qJhAAAAvkLAAgAAGGSvCVhVdXxVfbyqLq2qU1bYX1X1+9P+i6rqe/dEnSyWOcbVE6bxdFFV/V1VHbMn6mRxrDamlrT7/qq6uap+fHfWx2KaZ1xV1f2r6oKquriq3rW7a2SxzPH775ur6i+r6sJpTD15T9TJ4qiql1fVdVX1kR3sX5jP6ntFwKqqfZL8UZITkhyd5HFVdfSyZickOWp6nJzkRbu1SBbOnOPq8iQ/1N3fk+QFWbCbNNm95hxT29v978wWGYKva55xVVV3SvLHSX6ku78ryWN2e6EsjDn/rfqFJB/t7mOS3D/JC6dVp2FHzkhy/NfZvzCf1feKgJXkuCSXdvdl3f3vSV6b5JHL2jwyyZ/2zPuT3KmqDtndhbJQVh1X3f133f3Z6eX7M/u+ONiRef6tSpJfTPKGJNftzuJYWPOMq8cneWN3X5Ek3W1s8fXMM6Y6yR2qqpLcPslnkty0e8tkkXT3uzMbJzuyMJ/V95aAdWiSK5e8vmratrNtYKmdHTNPSfK2da2IRbfqmKqqQ5M8OsmLd2NdLLZ5/q369iQHVNW5VXV+VT1pt1XHIppnTP1hknskuTrJh5M8vbtv2T3lcSu1MJ/Vd/l7sBZMrbBt+fr087SBpeYeM1X1gMwC1v3WtSIW3Txj6veSPLu7b579xzCsap5xtW+S70vyoCS3TfK+qnp/d//DehfHQppnTD0syQVJHpjkbknOrqr3dPfn17s4brUW5rP63hKwrkpy+JLXh2X2Pyo72waWmmvMVNX3JHlpkhO6+4bdVBuLaZ4xtSXJa6dwdVCSE6vqpu7+i91TIgto3t+B13f3F5N8sareneSYJAIWK5lnTD05yWk9+8LVS6vq8iTfmeS83VMit0IL81l9b7lE8INJjqqqu043WD42yVnL2pyV5EnTCiX3SfLP3X3N7i6UhbLquKqqI5K8MckT/U8wc1h1THX3Xbt7c3dvTvL6JD8vXLGKeX4HvjnJD1TVvlV1uyT3TnLJbq6TxTHPmLoisxnRVNXBSb4jyWW7tUpubRbms/peMYPV3TdV1VMzW3FrnyQv7+6Lq+rnpv0vTvLWJCcmuTTJv2T2Py+wQ3OOq/+R5FuS/PE043BTd2/ZUzWzsc05pmCnzDOuuvuSqnp7kouS3JLkpd294lLJMOe/VS9IckZVfTizS7ue3d3X77Gi2fCq6jWZrTh5UFVdleTXkuyXLN5n9ZrN3AIAALBWe8slggAAAOtOwAIAABhEwAIAABhEwAIAABhEwAIAABhEwAJgTarqW6vqtVX1j1X10ap6a1V9+xr7fEhVnV9VH55+PnDZ/ntVVVfVw9ZW/U7V9IzpO6J25pj7V9Vb1qsmADYeAQuAXVazL3h7U5Jzu/tu3X10kuckOXiNXV+f5BHd/d1JTkryimX7H5fkvdPP3eUZSXYqYAGw9xGwAFiLByT5j6VfgtzdFyTZp6reVVWvq6p/qKrTquoJVXXeNCt1tySpqjOq6sVV9Z6p3cOnPv6+u6+eurw4yf5VdZvpmEry40l+OslDq2r/7eeuql+e+r+wqk6btt29qv5m2vahJef+par6YFVdVFXPn7ZtrqqPVdWZ0/bXV9XtquppSe6S5J1V9c6p7UOr6n1Tn39eVbefth8/9fHeJD+6Tu87ABuUgAXAWtwzyfk72HdMkqcn+e4kT0zy7d19XJKXJvnFJe02J/mhJD+c5MVLA9Pkx5L8fXd/aXp93ySXd/c/Jjk3yYlJUlUnJHlUknt39zFJfmtq/6okfzRt+89JrqmqhyY5KslxSY5N8n1V9YNT++9Icnp3f0+Szyf5+e7+/SRXJ3lAdz+gqg5K8twkD+7u702yNcl/n2p/SZJHJPmBJN+6yvsHwK2MgAXAevlgd18zBaN/TPKOafuHMwtV272uu2/p7k8kuSzJd27fUVXfleR/J/nZJe0fl+S10/PX5iuXCT44yZ90978kSXd/pqrukOTQ7n7TtO3fpv0PnR5/n+RD0zmPmvq5srv/3/T8lUnut8Kf7T5Jjk7y/6rqgswuYzxy6ufy7v5Ed/d0PAB7kX33dAEALLSLM7tcbyVfWvL8liWvb8lX//7pZcd1klTVYZnd3/WkabYqVbVPZjNaP1JVv5qkknzLFKRqhb5qB7VVkv/V3f/3qzZWbd5RPSscf3Z3f9U9YFV17A7aA7CXMIMFwFr8bZLbVNXPbN9QVd+f2SV/83pMVX3DdG/UtyX5eFXdKclfJfmVJbNJyWyW6sLuPry7N3f3kUnekNmlge9I8l+2r/RXVQd29+eTXFVVj5q23Wba/9dT2+33TR1aVXeeznFEVf2n6fn2xTSS5MYkd5ievz/Jfavq7tPxt5tWTvxYkrtuv88ru3cRDgA2AAELgF02XQb36CQPmZZpvzjJqZndrzSvjyd5V5K3Jfm57v63JE9Ncvckz6uqC6bHnTMLLG9advwbkjy+u9+e5KwkW6fL9p417X9ikqdV1UVJ/i7Jt3b3O5K8Osn7qurDSV6fr4SnS5KcNLU/MMmLpu2nJ3lbVb2zu7dltsjGa6Z270/ynVPtJyf5q2mRi0/txPsAwK1AzX43AsDuV1VnJHlLd79+T9eSfPkSwbd09z33cCkALCgzWAAAAIOYwQIAABjEDBYAAMAgAhYAAMAgAhYAAMAgAhYAAMAgAhYAAMAg/x9fqGzqB2f31AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    1568.000000\n",
      "mean        0.008291\n",
      "std         0.090705\n",
      "min         0.000000\n",
      "25%         0.000000\n",
      "50%         0.000000\n",
      "75%         0.000000\n",
      "max         1.000000\n",
      "Name: Any_Complain, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    1568.000000\n",
      "mean        0.152423\n",
      "std         0.359546\n",
      "min         0.000000\n",
      "25%         0.000000\n",
      "50%         0.000000\n",
      "75%         0.000000\n",
      "max         1.000000\n",
      "Name: Response, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "numeric_distribution_plot(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Year_of_Birth</th>\n",
       "      <th>Disposable_Income</th>\n",
       "      <th>No_of_Kids_in_home</th>\n",
       "      <th>No_of_Teen_in_home</th>\n",
       "      <th>Recency</th>\n",
       "      <th>Discounted_Purchases</th>\n",
       "      <th>WebPurchases</th>\n",
       "      <th>CatalogPurchases</th>\n",
       "      <th>StorePurchases</th>\n",
       "      <th>Amount_on_Wines</th>\n",
       "      <th>Amount_on_Fruits</th>\n",
       "      <th>Amount_on_MeatProducts</th>\n",
       "      <th>Amount_on_FishProducts</th>\n",
       "      <th>Amount_on_SweetProducts</th>\n",
       "      <th>Amount_on_GoldProds</th>\n",
       "      <th>WebVisitsMonth</th>\n",
       "      <th>Cmp3Accepted</th>\n",
       "      <th>Cmp4Accepted</th>\n",
       "      <th>Cmp5Accepted</th>\n",
       "      <th>Cmp1Accepted</th>\n",
       "      <th>Cmp2Accepted</th>\n",
       "      <th>Any_Complain</th>\n",
       "      <th>Response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1568.000000</td>\n",
       "      <td>1552.000000</td>\n",
       "      <td>1568.000000</td>\n",
       "      <td>1568.000000</td>\n",
       "      <td>1568.000000</td>\n",
       "      <td>1568.000000</td>\n",
       "      <td>1568.000000</td>\n",
       "      <td>1568.000000</td>\n",
       "      <td>1568.000000</td>\n",
       "      <td>1568.000000</td>\n",
       "      <td>1568.000000</td>\n",
       "      <td>1568.000000</td>\n",
       "      <td>1568.000000</td>\n",
       "      <td>1568.000000</td>\n",
       "      <td>1568.000000</td>\n",
       "      <td>1568.000000</td>\n",
       "      <td>1568.000000</td>\n",
       "      <td>1568.000000</td>\n",
       "      <td>1568.000000</td>\n",
       "      <td>1568.000000</td>\n",
       "      <td>1568.000000</td>\n",
       "      <td>1568.000000</td>\n",
       "      <td>1568.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1970.073342</td>\n",
       "      <td>62381.186598</td>\n",
       "      <td>0.460459</td>\n",
       "      <td>0.497449</td>\n",
       "      <td>55.408801</td>\n",
       "      <td>2.292730</td>\n",
       "      <td>4.001276</td>\n",
       "      <td>2.665816</td>\n",
       "      <td>5.703444</td>\n",
       "      <td>303.190051</td>\n",
       "      <td>29.091199</td>\n",
       "      <td>168.376276</td>\n",
       "      <td>37.166454</td>\n",
       "      <td>26.800383</td>\n",
       "      <td>43.739158</td>\n",
       "      <td>5.326531</td>\n",
       "      <td>0.072704</td>\n",
       "      <td>0.072704</td>\n",
       "      <td>0.080357</td>\n",
       "      <td>0.064413</td>\n",
       "      <td>0.012755</td>\n",
       "      <td>0.008291</td>\n",
       "      <td>0.152423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11.920781</td>\n",
       "      <td>32089.169563</td>\n",
       "      <td>0.540361</td>\n",
       "      <td>0.544151</td>\n",
       "      <td>28.788037</td>\n",
       "      <td>1.937544</td>\n",
       "      <td>2.773748</td>\n",
       "      <td>3.043493</td>\n",
       "      <td>3.260553</td>\n",
       "      <td>340.141396</td>\n",
       "      <td>39.565637</td>\n",
       "      <td>233.419980</td>\n",
       "      <td>53.994951</td>\n",
       "      <td>40.857657</td>\n",
       "      <td>51.591557</td>\n",
       "      <td>2.468023</td>\n",
       "      <td>0.259733</td>\n",
       "      <td>0.259733</td>\n",
       "      <td>0.271932</td>\n",
       "      <td>0.245566</td>\n",
       "      <td>0.112252</td>\n",
       "      <td>0.090705</td>\n",
       "      <td>0.359546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1900.000000</td>\n",
       "      <td>2076.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1960.000000</td>\n",
       "      <td>41612.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1971.000000</td>\n",
       "      <td>60964.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1979.000000</td>\n",
       "      <td>81493.200000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>499.500000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>232.000000</td>\n",
       "      <td>49.250000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1997.000000</td>\n",
       "      <td>799999.200000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>1498.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>259.000000</td>\n",
       "      <td>263.000000</td>\n",
       "      <td>362.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Year_of_Birth  Disposable_Income  No_of_Kids_in_home  \\\n",
       "count    1568.000000        1552.000000         1568.000000   \n",
       "mean     1970.073342       62381.186598            0.460459   \n",
       "std        11.920781       32089.169563            0.540361   \n",
       "min      1900.000000        2076.000000            0.000000   \n",
       "25%      1960.000000       41612.400000            0.000000   \n",
       "50%      1971.000000       60964.200000            0.000000   \n",
       "75%      1979.000000       81493.200000            1.000000   \n",
       "max      1997.000000      799999.200000            2.000000   \n",
       "\n",
       "       No_of_Teen_in_home      Recency  Discounted_Purchases  WebPurchases  \\\n",
       "count         1568.000000  1568.000000           1568.000000   1568.000000   \n",
       "mean             0.497449    55.408801              2.292730      4.001276   \n",
       "std              0.544151    28.788037              1.937544      2.773748   \n",
       "min              0.000000     7.000000              0.000000      0.000000   \n",
       "25%              0.000000    31.000000              1.000000      2.000000   \n",
       "50%              0.000000    56.000000              2.000000      3.000000   \n",
       "75%              1.000000    80.000000              3.000000      6.000000   \n",
       "max              2.000000   106.000000             15.000000     27.000000   \n",
       "\n",
       "       CatalogPurchases  StorePurchases  Amount_on_Wines  Amount_on_Fruits  \\\n",
       "count       1568.000000     1568.000000      1568.000000       1568.000000   \n",
       "mean           2.665816        5.703444       303.190051         29.091199   \n",
       "std            3.043493        3.260553       340.141396         39.565637   \n",
       "min            0.000000        0.000000         5.000000          3.000000   \n",
       "25%            0.000000        3.000000        27.000000          4.000000   \n",
       "50%            2.000000        5.000000       167.000000         11.000000   \n",
       "75%            4.000000        8.000000       499.500000         36.000000   \n",
       "max           28.000000       13.000000      1498.000000        202.000000   \n",
       "\n",
       "       Amount_on_MeatProducts  Amount_on_FishProducts  \\\n",
       "count             1568.000000             1568.000000   \n",
       "mean               168.376276               37.166454   \n",
       "std                233.419980               53.994951   \n",
       "min                  0.000000                0.000000   \n",
       "25%                 15.000000                3.000000   \n",
       "50%                 64.000000               12.000000   \n",
       "75%                232.000000               49.250000   \n",
       "max               1725.000000              259.000000   \n",
       "\n",
       "       Amount_on_SweetProducts  Amount_on_GoldProds  WebVisitsMonth  \\\n",
       "count              1568.000000          1568.000000     1568.000000   \n",
       "mean                 26.800383            43.739158        5.326531   \n",
       "std                  40.857657            51.591557        2.468023   \n",
       "min                   0.000000             0.000000        0.000000   \n",
       "25%                   1.000000             8.000000        3.000000   \n",
       "50%                   8.000000            25.000000        6.000000   \n",
       "75%                  33.000000            56.000000        7.000000   \n",
       "max                 263.000000           362.000000       20.000000   \n",
       "\n",
       "       Cmp3Accepted  Cmp4Accepted  Cmp5Accepted  Cmp1Accepted  Cmp2Accepted  \\\n",
       "count   1568.000000   1568.000000   1568.000000   1568.000000   1568.000000   \n",
       "mean       0.072704      0.072704      0.080357      0.064413      0.012755   \n",
       "std        0.259733      0.259733      0.271932      0.245566      0.112252   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "max        1.000000      1.000000      1.000000      1.000000      1.000000   \n",
       "\n",
       "       Any_Complain     Response  \n",
       "count   1568.000000  1568.000000  \n",
       "mean       0.008291     0.152423  \n",
       "std        0.090705     0.359546  \n",
       "min        0.000000     0.000000  \n",
       "25%        0.000000     0.000000  \n",
       "50%        0.000000     0.000000  \n",
       "75%        0.000000     0.000000  \n",
       "max        1.000000     1.000000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Year_of_Birth</th>\n",
       "      <th>Disposable_Income</th>\n",
       "      <th>No_of_Kids_in_home</th>\n",
       "      <th>No_of_Teen_in_home</th>\n",
       "      <th>Recency</th>\n",
       "      <th>Discounted_Purchases</th>\n",
       "      <th>WebPurchases</th>\n",
       "      <th>CatalogPurchases</th>\n",
       "      <th>StorePurchases</th>\n",
       "      <th>Amount_on_Wines</th>\n",
       "      <th>Amount_on_Fruits</th>\n",
       "      <th>Amount_on_MeatProducts</th>\n",
       "      <th>Amount_on_FishProducts</th>\n",
       "      <th>Amount_on_SweetProducts</th>\n",
       "      <th>Amount_on_GoldProds</th>\n",
       "      <th>WebVisitsMonth</th>\n",
       "      <th>Cmp3Accepted</th>\n",
       "      <th>Cmp4Accepted</th>\n",
       "      <th>Cmp5Accepted</th>\n",
       "      <th>Cmp1Accepted</th>\n",
       "      <th>Cmp2Accepted</th>\n",
       "      <th>Any_Complain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>672.000000</td>\n",
       "      <td>664.000000</td>\n",
       "      <td>672.000000</td>\n",
       "      <td>672.000000</td>\n",
       "      <td>672.000000</td>\n",
       "      <td>672.000000</td>\n",
       "      <td>672.000000</td>\n",
       "      <td>672.000000</td>\n",
       "      <td>672.000000</td>\n",
       "      <td>672.000000</td>\n",
       "      <td>672.000000</td>\n",
       "      <td>672.000000</td>\n",
       "      <td>672.000000</td>\n",
       "      <td>672.000000</td>\n",
       "      <td>672.000000</td>\n",
       "      <td>672.000000</td>\n",
       "      <td>672.000000</td>\n",
       "      <td>672.000000</td>\n",
       "      <td>672.000000</td>\n",
       "      <td>672.000000</td>\n",
       "      <td>672.000000</td>\n",
       "      <td>672.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1969.181548</td>\n",
       "      <td>63434.170482</td>\n",
       "      <td>0.406250</td>\n",
       "      <td>0.526786</td>\n",
       "      <td>57.744048</td>\n",
       "      <td>2.400298</td>\n",
       "      <td>4.279762</td>\n",
       "      <td>2.653274</td>\n",
       "      <td>5.992560</td>\n",
       "      <td>322.342262</td>\n",
       "      <td>29.794643</td>\n",
       "      <td>163.622024</td>\n",
       "      <td>38.363095</td>\n",
       "      <td>27.675595</td>\n",
       "      <td>44.681548</td>\n",
       "      <td>5.293155</td>\n",
       "      <td>0.072917</td>\n",
       "      <td>0.078869</td>\n",
       "      <td>0.055060</td>\n",
       "      <td>0.063988</td>\n",
       "      <td>0.014881</td>\n",
       "      <td>0.011905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12.116416</td>\n",
       "      <td>25276.585476</td>\n",
       "      <td>0.532259</td>\n",
       "      <td>0.545293</td>\n",
       "      <td>29.321893</td>\n",
       "      <td>1.919125</td>\n",
       "      <td>2.782585</td>\n",
       "      <td>2.622899</td>\n",
       "      <td>3.221797</td>\n",
       "      <td>328.037431</td>\n",
       "      <td>40.279494</td>\n",
       "      <td>206.752060</td>\n",
       "      <td>56.112424</td>\n",
       "      <td>42.275184</td>\n",
       "      <td>53.520249</td>\n",
       "      <td>2.328867</td>\n",
       "      <td>0.260193</td>\n",
       "      <td>0.269735</td>\n",
       "      <td>0.228266</td>\n",
       "      <td>0.244914</td>\n",
       "      <td>0.121167</td>\n",
       "      <td>0.108538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1894.000000</td>\n",
       "      <td>5313.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1960.000000</td>\n",
       "      <td>44219.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>35.750000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1970.000000</td>\n",
       "      <td>64007.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>209.500000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1978.000000</td>\n",
       "      <td>83466.300000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>514.750000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>230.500000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>33.250000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1997.000000</td>\n",
       "      <td>194876.400000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>1483.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>981.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>198.000000</td>\n",
       "      <td>321.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Year_of_Birth  Disposable_Income  No_of_Kids_in_home  \\\n",
       "count     672.000000         664.000000          672.000000   \n",
       "mean     1969.181548       63434.170482            0.406250   \n",
       "std        12.116416       25276.585476            0.532259   \n",
       "min      1894.000000        5313.600000            0.000000   \n",
       "25%      1960.000000       44219.700000            0.000000   \n",
       "50%      1970.000000       64007.400000            0.000000   \n",
       "75%      1978.000000       83466.300000            1.000000   \n",
       "max      1997.000000      194876.400000            2.000000   \n",
       "\n",
       "       No_of_Teen_in_home     Recency  Discounted_Purchases  WebPurchases  \\\n",
       "count          672.000000  672.000000            672.000000    672.000000   \n",
       "mean             0.526786   57.744048              2.400298      4.279762   \n",
       "std              0.545293   29.321893              1.919125      2.782585   \n",
       "min              0.000000    7.000000              0.000000      0.000000   \n",
       "25%              0.000000   33.000000              1.000000      2.000000   \n",
       "50%              1.000000   58.000000              2.000000      4.000000   \n",
       "75%              1.000000   84.000000              3.000000      6.000000   \n",
       "max              2.000000  106.000000             15.000000     25.000000   \n",
       "\n",
       "       CatalogPurchases  StorePurchases  Amount_on_Wines  Amount_on_Fruits  \\\n",
       "count        672.000000      672.000000       672.000000        672.000000   \n",
       "mean           2.653274        5.992560       322.342262         29.794643   \n",
       "std            2.622899        3.221797       328.037431         40.279494   \n",
       "min            0.000000        0.000000         5.000000          3.000000   \n",
       "25%            1.000000        3.000000        35.750000          5.000000   \n",
       "50%            2.000000        5.000000       209.500000         11.000000   \n",
       "75%            4.000000        8.000000       514.750000         36.000000   \n",
       "max           11.000000       13.000000      1483.000000        202.000000   \n",
       "\n",
       "       Amount_on_MeatProducts  Amount_on_FishProducts  \\\n",
       "count              672.000000              672.000000   \n",
       "mean               163.622024               38.363095   \n",
       "std                206.752060               56.112424   \n",
       "min                  1.000000                0.000000   \n",
       "25%                 17.000000                2.750000   \n",
       "50%                 73.000000               11.000000   \n",
       "75%                230.500000               50.000000   \n",
       "max                981.000000              250.000000   \n",
       "\n",
       "       Amount_on_SweetProducts  Amount_on_GoldProds  WebVisitsMonth  \\\n",
       "count               672.000000           672.000000      672.000000   \n",
       "mean                 27.675595            44.681548        5.293155   \n",
       "std                  42.275184            53.520249        2.328867   \n",
       "min                   0.000000             0.000000        0.000000   \n",
       "25%                   2.000000            10.000000        4.000000   \n",
       "50%                   9.000000            24.000000        6.000000   \n",
       "75%                  33.250000            56.000000        7.000000   \n",
       "max                 198.000000           321.000000       14.000000   \n",
       "\n",
       "       Cmp3Accepted  Cmp4Accepted  Cmp5Accepted  Cmp1Accepted  Cmp2Accepted  \\\n",
       "count    672.000000    672.000000    672.000000    672.000000    672.000000   \n",
       "mean       0.072917      0.078869      0.055060      0.063988      0.014881   \n",
       "std        0.260193      0.269735      0.228266      0.244914      0.121167   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "max        1.000000      1.000000      1.000000      1.000000      1.000000   \n",
       "\n",
       "       Any_Complain  \n",
