Marketing_customer_segmentation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "da38d46f",
   "metadata": {},
   "source": [
    "## Problem Statement\n",
    "\n",
    "**Using data to have a better understanding of the target customers for insurance purchases.**\n",
    "<br>\n",
    "<br> **Identify any seasonal influences in purchasing a product.** There could be seasons/time that will alter a person's willingness to buy. In investigating this, the marketing department can often influence/dictate, how, and when people will spend their money.  \n",
    "\n",
    "<br> **What are the features that will distinguish one customer from each other?** The straightforward way to answer this question is using unsupervised learning technique (KMeans clustering). The goal is to group the customer data to different clusters. In such a way, each group/cluster will consist of features that will distinguish one from the other. And this will guide you to estimate how much the yearly/monthly premiums they are willing to pay or what policy the customer would most likely to purchase.  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71b5b245",
   "metadata": {},
   "source": [
    "### Contents:\n",
    "- [Data](#Data)\n",
    "- [Marketing calendar](#Marketing-calendar)\n",
    "- [Customer segmentation](#Customer-segmentation)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e885f10a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "import seaborn as sns\n",
    "import datetime as dt\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.cluster import KMeans\n",
    "from scipy.spatial import distance\n",
    "from sklearn import metrics\n",
    "from mpl_toolkits.mplot3d import Axes3D"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30b2748b",
   "metadata": {},
   "source": [
    "### Data\n",
    "\n",
    "Import data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e7dd03da",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('ins_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c9ccb1d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16671, 34)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9a83d896",
   "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>Unnamed: 0</th>\n",
       "      <th>policy_ref_dummy</th>\n",
       "      <th>issue_date</th>\n",
       "      <th>st_date</th>\n",
       "      <th>pol_st</th>\n",
       "      <th>product_category</th>\n",
       "      <th>prod_type</th>\n",
       "      <th>basic_cover_type</th>\n",
       "      <th>cover_type</th>\n",
       "      <th>insured_amt</th>\n",
       "      <th>premium</th>\n",
       "      <th>pay_term</th>\n",
       "      <th>pay_method</th>\n",
       "      <th>pay_mode</th>\n",
       "      <th>pay_age</th>\n",
       "      <th>life_to_be_covered_dummy</th>\n",
       "      <th>relationship</th>\n",
       "      <th>relationship_bin</th>\n",
       "      <th>entry_age</th>\n",
       "      <th>gender</th>\n",
       "      <th>occupation_class</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>nationality_dummy</th>\n",
       "      <th>region_dummy</th>\n",
       "      <th>owner_age</th>\n",
       "      <th>owner_gender</th>\n",
       "      <th>class_order</th>\n",
       "      <th>serv_branch_dummy</th>\n",
       "      <th>ANP</th>\n",
       "      <th>bmi</th>\n",
       "      <th>week</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>POLID_3215</td>\n",
       "      <td>2011-06-15</td>\n",
       "      <td>2011-06-08</td>\n",
       "      <td>inforce</td>\n",
       "      <td>Retirement</td>\n",
       "      <td>NUL</td>\n",
       "      <td>ANTICIPATED ENDOWMENT</td>\n",
       "      <td>Basic</td>\n",
       "      <td>500000.00</td>\n",
       "      <td>161515.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Annual</td>\n",
       "      <td>Cash/Check</td>\n",
       "      <td>58.0</td>\n",
       "      <td>PART_ID_8104</td>\n",
       "      <td>SELF</td>\n",
       "      <td>SELF</td>\n",
       "      <td>53</td>\n",
       "      <td>F</td>\n",
       "      <td>HOSPITAL, NURSING HOME, CLINIC, ASSYLUM</td>\n",
       "      <td>1.5748</td>\n",
       "      <td>54.00000</td>\n",
       "      <td>NATIONALITY_ID_1</td>\n",
       "      <td>REGION_2</td>\n",
       "      <td>53.0</td>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "      <td>SERV_BRANCH_452</td>\n",
       "      <td>161515.0</td>\n",
       "      <td>21.774237</td>\n",
       "      <td>24</td>\n",
       "      <td>6</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>POLID_54</td>\n",
       "      <td>2010-12-14</td>\n",
       "      <td>2010-12-13</td>\n",
       "      <td>inforce</td>\n",
       "      <td>Retirement</td>\n",
       "      <td>NUL</td>\n",
       "      <td>ANTICIPATED ENDOWMENT</td>\n",
       "      <td>Basic</td>\n",
       "      <td>100000.00</td>\n",
       "      <td>26745.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Annual</td>\n",
       "      <td>Cash/Check</td>\n",
       "      <td>24.0</td>\n",
       "      <td>PART_ID_5553</td>\n",
       "      <td>DAUGHTER</td>\n",
       "      <td>CHILDREN</td>\n",
       "      <td>19</td>\n",
       "      <td>F</td>\n",
       "      <td>STUDENT</td>\n",
       "      <td>1.5240</td>\n",
       "      <td>45.35920</td>\n",
       "      <td>NATIONALITY_ID_1</td>\n",
       "      <td>REGION_10</td>\n",
       "      <td>50.0</td>\n",
       "      <td>F</td>\n",
       "      <td>10</td>\n",
       "      <td>SERV_BRANCH_600</td>\n",
       "      <td>26745.0</td>\n",
       "      <td>19.529695</td>\n",
       "      <td>50</td>\n",
       "      <td>12</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>POLID_54</td>\n",
       "      <td>2010-12-14</td>\n",
       "      <td>2010-12-13</td>\n",
       "      <td>inforce</td>\n",
       "      <td>Protection</td>\n",
       "      <td>NUL</td>\n",
       "      <td>ACC DEATH &amp; DISMEMBERMENT</td>\n",
       "      <td>Rider</td>\n",
       "      <td>100000.00</td>\n",
       "      <td>443.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Annual</td>\n",
       "      <td>Cash/Check</td>\n",
       "      <td>24.0</td>\n",
       "      <td>PART_ID_5553</td>\n",
       "      <td>DAUGHTER</td>\n",
       "      <td>CHILDREN</td>\n",
       "      <td>19</td>\n",
       "      <td>F</td>\n",
       "      <td>STUDENT</td>\n",
       "      <td>1.5240</td>\n",
       "      <td>45.35920</td>\n",
       "      <td>NATIONALITY_ID_1</td>\n",
       "      <td>REGION_10</td>\n",
       "      <td>50.0</td>\n",
       "      <td>F</td>\n",
       "      <td>10</td>\n",
       "      <td>SERV_BRANCH_600</td>\n",
       "      <td>443.0</td>\n",
       "      <td>19.529695</td>\n",
       "      <td>50</td>\n",
       "      <td>12</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>POLID_54</td>\n",
       "      <td>2010-12-14</td>\n",
       "      <td>2010-12-13</td>\n",
       "      <td>inforce</td>\n",
       "      <td>Protection</td>\n",
       "      <td>NUL</td>\n",
       "      <td>LIVING BENEFIT</td>\n",
       "      <td>Rider</td>\n",
       "      <td>100000.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Annual</td>\n",
       "      <td>Cash/Check</td>\n",
       "      <td>19.0</td>\n",
       "      <td>PART_ID_5553</td>\n",
       "      <td>DAUGHTER</td>\n",
       "      <td>CHILDREN</td>\n",
       "      <td>19</td>\n",
       "      <td>F</td>\n",
       "      <td>STUDENT</td>\n",
       "      <td>1.5240</td>\n",
       "      <td>45.35920</td>\n",
       "      <td>NATIONALITY_ID_1</td>\n",
       "      <td>REGION_10</td>\n",
       "      <td>50.0</td>\n",
       "      <td>F</td>\n",
       "      <td>10</td>\n",
       "      <td>SERV_BRANCH_600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.529695</td>\n",
       "      <td>50</td>\n",
       "      <td>12</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>POLID_9796</td>\n",
       "      <td>2013-11-05</td>\n",
       "      <td>2013-11-06</td>\n",
       "      <td>inforce</td>\n",
       "      <td>Investment</td>\n",
       "      <td>UL</td>\n",
       "      <td>UNIT LINK</td>\n",
       "      <td>Basic</td>\n",
       "      <td>552125.00</td>\n",
       "      <td>441700.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Single Pay</td>\n",
       "      <td>Cash/Check</td>\n",
       "      <td>62.0</td>\n",
       "      <td>PART_ID_5084</td>\n",
       "      <td>SELF</td>\n",
       "      <td>SELF</td>\n",
       "      <td>61</td>\n",
       "      <td>F</td>\n",
       "      <td>LAWYER, ATTORNEY</td>\n",
       "      <td>1.5240</td>\n",
       "      <td>63.50288</td>\n",
       "      <td>NATIONALITY_ID_1</td>\n",
       "      <td>REGION_10</td>\n",
       "      <td>61.0</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>SERV_BRANCH_252</td>\n",
       "      <td>44170.0</td>\n",
       "      <td>27.341572</td>\n",
       "      <td>45</td>\n",
       "      <td>11</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8</td>\n",
       "      <td>POLID_9803</td>\n",
       "      <td>2013-07-31</td>\n",
       "      <td>2013-08-01</td>\n",
       "      <td>inforce</td>\n",
       "      <td>Investment</td>\n",
       "      <td>UL</td>\n",
       "      <td>UNIT LINK</td>\n",
       "      <td>Basic</td>\n",
       "      <td>375000.00</td>\n",
       "      <td>300000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Single Pay</td>\n",
       "      <td>Cash/Check</td>\n",
       "      <td>61.0</td>\n",
       "      <td>PART_ID_5084</td>\n",
       "      <td>SELF</td>\n",
       "      <td>SELF</td>\n",
       "      <td>60</td>\n",
       "      <td>F</td>\n",
       "      <td>LAWYER,ATTORNEY</td>\n",
       "      <td>1.5240</td>\n",
       "      <td>63.50288</td>\n",
       "      <td>NATIONALITY_ID_1</td>\n",
       "      <td>REGION_10</td>\n",
       "      <td>60.0</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>SERV_BRANCH_252</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>27.341572</td>\n",
       "      <td>31</td>\n",
       "      <td>7</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9</td>\n",
       "      <td>POLID_8286</td>\n",
       "      <td>2011-09-27</td>\n",
       "      <td>2011-09-28</td>\n",
       "      <td>inforce</td>\n",
       "      <td>Investment</td>\n",
       "      <td>UL</td>\n",
       "      <td>UNIT LINK</td>\n",
       "      <td>Basic</td>\n",
       "      <td>1500000.00</td>\n",
       "      <td>1000000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Single Pay</td>\n",
       "      <td>Cash/Check</td>\n",
       "      <td>35.0</td>\n",
       "      <td>PART_ID_11671</td>\n",
       "      <td>SELF</td>\n",
       "      <td>SELF</td>\n",
       "      <td>34</td>\n",
       "      <td>F</td>\n",
       "      <td>BUSINESSMAN/ / BUSINESSWOMAN</td>\n",
       "      <td>1.6500</td>\n",
       "      <td>50.00000</td>\n",
       "      <td>NATIONALITY_ID_1</td>\n",
       "      <td>REGION_14</td>\n",
       "      <td>34.0</td>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "      <td>SERV_BRANCH_314</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>18.365473</td>\n",
       "      <td>39</td>\n",
       "      <td>9</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10</td>\n",
       "      <td>POLID_4860</td>\n",
       "      <td>2012-05-08</td>\n",
       "      <td>2012-05-09</td>\n",
       "      <td>inforce</td>\n",
       "      <td>Protection</td>\n",
       "      <td>UL</td>\n",
       "      <td>TERM LIFE</td>\n",
       "      <td>Basic</td>\n",
       "      <td>312500.00</td>\n",
       "      <td>250000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Single Pay</td>\n",
       "      <td>Cash/Check</td>\n",
       "      <td>43.0</td>\n",
       "      <td>PART_ID_7097</td>\n",
       "      <td>SELF</td>\n",
       "      <td>SELF</td>\n",
       "      <td>42</td>\n",
       "      <td>F</td>\n",
       "      <td>BUSINESSMAN/ / BUSINESSWOMAN</td>\n",
       "      <td>1.4986</td>\n",
       "      <td>49.89512</td>\n",
       "      <td>NATIONALITY_ID_1</td>\n",
       "      <td>REGION_12</td>\n",
       "      <td>42.0</td>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "      <td>SERV_BRANCH_132</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>22.217061</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11</td>\n",
       "      <td>POLID_8348</td>\n",
       "      <td>2011-09-27</td>\n",
       "      <td>2011-09-28</td>\n",
       "      <td>inforce</td>\n",
       "      <td>Investment</td>\n",
       "      <td>UL</td>\n",
       "      <td>UNIT LINK</td>\n",
       "      <td>Basic</td>\n",
       "      <td>312500.00</td>\n",
       "      <td>250000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Single Pay</td>\n",
       "      <td>Cash/Check</td>\n",
       "      <td>57.0</td>\n",
       "      <td>PART_ID_13535</td>\n",
       "      <td>SELF</td>\n",
       "      <td>SELF</td>\n",
       "      <td>56</td>\n",
       "      <td>M</td>\n",
       "      <td>BUSINESSMAN/ / BUSINESSWOMAN</td>\n",
       "      <td>1.7272</td>\n",
       "      <td>74.84268</td>\n",
       "      <td>NATIONALITY_ID_1</td>\n",
       "      <td>REGION_3</td>\n",
       "      <td>56.0</td>\n",
       "      <td>M</td>\n",
       "      <td>2</td>\n",
       "      <td>SERV_BRANCH_198</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>25.087886</td>\n",
       "      <td>39</td>\n",
       "      <td>9</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12</td>\n",
       "      <td>POLID_3017</td>\n",
       "      <td>2015-10-15</td>\n",
       "      <td>2015-10-02</td>\n",
       "      <td>inforce</td>\n",
       "      <td>Retirement</td>\n",
       "      <td>NUL</td>\n",
       "      <td>ANTICIPATED ENDOWMENT</td>\n",
       "      <td>Basic</td>\n",
       "      <td>379738.37</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Annual</td>\n",
       "      <td>Cash/Check</td>\n",
       "      <td>38.0</td>\n",
       "      <td>PART_ID_10102</td>\n",
       "      <td>SELF</td>\n",
       "      <td>SELF</td>\n",
       "      <td>28</td>\n",
       "      <td>F</td>\n",
       "      <td>BUSINESSMAN/ / BUSINESSWOMAN</td>\n",
       "      <td>1.5748</td>\n",
       "      <td>49.89512</td>\n",
       "      <td>NATIONALITY_ID_1</td>\n",
       "      <td>REGION_15</td>\n",
       "      <td>28.0</td>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "      <td>SERV_BRANCH_70</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>20.119040</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 policy_ref_dummy  issue_date     st_date   pol_st  \\\n",
       "0           1       POLID_3215  2011-06-15  2011-06-08  inforce   \n",
       "1           3         POLID_54  2010-12-14  2010-12-13  inforce   \n",
       "2           4         POLID_54  2010-12-14  2010-12-13  inforce   \n",
       "3           5         POLID_54  2010-12-14  2010-12-13  inforce   \n",
       "4           7       POLID_9796  2013-11-05  2013-11-06  inforce   \n",
       "5           8       POLID_9803  2013-07-31  2013-08-01  inforce   \n",
       "6           9       POLID_8286  2011-09-27  2011-09-28  inforce   \n",
       "7          10       POLID_4860  2012-05-08  2012-05-09  inforce   \n",
       "8          11       POLID_8348  2011-09-27  2011-09-28  inforce   \n",
       "9          12       POLID_3017  2015-10-15  2015-10-02  inforce   \n",
       "\n",
       "  product_category prod_type           basic_cover_type cover_type  \\\n",
       "0       Retirement       NUL      ANTICIPATED ENDOWMENT      Basic   \n",
       "1       Retirement       NUL      ANTICIPATED ENDOWMENT      Basic   \n",
       "2       Protection       NUL  ACC DEATH & DISMEMBERMENT      Rider   \n",
       "3       Protection       NUL             LIVING BENEFIT      Rider   \n",
       "4       Investment        UL                  UNIT LINK      Basic   \n",
       "5       Investment        UL                  UNIT LINK      Basic   \n",
       "6       Investment        UL                  UNIT LINK      Basic   \n",
       "7       Protection        UL                  TERM LIFE      Basic   \n",
       "8       Investment        UL                  UNIT LINK      Basic   \n",
       "9       Retirement       NUL      ANTICIPATED ENDOWMENT      Basic   \n",
       "\n",
       "   insured_amt    premium  pay_term  pay_method    pay_mode  pay_age  \\\n",
       "0    500000.00   161515.0       5.0      Annual  Cash/Check     58.0   \n",
       "1    100000.00    26745.0       5.0      Annual  Cash/Check     24.0   \n",
       "2    100000.00      443.0       5.0      Annual  Cash/Check     24.0   \n",
       "3    100000.00        0.0       0.0      Annual  Cash/Check     19.0   \n",
       "4    552125.00   441700.0       1.0  Single Pay  Cash/Check     62.0   \n",
       "5    375000.00   300000.0       1.0  Single Pay  Cash/Check     61.0   \n",
       "6   1500000.00  1000000.0       1.0  Single Pay  Cash/Check     35.0   \n",
