04_customer_behaviour_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report, accuracy_score, roc_auc_score, roc_curve, confusion_matrix\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime, timedelta\n",
    "from lifelines import KaplanMeierFitter\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>CustomerID</th>\n",
       "      <th>Recency</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>MonetaryValue</th>\n",
       "      <th>Recency_Score</th>\n",
       "      <th>Frequency_Score</th>\n",
       "      <th>Monetary_Score</th>\n",
       "      <th>RFM_Score</th>\n",
       "      <th>RFM_Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12413.0</td>\n",
       "      <td>66</td>\n",
       "      <td>37</td>\n",
       "      <td>676.15</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Lost Customers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12426.0</td>\n",
       "      <td>194</td>\n",
       "      <td>29</td>\n",
       "      <td>510.73</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>Loyal Customers</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID  Recency  Frequency  MonetaryValue  Recency_Score  \\\n",
       "0     12413.0       66         37         676.15              1   \n",
       "1     12426.0      194         29         510.73              1   \n",
       "\n",
       "   Frequency_Score  Monetary_Score  RFM_Score        RFM_Label  \n",
       "0                2               1          4   Lost Customers  \n",
       "1                5               2          8  Loyal Customers  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmf = pd.read_csv('rmf.csv')\n",
    "rmf = rmf.drop(columns=['Unnamed: 0'])\n",
    "rmf.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "      <th>TotalSpend</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day_of_Week</th>\n",
       "      <th>Is_weekend</th>\n",
       "      <th>OrderMonth</th>\n",
       "      <th>CohortMonth</th>\n",
       "      <th>CohortIndex</th>\n",
       "      <th>FirstPurchaseDate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>15.30</td>\n",
       "      <td>2010</td>\n",
       "      <td>12</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-12</td>\n",
       "      <td>2010-12</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "      <td>2010</td>\n",
       "      <td>12</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-12</td>\n",
       "      <td>2010-12</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-12-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                         Description  Quantity  \\\n",
       "0    536365    85123A  WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                 WHITE METAL LANTERN         6   \n",
       "\n",
       "          InvoiceDate  UnitPrice  CustomerID         Country  TotalSpend  \\\n",
       "0 2010-12-01 08:26:00       2.55     17850.0  United Kingdom       15.30   \n",
       "1 2010-12-01 08:26:00       3.39     17850.0  United Kingdom       20.34   \n",
       "\n",
       "   Year  Month Day_of_Week  Is_weekend OrderMonth CohortMonth  CohortIndex  \\\n",
       "0  2010     12   Wednesday           0    2010-12     2010-12            0   \n",
       "1  2010     12   Wednesday           0    2010-12     2010-12            0   \n",
       "\n",
       "  FirstPurchaseDate  \n",
       "0        2010-12-01  \n",
       "1        2010-12-01  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cohort_data = pd.read_csv('cohort_data.csv')\n",
    "cohort_data = cohort_data.drop(columns=['Unnamed: 0'])\n",
    "cohort_data['InvoiceDate'] = pd.to_datetime(cohort_data['InvoiceDate'])\n",
    "cohort_data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Country</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>one_dollar_product</th>\n",
       "      <th>two_dollar_product</th>\n",
       "      <th>three_dollar_product</th>\n",
       "      <th>above_3_product</th>\n",
       "      <th>last_purchase_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EIRE</td>\n",
       "      <td>14016.0</td>\n",
       "      <td>44</td>\n",
       "      <td>35</td>\n",
       "      <td>24</td>\n",
       "      <td>59</td>\n",
       "      <td>2011-07-08 14:27:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EIRE</td>\n",
       "      <td>14156.0</td>\n",
       "      <td>229</td>\n",
       "      <td>404</td>\n",
       "      <td>275</td>\n",
       "      <td>511</td>\n",
       "      <td>2011-11-30 10:54:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EIRE</td>\n",
       "      <td>14911.0</td>\n",
       "      <td>1338</td>\n",
       "      <td>1411</td>\n",
       "      <td>1028</td>\n",
       "      <td>2119</td>\n",
       "      <td>2011-12-08 15:54:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>France</td>\n",
       "      <td>12413.0</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>2011-10-04 09:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>France</td>\n",
       "      <td>12437.0</td>\n",
       "      <td>59</td>\n",
       "      <td>60</td>\n",
       "      <td>27</td>\n",
       "      <td>39</td>\n",
