Churn Date Prediction Final.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import xgboost as xgb\n",
    "from sklearn import preprocessing\n",
    "import pickle\n",
    "from sklearn.metrics import mean_squared_error, accuracy_score, confusion_matrix, classification_report, auc, roc_curve, f1_score\n",
    "from sklearn.model_selection import cross_val_score, RandomizedSearchCV,GridSearchCV\n",
    "from scipy import stats\n",
    "from scipy.stats import norm\n",
    "from statsmodels.formula.api import ols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('/Users/naitochieri/Desktop/Hackathon/Datasets/combined.csv', dtype = object)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th>account_id_x</th>\n",
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       "      <th>client_country</th>\n",
       "      <th>unique_client</th>\n",
       "      <th>client_continent</th>\n",
       "      <th>...</th>\n",
       "      <th>app</th>\n",
       "      <th>web</th>\n",
       "      <th>awareness</th>\n",
       "      <th>consideration</th>\n",
       "      <th>conversion</th>\n",
       "      <th>account_id_y</th>\n",
       "      <th>products</th>\n",
       "      <th>account_id</th>\n",
       "      <th>onsite_sales</th>\n",
       "      <th>onsite_revenue_euro</th>\n",
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       "      <td>ga_bc66d247-ae5d-e311-a5f9-005056bb7e02_RUSSIA...</td>\n",
       "      <td>EMEA</td>\n",
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       "      <td>1</td>\n",
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       "      <td>70</td>\n",
       "      <td>ga_bc784dcb-17fe-e311-a3df-005056bb7e02</td>\n",
       "      <td>RETAIL</td>\n",
       "      <td>Gifts &amp; Flowers</td>\n",
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       "      <td>AMERICAS</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>1966.0</td>\n",
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       "      <td>1966.0</td>\n",
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       "      <td>183438.72</td>\n",
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       "      <td>2</td>\n",
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       "      <td>96</td>\n",
       "      <td>ga_bc784dcb-17fe-e311-a3df-005056bb7e02</td>\n",
       "      <td>RETAIL</td>\n",
       "      <td>Gifts &amp; Flowers</td>\n",
       "      <td>UNITED KINGDOM</td>\n",
       "      <td>ga_bc784dcb-17fe-e311-a3df-005056bb7e02_UNITED...</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1377.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1377.0</td>\n",
       "      <td>ga_a512e5f2-b196-e411-b0d2-005056bb7e02</td>\n",
       "      <td>13958.0</td>\n",
       "      <td>ga_bc66d247-ae5d-e311-a5f9-005056bb7e02</td>\n",
       "      <td>21107.0</td>\n",
       "      <td>3858810.09</td>\n",
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       "      <td>ga_bc784dcb-17fe-e311-a3df-005056bb7e02</td>\n",
       "      <td>RETAIL</td>\n",
       "      <td>Gifts &amp; Flowers</td>\n",
       "      <td>UNITED STATES</td>\n",
       "      <td>ga_bc784dcb-17fe-e311-a3df-005056bb7e02_UNITED...</td>\n",
       "      <td>AMERICAS</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>82.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>82.0</td>\n",
       "      <td>ga_a512e5f2-b196-e411-b0d2-005056bb7e02</td>\n",
       "      <td>4990.0</td>\n",
       "      <td>ga_bc66d247-ae5d-e311-a5f9-005056bb7e02</td>\n",
       "      <td>82.0</td>\n",
       "      <td>11705.52</td>\n",
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       "      <td>4</td>\n",
       "      <td>4</td>\n",
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       "      <td>376</td>\n",
       "      <td>ga_8fff21b1-4fad-e511-9606-005056bb2344</td>\n",
       "      <td>RETAIL</td>\n",
       "      <td>Accessories / Jewelry / Luxury Goods</td>\n",
       "      <td>RUSSIAN FEDERATION</td>\n",
       "      <td>ga_8fff21b1-4fad-e511-9606-005056bb2344_RUSSIA...</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>74.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>74.0</td>\n",
       "      <td>ga_a512e5f2-b196-e411-b0d2-005056bb7e02</td>\n",
       "      <td>5147.0</td>\n",
       "      <td>ga_bc66d247-ae5d-e311-a5f9-005056bb7e02</td>\n",
       "      <td>686.0</td>\n",
       "      <td>87426.68</td>\n",
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       "  Unnamed: 0 index Unnamed: 0.1 Unnamed: 0.1.1  \\\n",
       "0          0     0            0              0   \n",
       "1          1     1           70             70   \n",
       "2          2     2           96             96   \n",
       "3          3     3          136            136   \n",
       "4          4     4          376            376   \n",
       "\n",
       "                              account_id_x vertical  \\\n",
       "0  ga_bc66d247-ae5d-e311-a5f9-005056bb7e02   RETAIL   \n",
       "1  ga_bc784dcb-17fe-e311-a3df-005056bb7e02   RETAIL   \n",
       "2  ga_bc784dcb-17fe-e311-a3df-005056bb7e02   RETAIL   \n",
       "3  ga_bc784dcb-17fe-e311-a3df-005056bb7e02   RETAIL   \n",
       "4  ga_8fff21b1-4fad-e511-9606-005056bb2344   RETAIL   \n",
       "\n",
       "                               industry      client_country  \\\n",
       "0               Electronics / High Tech  RUSSIAN FEDERATION   \n",
       "1                       Gifts & Flowers              CANADA   \n",
       "2                       Gifts & Flowers      UNITED KINGDOM   \n",
       "3                       Gifts & Flowers       UNITED STATES   \n",
       "4  Accessories / Jewelry / Luxury Goods  RUSSIAN FEDERATION   \n",
       "\n",
       "                                       unique_client client_continent  ...  \\\n",
       "0  ga_bc66d247-ae5d-e311-a5f9-005056bb7e02_RUSSIA...             EMEA  ...   \n",
       "1     ga_bc784dcb-17fe-e311-a3df-005056bb7e02_CANADA         AMERICAS  ...   \n",
       "2  ga_bc784dcb-17fe-e311-a3df-005056bb7e02_UNITED...             EMEA  ...   \n",
       "3  ga_bc784dcb-17fe-e311-a3df-005056bb7e02_UNITED...         AMERICAS  ...   \n",
       "4  ga_8fff21b1-4fad-e511-9606-005056bb2344_RUSSIA...             EMEA  ...   \n",
       "\n",
       "   app     web awareness consideration conversion  \\\n",
       "0  NaN  1030.0       NaN           NaN     1030.0   \n",
       "1  NaN  1966.0       NaN           NaN     1966.0   \n",
       "2  NaN  1377.0       NaN           NaN     1377.0   \n",
       "3  NaN    82.0       NaN           NaN       82.0   \n",
       "4  NaN    74.0       NaN           NaN       74.0   \n",
       "\n",
       "                              account_id_y products  \\\n",
       "0  ga_a512e5f2-b196-e411-b0d2-005056bb7e02   4500.0   \n",
       "1  ga_a512e5f2-b196-e411-b0d2-005056bb7e02   4604.0   \n",
       "2  ga_a512e5f2-b196-e411-b0d2-005056bb7e02  13958.0   \n",
       "3  ga_a512e5f2-b196-e411-b0d2-005056bb7e02   4990.0   \n",
       "4  ga_a512e5f2-b196-e411-b0d2-005056bb7e02   5147.0   \n",
       "\n",
       "                                account_id onsite_sales onsite_revenue_euro  \n",
       "0  ga_bc66d247-ae5d-e311-a5f9-005056bb7e02       3093.0           416103.72  \n",
       "1  ga_bc66d247-ae5d-e311-a5f9-005056bb7e02        585.0           183438.72  \n",
       "2  ga_bc66d247-ae5d-e311-a5f9-005056bb7e02      21107.0          3858810.09  \n",
       "3  ga_bc66d247-ae5d-e311-a5f9-005056bb7e02         82.0            11705.52  \n",
       "4  ga_bc66d247-ae5d-e311-a5f9-005056bb7e02        686.0            87426.68  \n",
       "\n",
       "[5 rows x 45 columns]"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remove the top 3 (revenue) client for the later prediction with trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th>client_continent</th>\n",
       "      <th>...</th>\n",
       "      <th>app</th>\n",
       "      <th>web</th>\n",
       "      <th>awareness</th>\n",
       "      <th>consideration</th>\n",
       "      <th>conversion</th>\n",
       "      <th>account_id_y</th>\n",
       "      <th>products</th>\n",
       "      <th>account_id</th>\n",
       "      <th>onsite_sales</th>\n",
       "      <th>onsite_revenue_euro</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>6082</td>\n",
       "      <td>6082</td>\n",
       "      <td>6082</td>\n",
       "      <td>595166</td>\n",
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       "      <td>ga_5ac151c7-7c38-e211-9847-000c29ae377c</td>\n",
       "      <td>CLASSIFIED ADS</td>\n",
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       "      <td>AUSTRALIA</td>\n",
       "      <td>ga_5ac151c7-7c38-e211-9847-000c29ae377c_AUSTRALIA</td>\n",
       "      <td>APAC</td>\n",
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       "      <td>ga_249a039d-d882-e311-b5d4-005056bb7e02</td>\n",
       "      <td>18681.0</td>\n",
       "      <td>71173809.79</td>\n",
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       "      <td>14463</td>\n",
       "      <td>14463</td>\n",
       "      <td>14463</td>\n",
       "      <td>1413801</td>\n",
       "      <td>1413801</td>\n",
       "      <td>ga_89539ff9-1390-e211-a813-005056bb2836</td>\n",
       "      <td>RETAIL</td>\n",
       "      <td>Apparel</td>\n",
       "      <td>UNITED STATES</td>\n",
       "      <td>ga_89539ff9-1390-e211-a813-005056bb2836_UNITED...</td>\n",
       "      <td>AMERICAS</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>558004.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>558004.0</td>\n",
       "      <td>ga_aab751c7-7c38-e211-9847-000c29ae377c</td>\n",
       "      <td>1409.0</td>\n",
       "      <td>d5f4efb4-2816-b8d3-b730-940b6786051d</td>\n",
       "      <td>20600.0</td>\n",
       "      <td>505723.96</td>\n",
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       "      <td>12587</td>\n",
       "      <td>12587</td>\n",
       "      <td>12587</td>\n",
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       "      <td>1230243</td>\n",
       "      <td>ga_3a1033da-7c38-e211-9847-000c29ae377c</td>\n",
       "      <td>RETAIL</td>\n",
       "      <td>Home Improvement / Gardening / Interior Design</td>\n",
       "      <td>UNITED STATES</td>\n",
       "      <td>ga_3a1033da-7c38-e211-9847-000c29ae377c_UNITED...</td>\n",
       "      <td>AMERICAS</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>535661.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>535661.0</td>\n",
       "      <td>ga_f443ad18-8a95-e411-b0d2-005056bb7e02</td>\n",
       "      <td>2830.0</td>\n",
       "      <td>ga_88573191-860a-e511-9606-005056bb2344</td>\n",
       "      <td>4.0</td>\n",
       "      <td>30.83</td>\n",
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      "text/plain": [
       "      Unnamed: 0  index Unnamed: 0.1 Unnamed: 0.1.1  \\\n",
       "6082        6082   6082       595166         595166   \n",