       "7    312500.00   250000.0       1.0  Single Pay  Cash/Check     43.0   \n",
       "8    312500.00   250000.0       1.0  Single Pay  Cash/Check     57.0   \n",
       "9    379738.37    50000.0      10.0      Annual  Cash/Check     38.0   \n",
       "\n",
       "  life_to_be_covered_dummy relationship relationship_bin  entry_age gender  \\\n",
       "0             PART_ID_8104         SELF             SELF         53      F   \n",
       "1             PART_ID_5553     DAUGHTER         CHILDREN         19      F   \n",
       "2             PART_ID_5553     DAUGHTER         CHILDREN         19      F   \n",
       "3             PART_ID_5553     DAUGHTER         CHILDREN         19      F   \n",
       "4             PART_ID_5084         SELF             SELF         61      F   \n",
       "5             PART_ID_5084         SELF             SELF         60      F   \n",
       "6            PART_ID_11671         SELF             SELF         34      F   \n",
       "7             PART_ID_7097         SELF             SELF         42      F   \n",
       "8            PART_ID_13535         SELF             SELF         56      M   \n",
       "9            PART_ID_10102         SELF             SELF         28      F   \n",
       "\n",
       "                          occupation_class  height    weight  \\\n",
       "0  HOSPITAL, NURSING HOME, CLINIC, ASSYLUM  1.5748  54.00000   \n",
       "1                                  STUDENT  1.5240  45.35920   \n",
       "2                                  STUDENT  1.5240  45.35920   \n",
       "3                                  STUDENT  1.5240  45.35920   \n",
       "4                         LAWYER, ATTORNEY  1.5240  63.50288   \n",
       "5                          LAWYER,ATTORNEY  1.5240  63.50288   \n",
       "6             BUSINESSMAN/ / BUSINESSWOMAN  1.6500  50.00000   \n",
       "7             BUSINESSMAN/ / BUSINESSWOMAN  1.4986  49.89512   \n",
       "8             BUSINESSMAN/ / BUSINESSWOMAN  1.7272  74.84268   \n",
       "9             BUSINESSMAN/ / BUSINESSWOMAN  1.5748  49.89512   \n",
       "\n",
       "  nationality_dummy region_dummy  owner_age owner_gender  class_order  \\\n",
       "0  NATIONALITY_ID_1     REGION_2       53.0            F            4   \n",
       "1  NATIONALITY_ID_1    REGION_10       50.0            F           10   \n",
       "2  NATIONALITY_ID_1    REGION_10       50.0            F           10   \n",
       "3  NATIONALITY_ID_1    REGION_10       50.0            F           10   \n",
       "4  NATIONALITY_ID_1    REGION_10       61.0            F            3   \n",
       "5  NATIONALITY_ID_1    REGION_10       60.0            F            3   \n",
       "6  NATIONALITY_ID_1    REGION_14       34.0            F            2   \n",
       "7  NATIONALITY_ID_1    REGION_12       42.0            F            2   \n",
       "8  NATIONALITY_ID_1     REGION_3       56.0            M            2   \n",
       "9  NATIONALITY_ID_1    REGION_15       28.0            F            2   \n",
       "\n",
       "  serv_branch_dummy       ANP        bmi  week  month  year  \n",
       "0   SERV_BRANCH_452  161515.0  21.774237    24      6  2011  \n",
       "1   SERV_BRANCH_600   26745.0  19.529695    50     12  2010  \n",
       "2   SERV_BRANCH_600     443.0  19.529695    50     12  2010  \n",
       "3   SERV_BRANCH_600       0.0  19.529695    50     12  2010  \n",
       "4   SERV_BRANCH_252   44170.0  27.341572    45     11  2013  \n",
       "5   SERV_BRANCH_252   30000.0  27.341572    31      7  2013  \n",
       "6   SERV_BRANCH_314  100000.0  18.365473    39      9  2011  \n",
       "7   SERV_BRANCH_132   25000.0  22.217061    19      5  2012  \n",
       "8   SERV_BRANCH_198   25000.0  25.087886    39      9  2011  \n",
       "9    SERV_BRANCH_70   50000.0  20.119040    42     10  2015  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_columns', 50)\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e2c66358",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 16671 entries, 0 to 16670\n",
      "Data columns (total 34 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Unnamed: 0                16671 non-null  int64  \n",
      " 1   policy_ref_dummy          16671 non-null  object \n",
      " 2   issue_date                16671 non-null  object \n",
      " 3   st_date                   16671 non-null  object \n",
      " 4   pol_st                    16671 non-null  object \n",
      " 5   product_category          16671 non-null  object \n",
      " 6   prod_type                 16671 non-null  object \n",
      " 7   basic_cover_type          16671 non-null  object \n",
      " 8   cover_type                16671 non-null  object \n",
      " 9   insured_amt               16671 non-null  float64\n",
      " 10  premium                   16671 non-null  float64\n",
      " 11  pay_term                  16671 non-null  float64\n",
      " 12  pay_method                16671 non-null  object \n",
      " 13  pay_mode                  16671 non-null  object \n",
      " 14  pay_age                   16671 non-null  float64\n",
      " 15  life_to_be_covered_dummy  16671 non-null  object \n",
      " 16  relationship              16671 non-null  object \n",
      " 17  relationship_bin          16671 non-null  object \n",
      " 18  entry_age                 16671 non-null  int64  \n",
      " 19  gender                    16671 non-null  object \n",
      " 20  occupation_class          16671 non-null  object \n",
      " 21  height                    16671 non-null  float64\n",
      " 22  weight                    16671 non-null  float64\n",
      " 23  nationality_dummy         16671 non-null  object \n",
      " 24  region_dummy              16671 non-null  object \n",
      " 25  owner_age                 16671 non-null  float64\n",
      " 26  owner_gender              16671 non-null  object \n",
      " 27  class_order               16671 non-null  int64  \n",
      " 28  serv_branch_dummy         16671 non-null  object \n",
      " 29  ANP                       16671 non-null  float64\n",
      " 30  bmi                       16671 non-null  float64\n",
      " 31  week                      16671 non-null  int64  \n",
      " 32  month                     16671 non-null  int64  \n",
      " 33  year                      16671 non-null  int64  \n",
      "dtypes: float64(9), int64(6), object(19)\n",
      "memory usage: 4.3+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cbeea77b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0                  0\n",
       "policy_ref_dummy            0\n",
       "issue_date                  0\n",
       "st_date                     0\n",
       "pol_st                      0\n",
       "product_category            0\n",
       "prod_type                   0\n",
       "basic_cover_type            0\n",
       "cover_type                  0\n",
       "insured_amt                 0\n",
       "premium                     0\n",
       "pay_term                    0\n",
       "pay_method                  0\n",
       "pay_mode                    0\n",
       "pay_age                     0\n",
       "life_to_be_covered_dummy    0\n",
       "relationship                0\n",
       "relationship_bin            0\n",
       "entry_age                   0\n",
       "gender                      0\n",
       "occupation_class            0\n",
       "height                      0\n",
       "weight                      0\n",
       "nationality_dummy           0\n",
       "region_dummy                0\n",
       "owner_age                   0\n",
       "owner_gender                0\n",
       "class_order                 0\n",
       "serv_branch_dummy           0\n",
       "ANP                         0\n",
       "bmi                         0\n",
       "week                        0\n",
       "month                       0\n",
       "year                        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ff5f53d",
   "metadata": {},
   "source": [
    "### Marketing calendar"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39e95131",
   "metadata": {},
   "source": [
    "How to know if there is seasonality (additive or multiplicative) in the dataset? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7cab0cfd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# label_encode product category\n",
    "label_encoder = LabelEncoder()\n",
    "data['product_num'] = label_encoder.fit_transform(data['product_category'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "453a8085",
   "metadata": {},
   "source": [
    "0 - health\n",
    "<br> 1 - investment\n",
    "<br>2 - protection\n",
    "<br>3 - retirement\n",
    "<br>4 - savings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f7965ac9",
   "metadata": {},
   "outputs": [],
   "source": [
    "tsa_analysis = data[['issue_date','product_num']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "16de6111",
   "metadata": {},
   "outputs": [],
   "source": [
    "tsa_analysis.set_index('issue_date',inplace=True)\n",
    "analysis = tsa_analysis[['product_num']].copy()\n",
    "analysis = analysis[analysis['product_num']>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "88299050",
   "metadata": {},
   "outputs": [],
   "source": [
    "decompose_result_mult = seasonal_decompose(analysis,model='multiplicative',period=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b6d8a466",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "trend = decompose_result_mult.trend\n",
    "seasonal = decompose_result_mult.seasonal\n",
    "residual = decompose_result_mult.resid\n",
    "decompose_result_mult.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "698a1a14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "decompose_result_add = seasonal_decompose(analysis,model='additive',period=1)\n",
    "decompose_result_add.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ab0e33c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "decompose_result_add.seasonal.plot();\n",
    "plt.xticks([]);\n",
    "plt.xlabel('Issue date (2010-2016)')\n",
    "plt.ylabel('Seasonal')\n",
    "plt.title('Seasonal influences in ANP');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5c32229",
   "metadata": {},
   "source": [
    "**product category by week**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1660114a",
   "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>week</th>\n",
       "      <th>product_num</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>ANP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>871</td>\n",
       "      <td>461</td>\n",
       "      <td>728326</td>\n",
       "      <td>1.683885e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>458</td>\n",
       "      <td>224</td>\n",
       "      <td>450956</td>\n",
       "      <td>9.660095e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>442</td>\n",
       "      <td>207</td>\n",
       "      <td>416632</td>\n",
       "      <td>7.750650e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>535</td>\n",
       "      <td>248</td>\n",
       "      <td>499215</td>\n",
       "      <td>9.901785e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>896</td>\n",
       "      <td>671</td>\n",
       "      <td>800799</td>\n",
       "      <td>1.924647e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>473</td>\n",
       "      <td>432</td>\n",
       "      <td>434808</td>\n",
       "      <td>8.278534e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>499</td>\n",
       "      <td>460</td>\n",
       "      <td>462975</td>\n",
       "      <td>9.825304e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>479</td>\n",
       "      <td>450</td>\n",
       "      <td>452864</td>\n",
       "      <td>1.001534e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>866</td>\n",
       "      <td>992</td>\n",
       "      <td>795012</td>\n",
       "      <td>1.990126e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>358</td>\n",
       "      <td>549</td>\n",
       "      <td>368385</td>\n",
       "      <td>8.242585e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>458</td>\n",
       "      <td>678</td>\n",
       "      <td>454874</td>\n",
       "      <td>1.027738e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>485</td>\n",
       "      <td>693</td>\n",
       "      <td>465040</td>\n",
       "      <td>8.581450e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>417</td>\n",
       "      <td>688</td>\n",
       "      <td>457039</td>\n",
       "      <td>1.035680e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>594</td>\n",
       "      <td>1048</td>\n",
       "      <td>577741</td>\n",
       "      <td>1.264731e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>717</td>\n",
       "      <td>1288</td>\n",
       "      <td>648247</td>\n",
       "      <td>1.529265e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>467</td>\n",
       "      <td>1116</td>\n",
       "      <td>561931</td>\n",
       "      <td>1.553367e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>735</td>\n",
       "      <td>1368</td>\n",
       "      <td>688386</td>\n",
       "      <td>1.218160e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>440</td>\n",
       "      <td>945</td>\n",
       "      <td>408501</td>\n",
       "      <td>7.375282e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>1541</td>\n",
       "      <td>3840</td>\n",
       "      <td>1545442</td>\n",
       "      <td>3.870025e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>411</td>\n",
       "      <td>990</td>\n",
       "      <td>398562</td>\n",
       "      <td>8.107738e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>539</td>\n",
       "      <td>1365</td>\n",
       "      <td>549574</td>\n",
       "      <td>1.160776e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>701</td>\n",
       "      <td>1764</td>\n",
       "      <td>666080</td>\n",
       "      <td>1.309435e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>500</td>\n",
       "      <td>1458</td>\n",
       "      <td>489166</td>\n",
       "      <td>7.772507e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>450</td>\n",
       "      <td>1308</td>\n",
       "      <td>438835</td>\n",
       "      <td>8.444607e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>411</td>\n",
       "      <td>1254</td>\n",
       "      <td>420838</td>\n",
       "      <td>9.213805e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>973</td>\n",
       "      <td>2930</td>\n",
       "      <td>933801</td>\n",
       "      <td>1.996113e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>654</td>\n",
       "      <td>2264</td>\n",
       "      <td>674334</td>\n",
       "      <td>1.518586e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>509</td>\n",
       "      <td>1820</td>\n",
       "      <td>523490</td>\n",
       "      <td>1.165392e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>415</td>\n",
       "      <td>1568</td>\n",
       "      <td>451101</td>\n",
       "      <td>1.078233e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>567</td>\n",
       "      <td>2002</td>\n",
       "      <td>575654</td>\n",
       "      <td>1.291813e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>1389</td>\n",
       "      <td>6940</td>\n",
       "      <td>1921988</td>\n",
       "      <td>6.415763e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>424</td>\n",
       "      <td>1680</td>\n",
       "      <td>422610</td>\n",
       "      <td>8.005043e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>464</td>\n",
       "      <td>1896</td>\n",
       "      <td>476960</td>\n",
       "      <td>1.017049e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>514</td>\n",
       "      <td>1976</td>\n",
       "      <td>496908</td>\n",
       "      <td>1.196225e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>35</td>\n",
       "      <td>830</td>\n",
       "      <td>3214</td>\n",
       "      <td>752289</td>\n",
       "      <td>1.794426e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36</td>\n",
       "      <td>521</td>\n",
       "      <td>2277</td>\n",
       "      <td>509113</td>\n",
       "      <td>7.961868e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37</td>\n",
       "      <td>533</td>\n",
       "      <td>2412</td>\n",
       "      <td>539262</td>\n",
       "      <td>1.322211e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38</td>\n",
       "      <td>364</td>\n",
       "      <td>1512</td>\n",
       "      <td>338048</td>\n",
       "      <td>9.398850e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39</td>\n",
       "      <td>967</td>\n",
       "      <td>5168</td>\n",
       "      <td>1134311</td>\n",
       "      <td>2.429038e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>40</td>\n",
       "      <td>1142</td>\n",
       "      <td>5795</td>\n",
       "      <td>1215599</td>\n",
       "      <td>3.125012e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>41</td>\n",