       "count    672.000000  \n",
       "mean       0.011905  \n",
       "std        0.108538  \n",
       "min        0.000000  \n",
       "25%        0.000000  \n",
       "50%        0.000000  \n",
       "75%        0.000000  \n",
       "max        1.000000  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25a9838e3a0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(train_data['Disposable_Income'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Year_of_Birth</th>\n",
       "      <th>Education_Level</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Disposable_Income</th>\n",
       "      <th>No_of_Kids_in_home</th>\n",
       "      <th>No_of_Teen_in_home</th>\n",
       "      <th>Date_Customer</th>\n",
       "      <th>Recency</th>\n",
       "      <th>Discounted_Purchases</th>\n",
       "      <th>WebPurchases</th>\n",
       "      <th>CatalogPurchases</th>\n",
       "      <th>StorePurchases</th>\n",
       "      <th>Amount_on_Wines</th>\n",
       "      <th>Amount_on_Fruits</th>\n",
       "      <th>Amount_on_MeatProducts</th>\n",
       "      <th>Amount_on_FishProducts</th>\n",
       "      <th>Amount_on_SweetProducts</th>\n",
       "      <th>Amount_on_GoldProds</th>\n",
       "      <th>WebVisitsMonth</th>\n",
       "      <th>Cmp3Accepted</th>\n",
       "      <th>Cmp4Accepted</th>\n",
       "      <th>Cmp5Accepted</th>\n",
       "      <th>Cmp1Accepted</th>\n",
       "      <th>Cmp2Accepted</th>\n",
       "      <th>Any_Complain</th>\n",
       "      <th>Response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>ID_VIUX07W2</td>\n",
       "      <td>1978</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Together</td>\n",
       "      <td>799999.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2013-02-06</td>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              ID  Year_of_Birth Education_Level Marital_Status  \\\n",
       "190  ID_VIUX07W2           1978      Graduation       Together   \n",
       "\n",
       "     Disposable_Income  No_of_Kids_in_home  No_of_Teen_in_home Date_Customer  \\\n",
       "190           799999.2                   1                   0    2013-02-06   \n",
       "\n",
       "     Recency  Discounted_Purchases  WebPurchases  CatalogPurchases  \\\n",
       "190       30                     4             3                 1   \n",
       "\n",
       "     StorePurchases  Amount_on_Wines  Amount_on_Fruits  \\\n",
       "190               3               14                17   \n",
       "\n",
       "     Amount_on_MeatProducts  Amount_on_FishProducts  Amount_on_SweetProducts  \\\n",
       "190                      18                       8                        1   \n",
       "\n",
       "     Amount_on_GoldProds  WebVisitsMonth  Cmp3Accepted  Cmp4Accepted  \\\n",
       "190                   12               6             0             0   \n",
       "\n",
       "     Cmp5Accepted  Cmp1Accepted  Cmp2Accepted  Any_Complain  Response  \n",
       "190             0             0             0             0         0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.loc[train_data['Disposable_Income'] > 200000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = train_data.drop(190)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DATA PREPROCESSING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Divorced', 'Together', 'Single', 'Married', 'Widow', 'Alone',\n",
       "       'Absurd', 'YOLO'], dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['Marital_Status'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Married     615\n",
       "Together    408\n",
       "Single      329\n",
       "Divorced    159\n",
       "Widow        50\n",
       "Alone         3\n",
       "YOLO          2\n",
       "Absurd        1\n",
       "Name: Marital_Status, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['Marital_Status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.loc[train_data['Marital_Status'] == 'Absurd', 'Marital_Status'] = 1\n",
    "train_data.loc[train_data['Marital_Status'] == 'Alone', 'Marital_Status'] = 2\n",
    "train_data.loc[train_data['Marital_Status'] == 'Divorced', 'Marital_Status'] = 3\n",
    "train_data.loc[train_data['Marital_Status'] == 'Married', 'Marital_Status'] = 4\n",
    "train_data.loc[train_data['Marital_Status'] == 'Single', 'Marital_Status'] = 5\n",
    "train_data.loc[train_data['Marital_Status'] == 'Together', 'Marital_Status'] = 6\n",
    "train_data.loc[train_data['Marital_Status'] == 'Widow', 'Marital_Status'] = 7\n",
    "train_data.loc[train_data['Marital_Status'] == 'YOLO', 'Marital_Status'] = 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Education_Level</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Disposable_Income</th>\n",
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       "      <th>WebPurchases</th>\n",
       "      <th>CatalogPurchases</th>\n",
       "      <th>StorePurchases</th>\n",
       "      <th>Amount_on_Wines</th>\n",
       "      <th>Amount_on_Fruits</th>\n",
       "      <th>Amount_on_MeatProducts</th>\n",
       "      <th>Amount_on_FishProducts</th>\n",
       "      <th>Amount_on_SweetProducts</th>\n",
       "      <th>Amount_on_GoldProds</th>\n",
       "      <th>WebVisitsMonth</th>\n",
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       "      <th>Any_Complain</th>\n",
       "      <th>Response</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID_4A9AR6FQ</td>\n",
       "      <td>1955</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>3</td>\n",
       "      <td>77504.4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-06-22</td>\n",
       "      <td>56</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>71</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID_X28T3VEK</td>\n",
       "      <td>1958</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>6</td>\n",
       "      <td>56784.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2013-01-08</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>205</td>\n",
       "      <td>22</td>\n",
       "      <td>111</td>\n",
       "      <td>50</td>\n",
       "      <td>15</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID_AWXARH57</td>\n",
       "      <td>1962</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>5</td>\n",
       "      <td>103714.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013-11-21</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>469</td>\n",
       "      <td>31</td>\n",
       "      <td>873</td>\n",
       "      <td>29</td>\n",
       "      <td>18</td>\n",
       "      <td>37</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_FQVZHE81</td>\n",
       "      <td>1979</td>\n",
       "      <td>2n Cycle</td>\n",
       "      <td>5</td>\n",
       "      <td>46311.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2013-09-13</td>\n",
       "      <td>49</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>56</td>\n",
       "      <td>15</td>\n",
       "      <td>49</td>\n",
       "      <td>17</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID_QVLWGPRN</td>\n",
       "      <td>1959</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>4</td>\n",
       "      <td>87486.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014-01-21</td>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>412</td>\n",
       "      <td>117</td>\n",
       "      <td>445</td>\n",
       "      <td>181</td>\n",
       "      <td>165</td>\n",
       "      <td>203</td>\n",
       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "            ID  Year_of_Birth Education_Level Marital_Status  \\\n",
       "0  ID_4A9AR6FQ           1955      Graduation              3   \n",
       "1  ID_X28T3VEK           1958      Graduation              6   \n",
       "2  ID_AWXARH57           1962      Graduation              5   \n",
       "3  ID_FQVZHE81           1979        2n Cycle              5   \n",
       "4  ID_QVLWGPRN           1959      Graduation              4   \n",
       "\n",
       "   Disposable_Income  No_of_Kids_in_home  No_of_Teen_in_home Date_Customer  \\\n",
       "0            77504.4                   1                   1    2014-06-22   \n",
       "1            56784.0                   0                   1    2013-01-08   \n",
       "2           103714.8                   0                   0    2013-11-21   \n",
       "3            46311.6                   1                   0    2013-09-13   \n",
       "4            87486.0                   0                   0    2014-01-21   \n",
       "\n",
       "   Recency  Discounted_Purchases  WebPurchases  CatalogPurchases  \\\n",
       "0       56                     2             1                 1   \n",
       "1       17                     6             5                 1   \n",
       "2       17                     0             7                 4   \n",
       "3       49                     3             4                 1   \n",
       "4       59                     1             3                 7   \n",
       "\n",
       "   StorePurchases  Amount_on_Wines  Amount_on_Fruits  Amount_on_MeatProducts  \\\n",
       "0               4               71                 3                      16   \n",
       "1               8              205                22                     111   \n",
       "2               7              469                31                     873   \n",
       "3               3               56                15                      49   \n",
       "4               9              412               117                     445   \n",
       "\n",
       "   Amount_on_FishProducts  Amount_on_SweetProducts  Amount_on_GoldProds  \\\n",
       "0                       0                        6                   20   \n",
       "1                      50                       15                   19   \n",
       "2                      29                       18                   37   \n",
       "3                      17                       24                   24   \n",
       "4                     181                      165                  203   \n",
       "\n",
       "   WebVisitsMonth  Cmp3Accepted  Cmp4Accepted  Cmp5Accepted  Cmp1Accepted  \\\n",
       "0               3             0             0             0             0   \n",
       "1               6             0             0             0             0   \n",
       "2               2             0             0             0             1   \n",
       "3               8             0             0             0             0   \n",
       "4               1             0             0             0             1   \n",
       "\n",
       "   Cmp2Accepted  Any_Complain  Response  \n",
       "0             0             0         0  \n",
       "1             0             0         0  \n",
       "2             0             0         1  \n",
       "3             0             0         0  \n",
       "4             0             0         0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data.loc[test_data['Marital_Status'] == 'Absurd', 'Marital_Status'] = 1\n",
    "test_data.loc[test_data['Marital_Status'] == 'Alone', 'Marital_Status'] = 2\n",
    "test_data.loc[test_data['Marital_Status'] == 'Divorced', 'Marital_Status'] = 3\n",
    "test_data.loc[test_data['Marital_Status'] == 'Married', 'Marital_Status'] = 4\n",
    "test_data.loc[test_data['Marital_Status'] == 'Single', 'Marital_Status'] = 5\n",
    "test_data.loc[test_data['Marital_Status'] == 'Together', 'Marital_Status'] = 6\n",
    "test_data.loc[test_data['Marital_Status'] == 'Widow', 'Marital_Status'] = 7\n",
    "test_data.loc[test_data['Marital_Status'] == 'YOLO', 'Marital_Status'] = 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID_ZPMABNVX</td>\n",
       "      <td>1954</td>\n",
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       "      <td>5</td>\n",
       "      <td>48556.8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2013-11-01</td>\n",
       "      <td>85</td>\n",
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       "      <td>2</td>\n",
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       "      <td>20</td>\n",
       "      <td>118</td>\n",
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       "      <td>ID_WFE91NAA</td>\n",
       "      <td>1961</td>\n",
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       "      <td>7</td>\n",
       "      <td>57499.2</td>\n",
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       "      <td>1</td>\n",
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       "      <td>26</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID_JV11RBRK</td>\n",
       "      <td>1973</td>\n",
       "      <td>Basic</td>\n",
       "      <td>4</td>\n",
       "      <td>17025.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013-02-28</td>\n",
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       "      <th>3</th>\n",
       "      <td>ID_6B7SVKY9</td>\n",
       "      <td>1970</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>6</td>\n",
       "      <td>91983.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013-08-16</td>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>741</td>\n",
       "      <td>66</td>\n",
       "      <td>946</td>\n",
       "      <td>219</td>\n",
       "      <td>189</td>\n",
       "      <td>126</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID_GOVUZ545</td>\n",
       "      <td>1959</td>\n",
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       "      <td>6</td>\n",
       "      <td>78235.2</td>\n",
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       "      <td>2</td>\n",
       "      <td>2013-07-25</td>\n",
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       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>748</td>\n",
       "      <td>22</td>\n",
       "      <td>181</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>200</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ID  Year_of_Birth Education_Level Marital_Status  \\\n",
       "0  ID_ZPMABNVX           1954      Graduation              5   \n",
       "1  ID_WFE91NAA           1961      Graduation              7   \n",
       "2  ID_JV11RBRK           1973           Basic              4   \n",
       "3  ID_6B7SVKY9           1970      Graduation              6   \n",
       "4  ID_GOVUZ545           1959      Graduation              6   \n",
       "\n",
       "   Disposable_Income  No_of_Kids_in_home  No_of_Teen_in_home Date_Customer  \\\n",
       "0            48556.8                   0                   1    2013-11-01   \n",
       "1            57499.2                   0                   1    2012-11-22   \n",
       "2            17025.6                   0                   0    2013-02-28   \n",
       "3            91983.6                   0                   0    2013-08-16   \n",
       "4            78235.2                   0                   2    2013-07-25   \n",
       "\n",
       "   Recency  Discounted_Purchases  WebPurchases  CatalogPurchases  \\\n",
       "0       85                     6             8                 2   \n",
       "1       79                     5             7                 4   \n",
       "2       47                     1             2                 0   \n",
       "3       98                     1             4                 7   \n",
       "4       41                     2             7                 6   \n",
       "\n",
       "   StorePurchases  Amount_on_Wines  Amount_on_Fruits  Amount_on_MeatProducts  \\\n",
       "0               8              429                20                     118   \n",
       "1               6              510                 3                      26   \n",
       "2               4                7                10                      11   \n",
       "3              11              741                66                     946   \n",
       "4              11              748                22                     181   \n",
       "\n",
       "   Amount_on_FishProducts  Amount_on_SweetProducts  Amount_on_GoldProds  \\\n",
       "0                       7                       23                   41   \n",
       "1                       0                        0                   75   \n",
       "2                      16                       12                   27   \n",
       "3                     219                      189                  126   \n",
       "4                      12                        0                  200   \n",
       "\n",
       "   WebVisitsMonth  Cmp3Accepted  Cmp4Accepted  Cmp5Accepted  Cmp1Accepted  \\\n",
       "0               8             0             0             0             0   \n",
       "1               6             0             1             0             0   \n",
       "2               6             0             0             0             0   \n",
       "3               2             0             0             1             1   \n",
       "4               5             1             0             0             0   \n",
       "\n",
       "   Cmp2Accepted  Any_Complain  \n",
       "0             0             0  \n",
       "1             0             0  \n",
       "2             0             0  \n",
       "3             0             0  \n",
       "4             0             0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Graduation', '2n Cycle', 'Master', 'PhD', 'Basic'], dtype=object)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['Education_Level'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.loc[train_data['Education_Level'] == 'Graduation', 'Education_Level'] = 1\n",
    "train_data.loc[train_data['Education_Level'] == '2n Cycle', 'Education_Level'] = 2\n",
    "train_data.loc[train_data['Education_Level'] == 'Master', 'Education_Level'] = 3\n",
    "train_data.loc[train_data['Education_Level'] == 'PhD', 'Education_Level'] = 4\n",
    "train_data.loc[train_data['Education_Level'] == 'Basic', 'Education_Level'] = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data.loc[test_data['Education_Level'] == 'Graduation', 'Education_Level'] = 1\n",
    "test_data.loc[test_data['Education_Level'] == '2n Cycle', 'Education_Level'] = 2\n",
    "test_data.loc[test_data['Education_Level'] == 'Master', 'Education_Level'] = 3\n",
    "test_data.loc[test_data['Education_Level'] == 'PhD', 'Education_Level'] = 4\n",
    "test_data.loc[test_data['Education_Level'] == 'Basic', 'Education_Level'] = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Year_of_Birth</th>\n",
       "      <th>Education_Level</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Disposable_Income</th>\n",
       "      <th>No_of_Kids_in_home</th>\n",
       "      <th>No_of_Teen_in_home</th>\n",
       "      <th>Date_Customer</th>\n",
       "      <th>Recency</th>\n",
       "      <th>Discounted_Purchases</th>\n",
       "      <th>WebPurchases</th>\n",
       "      <th>CatalogPurchases</th>\n",
       "      <th>StorePurchases</th>\n",
       "      <th>Amount_on_Wines</th>\n",
       "      <th>Amount_on_Fruits</th>\n",
       "      <th>Amount_on_MeatProducts</th>\n",
       "      <th>Amount_on_FishProducts</th>\n",
       "      <th>Amount_on_SweetProducts</th>\n",
       "      <th>Amount_on_GoldProds</th>\n",
       "      <th>WebVisitsMonth</th>\n",
       "      <th>Cmp3Accepted</th>\n",
       "      <th>Cmp4Accepted</th>\n",
       "      <th>Cmp5Accepted</th>\n",
       "      <th>Cmp1Accepted</th>\n",
       "      <th>Cmp2Accepted</th>\n",
       "      <th>Any_Complain</th>\n",
       "      <th>Response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID_4A9AR6FQ</td>\n",
       "      <td>1955</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>77504.4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-06-22</td>\n",
       "      <td>56</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>71</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID_X28T3VEK</td>\n",
       "      <td>1958</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>56784.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2013-01-08</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>205</td>\n",
       "      <td>22</td>\n",
       "      <td>111</td>\n",
       "      <td>50</td>\n",
       "      <td>15</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID_AWXARH57</td>\n",
       "      <td>1962</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>103714.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013-11-21</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>469</td>\n",
       "      <td>31</td>\n",
       "      <td>873</td>\n",
       "      <td>29</td>\n",
       "      <td>18</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_FQVZHE81</td>\n",
       "      <td>1979</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>46311.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2013-09-13</td>\n",
       "      <td>49</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>56</td>\n",
       "      <td>15</td>\n",
       "      <td>49</td>\n",
       "      <td>17</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID_QVLWGPRN</td>\n",