       "      <td>2011-12-08 11:35:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4122</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>18280.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2011-03-07 09:52:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4123</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>18281.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2011-06-12 10:53:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4124</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>18282.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2011-12-02 11:43:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4125</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>18283.0</td>\n",
       "      <td>241</td>\n",
       "      <td>287</td>\n",
       "      <td>195</td>\n",
       "      <td>31</td>\n",
       "      <td>2011-12-06 12:02:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4126</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>18287.0</td>\n",
       "      <td>24</td>\n",
       "      <td>29</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>2011-10-28 09:29:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4127 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Country  CustomerID  one_dollar_product  two_dollar_product  \\\n",
       "0               EIRE     14016.0                  44                  35   \n",
       "1               EIRE     14156.0                 229                 404   \n",
       "2               EIRE     14911.0                1338                1411   \n",
       "3             France     12413.0                   9                   3   \n",
       "4             France     12437.0                  59                  60   \n",
       "...              ...         ...                 ...                 ...   \n",
       "4122  United Kingdom     18280.0                   0                   0   \n",
       "4123  United Kingdom     18281.0                   3                   1   \n",
       "4124  United Kingdom     18282.0                   4                   0   \n",
       "4125  United Kingdom     18283.0                 241                 287   \n",
       "4126  United Kingdom     18287.0                  24                  29   \n",
       "\n",
       "      three_dollar_product  above_3_product  last_purchase_date  \n",
       "0                       24               59 2011-07-08 14:27:00  \n",
       "1                      275              511 2011-11-30 10:54:00  \n",
       "2                     1028             2119 2011-12-08 15:54:00  \n",
       "3                       11               14 2011-10-04 09:00:00  \n",
       "4                       27               39 2011-12-08 11:35:00  \n",
       "...                    ...              ...                 ...  \n",
       "4122                     4                6 2011-03-07 09:52:00  \n",
       "4123                     1                2 2011-06-12 10:53:00  \n",
       "4124                     3                6 2011-12-02 11:43:00  \n",
       "4125                   195               31 2011-12-06 12:02:00  \n",
       "4126                    13                4 2011-10-28 09:29:00  \n",
       "\n",
       "[4127 rows x 7 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove the return products\n",
    "cohort_data = cohort_data[cohort_data['UnitPrice']>0]\n",
    "\n",
    "customer_data = cohort_data.groupby(['Country', 'CustomerID']).agg(\n",
    "    one_dollar_product=('UnitPrice', lambda x: ((x <= 1) & (x > 0)).sum()),\n",
    "    two_dollar_product=('UnitPrice', lambda x: ((x <= 2) & (x > 1)).sum()),\n",
    "    three_dollar_product=('UnitPrice', lambda x: ((x <= 3) & (x > 2)).sum()),\n",
    "    above_3_product=('UnitPrice', lambda x: (x > 3).sum()),\n",
    "    last_purchase_date=('InvoiceDate', 'max')\n",
    ").reset_index()\n",
    "customer_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merge the dataset\n",
    "only_rmf = rmf[['CustomerID', 'Recency', 'Frequency', 'MonetaryValue']]\n",
    "customer_behaviour = pd.merge(only_rmf, customer_data, how='left', on=['CustomerID'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last day of this dataset: 2011-12-09 12:50:00\n",
      "Chun date30: 2011-11-09 12:50:00\n"
     ]
    }
   ],
   "source": [
    "# Find the last date of this dataset\n",
    "max_date = max(cohort_data['InvoiceDate'])\n",
    "\n",
    "# Calculate the churn\n",
    "churn_threshold = 89 # The mean recency is 89 days\n",
    "churn_date89 = max_date - timedelta(days=churn_threshold)\n",
    "churn_date30 = max_date - timedelta(days=30)\n",
    "churn_date60 = max_date - timedelta(days=60)\n",
    "\n",
    "print('Last day of this dataset:', max_date)\n",
    "print('Chun date30:', churn_date30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Recency</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>MonetaryValue</th>\n",
       "      <th>Country</th>\n",
       "      <th>one_dollar_product</th>\n",
       "      <th>two_dollar_product</th>\n",
       "      <th>three_dollar_product</th>\n",
       "      <th>above_3_product</th>\n",
       "      <th>last_purchase_date</th>\n",
       "      <th>Churn_Status</th>\n",
       "      <th>Active</th>\n",
       "      <th>Churn_30days</th>\n",
       "      <th>Churn_60days</th>\n",
       "      <th>Churn_89days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12413.0</td>\n",
       "      <td>66</td>\n",
       "      <td>37</td>\n",
       "      <td>676.15</td>\n",