       "14463      14463  14463      1413801        1413801   \n",
       "12587      12587  12587      1230243        1230243   \n",
       "\n",
       "                                  account_id_x        vertical  \\\n",
       "6082   ga_5ac151c7-7c38-e211-9847-000c29ae377c  CLASSIFIED ADS   \n",
       "14463  ga_89539ff9-1390-e211-a813-005056bb2836          RETAIL   \n",
       "12587  ga_3a1033da-7c38-e211-9847-000c29ae377c          RETAIL   \n",
       "\n",
       "                                             industry client_country  \\\n",
       "6082                                    Mass Merchant      AUSTRALIA   \n",
       "14463                                         Apparel  UNITED STATES   \n",
       "12587  Home Improvement / Gardening / Interior Design  UNITED STATES   \n",
       "\n",
       "                                           unique_client client_continent  \\\n",
       "6082   ga_5ac151c7-7c38-e211-9847-000c29ae377c_AUSTRALIA             APAC   \n",
       "14463  ga_89539ff9-1390-e211-a813-005056bb2836_UNITED...         AMERICAS   \n",
       "12587  ga_3a1033da-7c38-e211-9847-000c29ae377c_UNITED...         AMERICAS   \n",
       "\n",
       "       ...  app       web awareness consideration conversion  \\\n",
       "6082   ...  NaN  470106.0       NaN           NaN   470106.0   \n",
       "14463  ...  NaN  558004.0       NaN           NaN   558004.0   \n",
       "12587  ...  NaN  535661.0       NaN           NaN   535661.0   \n",
       "\n",
       "                                  account_id_y products  \\\n",
       "6082   ga_c8b651c7-7c38-e211-9847-000c29ae377c  41939.0   \n",
       "14463  ga_aab751c7-7c38-e211-9847-000c29ae377c   1409.0   \n",
       "12587  ga_f443ad18-8a95-e411-b0d2-005056bb7e02   2830.0   \n",
       "\n",
       "                                    account_id onsite_sales  \\\n",
       "6082   ga_249a039d-d882-e311-b5d4-005056bb7e02      18681.0   \n",
       "14463     d5f4efb4-2816-b8d3-b730-940b6786051d      20600.0   \n",
       "12587  ga_88573191-860a-e511-9606-005056bb2344          4.0   \n",
       "\n",
       "      onsite_revenue_euro  \n",
       "6082          71173809.79  \n",
       "14463           505723.96  \n",
       "12587               30.83  \n",
       "\n",
       "[3 rows x 45 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "save_top3=df.iloc[[6082,14463,12587]]\n",
    "save_top3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_top3 = save_top3.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>index</th>\n",
       "      <th>month</th>\n",
       "      <th>time</th>\n",
       "      <th>vertical</th>\n",
       "      <th>industry</th>\n",
       "      <th>client_country</th>\n",
       "      <th>client_continent</th>\n",
       "      <th>client_region</th>\n",
       "      <th>OS</th>\n",
       "      <th>device</th>\n",
       "      <th>...</th>\n",
       "      <th>Desktop</th>\n",
       "      <th>Mobile</th>\n",
       "      <th>app</th>\n",
       "      <th>web</th>\n",
       "      <th>awareness</th>\n",
       "      <th>consideration</th>\n",
       "      <th>conversion</th>\n",
       "      <th>products</th>\n",
       "      <th>onsite_sales</th>\n",
       "      <th>onsite_revenue_euro</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>6082</td>\n",
       "      <td>6082</td>\n",
       "      <td>3</td>\n",
       "      <td>2017.03</td>\n",
       "      <td>CLASSIFIED ADS</td>\n",
       "      <td>Mass Merchant</td>\n",
       "      <td>AUSTRALIA</td>\n",
       "      <td>APAC</td>\n",
       "      <td>PACIFIC</td>\n",
       "      <td>Android</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>470106.0</td>\n",
       "      <td>0</td>\n",
       "      <td>470106.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>470106.0</td>\n",
       "      <td>41939.0</td>\n",
       "      <td>18681.0</td>\n",
       "      <td>71173809.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14463</td>\n",
       "      <td>14463</td>\n",
       "      <td>3</td>\n",
       "      <td>2017.03</td>\n",
       "      <td>RETAIL</td>\n",
       "      <td>Apparel</td>\n",
       "      <td>UNITED STATES</td>\n",
       "      <td>AMERICAS</td>\n",
       "      <td>US</td>\n",
       "      <td>Android</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>558004.0</td>\n",
       "      <td>0</td>\n",
       "      <td>558004.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>558004.0</td>\n",
       "      <td>1409.0</td>\n",
       "      <td>20600.0</td>\n",
       "      <td>505723.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12587</td>\n",
       "      <td>12587</td>\n",
       "      <td>3</td>\n",
       "      <td>2017.03</td>\n",
       "      <td>RETAIL</td>\n",
       "      <td>Home Improvement / Gardening / Interior Design</td>\n",
       "      <td>UNITED STATES</td>\n",
       "      <td>AMERICAS</td>\n",
       "      <td>US</td>\n",
       "      <td>Android</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>535661.0</td>\n",
       "      <td>0</td>\n",
       "      <td>535661.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>535661.0</td>\n",
       "      <td>2830.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>30.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       index month     time        vertical  \\\n",
       "6082    6082     3  2017.03  CLASSIFIED ADS   \n",
       "14463  14463     3  2017.03          RETAIL   \n",
       "12587  12587     3  2017.03          RETAIL   \n",
       "\n",
       "                                             industry client_country  \\\n",
       "6082                                    Mass Merchant      AUSTRALIA   \n",
       "14463                                         Apparel  UNITED STATES   \n",
       "12587  Home Improvement / Gardening / Interior Design  UNITED STATES   \n",
       "\n",
       "      client_continent client_region       OS  device  ... Desktop    Mobile  \\\n",
       "6082              APAC       PACIFIC  Android  Mobile  ...       0  470106.0   \n",
       "14463         AMERICAS            US  Android  Mobile  ...       0  558004.0   \n",
       "12587         AMERICAS            US  Android  Mobile  ...       0  535661.0   \n",
       "\n",
       "      app       web awareness consideration conversion products  onsite_sales  \\\n",
       "6082    0  470106.0         0             0   470106.0  41939.0       18681.0   \n",
       "14463   0  558004.0         0             0   558004.0   1409.0       20600.0   \n",
       "12587   0  535661.0         0             0   535661.0   2830.0           4.0   \n",
       "\n",
       "      onsite_revenue_euro  \n",
       "6082          71173809.79  \n",
       "14463           505723.96  \n",
       "12587               30.83  \n",
       "\n",
       "[3 rows x 33 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols1 = ['index', 'month','time','vertical', 'industry', 'client_country',\n",
    "         'client_continent', 'client_region', 'OS', 'device', 'environment', 'criteo_product', \n",
    "         'spend_criteo_euro', 'sales_criteo', 'CPO', 'revenue_criteo_euro', 'AOV', 'COS', 'churn_date',\n",
    "         'Android', 'MacOS', 'Other', 'iOs', 'Desktop', 'Mobile', 'app', 'web', 'awareness', 'consideration', \n",
    "         'conversion', 'products','onsite_sales', 'onsite_revenue_euro']\n",
    "save_top3 = save_top3[cols1]\n",
    "save_top3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_cols = ['index', 'month','time', 'spend_criteo_euro', 'sales_criteo', 'CPO', 'revenue_criteo_euro', 'AOV', 'COS',\n",
    "           'Android', 'MacOS', 'Other', 'iOs', 'Desktop', 'Mobile', 'app', 'web', 'awareness', 'consideration', \n",
    "         'conversion', 'products','onsite_sales', 'onsite_revenue_euro']\n",
    "save_top3[num_cols] = save_top3[num_cols].apply(pd.to_numeric, errors='coerce')\n",
    "cate_cols = save_top3.columns.drop('churn_date').drop(num_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_top3[num_cols] = save_top3[num_cols].apply(pd.to_numeric, errors='coerce')\n",
    "save_top3['churn_date'] = save_top3['churn_date'].apply(pd.to_numeric, errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compare_nan_array(func, a, thresh):\n",
    "    out = ~np.isnan(a)\n",
    "    out[out] = func(a[out] , thresh)\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 3 entries, 6082 to 12587\n",
      "Data columns (total 33 columns):\n",
      "index                  3 non-null int64\n",
      "month                  3 non-null int64\n",
      "time                   3 non-null float64\n",
      "vertical               3 non-null object\n",
      "industry               3 non-null object\n",
      "client_country         3 non-null object\n",
      "client_continent       3 non-null object\n",
      "client_region          3 non-null object\n",
      "OS                     3 non-null object\n",
      "device                 3 non-null object\n",
      "environment            3 non-null object\n",
      "criteo_product         3 non-null object\n",
      "spend_criteo_euro      3 non-null int64\n",
      "sales_criteo           3 non-null int64\n",
      "CPO                    3 non-null float64\n",
      "revenue_criteo_euro    3 non-null int64\n",
      "AOV                    3 non-null float64\n",
      "COS                    3 non-null int64\n",
      "churn_date             3 non-null int64\n",
      "Android                3 non-null float64\n",
      "MacOS                  3 non-null int64\n",
      "Other                  3 non-null int64\n",
      "iOs                    3 non-null int64\n",
      "Desktop                3 non-null int64\n",
      "Mobile                 3 non-null float64\n",
      "app                    3 non-null int64\n",
      "web                    3 non-null float64\n",
      "awareness              3 non-null int64\n",
      "consideration          3 non-null int64\n",
      "conversion             3 non-null float64\n",
      "products               3 non-null float64\n",
      "onsite_sales           3 non-null float64\n",
      "onsite_revenue_euro    3 non-null float64\n",
      "dtypes: float64(10), int64(14), object(9)\n",
      "memory usage: 816.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "save_top3.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_top3 = save_top3.drop('index', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder = preprocessing.LabelEncoder()\n",
    "save_top3['vertical'] = encoder.fit_transform(save_top3['vertical'])\n",
    "save_top3['industry'] = encoder.fit_transform(save_top3['industry'])\n",
    "save_top3['client_country'] = encoder.fit_transform(save_top3['client_country'])\n",
    "save_top3['client_continent'] = encoder.fit_transform(save_top3['client_continent'])\n",
    "save_top3['client_region'] = encoder.fit_transform(save_top3['client_region'])\n",
    "save_top3['OS'] = encoder.fit_transform(save_top3['OS'])\n",