       "      <td>1959</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>87486.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014-01-21</td>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>412</td>\n",
       "      <td>117</td>\n",
       "      <td>445</td>\n",
       "      <td>181</td>\n",
       "      <td>165</td>\n",
       "      <td>203</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ID  Year_of_Birth Education_Level Marital_Status  \\\n",
       "0  ID_4A9AR6FQ           1955               1              3   \n",
       "1  ID_X28T3VEK           1958               1              6   \n",
       "2  ID_AWXARH57           1962               1              5   \n",
       "3  ID_FQVZHE81           1979               2              5   \n",
       "4  ID_QVLWGPRN           1959               1              4   \n",
       "\n",
       "   Disposable_Income  No_of_Kids_in_home  No_of_Teen_in_home Date_Customer  \\\n",
       "0            77504.4                   1                   1    2014-06-22   \n",
       "1            56784.0                   0                   1    2013-01-08   \n",
       "2           103714.8                   0                   0    2013-11-21   \n",
       "3            46311.6                   1                   0    2013-09-13   \n",
       "4            87486.0                   0                   0    2014-01-21   \n",
       "\n",
       "   Recency  Discounted_Purchases  WebPurchases  CatalogPurchases  \\\n",
       "0       56                     2             1                 1   \n",
       "1       17                     6             5                 1   \n",
       "2       17                     0             7                 4   \n",
       "3       49                     3             4                 1   \n",
       "4       59                     1             3                 7   \n",
       "\n",
       "   StorePurchases  Amount_on_Wines  Amount_on_Fruits  Amount_on_MeatProducts  \\\n",
       "0               4               71                 3                      16   \n",
       "1               8              205                22                     111   \n",
       "2               7              469                31                     873   \n",
       "3               3               56                15                      49   \n",
       "4               9              412               117                     445   \n",
       "\n",
       "   Amount_on_FishProducts  Amount_on_SweetProducts  Amount_on_GoldProds  \\\n",
       "0                       0                        6                   20   \n",
       "1                      50                       15                   19   \n",
       "2                      29                       18                   37   \n",
       "3                      17                       24                   24   \n",
       "4                     181                      165                  203   \n",
       "\n",
       "   WebVisitsMonth  Cmp3Accepted  Cmp4Accepted  Cmp5Accepted  Cmp1Accepted  \\\n",
       "0               3             0             0             0             0   \n",
       "1               6             0             0             0             0   \n",
       "2               2             0             0             0             1   \n",
       "3               8             0             0             0             0   \n",
       "4               1             0             0             0             1   \n",
       "\n",
       "   Cmp2Accepted  Any_Complain  Response  \n",
       "0             0             0         0  \n",
       "1             0             0         0  \n",
       "2             0             0         1  \n",
       "3             0             0         0  \n",
       "4             0             0         0  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1567 entries, 0 to 1567\n",
      "Data columns (total 27 columns):\n",
      " #   Column                   Non-Null Count  Dtype         \n",
      "---  ------                   --------------  -----         \n",
      " 0   ID                       1567 non-null   object        \n",
      " 1   Year_of_Birth            1567 non-null   int64         \n",
      " 2   Education_Level          1567 non-null   object        \n",
      " 3   Marital_Status           1567 non-null   object        \n",
      " 4   Disposable_Income        1551 non-null   float64       \n",
      " 5   No_of_Kids_in_home       1567 non-null   int64         \n",
      " 6   No_of_Teen_in_home       1567 non-null   int64         \n",
      " 7   Date_Customer            1567 non-null   datetime64[ns]\n",
      " 8   Recency                  1567 non-null   int64         \n",
      " 9   Discounted_Purchases     1567 non-null   int64         \n",
      " 10  WebPurchases             1567 non-null   int64         \n",
      " 11  CatalogPurchases         1567 non-null   int64         \n",
      " 12  StorePurchases           1567 non-null   int64         \n",
      " 13  Amount_on_Wines          1567 non-null   int64         \n",
      " 14  Amount_on_Fruits         1567 non-null   int64         \n",
      " 15  Amount_on_MeatProducts   1567 non-null   int64         \n",
      " 16  Amount_on_FishProducts   1567 non-null   int64         \n",
      " 17  Amount_on_SweetProducts  1567 non-null   int64         \n",
      " 18  Amount_on_GoldProds      1567 non-null   int64         \n",
      " 19  WebVisitsMonth           1567 non-null   int64         \n",
      " 20  Cmp3Accepted             1567 non-null   int64         \n",
      " 21  Cmp4Accepted             1567 non-null   int64         \n",
      " 22  Cmp5Accepted             1567 non-null   int64         \n",
      " 23  Cmp1Accepted             1567 non-null   int64         \n",
      " 24  Cmp2Accepted             1567 non-null   int64         \n",
      " 25  Any_Complain             1567 non-null   int64         \n",
      " 26  Response                 1567 non-null   int64         \n",
      "dtypes: datetime64[ns](1), float64(1), int64(22), object(3)\n",
      "memory usage: 342.8+ KB\n"
     ]
    }
   ],
   "source": [
    "train_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = train_data.astype({'Education_Level': int, 'Marital_Status': int})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = test_data.astype({'Education_Level': int, 'Marital_Status': int})\n",
    "\n",
    "# feature = train.columns.drop(['Response', 'ID', 'Date_Customer'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1567, 27)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TREATING MISSING VALUES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No_of_Kids_in_home\n",
       "0    74785.532646\n",
       "1    45180.370233\n",
       "2    48075.563636\n",
       "Name: Disposable_Income, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.groupby(['No_of_Kids_in_home'])['Disposable_Income'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No_of_Kids_in_home\n",
       "0    72752.438049\n",
       "1    48619.214938\n",
       "2    44196.830769\n",
       "Name: Disposable_Income, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.groupby(['No_of_Kids_in_home'])['Disposable_Income'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Disposable_Income(cols):\n",
    "    Disposable_Income = cols[1]\n",
    "    No_of_Kids_in_home= cols[0]\n",
    "    if pd.isnull(Disposable_Income):\n",
    "        if No_of_Kids_in_home == 0:\n",
    "            return 72752.438049\n",
    "        elif No_of_Kids_in_home == 1:\n",
    "            return 48619.214938\n",
    "        else:\n",
    "            return 48075.563636\n",
    "    else:\n",
    "        return Disposable_Income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['Disposable_Income'] = train_data[['No_of_Kids_in_home','Disposable_Income']].apply(Disposable_Income,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Year_of_Birth</th>\n",
       "      <th>Education_Level</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Disposable_Income</th>\n",
       "      <th>No_of_Kids_in_home</th>\n",
       "      <th>No_of_Teen_in_home</th>\n",
       "      <th>Date_Customer</th>\n",
       "      <th>Recency</th>\n",
       "      <th>Discounted_Purchases</th>\n",
       "      <th>WebPurchases</th>\n",
       "      <th>CatalogPurchases</th>\n",
       "      <th>StorePurchases</th>\n",
       "      <th>Amount_on_Wines</th>\n",
       "      <th>Amount_on_Fruits</th>\n",
       "      <th>Amount_on_MeatProducts</th>\n",
       "      <th>Amount_on_FishProducts</th>\n",
       "      <th>Amount_on_SweetProducts</th>\n",
       "      <th>Amount_on_GoldProds</th>\n",
       "      <th>WebVisitsMonth</th>\n",
       "      <th>Cmp3Accepted</th>\n",
       "      <th>Cmp4Accepted</th>\n",
       "      <th>Cmp5Accepted</th>\n",
       "      <th>Cmp1Accepted</th>\n",
       "      <th>Cmp2Accepted</th>\n",
       "      <th>Any_Complain</th>\n",
       "      <th>Response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID_4A9AR6FQ</td>\n",
       "      <td>1955</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>77504.4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-06-22</td>\n",
       "      <td>56</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>71</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID_X28T3VEK</td>\n",
       "      <td>1958</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>56784.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2013-01-08</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>205</td>\n",
       "      <td>22</td>\n",
       "      <td>111</td>\n",
       "      <td>50</td>\n",
       "      <td>15</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID_AWXARH57</td>\n",
       "      <td>1962</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>103714.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013-11-21</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>469</td>\n",
       "      <td>31</td>\n",
       "      <td>873</td>\n",
       "      <td>29</td>\n",
       "      <td>18</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_FQVZHE81</td>\n",
       "      <td>1979</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>46311.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2013-09-13</td>\n",
       "      <td>49</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>56</td>\n",
       "      <td>15</td>\n",
       "      <td>49</td>\n",
       "      <td>17</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID_QVLWGPRN</td>\n",
       "      <td>1959</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>87486.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014-01-21</td>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>412</td>\n",
       "      <td>117</td>\n",
       "      <td>445</td>\n",
       "      <td>181</td>\n",
       "      <td>165</td>\n",
       "      <td>203</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ID  Year_of_Birth  Education_Level  Marital_Status  \\\n",
       "0  ID_4A9AR6FQ           1955                1               3   \n",
       "1  ID_X28T3VEK           1958                1               6   \n",
       "2  ID_AWXARH57           1962                1               5   \n",
       "3  ID_FQVZHE81           1979                2               5   \n",
       "4  ID_QVLWGPRN           1959                1               4   \n",
       "\n",
       "   Disposable_Income  No_of_Kids_in_home  No_of_Teen_in_home Date_Customer  \\\n",
       "0            77504.4                   1                   1    2014-06-22   \n",
       "1            56784.0                   0                   1    2013-01-08   \n",
       "2           103714.8                   0                   0    2013-11-21   \n",
       "3            46311.6                   1                   0    2013-09-13   \n",
       "4            87486.0                   0                   0    2014-01-21   \n",
       "\n",
       "   Recency  Discounted_Purchases  WebPurchases  CatalogPurchases  \\\n",
       "0       56                     2             1                 1   \n",
       "1       17                     6             5                 1   \n",
       "2       17                     0             7                 4   \n",
       "3       49                     3             4                 1   \n",
       "4       59                     1             3                 7   \n",
       "\n",
       "   StorePurchases  Amount_on_Wines  Amount_on_Fruits  Amount_on_MeatProducts  \\\n",
       "0               4               71                 3                      16   \n",
       "1               8              205                22                     111   \n",
       "2               7              469                31                     873   \n",
       "3               3               56                15                      49   \n",
       "4               9              412               117                     445   \n",
       "\n",
       "   Amount_on_FishProducts  Amount_on_SweetProducts  Amount_on_GoldProds  \\\n",
       "0                       0                        6                   20   \n",
       "1                      50                       15                   19   \n",
       "2                      29                       18                   37   \n",
       "3                      17                       24                   24   \n",
       "4                     181                      165                  203   \n",
       "\n",
       "   WebVisitsMonth  Cmp3Accepted  Cmp4Accepted  Cmp5Accepted  Cmp1Accepted  \\\n",
       "0               3             0             0             0             0   \n",
       "1               6             0             0             0             0   \n",
       "2               2             0             0             0             1   \n",
       "3               8             0             0             0             0   \n",
       "4               1             0             0             0             1   \n",
       "\n",
       "   Cmp2Accepted  Any_Complain  Response  \n",
       "0             0             0         0  \n",
       "1             0             0         0  \n",
       "2             0             0         1  \n",
       "3             0             0         0  \n",
       "4             0             0         0  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1567 entries, 0 to 1567\n",
      "Data columns (total 27 columns):\n",
      " #   Column                   Non-Null Count  Dtype         \n",
      "---  ------                   --------------  -----         \n",
      " 0   ID                       1567 non-null   object        \n",
      " 1   Year_of_Birth            1567 non-null   int64         \n",
      " 2   Education_Level          1567 non-null   int32         \n",
      " 3   Marital_Status           1567 non-null   int32         \n",
      " 4   Disposable_Income        1567 non-null   float64       \n",
      " 5   No_of_Kids_in_home       1567 non-null   int64         \n",
      " 6   No_of_Teen_in_home       1567 non-null   int64         \n",
      " 7   Date_Customer            1567 non-null   datetime64[ns]\n",
      " 8   Recency                  1567 non-null   int64         \n",
      " 9   Discounted_Purchases     1567 non-null   int64         \n",
      " 10  WebPurchases             1567 non-null   int64         \n",
      " 11  CatalogPurchases         1567 non-null   int64         \n",
      " 12  StorePurchases           1567 non-null   int64         \n",
      " 13  Amount_on_Wines          1567 non-null   int64         \n",
      " 14  Amount_on_Fruits         1567 non-null   int64         \n",
      " 15  Amount_on_MeatProducts   1567 non-null   int64         \n",
      " 16  Amount_on_FishProducts   1567 non-null   int64         \n",
      " 17  Amount_on_SweetProducts  1567 non-null   int64         \n",
      " 18  Amount_on_GoldProds      1567 non-null   int64         \n",
      " 19  WebVisitsMonth           1567 non-null   int64         \n",
      " 20  Cmp3Accepted             1567 non-null   int64         \n",
      " 21  Cmp4Accepted             1567 non-null   int64         \n",
      " 22  Cmp5Accepted             1567 non-null   int64         \n",
      " 23  Cmp1Accepted             1567 non-null   int64         \n",
      " 24  Cmp2Accepted             1567 non-null   int64         \n",
      " 25  Any_Complain             1567 non-null   int64         \n",
      " 26  Response                 1567 non-null   int64         \n",
      "dtypes: datetime64[ns](1), float64(1), int32(2), int64(22), object(1)\n",
      "memory usage: 330.5+ KB\n"
     ]
    }
   ],
   "source": [
    "train_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25a98353220>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.heatmap(train_data.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data['Disposable_Income'] = test_data[['No_of_Kids_in_home','Disposable_Income']].apply(Disposable_Income,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25a97efd700>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.heatmap(test_data.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'Year_of_Birth', 'Education_Level', 'Marital_Status',\n",
       "       'Disposable_Income', 'No_of_Kids_in_home', 'No_of_Teen_in_home',\n",
       "       'Date_Customer', 'Recency', 'Discounted_Purchases', 'WebPurchases',\n",
       "       'CatalogPurchases', 'StorePurchases', 'Amount_on_Wines',\n",
       "       'Amount_on_Fruits', 'Amount_on_MeatProducts', 'Amount_on_FishProducts',\n",
       "       'Amount_on_SweetProducts', 'Amount_on_GoldProds', 'WebVisitsMonth',\n",
       "       'Cmp3Accepted', 'Cmp4Accepted', 'Cmp5Accepted', 'Cmp1Accepted',\n",
       "       'Cmp2Accepted', 'Any_Complain'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'Year_of_Birth', 'Education_Level', 'Marital_Status',\n",
       "       'Disposable_Income', 'No_of_Kids_in_home', 'No_of_Teen_in_home',\n",
       "       'Date_Customer', 'Recency', 'Discounted_Purchases', 'WebPurchases',\n",
       "       'CatalogPurchases', 'StorePurchases', 'Amount_on_Wines',\n",
       "       'Amount_on_Fruits', 'Amount_on_MeatProducts', 'Amount_on_FishProducts',\n",
       "       'Amount_on_SweetProducts', 'Amount_on_GoldProds', 'WebVisitsMonth',\n",
       "       'Cmp3Accepted', 'Cmp4Accepted', 'Cmp5Accepted', 'Cmp1Accepted',\n",
       "       'Cmp2Accepted', 'Any_Complain', 'Response'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data['Response'] = 'test'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'Year_of_Birth', 'Education_Level', 'Marital_Status',\n",
       "       'Disposable_Income', 'No_of_Kids_in_home', 'No_of_Teen_in_home',\n",
       "       'Date_Customer', 'Recency', 'Discounted_Purchases', 'WebPurchases',\n",
       "       'CatalogPurchases', 'StorePurchases', 'Amount_on_Wines',\n",
       "       'Amount_on_Fruits', 'Amount_on_MeatProducts', 'Amount_on_FishProducts',\n",
       "       'Amount_on_SweetProducts', 'Amount_on_GoldProds', 'WebVisitsMonth',\n",
       "       'Cmp3Accepted', 'Cmp4Accepted', 'Cmp5Accepted', 'Cmp1Accepted',\n",
       "       'Cmp2Accepted', 'Any_Complain', 'Response'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.concat([train_data, test_data]).reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2239 entries, 0 to 2238\n",
      "Data columns (total 27 columns):\n",
      " #   Column                   Non-Null Count  Dtype         \n",
      "---  ------                   --------------  -----         \n",
      " 0   ID                       2239 non-null   object        \n",
      " 1   Year_of_Birth            2239 non-null   int64         \n",
      " 2   Education_Level          2239 non-null   int32         \n",
      " 3   Marital_Status           2239 non-null   int32         \n",
      " 4   Disposable_Income        2239 non-null   float64       \n",
      " 5   No_of_Kids_in_home       2239 non-null   int64         \n",
      " 6   No_of_Teen_in_home       2239 non-null   int64         \n",
      " 7   Date_Customer            2239 non-null   datetime64[ns]\n",
      " 8   Recency                  2239 non-null   int64         \n",
      " 9   Discounted_Purchases     2239 non-null   int64         \n",
      " 10  WebPurchases             2239 non-null   int64         \n",
      " 11  CatalogPurchases         2239 non-null   int64         \n",
      " 12  StorePurchases           2239 non-null   int64         \n",
      " 13  Amount_on_Wines          2239 non-null   int64         \n",
      " 14  Amount_on_Fruits         2239 non-null   int64         \n",
      " 15  Amount_on_MeatProducts   2239 non-null   int64         \n",
      " 16  Amount_on_FishProducts   2239 non-null   int64         \n",
      " 17  Amount_on_SweetProducts  2239 non-null   int64         \n",
      " 18  Amount_on_GoldProds      2239 non-null   int64         \n",
      " 19  WebVisitsMonth           2239 non-null   int64         \n",
      " 20  Cmp3Accepted             2239 non-null   int64         \n",
      " 21  Cmp4Accepted             2239 non-null   int64         \n",
      " 22  Cmp5Accepted             2239 non-null   int64         \n",
      " 23  Cmp1Accepted             2239 non-null   int64         \n",
      " 24  Cmp2Accepted             2239 non-null   int64         \n",
      " 25  Any_Complain             2239 non-null   int64         \n",
      " 26  Response                 2239 non-null   object        \n",
      "dtypes: datetime64[ns](1), float64(1), int32(2), int64(21), object(2)\n",
      "memory usage: 454.9+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2239, 27)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2014-12-06 00:00:00')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(data.Date_Customer) #maximum date of which a customer has enrolled on spent with the company"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['year'] = data.Date_Customer.dt.year  #Extracting year\n",
    "data['month'] = data.Date_Customer.dt.month # Extracting month  \n",
    "data['quater'] = data.Date_Customer.dt.quarter # Extracting quarter\n",
    "\n",
    "\n",
    "today = pd.to_datetime('2015-1-1') # setting the current date close to the date of which the last user as spent or enrolled which is 2014-12-06\n",
    "\n",
    "#Getting the no of days, month when th customer join and now \n",
    "data['duration_days'] = (today - data['Date_Customer']).dt.days  #No of days of which a customer has enrolled on spent with the company \n",
    "data['duration_months'] = ((today- data.Date_Customer)/np.timedelta64(1, 'M'))  #No of months of which a customer has enrolled on spent with the company\n",
    "data['duration_months'] = data['duration_months'].astype(int) \n",
    "\n",
    "#Getting information of customer that join at the start of the month and at the end of the month\n",
    "data['is_month_end'] = data.Date_Customer.dt.is_month_end.astype(int)\n",
    "data['is_month_start'] = data.Date_Customer.dt.is_month_start.astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FEATURES INTERACTION (ADDITION /MULTIPLICATION/ SUBTRACTION E.T.C)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['total_children'] = data['No_of_Kids_in_home'] + data['No_of_Teen_in_home'] #"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "poly_feature_1 = ['Discounted_Purchases', 'WebPurchases', 'CatalogPurchases',\n",
    "       'StorePurchases'] # 2 raise to power of 4 features\n",
    "poly_feature_2 = ['Amount_on_Wines', 'Amount_on_Fruits',\n",
    "       'Amount_on_MeatProducts', 'Amount_on_FishProducts',\n",
    "       'Amount_on_SweetProducts', 'Amount_on_GoldProds'] # 2 raise to power of 6 features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "poly = PolynomialFeatures(degree=2, interaction_only=True, include_bias=False)\n",
    "\n",
    "poly1 = poly.fit_transform(data[poly_feature_1])\n",
    "poly2 = poly.fit_transform(data[poly_feature_2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Renaming the features to poly1_.....n, poly2_....n\n",
    "df_poly1 = pd.DataFrame(poly1, columns=[f\"poly1_{i}\" for i in range(poly1.shape[1])])\n",
    "df_poly2 = pd.DataFrame(poly2, columns=[f\"poly2_{i}\" for i in range(poly2.shape[1])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['poly1_0', 'poly1_1', 'poly1_2', 'poly1_3', 'poly1_4', 'poly1_5',\n",
       "       'poly1_6', 'poly1_7', 'poly1_8', 'poly1_9'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_poly1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['poly2_0', 'poly2_1', 'poly2_2', 'poly2_3', 'poly2_4', 'poly2_5',\n",
       "       'poly2_6', 'poly2_7', 'poly2_8', 'poly2_9', 'poly2_10', 'poly2_11',\n",
       "       'poly2_12', 'poly2_13', 'poly2_14', 'poly2_15', 'poly2_16', 'poly2_17',\n",
       "       'poly2_18', 'poly2_19', 'poly2_20'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_poly2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'Year_of_Birth', 'Education_Level', 'Marital_Status',\n",
       "       'Disposable_Income', 'No_of_Kids_in_home', 'No_of_Teen_in_home',\n",
       "       'Date_Customer', 'Recency', 'Discounted_Purchases', 'WebPurchases',\n",
       "       'CatalogPurchases', 'StorePurchases', 'Amount_on_Wines',\n",
       "       'Amount_on_Fruits', 'Amount_on_MeatProducts', 'Amount_on_FishProducts',\n",
       "       'Amount_on_SweetProducts', 'Amount_on_GoldProds', 'WebVisitsMonth',\n",
       "       'Cmp3Accepted', 'Cmp4Accepted', 'Cmp5Accepted', 'Cmp1Accepted',\n",
       "       'Cmp2Accepted', 'Any_Complain', 'Response', 'year', 'month', 'quater',\n",
       "       'duration_days', 'duration_months', 'is_month_end', 'is_month_start',\n",
       "       'total_children'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2239, 35)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merging the datas created from polynomial to the whole datsets\n",
    "new_data = pd.concat([data.drop('Date_Customer', axis = 1), df_poly1], axis = 1)\n",
    "new_data = pd.concat([new_data, df_poly2], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data['total_purchase'] = new_data[poly_feature_1].sum(axis = 1) # sum of all features created from poly1\n",
    "new_data['total_amount_on'] = new_data[poly_feature_2].sum(axis = 1) # sum of all features created from poly2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# new_data['year_month'] = \n",
    "new_data['year_month'] = new_data['year'].apply(str) + \"_\"  + new_data['month'].apply(str) #Feting the strings total number of customer that join each month odf the year by using categorically method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# bin_label = [1,2,3,4,5]\n",
    "new_data['Recency_bin'] = pd.qcut(new_data.Recency, q = [0, .2, .4, .6, .8, 1], labels=False) #Arranginging recency in percentile of 20% increasing, Bin is done to convert your continous data into group data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculating mean, std, max, min for each of the recency groups e.g the mean of all the customer that makes a purchase in a month and in year e.t.c\n",
    "\n",
    "new_data['Recency' + '_mean_on_yr_mth'] = new_data['Recency'].groupby(new_data['year_month']).transform('mean')\n",
    "new_data['Recency' + '_std_on_yr_mth'] = new_data['Recency'].groupby(new_data['year_month']).transform('std')\n",
    "new_data['Recency' + '_max_on_yr_mth'] = new_data['Recency'].groupby(new_data['year_month']).transform('max')\n",
    "new_data['Recency' + '_min_on_yr_mth'] = new_data['Recency'].groupby(new_data['year_month']).transform('min')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data['year_month_int'], _ = pd.factorize(new_data['year_month']) # label encoding "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'Year_of_Birth', 'Education_Level', 'Marital_Status',\n",
       "       'Disposable_Income', 'No_of_Kids_in_home', 'No_of_Teen_in_home',\n",
       "       'Recency', 'Discounted_Purchases', 'WebPurchases', 'CatalogPurchases',\n",
       "       'StorePurchases', 'Amount_on_Wines', 'Amount_on_Fruits',\n",
       "       'Amount_on_MeatProducts', 'Amount_on_FishProducts',\n",
       "       'Amount_on_SweetProducts', 'Amount_on_GoldProds', 'WebVisitsMonth',\n",
       "       'Cmp3Accepted', 'Cmp4Accepted', 'Cmp5Accepted', 'Cmp1Accepted',\n",
       "       'Cmp2Accepted', 'Any_Complain', 'Response', 'year', 'month', 'quater',\n",
       "       'duration_days', 'duration_months', 'is_month_end', 'is_month_start',\n",
       "       'total_children', 'poly1_0', 'poly1_1', 'poly1_2', 'poly1_3', 'poly1_4',\n",
       "       'poly1_5', 'poly1_6', 'poly1_7', 'poly1_8', 'poly1_9', 'poly2_0',\n",
       "       'poly2_1', 'poly2_2', 'poly2_3', 'poly2_4', 'poly2_5', 'poly2_6',\n",
       "       'poly2_7', 'poly2_8', 'poly2_9', 'poly2_10', 'poly2_11', 'poly2_12',\n",
       "       'poly2_13', 'poly2_14', 'poly2_15', 'poly2_16', 'poly2_17', 'poly2_18',\n",
       "       'poly2_19', 'poly2_20', 'total_purchase', 'total_amount_on',\n",
       "       'year_month', 'Recency_bin', 'Recency_mean_on_yr_mth',\n",
       "       'Recency_std_on_yr_mth', 'Recency_max_on_yr_mth',\n",
       "       'Recency_min_on_yr_mth', 'year_month_int'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2239, 74)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'Year_of_Birth', 'Education_Level', 'Marital_Status',\n",
       "       'Disposable_Income', 'No_of_Kids_in_home', 'No_of_Teen_in_home',\n",
       "       'Recency', 'Discounted_Purchases', 'WebPurchases', 'CatalogPurchases',\n",
       "       'StorePurchases', 'Amount_on_Wines', 'Amount_on_Fruits',\n",
       "       'Amount_on_MeatProducts', 'Amount_on_FishProducts',\n",
       "       'Amount_on_SweetProducts', 'Amount_on_GoldProds', 'WebVisitsMonth',\n",
       "       'Cmp3Accepted', 'Cmp4Accepted', 'Cmp5Accepted', 'Cmp1Accepted',\n",
       "       'Cmp2Accepted', 'Any_Complain', 'Response', 'year', 'month', 'quater',\n",
       "       'duration_days', 'duration_months', 'is_month_end', 'is_month_start',\n",
       "       'total_children', 'poly1_0', 'poly1_1', 'poly1_2', 'poly1_3', 'poly1_4',\n",
       "       'poly1_5', 'poly1_6', 'poly1_7', 'poly1_8', 'poly1_9', 'poly2_0',\n",
       "       'poly2_1', 'poly2_2', 'poly2_3', 'poly2_4', 'poly2_5', 'poly2_6',\n",
       "       'poly2_7', 'poly2_8', 'poly2_9', 'poly2_10', 'poly2_11', 'poly2_12',\n",
       "       'poly2_13', 'poly2_14', 'poly2_15', 'poly2_16', 'poly2_17', 'poly2_18',\n",
       "       'poly2_19', 'poly2_20', 'total_purchase', 'total_amount_on',\n",
       "       'year_month', 'Recency_bin', 'Recency_mean_on_yr_mth',\n",
       "       'Recency_std_on_yr_mth', 'Recency_max_on_yr_mth',\n",
       "       'Recency_min_on_yr_mth', 'year_month_int'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2239 entries, 0 to 2238\n",
      "Data columns (total 74 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   ID                       2239 non-null   object \n",
      " 1   Year_of_Birth            2239 non-null   int64  \n",
      " 2   Education_Level          2239 non-null   int32  \n",
      " 3   Marital_Status           2239 non-null   int32  \n",
      " 4   Disposable_Income        2239 non-null   float64\n",
      " 5   No_of_Kids_in_home       2239 non-null   int64  \n",
      " 6   No_of_Teen_in_home       2239 non-null   int64  \n",
      " 7   Recency                  2239 non-null   int64  \n",
      " 8   Discounted_Purchases     2239 non-null   int64  \n",
      " 9   WebPurchases             2239 non-null   int64  \n",
      " 10  CatalogPurchases         2239 non-null   int64  \n",
      " 11  StorePurchases           2239 non-null   int64  \n",
      " 12  Amount_on_Wines          2239 non-null   int64  \n",
      " 13  Amount_on_Fruits         2239 non-null   int64  \n",
      " 14  Amount_on_MeatProducts   2239 non-null   int64  \n",
      " 15  Amount_on_FishProducts   2239 non-null   int64  \n",
      " 16  Amount_on_SweetProducts  2239 non-null   int64  \n",
      " 17  Amount_on_GoldProds      2239 non-null   int64  \n",
      " 18  WebVisitsMonth           2239 non-null   int64  \n",
      " 19  Cmp3Accepted             2239 non-null   int64  \n",
      " 20  Cmp4Accepted             2239 non-null   int64  \n",
      " 21  Cmp5Accepted             2239 non-null   int64  \n",
      " 22  Cmp1Accepted             2239 non-null   int64  \n",
      " 23  Cmp2Accepted             2239 non-null   int64  \n",
      " 24  Any_Complain             2239 non-null   int64  \n",
      " 25  Response                 2239 non-null   object \n",
      " 26  year                     2239 non-null   int64  \n",
      " 27  month                    2239 non-null   int64  \n",
      " 28  quater                   2239 non-null   int64  \n",
      " 29  duration_days            2239 non-null   int64  \n",
      " 30  duration_months          2239 non-null   int32  \n",
      " 31  is_month_end             2239 non-null   int32  \n",
      " 32  is_month_start           2239 non-null   int32  \n",
      " 33  total_children           2239 non-null   int64  \n",
      " 34  poly1_0                  2239 non-null   float64\n",
      " 35  poly1_1                  2239 non-null   float64\n",
      " 36  poly1_2                  2239 non-null   float64\n",
      " 37  poly1_3                  2239 non-null   float64\n",
      " 38  poly1_4                  2239 non-null   float64\n",
      " 39  poly1_5                  2239 non-null   float64\n",
      " 40  poly1_6                  2239 non-null   float64\n",
      " 41  poly1_7                  2239 non-null   float64\n",
      " 42  poly1_8                  2239 non-null   float64\n",
      " 43  poly1_9                  2239 non-null   float64\n",
      " 44  poly2_0                  2239 non-null   float64\n",
      " 45  poly2_1                  2239 non-null   float64\n",
      " 46  poly2_2                  2239 non-null   float64\n",
      " 47  poly2_3                  2239 non-null   float64\n",
      " 48  poly2_4                  2239 non-null   float64\n",
      " 49  poly2_5                  2239 non-null   float64\n",
      " 50  poly2_6                  2239 non-null   float64\n",
      " 51  poly2_7                  2239 non-null   float64\n",
      " 52  poly2_8                  2239 non-null   float64\n",
      " 53  poly2_9                  2239 non-null   float64\n",
      " 54  poly2_10                 2239 non-null   float64\n",
      " 55  poly2_11                 2239 non-null   float64\n",
      " 56  poly2_12                 2239 non-null   float64\n",
      " 57  poly2_13                 2239 non-null   float64\n",
      " 58  poly2_14                 2239 non-null   float64\n",
      " 59  poly2_15                 2239 non-null   float64\n",
      " 60  poly2_16                 2239 non-null   float64\n",
      " 61  poly2_17                 2239 non-null   float64\n",
      " 62  poly2_18                 2239 non-null   float64\n",
      " 63  poly2_19                 2239 non-null   float64\n",
      " 64  poly2_20                 2239 non-null   float64\n",
      " 65  total_purchase           2239 non-null   int64  \n",
      " 66  total_amount_on          2239 non-null   int64  \n",
      " 67  year_month               2239 non-null   object \n",
      " 68  Recency_bin              2239 non-null   int64  \n",
      " 69  Recency_mean_on_yr_mth   2239 non-null   float64\n",
      " 70  Recency_std_on_yr_mth    2239 non-null   float64\n",
      " 71  Recency_max_on_yr_mth    2239 non-null   int64  \n",
      " 72  Recency_min_on_yr_mth    2239 non-null   int64  \n",
      " 73  year_month_int           2239 non-null   int64  \n",
      "dtypes: float64(34), int32(5), int64(32), object(3)\n",
      "memory usage: 1.2+ MB\n"
     ]
    }
   ],
   "source": [
    "new_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = new_data.select_dtypes(include = 'number').columns # Selecting all numeric data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Year_of_Birth', 'Education_Level', 'Marital_Status',\n",
       "       'Disposable_Income', 'No_of_Kids_in_home', 'No_of_Teen_in_home',\n",
       "       'Recency', 'Discounted_Purchases', 'WebPurchases', 'CatalogPurchases',\n",
       "       'StorePurchases', 'Amount_on_Wines', 'Amount_on_Fruits',\n",
       "       'Amount_on_MeatProducts', 'Amount_on_FishProducts',\n",
       "       'Amount_on_SweetProducts', 'Amount_on_GoldProds', 'WebVisitsMonth',\n",
       "       'Cmp3Accepted', 'Cmp4Accepted', 'Cmp5Accepted', 'Cmp1Accepted',\n",
       "       'Cmp2Accepted', 'Any_Complain', 'year', 'month', 'quater',\n",
       "       'duration_days', 'duration_months', 'is_month_end', 'is_month_start',\n",
       "       'total_children', 'poly1_0', 'poly1_1', 'poly1_2', 'poly1_3', 'poly1_4',\n",
       "       'poly1_5', 'poly1_6', 'poly1_7', 'poly1_8', 'poly1_9', 'poly2_0',\n",
       "       'poly2_1', 'poly2_2', 'poly2_3', 'poly2_4', 'poly2_5', 'poly2_6',\n",
       "       'poly2_7', 'poly2_8', 'poly2_9', 'poly2_10', 'poly2_11', 'poly2_12',\n",
       "       'poly2_13', 'poly2_14', 'poly2_15', 'poly2_16', 'poly2_17', 'poly2_18',\n",
       "       'poly2_19', 'poly2_20', 'total_purchase', 'total_amount_on',\n",
       "       'Recency_bin', 'Recency_mean_on_yr_mth', 'Recency_std_on_yr_mth',\n",
       "       'Recency_max_on_yr_mth', 'Recency_min_on_yr_mth', 'year_month_int'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features # All the numeric datas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "#splitting data \n",
    "train_df = new_data.loc[(new_data['Response'] != 'test')].reset_index(drop = True)\n",
    "test_df = new_data.loc[(new_data['Response'] == 'test')].reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_df[features].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df['Response'] = train_df['Response'].apply(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = 'Response'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## MODELLING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "from catboost import CatBoostClassifier\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.metrics import log_loss, f1_score\n",
    "from xgboost import XGBClassifier\n",
    "from lightgbm import LGBMClassifier\n",
    "from sklearn.model_selection import train_test_split, cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install --upgrade pip lightgbm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = train_df[features]\n",
    "y = train_df[target]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BASELINE MODELLING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[00:50:04] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "0.5581395348837209\n"
     ]
    }
   ],
   "source": [
    "model = XGBClassifier(scale_pos_weight = 8,max_depth = 8, n_estimators = 500)\n",
    "model.fit(X_train, y_train)\n",
    "prediction = model.predict(X_test)\n",
    "\n",
    "print(f1_score(prediction, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "sm = SMOTE(random_state=42)\n",
    "X_train_res, y_train_res = sm.fit_resample(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5\n"
     ]
    }
   ],
   "source": [
    "model = LGBMClassifier(scale_pos_weight = 1,max_depth=6, n_estimators = 1000, learning_rate=0.01)\n",
    "model.fit(X_train_res, y_train_res)\n",
    "prediction = model.predict(X_test)\n",
    "\n",
    "print(f1_score(prediction, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "sm =SMOTE(sampling_strategy={1:1000, 0:1400},random_state=5555)\n",
    "X_, y_ = sm.fit_resample(train_df[features], train_df[target])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1, fold 1: 0.6435331230283912\n",
      "F1, fold 2: 0.9306930693069307\n",
      "F1, fold 3: 0.9707317073170733\n",
      "F1, fold 4: 0.9383886255924171\n",
      "F1, fold 5: 0.9496402877697843\n",
      "0.8865973626029193\n"
     ]
    }
   ],
   "source": [
    "oof_f1=[]\n",
    "oof_predictions=[]\n",
    "\n",
    "params = {'learning_rate': 0.3,\n",
    "         'subsample': 0.7845527188607021,\n",
    "         'colsample_bytree': 0.6474680443294045,\n",
    "         'max_depth': 8,\n",
    "         'scale_pos_weight': 1}\n",
    "\n",
    "fold=StratifiedKFold(n_splits=5)#15#5#10\n",
    "\n",
    "i=1\n",
    "for train_index, test_index in fold.split(X_,y_):\n",
    "        \n",
    "    X_train, X_test = X_.iloc[train_index], X_.iloc[test_index]\n",
    "    y_train, y_test = y_.iloc[train_index], y_.iloc[test_index]\n",
    "\n",
    "    model = LGBMClassifier(**params, n_estimators=1000)\n",
    "\n",
    "    model.fit(X_train,y_train,eval_set=[(X_train,y_train),(X_test, y_test)], early_stopping_rounds=300, verbose = False)#erly100\n",
    "    preds=model.predict(X_test)\n",
    "\n",
    "    print(f'F1, fold {i}: {f1_score(y_test,preds)}')\n",
    "    i+=1                \n",
    "    oof_f1.append(f1_score(y_test,preds))\n",
    "    p2 = model.predict_proba(test_df[features])[:, -1]\n",
    "    oof_predictions.append(p2)\n",
    "    \n",
    "\n",
    "print(np.mean(oof_f1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Model features importance:')"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_importance_df = pd.DataFrame(model.feature_importances_, columns=['importance'])\n",
    "feature_importance_df['feature'] = new_data[features].columns\n",
    "\n",
    "plt.figure(figsize=(20, 12));\n",
    "sns.barplot(x=\"importance\", y=\"feature\", data=feature_importance_df.sort_values(by = ['importance'], ascending = False).head(60))\n",
    "plt.title('Model features importance:')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_feat = feature_importance_df.sort_values(by = ['importance'], ascending = False).head(50)['feature'].to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Disposable_Income',\n",
       " 'Recency',\n",
       " 'Year_of_Birth',\n",
       " 'poly1_7',\n",
       " 'duration_days',\n",
       " 'Marital_Status',\n",
       " 'poly2_11',\n",
       " 'poly1_4',\n",
       " 'StorePurchases',\n",
       " 'Recency_bin',\n",
       " 'poly2_7',\n",
       " 'poly2_10',\n",
       " 'Amount_on_MeatProducts',\n",
       " 'Amount_on_Wines',\n",
       " 'poly2_8',\n",
       " 'poly2_17',\n",
       " 'Recency_mean_on_yr_mth',\n",
       " 'poly2_0',\n",
       " 'poly2_9',\n",
       " 'month',\n",
       " 'poly1_5',\n",
       " 'poly1_8',\n",
       " 'poly2_19',\n",
       " 'Education_Level',\n",
       " 'poly2_14',\n",
       " 'Recency_std_on_yr_mth',\n",