       "      <td>France</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>2011-10-04 09:00:00</td>\n",
       "      <td>Churn_60days</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12426.0</td>\n",
       "      <td>194</td>\n",
       "      <td>29</td>\n",
       "      <td>510.73</td>\n",
       "      <td>Germany</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>2011-05-29 12:26:00</td>\n",
       "      <td>Churn_89days</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID  Recency  Frequency  MonetaryValue  Country  one_dollar_product  \\\n",
       "0     12413.0       66         37         676.15   France                   9   \n",
       "1     12426.0      194         29         510.73  Germany                   2   \n",
       "\n",
       "   two_dollar_product  three_dollar_product  above_3_product  \\\n",
       "0                   3                    11               14   \n",
       "1                  12                     7                8   \n",
       "\n",
       "   last_purchase_date  Churn_Status  Active  Churn_30days  Churn_60days  \\\n",
       "0 2011-10-04 09:00:00  Churn_60days       0             0             1   \n",
       "1 2011-05-29 12:26:00  Churn_89days       0             0             0   \n",
       "\n",
       "   Churn_89days  \n",
       "0             0  \n",
       "1             1  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create Churn column\n",
    "def determine_churn(last_purchase_date):\n",
    "    if last_purchase_date > churn_date30:\n",
    "        return 'Active'\n",
    "    elif churn_date60 <= last_purchase_date <= churn_date30:\n",
    "        return 'Churn_30days'\n",
    "    elif churn_date89 <= last_purchase_date < churn_date60:\n",
    "        return 'Churn_60days'\n",
    "    elif last_purchase_date < churn_date89:\n",
    "        return 'Churn_89days'\n",
    "\n",
    "customer_behaviour['Churn_Status'] = customer_behaviour['last_purchase_date'].apply(determine_churn)\n",
    "churn_one_hot = pd.get_dummies(customer_behaviour['Churn_Status'])\n",
    "churn_one_hot = churn_one_hot.astype(int)\n",
    "customer_behaviour = pd.concat([customer_behaviour, churn_one_hot], axis=1)\n",
    "\n",
    "customer_behaviour.head(2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "churn_summary = customer_behaviour[['Active', 'Churn_30days', 'Churn_60days', 'Churn_89days']].sum()\n",
    "\n",
    "# Plot the data\n",
    "churn_summary.plot(kind='bar', color=['skyblue', 'orange', 'green'], figsize=(8, 5))\n",
    "plt.title('Number of Customers Who Churned at Different Time Periods')\n",
    "plt.ylabel('Number of Customers')\n",
    "plt.xlabel('Churn Period')\n",
    "plt.xticks(rotation=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The retention strategy should focus on the 30-day period, as a large portion of churn occurs within this time frame."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_____"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Product Price Ratios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "      <th>MonetaryValue</th>\n",
       "      <th>cheap_product_ratio</th>\n",
       "      <th>affortable_product_ratio</th>\n",
       "      <th>expensive_product_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12413.0</td>\n",
       "      <td>France</td>\n",
       "      <td>676.15</td>\n",
       "      <td>0.243243</td>\n",
       "      <td>0.378378</td>\n",
       "      <td>0.378378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12426.0</td>\n",
       "      <td>Germany</td>\n",
       "      <td>510.73</td>\n",
       "      <td>0.068966</td>\n",
       "      <td>0.655172</td>\n",
       "      <td>0.275862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12427.0</td>\n",
       "      <td>Germany</td>\n",
       "      <td>582.37</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.312500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12437.0</td>\n",
       "      <td>France</td>\n",
       "      <td>4392.66</td>\n",
       "      <td>0.318919</td>\n",
       "      <td>0.470270</td>\n",
       "      <td>0.210811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12441.0</td>\n",
       "      <td>France</td>\n",
       "      <td>155.55</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID  Country  MonetaryValue  cheap_product_ratio  \\\n",
       "0     12413.0   France         676.15             0.243243   \n",
       "1     12426.0  Germany         510.73             0.068966   \n",
       "2     12427.0  Germany         582.37             0.062500   \n",
       "3     12437.0   France        4392.66             0.318919   \n",
       "4     12441.0   France         155.55             0.300000   \n",
       "\n",
       "   affortable_product_ratio  expensive_product_ratio  \n",
       "0                  0.378378                 0.378378  \n",
       "1                  0.655172                 0.275862  \n",
       "2                  0.625000                 0.312500  \n",
       "3                  0.470270                 0.210811  \n",
       "4                  0.500000                 0.200000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_behaviour['total_products'] = customer_behaviour[['one_dollar_product', 'two_dollar_product', \n",
    "                                                           'three_dollar_product', 'above_3_product']].sum(axis=1)\n",