       "      <td>499</td>\n",
       "      <td>2390</td>\n",
       "      <td>480992</td>\n",
       "      <td>1.101542e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>42</td>\n",
       "      <td>579</td>\n",
       "      <td>3050</td>\n",
       "      <td>613911</td>\n",
       "      <td>1.389244e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>43</td>\n",
       "      <td>455</td>\n",
       "      <td>2140</td>\n",
       "      <td>430748</td>\n",
       "      <td>8.716112e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>44</td>\n",
       "      <td>937</td>\n",
       "      <td>4563</td>\n",
       "      <td>867039</td>\n",
       "      <td>2.322664e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>45</td>\n",
       "      <td>1007</td>\n",
       "      <td>5962</td>\n",
       "      <td>1091010</td>\n",
       "      <td>2.440231e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>46</td>\n",
       "      <td>553</td>\n",
       "      <td>2827</td>\n",
       "      <td>517229</td>\n",
       "      <td>1.021190e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>47</td>\n",
       "      <td>326</td>\n",
       "      <td>1881</td>\n",
       "      <td>344146</td>\n",
       "      <td>7.370198e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>48</td>\n",
       "      <td>1240</td>\n",
       "      <td>8386</td>\n",
       "      <td>1509179</td>\n",
       "      <td>3.670864e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>49</td>\n",
       "      <td>1063</td>\n",
       "      <td>5640</td>\n",
       "      <td>945638</td>\n",
       "      <td>2.265208e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>50</td>\n",
       "      <td>744</td>\n",
       "      <td>4092</td>\n",
       "      <td>686347</td>\n",
       "      <td>1.678010e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>51</td>\n",
       "      <td>665</td>\n",
       "      <td>3624</td>\n",
       "      <td>607685</td>\n",
       "      <td>1.544600e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>52</td>\n",
       "      <td>182</td>\n",
       "      <td>1200</td>\n",
       "      <td>201299</td>\n",
       "      <td>4.802579e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>53</td>\n",
       "      <td>121</td>\n",
       "      <td>660</td>\n",
       "      <td>110825</td>\n",
       "      <td>2.524416e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    week  product_num  month     year           ANP\n",
       "0      1          871    461   728326  1.683885e+07\n",
       "1      2          458    224   450956  9.660095e+06\n",
       "2      3          442    207   416632  7.750650e+06\n",
       "3      4          535    248   499215  9.901785e+06\n",
       "4      5          896    671   800799  1.924647e+07\n",
       "5      6          473    432   434808  8.278534e+06\n",
       "6      7          499    460   462975  9.825304e+06\n",
       "7      8          479    450   452864  1.001534e+07\n",
       "8      9          866    992   795012  1.990126e+07\n",
       "9     10          358    549   368385  8.242585e+06\n",
       "10    11          458    678   454874  1.027738e+07\n",
       "11    12          485    693   465040  8.581450e+06\n",
       "12    13          417    688   457039  1.035680e+07\n",
       "13    14          594   1048   577741  1.264731e+07\n",
       "14    15          717   1288   648247  1.529265e+07\n",
       "15    16          467   1116   561931  1.553367e+07\n",
       "16    17          735   1368   688386  1.218160e+07\n",
       "17    18          440    945   408501  7.375282e+06\n",
       "18    19         1541   3840  1545442  3.870025e+07\n",
       "19    20          411    990   398562  8.107738e+06\n",
       "20    21          539   1365   549574  1.160776e+07\n",
       "21    22          701   1764   666080  1.309435e+07\n",
       "22    23          500   1458   489166  7.772507e+06\n",
       "23    24          450   1308   438835  8.444607e+06\n",
       "24    25          411   1254   420838  9.213805e+06\n",
       "25    26          973   2930   933801  1.996113e+07\n",
       "26    27          654   2264   674334  1.518586e+07\n",
       "27    28          509   1820   523490  1.165392e+07\n",
       "28    29          415   1568   451101  1.078233e+07\n",
       "29    30          567   2002   575654  1.291813e+07\n",
       "30    31         1389   6940  1921988  6.415763e+07\n",
       "31    32          424   1680   422610  8.005043e+06\n",
       "32    33          464   1896   476960  1.017049e+07\n",
       "33    34          514   1976   496908  1.196225e+07\n",
       "34    35          830   3214   752289  1.794426e+07\n",
       "35    36          521   2277   509113  7.961868e+06\n",
       "36    37          533   2412   539262  1.322211e+07\n",
       "37    38          364   1512   338048  9.398850e+06\n",
       "38    39          967   5168  1134311  2.429038e+07\n",
       "39    40         1142   5795  1215599  3.125012e+07\n",
       "40    41          499   2390   480992  1.101542e+07\n",
       "41    42          579   3050   613911  1.389244e+07\n",
       "42    43          455   2140   430748  8.716112e+06\n",
       "43    44          937   4563   867039  2.322664e+07\n",
       "44    45         1007   5962  1091010  2.440231e+07\n",
       "45    46          553   2827   517229  1.021190e+07\n",
       "46    47          326   1881   344146  7.370198e+06\n",
       "47    48         1240   8386  1509179  3.670864e+07\n",
       "48    49         1063   5640   945638  2.265208e+07\n",
       "49    50          744   4092   686347  1.678010e+07\n",
       "50    51          665   3624   607685  1.544600e+07\n",
       "51    52          182   1200   201299  4.802579e+06\n",
       "52    53          121    660   110825  2.524416e+06"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# is product category important? sum by week. \n",
    "product_week = data[['product_num', 'week', 'month', 'year', 'ANP']].groupby(by='week').sum().reset_index()\n",
    "product_week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2eff6d0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "week_groups = data.groupby([data['week']])['product_category'].count()\n",
    "week_groups.plot(kind='bar',figsize=(15,5),legend=None);\n",
    "plt.title('Total number of products bought each week');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f738deb9",
   "metadata": {},
   "source": [
    "**product category by month**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "54f4842a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "month_groups = data.groupby([data['month']])['product_category'].count()\n",
    "month_groups.plot(kind='bar',figsize=(15,5),legend=None);\n",
    "plt.title('Total number of products bought each month');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78141f14",
   "metadata": {},
   "source": [
    "**product category by year**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1d96c504",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "year_groups = data.groupby([data['year']])['product_category'].count()\n",
    "year_groups.plot(kind='bar',figsize=(15,5),legend=None);\n",
    "plt.title('Total number of products bought by year');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "23e56cfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[data['product_category'] == 'Investment'][['month','year']].groupby(['year','month']).size().plot(kind='bar',figsize=(15,5));\n",
    "plt.xlabel('month/year',fontsize=10);\n",
    "plt.xticks(rotation='90',fontsize=10);\n",
    "plt.ylabel('number of investment policy',fontsize=10)\n",
    "plt.title('number of Investment products bought by month and year',fontsize=15); "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f60d7f0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[data['product_category'] == 'Retirement'][['month','year']].groupby(['year','month']).size().plot(kind='bar',figsize=(15,5));\n",
    "plt.xlabel('month/year',fontsize=10);\n",
    "plt.xticks(rotation='90',fontsize=10);\n",
    "plt.ylabel('number of retirement policy',fontsize=10)\n",
    "plt.title('number of retirement products bought by month and year',fontsize=15); "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "28def342",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[(data['product_category'] == 'Retirement')][['year']].groupby(['year']).size().plot(kind='bar',figsize=(15,5));\n",
    "plt.xlabel('year',fontsize=10);\n",
    "plt.xticks(rotation='horizontal',fontsize=10);\n",
    "plt.ylabel('number of retirement policy',fontsize=10)\n",
    "plt.title('number of retirement products bought by year',fontsize=15); "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0f3cbef7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[(data['product_category'] == 'Investment')][['year']].groupby(['year']).size().plot(kind='bar',figsize=(15,5));\n",
    "plt.xlabel('year',fontsize=10);\n",
    "plt.xticks(rotation='horizontal',fontsize=10);\n",
    "plt.ylabel('number of investment policy',fontsize=10)\n",
    "plt.title('number of investment products bought by year',fontsize=15); "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "439b8f5a",
   "metadata": {},
   "source": [
    "Most ANP can be earned from Investment policies and Retirement policies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7fdc8581",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "basic_cover_type     \n",
       "DREAD DISEASE            57\n",
       "DAILY HOSPITALIZATION    10\n",
       "MEDICAL EXPENSES         10\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['product_category'] == 'Health'][['basic_cover_type']].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "77bdb3e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "basic_cover_type\n",
       "UNIT LINK           5395\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['product_category'] == 'Investment'][['basic_cover_type']].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "dcb7cfe0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "basic_cover_type            \n",
       "ACC DEATH & DISMEMBERMENT       1851\n",
       "LIVING BENEFIT                  1651\n",
       "PAYOR'S BENEFIT                 1247\n",
       "WAIVER OF PREMIUM DISABILITY     711\n",
       "TERM LIFE                        575\n",
       "WHOLE LIFE                       436\n",
       "SPECIAL ACCIDENT RIDER            48\n",
       "ACCIDENTAL DEATH                  30\n",
       "TERM RIDER                        28\n",
       "DECREASING TERM RIDER             11\n",
       "ACC TPD                           10\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['product_category'] == 'Protection'][['basic_cover_type']].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4118bbcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "basic_cover_type     \n",
       "ANTICIPATED ENDOWMENT    3615\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['product_category'] == 'Retirement'][['basic_cover_type']].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c5cc6306",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "basic_cover_type\n",
       "DEPOSIT FUND        508\n",
       "ENDOWMENT           468\n",
       "ENDOWMENT RIDER      10\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['product_category'] == 'Savings'][['basic_cover_type']].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9d843a06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EyizJy15q40b2XWf9RtR5J6X0WhPPW0mlXM7lwZ9llfdvgbE0V6WxNfS7W5an8wLpdQq/97zAX6Wx1B0rzxdf+vxVGteLFcorXd/ykHe/Tkgp5c3QbnEppVci4i7q58FfFfjv0noR8T7wBNkM1ZtSbXIzN+f+y3sdLuseKtc7IlYqfGunVvLuzecaqP8czftwrjwlClT+e9DY9/nlIfd3l1J6v3Avls+czntef8PS39T4NlkKJchPBdTcD+PGkH2AVhoD2BkgItah/rddHiNbbLfumwd1Hx5vy9Lf5ngq577Me+9Yj6Yt5Lx+he06PViyrktTz9eQ8n8n3QkcllLKS4EnSVKjOANekqS2798Vylv8P4sR0dwA/qycskqLi+bVbY6GFuGrFITtWfhZvghbtZaarZuj/Kv/zZE37oV1aTTKFb5anzfDsqWuvzlyA3OFMefd6z1zysot67mudN0N3Ut5x0rPkzeuir8TKt+bzdWY1/KqOWUzWngcy3IUcPsy6qwJHAj8EXg1InauwTiac//l3UdNvYcqnWeZmvC+nXdvNjTO5n7Qkfe3qy0EORu67rzXa97zej1Lpxo6IiJ6FXLxl6+z8Vgh9U/VUrZIanku/DUjom6NlXr9kQXs62weEX1y6kH+zPqW+JtR+veypc/XFNvStA8OJElaigF4SZLavkpfiW7WQoSli96VaO5M6KbkDG5ufuFyKzdwrFKwti749GGF402Vlw6mXEt+xT1v3J0iolte5YjoTv4YW+r6myP391e4lrxvdTYUOKyzrOe60nU3FNzPO1Z6nrxxdYqISjN8G/NBQkPyXsfQuNfyrJyyvG+C1ExK6cOU0leBQcD5ZOtAvEPl97e1gb9ERF7O9eZozv2Xdx819R6qdJ7y8TTnfTvv3mzofXPtRp43V146j7r1OVZwTf1bstTzWvjA7Q9lxXWLseYtvtpSqagqpaEpTT8zubC+Q3mKoS/S+AB8S/zNKP1b1BLnqzb20Rd4oAnrikiStBQD8JIkqXzxszp5gYRBtRxIja1aWNguT6XregegkNIib+bvUSmlaMJjRAtcR1O8XaF8qyaWVzrP8lRpbJV+d5XSDTVa4fc+swljqXSs9PmrNOt+ywrlTXnN5X2bZKnXcWEm6zqNON9beePJy7teaymlF1NKP0opfTml1B/oDmwBHM/Sr821gC+38BCac//lvX6aeg/NLEvzUYv37bxxNrTIdXmqpc+K3N9dRKxJdu+Vq/Re9HuWfs2ewNIB+I+AW5oywAZUCsCXBtYfA0gpvQyUprfblaUXkJ5Plv6pXN699P0m/r0ctozzvdrE85Xn1K9kBEt/OLsBcH9rvPdJktoHA/CSJGlWhfK8wMtpNRzH8nBseUFEdAC+mVP3lZRSaWDvoZw6wyNimbPaI2LliPhS44fZYh4lP6XMcRXq55Uvon4qgtay1O+uYFiF8rEt1G9ewOprEdGrvDAidiI/KFl6jrxgFcAxFcqHNTi6+mbllOW9jk+hcd/GeCCnrBPwo4YaVfiGRW7AOCJ6NHCezpXSp6SU5qeUJqWUrgCuzanS1PUZlqU591/ePbRFRCy1IG/hvvpaTv3y959ZFfptzvv24zllgyJi6/LCiPg8+Yu2Lk9591TF+6kFfaOwGGm5Sq/h3PeilNK7LL3g7ucLj1K3tuC6Bv9k6aD/l6j/Ic1jFba/wdJpXMYW0jCVezCn7OjGfDMlIrpGxL6NON8mEZG3UHTeOQc38AF8uUfIvolQ/i28z5Et+trcbyVJkj6DDMBLkqTXyA9kXBwRq0GWQzgiLgKGLM+B1cD3I+LIiAiAQjDgD+TPPr6pbP+KnDpDgD9HxFIzTCNi7Yg4JCJGAe8C32/WyKuQUpoF/DXn0PCIOK3uw4OI6BgR/49sNnG5vxbO09p2j4iRdalaIqJDRHwbODGn7qMppf+0UL9X5pStAdwaEcXZroUg5fU5dedSMnu1EHQrT+0AcHJEHF/4QKguCPW/wB5NGOu/cspOi4htSsZ5KHBOI8/3IPBGhbFeXp6SISL6R8QPgZtz2uR9kwDggAb6Xxf4d0T8LiL2z5t9GhGbUX+R1jqV1paoVnPuv5uBvMVTbyj73awJ3Ar0yalbnoKkFu/bt5Cf+uvGiNiiZJybkP+hx/KWd0+tERE71LjfTYFRpR/CRcSBwAU5dd8EnmzgXL9uRH9570FVKXyL4qmy4n7U/0Cu9APX0veqNXJOmffhEsDVLH0vbQ7cU/igsp6IWD0i9o2IS8m+JfT7snG/RP7z+OeIOK78Q7/Cvb9tRHwvIsYD41iyoOwypZT+QvZBZbnBwB2FdG2SJDVa3if3kiTpMySl9GlEPATsXXZoMPB2RLwBrA8sNeO3DeoC3AD8X0S8T/Yf8rwZkx+ydADg3oi4l6Wfp4OAgyJiJtmigp3IAhUrylfVfwDsS/3rDOBXwPkRMYUsAJOX23su8MNaD7AJzgROiIg3yfLy5gUqAS5qqQ5TSndFxINA+UzLvYB3IuIVoCuVgzv/m1J6v6zsp8DdZWUdyRYR/WlE/Ics5UFTZ1reA+xWVrY6MC4iJpH9jhuTegaAlNKCiPgOcBvZPVNqOHB8REwmW3hyDbLfCWQzSMu9BswDymfH31QIEtd922RWSmloyfE+ZIGwUwAi4gNgGlmKiNWonN+8fLHJllDV/ZdSej8ifgZcWFZvAPBM4T12HllgN+//Zw+nlO4qO2eLv2+nlN6MiBvIZjqX2hx4KSJeIwuqbsrS90NreLFC+T8L11+XN/xfKaW8bzk1x9eBgyPiVbLXWKX84P+bUqq4nklK6cmIeBL4QoUqr6SU8j6wa46HgUoLFc8AJpbsL+vbT7kB+JTSSxFxOUt/QLUL2e9nDkvWclidpVP35H1g9R1gNPVfI6sCfwIujYh3yb4ZsgrZh3eV1r9olJTSZRGxDnBe2aHdyAL/B6WUWvqDPklSO+UMeEmSBDCyQvlKZF9NrwviTKxQry2YwZLZvGuRXVeldAWnpJTeyyk/Ani+QpveZDPpN2fFCb7X5fE9ivwZwb3J8hnnBd8XAkenlCbVcHhNUReA7Ek25krBzz+mlO5t4b4PB/Keh05kaQkqBd//Avy4vDCldA9Z0CjPGmTXVxd8H9eEcV4BfJBT3pEsNUld8H0O2YdFy5RSuh04u8LhIPug4PNUDkDWnWcB8PcKhzcAtis8tl7GkFYne419nsrB98do+bRJdb+Hau+/n5D/zQCADcnuo7zg+yvAYRXa1eJ9+3Ty820HsAmwWWH7U/KDr8ttEdWU0hSWns0N2f9xN2bJPfW5Fu667l7oRvY8V7r37wMub8T5GpoF31KLr5aqNGsd4J9lC+E+S5aDPs8nNDy7/zTg/grHepKt4fA58vPmLyWl9E+yHPl5H2h0JvvA6fNkH2w1K/he0uf5ZB+MltsXuKbuG0uSJC2LfzAkSRIppYdY9kznh6i/UFtbM4csnceEBup8CpyUUspLJUJK6UOyvMe/JwtQN9a7wN+aUL9FFYKou5IF8xrjNWC3lNJtNRtU0/0/yr6VkOMq4KSW7jil9AHZDNXraVyA8ROyWZOHNTD79USyNA0N+TNwdCOHSUppOtnM3LyczHXeJEtJ8loTzjuSLM1LcxfjPRvI+2CrkkXkB9sa8iDw1bIgYks4miw9TEMq3n+F8RxJlopqbiP6S2QB+/8q/F7zztni79uFe313Gn6vmAUcSn5u89mN7auFnEr23r48nU0WNG/oHruHxt+Ht5L/gdgi4JqmD2+ZHmfpRUbr1Jttn1JaROX1NB5PKVU6DymlT4F9yFLzNPSeVO4Dlk4BV3fOK8n+jjf6/YvsebwfmNyENqVOIv/v9xHAZVWeU5L0GWMAXpIkAZBS+jHZf2xvJwuSLSj8vIsskLhnITjTZqWU3iKbEfnfZIGGD8gCEZPJZrltnVJq8D/UKaW5KaVTyGbZfY8s0PI22SzBRWRpD14mm+37Q7KAff+U0v+1/BU1XkppLNmMw6+SBQonkuVQXkgWUJsEjCJbAHLzlFLegoytaXHhed+DLGD1DtkHJu+R3bN7p5S+VQgYtbiU0uyU0jfIZmxeRLaY4XuFMXxSGM+9wHeB9VNKFy4j9cSilNKxZClEbi8513/I7p0DU0qHUjlQVum895HN0P4j2X39KdnveSxZCoeBKaVnmnLOwnnvJpup/TWymfYTgOlk7xMzye6nv5O9tr5V4RyTyWa4/xx4gSxwWjFAWZjh3Ac4pNDmH8CrZEHeRWTPzXSyWdCXAHuklIZWClg30/yU0mHAV8iCce/SxPsvZX4KrEeWzuZuYApZQL7uXI8D/wsMSikdUfjQr6JavG+nlN4gm0n8HbLZ3rPJUoJMAn5RGNvfyVLRlGvKByzNllIaB2xD9vufSH7qkhp0m04nS0VyA9n7f929eB9wZEpp35RSYz5oIaW0kPxA7r2FNSNaVEppHpWD6nnpbip9m2R0I/palFIaQZbm7DSyNUneZMlreA7ZN9PuIfuWyJ7AOimliutUpJQeIfsmxgFk3zB4niXvRfOAqWSvo8vJ0in1TSntVXj/abLCa/oI8p+H4RFR6ZsokiQVRctPDpEkSZKqExEDyAI05TaoNoAiqWVFxEZkOdjLc/p/M6W0IizQ2qZExG/IPrwqdcgK9i0kSZJUJWfAS5IkSZIAiIjbIuKsiNiyPMd1RHSJiIPJUv2UB9/nsGStBjVSROwGHFdW/B/gjlYYjiRJqoE2FYCPiC9FxP9FxCMRMTsiUsljSAPt+kfEbyPilYj4pNB2fER8LyIqLb5GRGwREVdFxOSImBcRMyPisYg4OSIqLuwSETtExM0R8e+ImB8R0yPi/oj4ekRE854FSZIkSaqZrchS/kwAPoqIlyPi2Yh4mSzI/heyBS/L/SSlNGM5jrPNKvxf9OmIeIsslUv5/0l/U1i4WJIktQNtKgVNRNwOHFjh8O4ppdE5bYaQ5aPsWaHdJGBoSqnewjcRcQTZojeVAu3/BPZNKdVbaCgivkOWG7GS24Aj/AeVJEnS0kxBI7WuiHgN2KiJza4GvlWDxW/bpYho6Hl6nSzPflMWLpUkSSuwNjUDnmyRpneBO8kWvGlQRKwJ/Jklwfd3gR8Dv2XJglabAzeUzkyPiM+R/SOyLvj+MnA+cCVQt5jWzsDvyvobSv3g+3iyxdf+XFJ2cKFMkiRJklY0TQmizwBOxOB7S5kGfNXguyRJ7UtbmwHfo241+cLM9odLDi81Az4ifgx8v7CbyGYSvFQ4djJwSUn1oSmlBwvHrgOOKpR/TDbjalrh2M+Bs0rOuVlK6dXCscfIAvMAbxeOzSscuwU4tOScfctnz5fr06dPGjBgQENVJEmS2pX58+fz4osvLlW+5ZZb0rVr11YYkfTZsmDBAj788EPmzJnDvHnzWLhwIQsXLiSlRMeOHencuTM9evSgV69erLrqqnTo0NbmdLW+p59+urgdEXTp0oVVVlmFtddem86dK2Y6laQVwuqrr85uu+3GVlttRY8eFbM6S58Zc+fO5YUXXuCRRx7hvvvum55SWqO8TqfWGFi16oLvTfDVku2X6oLvBbdQPwB/MPBgYaGhr5SUP1wXfC9pVxeAD+AgYGRErMGS4DvA3+uC7yXt6gLwKwFfBm5taPADBgxg/PjxDVWRJEmSJEmSam7+/Pm8/fbb9O7dm169etG5c2dc6lCfZSklFixYwA477MChhx7Kl770pf/k1Wu30xUioitZepk6r5ceTylNB0pnoH++8HMj6ueLr9cuZ7+u3dbLqFepnSRJkiRJkrRCmzFjBr1796ZPnz506dLF4Ls+8+q+ydanTx969+7NkUce2SevXrsNwAO9qX99eeleSsvqnqDVG6iTt9/cdvVExLcjYnxEjJ82bVpeFUmSJEmSJGm5mjNnDr169WrtYUgrpF69erHDDjusmnesPQfgyz+Gy/tYrrQsVai3rP3mtqtfmNLlKaXBKaXBa6yxVMogSZIkSZIkablbtGiRa1VIFXTu3JmVVlqpY96x9hyAnwEsLtnP+4iutOyDws/pDdTJ229uO0mSJEmSJGmFZ9oZKV9Dr412G4BPKc0HJpUUbVx6vLBoammu9+cLP18H5lRqR5YjvlRdu+fKyhvbTpIkSZIkSZLUDrXbAHzB7SXbn4uILUv2Dyur+1eAlNJi4I6S8iERsWaFdqmuj5TSNODxkmMHRES3Cu3mAvc1YvySJEmSJEmSpDaqU2sPoCki4nBg+8Ju/7LDJ0XE/oXtcSmlm4HfACewZIHUf0TEFcAqhfI6Y4AHS/Z/ChwCdAV6AGMi4npgfWBYSb3rU0qvluyfD9xfMr4xEXE7sDXwtZJ6F6eUPlzW9UqSJEmSJEmS2q42FYAH9gGOqXCsdIb5KODmlNJ7EXEo8HdgZaAv8MOydi8DR6aUiouippReiohjgWvInqNNgQvK2j0OnFJakFJ6ICLOAkYWigYXHqX+Cvyo4hVKkiRJkiRJbcyAc+5q7SE0aPLP9mvtISx3I0aM4IILloQ0S8KfjTJ58mQ22GCD4v5VV13FsGHDWmp4nxntPQUNKaWHgYHAJcBrwHzgI+BZ4PvAdimlf+e0u5Fs5vrVwNvAp8CHZIH3U4EhKaXZOe1+AewE3Ar8B1hAtiDsQ8A3gENSSgta8holSZIkSZIk1d7kyZOJiOKjUkB6wIABxToDBgxYrmNsjKuvvrredUyePLm1h9RutakZ8CmlYdRPAdPYdm+TBc2b2u4l4Ngq2j0BPNHUdpIkSZIkSZKk9qPdz4CXJEmSJEmSJKk1GICXJEmSJEmSpOUkpcQtt9zC/vvvzzrrrEOXLl1YddVV2XXXXbn88stZuHDhUm2uvPJKjjjiCAYOHMiaa65Jly5dWHnlldliiy044YQTmDhxYqP6rkuhc+yx9ZN+bLDBBsV0NEOGDKnY/rHHHuNLX/oSvXr1YqWVVmL33Xdn7NixTbr+z5o2lYJGkiRJkiRJktqq+fPnc8ghh3DXXfUXrf3www8ZM2YMY8aM4cYbb+Suu+6iR48exeO///3vefrpp+u1WbBgAZMmTWLSpEmMGjWKe++9t8HgeXPdeOONPPDAAyxevLhYNnr0aPbYYw+eeeYZNt9885r13ZYZgJckSZIkSZKkKrz00kv84he/WKp89uzZufW/853vFIPvHTp04Gtf+xqDBg3irbfe4tprr2X+/PmMHj2a008/ncsvv7zYbo011mD//fdn4403pnfv3nTu3JmpU6fy17/+lSlTpjB//nxOPfVUXnzxxQbHu9pqqzFy5EjGjx/PzTffXCw/99xz6d27NwD9+/fPbXvfffex+eabc/DBB/Pcc89x9913A/DJJ5/w61//mksvvbTBvj+rDMBLkiRJkiRJUhXGjx/P+PHjG1V35syZ/OEPfyjuX3TRRXz3u98t7m+77bacfPLJQJZy5qc//Sl9+vQB4J577mHevHmMHTuW119/nTlz5tC/f3+GDh3KVVddBWQfBkyZMqViAB2gV69enHnmmVx99dX1AvDDhw9nwIABDY6/f//+PPXUU/Ts2bM43meffRaAp556qlHPwWeRAXhJkiRJkiRJqrGxY8fWy+9+9tlnc/bZZ+fWXbRoEWPHjmX//fcH4Ne//jXnnXdexZn1dd55550GA/DNcfTRRxeD7wCbbrppMQA/c+bMmvTZHrgIqyRJkiRJkiRV4ZhjjiGltNRj/fXXX6rujBkzmnTuadOmAfD3v/+d008/fZnBd8hyzNdK+Qz5rl27FrdL88KrPmfAS5IkSZIkSVKNrbbaavX2hw8fzqabblqx/uDBgwG46aabimUrrbQSf/7zn9ltt93o3r07d999N/vtt19tBlymc+fO9fYjYrn029YZgJckSZIkSZKkGtthhx3o1KlTMQ3N/PnzOfPMM5eqN2vWLO655x4GDRoEwPTp04vHNtxwQ/bee+/ifmlwvinKg+lz586t6jxaNgPwkiRJkiRJklRjvXv3Zvjw4Vx66aUAXHPNNUycOJGhQ4fSs2dP3n//fZ599lkef/xx+vbty5FHHgnAZpttxv333w/AhAkTOPzww9lyyy0ZPXo0Dz30UFVj6devX739k08+mb333ptOnToxZMiQ4ux7NZ8BeEmSJEmSJElaDi6++GLefvtt7rrrLgDGjRvHuHHjGmxz+umnc8011xRzwN9yyy3ccsstABx77LFcddVVTR7HjjvuSL9+/XjnnXcAeOSRR3jkkUcAGDlypAH4FmQAXpIkSZIkSVKzTP7Z8slD3tZ169aNO++8k9tuu41rrrmG8ePHM23aNLp160bfvn3ZeOON2WeffTjggAOKbTbaaCPGjBnDOeecw6OPPkpKia222opzzz2Xnj17VhWA79KlC/feey9nn302jz/+OLNmzSKl1JKXqoLwiV1xDR48OI0fP761hyFJkiRJkqTPuIkTJ7LFFlu09jCkFdYDDzzw6dChQ7uWl3dojcFIkiRJkiRJktTeGYCXJEmSJEmSJKkGDMBLkiRJkiRJklQDBuAlSZIkSZIkSaoBA/CSJEmSJEmSJNWAAXhJkiRJkiRJkmrAALwkSZIkSZIkSTVgAF6SJEmSJEmSpBowAC9JkiRJkiRJUg0YgJckSZIkSZIkqQYMwEuSJEmSJEmSVAMG4CVJkiRJkiRJqgED8JIkSZIkSZIk1UCn1h6AJEmSJEmSpDZuxCqtPYKGjfiwtUegzygD8JIkSZL0GXTJiQ81uc0pl+1Rg5FIktR2TJ48mQ022KC4f8wxx3D11Ve33oBqbNiwYYwaNQqA9ddfn8mTJ7fugGrk6quv5thjjy3uv/nmmwwYMKBFzm0KGkmSJEmSJEmSasAAvCRJkiRJkiRJNWAAXpIkSZIkSZKaacCAAUQEEcGwYcN47bXXOOqoo1hzzTXp2rUrW265ZTGdS51ddtml2ObLX/7yUud85plniscjgjvuuKN4LKXELbfcwv77788666xDly5dWHXVVdl11125/PLLWbhw4VLne/nllznuuOPYZJNN6N69O127dmWdddZh++2356STTuLBBx8EspQsEVFvvG+99Va9sYwYMaJe3brHK6+8woUXXsiGG25I9+7d+fznP8/NN98MwNy5czn77LPp378/3bp1Y+DAgVx11VW5z2c111c+vmeeeYYDDzyQ3r170717d/7rv/6Lu+66q1h/8uTJRES99DMAG2ywQfE8Q4YMyR1fY5kDXpIkSZIkSZJa0LPPPst2223H7Nmzi2UvvfQSw4YNA7Lc8QDHH388jz32GAAPPvggU6dOZe211y62ueGGG4rbffv2Zd999wVg/vz5HHLIIfWCyQAffvghY8aMYcyYMdx4443cdddd9OjRA8iC74MHD+ajjz6q12bq1KlMnTqV8ePH88knn7Dnnns269qPOuooxo8fX9x/4YUXOOKII/jwww+56qqrGDt2bPHYv/71L771rW/RoUOH4nNS7fWVu/fee7nooov49NNPi2Xjxo3jK1/5Cg888AC77757s66zsQzAS5IkSZIkSVILeuGFF+jduzdnnHEGn3zyCX/84x9ZtGgRAD/72c+KwebDDjuM0047jQ8//JBFixZx4403csYZZwCwePFibrrppuI5jz32WDp27AjAd77znWJwukOHDnzta19j0KBBvPXWW1x77bXMnz+f0aNHc/rpp3P55ZcDcNVVVxWD76uuuirHHnssffr04b333uP111/n0UcfLfa1/fbbM3LkSG6++eZiML13796ce+65xTo77bRT7rWPHz+eww8/nA033JDf/e53zJkzB4ATTjgBgCOPPJL111+f3/72t3z88cdLPSfVXl+5J598kn79+nHUUUcxZcqU4ocZixcv5uc//zm77747q622GiNHjmT8+PHFWfoA5557Lr179wagf//+uedvLAPwkiRJkiRJktSCIoIHH3yQbbbZBoBu3brxq1/9CoBJkyYxZ84cevbsSffu3TnyyCO57LLLALjuuuuKAfjRo0fz73//u3i+4447DoCZM2fyhz/8odjXRRddxHe/+93i/rbbbsvJJ58MwJVXXslPf/pT+vTpw7x584p1Dj/8cC6++OJ6Y16wYAHvvvsuAAMHDmTgwIG8+OKLxQB8r169OPPMM5d57ccffzx//OMf642vzgknnFC81rpAePlzUu31lVtppZUYO3Ys6667LpClv7n99tsBeOqpp+pd09VXX10vAD98+HAGDBiwzGttDHPAS5IkSZIkSVIL2nHHHYvBd4DNNtus3vGZM2cWt4cPH17cfuaZZ5g4cSIA119/fbF8jz32YIMNNgBg7Nix9fKfn3322fVyn9cFpwEWLVpUTPmy6667Fsv/8Ic/sO2223LUUUdx/vnnc9ttt/Hxxx+z/vrrN+u6IUtBU6c8iP31r3+9uL3JJpvUO1b3nFR7feUOPPDAYvAd6v8OSp//WjMAL0mSJEmSJEktqDzw3LVr13r7ixcvLm5vu+229YL11113HfPnz+cvf/lLsaw0SD9jxowmjWXatGkAHHzwwfzgBz+ge/fuQJan/oYbbuBHP/oRhxxyCOussw6XXHJJk86dpzTo3aVLl3rH+vbtW9zu1Kl+cpa656Ta6yvX0O8gpdSkPprDFDSSJEmSJEmS1II6d+5cbz8iGqx/3HHHceqppwLZzPdtttmGDz/8EIDVV1+dgw46qFh3tdVWq9d2+PDhbLrpphXPPXjw4OL2hRdeyDnnnMPYsWOZOHEir7/+Og8//DDPP/888+bN47TTTmOfffZhww03bNR15im/9lLlQfc8zbm+hsaxrN9BrRiAlyRJkiRJkqRWdNRRR3HWWWfxySef8NZbb3H22WcXjx199NH1Zm/vsMMOdOrUqZimZf78+bm52WfNmsU999zDoEGDAHjzzTdZddVV6d27N3vuuSd77rknkM04X3311YEspcuzzz5bDMCXBrHnzp3bwledr9rra47yYH1LXqsBeEmSJEmSJElqRauuuiqHHHII1113HQBvvPFG8djxxx9fr27v3r0ZPnw4l156KQDXXHMNEydOZOjQofTs2ZP333+fZ599lscff5y+ffty5JFHAvCXv/yF733ve+yyyy5sttlmrLPOOqSUuPfee+udv3QGer9+/Yrb06ZNY9iwYQwcOJCI4Oijj2attdZq2SeiGdfXHKXXCXDyySez995706lTJ4YMGVJxln1jGICXJEmSJEmSpFZ2/PHHFwPwdXbccUcGDhy4VN2LL76Yt99+m7vuuguAcePGMW7cuGX2sXDhQh5++GEefvjh3OM777xzvcVaDz74YC688EIWLVoEwKhRo4rHhgwZUpMAPFR/fdXacccd6devH++88w4AjzzyCI888ggAI0eONAAvSZIkSZIkqRWN+LC1R9Dm7bbbbmy66aa88sorxbLy2e91unXrxp133sltt93GNddcw/jx45k2bRrdunWjb9++bLzxxuyzzz4ccMABxTZf+cpXmDt3Lk888QSvvvoq06ZN4+OPP2aVVVZh880356CDDuLUU0+lY8eOxTaDBg3iL3/5CxdddBETJkxYbmloqrm+5ujSpQv33nsvZ599No8//jizZs1qsYVaY3mu+KqmGTx4cBo/fnxrD0OSJElSO3TJiQ81uc0pl+1Rg5FIktqCiRMnssUWW7T2MKQV1gMPPPDp0KFDu5aXOwNekiRJkj6D9hh9ShWtJrb4OCRJktqzDq09AEmSJEmSJEmS2iMD8JIkSZIkSZIk1YABeEmSJEmSJEmSasAAvCRJkiRJkiRJNWAAXpIkSZIkSZKkGjAAL0mSJEmSJElSDRiAlyRJkiRJkiSpBgzAS5IkSZIkSZJUAwbgJUmSJEmSJEmqAQPwkiRJkiRJkiTVgAF4SZIkSZIkSZJqwAC8JEmSJEmSJEk10Km1ByBJkiRJkiSpbRs0alBrD6FBE46Z0NpDUInRo0ez++67F/cffvhhhgwZ0noDqiFnwEuSJEmSJElSI0yePJmIyH106dKFddddlwMOOIBbb711uY5r2LBhxXEMGDBgufZdbsSIEfWel886Z8BLkiRJkiRJUjMtWLCAd999l3fffZc777yTgw8+mJtvvplOnQzBlttoo40YOXJkvf32yt++JEmSJEmSJFVh8ODBHH744aSUmDx5Mtdeey1z5swB4LbbbuOyyy7j1FNPXeZ55syZQ8+ePWs93BVG//79OfPMM1t7GMuFKWgkSZIkSZIkqQoDBw7kzDPP5KyzzuKSSy7hz3/+c73jdaloylPETJ8+nZNPPpl+/frRqVMnfvnLXxbbfPrpp1x22WXsvvvu9OnTh86dO7P66quz22678bvf/Y758+cX61599dVEBKNGjSqWvfXWW/VSwIwYMaLemJ588km++c1vsuGGG9K9e3dWWmklttpqK8477zxmzJiRe52LFy/mhhtuYP/996dv37507dqV3r17M2jQIE455RSmT5/O6NGjiQguuOCCem1LxzJs2DCAYt26x+jRo5fq8+677+bggw9m3XXXpUuXLvTq1Yutt96ac889l/fee2+p+kOGDCmeb8iQIbz33nucdNJJrLvuunTt2pVNNtmEkSNHklLKvcZacQa8JEmSJEmSJLWAnXfeud7+f/7zn6XqfPzxx+yyyy5MmjRpqWPTp0/ny1/+Ms8880y98hkzZvDoo4/y6KOPcsUVV3DfffexxhprNHl8P/rRjxgxYsRSQegJEyYwYcIERo0axX333cdmm21WPPbhhx9ywAEHMGbMmHptPv30U2bNmsWLL77I8OHDmzyWShYvXsxxxx3H1VdfXa98wYIFPP/88zz//PP88Y9/5O9//zs77rhj7jmmTJnCtttuy7vvvlsse+211/jud7/L3LlzOf/881tsvMtiAF6SJEmSJEmSWsA///nPevvrrLPOUnWmT5/O9OnT2XPPPfniF7/IzJkzWXfddQE4+uij6wXfv/zlL7PDDjswbtw47r77bgCee+45jjrqKO677z623357Ro4cyc0338z48eMB6N27N+eee27xHDvttBMAf/7zn+sFnnfeeWe+9KUv8fHHH3PttdcydepU3n77bb761a8yYcIEOnbsCMAxxxxTL/jev39/DjroIFZbbTUmTZrEHXfcASzJ637fffdx//33F+uX5nrfcsstl/kcjhw5sl7wfcstt+TAAw/kvffeY9SoUSxYsIDp06dz4IEH8uqrr7LKKqssdY433niDbt26cdJJJ9G9e3cuvfRSPvnkEwAuvvhizj33XDp37rzMsbQEA/CSJEmSJEmSVIWXXnqJX/ziF6SUeOutt7jmmmvqHT/00ENz25122mn86le/qlc2YcIE7r333uL+UUcdxXXXXVfcP+aYY4rnv//++3n22WfZZpttGDhwIC+++GIxAN+rV6/c/Oo/+9nPitv77rsvd955JxEBwLHHHsvnPvc5ACZOnMgdd9zBQQcdxEsvvcTf/va3Yrvtt9+ehx9+mJVWWqlYNn36dDp37swqq6zCmWeeyUcffVQvAN+UXO+LFy/mF7/4RXF/o402Yty4cXTr1g3IPkz41re+BcC0adO48sorOeOMM3LPddNNN3HggQcCsN5663H66acDMHv2bCZNmsSgQYMaPa7mMAe8JEmSJEmSJFVh/PjxnHXWWXz3u9/lkksuKS7ACnDggQdy4okn5rb74Q9/uFTZY489Vm//2GOPrbdfF3iuUz7bviFz586tN7P+7rvvpkOHDsWc6XXB9/KxPProo/XKv//979cLvgP06dMndxZ6NV5++WWmT59e3D/yyCOLwXfIviHQqdOSOeWVnoO+ffsWg+9AvZQ6ADNnzmyR8TaGAXhJkiRJkiRJaqbOnTuz9tprs++++3LjjTfy17/+tV6wuE6fPn1YffXVlyovXwB17bXXbnC/0oKpeWbOnNmkxUenTZuW28cGG2zQ6HNUY1nPQadOnejTp0/F+nUGDBhQb79r16719hcvXtyMUTaNKWgkSZIkSZIkqQrHHHPMUouFLkv5DPI6q622Wr39qVOnMnDgwHr7DdVvSO/evYmIYhB+jz32YJ999qlYv25GfHkfb775JltttVWj+22qvOeg1MKFC+vNkK/0HJTnd69LtdMaDMBLkiRJkiRJUivbeeed6+1fddVV7LnnnsX9K6+8smL90oDz3Llzlzp3jx492GabbYppaKZOncqJJ57IyiuvXK/eggULuOOOO9hxxx0B2HXXXesd/+lPf8qXvvQlevToUSybOXMmHTt2pFevXkuNpW48pfUbstlmm9GnT59ikP2mm27i+9//fjENzbXXXsvChQtzn4MVlQF4SZIkSZIkSWplW221FXvttRf33XcfANdffz3Tp09nhx12YPz48dx1113FunvuuSfbbLNNcb9fv37F7WnTpjFs2DAGDhxIRHD00Uez1lprcfbZZ3P44YcD8K9//YuBAwfy1a9+lbXXXpvZs2fz0ksvMXr0aGbPns2bb75J7969GThwIAceeGBxIdannnqKz33ucxx00EGsttpqvP7669x+++088sgjbL311kuNBeDrX/86O+64Ix07duQrX/kKm266acXnoEOHDvzP//wP5557LgCvvfYa22+/PQcddBBTp05l1KhRxbp9+vRZKk/+isgAvCRJkiRJkiStAK677jr22msvnnvuOQD+8Y9/8I9//KNenUGDBnH99dfXKzv44IO58MILWbRoEUC9QPWQIUNYa621OOyww5g4cSIXXHABKSXefvttfv3rXy9zTKNGjeKAAw5gzJgxALz11lsNtttnn31YeeWV+eijjwD429/+VgzgDxgwoMEAPMDZZ5/NxIkTufbaawF48cUXefHFF+vVWW211bj99ttZddVVlzn+1mYAXpIkSZIkSVKzTDhmQmsPoV1YY401GDt2LH/605+49dZbmTBhArNnz6Znz54MHDiQQw89lG9/+9vFlCx1Bg0axF/+8hcuuugiJkyYkJuGBuD8889nv/3249JLL2XMmDG88847QLbY6Xrrrceee+7JAQccUG8R01VWWYXRo0dz4403csMNN/DMM8/wwQcf0K1bN/r168euu+5ab9b7mmuuyT333MN5553H+PHjmTNnTpOegw4dOnDNNddw6KGHcsUVV/DUU08xffp0unTpwkYbbcQ+++zDaaedxjrrrNOk87aWaMrqt1q+Bg8enMaPH9/aw5AkSZLUDk3cfIsmt9li0sQajESS1BZMnDiRLbZo+t8O6bPigQce+HTo0KFdy8s7tMZgJEmSJEmSJElq7wzAS5IkSZIkSZJUAwbgJUmSJEmSJEmqAQPwkiRJkiRJkiTVgAF4SZIkSZIkSZJqwAC8JEmSJEmSJEk1YABekiRJkiRJkqQaMAAvSZIkSZIkSVINGICXJEmSJEmSJKkGDMBLkiRJkiRJklQDBuAlSZIkSZIkSaoBA/CSJEmSJEmSJNWAAXhJkiRJkiRJkmqgU2sPQJIkSZIkSVLbNnHzLVp7CA3aYtLE1h5CTQ0ZMoRHHnkEgN12243Ro0e37oBU5Ax4SZIkSZIkSWqEyZMnExFLPTp06MBKK63EJptswje+8Q0ee+yxFuuztJ8RI0a02HmVGTFiRL3nuKU5A16SJEmSJEmSmiGlxNy5c3nttdd47bXXuOGGG7jiiis49thjl0v/J510Evvvvz8A/fv3Xy59qnEMwEuSJEmSJElSFQYPHszhhx/OvHnzeOKJJ7j77ruBLCB/9tlnc8wxx9ChQ+2TkBx++OFVtZs3bx4dO3akc+fOLTwi1TEFjSRJkiRJkiRVYeDAgZx55pn84Ac/4K677uILX/hC8di0adOYNm1avfoffPABF1xwAdtvvz2rrLIKXbp0oV+/fnz9619n3Lhx9eoOGTJkqZQoF1xwQb10KZMnT65XNyIYMmRIvTblKWweffRRhg4dyqqrrkr37t3597//Xaw7ZcoUzjrrLLbaait69uxJ165d2XDDDRk+fDgvv/zyUtdfnr5l9uzZ/M///A/rrrsuPXr0YMcdd+SBBx4oXvsJJ5zAWmutRbdu3Rg8eDB33nln7vO6YMEC/vSnPzF06FDWWGMNunTpQp8+fdhrr7249dZbl6pfnhro6quv5sEHH2TPPfekV69erLTSSuy+++6MHTu22Gb06NFEBBdccEHF52vYsGG542sKZ8BLkiRJkiRJUgtYd911i9sdOnSgd+/exf3x48ez//77895779Vr8+9//5sbb7yRW265hV/96leceuqpNRvffffdx49//GMWLVq01LG7776bI444gjlz5tQrf/PNN/nTn/7Eddddx/XXX8/BBx9c8fx77rkn48ePL+6PHTuWvffemxtvvJHvfe97vP7668VjTz/9NAceeCAPPPAAu+++e7F8xowZ7L333kt9IPHBBx9w//33c//993PkkUdy3XXXVfx2wRVXXME///lPUkrFstGjR7PHHnvwzDPPsPnmm1e8hpZmAF6SJEmSJEmSmmHevHk8/vjj3H///cWyr33ta3Tp0gWAOXPmcMABBxSD72uttRZHHnkkq622Gg888ACPPvooixYt4rTTTmPrrbfmi1/8YjGv+1lnnVU855e+9CX22muv4v5qq63WpHE+8cQT9OjRg69//eust956TJgwgc6dO/PWW29x6KGHMnfuXAA22GADDjvsMLp168bf/vY3nnvuOebNm8dRRx3FSy+9xIYbbph7/meeeYbhw4ez8sor87vf/Y4FCxawaNEiDjvsMDp27Mi3v/1tunXrxu9//3sWLlzI4sWL+fnPf14vAP/Nb36zGHzv1q0bRxxxBBtvvDEvvfQSN998M4sXL+bGG29kyy235Nxzz80dx2OPPcbmm2/OwQcfzHPPPVdMDfTJJ5/w61//mksvvZSNNtqIkSNHct9999X7vY0cObK4veWWWzbp+c1jAF6SJEmSJEmSqjBq1ChGjRq1VPlBBx3E5ZdfXq/e1KlTAejatSvjxo0rLpb6gx/8gB133JEnn3ySxYsX84tf/IIvfvGLxbzupQH4nXbaiTPPPLPq8Xbs2JFHH32U7bbbrl75mWeeWQy+r7322jz33HP06tULgO9973tssskmTJkyhXnz5vGb3/yGX/3qV7nn/9GPfsT3v/99AKZOncqNN95YPPbjH/+Yc845p3jslltuAeCpp54q1nnxxRe56667ivujRo3isMMOK+7379+fn//85wD88pe/5Oyzz6Zjx45LjaN///489dRT9OzZE4Btt92WZ599tl5//fv358wzz+Sjjz6qF4BvzvObxwC8JEmSJEmSJLWQrbbaigsvvJBVVlmlWDZmzJji9vz581lvvfUqtn/sscdqNrZ99tlnqeA71B/f1KlT6429XEPjO+qoo4rbAwYMqHfs61//enF7k002KW7PnDkzdxyQLS5baYHZGTNmMHHixNxZ6kcffXQx+A6w6aabFgPwpf0tDy7CKkmSJEmSJElVGDx4MD//+c+LqVUAXnjhBXbZZZd6+c5nzJjR6HPOmDGDxYsXt/hYgYq5z5syvvKFZUuV5sCvS79Tp2/fvsXtTp2WzAsvzdPelHE0NJby4H/Xrl2L27V6bitxBrwkSZIkSZIkVWHgwIHFFDH77LMPX/3qVwGYNWsW//3f/13MPV6aq71Xr1788Ic/bPC8EVGT8a600kq55aXjW3/99RtcCLZ0Znm5zp07VzxWGnSvpDyn/dlnn02fPn0q1t9oo40aNY5aPZ+NYQBekiRJkiRJkprpoIMO4stf/jL/+Mc/ALjnnnsYM2YMu+yyC1/84heLOc9nz57NdtttV2/h0Tovvvgis2bNqhcw7tSpEwsXLgQo5mlvaV/84heLudHfe+899ttvP7bYYoul6o0dO7Y4079W4yjVtWvX3JzsU6dO5YknnmgwlU9jlQfr586dS48ePZp93joG4CVJkiRJkiSpBZx33nnFADzABRdcwAMPPMAxxxzDT37yE9577z1gyWz5z33uc6SUmDx5Mv/85z955ZVXOP/88+sFovv168fkyZMBuPrqq+natSurrLIKffr0YdiwYS0y7v/+7//m0ksv5ZNPPmHevHl84Qtf4Gtf+xobbbQRCxYs4LXXXuPRRx9lypQpXHXVVWy99dYt0m+5QYMGsc8++3DPPfcA2aKujz32GDvttBPdu3fn3XffZfz48YwbN45ddtml+I2D5ujXr1+9/a9//evsuOOOdOzYka985StsuummzTq/AXhJkiRJkiRJagE77bQTQ4YMYfTo0QA8+OCDPPbYY3zxi1/kjjvu4Ctf+QpTp05l/vz53HTTTY0656GHHsrIkSOBLOf5j3/8YyBLf9NSAfgBAwZw6623cuSRRzJnzhzmzJnDVVdd1SLnbqprr72WffbZh3HjxgHw0EMP8dBDD9Wsv3322YeVV16Zjz76CIC//e1v/O1vfwOy58UAvCRJkiRJkqRWtcWkia09hBXGD3/4w2IAHmDEiBE88MADbL/99rz00ktceuml3HnnnUyaNImPPvqIPn360LdvX7bbbjsOOOAAhg4dWu98F154IRHBLbfcwjvvvFNMR9PS9ttvP/71r39xySWXcN999/Hqq68yb9481lxzTdZdd1123HFHDjjgAHbdddea9F9n9dVX5/HHH+faa6/l5ptv5rnnnmPGjBmsvPLK9O3bly222IL99tuP/fbbr0X6W3PNNbnnnns477zzGD9+PHPmzGmR89aJ0lVmtWIZPHhwGj9+fGsPQ5IkSVI7NHHzpfO6LovBFUn67Jo4cWJuTnBJmQceeODToUOHdi0v79Aag5EkSZIkSZIkqb0zAC9JkiRJkiRJUg0YgJckSZIkSZIkqQYMwEuSJEmSJEmSVAMG4CVJkiRJkiRJqgED8JIkSZIkSZIk1YABeEmSJEmSJEmSasAAvCRJkiRJkiRJNWAAXpIkSZIkSZKkGjAAL0mSJEmSJElSDRiAlyRJkiRJkiSpBgzAS5IkSZIkSZJUAwbgJUmSJEmSJEmqgU6tPQBJkiRJkiRJbdslJz7U2kNo0CmX7dHaQ1guRowYwQUXXFDcTym14mgEzoCXJEmSJEmSpCa57bbb2H///VlnnXXo0qULPXv2ZL311mOHHXZg+PDh/OEPf2jtIWoF4Qx4SZIkSZIkSWqk448/niuuuKJe2YIFC/joo4+YMmUKTz75JLfeeisnnHDCch/bXnvtxcorr7zc+1VlBuAlSZIkSZIkqRHuu+++esH3bbbZhi9/+cusssoqzJgxgxdeeIFHHnmk1ca30047sdNOO7Va/1qaKWgkSZIkSZIkqRHuvffe4vZGG23EU089xUUXXcQ555zDz3/+c+69916mTZvGtddeW6/dlVdeyRFHHMHAgQNZc8016dKlCyuvvDJbbLEFJ5xwAhMnTqxX/+ijjyYiiAg222yzpcYxbdo0OnfuXKzz29/+FshywNeVRUS9NkOGDCmWDxkyhPfee4+TTjqJddddl65du7LJJpswcuTI3Lzxs2fP5jvf+Q79+/enW7dubLbZZvzsZz9j4cKF9fobMWJEvXb33XcfBx54YLGPHj16sN5667H77rvz3e9+l1deeaVJz39b5Ax4SZIkSZIkSWqERYsWFbdnzZrFm2++ySabbFKvzsorr8wBBxxQr+z3v/89Tz/9dL2yBQsWMGnSJCZNmsSoUaO49957GTJkCJClubnuuusAeOWVVxg3bhzbb799se3NN9/MwoULAejWrRvf+MY3mnQdU6ZMYdttt+Xdd98tlr322mt897vfZe7cuZx//vnF8o8//pjddtuN5557rlj2yiuv8L3vfY+xY8dW7OP666/PHdeUKVOYMmUKo0eP5nOf+xybbrppk8be1hiAlyRJkiRJkqRG2HbbbYvbH3zwAZttthmDBg1i8ODBbLPNNuyyyy58/vOfX6rdGmuswf7778/GG29M79696dy5M1OnTuWvf/0rU6ZMYf78+Zx66qm8+OKLAOy2225suummxRni1113Xb0A/PXXX1/cPuSQQ+jdu3eTruONN96gW7dunHTSSXTv3p1LL72UTz75BICLL76Yc889l86dOwNw3nnn1Qu+Dxo0iAMPPJBXX32Vm2++uWIfdbPyATbbbDMOPfRQunTpwjvvvMPEiRN54oknmjTmtsoAvCRJkiRJkiQ1wlFHHcWll17Kk08+CUBKiRdeeIEXXnihWKcuPctBBx1ULLvnnnuYN28eY8eO5fXXX2fOnDn079+foUOHctVVVwHw0ksvMWXKFPr37w/At771Lc455xwAbrrpJn75y1/SqVMnXn/99Xozz48//viqruWmm27iwAMPBGC99dbj9NNPB7J0M5MmTWLQoEEsXLiQP/3pT8U2G264IU8++STdu3cHYJNNNuHHP/5x7vnnzZtX3B4xYgRHHHFEveOzZ8+uV6e9Mge8JEmSJEmSJDVCp06dePDBBzn//PPp169fbp2XX36Zgw8+mDvuuKNY9utf/5q11lqL3XffneOPP54zzjiDs846qxh8r/POO+8Ut4cNG0anTtn86ffff5/7778fgBtuuKFYZ+ONN2a33XZr8nX07du3GHwHlsozP3PmzOK1zJ49u1h+5JFHFoPvAMcdd1zFPnbdddd617Lbbrtx3HHH8b//+788+OCDdO/enTXXXLPJY29rDMBLkiRJkiRJUiOttNJKjBgxgilTpvDyyy9zzTXXcMIJJ9QLJqeUuPjiiwH4+9//zumnn14vkF3J/Pnzi9trrbUW+++/f3G/Lid8aQD++OOPX2qx1cYYMGBAvf2uXbvW21+8eDGQ5bkvtc4669TbX3vttSv28dOf/pQDDzyQiGD+/Pk8+uijXHnllZxzzjkMHTqU9dZbj3/+859NHntbYwBekiRJkiRJkqqw6aabcvTRR3PZZZfx2muv0bdv3+Kxt956C8hSvdRZaaWVuOeee5g7dy4pJe66664Gz1+aXub222/nkUceYdKkSUA2G/+YY46patx1+d3rVArir7rqqvX233///Xr7U6dOrdjHyiuvzO23385//vMfbr/9dkaOHMm3v/3tYhB/6tSpHH300VWMvm0xAC9JkiRJkiRJjTBq1Ch+//vfLzUzHLJZ5F26dCnur7766gBMnz69WLbhhhuy9957F9O4lAbn8+y9997FVDdz587lW9/6VvHY/vvv3+AM9Jaw+eab06tXr+L+rbfeyoIFC4r7V1xxRcW2L774IvPnz2ettdbiwAMP5Mwzz+QPf/gDv/vd74p13nzzTT744IPaDH4F4SKskiRJkiRJktQIb775JhdccAGnn346O++8M9tssw1rrLEGc+bM4a677mLy5MnFuvvuuy+Q5Vevy98+YcIEDj/8cLbccktGjx7NQw891GB/HTt2ZNiwYcWFTt94443iseHDh7fw1eX3f/zxxxfT6UycOJEddtiB/fffn1deeaXBDxDOOeccxowZwx577MF6663HWmutxezZs7nxxhuLdbp27UqPHj1qfh2tyQC8JEmSJEmSJDXBggULGD16NKNHj849PnjwYM4880wATj/9dK655ppiDvhbbrmFW265BYBjjz12qYVYyx133HH85Cc/IaVULOvXrx9f/vKXW+BKlu1HP/oRDz30EM899xwAzzzzDM888wwA++23X700Oh061E+4Mnv2bG6//faK5z7jjDPqLeraHhmAlyRJkiRJktQsp1y2R2sPYbk4/fTT+fznP8/o0aMZN24cU6dO5f333+fTTz+ld+/eDBw4kEMOOYThw4cX09FstNFGjBkzhnPOOYdHH32UlBJbbbUV5557Lj179lxmAH7AgAEMHTq0OIsessB9x44da3qtdVZaaSUeeeQRRowYwS233MK0adNYb731OOaYYzj66KPrBeB79+5d3P7Od77DZpttxlNPPcXbb7/NtGnTWLhwIX369GHrrbfmmGOO4fDDD18u19CaovSTE61YBg8enMaPH9/aw5AkSZLUDk3cfIsmt9li0sQajESS1BZMnDiRLbZo+t8OtQ+ffPJJ7kz1//u//+N//ud/ivtjx47lC1/4wvIc2grjgQce+HTo0KFdy8udAS9JkiRJkiRJqmjLLbdk5513ZqeddqJfv37MmjWL0aNHM2rUqGKdnXfe+TMbfG+IAXhJkiRJkiRJUkUff/wx1157Lddee23u8UGDBhXz2qu+DsuuIkmSJEmSJEn6rDr77LMZOnQo6667Lt26daNLly6ss8467L333vzpT39i/Pjx9O3bt7WHuUJyBrwkSZIkSZIkqaIzzjiDM844o7WH0SY5A16SJEmSJEmSpBowAC9JkiRJkiRJUg2YgkaSJEmSPoMO+17T/zs4oQbjkCS1HSklIqK1hyGtcFJKFY85A16SJEmSJElSgzp27MiCBQtaexjSCmnBggV8/PHHi/KOGYCXJEmSJEmS1KCePXsye/bs1h6GtEKaPXs2Y8eOnZV3zAC8JEmSJEmSpAatttpqzJw5k+nTp/Ppp582mHJD+ixIKfHpp58yffp0Zs6cyY033jg9r5454CVJkiRJkiQ1qGvXrqy33nrMmDGDyZMns2hRbrYN6TOlY8eO9OzZk/XWW4+33nrr07w6BuAlSZIkSZIkLVPXrl1ZZ511WGeddVp7KFKbYQoaSZIkSZIkSZJqwAC8JEmSJEmSJEk1YABekiRJkiRJkqQaMAAvSZIkSZIkSVINGICXJEmSJEmSJKkGDMBLkiRJkiRJklQDBuAlSZIkSZIkSaqBdh+Aj8zXI+KuiPh3RMyPiE8j4j8RcV9EHBcRHXPa9Y+I30bEKxHxSUTMjojxEfG9iOjRQH9bRMRVETE5IuZFxMyIeCwiTo6IzrW9WkmSJEmSJEnSiqJTaw9gObgG+EZO+dqFx5eAgyNi/5RSAoiIIcDfgZ4l9bsB2xUe34yIoSmlf5eeMCKOKPRXGmjvCuxceHw9IvZNKc1ugeuSJEmSJEmSJK3A2vUM+IjYnvrB99eBC4GLgP+UlO8L7FJosybwZ5YE398Ffgz8FphfKNscuCEioqSvzwFXsyT4/jJwPnAlsLhQtjPwu+ZfmSRJkiRJkiRpRdfeZ8BvWLZ/REppPEBEPE0WaK+zRuHn/wNWL2wnYK+U0kuFNpOASwrHdgX2AB4s7J9LNtsd4GNgl5TStEK7D4CzCse+EREXppRebea1SZIkSZIkSZJWYO16Bjzwr7L9gyKiZ0SsBuxfUv4R8M/C9ldLyl+qC74X3FJ2voMBIqID8JWS8ofrgu857QI4qHHDlyRJkiRJkiS1Ve06AJ9SmgD8oqTo+8Bs4ANgWKHsZWC/lNLUiOhKll6mzutl55teaF/n84WfG1E/X3y9djn7n6eCiPh2YbHX8dOmTatUTZIkSZIkSZK0gmvXAXiAlNJZZMH2eTmHPwFuBp4u7Pem/nOSt1hqaVmfws/VG6iTt9+HClJKl6eUBqeUBq+xxhqVqkmSJEmSJEmSVnDtOgAfmV+RLY7aDXgV+Anwc7JFWLsD5wGPREQXsvQw9U6Rd9qS7VSh3rL2E5IkSZIkSZKkdq29L8J6LHBaYftD4AsppZkAEfFH4BWy4Ph2wDeBa4HFLPlgolfOOUvLPij8nN5Anbz9D5AkSZIkSZIktWvtegY8sF/J9it1wXeAlNJr1A+cb51Smg9MKinbuPRkEbEG9XO9P1/4+Towp1I7shzxpZ5HkiRJkiRJktSutfcAfOeS7U0jonfdTkRsTP1c7J8Uft5eUva5iNiyZP+wsvP/FSCltBi4o6R8SESsWaFdKutDkiRJkiRJktQOtfcUNA8BBxS2VwGejIhbyK77m9TPzX5P4edvgBNYsrDqPyLiikL7E0rqjwEeLNn/KXAI0BXoAYyJiOuB9ckWga1zfUrp1eZdliRJkiRJkiRpRdfeA/CXAgcDuxT2NwG+n1Pv8pTSQwAppfci4lDg78DKQF/gh2X1XwaOTCkVF1NNKb0UEccC15A9r5sCF5S1exw4pVlXJEmSJEmSJElqE9p1CppCTvc9gG8D9wPvAQuAT4F3gL8BB6WUTihr9zAwELgEeA2YD3wEPEsWwN8upfTvnP5uBLYGrgbeLvTzIVng/VRgSEppdgtfpiRJkiRJkiRpBdTeZ8CTUloI/LHwaEq7t8mC5k3t7yXg2Ka2kyRJkiRJkiS1L+16BrwkSZIkSZIkSa3FALwkSZIkSZIkSTVgAF6SJEmSJEmSpBowAC9JkiRJkiRJUg0YgJckSZIkSZIkqQYMwEuSJEmSJEmSVAMG4CVJkiRJkiRJqgED8JIkSZIkSZIk1YABeEmSJEmSJEmSasAAvCRJkiRJkiRJNWAAXpIkSZIkSZKkGjAAL0mSJEmSJElSDRiAlyRJkiRJkiSpBgzAS5IkSZIkSZJUAwbgJUmSJEmSJEmqAQPwkiRJkiRJkiTVQKfWHoAkSdKK5JITH2pym1Mu26MGI5EkSZIktXXOgJckSZIkSZIkqQYMwEuSJEmSJEmSVAMG4CVJkiRJkiRJqgED8JIkSZIkSZIk1YABeEmSJEmSJEmSasAAvCRJkiRJkiRJNWAAXpIkSZIkSZKkGjAAL0mSJEmSJElSDRiAlyRJkiRJkiSpBgzAS5IkSZIkSZJUAwbgJUmSJEmSJEmqAQPwkiRJkiRJkiTVgAF4SZIkSZIkSZJqwAC8JEmSJEmSJEk10Km1ByBJkiRJkiRJtXbJiQ9V1e6Uy/Zo4ZHos8QAvCRJUok9Rp9SRauJLT4OSZIkSVLbZwoaSZIkSZIkSZJqwAC8JEmSJEmSJEk1YABekiRJkiRJkqQaMAAvSZIkSZIkSVINGICXJEmSJEmSJKkGDMBLkiRJkiRJklQDBuAlSZIkSZIkSaoBA/CSJEmSJEmSJNWAAXhJkiRJkiRJkmrAALwkSZIkSZIkSTVgAF6SJEmSJEmSpBowAC9JkiRJkiRJUg0YgJckSZIkSZIkqQYMwEuSJEmSJEmSVAMG4CVJkiRJkiRJqgED8JIkSZIkSZIk1YABeEmSJEmSJEmSasAAvCRJkiRJkiRJNWAAXpIkSZIkSZKkGjAAL0mSJEmSJElSDXRq7QFIkiRJkiRJUq3tMfqUKltObNFx6LPFGfCSJEmSJEmSJNWAAXhJkiRJkiRJkmrAALwkSZIkSZIkSTVgAF6SJEmSJEmSpBowAC9JkiRJkiRJUg0YgJckSZIkSZIkqQYMwEuSJEmSJEmSVAMG4CVJkiRJkiRJqgED8JIkSZIkSZIk1YABeEmSJEmSJEmSasAAvCRJkiRJkiRJNWAAXpIkSZIkSZKkGjAAL0mSJEmSJElSDRiAlyRJkiRJkiSpBjrV4qQR0R34MrAxsAB4OqX0WC36kiRJkiRJkiRpRdTkAHxEdAaOLSlKwJ9SSqlwfCfgz8BaZe3GAAenlGZUP1xJkiRJkiRJktqGambA/xdwGVngHeCplNIfASJiJbLg+9o57XYBbiSbGS9JkrRCOux7Tf/n0YQajEOSJEmS1PZVkwP+i4WfUfh5W8mxI8iC7ynnEcDQiNituqFKkiRJkiRJktR2VBOA37psf3TJ9v5lx4KlHVJFn5IkSZIkSZIktSnVBOA3Zkn6mcXA8yXHdmTJbPeZwK+B1wv7dW22r2qkkiRJkiRJkiS1IdUE4PuUbL+TUvoUICJWA9YslCfgrJTSGcABJfUD2LCagUqSJEmSJEmS1JZUE4BfvWR7Vsn2ZoWfdWln/gmQUnoZmFJSr2cVfUqSJEmSJEmS1KZUE4DvWrLdrWR785LtBLxZsj+jZLtTFX1KkiRJkiRJktSmVBOA/7hke72IqAvIb11S/u+U0oKS/dKgfWl7SZIkSZIkSZLapWoC8G+XbHcDfhcRhwFHk818T8C/ytqsUbL9XhV9SpIkSZIkSZLUplQTgH+eLM97Kvz8FnAjsCpl+d+huDhrXd74BLxR5VglSZIkSZIkSWozqgnA31ayXReErwvI59XZsaz981X0KUmSJEmSJElSm1JNAP7vwHiWzHavSztT5/aUUmkKmr3L2j9ZRZ+SJEmSJEmSJLUpTQ7Ap5QS8BXgMZbMfq973AUMq6sbEZ2Bw+t2gcXAw80asSRJkiRJkiRJbUCnahqllKYCu0bEQGCLQvHzKaVXy6sC25fsL0opfVhNn5IkSZIkSZIktSVVBeDrpJReAl5q4PhC4K3m9CFJkiRJkiRJUlvU5AB8RHyzZHd6SunuFhyPJEmSJEmSJEntQjUz4K9myaKrTwPLDMBHxP3A5wu7KaW0VhX9SpIkSZIkSZLUZjQnBU00oe6qQJ/CdmqgniRJkiRJkiRJ7UKHZrRtaiDdwLskSZIkSZIk6TOjOQH4psyAX60Z/UiSJEmSJEmS1OY0JwXNMkVEN+AAYAOWzIB3JrwkSZIkSZIkqd1rMAAfEecD5+UdKvzcLiIWNbHPj5pYX5IkSZIkSZKkNqcxM+AbSjXT2DQ0qaTum41sI0mSJEmSJElSm9XYFDSlaWOiQnljz3NvE9tIkiRJkiRJktTm1DQHfI73gP9bzn1KkiRJkspMePPt1h6CJElSu9ehkfWi5FGpvKHHfOAWYMeU0vvNH7YkSZIkSZIkSSu2Zc2AvxoYXbIfwEMsST3zCnBiA+0/BWYCr6WUFlY3REmSJEmSJEmS2p4GA/AppbeAt0rLIupNgp+TUnqkBuOSJEmSJEmSJKlNqyYH/NssmQH/bguORZIkSZIkSZKkdqPJAfiU0oAajEOSJEmSJEmSpHalsYuwSpIkSZIkSZKkJmjyDPiI2AC4oqRoNnBoSmlBTt2uwM1Ar0JRAo5LKU1u+lAlSZIkSZIkSWo7qskBvw8whCV54C/MC74DpJTmR8TTwAUl9fcFfl9Fv5IkSZIkSZIktRnVpKD5UuFnFH5evYz6V1VoL0mSJEmSJElSu1VNAH4Tlsxmn7qsdDIppXeAdwu7UWgvSZIkSZIkSVK7Vk0Afq2S7fcb2WZayfbaVfQpSZIkSZIkSVKbUk0AvleF7ca2WbmKPiVJkiRJkiRJalOqCcDPKtkeEBF9G6ocEesAA0qKPqyiT0mSJEmSJEmS2pRqAvBTy/ZHLKP+CJYs2Arwnyr6lCRJkiRJkiSpTakmAP8kWUA9FX4eFxG/i4jVSitFRO+I+C0wvKRuAp5q3pAlSZIkSZIkSVrxdaqizT3A8YXtusD6ScDwiJgAzABWAwZVOP89VfQpSZIkSZIkSVKbUk0A/m/AFKBfYb8uCN8Z2LZkn5Ljdd4Gbq+iT0mSJEmSJEmS2pQmp6BJKS0mm/FeHmSvC7RHhbIEnJRSKg3IS5IkSZIkSZLULlWTA56U0t3A2XmHqB94hyXB97NTSvdW058kSZIkSZIkSW1NVQF4gJTSSOAQ4E2yIHulxxvAwSmlXzR7tJIkSZIkSZIktRHV5IAvSindHhF3AkOBPYH1gVWBWcBbwAPAgymlhc0bpiRJkiRJkiRJbUuzAvAAheD6vYWHJEmSJEmSJEmiGSloJEmSJEmSJElSZQbgJUmSJEmSJEmqgSanoImI85rbaUrpR809hyRJkiRJkiRJK7JqcsCPAFIz+zUAL0mSJEmSJElq15qzCGtU2a65wXtJkiRJkiRJklZ4zQnAVxNIrzZoL0mSJEmSJElSm1KrGfDlwXkD75IkSZIkSZKkz5RqAvCP0vDs9y5Af2AdoGOhbgL+DbxeRX+SJEmSJEmSJLU5TQ7Ap5SGNKZeRKwOnAqcDXQDegFnpJSea2qfkiRJkiRJkiS1NR1qdeKU0gcppQuAYwpFPYFbI6JrrfqUJEmSJEmSJGlFUbMAfJ2U0q3Af8jywG8InFjrPiVJkiRJkiRJam01D8AXzCjZPmw59SlJkiRJkiRJUqupeQA+IrYGNidbiDWAzWrdpyRJkiRJkiRJra3Ji7BGxK6NqNaZLOf7QOA0oCNZAB5gpab2KUmSJEmSJElSW9PkADwwmiXB9MaIsvrvVtGnJEmSJEmSJEltSjUB+DrRyHp1wfe6QPz9zehTkiRJkiRJkqQ2oTkB+KbMgq/zCfDzZvQpSZIkSZIkSVKbUPNFWMlmvgfwAfDVlNIby6FPSZIkSZIkSZJaVbUz4BuTfmYBMAv4F3APcEVK6YMq+5MkSZIkSZIkqU1pcgA+pbQ8Zs1LkiRJkiRJktSmGUyXJEmSJEmSJKkGDMBLkiRJkiRJklQDBuAlSZIkSZIkSaqBijngI2K9WnWaUnq7VueWJEmSJEmSJGlF0NAirJOBVIM+0zL6lSRJkiRJkiSpzVtWIDyWyygkSZIkSZIkSWpnlhWAb+kZ8Ab0JUmSJEmSJEmfCc1dhDVYdlC9MXUkSZIkSZIkSWpXWiIFTVpGvbrjBuElSZIkSZIktYrDvlfdspQTWngc+mxp6K7boIFjHYDfAPuRBdZHA1eR3Y+zgZ7AIGAYsAdZEH4ccDTwaTPHLEmSJEmSJEnSCq9iCpqU0luVHsB3gH3JAuu/SCntkVK6NqX0XErpjZTS8yml61JKQ4GRZEH67YELC+2Xu4jYNCJ+FxETI2JORHwSEW9HxD8i4vSc+v0j4rcR8Uqh7uyIGB8R34uIHg30s0VEXBURkyNiXkTMjIjHIuLkiOhc04uUJEmSJEmSJK0wmpwDPiKGACeTBdU/Ar6/jCY/BOYU6h8aEfs1tc/mioj/B/wLOAXYHFgZ6Ab0B/YCzimrPwR4CTgV2KRQtyewHfBT4OmIWDennyOA58lm/q8PdAVWBXYGLgEejoheLXt1kiRJkiRJkqQVUTWJj04q/EzAmymlBQ1VTil9GhFvAlsVio4H7qqi36pExNHAr0uKJgL3Ae+TBdXXB9Yrqb8m8OfCMYB3gSuBVYBvkwXVNwduiIghKaVUaPc54Gqgbpb7y8ANhfMPI/uwY2fgd8A3W/YqJUmSJEmSJEkrmmoC8DuyZGHVdZZVOSLq6tW12b6KPqsSESsBvyop+llK6XvLaPb/gNUL2wnYK6X0UuF8k8hmsgPsSpbf/sHC/rlkwXmAj4FdUkrTCu0+AM4qHPtGRFyYUnq1qouSJEmSJEmfOZec+FCT25xy2R41GIkkqSmanIIGWLNku09EHL+M+t8C1ijZX71SxRo4GFitsP0xMDkixkbErEJO98ci4qiyNl8t2X6pLvhecEvO+YmIDsBXSsofrgu+57QL4KAmXockSZIkSZIkqY2pZgb8DGAtlsxovzQiBgGjgBcLKWc6A1sCx5Dli08l7Wc2b8hN8sWS7ZWAy8qO7wzsXEglMzwi6tLL1Hm9tHJKaXpEzAbq8rh/vvBzI5akrFmqXc7+55EkSZIkSZIktWvVzIB/gizwDllgvSPZYqXjgE8iYj4wDxgP/DdLgvxRqP9EcwbcROULpSbgGuDH1A+KHx8RhwK9qf+czM45Z2lZn8LP8ln95e3K9/tQQUR8OyLGR8T4adOmVaomSZIkSZIkSVrBVROAv7xkuy6oHiWPzmX7qax9+Sz0WupStv+blNIxKaUfArsBn5YcO4YlHyzUKd8vL0sV6i1rv/w5WXIgpctTSoNTSoPXWGONStUkSZIkSZIkSSu4JgfgU0r/AK6mfnA9NfAodXVK6f5qB1uF8nQ3xRVLUkr/Bl4pObYxWXqdxSVlvVhaadkHhZ/TG6iTt/8BkiRJkiRJkqR2rZoZ8ADHA1eQP0O8XN1M+D8Bw6vsr1oTyvbLPxAo3f8kpTQfmFRStnFp5YhYg/q53p8v/HwdmFOpHVmO+FLPI0mSJEmSJElq16oKwKeUFqeUhgP7AY9TP+VM+eMxYN+U0rdTSosrnLJW7ijb371uIyLWATYrOTau8PP2krLPRcSWJfuHlZ3vr5A9H2V9DYmINSu0S2V9SJIkSZIkSZLaoU7LrlJZSuke4J5CMHtHoB/ZDPE5wDvAEyml/zR7lNWP7/mI+BtwYKHotIjoDUwBjmBJjviFwG8L278BTmDJwqr/iIgrgFUK5XXGAA+W7P8UOAToCvQAxkTE9cD6wLCSetenlF5t/tVJkiRJkiRJklZkzQrA1ykE2W9riXPVwPFAf2Bbshn/w8qOLwC+nVKaAJBSei8iDgX+DqwM9AV+WNbmZeDIlFIxhU1K6aWIOBa4hux53RS4oKzd48ApLXBNkiRJkiRJkqQVXLU54NuMlNJ0YGfgf4AngdlkQfe3yYLl26eUri5r8zAwELgEeA2YD3wEPAt8H9iusIhreV83AluTLVL7NvAp8CFZ4P1UYEhKaXYLX6IkSZIkSZIkaQXU7BnwhYVJDyPLr96fLFXLh2QB6IeBWwpB8FaTUpoH/F/h0dg2b5MFzZva10vAsU1tJ0mSJEmSJElqX5oVgI+IHwLfJct5Dtmiq3UGAwcD/xsRP0sp/aQ5fUmSJEmSJEmS1JZUFYCPiABuAr5G/aA7QCqU1ZWvBPwoIrZKKR1e7UAlSZIkSZIkSWpLqs0Bfy5wKFmQPZU9yCkL4GsRcW6zRitJkiRJkiRJUhvR5AB8RKxOFoAvDbgXD1N/9nuduiD8uYX2kiRJkiRJkiS1a9WkoDkW6M6S4HtdsH0S8AIwB+gJbAVsTv0gfXdgGPDLKvqVJEmSJEmSJKnNqCYAP6RkO4C3gK+nlJ4orxgROwHXAeuXFO+BAXhJkiRJkiRJUjtXTQ74LVmSUiYBB+UF3wFSSo8DB5fUjUJ7SZIkSZIkSZLatWoC8KU53N9IKT3fUOWU0nPA6xXaS5IkSZIkSZLULlUTgC9NWzO/kW1K63Wsok9JkiRJkiRJktqUagLwM0q2N4mINRqqHBFrApuWFM2sok9JkiRJkiRJktqUagLwr7Ekp3sn4JqIWDmvYqH8GpbMmk+F9pIkSZIkSZIktWudll1lKWOAXQrbAewFvBYRfwZeAOYAPYGtgK8Ba1B/0dZHmzlmSZIkSZIkSZJWeNUE4K8BvlfYrgusrwmclFM3SurV/bymij4lSZIkSZIkSWpTmpyCJqX0CnAD9YPrdYH48kfpsQTcUGgvSZIkSZIkSVK7Vk0OeMhmu7/MkiA8LAm2lz5KTQJOrrI/SZIkSZIkSZLalGpS0JBS+igidgZuBXavK86pWhegfxA4PKX0UTX9SZIkSZIkfZbtMfqUKlpNbPFxSJKaptoZ8KSUZqSU9gQOAG4DZlE//cysQvn+KaUvpZRmNHu0kiRJkiRJkiS1EVXNgC+VUroLuAsgIlYCVgE+TCl93NxzS5IkSZIkSZLUVjU5AB8RF5cVXZNSeg6gEHQ38C5JkiRJkiRJ+syrZgb86SzJ974I+GmLjUaSJEmSJEmSpHaimgD8bKAnWZ73d1JK01t2SJIkSZIkSSu+S058qKp2p1y2RwuPRJK0oqpmEdYpJdsftdRAJEmSJEmSJElqT6oJwN9DNvsdYMOI6NqC45EkSZIkSZIkqV2oJgD/G7I0NADdyXLCS5IkSZIkSZKkEk0OwKeU3gGOBuaTzYT/SUScFxErt/TgJEmSJEmSJElqq5q8CGtEXFnYfBXYkiyIfz7w3Yh4GniHLDhfSUopHdfUfiVJkiRJkiRJakuaHIAHhgGpZD+RzYTvAXxxGW2jUN8AvCRJkiRJkiSpXasmAF8nSrZTTpkkSZIkSZIkSZ9ZzQnAp0aWlTJAL0mSJEmSJEn6TKg2AG8gXZIkSZIkSZKkBlQTgB/V4qOQJEmSJEmSJKmdaXIAPqV0bC0GIkmSJEmSJElSe9KhtQcgSZIkSZIkSVJ7ZABekiRJkiRJkqQaqHYR1noiYm1gB6Af0BOYA0wBxqaU3muJPiRJkiRJkiRJakuaFYCPiL2Ac4FdGqjzCHBRSun+5vQlSZIkSZIkSVJbUlUAPiICuBQYXlfUQPUhwG4RcTlwckopVdOnJEmSJEnSimSP0adU2XJii45DkrTiqnYG/J+AY0v2GwqqR+Hx7UJ/wxuoK0mSJEmSJElSu9DkAHwh7cyxNBx0L1VXL4BvRcTNKaUHmtqvJEmSJEmS9Fl3yYkPVdXulMv2aOGRSGqMDlW0ObFkO0p+1j0Wle2XO6mKPiVJkiRJkiRJalOqCcDvQP3Z7wm4DPgC0COl1AXoDvwXWZ74VFIvgB2rHq0kSZIkSZIkSW1ENTngVyv8DLKg+ikppT+UVkgpzQfGA+Mj4nmyAH1dIL53lWOVJEmSJEmSJKnNqGYG/LSS7Q/Kg+85/ghML9l/v4o+JUmSJEmSJElqU6oJwD/Jktnv7y6rckqprl5dmyeq6FOSJEmSJEmSpDalmgD8lYWfAWwQEV0aqlw4vgFLUtBcWkWfkiRJkiRJkiS1KU0OwKeU7gZuKuyuDFy4jCY/BnoWti9NKT3S1D4lSZIkSZIkSWprmrwIa0SsB5xPNqv9C8CZEbEdcBXwAvARWWB+K+BYYHey2e93Ab8otM+VUnq7qeORJEmSJEmSJGlF1OQAPDCZJelkElkqmt0Ljzx1ud/3KzwqSVWOR5IkSZIkSZKkFU61Ae8o2U5l++WWdVySJEmSJEmSpHan2gB83Qz4YMkM90rqgu+NqSNJkiRJkiRJUrvQ3JQvDQXVm1JHkiRJkiRJkqR2pSVS0EiSJEmSJEmSpDLVBOA3aPFRSJIkSZIkSZLUzjQ5AJ9SeqsWA5EkSZIkSZIkqT1pbg54SZIkSZIkScvJHqNPqbLlxBYdh6TG6dDaA5AkSZIkSZIkqT0yAC9JkiRJkiRJUg0YgJckSZIkSZIkqQYMwEuSJEmSJEmSVAMG4CVJkiRJkiRJqgED8JIkSZIkSZIk1YABeEmSJEmSJEmSasAAvCRJkiRJkiRJNWAAXpIkSZIkSZKkGuhU6UBErFeyOz+l9N5yGI8kSZIkSZIkSe1CxQA8MBlIhe3xwBcAIuKhkjovp5ROqs3QJEmSJEmSJElquxoKwANE2U+AISwJzK/c0gOSJEmSJEmSJKk9WFYAPjVwLBo4JkmSJEmSJEnSZ1pjFmENYPVaD0SSJEmSJEmSpPakoRnw84Cuhe0BEXEG8HRZnZ4RsWtTO00pPdrUNpIkSZIkSZIktSUNBeCnAf3I0tAE8IuSY3XpZzYFHm5in2kZ/UqSJEmSJEmS1OY1lILmBZYE2uuC8OV536PKhyRJkiRJkiRJ7VpDAfhbyvYTSy/Kmpr4kCRJkiRJkiTpM6GhAPx1wGicsS5JkiRJkiRJUpNVDMCnlBKwLzACmAjMJwvGl85kN/WMJEmSJEmSJEk5GpoBT0ppXkrpRymlLVNKPVJKHVgShE/A+JRShyY+Oi6PC5MkSZIkSZIkqTV1qrKds9klSZIkSZIkSWpANQH4USXbb7bUQCRJkiRJkiRJak+aHIBPKR1bi4FIkiRJkiRJktSeVJuCpp6I6AXsCvQHVgE+BN4GxqSUZrdEH5IkSZIkSZIktSXNCsBHxGbAhcBBQN7iqosi4jbgvJTSK83pS5IkSZIkSZKktqRDtQ0j4qvAeOAQskB+5Dw6AYcCT0fEgc0erSRJkiRJkiRJbURVAfiI2BG4GViJLNCeGnhEod4tEfGFFhizJEmSJEmSJEkrvCYH4CMigKvJZrfXBdkbUlenMzCq0F6SJEmSJEmSpHatmhnwXwU2oX7gvTTtzOKy/VKbkOWLlyRJkiRJkiSpXasmAL9fyXZdgP2PwA5Aj5RSZ6BHYf9POe0PqKJPSZIkSZIkSZLalE5VtNmBJbndE3B6Sum3pRVSSvOAp4CnIuJF4FclbcwDL0mSJEmSJElq96qZAb9WyfZM4HfLqP87YEbJ/tpV9ClJkiRJkiRJUptSTQC+V8n2OymlBhdhTSktBt4pKVq5ij4lSZIkSZIkSWpTqgnAzy7Z7h8R5Qut1hMRHYD+JUVzquhTkiRJkiRJkqQ2pZoA/NSS7VWBU5dR/1Sgd4X2kiRJkiRJkiS1S9UE4J9gyQKsAfxfRPw+IgZHRBeAiOhS2L8UuJj6i7aObZmhS5IkSZIkSZK04upURZs7geMK24ksiH9C4UFELCw7b13gvc7fq+hTkiRJkiRJkqQ2pZoZ8H8HXinZr5vdXvfoXLafSuq9klIyAC9JkiRJkiRJaveaHIBPKSXgGGBhaXEDD8gC8QsK7SRJkiRJkiRJaveqmQFPSulJ4HDgE7LgekMCmAscnlJ6qpr+JEmSJEmSJElqa6oKwAOklG4HtgP+AiymftqZusdi4M/AtimlvzV3sJIkSZIkSZIktRXVLMJalFJ6GTg0IlYBdgXWA1YBPgTeBh5NKX3Y7FFKkiRJkiRJUhtxyYkPNbnNKZftUYORqLU1KwBfpxBkv6MlziVJkiRJkiRJUntQdQoaSZIkSZIkSZJUmQF4SZIkSZIkSZJqoEVS0EiSJEla8ZmLVJIkSVq+nAEvSZIkSZIkSVINGICXJEmSJEmSJKkGDMBLkiRJkiRJklQDBuAlSZIkSZIkSaoBA/CSJEmSJEmSJNWAAXhJkiRJkiRJkmrAALwkSZIkSZIkSTXQqakNIuK2sqKfp5TGttB4JEmSJEmSJElqF5ocgAcOAhIQwKfAN1tyQJIkSZIkSZIktQfVpKCZWbL9dkrpo5YajCRJkiRJkiRJ7UU1Afg3S7Y/bamBSJIkSZIkSZLUnlQTgL+DLP0MwMYR0bMFxyNJkiRJkiRJUrtQTQD+18DUwnZn4IKWG44kSZIkSZIkSe1DkxdhTSnNioivAncBqwGnRcSqwI9TSm+08PgkSZIkSZI+8w77XpNDOEyowTgkSU3T5HfviHiosDmLLAAfwDHAMRHxDvAOML+BU6SU0p5N7VeSJEmSJEmSpLak6R+fwhAglewnluSE7w/0a6BtlLX9/+3debhsZ1kn7N8TAgmQMJgQGWSwAWVSUOhWQeB4GBpxApWIqBhbhbTHAT/HKNpg23a30HaLpA3YKILg0NAM0jII4WAQHKAFDzEMYkIEBWOYQghT8n5/VG1PnUrtvVetWmsPte/7uuraVWt61lu19lOrnnrrXQAAAAD7Up9e6Yme6QAHSb93ioma+dsWTJ+n8A4AAAAAwIGxSgF+s4K6QjsAAAAAcGAdPnqkx1qXDL4f7L6+BfjNerkDAAAAAADpV4B/6uB7AQAAAAAAa2bpAnxrTQEeAAAAAAC2cdJu7wAAAAAAAKwjBXgAAAAAABhB34uwnqCqTkvywCR3THJGkhu01n5hiG0DAAAAAMB+tFIBvqrunOQXkjwmyQ3mZv9CVT00yc/MTHtpa+0Zq8QEAAD6OXz0SI+1Lhl8PwAA4KDoXYCvqkcleV6Smyapudlt+vfNSe6b5LTpMndNogAPAAAAAMDa61WAr6oHJfmDmfXb7OyNO621q6vqJUkeP51026r6itban/eJCwAAAAB70fnnXrj0OkcuODzCngB7ydIXYa2qG2bS8/3kTArvbes18vLp343lHrJsTAAAAAAA2G/69ID/jiR3yPGC+uzwM4uK8X869/hf94gJAAAAAAD7ytI94JN8/dzjv8hkbPe35vpjwae19qEkV04fVpIv7hETAAAAAAD2lT494O+bSU/3SnJtku9srb23qjIzfd4VST5vev/WPWICAAAAAPR27NLLd3sXOID69IC/1cz997fW/rbDOp+buX9aj5gAAAAAALCv9CnAz/Zwv6bjOrfrsQ4AAAAAAOxbfQrwH565f4eqOmWrhavqXkluucn6AAAAAACwlvoU4N+V473gb5zk3M0WrKqTkvzCzKSW5OIeMQEAAAAAYF/pcxHWP0lyOMcvuPr0qrptkptnZniaqnpsku9Pcmhm2TZdHwAAAAAA1lqfHvC/neTa6f2W5AZJfjzJnWeWqSQvyKT4Pjtm/OeSPL9HTAAAAAAA2FeWLsC31t6X5Dk5Xljf6N1ec4tuTJvt/f7s1to/9t5bAAAAAADYJ/r0gE+SH03y9pxYhG9zy8xPe3uSn+gZDwAAAAAA9pVeBfjW2jVJHpzklTmx93vLiYX3jXmvSnKotfaplfYWAAAAAAD2ib494NNa+3hr7euSPCLJi5J8JMcL7jV9/KIkX9tae2Rr7eMD7C8AAAAAAOwLJ6+6gdbaa5K8Jkmq6qZJbp7kY621q1fdNgAAAAAA7FcrF+BnTYvuCu8AAAAAABx4gxXgq+oWSb4gyelJrkry/tbaR4faPgAAAAAA7CcrFeCr6qwkP5DksUnuumD+u5P8bpJfb61dsUosAAAAAADYT3oX4Kvq25Ocn8mY77XJYl+c5D8keVJV/UBr7ff6xgMAAACAverw0SM91rpk8P0A9paT+qxUVU9M8jtJbpFJ8b1tcavpci+oqu9feY8HUFUXVFWbuV22yXK3r6pfq6p3V9U1VfXxqnpLVZ1XVTfZYvt3r6rfqqrLqupTVfWRqnpjVf1AVd1wtIYBAAAAALBnLN0DvqruluRXc7zwvp2NZSrJM6rqT1pr71o27lCq6uFJnthhuUNJXp7JmPYbTk1y3+nt8VX10NbaB+bWe2yS5yWZLbSfkuQB09vjquqRrbWPr9AMAAAAAA6gs8/rN6DFsYH3A+imTw/4JyW5UU4svtcWt1k3mq6/K6YXiv3NDsudleRFOV58/4ckv5jk15J8ejrtbkleWFU1s949kjw3x4vv78pkCJ7fTHLddNoDkjxzhWYAAAAAALAP9PnK7GE5sVd7klycSa/vY0k+nknh+kuSPD7JvXLicDQPX2F/V/XMJLdL8rEkb01yeJPlfjjJGdP7LcnDW2sXJ0lVvTOTse+T5EHTbbxu+vhnMuntniRXJ3ngxsVnq+rKJD8xnfedVfUfW2vvGaJRAAAAAADsPX16wN92+ndjCJrfTHLv1trTWmuvaq29qbX26tba05PcO8lzcmJP+NustMc9VdU3J/mO6cMfSvL3Wyz+6Jn7F28U36f+YG7Zb55u/6Qk3zgz/fUbxfcF61WSR3XYbQAAAAAA9qk+BfiPzdz/ZJIfaq1dt2jB1lpL8iPT5TZ8okfMlUyHlLlg+vDFrbXnb7HsKZkML7PhvbPzW2v/nEkv/w33nv69c04cL/6E9RY8vncAAAAAAFhbfQrwF+d47/e/a61ds9XCrbVPZlJ83ljnkh4xV/WsJLdK8qEk526z7C1z4vOy6GKps9POnP49Y4tlFj0+MwtU1ROq6i1V9ZYrrrhi0SIAAAAAAOwDfcaA/50kXzO9f1bHdWaX+/0eMXurqu/O8eFevm/ag33LVbZ5PD9tfjz8rttpWaC19uwkz06S+93vfguXYX84/9wLl17nyAWbXZYAAAAAANhv+vSAf26SN2VSUD6rqr5tq4Wr6rFJPn/68B2Z9EbfEVV1apJfnT58TmvtFR1W+3CS2SF1brZgmdlpV07/zhf259ebf3xlAAAAAABYW0sX4Kfjuj8qyVszKcI/t6qeXFW3m12uqm5XVU9O8lvTSe9N8vWttWtX2+WlnJrk5tP731tVbeOW5LtnlrvjxvTW2qeTvHNm3l1mN1hVt8qJY72/ffr3vUmu2my9TMaIn/X2AAAAAACwtjYdgqaq/m6bdU+d/j0lyVOTPLWqPpZJEfq0JLfY2FQmw63cIskbpkXu+WL0XvPSJPeY3r9HVd2rtfaO6eOz55Z9SZK01q6rqj9M8rjp9ENVdVZr7Z8WrNemMYBdZJggAAAAAMa01Rjwd8qkULxoDPTk+Bjms8vcIscL75mb/3mZXKh0J8c1/0ySF28y735J7ji9/8kkr5yZ94wkT8zxC6u+uqqek0lv+ifOLHdRktfNPP6lJN+SyZcSN0lyUVW9YBrnnJnlXtBae8+yjQEAAAAAYP/ochHWzQrmiy5Euqe01j6Z5FsXzauq5+b4MDRXtNa+dWa9D1XVY5K8PJPe/LdN8nNzm3hXkm+fDsmzsd7FVfU9SZ6XyXP7RZn8OmDWm5Ic6dsmAAAAAAD2hz4XYd3Q0r3wvicL9Ftprb0+yT2TnJ/kb5N8OsknkvxVkp9Nct/W2gcWrPe7Se6TycVqL8+kF/7HMim8/2CSQ621j4/fAgAAAAAAdtN2PeA3G35m32utnZMTh4VZtMzlmRTNl932xUm+p9eOAQAAAACwFrYqwCsgAwAAAABAT5sW4Ftrv72TOwIAAAAAAOtklTHgAQAAAACATSjAAwAAAADACBTgAQAAAABgBFtdhHVbVXXfJI9Ics8kt0xySofVWmvtIavEBQAAAACAva5XAb6qbpvkt5McXnbVJK1PTAAAAAAA2E+WLsBX1elJXp/kLpkU1LtSeAcAAAAA4MDo0wP+R5PcNZOCuqI6AAAAAAAs0KcA/+gF05bpCQ8AAAAAAGuvTwH+zjne872SfC7Ja5K8J8lHh9ktAAAAAADY3/oU4K+b/q0k1yZ5cGvtzcPtEgAAAAAA7H99CvDvSfLl0/vvVXyHxQ4fPdJjrUsG3w8AAGB/Ov/cC5de58gFh0fYEwCgr5N6rPOCHB/z/ZZVZfx3AAAAAACY06cA/6wc76Z7ZpKfHm53AAAAAABgPSw9BE1r7ZqqenSSVye5Y5JfrKpDSX4nybFMLsR63WbrT7dx+bJxAQAAAABgP+kzBnxaa++uqm9L8sYkN0jy0Omt0+p94wIAAAAAwH7RZwiaVNW3JHl9JsX3ZDIm/DI3AAAAAABYa0v3RK+qeyR5fpJTM+nNXtO/nVZfNh4AAAAAAOxHfXrA/3iOF9+T7sV3AAAAAAA4MPqMxX4oJxbd9WoHAAAAAIA5fQrwt5n+3Rh65miS85O8N8knknx2kD1jU+efe+HS6xy54PAIewIAAAAAwGb6FOCvSHK76f2rkjyitfaZ4XYJAAAAAAD2vz4F+Ncn+a5Mer//veI7sF8dPnqkx1qXDL4fAAAAAKynPhdh/bUk12YyBM1dq+qsYXcJAAAAAAD2v6UL8K21tyR58vThDZO8pKpuP+heAQAAAADAPrf0EDRV9fPTu+9JctckX5nkb6vqjUmOJflokuu22kZr7ReWjQsAAAAAAPtJnzHgn5LJ+O+Z/q1MesIfmt66UIAHAAAAAGCt9SnAb6jp3zb3eDtt+0UAAADgYDt89EiPtS4ZfD8AgP5WKcDPF9K7FNa7FukBAAAAAGBfG6IHPAAAAAAAMKdPAf5PYhgZAAAAAADY0tIF+NbaoRH2AwAAAAAA1spJu70DAAAAAACwjhTgAQAAAABgBArwAAAAAAAwgqXHgK+qB60atLX2J6tuAwAAAAAA9rKlC/BJjiZpK8RsPeMCAAAAAMC+sUohvAbbCwBgXzr/3At7rXfkgsMD7wkAAADsPasU4Pv0gle0BwAAAAAYQJ9OUTpE7ayduAhrReEdAAAAAIADpm8P+GUK6i2K8AAAAAAAHDBL94BvrZ201S3JKUnukuTRSd6QSeG9JfmbJGe01m4wZAMAAAAAAGAvWmUM+IVaa59N8nfT28uq6j8n+akkd0/y+0kePnRMAABgbzEeKQAA7MwY8D+f5OOZ9IR/SFV9yw7EBAAAAACAXTV6AX7aI/59M5O+e+yYAAAAAACw20YvwFfVLZP8qxy/GOv9xo4JAAAAAAC7bfAx4JOkqm6Q5PQk90zyK0lumkkBPkk+b4yYB8nho0d6rHXJ4PsBADDLmN8AAAAnWroAX1XXrhjzwyuuDwAAAAAAe16fHvDVY52N4Wdakjf3WB8AAAAAAPaVvkPQtO0X2dQzVlgXAAAAAAD2hdEvwjq10Wv+51trb9ihmAAAAAAAsGv69oBfZhiazyS5MMnTWmuv7xkPAAAAAIAZh48e6bHWJYPvB5vrU4D/mo7LfSbJR5P8bWvtsz3iAAAAAADAvrV0Ad4QMgDQ3/nnXrj0OkcuODzCngAAAABj6zsEDQAAwKb8HBoAAHbuIqwAAAAAAHCgKMADAAAAAMAIeg9BU1W3TPK9SR6R5B5JbpnkRh1Wba01Q98AAAAAALDWehXCq+qhSV6Y5IyNSYPtEQAAAAAArIGlC/BVdeckL0ty45nJrevqy8YDAAAAAID9qE8P+J/KpPjetegOAAAAAAAHTp8C/ENy/eK7nu0AAAAAADCjTwH+tjP3K8kVSf57kncm+USSz0XveAAAAAAADrg+Bfirk9wok+L7tUke1lr760H3CgAAAAAA9rmTeqzzzhwfcuadiu8AAAAAAHB9fXrAvzjJ/af3zxhwXwCAfebw0SM917xk0P0AAACAvahPD/hfT3Lp9P7nV9W3Dbg/AAAAAACwFpbuAd9a+1RVfWuS1ye5WZLnVtXdkzyztfbPQ+8gAAD7Q79fRPg1BAAcROefe2Gv9Y5ccHjgPQEY19IF+KrayJAfz6QAf0qSn0vyM1X1D0k+kOTTW2yitdYesmxcAAAAAADYT/qMAX8oSZt53DK5KOvJSe6Q5PZbrFtz6wIAAAAArJWzz1u+7HpshP0YSp9frfjFykSfAvyGmrnfNpmeTZYBAAB22Lp9EAQAgL1ulQL8ZgV1hXYAAAAAAA68vgX4zXq5AwAAABwIxy69fLd3AYA9rk8B/qmD7wUAAAAAAKyZpQvwrTUFeAAAAAAA2MZJu70DAAAAAACwjhTgAQAAAABgBArwAAAAAAAwAgV4AAAAAAAYgQI8AAAAAACM4OTd3gEAAAAADpbDR4/0XPOSQfcDYGwK8AAceOefe2Gv9Y5ccHjgPQEAAADWiQI8AADAHtLni2FfCgMA7E3GgAcAAAAAgBHoAQ8AO6jfWJfGuQQAAGD3+Czbnx7wAAAAAAAwAj3gAQAAAGBFZ5+3fJnt2Aj7AewtesADAAAAAMAI9IAHgDV0/rkXLr3OkQsOj7AnAAAAcHDpAQ8AAAAAACNQgAcAAAAAgBEYggYAAGAPOXz0SI+1Lhl8PwAAWJ0e8AAAAAAAMAI94AEA1pyL8gIAAOwOPeABAAAAAGAECvAAAAAAADACBXgAAAAAABiBMeABAACAHeG6JAAcNHrAAwAAAADACBTgAQAAAABgBArwAAAAAAAwAgV4AAAAAAAYgQI8AAAAAACM4OTd3gEAAIC+zj/3wqXXOXLB4RH2BAAArk8BHoAD7/DRIz3XvGTQ/QAAAADWiyFoAAAAAABgBHrAA8Aa6terX49+AADgOEO9weoU4IED6+zzlk+Bx0bYDwBYR95ngUV0Eujv2KWX7/YuANCDAjwAwJpT7AAAANgdCvAAAABwwBlmAgDG4SKsAAAAAAAwAgV4AAAAAAAYgSFoYCQuPAYAMD7XOAAAYC9TgOdAMa4hAAAAALBTFOABAABgCTr2AABdGQMeAAAAAABGoAAPAAAAAAAjMAQNAAD7iqEfAACA/UIPeAAAAAAAGIECPAAAAAAAjMAQNAAAAHDAHT56pMdalwy+H+w+Q70xS26A1ekBDwAAAAAAI9ADHgAAgLWg5y4AsNcowAMAAMAedPZ5y39kPzbCfgAA/SnAAwAAwBKMiQwAdGUMeAAAAAAAGIEe8AAAAACwTxy79PLd3gVgCQrwACzNBc4AAIBV9LnGQeI6B8D+owAPdKLgCizigxMAAABsTgEeANZQn8K4ojir2qnjzsUPAQBgwme/vU8BngPFB3bYX/zyAgAAANjPFOAB1kSfYnWiYA0AAAAwFgV4YM/R6xkAAACAdaAAz5YUQgEAwHnxfmHISVid/yOAYZ202zsAAAAAAADrSAEeAAAAAABGoAAPAAAAAAAjUIAHAAAAAIARuAjrPnT2ecu/bMdG2A8AgHXnwpsAAMAqFOBhn1MYAAAAAIC9SQEeYE0cPnqk55qXDLofALCO+r3Peo8FADjoFOABAAAAWFt+OQ7sJgV4YM/Rw4wN63gsuI4HAAAAHBwn7fYOAAAAAADAOtIDni2tY+9TAABg5xj6AQA4yBTgAQBglylQAgDAelr7AnxV3TLJNyZ5UJIvS3LrJGcm+VSS9yT5v0n+R2vtwwvWvX2Sn0zyb5PcPslnk7w7yYuT/Gpr7ZObxLz7dL2vmca7JsnFSV6Y5Ddaa58dsIlrwYfOvc+vIQAAAAAOJtcz62/tC/BJHpLkuQum3zDJl09v31dVh1tr79yYWVWHkrw8yekz65ya5L7T2+Or6qGttQ/MbrSqHpvkedPtbzglyQOmt8dV1SNbax9frVkAAAAAAOxlB6EAv+FjSV6VSU/0myd5XJLbTOfdJsmzkjw4SarqrCQvyvHi+z8k+c3pek/IpKB+tyQvrKpDrbU2Xe8emRT7N4rv78qk1/sdk5yTyUVvH5DkmUkeP0orAXaAX0QAAAAAbO8gFOA/nORJmQz98i9DxlTVf03yjiRnTSc9sKpOb61dleSHk5wxnd6SPLy1dvF0vXcmOX8670FJDid53fTxz2RSnE+Sq5M8sLV2xXS9K5P8xHTed1bVf2ytvWfIhgIALOLnogAAALtj7QvwrbULk1xvgPHW2hVVdVGSb5lOqiQ3mt5/9MyiF28U36f+IMcL8EnyzUleV1UnZTLW/IbXbxTfZ9bbKMBXkkcledpyrQGAg8v1QgAAANhv1r4Av5mqqkyGkdnw3tbalVV1yvz02fVaa/9cVR9PcrPppHtP/945J44Xf8J6Cx7fOwAAAHP8agUAYH0c2AJ8kp9Pcs+5x0lyy0zGat+w6GKpswX4M6d/z1iwzFaPz8wCVfWETMaZzx3ucIdFi8AJjMUNAAAAm/O5GdhNJ22/yHqpqpOq6leSPGVm8lNbay/cWGR+lUWbmbnfOq43/7hlgdbas1tr92ut3e9Wt7rVokUAAAAAANgHDlQP+Ko6PcnvJvm66aSW5Cdba0+fWezDSa7L8S8nbpbrm5125fTvP2+xzKLHVwYAOLD6jGmfGNd+p+kxBwAArOLAFOCr6g5JXpHkS6aTPpnk8a21F88u11r7dFW9M8k9ppPuMredW+XEsd7fPv373iRXzcw7Yb1Mxoif9fYAsCf0GWs3Md4uAMCyXOMAgIPmQBTgq+ork7w0yedPJ70/yTe21v5qk1VemuMF+HtU1b1aa++YPj57btmXJElr7bqq+sMkj5tOP1RVZ7XW/mnBem0aAwAA9LQHAIA1tfYF+Kq6f5LXJTl1OunaJL+f5CFV9ZC5xX+/tfb3SZ6R5Ik5fmHVV1fVc5LcfDp9w0XTbW/4pSTfkuSUJDdJclFVvSDJHZOcM7PcC1pr71mxaQAAAAAA7GFrX4BP8kU5XnxPkhsk+bFNln1Lkr9vrX2oqh6T5OVJTkty2yQ/N7fsu5J8e2vtXy6m2lq7uKq+J8nzMnluvyjJU+fWe1OSPl2cAAAA9h2/8AAADrKDUIDvpbX2+qq6Z5KfTPJvk9w+yWeTvCfJi5L8amvt6gXr/W5V/XWSH09yOMmtk1yT5OIkL0zy7NbaZ3emFfuHk3IAAPownjQAAHvZ2hfgW2vPTfLcnutenuQHe6x3cZLv6RMTAAAAAID1cNJu7wAAAAAAAKwjBXgAAAAAABiBAjwAAAAAAIxAAR4AAAAAAEaw9hdhBQDWw+GjR3qsdcng+wEA6+js85YvDxwbYT8AYN0owAOM7PxzL1x6nSMXHB5hTwAAAADYSYagAQAAAACAEegBDwCwC/oNqZMYVgcAAGD/0AMeAAAAAABGoAAPAAAAAAAjMAQNAHvW2ect/zZ1bIT9AAD2B+cOAMOSV2F1CvAAI+s3zrMxngEAAAD2OwV4AACAbegBCABAH8aABwAAAACAEegBDwDAvqInMgCMx/sswLAU4AEAAACAXXP+uRcuvc6RCw6PsCcwPAV4AAAAWIIewgBAVwrwAAAAwFrRmxaAvUIBngNFTxUAAAAAYKectNs7AAAAAAAA60gPeIA10ecXHolfeQAAAACMRQEeANbQsUsv3+1dAAAAgAPPEDQAAAAAADACBXgAAAAAABiBIWgAAAAAgF1z+OiRHmtdMvh+wBgU4AFYWp8LvrrYKwAAAHDQGIIGAAAAAABGoAAPAAAAAAAjMAQNAAAAsFaMJw3AXqEHPAAAAAAAjEABHgAAAAAARmAIGrZ09nnLHyLHRtgPAIDd4FwIAABYhQI80IkCBACMx/ss68zxDQAcZArwwJ7jQxoAAAAA68AY8AAAAAAAMAIFeAAAAAAAGIECPAAAAAAAjMAY8OwJxvzuz3MHAHR17NLLd3sXAADgQFGABwAAAGBt6bgG7CZD0AAAAAAAwAj0gAcA2AV9emIlemMBAADsJ3rAAwAAAADACBTgAQAAAABgBArwAAAAAAAwAmPAAwD7Qp8x042XDgAAwG5SgAcYmaIhAAAAwMFkCBoAAAAAABiBAjwAAAAAAIxAAR4AAAAAAEagAA8AAAAAACNwEVYAAAAAYNecfd7yJcpjI+wHjEEPeAAAAAAAGIECPAAAAAAAjEABHgAAAAAARqAADwAAAAAAI1CABwAAAACAESx/iWEAAAAAWMGxSy/f7V0A2BF6wAMAAAAAwAgU4AEAAAAAYAQK8AAAAAAAMAIFeAAAAAAAGIECPAAAAAAAjEABHgAAAAAARqAADwAAAAAAI1CABwAAAACAEZy82zsAAACsn2OXXr7buwAAALtOAR4AAABYK2eft3y549gI+wEAhqABAAAAAIARKMADAAAAAMAIFOABAAAAAGAECvAAAAAAADACBXgAAAAAABjB8pcFB4AdcuzSy3d7FwAAAIAddP65Fy69zpELDo+wJ8NQgAfgwFPoB2Av8b4EALA+FOCBA8uHWwAAAADGZAx4AAAAAAAYgR7wAAAAALAiv7KGYRw+eqTHWpcMvh9DUYAHAFhzPgwCAADsDkPQAAAAAADACPSABwAA2IZfkgAA0Ice8AAAAAAAMAI94AGA3vQIBXabPAQAwF6mBzwAAAAAAIxAD3gAAAahJzIAAMCJFOABAACAHeHLWgAOGkPQAAAAAADACPSABwAAYC3oXQ0A7DV6wAMAAAAAwAgU4AEAAAAAYAQK8AAAAAAAMAJjwAMAzDB+MAAAAENRgAcAgF3mix8AAJg4+7zlS9bHRtiPoSjAsyUfBgEAAHaHz2MAsP8pwAOwNB8GAQAAALanAA8AAMBofHEPABxkCvAAAAAAAAPyBTQbFOABANhXfJgBAAD2CwV4GIniAAAArCfn+gDDkldZZwrwALCDnFgCAADAwaEADwAAAAecTgIAMI6TdnsHAAAAAABgHekBDwAAm9AjFAAAWIUCPMDIFG8AAAAADiYFeAAAAACS6EAE+43/2b1PAZ4DRVJinTm+YX/xPwsAALD+FOCBThSKAAAAAGA5J+32DgAAAAAAwDrSAx7Yc/S2BwAAAGAdKMADAAAAsLZ08urPcwerU4AHAAAAAOBAOf/cC5de58gFh5dexxjwAAAAAAAwAj3gAYB9wc9fAQAA2G8U4AEAAAAA2JQOUf0pwLMn+CcGAAAAAHbK4aNHeqx1ydJrGAMeAAAAAABGoAAPAAAAAAAjUIAHAAAAAIARGAMeAAAAAIA9Yd2uFakAD/vcuiUlAACAVfmcBMBeYQgaAAAAAAAYgQI8AAAAAACMQAEeAAAAAABGoAAPAAAAAAAjUIAHAAAAAIARKMADAAAAAMAITt7tHQAAAAAAgJ109nnLl8aP9YijBzwAAAAAAIxAAR4AAAAAAEagAA8AAAAAACNQgAcAAAAAgBEowAMAAAAAwAgU4AEAAAAAYAQn7/YOsLxjl16+27sAAAAAAMA29IAHAAAAAIARKMADAAAAAMAIFOABAAAAAGAECvAAAAAAADACBXgAAAAAABiBAjwAAAAAAIxAAR4AAAAAAEagAA8AAAAAACNQgAcAAAAAgBEowAMAAAAAwAgU4AEAAAAAYAQn7/YOAAAAAADATjp26eU7EkcPeAAAAAAAGIECPAAAAAAAjMAQNAAAsIk7feqFS69z2fC7AQAA7FN6wI+gqs6oql+qqmNV9Ynp7dh02hm7vX8AAAAAAIxPD/iBVdW9krwmyW3mZt1rejunqh7eWnvHju8cAAysT+/gRA9hAAAADgYF+AFV1Y2T/J8cL75/NMlvJGlJnpDkFtN5L66qe7fWPrULuwmsKYXQ/cFwFgAAAAzFZ8y9TwF+WN+R5K4zjx/XWntlklTVG5L83+n0L5ou+5yd3T3W0U4l2p1M6N48ADho1vH9HAAAUIAf2qNn7n88yatmHr8qyVVJTp8+/uYowO84RWQAGI/3PhiG/yVgEbmhP19097eObYKdpgA/rPvM3L+0tdY2HrTWrquqS5N86XTSvfsGWccisoTObnB8732eu/48d/157mB/8T8Lw1i3/6W9fK7fN9a68dyx7uTVvd2eZOfaVDM1YlZUVZ9Kcsr04UWttQfNzb8oyVdPH366tXbqgm08IZPx4pPki5O8a8ndODPJPy+5Th87FWcnY4mz92OtW5ydjLVucXYy1rrF2clY6xZnJ2OtW5ydjLVucXYy1rrF2clY4uz9WOsWZydjrVucnYy1bnF2Mta6xdnJWOsWZydjrVucnYy1bnH6xrpja+1W8xP1gB9PbTNt4TcfrbVnJ3l276BVb2mt3a/v+nstzk7GEmfvx1q3ODsZa93i7GSsdYuzk7HWLc5Oxlq3ODsZa93i7GSsdYuzk7HE2fux1i3OTsZatzg7GWvd4uxkrHWLs5Ox1i3OTsZatzg7GWvd4gwd66QhNsK/uHLm/s0WzJ+dduWC+QAAAAAArAkF+GG9beb+F1bVvzy/0/tfODP/7Tu1UwAAAAAA7DwF+GG9dOb+6Um+dubx1yY5bebxS0bah97D1+zRODsZS5y9H2vd4uxkrHWLs5Ox1i3OTsZatzg7GWvd4uxkrHWLs5Ox1i3OTsYSZ+/HWrc4Oxlr3eLsZKx1i7OTsdYtzk7GWrc4Oxlr3eLsZKx1izNoLBdhHVBV3TiTnu13nU76WJJnTe8/Icktpvffk+TerbVrdnQHAQAAAADYMQrwA6uqeyX54yS33mSRDyZ5WGvtHTu3VwAAAAAA7DQF+BFU1RlJfjzJNya503TyZUlenuTprTUXYAUAAAAAWHPGgB9Ba+3K1tp5rbV7ttZuOr3dczpt0OJ7Vd2lqn6jqi6uquuqqlXV54aMMY3z+Kp6SVVdWlWfrKoPVdXrquprBo7zkKp6VVW9v6o+VVXXVNXfVNUvVdVp22+hd9yHTp+7jdtXD7jtc+a2PXt76VBx5mJ+V1W9qaquqqqrq+qdVfXLA25/s/Zs3A4NFWsa7/FV9eaqumLanndV1X+uqlsOHOeRVXXRNMZVVXXhKsfCMv+fVfW9VfX26XF/RVX9TlXdfsg4VfUD0/+vj868Vk8euk1Vdaeq+m9V9ZZpWz5ZVZdU1X+pqpsP3KZnVdWxqvpIVX12Gu+VXY/BPjl0eoxsPH+v7RJnyTZdtsX/1n2GbFNVfcF02b+vqs9U1T9V1Wuq6l8PEaeqnrJNrji6XZxl2jQ99p5dVe+tyfvHB6vqtVX18IHj3KKqfm36vH26Ju+N/7mqbtIxTuf31FotN3SKUyvmhi5xaoC8sGSbVs0NS5/3VI/csER7VsoLy7apVssNXY6HlXPDEs/dqnmha5yV8sJ0G53Pg2u13NApTq2eG7aNU8Plhq5tWjU3LP1Zpfrlhq7tGSI3LHPcrZIbuhwPQ+SGrs/dSrlhyVgr54eZbW35ubVWyA3LxKoBPldsF6cGyg8d27NSblgm1txyvT5XdGzTyvmha3tqhdzQNdYQ+aFrm4bIDx3jrPqZonOtadXc0DXWqrmhS5wapt7QtT3D5YbWmts+viV5VJI2d/vcCHHeuSDOxu2xA8b56S3ivHKk5/CGSS6Zi/XVA27/nC3a9NIR2vPMTWK9f8AYm7Vn43a/AWP9xBZx/mzAOI9Pct2CGJ9J8pCe2+z0/5nkyZu07++T3HrAOG9bsNyTh25Tksdu8Zq9PcmNBmzTpzaJ87kk9x8qzszy3zW37GtHOB4u2+L5u8+Ace6e5J82ifN9Ax0LT9miLS3JKwY87m6W5B82iXNdkkcOFOcmSf56kzh/nOSkDnE6vadm9dzQNc7bFszvnBu6xMkAeWHJNq2aG5Y670nP3LBEey7bYrlt88KSsVbNDV2Oh6dssUyn3NAxzhB5oUuclfPCdDudzoOzem7oGudtC+Yvkxu2jZPhckPXNq2aG5b6rJL+uaFrey7bYrmuuaFrrFVzQ5fj4SlbLNPSLTd0ibNyblgi1iD5YbqtLT+3ZsXcsGSsty2Is9Tniu3iZKD80LE9K+WGZWKtmh+WaNNlWzx/nfJDxzgr5YYljoenbNGeTvmhY5xB8kOHOEN8pjhni+fjpUPmhiVivW3B/GXOHbaNk2HqDV3bM1hu0AN+//tAkl9K8g1J/mLEOB/N5J/2TpkkpF+amfdzA8Z5W5LHJLldJgnpm5J8ejrvEVX1eQPG2vCjSe6W5JMjbHvW+1prNXd71JABqurrkxyZPvzfSb4ok+fxXjnxNVvJfDsyeWP54HT2u5O8dahYSb5z+vfaJA9OcmaOH+tfUVX3WDVAVZ2c5L8lqSR/l+QLk9w5yfsyaduvV1X12PS2/59VdcckPz99+OdJbpPJyViSfEEmJxorx5l6SZIfSPLvO2xz1VgXJvm3SW6a5MsyeS6T5Esz+b8eKs4vJblPktMyeb5ePp1+gyTfNmCcVNXNkvxy+ueKZfP19yzIGW8bMM7zktwqyUeSnJ3JhcLPyqQIfWyIOK21pyzIFz80s8gLO8TpFCvJQzL5/0mSl2XyXvW46ePK5CRriDhPSPIl0/s/neT0TN4fk+ShSb69Q5yPZpv31IFyw7ZxplbNDV3jrJoXlom1am7oGmfV3NA5zlTfvLBMrFVzw7ZxBsoNXdozRF7oEmeIvJB0OA8eKDdsG2d6f9Xc0DXOELmha6xVc0PXOKvmhs5xplbJDV1jrZobto0zUG7o0p4hckPXWEPlh2SLz60D5YZOsaaG+FzRJc4Q+aFLnFVzwzKxhvhc0SnO1Cr5oUucVXNDp1gDfq7YMk6Gyw/bxRkyN2xaaxohN2xX1xoqN2wXZ6jcsF2c4XLDMtV6t719S3I0029iRtj2aXOPK8nHpvE+PXK73prj3zKdPvC2b5vkqiQfSvI/ZuKM0QP+sh04Bl4zjXVplugRMEDcb5157v6/gbf9/6bbff/MtF+ciXffAWLcZ2Z7vzwz/Vdmpv+bFWMs/P9M8mMzMc6emf4302kfzXI9Y7bNA0kOzcRcuqdKhzadtmDZ2XaeN0ScTZb9hpk4Tx8yzszxcN5MjKV6qnSJleO9Vc4Z4Nje7DW6/0wbnjBWnE2WPTZd9ookpwzYpm+cadP3TafdcGbaHw4U56Uz27zJdNrNZqb9UYdtb/ueOkRu6BJnbn6v3NCxPYPkhWXbNLPcUrlhmThZITd0jZMB8kLH12nl3LDCa7RUbujYnpXzQsc4L53ZZq+8sEX8E86DM/B5w2Zx5uYdmpnX+7xhk/YMes7QtU0zy/Q+b9guTgY8b9gszhC5oePrNOh5w5Kv0UrnDZu0Z9Bzhm1iDZIfss3n1iFzw3ax5pbtnR86tGmoc4elP/OnZ27oGisr5ocucTLMucN2r9FguaHn67R0fujQpkHyQ4c4L52Z1vczxTnTZS/bYplBckOXWHPLH5qJ26cH/FZtWjk3LNuemfV6nzfoAU8nrbVPzE26USbf+CSTHoODq6obV9U3JbnndNILWmtXDRzmv2XyTdZPZZJ4xnTbqrqyJmOivbuqfqGqThlq41V1gyQPmD58f5KXTMfd+nBVvaCqbrPF6qt64vTvp5I8d+BtP3v69zZV9aCaXOT4YdNp/5DkHQPEuPHM/bbJMl82QJxFvnzm/rsX3L95Jj3y940F+SJJTp25P3jOqInbJ/n+6aRPZtIbY6jt3zOTHhbvziRv7ISnT/PFR6rqj6rqqwbc9oNn7t+jqv52Oh7gO6rquzZda0VV9YBMfpGTJL/VWvv0Vssv6bWZfPmYJF9fVadn0ittw2sGinPjbeZvmys6vqeunBt26r27S5yh8sKybeqbG7rGWTU39HiNeueFjrFWzg19jrs+uaFjnJXzQsc4K+eFeVucBw963rBD59ubxhnjnKFLm4Y4b9gqzpDnDR1fo0HOGbaINeh5Q9fjbtXzhi3iDH7OsEWsofLDdp9bh8wNO/UZecs4A+aHzu0ZIDdsG2ug/LDMa7RKftguzpC5YanjboX8sF2cofLDdnGGPHfYqtY0dL1h1LpWlzgDnzt0as8g9YZlqvVue/uWEXvAL4j1szn+rc/PDrzt02a2vXF7cQbu0Z3Jm0VL8qeZ9GZ6yky8nRoD/lUDxjlrizgtybsy/WZ14OfxLjk+dvpvj3S8/UiuPz77/8sSY9dts/1bZjLWe0vy3iR3zORN6LKZeD+zYoyF/59JXj0T41/NTH/ezPSvWjXO3DKHZrY9eA/4BcudmePj6F2Z5GZDxkny9Llj42PpMW7/VnGSvH467+HTxxuxxuwBP3/7TJIHDXTc/c9t8sWy4zV2PRaeP13uuiR3HuG5u22O94TZuH0yyX/Kkj1Ct3juZo+3n8rkZ4+z74mf6dGm672nZuDcsFmcufmHZuavkhu2PUfIinmhS6wMlBu2ipPhc8NmcS7b5H916bywzXE3aG5Y4ngYIjds9twNlhe2eN4GywvZ5jw4A+WG7eLMLXtoZplle7gudV6fFXJD11hZMTd0iZMBckPHOJctWGbp3NDhuBskN/Q4Hnrlho7P3SC5ocNzt3J+SIfPrRkuN2wba4j8sGycvvlhmThZPTd0ipUV88MScS5bcGx2zg8dj7uhckOf42Hp/LDEc7dSfuj43A2RG87Z4rl/1cC5YdtYA+WGpeKskBs6x8lQ9YZlV3Dbu7fsUAE+k4tVXjuNdWGSGw68/UUnMS2TXgRDxTh5mlA/l2kRd7PkO0CsByT5d5kUdW88TUQfnIl1aKA4t5t7vh6fyc8eL5iZ9v0jHA9PWyZx99j+45J8dsHx8A9JvnvAOL+yIMbs7SdW3P7C/88cHzZo/g3x+TPTv3LVOHPLHJrZ9qgF+EzGAPzL6XKfTfKIoeMseEPceFNcaniiLV6jjYu8vHhm2kacMQrwP53JzzlvluTz5/6Hjw7Upt+Y2eZfZvIF3n1yfFiFD4xwLHxekmumy7166OMuk/eOP19wLFybyfiNZw0U546ZjHG5Wa64esk4C99TM3xu2Pa9OwPkho5xbpEV80KXWBkuN2z2Gg2aG7ZqTwbMC9u0aejc0OV4WDk3bNGeofPCZnEGywvZ5jw4A+WG7eLMLXtoZv4QBfjN4twiK+SGrrGyYm7o8BoNkhu6tCcD5YYObRokNyx5PPTODR3aM1hu6BBrpfyQjp9bM0Bu6Bpr1fzQM84tsmR+WDZOVsgNS7xOK+WHZdqUFfLDEu1ZOTf0PB6Wzg9LtGml/LBEnJVyw3Qb29aaMtx5w1J1rfQvwC8b5xbpce6wTJwM9ZlimYXd9vYtO1CAT/LdOf7B46IMPCb7XKybZPLN4eUzB/nK431Pt70xZvkfZvIGcZ+c+IZ0TpK7jNi22bHeVirszj1fG73EPzwz/UtnYj1z4HbcKJPx1lqSt4/wPJ2UyTeYLZNhde6eyZcKz51Ouy7J/QaMdV4mPzf7VCZvms+aee4eu+L2F/5/JnnBTIz7zEx/6cz0ZXr8bJsHskMF+Ex+WfCWHH8zfMwYcWZev9vlxC9Shhrz+x3TvPeNM/liI8afTR9fbxy6Vds0s+zJSa5OxxOxjm36LzNt+OGZ6f9nZvqZA79G/9/Mth899HGX5Ekz2/9PmeTEr0jyiSGPh+m8L0nyyum2r8jkok+XTJd/5xIxNn1PzYC5Yas4c8sdygq5oUucDJcXurZppdywzWs0WG7o2p6Z5VfJC1u1abDcsMRrtFJu2KY9T8pAeWG79mSgvDCzvYXnwRn+vGHb8+0M8+XclnEyUG5Yok1DnDds9hoNet7QpT0zy/bODdu0aejzhi6v0crnDVu050kzj1c+Z9iuTVkhP6Tj59YMkBu6xppb59DM/K4F+KXipGd+6NmeXrlhiddppfzQp0198sMS7Vk5N/R8nZbOD0u06Ukz05bOD8u0JwOfO0y3eUKtKQOfN2wVa9XcsGycDHju0KE9q583rLJzbnvrlpEL8NNEsfHB43VJbrpD7frvMwf4SkXQuba0bW5HB4p1vZ8oZfJt9EacHxvwudpI1lfOTJstwD9t4NfmcTPbPneE1/7WM9t/4cz0h2+WGAeOv3FCcW2SL1hxWwv/P7OGF2Gdzpt9M/x0km8aI86CZW8+07alTlq2eI0u65AvDg0Ua1G+uEGOn/BdNVCcb5vZ9x+amT57srxMcaDLcffO6TLvT3Ly0MdDTjyx/aKZ6W9Khw8aKx53Z868Rs/quP1zssV76lC5Ybs4c8semom5bC/XbeNkoLywTJtm1lk6N3R4jS7L9XPB0rmhQ5zB8kKHWIPkhiWPu965oUN7BskLPY+5pfPCJtv57zNteGzGuwjrCXHm5h2ambfqh+jrxcmA5wxd2zSzzNK5ocNrdNnM4965oUOcQc8Ztok16HlDx+NukPOGTdoz6DlDj+Ouc35Ix8+tGebi7Z1iza1zaGZe1wJ85zhZIT/0ac/MukvlhiVep8s6LLdpflgizkr5YYk4K+eGnsfd0vlhiTatlB9WPO6WOnfY5HU+odaU4T5TLFXXSv8e8J3iZMVzh2Xb0zc3zN5chHWfq6obVtWZVXVmJldm3ph+5vQ2yMUQqup7kjwnk299XpXk61prVw+x7bk4z6yqh1XVWVV16vTCGt8ys8jfDR1zB7y8qn64qu4wbdOhTL5V3fCmAWP93vTv51XV46vqtCQ/MDP/DQPGSo5ffPUTmXyzOrSPZNIbPUkeVFV3m14E5XEzy3x0iEBV9dCqenBV3ayqzqiqJyb50ens/9Nae3+PbXb5//zfmXxbmyQ/WlW3rqrvyKS3f5L8XmvtugHipKpuPl3m5jOr32S6zC2HatN0W6/NpIfRp5I8qrX2si7bXzLON1XVj06Pi1Or6vOT/NzMZrbNFzuVQ5eI9fVV9ftVdaiqbjJt0/mZjAmYdMgXHeP8UZKPT2c9fppz753kIdNpb22LL26zbJyNaV+T5IunD/9Xa+1z27WjR6x/nFnlu6fP31dk8iVk0u1iTl2Ou5Oq6ger6k7T4+5LMxnr9aaZFOj+Z4c4Xd5Th8gNnd67V80NXeIMkReWiDVEbtip854ucVbOC0vEGiI3dH7uVskNHeMMkRe6HHMr54XpdrqcBw+RGzqdbw+QG7aNM2Bu6BJriNywI59VOsYZKjd0iTVEbuj83K2YG7rEWTk3dI01VH7oYOXcsIxV80PHGIPkhw5xVs4Ne9Ag+aGDlXPDslbJDx0Nkh+2M1Bu6FJrGio3dKprDZAbto0zUG7oEmfY3LBMtd5t791y4rdKi27nDBTnsm3i3GmgOB/dIsbLRn4unzITa8gx4N+2RZsGG9d+Guu0HP8mc/72miQ1YKy7z2z7ghFfl63GZv9gkjMGivOLm8R4T5Jb99xmp//PJE/eZP77u8ReIs7RLZa5bKg2Zftv/Z87UJwnbTH/miT3H+q5W7DexvxlxnLt0qZHbTH/E0m+bMDj4Xs3mf+ZdOu12/m5S/L702mfS49fk3R87u6Qrd9Dtr1geMc4J28x/8c7tueybeLcaaDc0DXO0S2W2TY3dImTAfLCErGetMX8rrmh03O3am7o2J5HbTG/U15Y8nhYNTd0fu6yQm7o+NwNkRe6xFk5L0xjbbWvL5tZbtXc0DXO0S2W65Ibto2T4XJDl1hP2mKZrrmh03M3QG7o0p5HbbHMMrmh6/Gwam7o/NxltdzQ5blbOTcsEWuQ/DAX9ykz25gd83ul3LBkrKNbtGvb/NAlTgbKDx3iPGmLGJ1ywzLP3YLllsoPHdv0qC3a1Dk/dDwWVsoNyz53WfFzRYfnbpD80CHOEJ8p3rbFNmavF7Jyblgi1tEtlts2N3SJk2HqDV3iPGmLZZbODXrAs9ecn8nPSK7M5Fu/jyV5c5IfyWQsrf3o5zN5k3hvJv+kn0zy1iQ/mOS7hgzUJt8uPzjJs5N8KJNvOv8uk+LyN7RpFhnIE2fuXzDgduf9RCaJ768yee4+l+QDmVw05P6ttSsHivPnmYy795FMThYuzaT4/xWttQ8OFGOh1tovJvm+JH+dyc+nrsxk/Lf7jx17n3tzJj9vfF8m/1ufnd5/fpJ/01ob8tclO+XNSZ6ayfF4RSbH+z9mcjzct7X2V0MFaq09J5O8+peZ9By4KpMv6h7cWjs6VJyqOiuTDwFJ8orW49ckXbTWLk/yVZnk2w9m8h5ydSbtO7e19p8GCnVdJr82el8m/68fz+RijF/XWnv6QDGSyA0rWLfcsGN5IVmv3LAP80Kn8+ABcsNOnW/v5Hl9l1hD5Ia99NwNlRu6Hner5oZOcQbIDdvGGTA3dGmT84a9b93OG5I1/EyROHdYoFOtaaDcsFN1rb0UZ9DcUMPW4wAAAAAAgCR6wAMAAAAAwBgU4AEAAAAAYAQK8AAAAAAAMAIFeAAAAAAAGIECPAAAAAAAjEABHgAAAAAARqAADwAArJ2qagtuh3Z7vwAAOFgU4AEAAAAAYAQK8AAAAAAAMAIFeAAAAAAAGIECPAAAAAAAjEABHgAAAAAARqAADwAA+1xVva2q2tztYQuW+7EFy33JguWetmC5p20S+6yq+smq+qOqel9VfaKqrqmqv6+q/1tVP1RVpy3RlhtU1WOq6req6m+q6sqq+kxVXVFVb5nu2z2We4a2jPegqrp6QXv/sqpuOVQcAAAOJgV4AADY/16/YNoDF0z76o7LLZp2Qoxpofw/Jnlfkv+a5GuT3CHJTZOcmuQLkjwyyTOSXFpVj9l0749v82uSvCfJHyQ5J8ndk3xekhsmOTPJfZP8eJJjVfXsqjp1u21uE+8rk7wiyU3mZr05yUNbax9ZZfsAAKAADwAA+9+iAvyiYvsDFkw7odheVTdJ8uVzy1yb5KKZZW6Y5I+SPDmTYvt2zkzyB1X1w5stUFX/Lslrk3xhh+2dlOT7k7y2bxG+qr48yauSnD4366IkD2+tfazPdgEAYJYCPAAA7H9/kuS6uWlfUVUnbzyoqrsludWCded7u39lJj3OZ72ltXbVzOP/keThC7b1mSTvnd4+t2D+r1TV9XrXV9X9kzwriz+f/GOSi5NctWDeAzLpYb+U6bA7r0ly87lZFyZ5RGvtE8tuEwAAFlGABwCAfa619tEkfzU3eb4n+6Ie8Ulyu6q608zjLYefqaq7Jzl3wTK/nOTzW2t3aa3dJcmdMuklP+sG0+XmPT3JyXPT/jLJvVtrt22t3SuToWh+OEmbW+57lxkTfrr/r01yxtysVyf5+tbaJ7tuCwAAtqMADwAA62G7YWhmC+vvn1vugZvcX7Ttf5frf454UWvtp6ZfBCRJWmsfSHJ2Jr3iZ33lbMG8qu6Z5KvmlvlUkke21v56Znufa639WpIXzi17UibjxXdxl0yK72fNTX9Fkm9qrV3TcTsAANDJfC8TAABgf3p9JhconfXVSX5l5v6GlyV5RJI7Tx8/MMnzp0PWfOXcNj6b5E9nHh9eEPvfVNVbltjXr0nyN1ts77okr6qqReveZpPtdXF+khvNTXtJkm9rrX224zYAAKAzBXgAAFgPF2Uy7vrsOf4DkqSqbpPkX81Mf2OS03JiAT6ZDFlz07nt/kVr7eqZx3dcEPsO01tXd9zk/oabJLlvz+1tZb74/ookZ7fWFo1XDwAAKzMEDQAArIHpRVLfOjf5rKr6olx/WJk3ZlKw33C3qjpzwXLJ9Ye2mb9waR+z468Pvb1lfHmW++IAAACWogAPAADrY7Nx4GeHn7mstfb+TIrw88t1KcB/rP/u/YsbDLy9vp9rbpvktVV12wH2AQAArkcBHgAA1sdmBfjZwvobk6S19q4k/zQz/UE5sVCfJJ9O8ua5aZcviPGzrbVa4nbONtt7z5LbWzhY/AJPmbZp1hcm+eOq6tuLHgAANqUADwAA6+NPM7lo6qyHJfmSmcdv3OT+d+b6Q7n8WWvtmrlpr1sQ97uqan7s+OupqlOq6pEdtnfXqnrIdtubbvN+VXX7LssmeUOS78jkIq+z7pHJRV9P77gdAADoRAEeAADWxPRiqX8xN/kLcuKQL7Njv88W4G+1YJOLetQ/N9cvYN8tySur6v7zC1fVGVX1yKr69STvT/I/5/b54iR/viDOi6rqe6vq1Lnt3aiqvryqzquqtyT5yxy/mOy2WmsvTnJkwaz7JfnDqrpx120BAMB2Tt7tHQAAAAb1+iQP2GTeh5NcMvP4ok2Wm93WCVprF1fVs5OcOzfrgUn+tKquyqTQ3jLpUf/5c8tdvSDOjyU5mhM/n9wiyf9K8utV9Q9JPprJBVtvl+SG2+z3llprF1TVbZL8/NysB2dS+H9Ua23+lwQAALA0PeABAGC9LOq1vuFPW2tt5vFfJfnEJstek8U905PkR5L88SbzTk9y90yGdZkvvi/UWvvTJE/M9XvWJ5Ni+x2T3DvJnbJi8X0m5n9I8hsLZj0yyfOqymclAABW5qQSAADWy5ty/QuNbpgdciattWuT/Nlm22mtLdxOa+0zSb42yVMzKdR3dWWS39tkm7+Z5HCSv11ie9dm8kXAZUusM+vfJ3nZgumPTXJBz20CAMC/MAQNAACskdbap6rqzzIZTmXeGxdMuyjJQxdMP7pNnGuTPKWqnpHJBVwPJblPJsPO3DTJJ5NckeRdSf5fkguTXLTV0C6ttTdU1Rdn0gv9G5J8RSZDztw8k2L7R5P8XZJ3JPmTJH/cWvunrfZzuzZU1WOTvCaTIXRmfX9Vfay19hN9tw8AAHXiL1ABAAAAAIAhGIIGAAAAAABGoAAPAAAAAAAjUIAHAAAAAIARKMADAAAAAMAIFOABAAAAAGAECvAAAAAAADACBXgAAAAAABiBAjwAAAAAAIxAAR4AAAAAAEagAA8AAAAAACNQgAcAAAAAgBH8/y5zrXHSqHb9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1800x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_week = pd.pivot_table(data[['week','product_category','ANP']], index='week', columns='product_category', values = 'ANP' , aggfunc='count')\n",
    "df_week.plot.bar(stacked=True, figsize=(25,15));\n",
    "plt.legend(bbox_to_anchor=(1,1),loc='upper right',borderaxespad=0,fontsize=20);\n",
    "plt.xlabel('week',fontsize=30)\n",
    "plt.xticks(rotation='horizontal',fontsize=15)\n",