       " 'poly1_9',\n",
       " 'WebVisitsMonth',\n",
       " 'Amount_on_FishProducts',\n",
       " 'duration_months',\n",
       " 'total_amount_on',\n",
       " 'poly2_6',\n",
       " 'year_month_int',\n",
       " 'year',\n",
       " 'No_of_Teen_in_home',\n",
       " 'poly1_6',\n",
       " 'poly2_12',\n",
       " 'poly2_13',\n",
       " 'poly1_1',\n",
       " 'poly2_5',\n",
       " 'poly2_2',\n",
       " 'Amount_on_GoldProds',\n",
       " 'poly1_2',\n",
       " 'total_purchase',\n",
       " 'poly2_20',\n",
       " 'poly2_18',\n",
       " 'poly1_0',\n",
       " 'poly1_3',\n",
       " 'quater',\n",
       " 'poly2_15']"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_feat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "import optuna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = train_df[features]\n",
    "y = train_df[target]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = train_df[new_feat]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "def objective(trial):\n",
    "    oof_f1=[]\n",
    "    oof_predictions=[]\n",
    "\n",
    "\n",
    "    param = {}\n",
    "#     param['objective'] = \"Logloss\"\n",
    "    param['learning_rate'] = trial.suggest_discrete_uniform(\"learning_rate\", 0.001, 0.1, 0.001)\n",
    "    param['depth'] = trial.suggest_int('depth', 3, 15)\n",
    "    param['l2_leaf_reg'] = trial.suggest_discrete_uniform('l2_leaf_reg', 1.0, 5.5, 0.5)\n",
    "    param['min_child_samples'] = trial.suggest_categorical('min_child_samples', [1, 4, 8, 16, 32])\n",
    "    param['iterations'] = trial.suggest_discrete_uniform(\"iterations\", 200, 2000, 50)\n",
    "    param['use_best_model'] = True\n",
    "    param[\"colsample_bylevel\"] =  trial.suggest_float(\"colsample_bylevel\", 0.01, 0.1)\n",
    "    param['random_state'] = 0\n",
    "    param['scale_pos_weight'] = trial.suggest_int('scale_pos_weight', 1, 10)\n",
    "    param['bootstrap_type'] = trial.suggest_categorical(\"bootstrap_type\", [\"Bayesian\", \"Bernoulli\", \"MVS\"])\n",
    "    param['logging_level'] = 'Silent'\n",
    "\n",
    "\n",
    "\n",
    "    fold=StratifiedKFold(n_splits=5)#15#5#10\n",
    "    i=1\n",
    "    for train_index, test_index in fold.split(X,y):\n",
    "        \n",
    "        X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n",
    "        y_train, y_test = y.iloc[train_index], y.iloc[test_index]\n",
    "        model = CatBoostClassifier(**param)\n",
    "#         model = XGBClassifier(**param)\n",
    "    \n",
    "\n",
    "        model.fit(X_train,y_train,eval_set=[(X_train,y_train),(X_test, y_test)], early_stopping_rounds=200, verbose = False)#early100\n",
    "        preds=model.predict(X_test)\n",
    "\n",
    "        oof_f1.append(f1_score(y_test,preds))\n",
    "\n",
    "    return np.mean(oof_f1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2022-03-13 00:50:14,460]\u001b[0m A new study created in memory with name: no-name-01c7d0c7-2b9b-49f6-9302-e0ecb11e5a3a\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 00:50:26,502]\u001b[0m Trial 0 finished with value: 0.5484049107858632 and parameters: {'learning_rate': 0.039, 'depth': 4, 'l2_leaf_reg': 5.0, 'min_child_samples': 4, 'iterations': 1600.0, 'colsample_bylevel': 0.04543987003715986, 'scale_pos_weight': 2, 'bootstrap_type': 'Bernoulli'}. Best is trial 0 with value: 0.5484049107858632.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 00:50:33,634]\u001b[0m Trial 1 finished with value: 0.5148766117618774 and parameters: {'learning_rate': 0.092, 'depth': 15, 'l2_leaf_reg': 4.5, 'min_child_samples': 1, 'iterations': 1650.0, 'colsample_bylevel': 0.019263683067890863, 'scale_pos_weight': 2, 'bootstrap_type': 'MVS'}. Best is trial 0 with value: 0.5484049107858632.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 00:50:42,916]\u001b[0m Trial 2 finished with value: 0.5475686290228005 and parameters: {'learning_rate': 0.026000000000000002, 'depth': 7, 'l2_leaf_reg': 5.5, 'min_child_samples': 4, 'iterations': 1650.0, 'colsample_bylevel': 0.03440433340735815, 'scale_pos_weight': 7, 'bootstrap_type': 'Bayesian'}. Best is trial 0 with value: 0.5484049107858632.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 00:50:53,968]\u001b[0m Trial 3 finished with value: 0.5139747246580507 and parameters: {'learning_rate': 0.063, 'depth': 10, 'l2_leaf_reg': 1.5, 'min_child_samples': 16, 'iterations': 1250.0, 'colsample_bylevel': 0.06210663044969937, 'scale_pos_weight': 10, 'bootstrap_type': 'Bayesian'}. Best is trial 0 with value: 0.5484049107858632.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:06:56,224]\u001b[0m Trial 4 finished with value: 0.5262494867337083 and parameters: {'learning_rate': 0.002, 'depth': 14, 'l2_leaf_reg': 4.0, 'min_child_samples': 8, 'iterations': 1800.0, 'colsample_bylevel': 0.07391971798497753, 'scale_pos_weight': 5, 'bootstrap_type': 'MVS'}. Best is trial 0 with value: 0.5484049107858632.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:07:13,274]\u001b[0m Trial 5 finished with value: 0.5247327475537614 and parameters: {'learning_rate': 0.053000000000000005, 'depth': 10, 'l2_leaf_reg': 2.0, 'min_child_samples': 8, 'iterations': 1600.0, 'colsample_bylevel': 0.07811056742895017, 'scale_pos_weight': 7, 'bootstrap_type': 'MVS'}. Best is trial 0 with value: 0.5484049107858632.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:07:24,420]\u001b[0m Trial 6 finished with value: 0.4369827655516857 and parameters: {'learning_rate': 0.094, 'depth': 9, 'l2_leaf_reg': 4.5, 'min_child_samples': 4, 'iterations': 1450.0, 'colsample_bylevel': 0.06033505181421906, 'scale_pos_weight': 2, 'bootstrap_type': 'Bayesian'}. Best is trial 0 with value: 0.5484049107858632.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:07:29,902]\u001b[0m Trial 7 finished with value: 0.5065650683989802 and parameters: {'learning_rate': 0.042, 'depth': 15, 'l2_leaf_reg': 1.0, 'min_child_samples': 32, 'iterations': 250.0, 'colsample_bylevel': 0.02296191457190394, 'scale_pos_weight': 7, 'bootstrap_type': 'MVS'}. Best is trial 0 with value: 0.5484049107858632.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:07:47,844]\u001b[0m Trial 8 finished with value: 0.4895953084439907 and parameters: {'learning_rate': 0.013000000000000001, 'depth': 13, 'l2_leaf_reg': 5.5, 'min_child_samples': 4, 'iterations': 1400.0, 'colsample_bylevel': 0.022320130262689523, 'scale_pos_weight': 2, 'bootstrap_type': 'MVS'}. Best is trial 0 with value: 0.5484049107858632.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:08:05,751]\u001b[0m Trial 9 finished with value: 0.29483984942950786 and parameters: {'learning_rate': 0.063, 'depth': 10, 'l2_leaf_reg': 2.0, 'min_child_samples': 8, 'iterations': 1700.0, 'colsample_bylevel': 0.07310936553670307, 'scale_pos_weight': 1, 'bootstrap_type': 'Bayesian'}. Best is trial 0 with value: 0.5484049107858632.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:08:16,790]\u001b[0m Trial 10 finished with value: 0.5685489415429053 and parameters: {'learning_rate': 0.026000000000000002, 'depth': 3, 'l2_leaf_reg': 3.0, 'min_child_samples': 1, 'iterations': 800.0, 'colsample_bylevel': 0.09625315005886909, 'scale_pos_weight': 4, 'bootstrap_type': 'Bernoulli'}. Best is trial 10 with value: 0.5685489415429053.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:08:25,130]\u001b[0m Trial 11 finished with value: 0.5858552211031721 and parameters: {'learning_rate': 0.031, 'depth': 3, 'l2_leaf_reg': 3.5, 'min_child_samples': 1, 'iterations': 700.0, 'colsample_bylevel': 0.09973276320002467, 'scale_pos_weight': 4, 'bootstrap_type': 'Bernoulli'}. Best is trial 11 with value: 0.5858552211031721.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:08:34,141]\u001b[0m Trial 12 finished with value: 0.5731464057559339 and parameters: {'learning_rate': 0.024, 'depth': 3, 'l2_leaf_reg': 3.0, 'min_child_samples': 1, 'iterations': 800.0, 'colsample_bylevel': 0.09950529940135312, 'scale_pos_weight': 4, 'bootstrap_type': 'Bernoulli'}. Best is trial 11 with value: 0.5858552211031721.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:08:46,661]\u001b[0m Trial 13 finished with value: 0.5858549193140039 and parameters: {'learning_rate': 0.023, 'depth': 5, 'l2_leaf_reg': 3.0, 'min_child_samples': 1, 'iterations': 800.0, 'colsample_bylevel': 0.09968467039349817, 'scale_pos_weight': 4, 'bootstrap_type': 'Bernoulli'}. Best is trial 11 with value: 0.5858552211031721.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:09:02,028]\u001b[0m Trial 14 finished with value: 0.5042812460711164 and parameters: {'learning_rate': 0.004, 'depth': 6, 'l2_leaf_reg': 3.5, 'min_child_samples': 1, 'iterations': 800.0, 'colsample_bylevel': 0.08752933103535304, 'scale_pos_weight': 4, 'bootstrap_type': 'Bernoulli'}. Best is trial 11 with value: 0.5858552211031721.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:09:10,821]\u001b[0m Trial 15 finished with value: 0.5499640830277837 and parameters: {'learning_rate': 0.039, 'depth': 5, 'l2_leaf_reg': 2.5, 'min_child_samples': 1, 'iterations': 400.0, 'colsample_bylevel': 0.0865192974594787, 'scale_pos_weight': 6, 'bootstrap_type': 'Bernoulli'}. Best is trial 11 with value: 0.5858552211031721.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:09:26,449]\u001b[0m Trial 16 finished with value: 0.5291202941699156 and parameters: {'learning_rate': 0.016, 'depth': 8, 'l2_leaf_reg': 3.5, 'min_child_samples': 32, 'iterations': 550.0, 'colsample_bylevel': 0.08839193130976022, 'scale_pos_weight': 9, 'bootstrap_type': 'Bernoulli'}. Best is trial 11 with value: 0.5858552211031721.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:09:31,998]\u001b[0m Trial 17 finished with value: 0.5572641477228947 and parameters: {'learning_rate': 0.07100000000000001, 'depth': 5, 'l2_leaf_reg': 2.5, 'min_child_samples': 16, 'iterations': 1050.0, 'colsample_bylevel': 0.04974904555976658, 'scale_pos_weight': 5, 'bootstrap_type': 'Bernoulli'}. Best is trial 11 with value: 0.5858552211031721.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:09:42,182]\u001b[0m Trial 18 finished with value: 0.5579374445412276 and parameters: {'learning_rate': 0.033, 'depth': 6, 'l2_leaf_reg': 4.0, 'min_child_samples': 1, 'iterations': 1050.0, 'colsample_bylevel': 0.06746385320914944, 'scale_pos_weight': 3, 'bootstrap_type': 'Bernoulli'}. Best is trial 11 with value: 0.5858552211031721.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:09:49,798]\u001b[0m Trial 19 finished with value: 0.5552599898239652 and parameters: {'learning_rate': 0.051000000000000004, 'depth': 3, 'l2_leaf_reg': 2.5, 'min_child_samples': 1, 'iterations': 550.0, 'colsample_bylevel': 0.08296363013403567, 'scale_pos_weight': 6, 'bootstrap_type': 'Bernoulli'}. Best is trial 11 with value: 0.5858552211031721.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:11:38,954]\u001b[0m Trial 20 finished with value: 0.4847568946277698 and parameters: {'learning_rate': 0.014000000000000002, 'depth': 12, 'l2_leaf_reg': 4.0, 'min_child_samples': 1, 'iterations': 650.0, 'colsample_bylevel': 0.09490718605909236, 'scale_pos_weight': 3, 'bootstrap_type': 'Bernoulli'}. Best is trial 11 with value: 0.5858552211031721.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:11:50,085]\u001b[0m Trial 21 finished with value: 0.5704526757052328 and parameters: {'learning_rate': 0.021, 'depth': 3, 'l2_leaf_reg': 3.0, 'min_child_samples': 1, 'iterations': 850.0, 'colsample_bylevel': 0.09995294085744649, 'scale_pos_weight': 4, 'bootstrap_type': 'Bernoulli'}. Best is trial 11 with value: 0.5858552211031721.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:11:58,901]\u001b[0m Trial 22 finished with value: 0.5569309824027406 and parameters: {'learning_rate': 0.031, 'depth': 4, 'l2_leaf_reg': 3.0, 'min_child_samples': 1, 'iterations': 2000.0, 'colsample_bylevel': 0.09995756292834332, 'scale_pos_weight': 4, 'bootstrap_type': 'Bernoulli'}. Best is trial 11 with value: 0.5858552211031721.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:12:17,443]\u001b[0m Trial 23 finished with value: 0.5544615030763038 and parameters: {'learning_rate': 0.009000000000000001, 'depth': 5, 'l2_leaf_reg': 3.5, 'min_child_samples': 1, 'iterations': 950.0, 'colsample_bylevel': 0.09190618497062988, 'scale_pos_weight': 3, 'bootstrap_type': 'Bernoulli'}. Best is trial 11 with value: 0.5858552211031721.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:12:26,522]\u001b[0m Trial 24 finished with value: 0.5518468331386774 and parameters: {'learning_rate': 0.022000000000000002, 'depth': 4, 'l2_leaf_reg': 2.0, 'min_child_samples': 1, 'iterations': 650.0, 'colsample_bylevel': 0.08008358700841114, 'scale_pos_weight': 5, 'bootstrap_type': 'Bernoulli'}. Best is trial 11 with value: 0.5858552211031721.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:12:36,253]\u001b[0m Trial 25 finished with value: 0.5269920280730787 and parameters: {'learning_rate': 0.044000000000000004, 'depth': 7, 'l2_leaf_reg': 3.0, 'min_child_samples': 32, 'iterations': 1200.0, 'colsample_bylevel': 0.010287210319734313, 'scale_pos_weight': 6, 'bootstrap_type': 'Bernoulli'}. Best is trial 11 with value: 0.5858552211031721.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:12:39,711]\u001b[0m Trial 26 finished with value: 0.5059678400772476 and parameters: {'learning_rate': 0.033, 'depth': 3, 'l2_leaf_reg': 2.5, 'min_child_samples': 16, 'iterations': 200.0, 'colsample_bylevel': 0.09125261423267979, 'scale_pos_weight': 3, 'bootstrap_type': 'Bernoulli'}. Best is trial 11 with value: 0.5858552211031721.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:12:53,726]\u001b[0m Trial 27 finished with value: 0.5653514579012553 and parameters: {'learning_rate': 0.02, 'depth': 6, 'l2_leaf_reg': 3.5, 'min_child_samples': 1, 'iterations': 900.0, 'colsample_bylevel': 0.08169526956853308, 'scale_pos_weight': 5, 'bootstrap_type': 'Bernoulli'}. Best is trial 11 with value: 0.5858552211031721.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:13:01,670]\u001b[0m Trial 28 finished with value: 0.38266632666071654 and parameters: {'learning_rate': 0.030000000000000002, 'depth': 4, 'l2_leaf_reg': 4.0, 'min_child_samples': 1, 'iterations': 400.0, 'colsample_bylevel': 0.09399504572746696, 'scale_pos_weight': 1, 'bootstrap_type': 'Bayesian'}. Best is trial 11 with value: 0.5858552211031721.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:13:10,908]\u001b[0m Trial 29 finished with value: 0.5460927567464393 and parameters: {'learning_rate': 0.047, 'depth': 5, 'l2_leaf_reg': 4.5, 'min_child_samples': 1, 'iterations': 700.0, 'colsample_bylevel': 0.038820719364919876, 'scale_pos_weight': 8, 'bootstrap_type': 'Bernoulli'}. Best is trial 11 with value: 0.5858552211031721.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:13:19,326]\u001b[0m Trial 30 finished with value: 0.5700205287607961 and parameters: {'learning_rate': 0.037000000000000005, 'depth': 4, 'l2_leaf_reg': 3.0, 'min_child_samples': 8, 'iterations': 450.0, 'colsample_bylevel': 0.07468319300523943, 'scale_pos_weight': 4, 'bootstrap_type': 'Bernoulli'}. Best is trial 11 with value: 0.5858552211031721.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:13:30,425]\u001b[0m Trial 31 finished with value: 0.5817118695387082 and parameters: {'learning_rate': 0.021, 'depth': 3, 'l2_leaf_reg': 3.0, 'min_child_samples': 1, 'iterations': 850.0, 'colsample_bylevel': 0.0997894600515491, 'scale_pos_weight': 4, 'bootstrap_type': 'Bernoulli'}. Best is trial 11 with value: 0.5858552211031721.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:13:41,565]\u001b[0m Trial 32 finished with value: 0.5437082431509783 and parameters: {'learning_rate': 0.009000000000000001, 'depth': 3, 'l2_leaf_reg': 3.5, 'min_child_samples': 1, 'iterations': 950.0, 'colsample_bylevel': 0.09941749489917573, 'scale_pos_weight': 3, 'bootstrap_type': 'Bernoulli'}. Best is trial 11 with value: 0.5858552211031721.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:13:49,748]\u001b[0m Trial 33 finished with value: 0.5771120720132713 and parameters: {'learning_rate': 0.026000000000000002, 'depth': 3, 'l2_leaf_reg': 2.5, 'min_child_samples': 1, 'iterations': 750.0, 'colsample_bylevel': 0.08659800003760203, 'scale_pos_weight': 4, 'bootstrap_type': 'Bernoulli'}. Best is trial 11 with value: 0.5858552211031721.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:13:56,113]\u001b[0m Trial 34 finished with value: 0.5098013639477055 and parameters: {'learning_rate': 0.082, 'depth': 4, 'l2_leaf_reg': 1.5, 'min_child_samples': 4, 'iterations': 700.0, 'colsample_bylevel': 0.08608271682702945, 'scale_pos_weight': 2, 'bootstrap_type': 'Bernoulli'}. Best is trial 11 with value: 0.5858552211031721.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:14:04,805]\u001b[0m Trial 35 finished with value: 0.5885771776382805 and parameters: {'learning_rate': 0.029, 'depth': 5, 'l2_leaf_reg': 2.5, 'min_child_samples': 16, 'iterations': 500.0, 'colsample_bylevel': 0.0919260168756181, 'scale_pos_weight': 5, 'bootstrap_type': 'MVS'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:14:15,795]\u001b[0m Trial 36 finished with value: 0.5475336997643355 and parameters: {'learning_rate': 0.036000000000000004, 'depth': 7, 'l2_leaf_reg': 2.0, 'min_child_samples': 16, 'iterations': 550.0, 'colsample_bylevel': 0.09261734408620054, 'scale_pos_weight': 5, 'bootstrap_type': 'MVS'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:14:28,456]\u001b[0m Trial 37 finished with value: 0.5351801975118635 and parameters: {'learning_rate': 0.057, 'depth': 8, 'l2_leaf_reg': 5.0, 'min_child_samples': 16, 'iterations': 300.0, 'colsample_bylevel': 0.09364915483413953, 'scale_pos_weight': 6, 'bootstrap_type': 'MVS'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:14:42,589]\u001b[0m Trial 38 finished with value: 0.564552460679668 and parameters: {'learning_rate': 0.017, 'depth': 5, 'l2_leaf_reg': 4.0, 'min_child_samples': 16, 'iterations': 1150.0, 'colsample_bylevel': 0.06829358633190294, 'scale_pos_weight': 5, 'bootstrap_type': 'MVS'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:14:50,935]\u001b[0m Trial 39 finished with value: 0.4956442564864652 and parameters: {'learning_rate': 0.007, 'depth': 6, 'l2_leaf_reg': 1.5, 'min_child_samples': 16, 'iterations': 500.0, 'colsample_bylevel': 0.05652473078414764, 'scale_pos_weight': 7, 'bootstrap_type': 'MVS'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:15:03,692]\u001b[0m Trial 40 finished with value: 0.5188504196057657 and parameters: {'learning_rate': 0.029, 'depth': 8, 'l2_leaf_reg': 2.5, 'min_child_samples': 4, 'iterations': 1000.0, 'colsample_bylevel': 0.07521502582895946, 'scale_pos_weight': 3, 'bootstrap_type': 'Bayesian'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:15:17,682]\u001b[0m Trial 41 finished with value: 0.575775671058076 and parameters: {'learning_rate': 0.024, 'depth': 4, 'l2_leaf_reg': 2.5, 'min_child_samples': 1, 'iterations': 700.0, 'colsample_bylevel': 0.08447336208333198, 'scale_pos_weight': 4, 'bootstrap_type': 'MVS'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:15:25,270]\u001b[0m Trial 42 finished with value: 0.5658072539052246 and parameters: {'learning_rate': 0.028, 'depth': 3, 'l2_leaf_reg': 2.0, 'min_child_samples': 32, 'iterations': 600.0, 'colsample_bylevel': 0.09560458665722156, 'scale_pos_weight': 5, 'bootstrap_type': 'MVS'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:15:35,659]\u001b[0m Trial 43 finished with value: 0.5664395299923707 and parameters: {'learning_rate': 0.04, 'depth': 5, 'l2_leaf_reg': 3.0, 'min_child_samples': 1, 'iterations': 750.0, 'colsample_bylevel': 0.09057963074961807, 'scale_pos_weight': 4, 'bootstrap_type': 'Bayesian'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:15:41,518]\u001b[0m Trial 44 finished with value: 0.023999999999999997 and parameters: {'learning_rate': 0.001, 'depth': 3, 'l2_leaf_reg': 2.5, 'min_child_samples': 8, 'iterations': 350.0, 'colsample_bylevel': 0.07870570341944924, 'scale_pos_weight': 2, 'bootstrap_type': 'Bernoulli'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:16:28,968]\u001b[0m Trial 45 finished with value: 0.517406243710023 and parameters: {'learning_rate': 0.018000000000000002, 'depth': 11, 'l2_leaf_reg': 3.5, 'min_child_samples': 16, 'iterations': 850.0, 'colsample_bylevel': 0.08858531930449454, 'scale_pos_weight': 5, 'bootstrap_type': 'MVS'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:16:45,063]\u001b[0m Trial 46 finished with value: 0.5600369915870413 and parameters: {'learning_rate': 0.011, 'depth': 4, 'l2_leaf_reg': 2.0, 'min_child_samples': 1, 'iterations': 1250.0, 'colsample_bylevel': 0.0970044887151354, 'scale_pos_weight': 3, 'bootstrap_type': 'Bernoulli'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:16:53,342]\u001b[0m Trial 47 finished with value: 0.5626513892533309 and parameters: {'learning_rate': 0.045, 'depth': 4, 'l2_leaf_reg': 1.0, 'min_child_samples': 4, 'iterations': 1450.0, 'colsample_bylevel': 0.09637347482240993, 'scale_pos_weight': 4, 'bootstrap_type': 'Bayesian'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:17:03,857]\u001b[0m Trial 48 finished with value: 0.5398086235020615 and parameters: {'learning_rate': 0.027000000000000003, 'depth': 7, 'l2_leaf_reg': 3.0, 'min_child_samples': 1, 'iterations': 900.0, 'colsample_bylevel': 0.0689784847503537, 'scale_pos_weight': 6, 'bootstrap_type': 'Bernoulli'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:20:34,719]\u001b[0m