    "\n",
    "customer_behaviour['cheap_product_ratio'] = customer_behaviour['one_dollar_product'] / customer_behaviour['total_products']\n",
    "customer_behaviour['affortable_product_ratio'] = (customer_behaviour['two_dollar_product'] + customer_behaviour['three_dollar_product']) / customer_behaviour['total_products']\n",
    "customer_behaviour['expensive_product_ratio'] = customer_behaviour['above_3_product'] / customer_behaviour['total_products']\n",
    "\n",
    "ratio = customer_behaviour[['CustomerID', 'Country', 'MonetaryValue', 'cheap_product_ratio', 'affortable_product_ratio', 'expensive_product_ratio']]\n",
    "ratio.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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i9OzZs9Dwi6df38zMTCQkJKBZs2YAUOj9UFJ79+5FkyZN0KpVK3WbhYUFxo4di+joaFy9ehUAYG1tjXv37uH06dOl2s+wYcM0hqe8/vrrcHJyUn8eC/qcPHlS/U0LkP+ZcnV1feZ1H6mpqQBQ5PCXouzduxcGBgaYOHGiRvvUqVMhhMC+fftKtJ2ivP322xrPW7dujcTERHWMpdkGkH9uCr65K7B161aYmpqib9++pY6X6GXHYTBUbpo0aQI/P79C7TY2NhrDY27evAkhBKpXr17kdgqGDADAnTt3EBQUhJ9//hmPHj3S6JeSklLi2P79NXRBcvTvoQcF7U/v68qVK/jwww9x8ODBQr/c/h2Ds7NzoQuvatSoASB/CEFBEvNvt2/fhkKhgLe3t0a7o6MjrK2tcfv27WceX3GuXbuGv/76C8OGDUNERIS6vW3btlixYgVSU1NhaWmJBg0aoFatWti2bRtGjRoFIH8IjJ2dnXrsdnx8PJKTk7F69WqsXr26yP09PZYZyB+f/G8qlQqff/45Vq5ciaioKI2x/08PObl9+za8vLwKJZ3/Pkc3b94EgEJDeJ6WkpICGxubYpcD+WOQ+/XrB4VCAWtra/X483/79zEVvDY1a9bUaDc2Noanp6d6ecG//37fGxkZwdPT85mxFacggSyPqVHd3d2xZs0aqFQqREZGYuHChYiPjy807CMpKQnBwcH4/vvvC73+2nxGn3b79m31H9RPq127tnp5vXr1MGPGDBw4cABNmjSBt7c3OnXqhMGDB6Nly5Yl2s+/XwtJkuDt7a1xrcCAAQMwefJkbN26FUFBQUhJScHu3bsxZcqUZ/5BZGlpCSD/D/ySuH37NpydnQsl908fc2n9++dfwWfh0aNH6jifp6jPcseOHeHk5IStW7eiQ4cOUKlU+O6779CrV68S/5FCRIUxWSfZqVQqSJKEffv2FTmjhIWFBYD8qmvHjh2RlJSEGTNmoFatWjA3N0dMTAxGjBih1XzPxc1cUVy7+PuCruTkZLRp0waWlpaYN28evLy8YGJignPnzmHGjBmlnnO6OC9aDf23b775BgAwZcoUTJkypdDyHTt2YOTIkQDyk5KFCxciISEBlSpVws8//4xBgwapv3koONYhQ4YUmxj/+1uUf1fVgfyLz2bPno0333wT8+fPh62tLRQKBSZPnlyq81mwzuLFi4u9QVTBe+pZqlevDn9//+f2K+qYylpx7wNdzsltbm6ucT5atmyJRo0aYdasWfjiiy/U7f3798cff/yB9957D76+vrCwsIBKpULnzp3L/PPxb7Vr10Z4eDh2796N/fv3Y8eOHeqpEctqKlcbGxt0795dnaxv374dWVlZGjPlFMXb2xuGhoa4dOlSmcRRoDTvjef9nCuJot73BgYGGDx4MNasWYOVK1fi+PHjuH///nPPDRE9G5N1kp2XlxeEEPDw8FBXnYty6dIl3LhxA5s2bcKwYcPU7b///nuhvmWd5BY4dOgQEhMTsXPnTrz66qvq9oIZQv7t/v37haY1u3HjBoD8SmVx3NzcoFKpcPPmTXUlDci/0C45ORlubm5axy6EwLfffot27dph/PjxhZbPnz8fW7du1UjWg4ODsWPHDjg4OCA1NRUDBw5U97e3t0elSpWQl5dXoqS2ONu3b0e7du2wbt06jfbk5GT1hYdA/jm5evUqhBAar+/T3xAA/1wIZ2lp+UJxlVbBaxMeHq5RIc/OzkZUVJQ6poJ+N2/eVH9bAeRfgB0VFYUGDRqo2woqn/+eTebf1dWCY798+fIzj70sPh8+Pj4YMmQIvv76a0ybNg3VqlXDo0ePEBoaiuDgYAQFBan7FnzbUdoY3NzcEB4eXqi9YNjZ058Hc3NzDBgwAAMGDEB2djb69OmDhQsXYubMmcVe/FlcnEIIREREFPqjc9iwYejVqxdOnz6tvqCybt26z9y2mZkZ2rdvj4MHD+Lu3bvPvYDYzc0NBw4cwOPHjzWq0v8+5pK+N7RV2vfIsGHD8Nlnn+GXX37Bvn37YG9vr74gnYhKh2PWSXZ9+vSBgYEBgoODC1V2hBDqKd8KqkFP9xFCqKeke1pBclzWd0csKobs7GysXLmyyP65ubkac3JnZ2fj66+/hr29PRo3blzsfgpubLRs2TKN9iVLlgAAunXrpnXsx48fR3R0NEaOHInXX3+90GPAgAEICwvD/fv3AeRXKevXr49t27Zh27ZtcHJy0vgDxcDAAH379sWOHTuKnBu6qOntimJgYFDodf/xxx8LTfMWEBCAmJgY/Pzzz+q2zMzMQjfPady4Mby8vPDpp58iLS2t1HGVlr+/P4yNjfHFF19oHNe6deuQkpKifu38/Pxgb2+PVatWITs7W91v48aNhd63BUn4kSNH1G15eXmFhh81atQIHh4eWLZsWaFtPB1LWX0+pk+fjpycHPX7sqjPB1D4faxtDF27dsWpU6dw4sQJdVt6ejpWr14Nd3d31KlTBwA0pocE8oce1alTB0KIImeh+rfNmzdrDFPZvn07Hjx4gC5dumj069KlC+zs7LBo0SIcPny4xJXjOXPmQAiBoUOHFvnePHv2rHqWpq5duyIvLw/Lly/X6LN06VJIkqSOydLSEnZ2dhrvDQDF/kwqKXNz81K9P3x8fODj44O1a9dix44dGDhwoMZ1QESkPX6CSHZeXl5YsGABZs6ciejoaPTu3RuVKlVCVFQUfvrpJ4wdOxbTpk1DrVq14OXlhWnTpiEmJgaWlpbYsWNHobHrANSJ8MSJExEQEAADAwONqnBptWjRAjY2Nhg+fDgmTpwISZKwZcuWYr8+dnZ2xqJFixAdHY0aNWpg27ZtOH/+PFavXq0xFv/fGjRogOHDh2P16tXqoTenTp3Cpk2b0Lt3b7Rr107r2Ldu3QoDA4NiE/2ePXvigw8+wPfff6++sHXAgAEICgqCiYkJRo0aVegGRh9//DHCwsLQtGlTjBkzBnXq1EFSUhLOnTuHAwcOFJqjvijdu3fHvHnzMHLkSLRo0QKXLl3C1q1bC43bfuutt7B8+XIMGjQIkyZNUo+NLaiWFlQCFQoF1q5diy5duqBu3boYOXIkXFxcEBMTg7CwMFhaWuKXX37R+vyVlL29PWbOnIng4GB07twZPXv2RHh4OFauXIlXXnlFndgZGRlhwYIFeOutt9C+fXsMGDAAUVFR2LBhQ6Fjr1u3Lpo1a4aZM2ciKSkJtra2+P7775Gbm6vRT6FQ4KuvvkKPHj3g6+uLkSNHwsnJCdevX8eVK1fw66+/Aii7z0edOnXQtWtXrF27FrNnz0blypXx6quv4pNPPkFOTg5cXFzw22+/FfnNU0EMH3zwAQYOHAgjIyP06NGjyJvrvP/++/juu+/QpUsXTJw4Eba2tti0aROioqKwY8cO9fuyU6dOcHR0RMuWLeHg4IBr165h+fLlGhdUP4utrS1atWqFkSNHIjY2FsuWLYO3t3ehi5iNjIwwcOBALF++HAYGBhg0aFCJzleLFi2wYsUKjB8/HrVq1cLQoUNRvXp1PH78GIcOHcLPP/+MBQsWAAB69OiBdu3a4YMPPkB0dDQaNGiA3377Df/73/8wefJkjakUR48ejY8//hijR4+Gn58fjhw5ov4Gr7QaN26MAwcOYMmSJXB2doaHh0eR1w0UZdiwYZg2bRoAcAgMUVnQ4cwz9JIomGbu9OnTRS5v06ZNkdOr7dixQ7Rq1UqYm5sLc3NzUatWLfHOO++I8PBwdZ+rV68Kf39/YWFhIezs7MSYMWPUU6I9PTVYbm6uePfdd4W9vb2QJEk9TV3BNGeLFy/W2HfB1G0//vjjc4/l+PHjolmzZsLU1FQ4OzuL6dOni19//bXQ1G0Fx3nmzBnRvHlzYWJiItzc3MTy5ctLdB5zcnJEcHCw8PDwEEZGRsLV1VXMnDlTY3pIIUo2dWN2draoXLmyaN269TP7eXh4iIYNG6qf37x5Uz3d5rFjx4pcJzY2VrzzzjvC1dVVGBkZCUdHR9GhQwexevVqdZ/izq8Q+VM3Tp06VTg5OQlTU1PRsmVLceLECdGmTRuN6QuFEOLWrVuiW7duwtTUVNjb24upU6eKHTt2CADizz//1Oj7119/iT59+ojKlSsLpVIp3NzcRP/+/UVoaOgzz0Fx75F/e977fPny5aJWrVrCyMhIODg4iHHjxhWaTlEIIVauXCk8PDyEUqkUfn5+4siRI0Uee2RkpPD39xdKpVI4ODiIWbNmid9//73IKQOPHTsmOnbsKCpVqiTMzc2Fj4+P+PLLL9XLi/t8FKe4z6wQQhw6dEhjisB79+6J1157TVhbWwsrKyvRr18/cf/+/ULTCAohxPz584WLi4tQKBQa0zj+e+rGguN//fXXhbW1tTAxMRFNmjQRu3fv1ujz9ddfi1dffVX9mnt5eYn33ntPpKSkPPP4Ct6f3333nZg5c6aoUqWKMDU1Fd26ddOYEvZpp06dEgBEp06dnrntopw9e1YMHjxYODs7CyMjI2FjYyM6dOggNm3apDHt4ePHj8WUKVPU/apXry4WL16sMQ2nEPnTZY4aNUpYWVmJSpUqif79+4u4uLhip26Mj4/XWL+oqUGvX78uXn31VWFqaioAqF+P4rbxtAcPHggDAwNRo0YNrc8NERUmCaHFFSVEVGJt27ZFQkLCf/L24f8ly5Ytw5QpU3Dv3j3edIV05sKFC/D19cXmzZsxdOhQucPRKwkJCXByckJQUBBmz54tdzhE/3kcs05E/xkZGRkazzMzM/H111+jevXqTNRJp9asWQMLC4tC8+5T/nUXeXl5/COGqIxwzDoR/Wf06dMH1apVg6+vL1JSUvDNN9/g+vXrGjd0IipPv/zyC65evYrVq1djwoQJRY6xf1kdPHgQV69excKFC9G7d+9nznhFRCXHYTBE5YTDYMresmXLsHbtWkRHRyMvLw916tTB9OnT1XdVJSpv7u7uiI2NRUBAALZs2cKb/Tylbdu2+OOPP9CyZUt88803/LaLqIwwWSciIiIi0lMcs05EREREpKeYrBMRERER6Skm60REREREeoqzwRARERGRzuwxqqmT/XTLCdfJfspbhU3WW/U4LHcIRLI69ksbPFkXJHcYRLIyGzUPMTcuyR0GkaxcatSXOwR6ARU2WSciIiIi/SMZSXKH8J/CMetERERERHqKlXUiIiIi0hmFISvr2mBlnYiIiIhIT7GyTkREREQ6IxmxVqwNni0iIiIiIj3FZJ2IiIiISE9xGAwRERER6QwvMNUOK+tERERERHqKlXUiIiIi0hneFEk7rKwTEREREekpVtaJiIiISGc4Zl07rKwTEREREekpVtaJiIiISGc4Zl07rKwTEREREekpVtaJiIiISGc4Zl07rKwTEREREekpVtaJiIiISGckA1bWtcHKOhERERGRnmJlnYiIiIh0RsHKulZYWSciIiIi0lOsrBMRERGRzkgKVta1wco6EREREZGeYmWdiIiIiHRGMmCtWBs8W0REREREeoqVdSIiIiLSGc4Gox1W1omIiIiI9BQr60RERESkM5wNRjusrBMRERER6SlW1omIiIhIZzhmXTusrBMRERER6SlW1omIiIhIZyRW1rXCyjoRERERkZ5iZZ2IiIiIdEZSsFasDZ4tIiIiIiI9xco6EREREekM51nXDivrRERERER6ipV1IiIiItIZzrOuHVbWiYiIiIj0FCvrRERERKQzHLOuHVbWiYiIiIj0FCvrRERERKQznGddOzxbRERERER6ipV1IiIiItIZjlnXDivrRERERER6ipV1IiIiItIZzrOuHVbWiYiIiIj0FCvrRERERKQzHLOuHVbWiYiIiIj0FCvrRERERKQznGddOzxbRERERER6StZkPS4u7pnLc3NzcerUKR1FQ0RERESkX2RN1p2cnDQS9vr16+Pu3bvq54mJiWjevLkcoRERERFROZAUkk4eFYWsyboQQuN5dHQ0cnJyntmHiIiIiOhlofcXmEpSxfnLiIiIiOhlV5Gq3rrAC0yJiIiIiPSUrJV1SZLw+PFjmJiYQAgBSZKQlpaG1NRUAFD/S0REREQVAyvr2pE1WRdCoEaNGhrPGzZsqPGcw2CIiIiI6GUla7IeFhYm5+5JC326OmNQH1fY2hgjMioNS7+OwLWbj4vs26OTIzq3d4SnmxkAIDwiDV9vjlL3NzCQMHaIO5r52cLZ0RTp6bk4c+ERvtoUhcSkbJ0dE5G2tp27iU2nriMxPRM1qlhjhn8j1HOq/Nz19l+7g5m/nEBbbxcs7dMKAJCTp8LKo5dw7NYD3EtJg4WxEZq6O2Diqw1QpZJpeR8KUans2rMP23b+jKRHyfDycMO7b41C7RrVi+x75I8/8e2POxHz4CHycvPg4uyEfr17oFP7Nhr9bt+9h9Ubv8HFy1eRl5cHN9eqmDtzGhyq2OvikEgGvCmSdmRN1tu0afP8TiS79q3sMWG0Fz5dcQNXbzxG/54uWDKvPga9fRrJKTmF+jesb40DR+Jw6VoKsnNUeKNvNSyZ54Oh75xGQlI2TJQK1PCqhE3b7uBmVBosLQwxaYw3Fn1YD6MDz8lwhETP9+u1O/gs7Dw+6NQY9Zwq49szNzD+h8PYNborbM1Nil3vfko6loadR8OqmolHZm4ursU+wpgWdVDD3hqpWdlYHPoXJu88im+HdyrvwyHSWtjR4/hq7SZMfmcsateojh0/78GMoAXYtOoL2FhbFepvWckCb/Tvi2pVXWBoaIg/T5/FJ5+vgI21FV5p5AsAiHnwEJNmfIguHTtgxOD+MDMzQ/SduzA2Ntbx0RHpL73+0+bcuXPo3r273GG89Ab2ropffn2AvaGxiL77BItX3kRmlgrdOzoW2X/eZ9fx0977iIhKx517GVj0ZTgUCsCvgQ0AIP1JHqYEXcTBY/G4G5OBK+GPseTrCNSqXgkO9kpdHhpRiX1zJhx9fDzRq74nvOys8EGAH0yMDLHrUlSx6+SpVJi1+wTeblUPVa3NNZZVUhpj1YC26FSrGtwrW8LH2Q7v+zfCtdhHeJCaXt6HQ6S1H3f9gq4B/uji3x7u1VwxZfxYKJVK7Pv9YJH9fevXQ+vmTeHmWhUuTo7o27MbPN3dcOnqNXWf9Vu+RZPGjfDWyKGo7uUJFydHtGz6SpHJP1UcCgNJJ4+KQvZk/ddff8W0adMwa9Ys3Lp1CwBw/fp19O7dG6+88gpUKpXMEb7cDA0l1PCuhDMXHqnbhADOnH+EujUtS7QNpdIAhgYSUtMKV+ELWJgZQKUSeJyW+8IxE5W1nLw8XHv4CE3dHdRtCklCUzcHXLyfUOx6q/+4ClszE7zm41mi/TzOyoGE/ESeSJ/k5OTgRsQtNG7go25TKBRo7FsfV8PDn7u+EALnLlzEvZj78KlbBwCgUqnw55lzcHVxwvSg+egz5E2Mn/o+jp3gncuJnibrMJh169ZhzJgxsLW1xaNHj7B27VosWbIE7777LgYMGIDLly+jdu3acob40rOyNIKhgYSkR5qJdlJyDtyqmpVoG+NHeCAhKRtnzj8qcrmxkYRxIzxx4EgcnmTkvXDMRGXt0ZNs5AkBWzPN4S6VzU0QnVT0rFV/3YvHrou38P2IgBLtIys3D18cvojOtavBQmn0wjETlaWU1MdQqVSwsdGseNtYW+POvZhi10tLT0f/EW8hJycHCoUCk8eNhl/DBgCA5JQUZGRk4rvtuzByyECMHTEEp86ex5yQxViycC4a1K9brsdE8uFsMNqRNVn//PPPsWjRIrz33nvYsWMH+vXrh5UrV+LSpUuoWrVqibaRlZWFrKwsjTalkkMp9MWQ113RoXUVvDvrArJzCt+N1sBAwrwZdQAJ+HTlTRkiJCp76Vk5+HDPSczu/ApszJ7/8ygnT4Xp//sDQgjM6uSngwiJdMPM1BRrPl+MjMxMnLtwCSvXbYKTowN869eDSpX/O6FF01fQr3cPAIC3pweuXA/Hz/t/Y7JO9DdZk/XIyEj069cPANCnTx8YGhpi8eLFJU7UASAkJATBwcEabXPmzAHQrixDfWmlpOYgN0/A1kaz0mdrbYTER8+euWXQa1XxRt9qmDz7AiKjC4/BNTCQMH9GHThWMcHEDy6wqk56y8bMGAaShKQnmRrtiemZqFzExaX3ktNwPyUdk3ccVbepRH5i4rf4B/w0uitcbSwA5CfqM37+Aw9S07F6YDtW1UkvWVlWgkKhwKNHKRrtj5KTYWtjXex6CoUCLs5OAPIT8Tt3Y/Dtjz/Bt349WFlWgoGBAdyqaf7Od3N1waWr18v8GEh/cDYY7ciarGdkZMDMLH8ohSRJUCqVcHJy0mobM2fORGBgoEabUqnEgdf/LLM4X2a5uQI3Ih6jsY8Njv6ZCACQJKBxAxvs3FP8V5+D+7hiWP9qmDrnIsIj0gotL0jUqzqbYuKsC0h9zLHqpL+MDAxQ29EGJ2/Hol31/MRCJQRO3Y7FgEaFp61zr2yJH0dqDn9ZcfQynmTn4L0ODeFomT81Y0GifufRY6we2A7WpvxWkPSTkZERanh74tzFS2jVvAmA/DHn5y5cQu9uXUq8HZVQIScnR73NmtW9cPfefY0+d2MewMGe0zYSFZA1WQeAtWvXwsIiv8KUm5uLjRs3ws7OTqPPxIkTi11fqVRy2Es5+37XPXwwpRauRzzGtRuP0b+XC0xNFNhz4CEA4MMpNRGfmI2vN+fPivFGX1eMesMdwZ9ew4PYTNha51cKMzLzkJGpgoGBhAXv10ENLwvMmHcZCgXUfVLTcpGbW3i4DJHchvjVRNDek6jjaPv31I3hyMjJRa/6HgCAD/f8iSoWZpjYxgdKQwN421trrF/JJP89XtCek6fCe/87juuxj/B539ZQqQQS0jIAAFamxjAyMNDZsRGVRL/ePfDx0uWo6e2FWjW8seN/e5CZmYXO/vnfZIcs+QJ2lStjzPA3AADf/rgTNby94OzkiJycHJw8cw6/hx3B5HFj1Nsc0KcX5n+yFD71aqNh/Xo4de48Tpw6g6UfBRcZA1UMHLOuHVmT9WrVqmHNmjXq546OjtiyZYtGH0mSnpmsU/k7eCwe1lZGGP2GO2xtjBFxKw1T51zCo+T86oiDvQlUT+XXvbs4w9hIgYUzNccbrv82Guu/uw37ysZo3Sz/D7KNX2qOz3135nn8dVnza1YifRBQuxoeZWThq2OXkZieiZpVrLGiXxv1MJiHqU+g0OKOy/FpGTgckV9RHLjxN41lawa2g1+1KmUXPFEZaNe6JZJTUrFh6/d49CgZXp7uWBT8gXoYTFx8AhTSP8MbMjKz8PlXaxCfmASlsTFcqzpj1tSJaNe6pbpP6+ZNMWX8GHz7409YvnoDXF2cETxzGurX5eQSRAUkIUSFLGO26nFY7hCIZHXslzZ4si5I7jCIZGU2ah5iblySOwwiWbnUqC93CBpuj+2tk/24rd6l9TorVqzA4sWL8fDhQzRo0ABffvklmjRp8tz1vv/+ewwaNAi9evXCrl3/7HfEiBHYtGmTRt+AgADs37+/xDFxhD8RERERvfS2bduGwMBAzJkzB+fOnUODBg0QEBCAuLi4Z64XHR2NadOmoXXr1kUu79y5Mx48eKB+fPfdd1rFJeswmC+++KJE/TgMhoiIiKhi0NfZYJYsWYIxY8Zg5MiRAIBVq1Zhz549WL9+Pd5///0i18nLy8Mbb7yB4OBgHD16FMnJyYX6KJVKODoWfdf3kpA1WV+6dOlz+3DMOhERERFpq7h78RQ1MUl2djbOnj2LmTNnqtsUCgX8/f1x4sSJYvcxb948VKlSBaNGjcLRo0eL7HPo0CFUqVIFNjY2aN++PRYsWIDKlSuX+DhkTdajoqLk3D0RERER6ZiuZoMp7l48c+fOLdQ3ISEBeXl5cHBw0Gh3cHDA9etFz/t/7NgxrFu3DufPny82hs6dO6NPnz7w8PBAZGQkZs2ahS5duuDEiRMwKOGsX7J+D9G1a1ekpPwz88fHH3+s8fVBYmIi6tSpI0NkRERERPRfNnPmTKSkpGg8nq6cv4jHjx9j6NChWLNmTaEpx582cOBA9OzZE/Xr10fv3r2xe/dunD59GocOHSrxvmStrO/fv1/j64mPPvoI/fv3h7W1NYD8edfDw8Nlio6IiIiIypquxqxrcy8eOzs7GBgYIDY2VqM9Nja2yPHmkZGRiI6ORo8ePdRtKpUKAGBoaIjw8HB4eXkVWs/T0xN2dnaIiIhAhw4dShSbXo3wr6CzSBIRERGRHjM2Nkbjxo0RGhqqblOpVAgNDUXz5s0L9a9VqxYuXbqE8+fPqx89e/ZEu3btcP78ebi6uha5n3v37iExMRFOTk4ljk32O5gSERER0UtEixvI6VJgYCCGDx8OPz8/NGnSBMuWLUN6erp6dphhw4bBxcUFISEhMDExQb169TTWLxgZUtCelpaG4OBg9O3bF46OjoiMjMT06dPh7e2NgICAEscla7IuSRKkf71g/35ORERERFTeBgwYgPj4eAQFBeHhw4fw9fXF/v371Red3rlzBwothvAYGBjg4sWL2LRpE5KTk+Hs7IxOnTph/vz5JR6eA8icrAshMGLECHXAmZmZePvtt2Fubg4AhabbISIiIqL/Nl3NBlMaEyZMwIQJE4pc9ryLQjdu3Kjx3NTUFL/++usLxyRrsj58+HCN50OGDCnUZ9iwYboKh4iIiIhIr8iarG/YsEHO3RMRERGRjunrHUz1Fc8WEREREZGe4mwwRERERKQz+jxmXR+xsk5EREREpKdYWSciIiIineGYde3wbBERERER6SlW1omIiIhIZzhmXTusrBMRERER6SlW1omIiIhIZ1hZ1w4r60REREREeoqVdSIiIiLSHc4GoxWeLSIiIiIiPcXKOhERERHpjCRxzLo2WFknIiIiItJTTNaJiIiIiPQUh8EQERERkc5IvMBUKzxbRERERER6ipV1IiIiItIZ3hRJO6ysExERERHpKVbWiYiIiEh3OGZdKzxbRERERER6ipV1IiIiItIZjlnXDivrRERERER6ipV1IiIiItIZSWKtWBs8W0REREREeoqVdSIiIiLSHY5Z1wor60REREREeoqVdSIiIiLSGYnzrGuFZ4uIiIiISE+xsk5EREREOsN51rXDyjoRERERkZ5iZZ2IiIiIdIfzrGuFZ4uIiIiISE+xsk5EREREOsMx69phZZ2IiIiISE+xsk5EREREusN51rXCs0VEREREpKdYWSciIiIinZEkjlnXBivrRERERER6ipV1IiIiItIdjlnXCs8WEREREZGeYmWdiIiIiHSG86xrh5V1IiIiIiI9xco6EREREemOxFqxNni2iIiIiIj0FCvrRERERKQ7HLOuFVbWiYiIiIj0FCvrRERERKQzEsesa4Vni4iIiIhIT0lCCCF3EERERET0ckhf86FO9mM+ZoFO9lPeKuwwmHeXpcodApGsvpxsif2WteUOg0hWnVOv4c3gOLnDIJLV+jlV5A6BXkCFTdaJiIiISP9ICo7C1gbPFhERERGRnmJlnYiIiIh0R+I869pgZZ2IiIiISE+xsk5EREREusMx61rh2SIiIiIi0lOsrBMRERGR7nDMulZYWSciIiIi0lNM1omIiIiI9BSHwRARERGRzvCmSNrh2SIiIiIi0lOsrBMRERGR7kisFWuDZ4uIiIiISE+xsk5EREREuqPg1I3aYGWdiIiIiEhPsbJORERERDojccy6Vni2iIiIiIj0FCvrRERERKQ7HLOuFVbWiYiIiIj0FCvrRERERKQ7HLOuFZ4tIiIiIiI9xco6EREREemOxDHr2mBlnYiIiIhIT7GyTkRERES6o2CtWBs8W0REREREeoqVdSIiIiLSHc4GoxWeLSIiIiIiPcXKOhERERHpDu9gqhVW1omIiIiI9BQr60RERESkOxyzrhWeLSIiIiIiPcXKOhERERHpDu9gqhVW1omIiIiI9BQr60RERESkO7yDqVZ4toiIiIiIAKxYsQLu7u4wMTFB06ZNcerUqRKt9/3330OSJPTu3VujXQiBoKAgODk5wdTUFP7+/rh586ZWMTFZJyIiIiLdkSTdPLS0bds2BAYGYs6cOTh37hwaNGiAgIAAxMXFPXO96OhoTJs2Da1bty607JNPPsEXX3yBVatW4eTJkzA3N0dAQAAyMzNLHBeTdSIiIiJ66S1ZsgRjxozByJEjUadOHaxatQpmZmZYv359sevk5eXhjTfeQHBwMDw9PTWWCSGwbNkyfPjhh+jVqxd8fHywefNm3L9/H7t27SpxXEzWiYiIiEh3JIVuHlrIzs7G2bNn4e/vr25TKBTw9/fHiRMnil1v3rx5qFKlCkaNGlVoWVRUFB4+fKixTSsrKzRt2vSZ2/w3XmBKRERERBVOVlYWsrKyNNqUSiWUSmWhvgkJCcjLy4ODg4NGu4ODA65fv17k9o8dO4Z169bh/PnzRS5/+PChehv/3mbBspJgZZ2IiIiIdEeh0MkjJCQEVlZWGo+QkJAyOYTHjx9j6NChWLNmDezs7Mpkm8VhZZ2IiIiIKpyZM2ciMDBQo62oqjoA2NnZwcDAALGxsRrtsbGxcHR0LNQ/MjIS0dHR6NGjh7pNpVIBAAwNDREeHq5eLzY2Fk5OThrb9PX1LfFxsLJORERERLqjo9lglEolLC0tNR7FJevGxsZo3LgxQkND1W0qlQqhoaFo3rx5of61atXCpUuXcP78efWjZ8+eaNeuHc6fPw9XV1d4eHjA0dFRY5upqak4efJkkdssDivrRERERPTSCwwMxPDhw+Hn54cmTZpg2bJlSE9Px8iRIwEAw4YNg4uLC0JCQmBiYoJ69epprG9tbQ0AGu2TJ0/GggULUL16dXh4eGD27NlwdnYuNB/7szBZJyIiIiLd0XKmFl0ZMGAA4uPjERQUhIcPH8LX1xf79+9XXyB6584dKLS8++r06dORnp6OsWPHIjk5Ga1atcL+/fthYmJS4m1IQgih1V7LSXJyMrZv347IyEi89957sLW1xblz5+Dg4AAXFxett/fustRyiJLov+PLyZbYb1lb7jCIZNU59RreDH72DU2IKrr1c6rIHYKGzN826GQ/Jp1G6mQ/5U0vKusXL16Ev78/rKysEB0djTFjxsDW1hY7d+7EnTt3sHnzZrlDJCIiIqKyUIq7i77M9OJ7iMDAQIwYMQI3b97U+Fqga9euOHLkiIyRERERERHJRy8q66dPn8bXX39dqN3FxUWrSeOJiIiISM9pOe77ZacXZ0upVCI1tfAY8xs3bsDe3l6GiIiIiIiI5KcXyXrPnj0xb9485OTkAAAkScKdO3cwY8YM9O3bV+boiIiIiKisCEnSyaOi0Itk/bPPPkNaWhqqVKmCjIwMtGnTBt7e3qhUqRIWLlwod3hERERERLLQizHrVlZW+P3333H8+HFcuHABaWlpaNSoEfz9/eUOjYiIiIjKkp7Os66v9CJZL9CyZUu0bNlS7jCIiIiIiPSCXvxpM3HiRHzxxReF2pcvX47JkyfrPiAiIiIiIj2gF8n6jh07iqyot2jRAtu3b5chIiIiIiIqF5JCN48KQi+OJDExEVZWVoXaLS0tkZCQIENERERERETy04tk3dvbG/v37y/Uvm/fPnh6esoQERERERGVB07dqB29uMA0MDAQEyZMQHx8PNq3bw8ACA0NxWeffYZly5bJGxwRERERkUz0Ill/8803kZWVhYULF2L+/PkAAHd3d3z11VcYNmyYzNERALT2MUIHPyUszSTEJKiwPSwDt2NVRfZtUc8ITWobwamyAQDgblwefjmeWai/g40CvVop4V3VEAoF8DBRhXV7nuDRY1Hux0OkrWpjBsNj4pswdrDD48vXce29hUg5e+m56zn27QrfDZ8hdvcB/DX4XXW7gbkZagQHwqFbBxjZWiPj9j3cXvUN7q7fVp6HQfRC2r9iis4tzGBlocDdh7nYuu8xou7nFtn31UYmaOFjApcq+anG7Qe52BGaptH/zV6V0MrXVGO9SxFZWLo1pfwOguRXgcaT64JeJOsAMG7cOIwbNw7x8fEwNTWFhYWF3CHR3xrVMMRrr5pg28FM3H6Yh7YNjTH+NXPM35SGtIzCibV3VUOcDc/BrQeZyM0F/P2MMb6POT7anIaU9Pz+dlYSpvQ3w4krOdj7ZzoyswUcKxsgp+if+USycuzTBbU+moErk+ci+cxFuI8fBr+da3C0cVdkJyQVu55pNWfUWvAeko6fKbSs1kczYNumKS6OmY6MOzGo3L4l6iwJQuaDOMTvCyvPwyEqlVfqKjGgkwW27HmMW/dy0LGZGQKHWGPW8kQ8flL4d0FNN2OcvJyFiLtpyMkV6NrSDFOHWuPDlUlIfvxP8ebSzSys+99j9fPcPBZsiJ6md3/a2NvbM1HXM+0aKXHicg5OXs3BwyQVtoVmIjtXoHldoyL7b96fgaMXcxATr0LsIxW+PZAJCUDNav/8bdi9hQmuROfif8eycC9ehYQUgcu3cotM/onk5j5hOO5u+hExW39Cengkrkyei7yMTLgM7VP8SgoFfNYuxs2PliMj+m6hxdZNG+L+t/9D0rHTyLhzH/c2/ojHl8Jh7edTjkdCVHoBzcxw5FwGjp3PxP2EPGze/RjZOQKtG5oW2X/NT6kIO5OBu7G5eJiYhw2/PIYkAXU8jDX65eQBqekq9eNJJn8PVHiSpJtHBaEXyXpsbCyGDh0KZ2dnGBoawsDAQONB8jFQAK5VFAi/+0/JWwAIv5MLd6eSvTbGhoCBAZD+9w9gCUBdD0PEPVJh/Gtm+GisBaYONIePl9580UOkJhkZwdK3LhLDTvzTKAQSD52AdRPfYtfzfn88suOTELNlR5HLk0/+hSpd20HpVAUAYNu6Ccy93ZEQerwswycqEwYKwM3ZEFdvZavbBICrt7LhVbXows2/KY0kGCgkpGdoDoms5W6EZdPs8NE7thjazQLmphUnySIqC3qRHY0YMQJ37tzB7Nmz4eTkBKkC/TX0X2dumv/DNfVfX3E+fiLgYFuyZL1XKxOkpAmE38lP+C3MJJgYS+j4ihJ7/sjC/47loo6bIUZ1N8WX258gIiavzI+DqLSMK1tDYWiI7PhEjfasuESY1/Aoch3rZo1QdWhfHG/5WrHbvfreAtT7Yh7ahR+GKicHUAlcnhiER38UHjJDJLdKZor83wXpmol2aroKTnYlSyVe9zdH8mMVrjyV8F+OyMa5a1mIT85DFRsD9O1ggSlvGGHhukcQLLBXXAq9qBX/Z+hFsn7s2DEcPXoUvr6+Wq+blZWFrKwsjTalUllGkdGL6uhnjEY1jfDF9nTk/p2DF/wtdikyF2F/5f/QjonPhoeTAVr5GCMiJkOmaIlenIGFGXxWL8LliUHISUoutp/bW0Ng/UoDnO0/Dhl378O2pR/