    "save_top3['device'] = encoder.fit_transform(save_top3['device'])\n",
    "save_top3['environment'] = encoder.fit_transform(save_top3['environment'])\n",
    "save_top3['criteo_product'] = encoder.fit_transform(save_top3['criteo_product'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop([6082,14463,12587], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Now prepare the data for training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Unnamed: 0'], inplace = True)\n",
    "df = df.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 16247 entries, 0 to 16246\n",
      "Data columns (total 45 columns):\n",
      "level_0                16247 non-null int64\n",
      "index                  16247 non-null object\n",
      "Unnamed: 0.1           16247 non-null object\n",
      "Unnamed: 0.1.1         16247 non-null object\n",
      "account_id_x           16247 non-null object\n",
      "vertical               16247 non-null object\n",
      "industry               16247 non-null object\n",
      "client_country         16247 non-null object\n",
      "unique_client          16247 non-null object\n",
      "client_continent       16247 non-null object\n",
      "client_region          16247 non-null object\n",
      "OS                     16247 non-null object\n",
      "device                 16247 non-null object\n",
      "environment            16247 non-null object\n",
      "criteo_product         16247 non-null object\n",
      "month                  16247 non-null object\n",
      "time                   16247 non-null object\n",
      "year                   16247 non-null object\n",
      "spend_criteo_euro      16247 non-null object\n",
      "sales_criteo           16247 non-null object\n",
      "CPO                    16247 non-null object\n",
      "revenue_criteo_euro    16247 non-null object\n",
      "AOV                    16247 non-null object\n",
      "COS                    16247 non-null object\n",
      "churn_date             16247 non-null object\n",
      "churn                  16247 non-null object\n",
      "start_time             16247 non-null object\n",
      "Android                16247 non-null object\n",
      "MacOS                  16247 non-null object\n",
      "Other                  16247 non-null object\n",
      "Unknown_x              16247 non-null object\n",
      "iOs                    16247 non-null object\n",
      "Desktop                16247 non-null object\n",
      "Mobile                 16247 non-null object\n",
      "Unknown_y              16247 non-null object\n",
      "app                    16247 non-null object\n",
      "web                    16247 non-null object\n",
      "awareness              16247 non-null object\n",
      "consideration          16247 non-null object\n",
      "conversion             16247 non-null object\n",
      "account_id_y           16247 non-null object\n",
      "products               16247 non-null object\n",
      "account_id             16247 non-null object\n",
      "onsite_sales           16247 non-null object\n",
      "onsite_revenue_euro    16247 non-null object\n",
      "dtypes: int64(1), object(44)\n",
      "memory usage: 5.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['index',\n",
       " 'month',\n",
       " 'time',\n",
       " 'vertical',\n",
       " 'industry',\n",
       " 'client_country',\n",
       " 'client_continent',\n",
       " 'client_region',\n",
       " 'OS',\n",
       " 'device',\n",
       " 'environment',\n",
       " 'criteo_product',\n",
       " 'spend_criteo_euro',\n",
       " 'sales_criteo',\n",
       " 'CPO',\n",
       " 'revenue_criteo_euro',\n",
       " 'AOV',\n",
       " 'COS',\n",
       " 'churn_date',\n",
       " 'Android',\n",
       " 'MacOS',\n",
       " 'Other',\n",
       " 'iOs',\n",
       " 'Desktop',\n",
       " 'Mobile',\n",
       " 'app',\n",
       " 'web',\n",
       " 'awareness',\n",
       " 'consideration',\n",
       " 'conversion',\n",
       " 'products',\n",
       " 'onsite_sales',\n",
       " 'onsite_revenue_euro']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df.columns) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>month</th>\n",
       "      <th>time</th>\n",
       "      <th>vertical</th>\n",
       "      <th>industry</th>\n",
       "      <th>client_country</th>\n",
       "      <th>client_continent</th>\n",
       "      <th>client_region</th>\n",
       "      <th>OS</th>\n",
       "      <th>device</th>\n",
       "      <th>environment</th>\n",
       "      <th>...</th>\n",
       "      <th>Desktop</th>\n",
       "      <th>Mobile</th>\n",
       "      <th>app</th>\n",
       "      <th>web</th>\n",
       "      <th>awareness</th>\n",
       "      <th>consideration</th>\n",
       "      <th>conversion</th>\n",
       "      <th>products</th>\n",
       "      <th>onsite_sales</th>\n",
       "      <th>onsite_revenue_euro</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2017.03</td>\n",
       "      <td>RETAIL</td>\n",
       "      <td>Electronics / High Tech</td>\n",
       "      <td>RUSSIAN FEDERATION</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>RUSSIA</td>\n",
       "      <td>Android</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>web</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1030.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1030.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1030.0</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>3093.0</td>\n",
       "      <td>416103.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2017.04</td>\n",
       "      <td>RETAIL</td>\n",
       "      <td>Gifts &amp; Flowers</td>\n",
       "      <td>CANADA</td>\n",
       "      <td>AMERICAS</td>\n",
       "      <td>CANADA</td>\n",
       "      <td>Android</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>web</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>4604.0</td>\n",
       "      <td>585.0</td>\n",
       "      <td>183438.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>2017.09</td>\n",
       "      <td>RETAIL</td>\n",
       "      <td>Gifts &amp; Flowers</td>\n",
       "      <td>UNITED KINGDOM</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>UK</td>\n",
       "      <td>Android</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>web</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1377.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1377.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1377.0</td>\n",
       "      <td>13958.0</td>\n",
       "      <td>21107.0</td>\n",
       "      <td>3858810.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2019.07</td>\n",
       "      <td>RETAIL</td>\n",
       "      <td>Gifts &amp; Flowers</td>\n",
       "      <td>UNITED STATES</td>\n",
       "      <td>AMERICAS</td>\n",
       "      <td>US</td>\n",
       "      <td>Android</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>web</td>\n",
       "      <td>...</td>\n",
       "      <td>82.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>4990.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>11705.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2017.06</td>\n",
       "      <td>RETAIL</td>\n",
       "      <td>Accessories / Jewelry / Luxury Goods</td>\n",
       "      <td>RUSSIAN FEDERATION</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>RUSSIA</td>\n",
       "      <td>Android</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>web</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>5147.0</td>\n",
       "      <td>686.0</td>\n",
       "      <td>87426.68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   month     time vertical                              industry  \\\n",
       "0      3  2017.03   RETAIL               Electronics / High Tech   \n",
       "1      4  2017.04   RETAIL                       Gifts & Flowers   \n",
       "2      9  2017.09   RETAIL                       Gifts & Flowers   \n",
       "3      7  2019.07   RETAIL                       Gifts & Flowers   \n",
       "4      6  2017.06   RETAIL  Accessories / Jewelry / Luxury Goods   \n",
       "\n",
       "       client_country client_continent client_region       OS   device  \\\n",
       "0  RUSSIAN FEDERATION             EMEA        RUSSIA  Android   Mobile   \n",
       "1              CANADA         AMERICAS        CANADA  Android   Mobile   \n",
       "2      UNITED KINGDOM             EMEA            UK  Android   Mobile   \n",
       "3       UNITED STATES         AMERICAS            US  Android  Desktop   \n",
       "4  RUSSIAN FEDERATION             EMEA        RUSSIA  Android   Mobile   \n",
       "\n",
       "  environment  ... Desktop  Mobile  app     web  awareness  consideration  \\\n",
       "0         web  ...     0.0  1030.0  0.0  1030.0        0.0            0.0   \n",
       "1         web  ...     0.0  1966.0  0.0  1966.0        0.0            0.0   \n",
       "2         web  ...     0.0  1377.0  0.0  1377.0        0.0            0.0   \n",
       "3         web  ...    82.0     0.0  0.0    82.0        0.0            0.0   \n",
       "4         web  ...     0.0    74.0  0.0    74.0        0.0            0.0   \n",
       "\n",
       "   conversion  products  onsite_sales  onsite_revenue_euro  \n",
       "0      1030.0    4500.0        3093.0            416103.72  \n",
       "1      1966.0    4604.0         585.0            183438.72  \n",
       "2      1377.0   13958.0       21107.0           3858810.09  \n",
       "3        82.0    4990.0          82.0             11705.52  \n",
       "4        74.0    5147.0         686.0             87426.68  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = ['month','time','vertical', 'industry', 'client_country', 'client_continent', 'client_region', 'OS',\n",
    "        'device', 'environment', 'criteo_product', 'spend_criteo_euro', 'sales_criteo', 'CPO', 'revenue_criteo_euro',\n",
    "        'AOV', 'COS', 'churn_date','Android', 'MacOS', 'Other', 'iOs', 'Desktop', 'Mobile', 'app', 'web', \n",
    "        'awareness', 'consideration', 'conversion', 'products','onsite_sales', 'onsite_revenue_euro']\n",
    "df = df[cols]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_cols = ['month','time', 'spend_criteo_euro', 'sales_criteo', 'CPO', 'revenue_criteo_euro', 'AOV', 'COS',\n",
    "           'Android', 'MacOS', 'Other', 'iOs', 'Desktop', 'Mobile', 'app', 'web', 'awareness', 'consideration', \n",
    "         'conversion', 'products','onsite_sales', 'onsite_revenue_euro']\n",
    "cate_cols = df.columns.drop('churn_date').drop(num_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[num_cols] = df[num_cols].apply(pd.to_numeric, errors='coerce')\n",
    "df['churn_date'] = df['churn_date'].apply(pd.to_numeric, errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        2018.06\n",
       "1        2018.09\n",
       "2        2018.05\n",
       "3           0.00\n",