Trial 49 finished with value: 0.5099452434362733 and parameters: {'learning_rate': 0.024, 'depth': 14, 'l2_leaf_reg': 3.5, 'min_child_samples': 8, 'iterations': 750.0, 'colsample_bylevel': 0.08448871547166456, 'scale_pos_weight': 10, 'bootstrap_type': 'MVS'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:20:41,863]\u001b[0m Trial 50 finished with value: 0.5582127264546717 and parameters: {'learning_rate': 0.057, 'depth': 6, 'l2_leaf_reg': 1.5, 'min_child_samples': 32, 'iterations': 450.0, 'colsample_bylevel': 0.091747374013103, 'scale_pos_weight': 4, 'bootstrap_type': 'Bernoulli'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:20:50,636]\u001b[0m Trial 51 finished with value: 0.5545551602702339 and parameters: {'learning_rate': 0.035, 'depth': 5, 'l2_leaf_reg': 2.5, 'min_child_samples': 1, 'iterations': 650.0, 'colsample_bylevel': 0.08454157097327364, 'scale_pos_weight': 4, 'bootstrap_type': 'MVS'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:20:59,088]\u001b[0m Trial 52 finished with value: 0.5595216000101788 and parameters: {'learning_rate': 0.024, 'depth': 4, 'l2_leaf_reg': 2.5, 'min_child_samples': 1, 'iterations': 750.0, 'colsample_bylevel': 0.08762802847890624, 'scale_pos_weight': 4, 'bootstrap_type': 'MVS'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:21:08,357]\u001b[0m Trial 53 finished with value: 0.5430558383322577 and parameters: {'learning_rate': 0.014000000000000002, 'depth': 3, 'l2_leaf_reg': 3.0, 'min_child_samples': 1, 'iterations': 600.0, 'colsample_bylevel': 0.09779280301053579, 'scale_pos_weight': 5, 'bootstrap_type': 'MVS'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:21:20,103]\u001b[0m Trial 54 finished with value: 0.5577529973316213 and parameters: {'learning_rate': 0.02, 'depth': 3, 'l2_leaf_reg': 2.5, 'min_child_samples': 1, 'iterations': 850.0, 'colsample_bylevel': 0.08944765825946024, 'scale_pos_weight': 3, 'bootstrap_type': 'MVS'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:21:30,623]\u001b[0m Trial 55 finished with value: 0.5691494118351084 and parameters: {'learning_rate': 0.025, 'depth': 5, 'l2_leaf_reg': 3.0, 'min_child_samples': 1, 'iterations': 1000.0, 'colsample_bylevel': 0.04378655086275496, 'scale_pos_weight': 4, 'bootstrap_type': 'MVS'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:21:38,807]\u001b[0m Trial 56 finished with value: 0.5472856090319466 and parameters: {'learning_rate': 0.032, 'depth': 4, 'l2_leaf_reg': 2.0, 'min_child_samples': 1, 'iterations': 1100.0, 'colsample_bylevel': 0.08119921091159038, 'scale_pos_weight': 5, 'bootstrap_type': 'Bernoulli'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:21:47,401]\u001b[0m Trial 57 finished with value: 0.5608943453753173 and parameters: {'learning_rate': 0.04, 'depth': 3, 'l2_leaf_reg': 2.5, 'min_child_samples': 1, 'iterations': 800.0, 'colsample_bylevel': 0.09566519246754218, 'scale_pos_weight': 2, 'bootstrap_type': 'Bernoulli'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:21:52,726]\u001b[0m Trial 58 finished with value: 0.4824899238692343 and parameters: {'learning_rate': 0.015, 'depth': 4, 'l2_leaf_reg': 3.0, 'min_child_samples': 16, 'iterations': 500.0, 'colsample_bylevel': 0.02939992532984241, 'scale_pos_weight': 3, 'bootstrap_type': 'Bernoulli'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:22:03,494]\u001b[0m Trial 59 finished with value: 0.5368502776605508 and parameters: {'learning_rate': 0.1, 'depth': 9, 'l2_leaf_reg': 3.5, 'min_child_samples': 1, 'iterations': 700.0, 'colsample_bylevel': 0.0634691592519606, 'scale_pos_weight': 4, 'bootstrap_type': 'MVS'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:22:17,709]\u001b[0m Trial 60 finished with value: 0.5460283161799602 and parameters: {'learning_rate': 0.019000000000000003, 'depth': 3, 'l2_leaf_reg': 2.5, 'min_child_samples': 1, 'iterations': 1300.0, 'colsample_bylevel': 0.07703578739743716, 'scale_pos_weight': 6, 'bootstrap_type': 'Bernoulli'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:22:25,646]\u001b[0m Trial 61 finished with value: 0.5607522248592788 and parameters: {'learning_rate': 0.025, 'depth': 3, 'l2_leaf_reg': 3.0, 'min_child_samples': 1, 'iterations': 600.0, 'colsample_bylevel': 0.09932950181304522, 'scale_pos_weight': 4, 'bootstrap_type': 'Bernoulli'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:22:35,646]\u001b[0m Trial 62 finished with value: 0.5537196368861577 and parameters: {'learning_rate': 0.029, 'depth': 5, 'l2_leaf_reg': 3.5, 'min_child_samples': 1, 'iterations': 800.0, 'colsample_bylevel': 0.09396424753490673, 'scale_pos_weight': 5, 'bootstrap_type': 'Bernoulli'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:22:48,858]\u001b[0m Trial 63 finished with value: 0.5265753889834073 and parameters: {'learning_rate': 0.006, 'depth': 4, 'l2_leaf_reg': 3.0, 'min_child_samples': 1, 'iterations': 950.0, 'colsample_bylevel': 0.09018231798150927, 'scale_pos_weight': 4, 'bootstrap_type': 'Bernoulli'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:22:59,942]\u001b[0m Trial 64 finished with value: 0.5595885337729314 and parameters: {'learning_rate': 0.022000000000000002, 'depth': 3, 'l2_leaf_reg': 2.5, 'min_child_samples': 1, 'iterations': 850.0, 'colsample_bylevel': 0.09998779470342617, 'scale_pos_weight': 3, 'bootstrap_type': 'Bernoulli'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:23:12,244]\u001b[0m Trial 65 finished with value: 0.5382279390840189 and parameters: {'learning_rate': 0.012, 'depth': 4, 'l2_leaf_reg': 3.0, 'min_child_samples': 1, 'iterations': 650.0, 'colsample_bylevel': 0.08618431316028573, 'scale_pos_weight': 5, 'bootstrap_type': 'Bernoulli'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:23:21,805]\u001b[0m Trial 66 finished with value: 0.5532361207206297 and parameters: {'learning_rate': 0.034, 'depth': 6, 'l2_leaf_reg': 2.5, 'min_child_samples': 8, 'iterations': 750.0, 'colsample_bylevel': 0.09702581114382798, 'scale_pos_weight': 4, 'bootstrap_type': 'Bernoulli'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:23:28,016]\u001b[0m Trial 67 finished with value: 0.5536803690697514 and parameters: {'learning_rate': 0.022000000000000002, 'depth': 3, 'l2_leaf_reg': 3.5, 'min_child_samples': 32, 'iterations': 500.0, 'colsample_bylevel': 0.09233782888293246, 'scale_pos_weight': 3, 'bootstrap_type': 'Bayesian'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:23:37,551]\u001b[0m Trial 68 finished with value: 0.5571100247388907 and parameters: {'learning_rate': 0.038, 'depth': 5, 'l2_leaf_reg': 4.0, 'min_child_samples': 16, 'iterations': 900.0, 'colsample_bylevel': 0.08310703653359865, 'scale_pos_weight': 4, 'bootstrap_type': 'Bernoulli'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:23:44,124]\u001b[0m Trial 69 finished with value: 0.5677628249560489 and parameters: {'learning_rate': 0.074, 'depth': 4, 'l2_leaf_reg': 2.0, 'min_child_samples': 4, 'iterations': 700.0, 'colsample_bylevel': 0.07102937293090333, 'scale_pos_weight': 5, 'bootstrap_type': 'Bernoulli'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:23:53,177]\u001b[0m Trial 70 finished with value: 0.5165698687057165 and parameters: {'learning_rate': 0.031, 'depth': 3, 'l2_leaf_reg': 3.0, 'min_child_samples': 1, 'iterations': 1000.0, 'colsample_bylevel': 0.09390582469203487, 'scale_pos_weight': 9, 'bootstrap_type': 'MVS'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:24:04,477]\u001b[0m Trial 71 finished with value: 0.5661640180617067 and parameters: {'learning_rate': 0.021, 'depth': 3, 'l2_leaf_reg': 3.0, 'min_child_samples': 1, 'iterations': 900.0, 'colsample_bylevel': 0.09783868400579628, 'scale_pos_weight': 4, 'bootstrap_type': 'Bernoulli'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:24:17,347]\u001b[0m Trial 72 finished with value: 0.5646065676140506 and parameters: {'learning_rate': 0.017, 'depth': 4, 'l2_leaf_reg': 3.0, 'min_child_samples': 1, 'iterations': 800.0, 'colsample_bylevel': 0.09861963808073582, 'scale_pos_weight': 4, 'bootstrap_type': 'Bernoulli'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:24:24,407]\u001b[0m Trial 73 finished with value: 0.5692861401966189 and parameters: {'learning_rate': 0.026000000000000002, 'depth': 3, 'l2_leaf_reg': 2.5, 'min_child_samples': 1, 'iterations': 600.0, 'colsample_bylevel': 0.09439893797874321, 'scale_pos_weight': 3, 'bootstrap_type': 'Bernoulli'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:24:34,282]\u001b[0m Trial 74 finished with value: 0.5619751892062204 and parameters: {'learning_rate': 0.027000000000000003, 'depth': 4, 'l2_leaf_reg': 3.5, 'min_child_samples': 1, 'iterations': 850.0, 'colsample_bylevel': 0.08997130631086672, 'scale_pos_weight': 5, 'bootstrap_type': 'Bernoulli'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:24:49,769]\u001b[0m Trial 75 finished with value: 0.574237517732241 and parameters: {'learning_rate': 0.016, 'depth': 3, 'l2_leaf_reg': 3.0, 'min_child_samples': 1, 'iterations': 1100.0, 'colsample_bylevel': 0.0959374827513777, 'scale_pos_weight': 4, 'bootstrap_type': 'Bernoulli'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:25:08,223]\u001b[0m Trial 76 finished with value: 0.5550567876015687 and parameters: {'learning_rate': 0.011, 'depth': 5, 'l2_leaf_reg': 4.5, 'min_child_samples': 1, 'iterations': 1050.0, 'colsample_bylevel': 0.08657650460272029, 'scale_pos_weight': 3, 'bootstrap_type': 'Bernoulli'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:25:20,631]\u001b[0m Trial 77 finished with value: 0.5673638119485537 and parameters: {'learning_rate': 0.017, 'depth': 4, 'l2_leaf_reg': 2.5, 'min_child_samples': 16, 'iterations': 1600.0, 'colsample_bylevel': 0.09580978140508395, 'scale_pos_weight': 6, 'bootstrap_type': 'Bernoulli'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:25:42,385]\u001b[0m Trial 78 finished with value: 0.543641853619339 and parameters: {'learning_rate': 0.029, 'depth': 10, 'l2_leaf_reg': 4.0, 'min_child_samples': 1, 'iterations': 1150.0, 'colsample_bylevel': 0.09208295412429865, 'scale_pos_weight': 4, 'bootstrap_type': 'Bayesian'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:25:48,630]\u001b[0m Trial 79 finished with value: 0.5599386464719318 and parameters: {'learning_rate': 0.042, 'depth': 3, 'l2_leaf_reg': 3.5, 'min_child_samples': 1, 'iterations': 1900.0, 'colsample_bylevel': 0.08908767834501477, 'scale_pos_weight': 5, 'bootstrap_type': 'MVS'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:25:54,696]\u001b[0m Trial 80 finished with value: 0.5331380833873907 and parameters: {'learning_rate': 0.014000000000000002, 'depth': 6, 'l2_leaf_reg': 2.0, 'min_child_samples': 32, 'iterations': 350.0, 'colsample_bylevel': 0.0526436586232732, 'scale_pos_weight': 4, 'bootstrap_type': 'Bernoulli'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:26:04,177]\u001b[0m Trial 81 finished with value: 0.5628691409245332 and parameters: {'learning_rate': 0.022000000000000002, 'depth': 3, 'l2_leaf_reg': 3.0, 'min_child_samples': 1, 'iterations': 750.0, 'colsample_bylevel': 0.099361842943019, 'scale_pos_weight': 4, 'bootstrap_type': 'Bernoulli'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:26:18,089]\u001b[0m Trial 82 finished with value: 0.5688201859155261 and parameters: {'learning_rate': 0.019000000000000003, 'depth': 3, 'l2_leaf_reg': 3.0, 'min_child_samples': 1, 'iterations': 950.0, 'colsample_bylevel': 0.09720403921145629, 'scale_pos_weight': 4, 'bootstrap_type': 'Bernoulli'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:26:28,792]\u001b[0m Trial 83 finished with value: 0.5661521020562214 and parameters: {'learning_rate': 0.023, 'depth': 4, 'l2_leaf_reg': 2.5, 'min_child_samples': 1, 'iterations': 700.0, 'colsample_bylevel': 0.09990500429196729, 'scale_pos_weight': 3, 'bootstrap_type': 'Bernoulli'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:26:35,137]\u001b[0m Trial 84 finished with value: 0.5591001716001716 and parameters: {'learning_rate': 0.031, 'depth': 3, 'l2_leaf_reg': 3.0, 'min_child_samples': 1, 'iterations': 550.0, 'colsample_bylevel': 0.09445669262243109, 'scale_pos_weight': 5, 'bootstrap_type': 'Bernoulli'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:26:44,228]\u001b[0m Trial 85 finished with value: 0.5306787330316742 and parameters: {'learning_rate': 0.010000000000000002, 'depth': 4, 'l2_leaf_reg': 3.5, 'min_child_samples': 16, 'iterations': 650.0, 'colsample_bylevel': 0.09198410702528964, 'scale_pos_weight': 4, 'bootstrap_type': 'Bernoulli'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:26:52,598]\u001b[0m Trial 86 finished with value: 0.5815278048699772 and parameters: {'learning_rate': 0.035, 'depth': 5, 'l2_leaf_reg': 3.0, 'min_child_samples': 4, 'iterations': 800.0, 'colsample_bylevel': 0.09617004491870154, 'scale_pos_weight': 4, 'bootstrap_type': 'MVS'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:27:00,791]\u001b[0m Trial 87 finished with value: 0.5469221835075494 and parameters: {'learning_rate': 0.035, 'depth': 5, 'l2_leaf_reg': 3.0, 'min_child_samples': 4, 'iterations': 850.0, 'colsample_bylevel': 0.08370322168896238, 'scale_pos_weight': 5, 'bootstrap_type': 'MVS'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:27:09,478]\u001b[0m Trial 88 finished with value: 0.5250303666367785 and parameters: {'learning_rate': 0.048, 'depth': 5, 'l2_leaf_reg': 2.5, 'min_child_samples': 4, 'iterations': 550.0, 'colsample_bylevel': 0.09698602630332943, 'scale_pos_weight': 3, 'bootstrap_type': 'MVS'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:27:19,607]\u001b[0m Trial 89 finished with value: 0.556790600790401 and parameters: {'learning_rate': 0.027000000000000003, 'depth': 6, 'l2_leaf_reg': 3.5, 'min_child_samples': 4, 'iterations': 800.0, 'colsample_bylevel': 0.08739887237061034, 'scale_pos_weight': 4, 'bootstrap_type': 'MVS'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:27:26,654]\u001b[0m Trial 90 finished with value: 0.56324753635741 and parameters: {'learning_rate': 0.034, 'depth': 5, 'l2_leaf_reg': 5.5, 'min_child_samples': 4, 'iterations': 450.0, 'colsample_bylevel': 0.07939775014647944, 'scale_pos_weight': 3, 'bootstrap_type': 'MVS'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:27:34,548]\u001b[0m Trial 91 finished with value: 0.5637334193097316 and parameters: {'learning_rate': 0.025, 'depth': 3, 'l2_leaf_reg': 3.0, 'min_child_samples': 8, 'iterations': 700.0, 'colsample_bylevel': 0.09498956227372812, 'scale_pos_weight': 4, 'bootstrap_type': 'MVS'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:27:44,975]\u001b[0m Trial 92 finished with value: 0.5775813530561058 and parameters: {'learning_rate': 0.02, 'depth': 4, 'l2_leaf_reg': 2.5, 'min_child_samples': 1, 'iterations': 900.0, 'colsample_bylevel': 0.09291899212558831, 'scale_pos_weight': 4, 'bootstrap_type': 'Bernoulli'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:27:57,143]\u001b[0m Trial 93 finished with value: 0.574894085306551 and parameters: {'learning_rate': 0.02, 'depth': 4, 'l2_leaf_reg': 2.5, 'min_child_samples': 1, 'iterations': 1350.0, 'colsample_bylevel': 0.09095293261030103, 'scale_pos_weight': 4, 'bootstrap_type': 'Bernoulli'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:28:11,507]\u001b[0m Trial 94 finished with value: 0.5546425633844273 and parameters: {'learning_rate': 0.016, 'depth': 4, 'l2_leaf_reg': 2.0, 'min_child_samples': 1, 'iterations': 1400.0, 'colsample_bylevel': 0.09075852269476362, 'scale_pos_weight': 5, 'bootstrap_type': 'MVS'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:28:37,739]\u001b[0m Trial 95 finished with value: 0.535316888413552 and parameters: {'learning_rate': 0.007, 'depth': 7, 'l2_leaf_reg': 2.5, 'min_child_samples': 1, 'iterations': 1700.0, 'colsample_bylevel': 0.09298970562110397, 'scale_pos_weight': 4, 'bootstrap_type': 'Bernoulli'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:28:51,512]\u001b[0m Trial 96 finished with value: 0.5555695611577964 and parameters: {'learning_rate': 0.019000000000000003, 'depth': 4, 'l2_leaf_reg': 2.5, 'min_child_samples': 4, 'iterations': 1300.0, 'colsample_bylevel': 0.08844485209126603, 'scale_pos_weight': 3, 'bootstrap_type': 'Bernoulli'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:28:59,078]\u001b[0m Trial 97 finished with value: 0.5732220095403032 and parameters: {'learning_rate': 0.029, 'depth': 4, 'l2_leaf_reg': 2.0, 'min_child_samples': 1, 'iterations': 1150.0, 'colsample_bylevel': 0.08573352615762951, 'scale_pos_weight': 4, 'bootstrap_type': 'MVS'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:29:11,048]\u001b[0m Trial 98 finished with value: 0.5337529212158515 and parameters: {'learning_rate': 0.032, 'depth': 5, 'l2_leaf_reg': 2.5, 'min_child_samples': 16, 'iterations': 1100.0, 'colsample_bylevel': 0.08082033157310395, 'scale_pos_weight': 2, 'bootstrap_type': 'Bernoulli'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:29:22,572]\u001b[0m Trial 99 finished with value: 0.4981560054636424 and parameters: {'learning_rate': 0.013000000000000001, 'depth': 6, 'l2_leaf_reg': 2.5, 'min_child_samples': 1, 'iterations': 1450.0, 'colsample_bylevel': 0.01179131788057107, 'scale_pos_weight': 5, 'bootstrap_type': 'Bayesian'}. Best is trial 35 with value: 0.5885771776382805.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "study = optuna.create_study(direction=\"maximize\")\n",
    "study.optimize(objective, n_trials=100) #10000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of finished trials: 100\n",
      "Best trial:\n",
      "  Value: 0.5885771776382805\n",
      "  Params: \n",
      "    learning_rate: 0.029\n",
      "    depth: 5\n",
      "    l2_leaf_reg: 2.5\n",
      "    min_child_samples: 16\n",
      "    iterations: 500.0\n",
      "    colsample_bylevel: 0.0919260168756181\n",
      "    scale_pos_weight: 5\n",
      "    bootstrap_type: MVS\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of finished trials: {}\".format(len(study.trials)))\n",
    "\n",
    "print(\"Best trial:\")\n",
    "trial = study.best_trial\n",
    "\n",
    "print(\"  Value: {}\".format(trial.value))\n",
    "\n",
    "print(\"  Params: \")\n",
    "for key, value in trial.params.items():\n",
    "    print(\"    {}: {}\".format(key, value))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_feat2 = ['Year_of_Birth', 'Education_Level', 'Marital_Status',\n",
    "       'Disposable_Income', 'No_of_Kids_in_home', 'No_of_Teen_in_home',\n",
    "       'Recency', 'Discounted_Purchases', 'WebPurchases', 'CatalogPurchases',\n",
    "       'StorePurchases', 'Amount_on_Wines', 'Amount_on_Fruits',\n",
    "       'Amount_on_MeatProducts', 'Amount_on_FishProducts',\n",
    "       'Amount_on_SweetProducts', 'Amount_on_GoldProds', 'WebVisitsMonth',\n",
    "       'Cmp3Accepted', 'Cmp4Accepted', 'Cmp5Accepted', 'Cmp1Accepted',\n",
    "       'Cmp2Accepted', 'Any_Complain', 'year', 'month', 'quater',\n",
    "       'duration_days', 'duration_months', 'is_month_end', 'is_month_start']\n",
    "\n",
    "X = train_df[new_feat2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "def objective(trial):\n",
    "    oof_f1=[]\n",
    "    oof_predictions=[]\n",
    "\n",
    "\n",
    "    param = {}\n",
    "#     param['objective'] = \"Logloss\"\n",
    "#     param['learning_rate'] = trial.suggest_discrete_uniform(\"learning_rate\", 0.001, 0.1, 0.001)\n",
    "#     param['depth'] = trial.suggest_int('depth', 3, 15)\n",
    "#     param['l2_leaf_reg'] = trial.suggest_discrete_uniform('l2_leaf_reg', 1.0, 5.5, 0.5)\n",
    "#     param['min_child_samples'] = trial.suggest_categorical('min_child_samples', [1, 4, 8, 16, 32])\n",
    "#     param['iterations'] = trial.suggest_discrete_uniform(\"iterations\", 200, 2000, 50)\n",
    "#     param['use_best_model'] = True\n",
    "#     param[\"colsample_bylevel\"] =  trial.suggest_float(\"colsample_bylevel\", 0.01, 0.1)\n",
    "#     param['random_state'] = 0\n",
    "    \n",
    "#     param['bootstrap_type'] = trial.suggest_categorical(\"bootstrap_type\", [\"Bayesian\", \"Bernoulli\", \"MVS\"])\n",
    "#     param['logging_level'] = 'Silent'\n",
    "    \n",
    "    param[\"learning_rate\"] = trial.suggest_float(\"learning_rate\", 1e-2, 0.25, log=True)\n",
    "#     param['reg_lambda'] = trial.suggest_loguniform(\"reg_lambda\", 1e-8, 100.0)\n",
    "#     param['reg_alpha'] = trial.suggest_loguniform(\"reg_alpha\", 1e-8, 100.0)\n",
    "    param['subsample'] = trial.suggest_float(\"subsample\", 0.1, 1.0)\n",
    "    param['colsample_bytree'] = trial.suggest_float(\"colsample_bytree\", 0.1, 1.0)\n",
    "    param['max_depth'] = trial.suggest_int(\"max_depth\", 5, 20)\n",
    "    param['scale_pos_weight'] = trial.suggest_int('scale_pos_weight', 3, 10)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    fold=StratifiedKFold(n_splits=5)#15#5#10\n",
    "    i=1\n",
    "    for train_index, test_index in fold.split(X,y):\n",
    "        \n",
    "        X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n",
    "        y_train, y_test = y.iloc[train_index], y.iloc[test_index]\n",
    "\n",
    "        model = LGBMClassifier(**param ,n_estimators=10000)\n",
    "\n",
    "    \n",
    "        model.fit(X_train,y_train,eval_set=[(X_train,y_train),(X_test, y_test)], early_stopping_rounds=300, verbose = False)#erly100\n",
    "        preds=model.predict(X_test)\n",
    "        oof_f1.append(f1_score(y_test,preds))\n",
    "\n",
    "    \n",
    "\n",