    "plt.ylabel('number of products',fontsize=30);\n",
    "plt.yticks(fontsize=20)\n",
    "plt.title('number of products bought by week',fontsize=30); "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d5b7b2ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_month = pd.pivot_table(data[['month','product_category','ANP']], index='month', columns='product_category', values = 'ANP' , aggfunc='count')\n",
    "df_month.plot.bar(stacked=True);\n",
    "plt.legend(bbox_to_anchor=(1.3,1),loc='upper right',borderaxespad=0);\n",
    "plt.xlabel('month',fontsize=15)\n",
    "plt.xticks(rotation='horizontal',fontsize=10)\n",
    "plt.ylabel('number of products',fontsize=15);\n",
    "plt.yticks(fontsize=10)\n",
    "plt.title('number of products bought by month',fontsize=15);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f09d9f80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_year = pd.pivot_table(data[['year','product_category','ANP']], index='year', columns='product_category', values = 'ANP' , aggfunc='count')\n",
    "df_year.plot.bar(stacked=True);\n",
    "plt.legend(bbox_to_anchor=(1.3,1),loc='upper right',borderaxespad=0);\n",
    "plt.xlabel('year',fontsize=12)\n",
    "plt.xticks(rotation='horizontal',fontsize=12)\n",
    "plt.ylabel('number of products',fontsize=15);\n",
    "plt.yticks(fontsize=10)\n",
    "plt.title('number of products bought by year',fontsize=15);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0678bd75",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # double checking \n",
    "# data[(data['product_category'] == 'Retirement')  & (data['year'] == 2016)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "584a0b15",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # double checking \n",
    "# data[data['product_category'] == 'Health'].groupby('year').count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e1db219",
   "metadata": {},
   "source": [
    "### Customer segmentation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "401bd5fa",
   "metadata": {},
   "source": [
    "A new customer wants to buy life insurance. He comes into the firm and fills in basic demographic information about himself, such as age, gender and employment status, retirement plans, homeownership status, vehicle ownership and behavioural data (questions about the lifestyle of the individual). \n",
    "\n",
    "KMeans algorithm addresses the problem linked to how to classify the new customer in terms of what segment does he belong to. Once he can be classified then the KMeans algorithm will also help estimate how much the yearly/monthly premiums should be. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "af238725",
   "metadata": {},
   "outputs": [],
   "source": [
    "# possible owner features of interest\n",
    "# 'nationality_dummy'\n",
    "# 'region_dummy'\n",
    "# 'owner_age'\n",
    "# 'owner_gender'\n",
    "# 'class_order'\n",
    "# 'relationship_bin'\n",
    "# 'ANP'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01b79e46",
   "metadata": {},
   "source": [
    "**owner_gender**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "eb985620",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['F', 'M'], dtype=object)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['owner_gender'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "631512d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert gender to numerical\n",
    "data['owner_gender']=data['owner_gender'].apply(lambda x: 1 if x=='M' else 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85503ff0",
   "metadata": {},
   "source": [
    "**occupation_class**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3ea28cef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['HOSPITAL, NURSING HOME, CLINIC, ASSYLUM', 'STUDENT',\n",
       "       'LAWYER, ATTORNEY', 'LAWYER,ATTORNEY',\n",
       "       'BUSINESSMAN/ / BUSINESSWOMAN', 'BANK', 'PENSIONER',\n",
       "       'OFFICE WORKER', 'ENGINEER', 'UNEMPLOYED', 'HOUSEWIFE',\n",
       "       'AGRICULTURE', 'ACCOUNTANT', 'DENTAL', 'OPTICIAN/OPTOMETRIST',\n",
       "       'TEACHER, LECTURER, TUTOR, PROFESSOR', 'MARINE INDUSTRY',\n",
       "       'JUVENILE', 'PRINTING & PUBLISHING',\n",
       "       'HOSPITAL,NURSING HOME,CLINIC,ASSYLUM', 'SEAMAN / SEAFARER',\n",
       "       'OVERSEAS CONTRACT WORKER (OCW)', 'COMPUTER INDUSTRY',\n",
       "       'SALESPERSON', 'VETERINARY PRACTICE', 'DECORATOR, INTERIOR',\n",
       "       'FASHION DESIGNER', 'RELIGIOUS GROUP',\n",
       "       'BUSINESS MACHINES SALES & SERVICES', 'POLICE', 'SECURITY GUARD',\n",
       "       'CASHIER', 'COLLECTOR', 'MERCHANT - RETAIL', 'TRAVEL INDUSTRY',\n",
       "       'RICE, CORN, FEED & FLOUR MILLS (SEE GRAIN)', 'DRIVER',\n",
       "       'ARCHITECT', 'AIR TRANSPORTATION', 'CONTRACTOR',\n",
       "       'AUTOMOBILE INDUSTRY', 'POLITICIANS',\n",
       "       'OIL AND NATURAL GAS INDUSTRY', 'FERRY', 'MUSICIAN',\n",
       "       'FISHING INDUSTRY', 'LIQUOR INDUSTRY',\n",
       "       'LAUNDRY WORKERS (SEE DRY CLEANING)', 'LABORATORY',\n",
       "       'BAKERIES (FACTORIES & SHOPS)', 'HOTEL, RESTAURANT',\n",
       "       'ELECTRICAL INDUSTRY', 'DELIVERY', 'DOMESTIC HELPER/SERVANT',\n",
       "       'RADIO & TELEVISION INDUSTRY', 'BARBER SHOP/BEAUTY PARLOR',\n",
       "       'SUPERMARKET', 'WAREHOUSE', 'MACHINE SHOP', 'PACKER',\n",
       "       'GAS DISTRIBUTION (COOKING)', 'BUILDING & CONSTRUCTION INDUSTRY',\n",
       "       'MARKET VENDOR', 'ELECTRONICS INDUSTRY', 'CASINO',\n",
       "       'ENTERTAINMENT INDUSTRY', 'COURT', 'ARTIST', 'MECHANIC',\n",
       "       'JUNK SHOP', 'SOCIAL WORKER', 'CARPENTER', 'CHEMIST', 'LIBRARIAN',\n",
       "       'CLINIC (REFER TO HOSPITAL)', 'MILITARY',\n",
       "       'PAINTER, LAQUER, VARNISHER', 'MIDWIFE', 'WELDER & CUTTER',\n",
       "       'FIRE DEPARTMENT', 'HOTEL,RESTAURANT', 'MINING INDUSTRY',\n",
       "       'MERCHANT-RETAIL', 'MEDICAL SHOP', 'WATERWORKS', 'PHOTO STUDIO',\n",
       "       'JEWELRY', 'NEWSPAPER', 'TOBACCO, CIGAR & CIGARETTE MANUFACTURE',\n",
       "       'MESSENGER', 'PAPER & PULP INDUSTRY',\n",
       "       'CLOTHING MANUFACTURING INDUSTRY', 'HEALTH & SANITARY INSPECTOR',\n",
       "       'PLASTIC', 'TELEPHONE & TELEGRAM', 'GEOLOGIST', 'AMUSEMENT',\n",
       "       'SHIP & BOAT BUILDING (DRY DOCK)', 'SUGAR PLANTATION WORKER'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['occupation_class'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e897b4b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2     8653\n",
       "1     2760\n",
       "3     1413\n",
       "4     1032\n",
       "10     969\n",
       "6      871\n",
       "5      407\n",
       "7      274\n",
       "9      118\n",
       "0      112\n",
       "8       62\n",
       "Name: class_order, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# too broad 'occupation_class', 'owner_occupation_class' dropped, will use 'class_order' instead\n",
    "data['class_order'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "28df5238",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['SELF', 'DAUGHTER', 'MOTHER', 'SISTER', 'FATHER', 'GRANDMOTHER',\n",
       "       'BROTHER', 'SON', 'AUNT', 'GRANDFATHER', 'HUSBAND', 'GODMOTHER',\n",
       "       'SPOUSE', 'WIFE', 'EMPLOYER', 'GRANDSON', 'NEPHEW', 'STEPFATHER',\n",
       "       'MOTHER-IN-LAW', 'NIECE', 'PARENT', 'ADOPTED CHILD',\n",
       "       'GRANDDAUGHTER', 'UNCLE', 'CHILD', 'STEPMOTHER', 'COUSIN',\n",
       "       'GUARDIAN', 'OTHERS', 'GRAND-AUNT', 'MISSING'], dtype=object)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['relationship'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "068a1464",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SELF             0.607882\n",
       "PARENTS          0.321276\n",
       "GRANDPARENTS     0.026153\n",
       "CHILDREN         0.013377\n",
       "RELATIVE         0.012057\n",
       "SPOUSE           0.009358\n",
       "EMPLOYER         0.004499\n",
       "SIBLINGS         0.004079\n",
       "GRANDCHILDREN    0.001140\n",
       "OTHERS           0.000120\n",
       "MISSING          0.000060\n",
       "Name: relationship_bin, dtype: float64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['relationship_bin'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ea213ab",
   "metadata": {},
   "source": [
    "should 'relationship_bin' be included and grouped? perhaps, drop 'relationship' and 'relationship_bin' features for now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "e28d7357",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NATIONALITY_ID_1     0.982245\n",
       "NATIONALITY_ID_4     0.006718\n",
       "NATIONALITY_ID_6     0.003119\n",
       "NATIONALITY_ID_3     0.001979\n",
       "NATIONALITY_ID_7     0.000840\n",
       "NATIONALITY_ID_9     0.000600\n",
       "NATIONALITY_ID_5     0.000540\n",
       "NATIONALITY_ID_21    0.000480\n",
       "NATIONALITY_ID_13    0.000360\n",
       "NATIONALITY_ID_16    0.000300\n",
       "NATIONALITY_ID_17    0.000300\n",
       "NATIONALITY_ID_15    0.000300\n",
       "NATIONALITY_ID_20    0.000240\n",
       "NATIONALITY_ID_11    0.000240\n",
       "NATIONALITY_ID_22    0.000240\n",
       "NATIONALITY_ID_34    0.000180\n",
       "NATIONALITY_ID_31    0.000180\n",
       "NATIONALITY_ID_24    0.000180\n",
       "NATIONALITY_ID_35    0.000120\n",
       "NATIONALITY_ID_18    0.000120\n",
       "NATIONALITY_ID_28    0.000120\n",
       "NATIONALITY_ID_29    0.000060\n",
       "NATIONALITY_ID_14    0.000060\n",
       "NATIONALITY_ID_26    0.000060\n",
       "NATIONALITY_ID_25    0.000060\n",
       "NATIONALITY_ID_12    0.000060\n",
       "NATIONALITY_ID_27    0.000060\n",
       "NATIONALITY_ID_23    0.000060\n",
       "NATIONALITY_ID_10    0.000060\n",
       "NATIONALITY_ID_19    0.000060\n",
       "NATIONALITY_ID_30    0.000060\n",
       "Name: nationality_dummy, dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['nationality_dummy'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58c59282",
   "metadata": {},
   "source": [
    "chances are ID_1 is the Phillipines or capital city. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67fe6e06",
   "metadata": {},
   "source": [
    "ID_1 dominates nationality, so not useful. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "7d72664d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "REGION_10    2963\n",
       "REGION_14    2423\n",
       "REGION_3     2043\n",
       "REGION_2     1667\n",
       "REGION_1     1600\n",
       "REGION_11    1550\n",
       "REGION_12    1445\n",
       "REGION_8     1279\n",
       "REGION_13    1077\n",
       "REGION_15     534\n",
       "REGION_4       29\n",
       "REGION_18      24\n",
       "REGION_23      21\n",
       "REGION_22       6\n",
       "REGION_5        3\n",
       "REGION_16       3\n",
       "REGION_6        2\n",
       "REGION_20       1\n",
       "REGION_19       1\n",
       "Name: region_dummy, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['region_dummy'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c533d8aa",
   "metadata": {},
   "source": [
    "region may be more helpful, given a more even spread of customers from some regions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62ae3e0b",
   "metadata": {},
   "source": [
    "KMeans works only with continuous variables. Other types (like gender, class_order, region) is already pushing it. \n",
    "<br> but 3+ features per customer would provide more insights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b7afe413",
   "metadata": {},
   "outputs": [],
   "source": [
    "# customer segmentation features\n",
    "features = ['region_dummy','owner_age','owner_gender','class_order','ANP']\n",
    "cust_data = data[features]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ff3c182",
   "metadata": {},
   "source": [
    "'ANP' will perhaps give idea of spending score of a person. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a9c993e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region_dummy</th>\n",
       "      <th>owner_age</th>\n",
       "      <th>owner_gender</th>\n",
       "      <th>class_order</th>\n",
       "      <th>ANP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>REGION_2</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>161515.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>REGION_10</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>26745.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>REGION_10</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>443.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>REGION_10</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>REGION_10</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>44170.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>REGION_10</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>30000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>REGION_14</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>100000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>REGION_12</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>25000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>REGION_3</td>\n",
       "      <td>56.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>25000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>REGION_15</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>50000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  region_dummy  owner_age  owner_gender  class_order       ANP\n",
       "0     REGION_2       53.0             0            4  161515.0\n",
       "1    REGION_10       50.0             0           10   26745.0\n",
       "2    REGION_10       50.0             0           10     443.0\n",
       "3    REGION_10       50.0             0           10       0.0\n",
       "4    REGION_10       61.0             0            3   44170.0\n",
       "5    REGION_10       60.0             0            3   30000.0\n",
       "6    REGION_14       34.0             0            2  100000.0\n",
       "7    REGION_12       42.0             0            2   25000.0\n",
       "8     REGION_3       56.0             1            2   25000.0\n",
       "9    REGION_15       28.0             0            2   50000.0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cust_data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f4c57e96",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-45-a6f272dc4a3b>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cust_data['region'] = data['region_dummy'].replace({'REGION_':''},regex=True).astype(int)\n"
     ]
    }
   ],
   "source": [
    "cust_data['region'] = data['region_dummy'].replace({'REGION_':''},regex=True).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "c355fdb9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\anaconda3\\envs\\dsi24\\lib\\site-packages\\pandas\\core\\frame.py:4308: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  return super().drop(\n"
     ]
    }
   ],
   "source": [
    "cust_data.drop(columns=['region_dummy'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a788ee6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>owner_age</th>\n",
       "      <th>owner_gender</th>\n",
       "      <th>class_order</th>\n",
       "      <th>ANP</th>\n",
       "      <th>region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>50.0</td>\n",
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       "      <td>26745.0</td>\n",
       "      <td>10</td>\n",
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       "      <th>2</th>\n",
       "      <td>50.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>44170.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   owner_age  owner_gender  class_order       ANP  region\n",
       "0       53.0             0            4  161515.0       2\n",
       "1       50.0             0           10   26745.0      10\n",
       "2       50.0             0           10     443.0      10\n",
       "3       50.0             0           10       0.0      10\n",
       "4       61.0             0            3   44170.0      10"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cust_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "001d4ae0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 16671 entries, 0 to 16670\n",
      "Data columns (total 5 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   owner_age     16671 non-null  float64\n",
      " 1   owner_gender  16671 non-null  int64  \n",
      " 2   class_order   16671 non-null  int64  \n",
      " 3   ANP           16671 non-null  float64\n",
      " 4   region        16671 non-null  int32  \n",
      "dtypes: float64(2), int32(1), int64(2)\n",
      "memory usage: 586.2 KB\n"
     ]
    }
   ],
   "source": [
    "cust_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "3b0b797f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>owner_age</th>\n",
       "      <th>ANP</th>\n",
       "    </tr>\n",
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       "      <th>0</th>\n",