       "4        2018.02\n",
       "          ...   \n",
       "16242    2019.12\n",
       "16243       0.00\n",
       "16244       0.00\n",
       "16245       0.00\n",
       "16246       0.00\n",
       "Name: churn_date, Length: 16247, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['churn_date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#for i in range(df.shape[0]):\n",
    "    #if df.loc[i,'churn_date'] == 0:\n",
    "        #df.loc[i,'churn_date'] = int(df['time'][i]) + 7.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        2018.06\n",
       "1        2018.09\n",
       "2        2018.05\n",
       "3        2025.01\n",
       "4        2018.02\n",
       "          ...   \n",
       "16242    2019.12\n",
       "16243    2025.01\n",
       "16244    2025.01\n",
       "16245    2025.01\n",
       "16246    2025.01\n",
       "Name: churn_date, Length: 16247, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['churn_date'] = df['churn_date'].replace([0], 2025.01)\n",
    "df['churn_date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2025.01"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(df['churn_date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2017.03"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(df['churn_date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rescale the month (months in the scale of years) for the correct prediction of month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "eps = 1e-15\n",
    "for i in range(df.shape[0]):\n",
    "    number_dec_str = str(df['churn_date'][i]-int(df['churn_date'][i]))[1:]\n",
    "    number_dec = float(number_dec_str)\n",
    "    for j in range(1,13):\n",
    "            if abs(number_dec - 0.01*j) < eps:\n",
    "                df.loc[i,'churn_date'] = int(df['churn_date'][i]) + j/13"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create function to reverse the scale after the regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "def identify_month(churn_date):\n",
    "    eps = 1e-15\n",
    "    number_dec_str = str(churn_date-int(churn_date))[1:]\n",
    "    number_dec = float(number_dec_str)\n",
    "    entry = False\n",
    "    for j in range(1,13):\n",
    "        if (number_dec < j/13.0 or abs(number_dec - j/13) < eps) and number_dec > (j-1)/13.0:\n",
    "            entry = True\n",
    "            return j\n",
    "    if not entry:\n",
    "        return 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Identify the churn dates in the format (year, month) for all good clients in each cluster\n",
    "def get_final_date(pred_list):\n",
    "    final_dates = []\n",
    "    for i in range(len(pred_list)):\n",
    "        final_dates.append((int(pred_list[i]), identify_month(pred_list[i])))\n",
    "    return final_dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compare_nan_array(func, a, thresh):\n",
    "    out = ~np.isnan(a)\n",
    "    out[out] = func(a[out] , thresh)\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>month</th>\n",
       "      <th>time</th>\n",
       "      <th>spend_criteo_euro</th>\n",
       "      <th>sales_criteo</th>\n",
       "      <th>CPO</th>\n",
       "      <th>revenue_criteo_euro</th>\n",
       "      <th>AOV</th>\n",
       "      <th>COS</th>\n",
       "      <th>Android</th>\n",
       "      <th>MacOS</th>\n",
       "      <th>...</th>\n",
       "      <th>Desktop</th>\n",
       "      <th>Mobile</th>\n",
       "      <th>app</th>\n",
       "      <th>web</th>\n",
       "      <th>awareness</th>\n",
       "      <th>consideration</th>\n",
       "      <th>conversion</th>\n",
       "      <th>products</th>\n",
       "      <th>onsite_sales</th>\n",
       "      <th>onsite_revenue_euro</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2017.03</td>\n",
       "      <td>1030</td>\n",
       "      <td>168</td>\n",
       "      <td>6.0</td>\n",
       "      <td>22929</td>\n",
       "      <td>136.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1030.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1030.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1030.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1030.0</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>3093.0</td>\n",
       "      <td>416103.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2017.04</td>\n",
       "      <td>1966</td>\n",
       "      <td>4</td>\n",
       "      <td>492.0</td>\n",
       "      <td>246</td>\n",
       "      <td>61.0</td>\n",
       "      <td>8</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>4604.0</td>\n",
       "      <td>585.0</td>\n",
       "      <td>183438.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>2017.09</td>\n",
       "      <td>1377</td>\n",
       "      <td>168</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8606</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1377.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1377.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1377.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1377.0</td>\n",
       "      <td>13958.0</td>\n",
       "      <td>21107.0</td>\n",
       "      <td>3858810.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2019.07</td>\n",
       "      <td>82</td>\n",
       "      <td>4</td>\n",
       "      <td>21.0</td>\n",
       "      <td>72</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1</td>\n",
       "      <td>82.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>82.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>4990.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>11705.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2017.06</td>\n",
       "      <td>74</td>\n",
       "      <td>20</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1174</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>5147.0</td>\n",
       "      <td>686.0</td>\n",
       "      <td>87426.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16242</td>\n",
       "      <td>6</td>\n",
       "      <td>2018.06</td>\n",
       "      <td>16</td>\n",
       "      <td>43</td>\n",
       "      <td>0.0</td>\n",
       "      <td>380</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1888796.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>5421.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16243</td>\n",
       "      <td>4</td>\n",
       "      <td>2018.04</td>\n",
       "      <td>168</td>\n",
       "      <td>27</td>\n",
       "      <td>6.0</td>\n",
       "      <td>371</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>1715007.0</td>\n",
       "      <td>1755.0</td>\n",
       "      <td>144868.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16244</td>\n",
       "      <td>11</td>\n",
       "      <td>2018.11</td>\n",
       "      <td>254</td>\n",
       "      <td>20</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1261</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>1319882.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>528.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16245</td>\n",
       "      <td>3</td>\n",
       "      <td>2017.03</td>\n",
       "      <td>881</td>\n",
       "      <td>82</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2685</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>881.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>881.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>881.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>881.0</td>\n",
       "      <td>1033853.0</td>\n",
       "      <td>218.0</td>\n",
       "      <td>11588.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16246</td>\n",
       "      <td>3</td>\n",
       "      <td>2017.03</td>\n",
       "      <td>2683</td>\n",
       "      <td>1112</td>\n",
       "      <td>2.0</td>\n",
       "      <td>49505</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2683.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2683.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2683.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2683.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>8208.55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16247 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       month     time  spend_criteo_euro  sales_criteo    CPO  \\\n",
       "0          3  2017.03               1030           168    6.0   \n",
       "1          4  2017.04               1966             4  492.0   \n",
       "2          9  2017.09               1377           168    8.0   \n",
       "3          7  2019.07                 82             4   21.0   \n",
       "4          6  2017.06                 74            20    4.0   \n",
       "...      ...      ...                ...           ...    ...   \n",
       "16242      6  2018.06                 16            43    0.0   \n",
       "16243      4  2018.04                168            27    6.0   \n",
       "16244     11  2018.11                254            20   13.0   \n",
       "16245      3  2017.03                881            82   11.0   \n",
       "16246      3  2017.03               2683          1112    2.0   \n",
       "\n",
       "       revenue_criteo_euro    AOV  COS  Android  MacOS  ...  Desktop  Mobile  \\\n",
       "0                    22929  136.0    0   1030.0    0.0  ...      0.0  1030.0   \n",
       "1                      246   61.0    8   1966.0    0.0  ...      0.0  1966.0   \n",
       "2                     8606   51.0    0   1377.0    0.0  ...      0.0  1377.0   \n",
       "3                       72   18.0    1     82.0    0.0  ...     82.0     0.0   \n",
       "4                     1174   59.0    0     74.0    0.0  ...      0.0    74.0   \n",
       "...                    ...    ...  ...      ...    ...  ...      ...     ...   \n",
       "16242                  380    9.0    0     16.0    0.0  ...      0.0    16.0   \n",
       "16243                  371   14.0    0    168.0    0.0  ...      0.0   168.0   \n",
       "16244                 1261   63.0    0    254.0    0.0  ...      0.0   254.0   \n",
       "16245                 2685   33.0    0    881.0    0.0  ...      0.0   881.0   \n",
       "16246                49505   45.0    0   2683.0    0.0  ...      0.0  2683.0   \n",
       "\n",
       "       app     web  awareness  consideration  conversion   products  \\\n",
       "0      0.0  1030.0        0.0            0.0      1030.0     4500.0   \n",
       "1      0.0  1966.0        0.0            0.0      1966.0     4604.0   \n",
       "2      0.0  1377.0        0.0            0.0      1377.0    13958.0   \n",
       "3      0.0    82.0        0.0            0.0        82.0     4990.0   \n",
       "4      0.0    74.0        0.0            0.0        74.0     5147.0   \n",
       "...    ...     ...        ...            ...         ...        ...   \n",
       "16242  0.0    16.0        0.0            0.0        16.0  1888796.0   \n",
       "16243  0.0   168.0        0.0            0.0       168.0  1715007.0   \n",
       "16244  0.0   254.0        0.0            0.0       254.0  1319882.0   \n",
       "16245  0.0   881.0        0.0            0.0       881.0  1033853.0   \n",
       "16246  0.0  2683.0        0.0            0.0      2683.0        0.0   \n",
       "\n",
       "       onsite_sales  onsite_revenue_euro  \n",
       "0            3093.0            416103.72  \n",
       "1             585.0            183438.72  \n",
       "2           21107.0           3858810.09  \n",
       "3              82.0             11705.52  \n",
       "4             686.0             87426.68  \n",
       "...             ...                  ...  \n",
       "16242         101.0              5421.47  \n",
       "16243        1755.0            144868.98  \n",
       "16244          16.0               528.64  \n",
       "16245         218.0             11588.50  \n",
       "16246         179.0              8208.55  \n",
       "\n",
       "[16247 rows x 22 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[num_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        2018.06\n",
       "1        2018.09\n",
       "2        2018.05\n",
       "3        2025.01\n",
       "4        2018.02\n",
       "          ...   \n",
       "16242    2019.12\n",
       "16243    2025.01\n",
       "16244    2025.01\n",
       "16245    2025.01\n",
       "16246    2025.01\n",
       "Name: churn_date, Length: 16247, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['churn_date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14052, 33)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#np.seterr(divide='ignore', invalid='ignore')\n",
    "normalized_num_cols = preprocessing.normalize(df[num_cols])\n",
    "normalized_num_cols\n",
    "z = np.abs(stats.zscore(normalized_num_cols))\n",
    "#z = np.delete(z, 6, axis=1)\n",
    "df = df[(compare_nan_array(np.less, z, 3)).all(axis=1)].copy()#\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "index                    int64\n",
       "month                    int64\n",
       "time                   float64\n",
       "vertical                object\n",
       "industry                object\n",
       "client_country          object\n",
       "client_continent        object\n",
       "client_region           object\n",
       "OS                      object\n",
       "device                  object\n",
       "environment             object\n",
       "criteo_product          object\n",
       "spend_criteo_euro        int64\n",
       "sales_criteo             int64\n",
       "CPO                    float64\n",
       "revenue_criteo_euro      int64\n",
       "AOV                    float64\n",
       "COS                      int64\n",
       "churn_date             float64\n",
       "Android                float64\n",
       "MacOS                  float64\n",
       "Other                  float64\n",
       "iOs                    float64\n",
       "Desktop                float64\n",
       "Mobile                 float64\n",
       "app                    float64\n",
       "web                    float64\n",
       "awareness              float64\n",
       "consideration          float64\n",
       "conversion             float64\n",
       "products               float64\n",
       "onsite_sales           float64\n",
       "onsite_revenue_euro    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_cols = [\n",
    "    'month',\n",
    " 'time',\n",
    " 'spend_criteo_euro',\n",
    " 'sales_criteo',\n",
    " 'CPO',\n",
    " 'revenue_criteo_euro',\n",
    " 'AOV',\n",
    " 'COS',\n",
    " 'Android',\n",
    " 'MacOS',\n",
    " 'Other',\n",
    " 'iOs',\n",
    " 'Desktop',\n",
    " 'Mobile',\n",
    " 'app',\n",
    " 'web',\n",
    " 'awareness',\n",
    " 'consideration',\n",
    " 'conversion',\n",
    " 'products',\n",
    " 'onsite_sales',\n",
    " 'onsite_revenue_euro']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[num_cols] = df[num_cols].apply(pd.to_numeric, errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "vertical             3\n",
       "industry            25\n",
       "client_country      34\n",
       "client_continent     3\n",
       "client_region       20\n",
       "OS                   5\n",
       "device               3\n",
       "environment          2\n",
       "criteo_product       3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[cate_cols].apply(lambda x: x.nunique(), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14052, 33)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(columns=['churn_date', 'index'])\n",
    "y = df['churn_date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2018.06, 2018.09, 2018.05, 2025.01, 2018.02, 2019.09, 2019.06,\n",
       "       2017.1 , 2018.03, 2018.11, 2017.07, 2019.02, 2018.07, 2018.04,\n",
       "       2019.07, 2017.06, 2017.04, 2018.01, 2019.08, 2019.11, 2018.12,\n",
       "       2019.1 , 2019.01, 2018.1 , 2017.12, 2018.08, 2019.12, 2019.03,\n",
       "       2017.09, 2017.03, 2019.04, 2017.11, 2017.05, 2017.08, 2019.05])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['churn_date'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder = preprocessing.LabelEncoder()\n",
    "X['vertical'] = encoder.fit_transform(X['vertical'])\n",
    "X['industry'] = encoder.fit_transform(X['industry'])\n",
    "X['client_country'] = encoder.fit_transform(X['client_country'])\n",
    "X['client_continent'] = encoder.fit_transform(X['client_continent'])\n",
    "X['client_region'] = encoder.fit_transform(X['client_region'])\n",
    "X['OS'] = encoder.fit_transform(X['OS'])\n",
    "X['device'] = encoder.fit_transform(X['device'])\n",
    "X['environment'] = encoder.fit_transform(X['environment'])\n",
    "X['criteo_product'] = encoder.fit_transform(X['criteo_product'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['month', 'time', 'vertical', 'industry', 'client_country',\n",
       "       'client_continent', 'client_region', 'OS', 'device', 'environment',\n",
       "       'criteo_product', 'spend_criteo_euro', 'sales_criteo', 'CPO',\n",
       "       'revenue_criteo_euro', 'AOV', 'COS', 'Android', 'MacOS', 'Other', 'iOs',\n",
       "       'Desktop', 'Mobile', 'app', 'web', 'awareness', 'consideration',\n",
       "       'conversion', 'products', 'onsite_sales', 'onsite_revenue_euro'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/naitochieri/opt/anaconda3/lib/python3.7/site-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
      "  if getattr(data, 'base', None) is not None and \\\n",
      "/Users/naitochieri/opt/anaconda3/lib/python3.7/site-packages/xgboost/core.py:588: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
      "  data.base is not None and isinstance(data, np.ndarray) \\\n"
     ]
    }
   ],
   "source": [
    "data_dmatrix = xgb.DMatrix(data=X,label=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Grid Search Cross Validation, find the best parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "gbm = xgb.XGBRegressor()\n",
    "xg_reg = GridSearchCV(gbm, {\"colsample_bytree\":[1.0],\"min_child_weight\":[1.0,1.2],'max_depth': [3,6], 'n_estimators': [500,1000]}, verbose=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 2 candidates, totalling 10 fits\n",
      "[12:50:09] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "/Users/naitochieri/opt/anaconda3/lib/python3.7/site-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
      "  if getattr(data, 'base', None) is not None and \\\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[12:50:19] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/naitochieri/opt/anaconda3/lib/python3.7/site-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
      "  if getattr(data, 'base', None) is not None and \\\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[12:50:29] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/naitochieri/opt/anaconda3/lib/python3.7/site-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
      "  if getattr(data, 'base', None) is not None and \\\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[12:50:38] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/naitochieri/opt/anaconda3/lib/python3.7/site-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
      "  if getattr(data, 'base', None) is not None and \\\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[12:50:47] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/naitochieri/opt/anaconda3/lib/python3.7/site-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
      "  if getattr(data, 'base', None) is not None and \\\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[12:50:57] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/naitochieri/opt/anaconda3/lib/python3.7/site-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
      "  if getattr(data, 'base', None) is not None and \\\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[12:51:07] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/naitochieri/opt/anaconda3/lib/python3.7/site-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
      "  if getattr(data, 'base', None) is not None and \\\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[12:51:17] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/naitochieri/opt/anaconda3/lib/python3.7/site-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
      "  if getattr(data, 'base', None) is not None and \\\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[12:51:26] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/naitochieri/opt/anaconda3/lib/python3.7/site-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
      "  if getattr(data, 'base', None) is not None and \\\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[12:51:36] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:  1.6min finished\n",
      "/Users/naitochieri/opt/anaconda3/lib/python3.7/site-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
      "  if getattr(data, 'base', None) is not None and \\\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[12:51:45] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=None, error_score=nan,\n",
       "             estimator=XGBRegressor(base_score=0.5, booster='gbtree',\n",
       "                                    colsample_bylevel=1, colsample_bynode=1,\n",
       "                                    colsample_bytree=1, gamma=0,\n",
       "                                    importance_type='gain', learning_rate=0.1,\n",
       "                                    max_delta_step=0, max_depth=3,\n",
       "                                    min_child_weight=1, missing=None,\n",
       "                                    n_estimators=100, n_jobs=1, nthread=None,\n",
       "                                    objective='reg:linear', random_state=0,\n",
       "                                    reg_alpha=0, reg_lambda=1,\n",