    "    return np.mean(oof_f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2022-03-13 01:29:22,758]\u001b[0m A new study created in memory with name: no-name-c187079d-4e9b-4a47-9f5b-e70a18af9a36\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:29:25,414]\u001b[0m Trial 0 finished with value: 0.5855883680713553 and parameters: {'learning_rate': 0.07281239404138323, 'subsample': 0.9470997713630432, 'colsample_bytree': 0.7157391749824287, 'max_depth': 8, 'scale_pos_weight': 6}. Best is trial 0 with value: 0.5855883680713553.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:29:27,139]\u001b[0m Trial 1 finished with value: 0.5917402673235419 and parameters: {'learning_rate': 0.18907949066322266, 'subsample': 0.9030314824852131, 'colsample_bytree': 0.5169486686263819, 'max_depth': 5, 'scale_pos_weight': 10}. Best is trial 1 with value: 0.5917402673235419.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:29:29,705]\u001b[0m Trial 2 finished with value: 0.5814360396884668 and parameters: {'learning_rate': 0.15843351491598617, 'subsample': 0.8268347533201383, 'colsample_bytree': 0.36313366826268656, 'max_depth': 15, 'scale_pos_weight': 7}. Best is trial 1 with value: 0.5917402673235419.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:29:36,157]\u001b[0m Trial 3 finished with value: 0.5266635711265294 and parameters: {'learning_rate': 0.02397333881127079, 'subsample': 0.337469321434311, 'colsample_bytree': 0.14742294443143628, 'max_depth': 10, 'scale_pos_weight': 4}. Best is trial 1 with value: 0.5917402673235419.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:29:42,898]\u001b[0m Trial 4 finished with value: 0.5892880281515259 and parameters: {'learning_rate': 0.010435834922377804, 'subsample': 0.6569042343731566, 'colsample_bytree': 0.36762241881736535, 'max_depth': 8, 'scale_pos_weight': 6}. Best is trial 1 with value: 0.5917402673235419.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:29:45,696]\u001b[0m Trial 5 finished with value: 0.5656925790259124 and parameters: {'learning_rate': 0.05274308109541711, 'subsample': 0.12092809857489317, 'colsample_bytree': 0.35820718878748603, 'max_depth': 11, 'scale_pos_weight': 3}. Best is trial 1 with value: 0.5917402673235419.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:29:52,414]\u001b[0m Trial 6 finished with value: 0.5871948211254477 and parameters: {'learning_rate': 0.01367061039665939, 'subsample': 0.4643486161829219, 'colsample_bytree': 0.34117370160563787, 'max_depth': 9, 'scale_pos_weight': 8}. Best is trial 1 with value: 0.5917402673235419.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:29:54,638]\u001b[0m Trial 7 finished with value: 0.5828060962846312 and parameters: {'learning_rate': 0.1595827181692645, 'subsample': 0.6745322806866705, 'colsample_bytree': 0.29324017947636793, 'max_depth': 7, 'scale_pos_weight': 5}. Best is trial 1 with value: 0.5917402673235419.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:29:59,571]\u001b[0m Trial 8 finished with value: 0.5861758802318677 and parameters: {'learning_rate': 0.016987375479860213, 'subsample': 0.17040897960511764, 'colsample_bytree': 0.5248476669216224, 'max_depth': 16, 'scale_pos_weight': 10}. Best is trial 1 with value: 0.5917402673235419.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:30:01,871]\u001b[0m Trial 9 finished with value: 0.5775365932284152 and parameters: {'learning_rate': 0.070319040403063, 'subsample': 0.20302503827403542, 'colsample_bytree': 0.3953869595456052, 'max_depth': 6, 'scale_pos_weight': 3}. Best is trial 1 with value: 0.5917402673235419.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:30:04,059]\u001b[0m Trial 10 finished with value: 0.6114051533520561 and parameters: {'learning_rate': 0.23703441733275088, 'subsample': 0.9719060445717111, 'colsample_bytree': 0.9175405877413312, 'max_depth': 20, 'scale_pos_weight': 10}. Best is trial 10 with value: 0.6114051533520561.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:30:06,176]\u001b[0m Trial 11 finished with value: 0.5924846931562472 and parameters: {'learning_rate': 0.23906607900385543, 'subsample': 0.9624208514837482, 'colsample_bytree': 0.9809323028224185, 'max_depth': 20, 'scale_pos_weight': 10}. Best is trial 10 with value: 0.6114051533520561.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:30:08,558]\u001b[0m Trial 12 finished with value: 0.6184226563558868 and parameters: {'learning_rate': 0.2391065076287467, 'subsample': 0.9991079983441716, 'colsample_bytree': 0.9982273420501954, 'max_depth': 20, 'scale_pos_weight': 9}. Best is trial 12 with value: 0.6184226563558868.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:30:12,030]\u001b[0m Trial 13 finished with value: 0.6035057377789825 and parameters: {'learning_rate': 0.10973612439968046, 'subsample': 0.7686819341561727, 'colsample_bytree': 0.9715287168381153, 'max_depth': 20, 'scale_pos_weight': 8}. Best is trial 12 with value: 0.6184226563558868.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:30:16,617]\u001b[0m Trial 14 finished with value: 0.6086120290641504 and parameters: {'learning_rate': 0.03121752328710432, 'subsample': 0.7992195620845265, 'colsample_bytree': 0.7878203095663705, 'max_depth': 17, 'scale_pos_weight': 9}. Best is trial 12 with value: 0.6184226563558868.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:30:19,195]\u001b[0m Trial 15 finished with value: 0.5889908218606592 and parameters: {'learning_rate': 0.11288930576534702, 'subsample': 0.997092017269006, 'colsample_bytree': 0.8282208500233121, 'max_depth': 18, 'scale_pos_weight': 9}. Best is trial 12 with value: 0.6184226563558868.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:30:20,911]\u001b[0m Trial 16 finished with value: 0.576511004887995 and parameters: {'learning_rate': 0.24403306311311948, 'subsample': 0.5296058673446989, 'colsample_bytree': 0.6528819534918485, 'max_depth': 13, 'scale_pos_weight': 8}. Best is trial 12 with value: 0.6184226563558868.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:30:23,617]\u001b[0m Trial 17 finished with value: 0.573850463385522 and parameters: {'learning_rate': 0.10554479271726015, 'subsample': 0.6591691135497282, 'colsample_bytree': 0.8679717933493661, 'max_depth': 14, 'scale_pos_weight': 9}. Best is trial 12 with value: 0.6184226563558868.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:30:27,312]\u001b[0m Trial 18 finished with value: 0.5882609819218223 and parameters: {'learning_rate': 0.03994113222654155, 'subsample': 0.8649219541564556, 'colsample_bytree': 0.9135001377691908, 'max_depth': 18, 'scale_pos_weight': 7}. Best is trial 12 with value: 0.6184226563558868.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:30:30,278]\u001b[0m Trial 19 finished with value: 0.5892162691319646 and parameters: {'learning_rate': 0.1389265409046068, 'subsample': 0.7591258634953542, 'colsample_bytree': 0.6413117895803957, 'max_depth': 19, 'scale_pos_weight': 9}. Best is trial 12 with value: 0.6184226563558868.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:30:33,236]\u001b[0m Trial 20 finished with value: 0.6077396359896092 and parameters: {'learning_rate': 0.07613520308855677, 'subsample': 0.39332225171975144, 'colsample_bytree': 0.7590657793271944, 'max_depth': 16, 'scale_pos_weight': 10}. Best is trial 12 with value: 0.6184226563558868.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:30:37,102]\u001b[0m Trial 21 finished with value: 0.6009650962744106 and parameters: {'learning_rate': 0.032096720833307356, 'subsample': 0.8234449026044102, 'colsample_bytree': 0.8069844607263361, 'max_depth': 18, 'scale_pos_weight': 9}. Best is trial 12 with value: 0.6184226563558868.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:30:41,254]\u001b[0m Trial 22 finished with value: 0.5968138817453338 and parameters: {'learning_rate': 0.025722530760705676, 'subsample': 0.9064856608105545, 'colsample_bytree': 0.9120200964857343, 'max_depth': 17, 'scale_pos_weight': 8}. Best is trial 12 with value: 0.6184226563558868.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:30:45,477]\u001b[0m Trial 23 finished with value: 0.6026126586101364 and parameters: {'learning_rate': 0.04577894801348491, 'subsample': 0.7623970849141292, 'colsample_bytree': 0.9112275781702639, 'max_depth': 20, 'scale_pos_weight': 9}. Best is trial 12 with value: 0.6184226563558868.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:30:47,719]\u001b[0m Trial 24 finished with value: 0.5861067099549901 and parameters: {'learning_rate': 0.20387526429852523, 'subsample': 0.9604292826354857, 'colsample_bytree': 0.979751917140251, 'max_depth': 19, 'scale_pos_weight': 10}. Best is trial 12 with value: 0.6184226563558868.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:30:52,213]\u001b[0m Trial 25 finished with value: 0.5926700732861008 and parameters: {'learning_rate': 0.021556806319660924, 'subsample': 0.9909494184562991, 'colsample_bytree': 0.7370774159505412, 'max_depth': 17, 'scale_pos_weight': 7}. Best is trial 12 with value: 0.6184226563558868.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:30:55,915]\u001b[0m Trial 26 finished with value: 0.5951633171714332 and parameters: {'learning_rate': 0.034541352047148303, 'subsample': 0.8640005215177823, 'colsample_bytree': 0.8184247217597066, 'max_depth': 19, 'scale_pos_weight': 9}. Best is trial 12 with value: 0.6184226563558868.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:30:58,965]\u001b[0m Trial 27 finished with value: 0.6014038212397763 and parameters: {'learning_rate': 0.08907151072299353, 'subsample': 0.5912005055827483, 'colsample_bytree': 0.6151095145936485, 'max_depth': 15, 'scale_pos_weight': 8}. Best is trial 12 with value: 0.6184226563558868.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:31:02,093]\u001b[0m Trial 28 finished with value: 0.5979209621993128 and parameters: {'learning_rate': 0.06012133954638788, 'subsample': 0.7300520230888582, 'colsample_bytree': 0.9994973592350606, 'max_depth': 12, 'scale_pos_weight': 10}. Best is trial 12 with value: 0.6184226563558868.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:31:04,276]\u001b[0m Trial 29 finished with value: 0.5842686130657034 and parameters: {'learning_rate': 0.1663568091650703, 'subsample': 0.9127779211411076, 'colsample_bytree': 0.7204322477648669, 'max_depth': 17, 'scale_pos_weight': 5}. Best is trial 12 with value: 0.6184226563558868.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:31:06,689]\u001b[0m Trial 30 finished with value: 0.608388162809534 and parameters: {'learning_rate': 0.12297883639540504, 'subsample': 0.8467344701016221, 'colsample_bytree': 0.9002240890479253, 'max_depth': 20, 'scale_pos_weight': 9}. Best is trial 12 with value: 0.6184226563558868.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:31:08,478]\u001b[0m Trial 31 finished with value: 0.5843487293431621 and parameters: {'learning_rate': 0.24913840315236085, 'subsample': 0.8427958116850514, 'colsample_bytree': 0.8744648422750889, 'max_depth': 20, 'scale_pos_weight': 9}. Best is trial 12 with value: 0.6184226563558868.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:31:10,380]\u001b[0m Trial 32 finished with value: 0.558798015763194 and parameters: {'learning_rate': 0.19890710679744486, 'subsample': 0.9258773608654627, 'colsample_bytree': 0.7860148375610723, 'max_depth': 19, 'scale_pos_weight': 10}. Best is trial 12 with value: 0.6184226563558868.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:31:12,717]\u001b[0m Trial 33 finished with value: 0.6202306058867416 and parameters: {'learning_rate': 0.13895156220108534, 'subsample': 0.8009268868261007, 'colsample_bytree': 0.8703625692753638, 'max_depth': 18, 'scale_pos_weight': 8}. Best is trial 33 with value: 0.6202306058867416.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:31:15,092]\u001b[0m Trial 34 finished with value: 0.575330568101187 and parameters: {'learning_rate': 0.1861991048603656, 'subsample': 0.8014000893712612, 'colsample_bytree': 0.6892412869271229, 'max_depth': 16, 'scale_pos_weight': 7}. Best is trial 33 with value: 0.6202306058867416.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:31:17,467]\u001b[0m Trial 35 finished with value: 0.5874778314611879 and parameters: {'learning_rate': 0.13589495631915807, 'subsample': 0.8904026985668775, 'colsample_bytree': 0.5756150029601196, 'max_depth': 18, 'scale_pos_weight': 8}. Best is trial 33 with value: 0.6202306058867416.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:31:19,483]\u001b[0m Trial 36 finished with value: 0.6025279001625213 and parameters: {'learning_rate': 0.20583891333962764, 'subsample': 0.9476533299034318, 'colsample_bytree': 0.9371399175536255, 'max_depth': 15, 'scale_pos_weight': 6}. Best is trial 33 with value: 0.6202306058867416.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:31:21,610]\u001b[0m Trial 37 finished with value: 0.5917915089430198 and parameters: {'learning_rate': 0.15481586832832478, 'subsample': 0.7066877640431114, 'colsample_bytree': 0.8548912309026588, 'max_depth': 17, 'scale_pos_weight': 7}. Best is trial 33 with value: 0.6202306058867416.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:31:23,698]\u001b[0m Trial 38 finished with value: 0.572848839209664 and parameters: {'learning_rate': 0.1785027686043549, 'subsample': 0.7988117273863862, 'colsample_bytree': 0.44732499504599604, 'max_depth': 19, 'scale_pos_weight': 8}. Best is trial 33 with value: 0.6202306058867416.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:31:27,616]\u001b[0m Trial 39 finished with value: 0.5817089845824769 and parameters: {'learning_rate': 0.028044829510415935, 'subsample': 0.5723779199502464, 'colsample_bytree': 0.9509641107858953, 'max_depth': 14, 'scale_pos_weight': 10}. Best is trial 33 with value: 0.6202306058867416.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:31:32,243]\u001b[0m Trial 40 finished with value: 0.5746147469551726 and parameters: {'learning_rate': 0.04958853703813348, 'subsample': 0.501719575508558, 'colsample_bytree': 0.2230880711837705, 'max_depth': 18, 'scale_pos_weight': 9}. Best is trial 33 with value: 0.6202306058867416.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:31:34,940]\u001b[0m Trial 41 finished with value: 0.5750929154888594 and parameters: {'learning_rate': 0.13355470149094512, 'subsample': 0.6117370439027994, 'colsample_bytree': 0.8883951748888778, 'max_depth': 20, 'scale_pos_weight': 9}. Best is trial 33 with value: 0.6202306058867416.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:31:37,879]\u001b[0m Trial 42 finished with value: 0.6100364805014339 and parameters: {'learning_rate': 0.08687310595145703, 'subsample': 0.8669160004545376, 'colsample_bytree': 0.8490194688545102, 'max_depth': 20, 'scale_pos_weight': 10}. Best is trial 33 with value: 0.6202306058867416.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:31:40,813]\u001b[0m Trial 43 finished with value: 0.5960102218800672 and parameters: {'learning_rate': 0.08242188971289052, 'subsample': 0.8918944074084938, 'colsample_bytree': 0.7837498525712562, 'max_depth': 19, 'scale_pos_weight': 10}. Best is trial 33 with value: 0.6202306058867416.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:31:43,807]\u001b[0m Trial 44 finished with value: 0.585188574357711 and parameters: {'learning_rate': 0.06607147291941971, 'subsample': 0.9635594725951707, 'colsample_bytree': 0.8243070518211086, 'max_depth': 20, 'scale_pos_weight': 10}. Best is trial 33 with value: 0.6202306058867416.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:31:46,800]\u001b[0m Trial 45 finished with value: 0.6051194714895203 and parameters: {'learning_rate': 0.09086959441063018, 'subsample': 0.9945462431928641, 'colsample_bytree': 0.9455576091944407, 'max_depth': 18, 'scale_pos_weight': 10}. Best is trial 33 with value: 0.6202306058867416.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:31:53,589]\u001b[0m Trial 46 finished with value: 0.5299630761637101 and parameters: {'learning_rate': 0.016913612554219064, 'subsample': 0.7069763694569811, 'colsample_bytree': 0.11921220606758698, 'max_depth': 9, 'scale_pos_weight': 5}. Best is trial 33 with value: 0.6202306058867416.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:31:55,364]\u001b[0m Trial 47 finished with value: 0.5971289174901712 and parameters: {'learning_rate': 0.21757808290414096, 'subsample': 0.2526842002171391, 'colsample_bytree': 0.85044980419812, 'max_depth': 5, 'scale_pos_weight': 8}. Best is trial 33 with value: 0.6202306058867416.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:31:58,275]\u001b[0m Trial 48 finished with value: 0.6041500292174751 and parameters: {'learning_rate': 0.09655471721296449, 'subsample': 0.8013263947270669, 'colsample_bytree': 0.9395511973453787, 'max_depth': 19, 'scale_pos_weight': 9}. Best is trial 33 with value: 0.6202306058867416.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:32:00,626]\u001b[0m Trial 49 finished with value: 0.5724823074635742 and parameters: {'learning_rate': 0.15610511527767168, 'subsample': 0.9392420081631231, 'colsample_bytree': 0.7575832128494855, 'max_depth': 16, 'scale_pos_weight': 10}. Best is trial 33 with value: 0.6202306058867416.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:32:04,629]\u001b[0m Trial 50 finished with value: 0.5856180360304071 and parameters: {'learning_rate': 0.059320542879626355, 'subsample': 0.8821863098881706, 'colsample_bytree': 0.6841553305999558, 'max_depth': 11, 'scale_pos_weight': 4}. Best is trial 33 with value: 0.6202306058867416.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:32:07,846]\u001b[0m Trial 51 finished with value: 0.6142959490857927 and parameters: {'learning_rate': 0.11800390980761749, 'subsample': 0.8400112855477099, 'colsample_bytree': 0.8986090688217667, 'max_depth': 20, 'scale_pos_weight': 9}. Best is trial 33 with value: 0.6202306058867416.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:32:11,011]\u001b[0m Trial 52 finished with value: 0.614827503642993 and parameters: {'learning_rate': 0.12003253003453296, 'subsample': 0.8148543464275224, 'colsample_bytree': 0.849868982018811, 'max_depth': 20, 'scale_pos_weight': 9}. Best is trial 33 with value: 0.6202306058867416.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:32:14,077]\u001b[0m Trial 53 finished with value: 0.5962140022732257 and parameters: {'learning_rate': 0.1216221263367133, 'subsample': 0.8403030361185841, 'colsample_bytree': 0.9939153913037628, 'max_depth': 20, 'scale_pos_weight': 9}. Best is trial 33 with value: 0.6202306058867416.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:32:17,301]\u001b[0m Trial 54 finished with value: 0.6258987085247679 and parameters: {'learning_rate': 0.10710006520631823, 'subsample': 0.7434475633453672, 'colsample_bytree': 0.8498991447226938, 'max_depth': 20, 'scale_pos_weight': 10}. Best is trial 54 with value: 0.6258987085247679.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:32:20,518]\u001b[0m Trial 55 finished with value: 0.5793774660672459 and parameters: {'learning_rate': 0.1050707281476422, 'subsample': 0.636357450848321, 'colsample_bytree': 0.8871180221266088, 'max_depth': 19, 'scale_pos_weight': 9}. Best is trial 54 with value: 0.6258987085247679.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:32:23,518]\u001b[0m Trial 56 finished with value: 0.6093929879644167 and parameters: {'learning_rate': 0.14636712430650875, 'subsample': 0.733949079410348, 'colsample_bytree': 0.9585310225772345, 'max_depth': 18, 'scale_pos_weight': 8}. Best is trial 54 with value: 0.6258987085247679.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:32:25,930]\u001b[0m Trial 57 finished with value: 0.6012447639753837 and parameters: {'learning_rate': 0.2223196032409012, 'subsample': 0.6817103796670269, 'colsample_bytree': 0.9204488233513963, 'max_depth': 20, 'scale_pos_weight': 10}. Best is trial 54 with value: 0.6258987085247679.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:32:28,389]\u001b[0m Trial 58 finished with value: 0.5974647288195676 and parameters: {'learning_rate': 0.17516030223645349, 'subsample': 0.7681710612959087, 'colsample_bytree': 0.4483932924665858, 'max_depth': 19, 'scale_pos_weight': 9}. Best is trial 54 with value: 0.6258987085247679.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:32:31,200]\u001b[0m Trial 59 finished with value: 0.5968958260865842 and parameters: {'learning_rate': 0.12134216883733417, 'subsample': 0.8207359229160769, 'colsample_bytree': 0.8460232571864114, 'max_depth': 20, 'scale_pos_weight': 8}. Best is trial 54 with value: 0.6258987085247679.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:32:34,542]\u001b[0m Trial 60 finished with value: 0.5886819018342552 and parameters: {'learning_rate': 0.10046249830697195, 'subsample': 0.40771843342464476, 'colsample_bytree': 0.8062693827604459, 'max_depth': 19, 'scale_pos_weight': 9}. Best is trial 54 with value: 0.6258987085247679.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:32:37,898]\u001b[0m Trial 61 finished with value: 0.5948577675393529 and parameters: {'learning_rate': 0.08061890905524723, 'subsample': 0.8669479048919113, 'colsample_bytree': 0.855307653612137, 'max_depth': 20, 'scale_pos_weight': 10}. Best is trial 54 with value: 0.6258987085247679.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:32:41,462]\u001b[0m Trial 62 finished with value: 0.5901056454220958 and parameters: {'learning_rate': 0.12078221120803347, 'subsample': 0.9710313606914935, 'colsample_bytree': 0.7556377854358991, 'max_depth': 20, 'scale_pos_weight': 10}. Best is trial 54 with value: 0.6258987085247679.