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       "      <td>1.050262</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.517396</td>\n",
       "      <td>-0.180560</td>\n",
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       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.517396</td>\n",
       "      <td>-0.420770</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.517396</td>\n",
       "      <td>-0.424815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1.398417</td>\n",
       "      <td>-0.021422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>16666</th>\n",
       "      <td>16666</td>\n",
       "      <td>-0.363624</td>\n",
       "      <td>1.958694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16667</th>\n",
       "      <td>16667</td>\n",
       "      <td>-0.363624</td>\n",
       "      <td>-0.396552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16668</th>\n",
       "      <td>16668</td>\n",
       "      <td>-0.523810</td>\n",
       "      <td>0.302462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16669</th>\n",
       "      <td>16669</td>\n",
       "      <td>-0.523810</td>\n",
       "      <td>-0.411609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16670</th>\n",
       "      <td>16670</td>\n",
       "      <td>-0.523810</td>\n",
       "      <td>-0.424815</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16671 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       index  owner_age       ANP\n",
       "0          0   0.757675  1.050262\n",
       "1          1   0.517396 -0.180560\n",
       "2          2   0.517396 -0.420770\n",
       "3          3   0.517396 -0.424815\n",
       "4          4   1.398417 -0.021422\n",
       "...      ...        ...       ...\n",
       "16666  16666  -0.363624  1.958694\n",
       "16667  16667  -0.363624 -0.396552\n",
       "16668  16668  -0.523810  0.302462\n",
       "16669  16669  -0.523810 -0.411609\n",
       "16670  16670  -0.523810 -0.424815\n",
       "\n",
       "[16671 rows x 3 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# standard scaler age and ANP\n",
    "col = ['owner_age','ANP']\n",
    "ss_features = cust_data[col]\n",
    "scaler = StandardScaler().fit(ss_features.values)\n",
    "ss_features = scaler.transform(ss_features.values)\n",
    "scaled_features = pd.DataFrame(ss_features, columns = col)\n",
    "scaled_features.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "29e31efe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16671, 2)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking rows\n",
    "scaled_features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "31288fea",
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = cust_data[['owner_gender','class_order','region']].reset_index()\n",
    "X = pd.concat([scaled_features,temp],ignore_index=False,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "bee38896",
   "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>owner_age</th>\n",
       "      <th>ANP</th>\n",
       "      <th>index</th>\n",
       "      <th>owner_gender</th>\n",
       "      <th>class_order</th>\n",
       "      <th>region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.757675</td>\n",
       "      <td>1.050262</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.517396</td>\n",
       "      <td>-0.180560</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.517396</td>\n",
       "      <td>-0.420770</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.517396</td>\n",
       "      <td>-0.424815</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.398417</td>\n",
       "      <td>-0.021422</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.318324</td>\n",
       "      <td>-0.150833</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.764088</td>\n",
       "      <td>0.488460</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.123346</td>\n",
       "      <td>-0.196497</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.997953</td>\n",
       "      <td>-0.196497</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-1.244645</td>\n",
       "      <td>0.031822</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   owner_age       ANP  index  owner_gender  class_order  region\n",
       "0   0.757675  1.050262      0             0            4       2\n",
       "1   0.517396 -0.180560      1             0           10      10\n",
       "2   0.517396 -0.420770      2             0           10      10\n",
       "3   0.517396 -0.424815      3             0           10      10\n",
       "4   1.398417 -0.021422      4             0            3      10\n",
       "5   1.318324 -0.150833      5             0            3      10\n",
       "6  -0.764088  0.488460      6             0            2      14\n",
       "7  -0.123346 -0.196497      7             0            2      12\n",
       "8   0.997953 -0.196497      8             1            2       3\n",
       "9  -1.244645  0.031822      9             0            2      15"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "13589664",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 16671 entries, 0 to 16670\n",
      "Data columns (total 6 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   owner_age     16671 non-null  float64\n",
      " 1   ANP           16671 non-null  float64\n",
      " 2   index         16671 non-null  int64  \n",
      " 3   owner_gender  16671 non-null  int64  \n",
      " 4   class_order   16671 non-null  int64  \n",
      " 5   region        16671 non-null  int32  \n",
      "dtypes: float64(2), int32(1), int64(3)\n",
      "memory usage: 716.5 KB\n"
     ]
    }
   ],
   "source": [
    "X.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "4bfd9ae4",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X.drop(columns=['index'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "8ebcf137",
   "metadata": {},
   "outputs": [],
   "source": [
    "# X.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "a01a3d82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# KMeans\n",
    "# determine k\n",
    "distortions = []\n",
    "for k in range(1,10):\n",
    "    kmeanModel = KMeans(n_clusters=k, init='k-means++').fit(X)\n",
    "    kmeanModel.fit(X)\n",
    "    distortions.append(sum(np.min(distance.cdist(X,kmeanModel.cluster_centers_, 'euclidean'), axis=1)) / X.shape[0])\n",
    "    \n",
    "plt.plot(range(1,10), distortions, 'bx-')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Distortion')\n",
    "plt.title('Finding optimal k');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "05889579",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "SSE = []\n",
    "for k in range(1,10):\n",
    "    kmeans = KMeans(n_clusters = k, init='k-means++')\n",
    "    kmeans.fit(X)\n",
    "    SSE.append(kmeans.inertia_)\n",
    "\n",
    "frame = pd.DataFrame({'Cluster':range(1,10), 'SSE':SSE})\n",
    "plt.plot(frame['Cluster'], frame['SSE'], marker='o')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('SSE')\n",
    "plt.title('Finding optimal k');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40c94058",
   "metadata": {},
   "source": [
    "optimal number of k=5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "d36d4c09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.44557147947270076\n"
     ]
    }
   ],
   "source": [
    "# Build a model with 5 clusters\n",
    "kmeans = KMeans(n_clusters = 5, init='k-means++')\n",
    "clusters = kmeans.fit_predict(X)\n",
    "\n",
    "# print silhouette score of modek\n",
    "print(metrics.silhouette_score(X, kmeans.labels_, metric='euclidean'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a32c9d83",
   "metadata": {},
   "source": [
    "Silhouette score is 0.45, not bad at all."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "84efa5c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-59-083a732311b7>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cust_data['label'] = clusters\n"
     ]
    }
   ],
   "source": [
    "X['label'] = clusters\n",
    "cust_data['label'] = clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "a14f57cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    5139\n",
       "2    4691\n",
       "0    4474\n",
       "4    1499\n",
       "3     868\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cust_data['label'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "dcdad046",
   "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>owner_age</th>\n",
       "      <th>owner_gender</th>\n",
       "      <th>class_order</th>\n",
       "      <th>ANP</th>\n",
       "      <th>region</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>53.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>161515.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>26745.0</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>443.0</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>44170.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>60.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>42.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>56.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   owner_age  owner_gender  class_order       ANP  region  label\n",
       "0       53.0             0            4  161515.0       2      0\n",
       "1       50.0             0           10   26745.0      10      4\n",
       "2       50.0             0           10     443.0      10      4\n",
       "3       50.0             0           10       0.0      10      4\n",
       "4       61.0             0            3   44170.0      10      1\n",
       "5       60.0             0            3   30000.0      10      1\n",
       "6       34.0             0            2  100000.0      14      2\n",
       "7       42.0             0            2   25000.0      12      2\n",
       "8       56.0             1            2   25000.0       3      0\n",
       "9       28.0             0            2   50000.0      15      2"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cust_data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "ca77529d",
   "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>label</th>\n",
       "      <th>owner_age</th>\n",
       "      <th>owner_gender</th>\n",
       "      <th>class_order</th>\n",
       "      <th>ANP</th>\n",
       "      <th>region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>22065.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>22040.00</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>19184.76</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>20000.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>15821.60</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   label  owner_age  owner_gender  class_order       ANP  region\n",
       "0      0       45.0             0            2  22065.00       2\n",
       "1      1       44.0             0            2  22040.00      10\n",
       "2      2       42.0             0            2  19184.76      14\n",
       "3      3       41.0             0            7  20000.00       3\n",
       "4      4       40.0             0            8  15821.60      12"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# average of clusters\n",
    "avg_df = cust_data.groupby(['label'],as_index=False).median()\n",
    "avg_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "bba6f4ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='label',y='owner_age',data=avg_df);\n",
    "plt.title('age for each cluster');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "608bde41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='label',y='region',data=avg_df);\n",
    "plt.title('region vs label vs ANP for each cluster');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "5b8a3604",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "region\n",
       "10        2597\n",
       "11        1325\n",
       "8         1215\n",
       "6            2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cust_data[cust_data['label'] == 1][['region']].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "48b7b3ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='label',y='class_order',data=avg_df);\n",
    "plt.title('region vs label vs ANP for each cluster');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "fe546511",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class_order\n",
       "10             654\n",
       "6              514\n",
       "7              172\n",
       "9               95\n",
       "8               49\n",
       "5               15\n",
       "dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cust_data[cust_data['label'] == 4][['class_order']].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "eb1fb463",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='label',y='ANP',data=avg_df);\n",
    "plt.title('region vs label vs ANP for each cluster');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "6c0882c3",
   "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></th>\n",
       "      <th>owner_gender</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>label</th>\n",
       "      <th>owner_gender</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">0</th>\n",
       "      <th>0</th>\n",
       "      <td>3072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
       "      <th>0</th>\n",
       "      <td>3352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
       "      <th>0</th>\n",
       "      <td>3157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
       "      <th>0</th>\n",
       "      <td>463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">4</th>\n",
       "      <th>0</th>\n",
       "      <td>952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>547</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    owner_gender\n",
       "label owner_gender              \n",
       "0     0                     3072\n",
       "      1                     1402\n",
       "1     0                     3352\n",
       "      1                     1787\n",
       "2     0                     3157\n",
       "      1                     1534\n",
       "3     0                      463\n",
       "      1                      405\n",
       "4     0                      952\n",
       "      1                      547"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predominantly female in all clusters\n",
    "df_gender = pd.DataFrame(cust_data.groupby(['label','owner_gender'])['owner_gender'].count())\n",
    "df_gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "a8e79367",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20,10))\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.scatter(cust_data.owner_age[cust_data.label == 0], cust_data['ANP'][cust_data.label == 0], cust_data['region'][cust_data.label == 0], c='blue', s=60)\n",
    "ax.scatter(cust_data.owner_age[cust_data.label == 1], cust_data['ANP'][cust_data.label == 1], cust_data['region'][cust_data.label == 1], c='red', s=60)\n",
    "ax.scatter(cust_data.owner_age[cust_data.label == 2], cust_data['ANP'][cust_data.label == 2], cust_data['region'][cust_data.label == 2], c='green', s=60)\n",
    "ax.scatter(cust_data.owner_age[cust_data.label == 3], cust_data['ANP'][cust_data.label == 3], cust_data['region'][cust_data.label == 3], c='orange', s=60)\n",
    "ax.scatter(cust_data.owner_age[cust_data.label == 4], cust_data['ANP'][cust_data.label == 4], cust_data['region'][cust_data.label == 4], c='purple', s=60)\n",
    "ax.view_init(0, 90)\n",
    "plt.xlabel('age')\n",
    "plt.ylabel('ANP')\n",
    "ax.set_zlabel('region')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d0afd6b",
   "metadata": {},
   "source": [
    "With a few outliers, there is an obvious segregation due to region. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "f96f3d84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20,10))\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.scatter(cust_data.region[cust_data.label == 0], cust_data['ANP'][cust_data.label == 0], cust_data['class_order'][cust_data.label == 0], c='blue', s=60)\n",
    "ax.scatter(cust_data.region[cust_data.label == 1], cust_data['ANP'][cust_data.label == 1], cust_data['class_order'][cust_data.label == 1], c='red', s=60)\n",
    "ax.scatter(cust_data.region[cust_data.label == 2], cust_data['ANP'][cust_data.label == 2], cust_data['class_order'][cust_data.label == 2], c='green', s=60)\n",
    "ax.scatter(cust_data.region[cust_data.label == 3], cust_data['ANP'][cust_data.label == 3], cust_data['class_order'][cust_data.label == 3], c='orange', s=60)\n",
    "ax.scatter(cust_data.region[cust_data.label == 4], cust_data['ANP'][cust_data.label == 4], cust_data['class_order'][cust_data.label == 4], c='purple', s=60)\n",
    "ax.view_init(45, 90)\n",
    "plt.xlabel('region')\n",
    "plt.ylabel('ANP')\n",
    "ax.set_zlabel('class order')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "747px",
    "left": "858px",
    "top": "214.133px",
    "width": "226.333px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}