       "                                    scale_pos_weight=1, seed=None, silent=None,\n",
       "                                    subsample=1, verbosity=1),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid={'colsample_bytree': [1.0], 'max_depth': [3],\n",
       "                         'min_child_weight': [1.0, 1.2],\n",
       "                         'n_estimators': [500]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=1)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xg_reg.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'colsample_bytree': 1.0,\n",
       " 'max_depth': 3,\n",
       " 'min_child_weight': 1.0,\n",
       " 'n_estimators': 500}"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xg_reg.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the model with best parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[13:20:37] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "             colsample_bynode=1, colsample_bytree=1.0, gamma=0,\n",
       "             importance_type='gain', learning_rate=0.1, max_delta_step=0,\n",
       "             max_depth=3, min_child_weight=1.0, missing=None, n_estimators=500,\n",
       "             n_jobs=1, nthread=None, objective='reg:linear', random_state=0,\n",
       "             reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
       "             silent=None, subsample=1, verbosity=1)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbm = xgb.XGBRegressor(**xg_reg.best_params_)\n",
    "gbm.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2022.1375, 2022.3774, 2022.39  , ..., 2022.2303, 2021.3406,\n",
       "       2023.6055], dtype=float32)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = gbm.predict(X_test)\n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 3.111076\n"
     ]
    }
   ],
   "source": [
    "rmse = np.sqrt(mean_squared_error(y_test, predictions))\n",
    "print(\"RMSE: %f\" % (rmse))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3898591951646139"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Normalized root-mean-square deviation\n",
    "rmse/(max(df['churn_date'])-min(df['churn_date']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "xgb.plot_importance(gbm, max_num_features=10)\n",
    "plt.rcParams['figure.figsize'] = [10, 10]\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x684 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import shap\n",
    "X_importance = X_test\n",
    "\n",
    "# Explain model predictions using shap library:\n",
    "explainer = shap.TreeExplainer(gbm)\n",
    "shap_values = explainer.shap_values(X_importance)\n",
    "shap.summary_plot(shap_values, X_importance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Lg92xAoEjBu7f9eBCiLYlxdeiVdIcxbzhE+hTVg5AflWAE8t3QM+hMO34+E/007PcRwudfXAKj80OsHBTFMdShFJ9vLDE4amWn6pF8jMtZl2TxjMLIgzLs/ielo+YEKLtJGPuuC75xUyQtw4+htRI/Xu67t3br59yVqrivz/JYeK/gnyxFVCK8X3a58M8preHO0+VHLIQou1JUBatEvD48HugKpKKLxSmcEgeN57Xv12uvWSn4YK3omyvglsO83FCmcEAPztSAqUQomuT4mvRKm8P6ctp67cSzsrg/UF98KT7CEYhPZ7M8oYdcN/bkJUG/3cWZLas689NHzss3eUu3zLLsOU6D/mdaJg8IYRoLckpi1ZZ0SObCq8XD7AxN52+ZQFe+LiKq05rZuYvx4FJv4M1sWmel22CF3/WomuHndp7SWNga4XhzeVRbnovTIYfXjjfzwkFkmsWQojORrpEJYjeUsKxO8s4emcZEzcVYyn44O0iNpU5XPl2hDNfjvBx4V5mXimtqg3IAF+vbfG17z3eQ79M8FpgOQ7jHgtz7VthqsKwoxJ+9E54H16ZEEJ0nGTvEtVsUNZae7XWi7TW8XeSFQysClLttaj0eugfCKECEXqsXMugB8I8tdjw1mrD1FeibK9sUEPSLRNOObj2+UUTW3xt3Vux6Tovw9IdorFuwnUyzw3HNBFCiC7DNHgkm2aLr23bjmitc0nO158wy7plsLxXDkYphhZXsFtZfDrsgFhwNKAUVWHYWgn5DSc7evOX8PZXkJ0Gk8fFdb3AlirCZWGyDsipWdcjXUGxe62UVEW2ZchKUTwytX1agZdWOXy+OsyQnh4O6CM1JUKIfZeMueO64v2lvB/4k9b6F7Ztt+0QTUlqRX42JjbZxJpuGaRUh1jXOx8cAAMeOGGQxegeeznY74MTD4Kc+KYmLHq5kK+v/RwTMQy4dAiHPHgkAE+c4eW69yOUBuHPx/s5qaD9ssjl1Q6n31vC2h0OXgv+dXU2J43xt9v1hRDJKrmDcry/0tcCNwGlWuvVWuuVex4JTFuX1vBjY5kIOVXlNc9PGAAzLvLgsWJ7vjIXHvkANu2CI38BudNg/E9hd0Wz11p9/1JMrE/0xmfXEthSBcCw7or/m2Bx8LJNzPzLclZ8VdImry0eXxVGWLvDrTOPOPDaV8F2u7YQInkle51yvDnlPyY0FUkoGozg97otnEPGkBOo5oG3nuCcS38KwDkHePDuCci/ehb+/Iq73P9lNzADzF8Hj37Y9ExSQGq/dEq/2e1eN82Lfh4OGhTmrske7rtjHacsLQTg7UVb6fvKUWTlJ755wJB8D6k+CMTalI3qK629hRD7LtnrUeMKyrZtP5XohCSbMSVVGBSVXg/pkQhLu+VQ5k/FH40SUorReXV2fuer2uU9AXmPrOb7KI+7/3CWpH/Flk0Bbh00kiUVHpYscchLg151ctqesEPJhqp2CcoD8zw894McXvoywPDeHq45XqZZFPEJRgx+D6h45hoX+51kzB3XFXfrG631BOC7wABgI/C4bdvzEpWwri5oWayK1Ql7HId+VSG+d+H1RDwWlmO4e1aIwd1S6Z8N/mNGwcJC98BhveH4MfDRN3D8aLh6crPXSu2VxqGPT+TlpVGWvFxb5e+1IEXn47y1Ewvw9kojf1R2Al7t3h0xzMcRw9pvaFHR9p75X4CHZwUY1N3i7xdlkpeZ2HYJP/k4yn22Q+8MePc8Lwf3iv8HuLg0ygMvlFJS4XDplCzGj+p6M4nN2uDw+kqD7qO4ZLR0k9gbySkDWuuzgeeB14D5wBDgU631pbZtv5bA9HVZpT4FKRZYimjEYtCOUoq6u0HasRTz1kUZ+nCYkXkw+09X0ePAAbCzzA3Cfbq36ppTR1hMLlB8tM4wpBv8+AgvA0/pw8fHpmPtrObIST1IyZBW0CI+a3dEufE/lTgGFhdF6ZZexT8vaWbwm32wdKfhb7bbDmFLJfx6dpS3z4v/8/q3Z0v5fGEAgOWFxbx4Vy8y0rpOYFu03TD5+SjhOsMXSGD+NkdyygD8DjjXtu1396zQWk8B7sQN1KKBQKrfzaoC+BVBr+UOrxUrkgvEOgsv3wVPL1f8+Aen7vM1U7yKD6f52VVlyE0Fj6V4eUWUC+ek4UTTON/Ai9/Z58uI/URptanXv724ai+D3bShVK/bQHLPJeMakraOXSXRmuVA0FBZbcjoQrUmC7aZegH5y82GS0Z3XHo6q2Qvvo73Nmww8H6DdR8Ag9o0NUkk7K3fsGmT31sTkDEGFYq6I3pY8NlmQ8Rpu0KZvHRV06r7u29GcEIGooaXFkWZuS7azNFCuA4e4OE7h7jd2HLTFT+ZnNgINyRXcf+JFv2z4Kh+ir8c37LGgReeksmer90pR6aR371rNS48fpAiL/YWey04c3hyB5/W2u8HD4lZD0wGZtRZdyKwoc1TlCQCRsWCrnL7BHnr3P8oxZBtu1nUvyd4PbyyGu6ZZ/jF4d/+Es7dGCUchWMHN/8Ds7nc8OxSh76ZiksOdM8VDNf/2M5cH+V4GfdaxEEpxeNXZvGncxxy0hTp/sQHiRsO9XDDoa37fB53aBoHDfNTUe0wsHfXa8swIFvx9VVePllvGNdLtag+fX+S7DnleIPyH4A3tNYvA+twc87nAlckKF1dXmokSnUg9uFRkBFx3MkmHEPe7kqWZmZCaQACHuiexsrd377nu2VGmLs/cxtuff9QD9PPbHzwjaqw4ehnIhSWus8XbbeYsc4hZCxiI5agFFw0WuqURcv0yek69Zrdczx0z+m6N50DcxRXjE3uoLOvkj0ox/Vts237FdyccRWggWrgJNu2X05g2rq03EAIMG49MoZijwdfeQiqo+xKSSG6J+ccjOKvDvLq/CB3zw7VO8djX9e2pH58fhSnQRH3kh0O57wc5qLXIszZaNyA7BgIRHjof2Hmb43l1L2KQ3or5l3tZ2TPrvMDK4QQDe23xdda61ds2z43tnyVbdtPAJ+3W8q6uC2ZKRCK1tQjl6T6YE91rlJkKKiMPX31qXuYetFPueUDw9mjvIzo4QbOkT0sPt/o5nJH5Cksq/4d4pQXImwsNWCgaEYRl5dV8PygQYQti7KIAY9xi83DUeYXGeasUxzad/8c6nJmocOi7YbTh1sM6Zbcd9pCJLP9Oad8Yp3l+xOdkKRTXRuQAbAUnjo53cFVAVIch0HVxZSlpmOU+68I12mH9cqFfq7THr433sN7l9UPphHHUFQOGDikaAdnf7OWERt3Ek71Qo4Psn1uU9aaEyp+OmP/nLLxpaVRTng6zI0fRJjwrxCby5Px/lqI/cP+PMzmEq3188AiwK+1vnVvO9m2/eeEpKyrizpQrwW24qhdJWxKTSEzEuWIHSWsHtQHX14uv7rih3gq4CdH+Rjdq/Y+qXeW4qGpe8/Zei3FTRMs/jY3Su9yd6zrjblpkOZxbwYUkOqBynDNzUEyfoDj8f6a2n4mxdXwRZHDOSO7br2jECJ5NRWULwN+AZwAeICT9rKPASQo74VyIhhvihscIw7p5dUs65XDmau2kBKN8ln3HIIeiwmj0vn3d1Kx4FvF03tUhQ2LdhiG5ip3OsaYv0728uLiKN/0yuPgrTt58tAD6x9o3K5QbnmIwetTBCKGVO/+FZyPHWjx+AI3MGf64ZDeUq8uRFeV7OVcjQZl27bX4c4OhdZ6gW3bJ7RbqpKAyUx3G1kBeC1OXbaWVycexNx+PehWEeSbnAyw4PkFDmWRMG9ftPcccUnAcNQzEZbtAo+CyQPh+bO8dEtzz33KUIvHyzJ5YMJogj6/G4Q9kBoOEQiAJxxl6pK1dKsK8tGogawuTmNM/v4VlK8Y5yE7Bb7ZZjhnpMXg3P3r9QuRTJK9xC/eCSkOTnRCkk7dz41SbOiey4E7KuhdGaTUo/j9Z69xZNFq3hsyjr/5Tmd3taFbuJryhz6iGB95109ibZWfL7Y4LIvNURE18ME6wy0fR7lrkoepL4T5ssjQMwOyd1QztGwL8/sNwR8Kcc3/ZvD3I6Zw6VeLOX1ZIQBHFW4hePMUaIcJKTqbc0Z6OGdkR6dCCLGv9tucstg32eXVlGWnu/W5UYfMYIigMWzLTOH41XP57WdvAHBS4RLWDhpIdsoESo/7IznzlpMFvPDCUi46/wYIRMDExtH2Wm5L63LDOS+H+Wyj+/HcUQU7+nTDMtmcsnI+vkCYf044GfwWBcVltWkKhsgorQb2v6AshEgOyZ5Tlsq1BMmrDJBRFsBbFSKjPMDm3HQW9c1hSZ8c+lYU19v32NUb+Oe7FeTMW16z7rhVi6E64o77YQwE3GE5s32wuQo+rTuWmmOgKoJTbfhg6DjePmA8TooXjOHzwb1rdtvaJ4cBo7MS/MqFECJxkr31tQTlBMkMV5MbDNO7IkB2OMohRSsh5DY2enbcMWxLd6dQXJHXh+fGHc57H5Wz4IBRNcfPGDHWLadR1BSFr7nOy6YbfSzdhfuf21NlHa3TjyoYdeuyDRBy+HDoAH590uH8YZLm5pOPJvOPAW58M5joly+EEAnhNHjESyn1O6WUUUqNiT0/Qim1UCm1Uik1QymVX2ffRrclWrsUX2uthwO7bdveqbXOAP4PdyiNv9i2HWija4wAngLygF3A5bZtr2qwjwf4O3Aqbti607btx9ri+g1FfX56OO59XEA5bMvKIX13JZFuaThOOmNuvpeBZTtZ1rMf1f4URu0o5egLfs6VC2YR8Pl46uBjoW63YgV5qQ5ZKV7OGaF4YRngVVw6WjFvVYSVdTPfBjf3rABLsapnrlv0Hesa9Y+5Ya45zMuY3tItSAjRtZhGeqk0RSk1HjgCdx4HlFIW8AxwpTFmjlLq17izHn63qW1t9BKaFFdOWWvt0Vr/Wmu9SmtdGlt3itb6ujiv8zywpxz1z8B3gLOA+1qa4CY8DDxg2/YI4AHgkb3scykwDBgOHAncprUe3IZpqOH402oKVlKNYnbBgfQuLWfc+o1syExnZ0YmX/cbQrXfnYh9ZW4GValpPHjUqTw+4USiXi9ZgSB9SitiUz7ClIfd/sjPTPXwn7M8vHCWh1s0hKN1mj54lBt8LQU+DwX9UyDNC57af7VSkOZLvmIfIUTyM6r+ozlKqRTcmPCDOqsPBQLGmDmx5w8DF8SxLeHiLb7+A3AmcAu1jd9WEusyFYchwJLY8nmxc50S+7vPtNb5wHjc4E/s73itdc8Gu14IPGrbtmPb9g7gdeD8tkhDQ+E6QTCiFIdvKWJbVha/nPER6ZVVUBWGSNQNuFUhauJq1IFwlIM2bOOuNz7lD+/M4brZC/BVh5m32RCJGryW4sJRFheMsvjRWyHWFe8Z49oCy3L/q7G7yTQ//Pd7aTx+XgoTB1sMzFXcf4afoXlScyGE6HqMpeo94nA78IwxprDOuoHEcs0AxpidgKWU6t7MtoSLt/j6EuBI27a3aK33FPcW4s4WFQ8FeLTWw4Aq27YLAbTWbdXqaABQZNt2FMC27ajWenNs/Y46+9V7s3GnnhzQRmmop8QCrw+8jsO2tFTW+XpQmeJnZp+RZAdC7PSnQmWdCSj8Hkj319zynPzNGvxRt8ZEb9rGnIJ+lGekEXWyawYKK94VwbM9gD/iIWQAv0WPcITSjJSakm8VdJhU4IECD1eNr53ObtEOw1MLI2RahqsP9dK/C80EJITYf5kGP1VKqWuAa+qsmm6MmR7bdiTuJEq/aK/07at4g3I6sL3BOj8Qb33wF7jFB72BdwFixcbFTRzTobTWNf/o3r17N7P3t12weC73f/gEPsfh0XEn8vtjz8cfCvN5wQAuXF7EnUfmYix3KkcAUrwoj+XG5IhDSXptt6WIpehp4Jxla3j7yQhTLu3Dju1h7vrDJgoChl5ei1l5OYwoD5IejBAs9rAlI5XsUIR+I2rPs6oozOotUTJ6eDjxPw6RCncWqgf+F2bJjenkZ0qRthCiczOe+r9TsQA8vZHdjwNGAeuU26amP/ABbtuiQXt2Ukr1ABxjTLFSakNj29rwZTQq3qD8NXAVULdR1CXAl3Eefy1uXXIxblECwGHAc3Ee35yNQD+ttSeWS/YAfWPr69rzZs+LPW+Yc65h23bNP8ryuSQAACAASURBVHratGkt7q9+07x38TluTvf7C//L3w8/naPKDKk7dvPMESMxOalu0XVJIFbJ66/tFG/gpUMOAAXdqgJ8MHoI+VEo7NubwllBvlpbxNgDUwkG3CPSIg6TdpbWTHgRtKKkRxwiCqbqHD5dF+XpOdU8tdZDxLLoW1GJ32MRsdws984qsIuinHaAF7aVwNMzoVcuXHZc/Uk1hBCigzktaOhljLkTt5EWAEqpQuAMYClwjVJqYqzu+DrgpdhuXwFpjWxLuHiD8s+AmVrri4B0rfVbuEUC8Q69qWzbvrTuCtu2X9RafxF/Uhtn2/Z2rfUC4GLcVnMXA/Nj9cZ1vQR8X2v9Km4r7bOBY9oiDQ1Vp2TULFf401ib352CzSVYOelszI1tU4p8y2HCui28M3ZYzf6WMWRGDP865pCadRev2AixmaQ2bQoz6uAMPuyXR7nPy7hdZQyuqC20qPJ5WJGZysmBNZSr3hz/WAB8nprGXpsz0zh11SqOWPc1i/IH8N7YwziolwXBMBzzK1i1xT3Rsk3w58sS8fYIIUSrNCy+btU5jHGUUtOAR5RSqbjVsZc1t609xDvM5mKt9SjgcmA5bu7yatu2t8V5nW+A7L2snw+0VeX5dcBTWuvfArtx04rW+l3gt7Zt28DTwOHAnq5St8fG+G5z/5h4KT+c+zLpwWpeGH8aAcvLVz2zKe2eFWtN7d7t5ZVXceKK9azO78aK3nlgDI5SbM1OqzlXt+ogI/t5WL3ZzQlnZVnM9GawMdOgIlFm9cjGC/SvCOAA3+RlUZidzlYzhrNe2szFRdWs6J7N133zALAch/vefZgDdm0GYMPEHzIgdzKs2lobkAH+uygRb40QQrRaa7pE1RxrzOA6y58DBzWyX6PbEi3ufsqxXOe9rbzOt95FrbWPNhzG1Lbt5bgBt+H60+osR6nfLD5hqsMWK5yxeD0OkXIfTsSww+MhJRiGqIM/4uCLOOy2/Mwa1J/rP53P44ePZkH/XhBxcIwByzB+WzEnFW7hhKv6clhmOsXFEY47LoubPgcrEMJb5TYWm5WdTm5+FgE8lPvdf+uB28sYtGYnAEPLK9md4qM61cfktStqAjJAdPYy+PFkGNgTRvSFlbFtk8e2x1slhBBxi6cbVFcWV1BubC5laHo+Za31h7iBN0VrPaPB5oG4ddVJ6dCVG/HG6pTHrt1MNz2K3dnpeK0QHkuRt6uqZt9NOZkUZ6Sxold3CEYYXrSB+2c+T2o0wn8OnUJWSgYjD8+l54Da3POvJjq882VlzYg2KuRw+rGZ2JscFrtxmN7RuqOPwAG7y5k9qBcfFQxiR3omPasqAHgwfxx/AUjxwew/wjOzoHcuXJyQkn0hhGi1fckpdwXx5pQbzqXcFygA5tD0fMp7Ol8fB3xWZ70DbKUdK8/b26oeudx3/Hh2Z6VhvBYBrwdQjNy+kQU9RxH2WfjCbkjtGwjwTUFfqlP8UFrNc+9OR293258NK93BjLt/z9Y1VeT1S62Zc/mQPhbnjvHy0nw3p9w7W/HQKV78Xjj+32FmbzDMzsllqHc3aZEoFV4vJamp5FQF+NFHC3ir11ns7KZ49+ACMo8fU5vw/Fz4SZt0HxdCCNFC8dYpf6tBl9b6R0DDwTkaHvf72L7LbNt+sVUp7KIeP3IMYa8HUjy1LZiN4T9vP8Jvw+fzxugjKNhRxrhtuxleVsniHhlgDD7j0LvKndnJAO8eeAabXy9iA7BxeQWnXVfTUp+HLsykf24VxZWGm09IJTU2SldplQMRw660FB44eDifnmlYXwY9tjpU/GcpfUvdHHKvYkO/YX2453SZLEwI0TU4yZ1R3qcJKR7CbVwVj4jWekzdFVrrg7TWZ+/D9Tu1qGXFJpOo/wm6dfIlnLJiIcN6efEbQ1rUYUluJu8N7A9eRTg7jd9PPJuoUlT50tic27fm2NV2Wb1zZaUq7jwrg+mXZHJgHzewfrgywtINYXfKx6owpx/k4zCdyfmTMvnVJdn0za3/L1+9Nkjh5vrF3EII0Vm1YkSvLmVfskjj2EsDrkbchVuEXVcx8AruUJdJx/FbEPl2O7ZXxkxgRr8DGFtWRWUwyhPDYzlfB1LLg/gUPHbkSbx10GFUen1cunwTvardWZ0Gjcls9rr/WRghsqei2cClmSWYihxUpjvGdtHZB7JjZTm9Sir4ZEwBhd1zeH9hkCMP8LfJ6xZCiETqbA29lFJv4