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:32:45,081]\u001b[0m Trial 63 finished with value: 0.6029175021641746 and parameters: {'learning_rate': 0.11035638677388482, 'subsample': 0.9093105053936397, 'colsample_bytree': 0.8874748001262993, 'max_depth': 18, 'scale_pos_weight': 10}. Best is trial 54 with value: 0.6258987085247679.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:32:48,176]\u001b[0m Trial 64 finished with value: 0.5910260403775551 and parameters: {'learning_rate': 0.13676512160577214, 'subsample': 0.936468404703175, 'colsample_bytree': 0.9652400716550058, 'max_depth': 19, 'scale_pos_weight': 10}. Best is trial 54 with value: 0.6258987085247679.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:32:52,328]\u001b[0m Trial 65 finished with value: 0.5974259064153505 and parameters: {'learning_rate': 0.07192655911141925, 'subsample': 0.7802082689570649, 'colsample_bytree': 0.9243245035151777, 'max_depth': 20, 'scale_pos_weight': 9}. Best is trial 54 with value: 0.6258987085247679.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:32:54,646]\u001b[0m Trial 66 finished with value: 0.5744669505408587 and parameters: {'learning_rate': 0.22530831277713978, 'subsample': 0.7502701164504073, 'colsample_bytree': 0.8315797595379264, 'max_depth': 18, 'scale_pos_weight': 10}. Best is trial 54 with value: 0.6258987085247679.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:32:57,038]\u001b[0m Trial 67 finished with value: 0.6057574604372736 and parameters: {'learning_rate': 0.16534825551608165, 'subsample': 0.8587817385310617, 'colsample_bytree': 0.7956891114747157, 'max_depth': 19, 'scale_pos_weight': 9}. Best is trial 54 with value: 0.6258987085247679.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:33:00,095]\u001b[0m Trial 68 finished with value: 0.603789981496167 and parameters: {'learning_rate': 0.09098151255361436, 'subsample': 0.8267034600662025, 'colsample_bytree': 0.8762408570821116, 'max_depth': 20, 'scale_pos_weight': 10}. Best is trial 54 with value: 0.6258987085247679.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:33:07,455]\u001b[0m Trial 69 finished with value: 0.5906743705127426 and parameters: {'learning_rate': 0.010087547848704925, 'subsample': 0.9358577287879074, 'colsample_bytree': 0.984731189492835, 'max_depth': 17, 'scale_pos_weight': 8}. Best is trial 54 with value: 0.6258987085247679.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:33:10,471]\u001b[0m Trial 70 finished with value: 0.5911750704030762 and parameters: {'learning_rate': 0.1491072034432331, 'subsample': 0.9721239604231718, 'colsample_bytree': 0.9097800318844435, 'max_depth': 19, 'scale_pos_weight': 9}. Best is trial 54 with value: 0.6258987085247679.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:33:12,667]\u001b[0m Trial 71 finished with value: 0.6125670112351578 and parameters: {'learning_rate': 0.19222565323677876, 'subsample': 0.738483888057043, 'colsample_bytree': 0.9532359410642574, 'max_depth': 18, 'scale_pos_weight': 8}. Best is trial 54 with value: 0.6258987085247679.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:33:14,989]\u001b[0m Trial 72 finished with value: 0.613639678926866 and parameters: {'learning_rate': 0.17982617036749726, 'subsample': 0.7037459090670083, 'colsample_bytree': 0.9736063544311836, 'max_depth': 20, 'scale_pos_weight': 7}. Best is trial 54 with value: 0.6258987085247679.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:33:16,955]\u001b[0m Trial 73 finished with value: 0.5830482008852179 and parameters: {'learning_rate': 0.1853640046911035, 'subsample': 0.6958190936574045, 'colsample_bytree': 0.9700623725287943, 'max_depth': 18, 'scale_pos_weight': 7}. Best is trial 54 with value: 0.6258987085247679.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:33:18,721]\u001b[0m Trial 74 finished with value: 0.5870889517837521 and parameters: {'learning_rate': 0.24737577411089687, 'subsample': 0.7469194138768882, 'colsample_bytree': 0.9991064767005207, 'max_depth': 19, 'scale_pos_weight': 6}. Best is trial 54 with value: 0.6258987085247679.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:33:20,617]\u001b[0m Trial 75 finished with value: 0.5897512287545548 and parameters: {'learning_rate': 0.20213013234613805, 'subsample': 0.6535680667201071, 'colsample_bytree': 0.9293442507063964, 'max_depth': 16, 'scale_pos_weight': 7}. Best is trial 54 with value: 0.6258987085247679.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:33:23,200]\u001b[0m Trial 76 finished with value: 0.5860539224257311 and parameters: {'learning_rate': 0.16988588461602436, 'subsample': 0.7821361524807143, 'colsample_bytree': 0.9585592462233425, 'max_depth': 17, 'scale_pos_weight': 8}. Best is trial 54 with value: 0.6258987085247679.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:33:25,321]\u001b[0m Trial 77 finished with value: 0.5889362106842222 and parameters: {'learning_rate': 0.1867099157615321, 'subsample': 0.7193285563981859, 'colsample_bytree': 0.8924377142497063, 'max_depth': 20, 'scale_pos_weight': 7}. Best is trial 54 with value: 0.6258987085247679.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:33:27,181]\u001b[0m Trial 78 finished with value: 0.5840383116133366 and parameters: {'learning_rate': 0.2268773136566486, 'subsample': 0.6694801727718415, 'colsample_bytree': 0.9076679421851975, 'max_depth': 7, 'scale_pos_weight': 8}. Best is trial 54 with value: 0.6258987085247679.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:33:29,171]\u001b[0m Trial 79 finished with value: 0.5883584421742317 and parameters: {'learning_rate': 0.20420774470925532, 'subsample': 0.817739816991099, 'colsample_bytree': 0.938801120051906, 'max_depth': 18, 'scale_pos_weight': 7}. Best is trial 54 with value: 0.6258987085247679.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:33:31,584]\u001b[0m Trial 80 finished with value: 0.5870653367088341 and parameters: {'learning_rate': 0.1358127841442942, 'subsample': 0.6188215955578039, 'colsample_bytree': 0.8693174789062423, 'max_depth': 20, 'scale_pos_weight': 8}. Best is trial 54 with value: 0.6258987085247679.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:33:34,089]\u001b[0m Trial 81 finished with value: 0.6052137470108303 and parameters: {'learning_rate': 0.12603301113240606, 'subsample': 0.876209428115436, 'colsample_bytree': 0.8232876223932765, 'max_depth': 20, 'scale_pos_weight': 9}. Best is trial 54 with value: 0.6258987085247679.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:33:36,658]\u001b[0m Trial 82 finished with value: 0.5783545103317909 and parameters: {'learning_rate': 0.11449531163587282, 'subsample': 0.9995215329450237, 'colsample_bytree': 0.8462499425883476, 'max_depth': 19, 'scale_pos_weight': 6}. Best is trial 54 with value: 0.6258987085247679.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:33:39,613]\u001b[0m Trial 83 finished with value: 0.6010959876898732 and parameters: {'learning_rate': 0.14798465727437976, 'subsample': 0.7889924501820151, 'colsample_bytree': 0.9687558994186274, 'max_depth': 20, 'scale_pos_weight': 9}. Best is trial 54 with value: 0.6258987085247679.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:33:42,531]\u001b[0m Trial 84 finished with value: 0.5712632948286862 and parameters: {'learning_rate': 0.16399274284693607, 'subsample': 0.9172652555095837, 'colsample_bytree': 0.28018710538898073, 'max_depth': 19, 'scale_pos_weight': 10}. Best is trial 54 with value: 0.6258987085247679.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:33:45,429]\u001b[0m Trial 85 finished with value: 0.5784272809872081 and parameters: {'learning_rate': 0.1912007309874467, 'subsample': 0.7453042504732208, 'colsample_bytree': 0.7836879856920103, 'max_depth': 14, 'scale_pos_weight': 8}. Best is trial 54 with value: 0.6258987085247679.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:33:48,960]\u001b[0m Trial 86 finished with value: 0.5907568395898337 and parameters: {'learning_rate': 0.10039776870607547, 'subsample': 0.8447392807182801, 'colsample_bytree': 0.8679475472248416, 'max_depth': 20, 'scale_pos_weight': 10}. Best is trial 54 with value: 0.6258987085247679.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:33:51,224]\u001b[0m Trial 87 finished with value: 0.5756033929350125 and parameters: {'learning_rate': 0.21486331306886786, 'subsample': 0.892477189610694, 'colsample_bytree': 0.904702501332489, 'max_depth': 19, 'scale_pos_weight': 9}. Best is trial 54 with value: 0.6258987085247679.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:33:53,683]\u001b[0m Trial 88 finished with value: 0.5987748653955688 and parameters: {'learning_rate': 0.22998548853468914, 'subsample': 0.8107784882465212, 'colsample_bytree': 0.94574881576473, 'max_depth': 20, 'scale_pos_weight': 9}. Best is trial 54 with value: 0.6258987085247679.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:33:57,221]\u001b[0m Trial 89 finished with value: 0.6011905883250478 and parameters: {'learning_rate': 0.08294481679577616, 'subsample': 0.7188756399409366, 'colsample_bytree': 0.982541624561136, 'max_depth': 18, 'scale_pos_weight': 8}. Best is trial 54 with value: 0.6258987085247679.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:33:59,948]\u001b[0m Trial 90 finished with value: 0.5780507647163572 and parameters: {'learning_rate': 0.1321808441976955, 'subsample': 0.7672693537427915, 'colsample_bytree': 0.7177814364397971, 'max_depth': 19, 'scale_pos_weight': 7}. Best is trial 54 with value: 0.6258987085247679.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:34:07,039]\u001b[0m Trial 91 finished with value: 0.606076828957401 and parameters: {'learning_rate': 0.01173418522002688, 'subsample': 0.7332822230923691, 'colsample_bytree': 0.9543003217347849, 'max_depth': 17, 'scale_pos_weight': 8}. Best is trial 54 with value: 0.6258987085247679.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:34:09,906]\u001b[0m Trial 92 finished with value: 0.6120371037112629 and parameters: {'learning_rate': 0.1454906585840168, 'subsample': 0.6850504866478555, 'colsample_bytree': 0.9265801957760087, 'max_depth': 18, 'scale_pos_weight': 8}. Best is trial 54 with value: 0.6258987085247679.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:34:12,699]\u001b[0m Trial 93 finished with value: 0.5973242299978092 and parameters: {'learning_rate': 0.1135896430646188, 'subsample': 0.6946295316845783, 'colsample_bytree': 0.9227175967854889, 'max_depth': 20, 'scale_pos_weight': 8}. Best is trial 54 with value: 0.6258987085247679.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:34:14,869]\u001b[0m Trial 94 finished with value: 0.5939045543676914 and parameters: {'learning_rate': 0.1567140244708644, 'subsample': 0.5508143604740139, 'colsample_bytree': 0.8387604011574974, 'max_depth': 13, 'scale_pos_weight': 10}. Best is trial 54 with value: 0.6258987085247679.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:34:16,901]\u001b[0m Trial 95 finished with value: 0.5912634376768116 and parameters: {'learning_rate': 0.1792032954871495, 'subsample': 0.11677816562849685, 'colsample_bytree': 0.861604183693838, 'max_depth': 18, 'scale_pos_weight': 9}. Best is trial 54 with value: 0.6258987085247679.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:34:19,178]\u001b[0m Trial 96 finished with value: 0.6031837574070137 and parameters: {'learning_rate': 0.14226383773512313, 'subsample': 0.8471229920322192, 'colsample_bytree': 0.8874203311259348, 'max_depth': 19, 'scale_pos_weight': 8}. Best is trial 54 with value: 0.6258987085247679.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:34:21,726]\u001b[0m Trial 97 finished with value: 0.5728803349409826 and parameters: {'learning_rate': 0.10649928783857468, 'subsample': 0.6524005071798873, 'colsample_bytree': 0.5629928088998172, 'max_depth': 20, 'scale_pos_weight': 7}. Best is trial 54 with value: 0.6258987085247679.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:34:23,497]\u001b[0m Trial 98 finished with value: 0.5941636722745759 and parameters: {'learning_rate': 0.24946938940100638, 'subsample': 0.7881131685435409, 'colsample_bytree': 0.934409351290752, 'max_depth': 15, 'scale_pos_weight': 10}. Best is trial 54 with value: 0.6258987085247679.\u001b[0m\n",
      "\u001b[32m[I 2022-03-13 01:34:26,037]\u001b[0m Trial 99 finished with value: 0.5920928001120953 and parameters: {'learning_rate': 0.12607919638368906, 'subsample': 0.8305528384784538, 'colsample_bytree': 0.8087949169998349, 'max_depth': 19, 'scale_pos_weight': 3}. Best is trial 54 with value: 0.6258987085247679.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "study = optuna.create_study(direction=\"maximize\")\n",
    "study.optimize(objective, n_trials=100) #10000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of finished trials: 100\n",
      "Best trial:\n",
      "  Value: 0.6258987085247679\n",
      "  Params: \n",
      "    learning_rate: 0.10710006520631823\n",
      "    subsample: 0.7434475633453672\n",
      "    colsample_bytree: 0.8498991447226938\n",
      "    max_depth: 20\n",
      "    scale_pos_weight: 10\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of finished trials: {}\".format(len(study.trials)))\n",
    "\n",
    "print(\"Best trial:\")\n",
    "trial = study.best_trial\n",
    "\n",
    "print(\"  Value: {}\".format(trial.value))\n",
    "\n",
    "print(\"  Params: \")\n",
    "for key, value in trial.params.items():\n",
    "    print(\"    {}: {}\".format(key, value))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'learning_rate': 0.10710006520631823,\n",
       " 'subsample': 0.7434475633453672,\n",
       " 'colsample_bytree': 0.8498991447226938,\n",
       " 'max_depth': 20,\n",
       " 'scale_pos_weight': 10}"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trial.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of finished trials: 100\n",
      "Best trial:\n",
      "  Value: 0.6258987085247679\n",
      "  Params: \n",
      "    learning_rate: 0.10710006520631823\n",
      "    subsample: 0.7434475633453672\n",
      "    colsample_bytree: 0.8498991447226938\n",
      "    max_depth: 20\n",
      "    scale_pos_weight: 10\n",
      "err:  0.7666666666666667\n",
      "err:  0.912529550827423\n",
      "err:  0.9411764705882353\n",
      "err:  0.9195402298850576\n",
      "err:  0.9408983451536643\n",
      "0.8961622526242093\n"
     ]
    }
   ],
   "source": [
    "oof_f1=[]\n",
    "lgb_oof_predictions=[]\n",
    "\n",
    "\n",
    "param = trial.params \n",
    "print(\"Number of finished trials: {}\".format(len(study.trials)))\n",
    "\n",
    "print(\"Best trial:\")\n",
    "trial = study.best_trial\n",
    "\n",
    "print(\"  Value: {}\".format(trial.value))\n",
    "\n",
    "print(\"  Params: \")\n",
    "for key, value in trial.params.items():\n",
    "    print(\"    {}: {}\".format(key, value))\n",
    "\n",
    "\n",
    "\n",
    "fold=StratifiedKFold(n_splits=5)#15#5#10\n",
    "i=1\n",
    "\n",
    "for train_index, test_index in fold.split(X_,y_):\n",
    "        \n",
    "    X_train, X_test = X_.iloc[train_index], X_.iloc[test_index]\n",
    "    y_train, y_test = y_.iloc[train_index], y_.iloc[test_index]\n",
    "    \n",
    "    model = LGBMClassifier(**param ,n_estimators=10000)\n",
    "\n",
    "    model.fit(X_train,y_train,eval_set=[(X_train,y_train),(X_test, y_test)], early_stopping_rounds=500, verbose = False)#erly100\n",
    "    preds=model.predict(X_test)\n",
    "    print(\"err: \",f1_score(y_test,preds))\n",
    "    oof_f1.append(f1_score(y_test,preds))\n",
    "    p2 = model.predict_proba(test_df[X_.columns])[:, -1]\n",
    "    lgb_oof_predictions.append(p2)\n",
    "    \n",
    "print(np.mean(oof_f1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_lgb = pd.DataFrame(lgb_oof_predictions).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_cat = pd.DataFrame(oof_predictions).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.014229</td>\n",
       "      <td>0.002802</td>\n",
       "      <td>0.000092</td>\n",
       "      <td>0.001028</td>\n",
       "      <td>1.608459e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.569537</td>\n",
       "      <td>0.898509</td>\n",
       "      <td>0.965617</td>\n",
       "      <td>0.937945</td>\n",
       "      <td>8.589057e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.003036</td>\n",
       "      <td>0.001038</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000431</td>\n",
       "      <td>5.255449e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.073400</td>\n",
       "      <td>0.292327</td>\n",
       "      <td>0.487805</td>\n",
       "      <td>0.659851</td>\n",
       "      <td>4.094120e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.078206</td>\n",
       "      <td>0.003957</td>\n",
       "      <td>0.001096</td>\n",
       "      <td>0.007918</td>\n",
       "      <td>1.530104e-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3             4\n",
       "0  0.014229  0.002802  0.000092  0.001028  1.608459e-05\n",
       "1  0.569537  0.898509  0.965617  0.937945  8.589057e-01\n",
       "2  0.003036  0.001038  0.000006  0.000431  5.255449e-07\n",
       "3  0.073400  0.292327  0.487805  0.659851  4.094120e-01\n",
       "4  0.078206  0.003957  0.001096  0.007918  1.530104e-03"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_lgb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_pred = pred_lgb.join(pred_cat, lsuffix=\"_lgb\", rsuffix=\"_cat\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "\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>0_lgb</th>\n",
       "      <th>1_lgb</th>\n",
       "      <th>2_lgb</th>\n",
       "      <th>3_lgb</th>\n",
       "      <th>4_lgb</th>\n",
       "      <th>0_cat</th>\n",
       "      <th>1_cat</th>\n",
       "      <th>2_cat</th>\n",
       "      <th>3_cat</th>\n",
       "      <th>4_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.014229</td>\n",
       "      <td>0.002802</td>\n",
       "      <td>0.000092</td>\n",
       "      <td>0.001028</td>\n",
       "      <td>1.608459e-05</td>\n",
       "      <td>0.082831</td>\n",
       "      <td>0.004536</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>0.005147</td>\n",
       "      <td>0.007226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.569537</td>\n",
       "      <td>0.898509</td>\n",
       "      <td>0.965617</td>\n",
       "      <td>0.937945</td>\n",
       "      <td>8.589057e-01</td>\n",
       "      <td>0.214366</td>\n",
       "      <td>0.661427</td>\n",
       "      <td>0.150492</td>\n",
       "      <td>0.710808</td>\n",
       "      <td>0.387974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.003036</td>\n",
       "      <td>0.001038</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000431</td>\n",
       "      <td>5.255449e-07</td>\n",
       "      <td>0.025720</td>\n",
       "      <td>0.005377</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.001209</td>\n",
       "      <td>0.000486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.073400</td>\n",
       "      <td>0.292327</td>\n",
       "      <td>0.487805</td>\n",
       "      <td>0.659851</td>\n",
       "      <td>4.094120e-01</td>\n",
       "      <td>0.113783</td>\n",
       "      <td>0.408847</td>\n",
       "      <td>0.284758</td>\n",
       "      <td>0.025756</td>\n",
       "      <td>0.243567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.078206</td>\n",
       "      <td>0.003957</td>\n",
       "      <td>0.001096</td>\n",
       "      <td>0.007918</td>\n",
       "      <td>1.530104e-03</td>\n",
       "      <td>0.021039</td>\n",
       "      <td>0.017409</td>\n",
       "      <td>0.001490</td>\n",
       "      <td>0.006232</td>\n",
       "      <td>0.058747</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      0_lgb     1_lgb     2_lgb     3_lgb         4_lgb     0_cat     1_cat  \\\n",
       "0  0.014229  0.002802  0.000092  0.001028  1.608459e-05  0.082831  0.004536   \n",
       "1  0.569537  0.898509  0.965617  0.937945  8.589057e-01  0.214366  0.661427   \n",
       "2  0.003036  0.001038  0.000006  0.000431  5.255449e-07  0.025720  0.005377   \n",
       "3  0.073400  0.292327  0.487805  0.659851  4.094120e-01  0.113783  0.408847   \n",
       "4  0.078206  0.003957  0.001096  0.007918  1.530104e-03  0.021039  0.017409   \n",
       "\n",
       "      2_cat     3_cat     4_cat  \n",
       "0  0.000040  0.005147  0.007226  \n",
       "1  0.150492  0.710808  0.387974  \n",
       "2  0.000002  0.001209  0.000486  \n",
       "3  0.284758  0.025756  0.243567  \n",
       "4  0.001490  0.006232  0.058747  "
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_pred.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_pred = all_pred.mean(axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = np.where(mean_pred > 0.45, 1 , 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "dtype: int32"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(pred).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub = pd.read_csv('SampleSubmission.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub['Response'] = pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub.to_csv('submit_lgb_cat_5fold.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
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