o6P/Z4xZp8bL8fb0GtPg609MnDHmv5rnNfpZdv25rorbNsu0lr3ifP4LsevIOSzwHFqc8uxYFnu9fPZJgd/amq97lEpgSg9QmFyrSCbc7KIWop/jyrg0O3FBL0efv3D5t+uPt0s0n2KzFCEiqih4LKHCaSFSZl9M1ZBHlcel47ecByVEfCFIvTfUs67m32EZ0f540QLSwYLEWKfrNnl8NP3QwQjcMfJPg7uI7OxtSXT+X6jdgEvAruUUo8B/zLGbG7mmEbFW3w9B7eh1p7HK8Cptm3/Ks7jN2utR9ddEXu+Nd6EdjUhE5utCQWOISPWdckKRtiTlQ1ZFj0D7nqf45Addoe99DuGHqEQKKj2eZnTL5/qg3rg8zf95S6uNrzwVZiTthQzcWcZp+3cRf/yMkxRKZF/zQVg+jeGSvcyRP0etuelY0dSuOMLw/SFbdZDTYhOY0Wx4doPovxyVpSKUOI/4xe9GOSNZVHeXxXljKdl7vK25qj6j45mjLkKt/Hz3bgzIBYqpd5QSk1RquV3EPE29Pp9S0/cwL+BF7TWP8MduGM47gt4ah/P23kZQ93S/UlrNlIQDLPOF+CdvsNwLIvDd+9kiOOl0u9hZvdsUpwoRlkoZRhWXMFIypjXqztVPg/vXZpS7/SRqOGXH0X4osjhO6M83Hykl+XFkL2tGk/sd8fx+FnWZwCHbliD6pfDnHURps+shhCQ6cdJ8VCZWXveTRUSlEVyCUYMk16Istlt28j6MofnzkhsznVTae33aEt5bGY3TyeIHkmiM9YjG2PKcOd3eEApdTjuvAk1uWdjzB/jPVejQVlr3bexbXU1LJZuxN1ADm4XqAygEreh2J1NHdSVHbJpHfMHDAEFVijC0sG9UNtK+PmsN3jo/ekEvD4cfzq3T7mRC07N4s15HhyvxeiynZTk9OSzAT3xRx1OW7eFmQN7kp1aP8g/OC/KPZ+7Wd7Z6x3G5Csm9LcIpXuhxN3HATZOGMbhFw3Ge83RXHlvJZWx8bIpD3LU4DR2BRQrdkOfDPjuGJkpSiSX4gA1ARnc2dES7ZfH+bj53RDGwC3H+CQgt7HOVqfckDHmC+ALpdQ/cEuVfw/se1AGNtH0iFsqtr3Z207btiO4czHforXuuZcxqZNOjlNd0/raSfWxJjeNiNfHnZOu5J+v3c3Q3Vtwxg/hwbt7s+OUVzhm5KHMHjaQrdHurO+eBbE66f/17k5eZZDFOzI4qn/t+YvK6v9risoNk3ygj8pg7qeG7GCEjTlpXHvlFBioeGedoSwIeNzidK+Cx06C3pkWmyoVBVuLyPznXBjZD847ql3fKyESpXcGnDxYMaPQ/b5cMTrxN543HunjrJEeQlEY3kNudNtaJ6xTrqGU8gLn4OaUT8Ct7v1NS87RVFAuaH3SGrc/BGSAmQV1eoAZwHEIei0cy8OWnJ4M9lRi3fddou+v5amUfGaPHQrAFpWBssCkuPc6ZVkpmBKHcfn1P4hXH+rh3wsjbK2Ag3op1qwKMOqJanrkeXCGZWOXwsWjFJMHwcn/ifLJegcsP2QqMIZIdZjR94cwwA8OivLg9bdCcSxL8eA18INT2+FdEiKxlFK8/R2LGYWG7qmKI/u1zw/6oG4SjBOlM9QjN6SUGgF8H3fOBS9u1ewNxpjlLT1Xo0HZtu29TmnYGlrrMI3kum3b3i/64lhRQ8Fud2jNTQ/cDKflAuCtXs/sEbUDgjh+L9mRMKWxoFyV6mNC7yAZ/vqfxOF5FitvTGVDqSFcHeW8e8oB2LYjypT+VSz9SRZeS7G+1PDJhthbv+cOUynweTAht8HZQ4s8XO3LYzyxoPzsLBjSC045BCG6Op9HcfrQTvhLLlqls+WUlVKfAhNxc8U/BV4yxrS6hV/c/ZS11iOB43FH8ap5V2zbvr2xY+qY3OB5P+DHwBPxXr/L8SqImlgBv8IKREgJRQFYuCLM+bFpMjJPHsSx89fzibuJA7fuoGdVJf8dHZvKMWrYGNz7XXdWimJ0vmLlFqfeep8HvLHGED3ToVcGbKuof6wnaojWeZ6Wkwp75vz6bDmc+gfKfnAGaf+4Cp/UiQkhOolOWKc8H7jOGLOsLU4Wbz/li4EncadgHBv7Ow6YFc/xtm1/updzfg78B3gwzrR2KZ6oQ9QXe3sdw5DyStJiE1QcOLx+S+rbbhnEsMVR1uyG8O2FjFiznv8WDAJLQWWYwmbGaBnRx8uPT0/nyZnVDMjz8NOptXM5p/sUH1/s5ZcfR/hmURWRCOQGw4wNVPFWXnfKo9AnU5H/5o/h7bnws9oG8ZX/ns3hg85j1vWZ9MyU4ri2Fo4aXlkcwWPBuaO9NeOaCyEa53SynLIx5mYApVQ6cCSQD+wAPjfGVDV17N7Em1P+FTDNtu0Xtda7bdueoLX+LjCypResowg4cB+O79QsRW1O1FLccl4W/l0h8vO8nHRU+rf2v2yMW1w9eWAfJn31DVSE2FMg4fU3HxCvPzWD60/NqLcuGDE8aEcpDcLniyo4aHMFfWN9oQH8qSHw+dhSanikKIdbf3oWPPExLNkIwPyeA1m+3eHJeSH+74TUlr8JokkXPR/g1SXu/+PKQ708cV5aM0cIITphThml1JW4g2nl1lldqpT6mTHmXy05V7xBeSDfntHp38BG4OfNHay1bticNwO4AmiT7H5nFK37yXEMB+cbxh+d0+xx6wt6cO35Z0CaD4JRsBSnqu1A80NsNnTtOxGe+iZWtO1JZZenuiYoG5/Cb4U5u3Axi3sN4N7F/Zm5M8rTb9xG8W9f4bXVirsmTAGgR0Yn/BZ0ccYYXl9ae4P02pIIT5zXgQkSoovohHXKp+OW+N4FPA1sxh1M5DLgfqXUVmPMO/GeL96gXILbz7gE2Ka1HoU7tFhGk0fVmtPgeQXwFfDdOI/vcrqXVLAzOxMUpFWHKIjznRrd38vabR5u//QrvI7Dq2OHstNxCOwOktotpfkT1PHZpjpt6zwWi7JSCXkUZw718OqmCPOm30q/8t0EvD5OvOY3fBgcya+ys3n0me9R8naAgpVhJg3zcYXeL9ritSulFIcPsJi7wb1pOmKADMUoRDw6W1DG7e77E2PMw3XWrQVuV0ptBX4BtHlQ/gi379UTuKOUfASEgffiOdi27f2uQnJnXiZEAAPVWSnsqAzQLY7jbhqr+PDZDaTHcrTnLVzN4qwMXr6okMs+aNhermlnjrD46/+ioCAlzSKYms3yiMPaiMN5G+fQr3w3AKmRMBcsnMvng0dSHnIDxt1T07gbKU5NpLevSOfvn4fwKLh5otz4CBGPTlh8PQ44rZFtzwP3tORk8Q6zWTdH+ztgOZBNMg+Tua8sC1Jq51EOp8WXy7UW7CA9VDuVogGOW7GBiDEYx6Ba0BjonskejuqvmP6NYcaG2EqvRcgolvbqT0RZeI2bU/umzyB6EOE3R7YsNy5ar3u64rbJ8n4L0cU19aNsmtn+LfG2vh5o2/YGANu2DfBcHMd8QtMjghE736R40tDV5JWUUZKdiWNZ5JaV09vyA803lgoGDCVZ2VBRgTcSJWdXOT7H0Gty3xYFZHBzvOeO8rC8xGHGhvrdphYMHsqZF/+U8xZ/wfKeAwmZfrwZWMm8t/rwWdhw9tnd6NXL16LrCSFEonXCsa8XAJcCj+xl28XAwpacLN7i67WxIPsv4DXbtuPpGP1RneUeuKOdvA6sAwYDZwOPxp/UruXZl/7B0ZtWEvZ4WZ+dR87N8c3p0Wt0NlUfV7O9Ww4YQ8+sEs48K5djrm79AGs/0YrVJYr31ziUlUfx+hXVjuKz4WNZnj+CXWl+BpWV82FViNWfuIOQrFoZ4J57B7b6mkIIkQidsE75LuAlpVRP4FncnkX9gEuAXwIXteRk8Qbl4cCVwB3Ag1rr/wCP27ZtN3aAbdt/2rOstX4TOMe27Q/rrJsM3NSSxHYlYzZvY1doJA4Ww6vXUl0dISuO42ZXplKREqtfVIrK4Xkce+0gWjEDWI00n+KJKR7cYcrd3O+WXVHOv7uUz3MyMUqxqGd3KoIBdg7y4TGGI7fvJhIxeL2d7gsghNiPdbacsjHmHaXUj4B7cSef2KMc+LEx5u2WnC+uBli2ba+zbft3tm0XAOfj9s/5RGsdb7b8eOC/DdZ9AhwXb0K7mi3qAAJkESKD9dYYSjLi69JUting1kfHdB+Xu08Bua6PCx2ufTfCA3aU3t0tDjsht95d54aUVMr9XkpSfMwvyJOALITofJSq/+gEjDGPA/2BKcA04FTc3PLnSqm/t+RccQ+zWcdM3EZeA4Bj4zxmI3Ahbku0Pc7DnYkqKQU8qWRRDYDBotvfXoXbzgdf02/58aP9LJ9d+/yiw9umVe7ynYYpL0SIjfTJR19W8eEuLymOIWhZ+C13qsc9rQB86dJFRwjR+XS2nPIexphKYIZSKgU33s0AjgDmtuQ8LRn7eixwFW45eRB38JDvx3n4z4FXtNbXAYW4dcqH4wbmpPQvPZL/+3Q+loFHDhvNX++YDiYKf76syeOOOjqL8oooy5YFmKAzGDvm26N/tcbSnaYmIBN1+LQwyuHFpXSPRCn1e0nD0O/UPB5dqvBZcN8prblfE0KIxOqEdcoAKKUOBK7FHTQkHbckeooxZkZLzhNX8bXWej7wP6AP7tRUg23b/rVt26vjOd627XeA0cAHQCXuHcRo27ZbVNbelRy782suu+pUzv3+VIaFV7tdjxZtaPa48oooH35WxWcLg7wzs4LqaqfZY+JKz0BF7wzAMVARYreyqPB6sIBuoQipoSivf1JBbjjAV1NDjLn2Tb7p9QSbb/uyTa4vhBBtwSir3qOjKaWmKaVmA4txq2Rvwy26LsZtmd0i8WaHHgWes227pKUX2MO27TXAn1t7fFdzz9FTWdqzPwC/mHwx35//CWnTmq9C/++nFazf6PZTXrUmxKy5lZwyKZ4mYk2zgKryMARqe6ltTvExKBBCAQGlKPZ6CFYYVv3yC/rP3gLA1t/b5JwxmAydv89pEEKIfdUJi6+fwh3h8nRjTM2AWq1tCxTv4CH7PJOT1vpc4GrcuuiNwGO2bb+yr+ftrCp8tX2SQx4vpTPvIO2YYc0el5ZW/84vLbVtPoDrdjuUBajXc3xTqp+l3X0ESsJstrwEM9z660965DONlTX7mWAUIYToDDph8fVvcGPb60qpd4HHacGwmg21S95fa30NMB133sm/xf4+orW+tj2u3xEynTBp4SAeJ8qBWzeRc8TQuI478dhMjp+YQa+eXk4+IZOJR8Q7vHjTPBZgjNta0Wu5K3weNkY9rMlOpzorxV3nsXjugGFs7pmNo+D1ww7gy3692iQNQgixz1SDRwczxvwJGII79oYBXsHtq5yLOzFFi7RXa56bgdNs2/5izwqt9eu42f69jYLS5d007xN+PekMgjj8YNEcfNYBNPYJqtpWzeIHloOCg340iuu/16PN0zMo14JgxA28qR7weyFq6LerjHB+FtVh3KCNW5Q99ebz8TiGqMdi2zKHSYM7vu6mvazd5fCkHWJArsX3Jvjimuc4EDF8sdkwMFtRkNsJfimESFKdMKeMMcbgzgXxnlKqD27O+bvAPKXUa8aYC+I9V3sF5b7AvAbrvgJ6t9P1290dx01hR053AP7vtAu5OhiB9L13b/rku3MoXuxW1xd/s5tTX2vbkUcrQ4aPNhgyMjxUBoCAAylAmodqv5ePJ4Z4siyVR+a5jcrKKg2eqipMmg8y/IzN73xfgkQpCxgmPljJlnL3BmVjicPtpzQ9PGogYjjuuShfbjH4PfD6OR6mDN1/bmKEaE+tqVNWSr0OFOD2/KwAbjDGLFBKjcDNHObh1gtfboxZFTum0W1Nps+YLcAflFJ/xO23fE1L0trsL4fWepjW+hyt9ZCWnLiB5bhjg9Z1MdSpuEwyAX9tAHaUYpfT+P1PycqymuXdK0rbNB3hqOGEF6Nc8JZDZVYapFiQ4we/B5QityCNYROyea1uO3qlMH4vvsogf5tk8cPx+0+AWVvs1ARkgDmFzdenz9ti+HKLe0woCo9+0zYt5oUQ32aUqveI0xXGmHHGmENwZ216PLb+YeABY8wI4AHql9w2ta35dLreNcac3ZLjmvy11Vp/B1iGW0a+VGvd2PRUzbkFtw55ttb631rrWbgv8OetPF+n1z9YRnppBf6yKoZuKeLcF8ON7jvk3EE1y0PPG9ym6VhdAvO2guUYvrNkPbfMX8nli9eREnKnhvRm+Zi90bC9qv6H2/F5yPArbpzgabMRxbqCET0shveo/VqcMar5wqSB2YqUOmOtDO+2/7xfQrS31gRlY0zd3E4O4Cil8oHx1A5q9TwwXinVs6ltbfIimtDcL86vgVuBB4EfxZbfbelFbNv+VGt9IG7ueABu2fvltm0XtvRcXUVa0W6qBrj/PxN0mL+l8dzTEXccyuAzBqAsRe+j2rbrUf8syE+HHoWlHLCzjG1pKbw2pD/BMIDD9rVBXtgcxufNIuwoiNQ2Brt4XCqRoIM/bf8Z3Svdr/j8+nReWRRhQI7itFHNz5Q1KEfx5rkepi9wGNZN8fuJ+0/JghDtrbV1ykqpx4CTcRv3nIobi4qMMVEAY0xUKbU5tl41sW3HPr+IJjQXlAuAe23bdrTWfwV+3NILaK29wP3AT23bvqMVaeyS/tevtrR/SY9+XD2i8aCslKLPxMS0cM7yK2Ze6GH6SwqzDD7pn0+5PxZoglG2VTu8UWFxZJ9qClNT2FCmIFZns+ulQh76dxkX3jOG3sPjG7s7GfTIsLj2iJYNb3pygcXJBRKMhUi0hkFZKXUN9ettpxtjpn/rOGOuju0/DfgLblemTqe5XxGPbdsOgG3bYaDFAzHbth3Bnboqnukek4by1cldpni457SOm8x+VJ7isrO78fBxB7Kuby74lNvSOhQh6PEQ8HqZtctiQ4kBx5ChHCZs2s5B24qpLosw78WiDku7EEI0xRgz3Rij6zy+FZAb7P80cALu3Av9lFIegNjfvrjjaGxsYltCNZdT9mutb63zPLXBc2zbjmeUrjeBc4GXW5i+LuuE1Zv5YGwBGDhqdRGB6oHktE2X41a57XNDCbEbBZ9FN4/D7kDsjjPF4zb8AnAME3o4nPJRYc2x6bnNF+EKIUR7aGnxtVIqE+hmjNkYez4VdwjM7bjDYF4MPBP7O98YsyO2X6PbEqm5oPw/4KQ6z79o8NwQ39CZPuCZOhNS1JTl2rbdoubiXcVN78zjuCUbCHssjlxRRPeHL+nQ9KTXiasKsK9J4YzHoyzbYdzBROpsPOVAL6f+eBgL3t5K3qA0Jl41sN3TK4QQe9OKOuUM4CWlVAYQxQ3IU40xRil1HfCUUuq3wG7cuR32aGpbwjQZlG3bPr6NrhOmthWbJ/ZIap8dMYQdjpeosijvns5JjfRRToiv1sC1D0M4Avd9F044iL8cZ7G5IsqGMvi/CRZDchXXT/TzozdDsUFD3A/60G6K7x3iJRDpxZVTk7YbuRCii2ppP2VjzDbcKRT3tm057oyFLdqWSK0aPERrrYDTgGtt2z6zuf1t275Ka50JnIE7EfQm4B3btstbc/0GaUkHngAOBSLAz/Y2+5TW+njcluN7+kYHbdtO2Bt+1yRNZE+9ctThzqiDx9NODYEuuw+Wx+qBz78Hdj7FwGzF7Ivr/7vPO9DLPXMdCksMGX6Hqw72cGCeou/fQkQc+MXRHu44UaZwFEJ0Hp1xRK+21KIoobXuq7X+LW4R9GtAWdNH1ByngTXAncBZwF3Amtj6ffUzoMy27WHAVOCx2A3A3iy1bfvg2COhd0A9qst4+NUneOLF6QzZvR3HNH9Mmymtql0urwZn7y2/e2UqvrnOz/cneBnbx0NOquLPc6JEYrvf+VmUylB7JlwIIZrWysFDuoxms0GxXPEU3MmbpwA7cQfaPtS27UVxXudB4K+2bd9V57w/Bx4CJrQ00Q1cCFwBYNv2Kq21HUvnS/t43n3yyTP3MXLDOgBOXrsM31/+2X4Xv+cKuOqfEHHg3ivBavze6501Do8udKPw3CJTp7YfclMhRTLKQohOJBkDcV1N/uRqrX8DfA+3Kfi7uC2o38NtFr6tBdcZBdzbYN1faZt+YgOB9XWeb8Dt4L03I7TWX+PWcT9o2/ZTbXD9vRpetKlmuU/JLohEwdtOVemXHAtnH+7mkDPTmty1qGEFgkfhiUbpka546Xw/3s43d6kQYj+2Xwdl4Pe4A3Gfbdt2zUherSh1XgCMif3d46AGz/cqFkQba/7bkhE3vgYG2LZdqrUuAD7SWhfZtv1RI9et6ZDeu3fLGzzt9uXTI+rW61ao7mRaVvvOMpYeX7/oS0db/GlmmN2Om5vuXh6gW1kQrxc8gRxa0jW9KmT4enOUgm4W/XJkIA3x/+3deZgb1Znv8e+Ruts7Bq8NGGMWm30JvDDDsA57AgkkrJ7QEAgYnGAuzEPYQoAJySQXSDIPAS4YAiRmSXDYJgwErjMQLlmGOYDDFnDABmMbj23ANl7a3ZLO/aOqsdzuRe1uSaXq3+d56qFUVZJeC1W/euucOicZHngjz5XP5RkxyHHfF7PsNlrfzVrW35NyE1Fi+o2ZvUo0iPf9RLdC9cQzwBNmdhdRVTuBaFqr6Wb22b1C3vsH2j/Re79PVy9sZvOBbVk/9Nl44NkOXmdl0fq8eOrIA4EOk7L3fjrRHNA0NTX1uGH1G0edl6OhxgAAGRxJREFUyg+feooMgesPPYLpJLPLeeNQxx8nZ7j0tuW81+xozkdXsFtz8LW7V3HcEZvxo6OyZLo5EVatCxx4+xpeXVxgcD08fc4gDpqga99SXSvXBb72RJ7WAsxfGZj6dJ7nz1BSrmX9Oil77+8H7jezXYiS87XADUT5xSh9HOxziC4Zn1W0LRdvbxOAjZJyCWYStXd7M5tI1EY9uf1BZrYlsNh7H8xsBNEYqFdvwvuV5O1x47jz6C+SAV7fsu/nR+5LO49v4Dc/GM3iT/Ls/f2VLMnW4YDh+QL/9l959hzjOHvvrn9SPDc3z6uLowbpNa3wM9+qpCxV15rns46LEH03pbaFdOfk0m6J8t7/FbjEzK4ATgXOI6p8vfd+/xKev13vwuzSjcC9ZvYO0Y3hU9putTKz7wKLvPe3E7WHTzWzVqJ/98+994+XK6hjlyz7rDI+YvEyXOismTsZnHOMHp7lo/p6CNEvpOVxG/iyNd1fKNhuhCObgXz8B3DHkapGpPpGDnb84LAsV/0+z/AB8KMjkni9SnqiX1fK7Xnv1wEzgBnxrE9VH43Le78aOKWTfdcUrd8CVKwLdC4fDWMG0XzKmWzyvkizFxc4+99zfNoSuOnIOg7eJk6sbVMIO8cuoxxf26v7P2S7jc3y0OSB/OKVHLuPzXDZIRUcLCUB7n2plSffznPwhAzT/qF//duT7vIDslyyf4a6DN02w0jyKSl3wnv/JnBxH8aSKh8MH8a45Z+SCYHFw4ayrrXAgPpkVY/nPpFj9v9EVfA/PZrjxF0ytAysh7U5Mhl44LQBfGW3OupL/EHxld3r+cru/W+c7Fnv5Dj74Wi+lZmvw6ghjsl79b/PIckaEvijWDZNoT8nZTP7G9106vLeT+rTiFJi2/2GM/elqMIcMXFw4hIybNi+ti4P760IUJ+B+gYKwOe2Lj0h92dvLy10+VhE+k6o7H0sFdddpfy9onUH3Ap8o3zhpMcBhw/ngQ/rCPnAt4+s4vRQXfjRUVlOfTjHmla46cgs4zfPMPnxHK0FOGGiY+KIakdYG07YtY7vP9fKh58GthgEp+2pKlmkXPr15ev2g2uY2Y/LOeBGmtx521zOencBzfUDeHrRcM6/Z9fEDcTx+R2zLP9WhofeyHHl73LUZ2DG8XVMHJlh77EOl/Ivf18ZNzzDa/9rMK8syrP72AyNw5J3VUQkLfp1UpZNt8+7C3izcUcAhrQ2s2ZNns2GJu/jXrwKzng0RyHu3HXmw60sunSgOsT00MjBjiN3TN7/X5G0SXtS1k/6MlnVsH54y+b6gaxbnatiNJ1btjZE81WEAPkCLbnA+8s1CUWb//hrK9c904z/IN/9wSIivaSkXCbNYS3ZuPwctXwRo8aUNuxlpe0xxrFXo4N1OWjJ49blWLFWCQjgkddaOf6etfzLrBYO/j+reWuJPheRagtuwyVtetr7ejMzm1N8jHpfd+zOXY1BhQL1BVgxZgtuzQca6pL3Dco4x/cPzXL83OhxCPCl+9cxe1odO2yRvHgr6fl565Nwcw5e/CDPzmM0+IRINfXrW6LYsPe19MDgDKzORr1wsyEkrpNXsQlbZMg4PpvzeVUuw/f+kOee40trI13bGqjPQF3Kbp86ZlKWn/4h+lyGDUDDhookQNrblHvU+1pKV58rQH1UVWUKgUIhkEloYt6tMcv3vjCQq55pgYyDgVkGl3hXz/W/a+Ha37UwrAEebRrI4TukJ3F9fud6np/qeHlhgaMnZtleQ4eKVF2/TspmVgc4731r0bavAXsDz3vvHylveLXrkOWf8J+bjyDvHIcs/Yi6ugnVDqlLVx7aQEsmy/TZeXYZ5bju4O4v0y5bHbhmVgsAK9fBZU+14C9MT1IGOHBCHQdOqHYUItIm7Zevu/vp/yvg7LYHZnY10XSGBxHNHnVuGWOraXOGDmGkyzPa5Xhn6BDyheT3aL724CwLpzUwa3I9owd3/8Wvz0JDUe4eqiGfRaTM0t7Rq7ukbMATRY+nAed67w04A5harsBq3TujN+P9UcOYP3IY87YaXu1wymL4QMcvTh3ADiMc+4/LcPuJA6sdkoikXMBtsKRNd9cat/DeLwKI51QeDjwU73uMqGqWDuSy63/vFDKO5tYCQwakr+fuaXvWa1hJEamY/n75erWZDY3XDXjde98cP3ZoRLBODWheP1iIW5dnUAInpBARqTXBuQ2WtOkuU/w/4Hoz2xk4H/ht0b6dgA/LFVitG9DcAqtaYXUrYXUrNdCkLCKSeP09KV8OHAu8CWwG/Lho31eBF8oUV81rztZBXQayGTID6zR0mohIHyi4DZe06e4+5XnALmY2wnv/cbvdNwAtZYusxrXWr28/LmQcrSEwIMGdEhasKPDmkgL7jcuyxaDkxiki/Vsaq+NiJbUJd5CQ8d4v7/tw0sM5CBkXtbznA/UJHTgE4KWFeQ67q5lVLTB+c8eLUwcxdmhy4xWR/quQ4OKmL+iqapm4ukw0OpZzkM2Q5CblB1/NsSq+5jF/eeCpOcmc0UpEJO1tyuo9XSaD6x2fxuOgJXV4zV+/XeD8/1sgl89AnYNcwDnYaZR+q4lIMqWxHbmY/vqWyc4jiaZcAiZsBtmEJeYQAmf/tsDHzbCy1bH5yHqOnZhh7KDAkXeu4Ybfr6t2iCIiGyk4t8HSHefcSOfck865t51zrznnHnHOjY73/b1z7i/OuTnOuWecc2OKntfpvnJSUi6TeZ+EaNLLQmDuJ4FcAu+JKv4+bzbAUcgHFn8aWNMKlz/VwvzlheoFJyLSNwJwQwhhpxDCHsC7wA+dcxngPuCbIYRJwPPADwG62lduSsplcuC49Rlv/y1J3NSNzjnuOTbDiIEwdjDceUxmgyTt4j5qIiJJ0tM25RDCxyGE54o2/RnYFtgXaA4htN3aeztwarze1b6yUptymaxYEyAfVZrLViUzvZ00KcNJk9b/LtvqCwOY9/FaFq0MXHvkALbZXL/ZRCRZetOmHFfAU4F/B8YD77ftCyEsc85lnHMjutoXQtjobqS+pKRcJm99VKCty/W8TwIt+UBDNpnJuc3ujVnevnRo9weKiFRJ+0konHNTgClFm6aHEDqbl+GnwCrgFuDLZQmwl1QKlcm5n1s/eMiZe2USn5BFRGpB+45eIYTpIQQrWjpMyM65m4CJwGkhhAIwn+gydtv+UUAhroS72ldWqpTL5Pp/rOe4iVmac4FDt9VvHxGRvrAps0Q55/6VqJ34uBBC260lLwGDnHMHxW3HFwAzS9hXVkrKZfT345SMRUT6Uk/blJ1zuwFXAnOAP7ooqc8LIXzZOdcE3OGcGwi8B5wBEEIodLav3JSUy2TB3LU8dMeH5FoLnHh2I5P2UFutiEhv9XSYzRDCG3RyM0kI4Y/AHj3dV04q5crkvpsXMv+dtSx6fx333rSAEJJ3n7KISK3RMJuySZrX5Nevr81TKEA228UTRESkWxpmUzbJl85sJFvncBk44cxGsup9LSLSaz0dZrPWqFIuk30OGs7u+w2jUAgMHKQSWUSkL6R96kYl5TJqGKALESIifSmf7pyspCwiIrUjjZesiykpl1GhEAgkb9pGEZFapY5eskkef62Fza78hMGXfcLd/6W5iUVE+kIBt8GSNkrKZTLtkTWsboGWPHzz4dUUEjifsohIrck7t8GSNjV/+drMzgAuA3YFLvbe39LFsecBlxON7vIUcJH3vlCOuAbWr19vyDpS+N0REZE+loZKeTZwOvBAVweZ2XbAtcABRDOFTKSMY5neO3kIO43JMGFEhgebhuCUlUVEeq3gNlzSpuYrZe/96wBm1l3FezLwmPd+aXz8ncDZwC/KEZdtU8dFhw2mOQcHbV/f/RNERKRb+RS2Ixer+aTcA+OB94sezwe2KdebnTWzmV++mgPg4Tda+cMFQ8r1ViIi/YbuU64yM3uZKKF2ZKz3Pt/Jvt6+7xRgCkBjY2OPn//s3PVh/fH9Ai25QENdyr9NIiJlpvuUq8x7v08fvdR8YNuix+OBD7p43+nAdICmpqYed50+emKWGa9ElfKh22WVkEVE+kAae1wXS3xS7kMPA8+b2b8AHwHn0U3nsN64+6SBHLZ9jubWwFn7qk1ZRKQv5KodQJnVfFI2s8nAjcAWwAlmdgVwtPf+TTP7LrDIe3+7936umV0P/Dl+6jPAfeWKqy7rOMeUjEVE+lLaK2UXgga16E5TU1OYMWNGtcMQEak1fZ5B97hwyQZJ67VbxqQqS9d8pSwiIv1HTrdEiYiIJENrunOykrKIiNSO1pS3KSspl8uylXD9TGhugStPggljqh2RiEjNa612AGWmpFwup/8YfvdqtP7s6zDn1urGIyKSAmtUKcsmmT13/frfPoSWVmjQLVIiIr2xNt05WUm5fIq+OQPqlJBFRPpAS8p7X6dh6sZkGlCUhAtAvixDdIuI9C+u3ZIySsrlcuOZ0FAHmQzcdBZks9WOSEREEk6Xr8vlnw6BE/aHfAE2G1ztaERE0iHlHb1UKZfTa+/DK/NAQ5mKiEgJVCmXy7fvh399OFo/+3C4+8LqxiMikgaqlGWT3DVr/fq9z0JOHb1ERHpNHb1kk+y89fr1HRuhTh29RER6r2dZ2Tl3k3NunnMuOOd2L9o+yTn3J+fcnPi/E0vZV25KyuXy0KUw9Rg45wh4+ppqRyMikg49r5QfAw4B3m+3/Xbg1hDCJOBW4I4S95WV2pTLZezmcNv51Y5CRCRdenjJOoTwAoAraot2zo0B9gGOijc9CNzinBsdv0OH+0IIS3sTeimUlEVEpIb0SUPyNsDCEEIeIISQd84tire7LvaVPSnr8rWIiNSOdpevnXNTnHO+aJlS5Qh7RZWyiIjUkA0r5RDCdGB6D1/kA2Br51w2roSzwFbxdtfFvrJTpSwiIrWjD26JCiEsAWYDk+NNk4FXQghLu9rXi6hLpqQsIiK1o4dJ2Tl3s3NuATAOmOWceyPedQEwzTk3B5gWP6aEfWWly9ciIlJDelYehxAuAi7qYPtbwN918pxO95WbkrKIiNSOFI7iVUxJWUREaofGvhYREZFKUFIWERFJCF2+FhGR2pHuq9dKyiIiUkvSnZWVlEVEpHakOycrKYuISA1RUhYREUmKdGdlJWUREakd6c7JuiVKREQkKVQpi4hI7Uh5paykLCIitUPDbIqIiEglqFIWEZHake5CWUlZRERqSbqzcs0nZTM7A7gM2BW42Ht/SyfHHQY8CcyJN63z3ldlEmsREdlE6c7JtZ+UgdnA6cAVJRz7pvfeyhyPiIjIJqn5pOy9fx3AzArVjqU/Wbu2wKKFLYxtrGfo0Gy1wxGR/kKVcqpMMrOXgVbgNu/9z6sdUC1asSLH969bwNKlOYZtluWqa7amsbGh2mGJiNS8xCflOImO72T3WO99vsSXehnYxnu/wsy2A2aZ2ULv/axO3ncKMAWgsbGxp2Gn2st+NUuX5gD4dGWeP73wKV8+eWSVoxKRfiHl9yknPil77/fpo9dZWbQ+z8weAw4EOkzK3vvpwHSApqam0BcxpMWo0XXtHtdXKRIRkXTpN4OHmNmWZubi9RHA0USdxKSH9thzCE1njWaPvQZz0ikjOOiQYdUOSUT6C9duSZnEV8rdMbPJwI3AFsAJZnYFcLT3/k0z+y6wyHt/O3ASMNXMWon+3T/33j9etcBr3OFHDefwo4ZXOwwRkVRxIejKbHeamprCjBkzqh2GiEit6fNa1v2gZYOkFa5sSFW9XPOVsoiI9CepysEbUVIWEZHake6c3H86eomIiCSdKmUREakdqpRFRESkElQpi4hI7VClLCIiIpWgSllERGqHxr4WERFJiHTnZI3oVQozWwq8v4lPHwUs68NwKkmxV08tx6/YqyOJsS/z3h9b7SBqSghBSxmXfffd11c7BsVee0stx6/YFbuWTV/U0UtERCQhlJRFREQSQkm5/KZXO4BeUOzVU8vxK/bqqOXYJaaOXiIiIgmhSllERCQhdJ9yHzCzScDPgZHAR8CZ3vu/tTsmC9wMHAsE4Ife+7sqHWt7Jcb+HeB0IA+0Ald575+udKztlRJ70bE7Aa8At3nvL61clJ0rNX4zOxX4DtEdmgE40nv/P5WMtYOYSvnejAHuAbYB6oFngYu897kKh1sc003AScAEYA/v/esdHJPIcxVKjj+R56uURpVy37gduNV7Pwm4Fbijg2O+CuwITAQOAK4zswkVi7BzpcT+IrCf935P4BzgV2Y2qIIxdqaU2Nv+yN4BPFbB2ErRbfxmZsB1wFHe+92Bg4AVlQyyE6V89lcBf42/N3sC+wJfqVyIHXoMOISuxx1I6rkKpcWf1PNVSqCk3EtxNbAP8GC86UFgHzMb3e7Q04A7vfcF7/1SopPrlMpFurFSY/feP+29XxM/fJWoYhtZsUA70IPPHeAK4AlgToXC61YP4r8EuMl7vxjAe7/Ce99cuUg31oPYAzDMzDLAAKABWFixQDvgvX/Be/9BN4cl7lxtU0r8STxfpXRKyr23DbDQe58HiP+7KN5ebDwb/rqd38ExlVZq7MXOBN713i+oQHxdKSl2M9sLOAb4ScUj7Fqpn/2uwPZm9ryZvWxmV5tZtQcaLDX264FJwIfAYuBp7/0fKhnoJkriubqpknK+SomUlKVkZnYo0R/aydWOpRRmVk90m8gFbQmkBmWJLv0eBRwKfB5oqmpEpTuFqFLbEtgaOMTMTq5uSP1HrZ2vElFS7r0PgK3jdsu29sut4u3F5gPbFj0e38ExlVZq7JjZAcB9wIne+7crGmXHSol9S2AH4Ekzew+4GDjPzJJwP2dPvje/9t6v895/CjwO7F/RSDdWauzTgPvjy8AriGL/x4pGummSeK72SALPVymRknIvee+XALNZ/2t0MvBK3BZVbCZRQsjEbW8nAr+uXKQbKzV2M9sP+BVwsvf+5cpG2bFSYvfez/fej/LeT/DeTwD+jaitcErFA26nB9+bB4CjzczFlf8RwF8qF+nGehD7PKIezJhZA3AksFFv4QRK3LnaE0k8X6V0Ssp94wJgmpnNIaoOLgAwsyfj3rMAM4C5wN+APwPf9d7Pq0aw7ZQS+23AIOAOM5sdL3tUJ9wNlBJ7kpUS/y+BJcCbRInwDeBnVYi1vVJivxg42MxeI4p9DnBnNYJtY2Y3m9kCYBwwy8zeiLfXwrlaavxJPV+lBBrRS0REJCFUKYuIiCSEkrKIiEhCKCmLiIgkhJKyiIhIQmhCChER2YCZ3Q0cDyyJx1zv6tifsP7+88HAGO/95mUOMbWUlEVqkJldTTRb1GHVjkVS6V7gFuAX3R3ovb+kbd3MpgGfK19Y6aekLNLPmdl1wEHe+yOrHYskg/f++fYzY5nZDkQzgo0G1gDnee/favfUycC1FQkypdSmLFJF8ShdIrVgOjDNe78vcCnRICWfMbNtge2A/6xCbKmhSlmkj8XjbN8NHA3sDbwFTPXe/7eZ3QvUE00+/yWi4RCnxpMH3ADsTDSr0k+893cUveZxwI1E4zA/B7zT7j0DcLD3/oX48WHALO99Xfy4HvgWcBbRONVLgMuJJry4CsiY2ar45fYECkRzJP8d0RSM84DJGke5fzKzocA/ADOLBssb0O6w04nGaa/VyV8SQUlZpDwuAL4IvAb8M9GkGDvE+04hmunp68AAM9sO+C0wlWgSAYuP/9h7PzN+3iPx8b8EDgceBf67B/F8L47nlDimrYER3vtXzWwX2l2+NrMHiCZm+BKQA3YDPunxpyBpkQGWe+/37uKY04FvViie1FJSFimPn3nvXwIws/8NfIOoNyvAC977X8Xra8xsMvCy9/7eeNufzewO4FyiyRFOB1703t8X73/GzB4jSqzdiudf/iZwmvf+1XjzgnjpTAvQCGzvvf8r0RSM0k9571ea2TwzOyX+oeiAPb33fwEws52BLYA/VTXQFFCbskh5vNe24r0PRFXnuPb7YtsQXR4u9m68nfh57Z/TkwkSRgNDiCaEKNW34vf4jZl9aGY/jS9hSj9gZg8SJdidzGyBmX0d+CrwdTP7C9HEKCcUPeV04Jfxd116QZWySHlMaFuJq4rxRJXprkTttcU+AL7Qbtv2rJ/DdyFwTGevH1tFlHjbbFW0vpSot+xEopmP2msfD/E0jBcBF5nZ9kRzIV8GXNPB8yVlvPeTO9l1bCfHX1e+aPoXJWWR8jjHzB4lar+9hGhQhf8g6vzV3oPAd8zsTKL5k/cBzidqY4aoHfma+DL3TOAwojl+fdFrvAScZWbPEiXkf27b4b0PZnYbcIOZzSeqcj5rUwYWA+PNrMF73wJgZqcBLxJV6CuILmerA49ImenytUh5TAduJuocdRpwnPd+RUcHxnP1fgG4EPiIaD7f73jvH4r3vwOcTFSlLidK8ne1e5kLgR2Bj4GHiAZ/KPbtePtjwKdEPbh3jPfNJKrKF5vZ8rjj2eeA3xNV4G8ALxP1/haRMtJ8yiJ9LL4l6uqijlkiIiVRpSwiIpIQSsoiIiIJocvXIiIiCaFKWUREJCGUlEVERBJCSVlERCQhlJRFREQSQklZREQkIZSURUREEuL/A9asjB5X+PEEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 540x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.dependence_plot(\"products\", shap_values, X_test, alpha = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now predict the churn date of top 3 clients which were excluded in the beginning with trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the model to disk\n",
    "filename = 'churn_date_model.sav'\n",
    "pickle.dump(xg_reg, open(filename, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[13:21:02] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
     ]
    }
   ],
   "source": [
    "model = pickle.load(open(filename, 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['month', 'time', 'vertical', 'industry', 'client_country',\n",
       "       'client_continent', 'client_region', 'OS', 'device', 'environment',\n",
       "       'criteo_product', 'spend_criteo_euro', 'sales_criteo', 'CPO',\n",
       "       'revenue_criteo_euro', 'AOV', 'COS', 'churn_date', 'Android', 'MacOS',\n",
       "       'Other', 'iOs', 'Desktop', 'Mobile', 'app', 'web', 'awareness',\n",
       "       'consideration', 'conversion', 'products', 'onsite_sales',\n",
       "       'onsite_revenue_euro'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "save_top3.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6082     0\n",
       "14463    0\n",
       "12587    0\n",
       "Name: churn_date, dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "save_top3['churn_date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "['month', 'time', 'vertical', 'industry', 'client_country', 'client_continent', 'client_region', 'OS', 'device', 'environment', 'criteo_product', 'spend_criteo_euro', 'sales_criteo', 'CPO', 'revenue_criteo_euro', 'AOV', 'COS', 'churn_date', 'Android', 'MacOS', 'Other', 'iOs', 'Desktop', 'Mobile', 'app', 'web', 'awareness', 'consideration', 'conversion', 'products', 'onsite_sales', 'onsite_revenue_euro']"
      ],
      "text/plain": [
       "['month',\n",
       " 'time',\n",
       " 'vertical',\n",
       " 'industry',\n",
       " 'client_country',\n",
       " 'client_continent',\n",
       " 'client_region',\n",
       " 'OS',\n",
       " 'device',\n",
       " 'environment',\n",
       " 'criteo_product',\n",
       " 'spend_criteo_euro',\n",
       " 'sales_criteo',\n",
       " 'CPO',\n",
       " 'revenue_criteo_euro',\n",
       " 'AOV',\n",
       " 'COS',\n",
       " 'churn_date',\n",
       " 'Android',\n",
       " 'MacOS',\n",
       " 'Other',\n",
       " 'iOs',\n",
       " 'Desktop',\n",
       " 'Mobile',\n",
       " 'app',\n",
       " 'web',\n",
       " 'awareness',\n",
       " 'consideration',\n",
       " 'conversion',\n",
       " 'products',\n",
       " 'onsite_sales',\n",
       " 'onsite_revenue_euro']"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(save_top3.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "['month', 'time', 'vertical', 'industry', 'client_country', 'client_continent', 'client_region', 'OS', 'device', 'environment', 'criteo_product', 'spend_criteo_euro', 'sales_criteo', 'CPO', 'revenue_criteo_euro', 'AOV', 'COS', 'Android', 'MacOS', 'Other', 'iOs', 'Desktop', 'Mobile', 'app', 'web', 'awareness', 'consideration', 'conversion', 'products', 'onsite_sales', 'onsite_revenue_euro']"
      ],
      "text/plain": [
       "['month',\n",
       " 'time',\n",
       " 'vertical',\n",
       " 'industry',\n",
       " 'client_country',\n",
       " 'client_continent',\n",
       " 'client_region',\n",
       " 'OS',\n",
       " 'device',\n",
       " 'environment',\n",
       " 'criteo_product',\n",
       " 'spend_criteo_euro',\n",
       " 'sales_criteo',\n",
       " 'CPO',\n",
       " 'revenue_criteo_euro',\n",
       " 'AOV',\n",
       " 'COS',\n",
       " 'Android',\n",
       " 'MacOS',\n",
       " 'Other',\n",
       " 'iOs',\n",
       " 'Desktop',\n",
       " 'Mobile',\n",
       " 'app',\n",
       " 'web',\n",
       " 'awareness',\n",
       " 'consideration',\n",
       " 'conversion',\n",
       " 'products',\n",
       " 'onsite_sales',\n",
       " 'onsite_revenue_euro']"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(X.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "X2 = save_top3.drop(columns=['churn_date'])\n",
    "y2 = save_top3['churn_date']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prediction result for top 3 clients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "[(2021, 7), (2021, 3), (2021, 12)]"
      ],
      "text/plain": [
       "[(2021, 7), (2021, 3), (2021, 12)]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_top3 = model.predict(X2)\n",
    "get_final_date(pred_top3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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