qfDobWQ/ikHjoRLHrEf0XdW1phib1TPDJxkfq3wUAcOrKP7+/Y+LycC82GYsm2aGWuxGuReXIECmR/tGLZN3V1RWilH9Ch4SEIDg4WKNtzpw5gHVgWYT20kvPEMhTCViaaX7bUcmscIXl39o3Mob/K0os35GO+wn/9E3PEMjLE3iYpFlBf/hIBS9nDnsi/ZKdmAxVbi6M7StrtCurVEZWbOGbtpl5VIOZe1U02rZS3Sb9XUXqlHQJRxt3RdaDONSYMxl/vTER8b8eBgCkXbmBSvVrw33iSCbrpHceP1Hl/y4w16yIWporkJL27N8FAc1N0bWVGT7dnIx7cc/+5jQ+WYXH6SpUsTVgsl6BVaQ50HVBL76HWLZsGd5//31ER0drve7MmTORkpKi8Zg5c2bZB/mSylMBd+NUqOH6z991EoAaroaIflD8D90OjY3RuakSX/30BHfjNH+Q56mA27F5qGKj+farYq1AUiq/9yT9InJykHr+Ciq3bfZPoyShcptmSD51vlD/9Bu3cKxpT/zRso/6Ebf3IJKOnMQfLfsg895DSEaGUBgbQ6j+leTk5akTeyJ9kqcCbt/PRW3Pfy4OlQDU9jRG5L3ik+rOLczQ41VzLPkmGdEPnj/dl00lBczNJKQ8fvYfAEQvE72orA8YMABPnjyBl5cXzMzMYGSkebFKUlLxU6Mplcpihr1kFdFGpRF2LgtDOpniTmxe/tSNjYyhNJLw59X8H9BDO5kgOV3gl+P559zfzxhdmymxaX8GElNVqPR3VT4rRyD775/poWezMbKrKSJj8nDjbi7quBuinqchvtj+RJZjJHqW6OWbUH9VCFL+uoyUM5fgPn4YDMxMEfPNTwCA+l9/jKz7sbgRvBSqrGykXbupsX5uSv60dAXteTk5SDp6CjXnvwdVRubfw2BegfOgXrg+a5FuD46ohH798wlG97ZE9P1cRMXkT92oNJJw7Hz+0MXRvSvh0WMVdoSmAwC6tDRD77bmWL0zFQnJKnVVPitbICtHQGkkoWdbM5y9moWUtPxqej9/C8Ql5eFyZHaxcVAFwHnWtaIXyTrvUqrfzt3IhYVpJro1V6LS3zdFWrnriXpeXRtLBQT+qYK08jGGkaGE0d3NNLaz988s7PszP6G/GJmLbaGZ6PiKMfq2NUHcIxXW7c7Arfu8uJT0z8Od+2BsZ4PqsyZC6WCH1EvXcKbvWPVFp6ZVnYB/V8mf4/zIqagxdwp81i6GkY0VMu7ex815y3B33fflcQhEL+z0lSxUMktD77bm6psiLd2ajNS/759ha2UA1VNfjrbzM4WRoYR3+ltpbOd/h9Lxv8PpUAkB1yqGaNnAFGYmUv7Fp5HZ+CksTWNcO9HLThKlHSyu595dlip3CESy+nKyJfZb1pY7DCJZdU69hjeD4+QOg0hW6+dUkTsEDWl//qyT/Vg066mT/ZQ3vaisPy0zMxPZ2Zpff1laWsoUDRERERGRfPRi0FB6ejomTJiAKlWqwNzcHDY2NhoPIiIiIqogeAdTrehFsj59+nQcPHgQX331FZRKJdauXYvg4GA4Oztj8+bNcodHRERERCQLvRgG88svv2Dz5s1o27YtRo4cidatW8Pb2xtubm7YunUr3njjDblDJCIiIqIyIDgbjFb04mwlJSXB09MTQP749IKpGlu1aoUjR47IGRoRERERkWz0Iln39PREVFQUAKBWrVr44YcfAORX3K2trWWMjIiIiIjKFMesa0UvkvWRI0fiwoULAID3338fK1asgImJCaZMmYL33ntP5uiIiIiIiOShF2PWp0yZov6/v78/rl+/jrNnz8Lb2xs+Pj4yRkZEREREZYpj1rUi+9nKyclBhw4dcPPmP7fndnNzQ58+fZioExEREdFLTfbKupGRES5evCh3GERERESkA6ICjSfXBdkr6wAwZMgQrFu3Tu4wiIiIiIj0iuyVdQDIzc3F+vXrceDAATRu3Bjm5uYay5csWSJTZERERERUpjhmXSuyJuu3bt2Cu7s7Ll++jEaNGgEAbty4odFH4lclRERERPSSkjVZr169Oh48eICwsDAAwIABA/DFF1/AwcFBzrCIiIiIqJwIsBCrDVm/hxBCaDzft28f0tPTZYqGiIiIiEi/6MWY9QL/Tt6JiIiIqGIRHLOuFVnPliRJhcakc4w6EREREVE+WSvrQgiMGDECSqUSAJCZmYm333670GwwO3fulCM8IiIiIiprrKxrRdZkffjw4RrPhwwZIlMkRERERET6R9ZkfcOGDXLunoiIiIh0jHcw1Q6/hyAiIiIi0lN6NRsMEREREVVsnA1GOzxbRERERER6ipV1IiIiItIdjlnXCivrRERERER6ipV1IiIiItIZjlnXDs8WEREREZGeYmWdiIiIiHRGgGPWtcHKOhERERGRnmKyTkRERESkpzgMhoiIiIh0hheYaodni4iIiIhIT7GyTkRERES6w5siaYWVdSIiIiIiPcXKOhERERHpjGCtWCulOlvDhw/HkSNHyjoWIiIiIiJ6SqmS9ZSUFPj7+6N69er46KOPEBMTU9ZxEREREVEFJCRJJ4+KolTJ+q5duxATE4Nx48Zh27ZtcHd3R5cuXbB9+3bk5OSUdYxERERERC+lUg8asre3R2BgIC5cuICTJ0/C29sbQ4cOhbOzM6ZMmYKbN2+WZZxEREREVAEISaGTR0Xxwkfy4MED/P777/j9999hYGCArl274tKlS6hTpw6WLl1aFjESEREREb2USjUbTE5ODn7++Wds2LABv/32G3x8fDB58mQMHjwYlpaWAICffvoJb775JqZMmVKmARMRERHRf5dAxRlPrgulStadnJygUqkwaNAgnDp1Cr6+voX6tGvXDtbW1i8YHhERERHRy6tUyfrSpUvRr18/mJiYFNvH2toaUVFRpQ6MiIiIiCqeijSeXBe0Pls5OTkYOXIkIiIiyiMeIiIiIiL6m9aVdSMjI1SrVg15eXnlEQ8RERERVWAVaQ50XSjV9xAffPABZs2ahaSkpLKOh4iIiIiI/laqMevLly9HREQEnJ2d4ebmBnNzc43l586dK5PgiIiIiKhi4Www2ilVst6rVy9I/AqDiIiIiKhclSpZnzt3bhmHQUREREQvA84Go51SnS1PT08kJiYWak9OToanp+cLB0VERERERKWsrEdHRxc5G0xWVhbu3bv3wkERERERUcXEMeva0SpZ//nnn9X///XXX2FlZaV+npeXh9DQUHh4eJRddERERERELzGtkvXevXsDACRJwvDhwzWWGRkZwd3dHZ999lmZBUdEREREFQvHrGtHq2RdpVIBADw8PHD69GnY2dmVS1BERERERFTKMetRUVFlHQcRERERvQQ4Zl07pUrWASA0NBShoaGIi4tTV9wLrF+//oUDIyIiIiJ62ZUqWQ8ODsa8efPg5+cHJycn3iCJiIiIiEqEY9a1U6pkfdWqVdi4cSOGDh1a1vEQEREREdHfSpWsZ2dno0WLFmUdCxERERFVcByzrp1SfQ8xevRofPvtt2UdCxERERERPUUSQghtV5o0aRI2b94MHx8f+Pj4wMjISGP5kiVLyixAIiIiIqo4Im/d0sl+vDw9dbKf8laqYTAXL16Er68vAODy5csay/TlYtNuoy8/vxNRBbZnbT3sMaopdxhEsuqWE455W3PlDoNIVkFvlHryP9IDpXr1wsLCyjoOIiIiInoJCKEfhd3/Cs6dQ0REREQEYMWKFXB3d4eJiQmaNm2KU6dOFdt3586d8PPzg7W1NczNzeHr64stW7Zo9BkxYgQkSdJ4dO7cWauYSlVZb9eu3TOHuxw8eLA0myUiIiKiCk7oaa1427ZtCAwMxKpVq9C0aVMsW7YMAQEBCA8PR5UqVQr1t7W1xQcffIBatWrB2NgYu3fvxsiRI1GlShUEBASo+3Xu3BkbNmxQP1cqlVrFVapkvWC8eoGcnBycP38ely9fxvDhw0uzSSIiIiIi2SxZsgRjxozByJEjAeTfV2jPnj1Yv3493n///UL927Ztq/F80qRJ2LRpE44dO6aRrCuVSjg6OpY6rlIl60uXLi2yfe7cuUhLSyt1MERERERUsenjPOvZ2dk4e/YsZs6cqW5TKBTw9/fHiRMnnru+EAIHDx5EeHg4Fi1apLHs0KFDqFKlCmxsbNC+fXssWLAAlStXLnFsZXp58JAhQ9CkSRN8+umnZblZIiIiIiKtZGVlISsrS6NNqVQWOQwlISEBeXl5cHBw0Gh3cHDA9evXi91HSkoKXFxckJWVBQMDA6xcuRIdO3ZUL+/cuTP69OkDDw8PREZGYtasWejSpQtOnDgBAwODEh1HmSbrJ06cgImJSVlukoiIiIgqEF1V1kNCQhAcHKzRNmfOHMydO7fM9lGpUiWcP38eaWlpCA0NRWBgIDw9PdVDZAYOHKjuW79+ffj4+MDLywuHDh1Chw4dSrSPUiXrffr00XguhMCDBw9w5swZzJ49uzSbJCIiIiIqMzNnzkRgYKBGW3EXd9rZ2cHAwACxsbEa7bGxsc8cb65QKODt7Q0g/5rOa9euISQkpNB49gKenp6ws7NDRERE+SbrVlZWhQKtWbMm5s2bh06dOpVmk0RERET0EtBVZb24IS9FMTY2RuPGjREaGorevXsDAFQqFUJDQzFhwoQS71OlUhUaevO0e/fuITExEU5OTiXeZqmS9aennyEiIiIi+q8LDAzE8OHD4efnhyZNmmDZsmVIT09Xzw4zbNgwuLi4ICQkBED+MBs/Pz94eXkhKysLe/fuxZYtW/DVV18BANLS0hAcHIy+ffvC0dERkZGRmD59Ory9vTVmi3meFxqzfvbsWVy7dg0AULduXTRs2PBFNkdEREREJIsBAwYgPj4eQUFBePjwIXx9fbF//371Rad37tyBQvHPHPHp6ekYP3487t27B1NTU9SqVQvffPMNBgwYAAAwMDDAxYsXsWnTJiQnJ8PZ2RmdOnXC/PnztZprXRJCCG0PJi4uDgMHDsShQ4dgbW0NAEhOTka7du3w/fffw97eXttNlrluoy/LHQKRrPasrYc9RjXlDoNIVt1ywjFva67cYRDJKuiNMp1P5IVdi4zRyX5qe7noZD/lrVS3kHr33Xfx+PFjXLlyBUlJSUhKSsLly5eRmpqKiRMnlnWMREREREQvpVL9qbV//34cOHAAtWvXVrfVqVMHK1as4AWmRERERFQsIfTvpkj6rFSVdZVKBSMjo0LtRkZGUKlULxwUERERERGVMllv3749Jk2ahPv376vbYmJiMGXKlBLPGUlERERELx8BSSePiqJUyfry5cuRmpoKd3d3eHl5wcvLCx4eHkhNTcWXX35Z1jESEREREb2USjVm3dXVFefOncOBAwdw/fp1AEDt2rXh7+9fpsERERERUcVSkareuqBVZf3gwYOoU6cOUlNTIUkSOnbsiHfffRfvvvsuXnnlFdStWxdHjx4tr1iJiIiIiF4qWiXry5Ytw5gxY2BpaVlomZWVFd566y0sWbKkzIIjIiIiooqFY9a1o1WyfuHCBXTu3LnY5Z06dcLZs2dfOCgiIiIiItJyzHpsbGyRUzaqN2ZoiPj4+BcOioiIiIgqJs6zrh2tKusuLi64fPlyscsvXrwIJyenFw6KiIiIiIi0TNa7du2K2bNnIzMzs9CyjIwMzJkzB927dy+z4IiIiIioYlFB0smjotBqGMyHH36InTt3okaNGpgwYQJq1qwJALh+/TpWrFiBvLw8fPDBB+USKBERERHRy0arZN3BwQF//PEHxo0bh5kzZ0IIAQCQJAkBAQFYsWIFHBwcyiVQIiIiIvrvq0gzteiC1jdFcnNzw969e/Ho0SNERERACIHq1avDxsamPOIjIiIiInppleoOpgBgY2ODV155pSxjISIiIqIKjrPBaEerC0yJiIiIiEh3Sl1ZJyIiIiLSFsesa4eVdSIiIiIiPcXKOhERERHpDMesa4eVdSIiIiIiPcXKOhERERHpDMesa0f2ynpYWJjcIRARERER6SXZk/XOnTvDy8sLCxYswN27d+UOh4iIiIjKkRCSTh4VhezJekxMDCZMmIDt27fD09MTAQEB+OGHH5CdnS13aEREREREspI9Wbezs8OUKVNw/vx5nDx5EjVq1MD48ePh7OyMiRMn4sKFC3KHSERERERlRKWjR0Uhe7L+tEaNGmHmzJmYMGEC0tLSsH79ejRu3BitW7fGlStX5A6PiIiIiEin9CJZz8nJwfbt29G1a1e4ubnh119/xfLlyxEbG4uIiAi4ubmhX79+codJRERERC+IY9a1I/vUje+++y6+++47CCEwdOhQfPLJJ6hXr556ubm5OT799FM4OzvLGCURERERke7JnqxfvXoVX375Jfr06QOlUllkHzs7O07xSERERFQBcJ517cierIeGhj63j6GhIdq0aaODaIiIiIiI9IfsyToA3Lx5E2FhYYiLi4NKpXn9blBQkExREREREVFZq0jjyXVB9mR9zZo1GDduHOzs7ODo6AhJ+ucFlCSJyToRERERvbRkT9YXLFiAhQsXYsaMGXKHQkRERETljGPWtSP71I2PHj3itIxEREREREWQPVnv168ffvvtN7nDICIiIiIdUAndPCoK2YfBeHt7Y/bs2fjzzz9Rv359GBkZaSyfOHGiTJEREREREclL9mR99erVsLCwwOHDh3H48GGNZZIkMVknIiIiqkA4Zl07sifrUVFRcodARERERKSXZE/WiYiIiOjlwXnWtaMXyfq9e/fw888/486dO8jOztZYtmTJEpmiIiIiIiKSl+zJemhoKHr27AlPT09cv34d9erVQ3R0NIQQaNSokdzhEREREVEZEhVophZdkH3qxpkzZ2LatGm4dOkSTExMsGPHDty9exdt2rTh/OtERERE9FKTPVm/du0ahg0bBgAwNDRERkYGLCwsMG/ePCxatEjm6IiIiIiI5CN7sm5ubq4ep+7k5ITIyEj1soSEBLnCIiIiIqJyoIKkk0dFIfuY9WbNmuHYsWOoXbs2unbtiqlTp+LSpUvYuXMnmjVrJnd4RERERESykT1ZX7JkCdLS0gAAwcHBSEtLw7Zt21C9enXOBKNHurWzRd8AO9hYGSLqbiZWffcAN6Iyiuwb0NoG7Ztbw93FBAAQcTsDm36K1eg/uGcVvPqKFextjZCbKxBxOwObf4pFeDHbJJKb27jB8AwcBaWjPVIvXseVyfORcvrSc9dz6t8VjbYuxcP/HcDZ199RtxtXqYxaIdNg798KRtaVkHj0DK5Mno8nEbfL8zCIXohfDQktaitgYQrEPgL2ncnD/cSi+zb0ktDAUwF7q/znD5IEDl5QafQ3MgQ6+CpQy1WCqTGQnA6cClfh7E1egViRcepG7cierHt6eqr/b25ujlWrVskYDRWl9SuWGNPfEcu/uY/wWxno7V8Z8ye7Y+yHN5DyOK9Q//o1zXHkVAq+jnyA7BwVXu9ij/lT3DE+6CYSk3MBADEPs7Dq2/t4GJ8NY2MFenesjPlT3DF61g2kphXeJpGcnPp1Qe3FM3H5nTlIPnUBHhOHo+medThUtzOy45OKXc/UzQW1F81A4tHThZb57VgBVU4uzvQdj9zUNHhMHoGm+zfgiE835D3hH62kf+q4SejUSIE9p1SISRBoWkuBN9oZYMUveXiSVbi/u4OEy9Eq3E0QyM0DWtZRYEh7A3y1Ow+P/36Ld2qkgIejhJ+Oq5CcLuDlJKHrKwo8fqLCjRgm7ESAHoxZf1paWhpSU1M1HiS/1zraYf/RRzhwPBl3H2Rh+Tf3kZmtQqdWNkX2/3TtPew5lIRbdzNx72E2vtgYA4UENKhtoe5z+FQKzl9Lx8OEHNy5n4U12x7C3MwAHlVNdHVYRCXmMXkk7q77Afc27UTatUhcGj8HeU8y4Tqib/ErKRTw3fwpbs77Ek+i7mosMq/uDptmDXF5wlyknLmE9BtRuPzOXBiYmsB5YLdyPhqi0mleS4FzEQIXbgkkpAJ7TqmQk5dfQS/KT3+ocOamQOwjIDEV+OWkCpIEeDj+09/VXsKFWyrcjhNISQfORQg8fAS42LHyWpEJoZtHRSF7sh4VFYVu3brB3NwcVlZWsLGxgY2NDaytrWFjU3QySLpjaCDB280U56+mqduEAM5fS0MtT7MSbUNprICBgYTH6UVXzA0NJHR51QZpT/IQdS+zTOImKiuSkRGsGtVFQugf/zQKgYSDf8C6WcNi16v+4TvIjkvE3Q3bCy1TKI0BAKrMp8qRQkCVlQ2blo3LLHaisqJQAE62QNRDzQwo6qFA1RIm1kYGgEICMp669+HdeIEaVRWoZJr/3N1BQmVLIPKBqqxCJ/rPk30YzJAhQyCEwPr16+Hg4ABJ4l/T+sTSwgAGBhKSU3M12pNTc+HqqCzRNka+7oCk5FyNhB8AXvGphBljq0JprEBSSi4+XBLNITCkd4ztbKAwNERWnObA3KzYRJjX9CxyHZuWjeE68nUc9etd5PK067fw5HYMai6Yikvjg5CXngGPSSNg6uoEE0f7sj4EohdmpgQUCgnpmZrJenomYGdZst/bHRoq8DgDuPXgn23sP6NC96YKTOljiDyVgBDA7pMq3Ikr0/BJz4gKNFOLLsierF+4cAFnz55FzZo1S7V+VlYWsrI0B8splSVLIqn89etih1ebWOH9xVHIydX8IX/xehrenRcJSwsDdG5ti/ffckXgR5FFjoMn+q8wsDCH74ZPcOnt2chJfFRkH5Gbi7P934XP6oUIiD8NVW4uEkJPIG7fYYAFC6qAWtaRUM9NwqYDech7qmjepKYEFzsJ3x/KQ3K6gFsVCV1eUeBxhqpQFZ/oZSV7sv7KK6/g7t27pU7WQ0JCEBwcrNE2Z84cAK+XQXSUmpaHvDwBa0vNt4q1pSEepeQWs1a+Pp0q4/Uu9vjgsyhE3yt89VFWtsCDuGw8iAPCb8Vg9cLq6NTKBj/u4/z6pD+yEx5BlZsLZZXKGu1Kh8rIelj4vWru5Qozj6rw2/WVuk1S5I847JJxBYfrdsaTW3eReu4Kjvn1hqGlBRTGRshOeIQWx39AytnL5XtARKXwJAtQqQTMTSQA/yTR5iZAWsazk+rmtSW0rKvAltA8xCX/025oALRvoMAPR1S4eT9/G3HJAg42As1rS0zWKzAVX1qtyJ6sr127Fm+//TZiYmJQr149GBkZaSz38fF55vozZ85EYGCgRptSqUSfd26Weawvo9y8/GkVfWtb4M/zjwHkF/58a1lgd1gx83UB6NvZDgO62mP2smhE3C7ZOHSFJMHISPbLKIg0iJwcpJy7Arv2zRH7c2h+oyShcrvmuL3ym0L9067fwmHf7hptNYMnw7CSOa4ELkTG3Ycay3JT84eHmXm7wbpxPdyY83n5HAjRC1CpgAdJ+ReHht/7J9PycJRwOrz48eUt6khoVVeBrQfz8OBfEycpJMDAQMK/8zYh+AUT0dNkT9bj4+MRGRmJkSNHqtskSYIQApIkIS/v2UMilEolh72Us59+T0Dgm1Vx83YGbkRloJd/ZZgoFfj9eP5X/IFvuiAxORebdsYCAF7vbIchvargkzX3EJeQA5u/q/IZWSpkZqmgNJYwoFsVnLyQiqTkXFhVMkC3dpVR2cYQx86kyHacRMWJWrYBDdYvQvLZy0g5fRHuE4fD0NwUdzftBAA02LAImTGxCP9wCVRZ2Ui7olksyEnJn9nq6XbHvvnTPmbcvQ/LejVRZ8ksPPzfASQcOK67AyPSwonrKvRursD9RAn3E/OnbjQyAM7fyk+3ezXPH5N+8Hx+8t6ijoS2PgrsPK5Ccnp+FR4AsnOBnNz8f6NjBfwbKpCTq0JKuoCbgwQfDwm/neMFphUZ51nXjuzJ+ptvvomGDRviu+++4wWmeuro6VRYWTzEkF5VYGNpiFt3MxG0LBrJqfl/SNlXNtaYIqlrW1sYGSnwwfhqGtvZ+nMcvv05DioV4OpkjA4tqsHKwgCp6Xm4GZWB6YuicOd+EZP1EsnswY/7YGxvixpzJubfFOnCNZzqPhrZf190aurqBKHSLrkwcbJHncXvQ+lQGZkP4hHzzf9wc+HK8gifqExcvS1grlShbQMFLEzyb4r0bVge0v/+8tTKPL/QVsCvugKGBhL6v2qgsZ3DF1U4fCn/87LjWB46+CrwWksFTI2BlHQg7AJvikT0NEkIeWeiNDc3x4ULF+Dt7V2m2+02muM+6eW2Z2097DEq3bUgRBVFt5xwzNv67OtriCq6oDdkr81q2HsuRyf76drI6Pmd/gNkHyDcvn17XLhwQe4wiIiIiIj0jux/avXo0QNTpkzBpUuXUL9+/UIXmPbs2VOmyIiIiIiorKk4z7pWZE/W3377bQDAvHnzCi0ryQWmREREREQVlezJukrLi7KIiIiI6L9L3qsl/3tkHbOek5MDQ0NDXL7Mi0GJiIiIiP5N1sq6kZERqlWrxqEuRERERC8JzrOuHdlng/nggw8wa9YsJCUlPb8zEREREdFLRPYx68uXL0dERAScnZ3h5uYGc3NzjeXnzp2TKTIiIiIiKmsqjlnXiuzJeu/eveUOgYiIiIhIL8merM+ZM0fuEIiIiIhIRzgbjHZkH7MOAMnJyVi7di1mzpypHrt+7tw5xMTEyBwZEREREZF8ZK+sX7x4Ef7+/rCyskJ0dDTGjBkDW1tb7Ny5E3fu3MHmzZvlDpGIiIiIyojgHUy1IntlPTAwECNGjMDNmzdhYmKibu/atSuOHDkiY2RERERERPKSvbJ++vRpfP3114XaXVxc8PDhQxkiIiIiIqLywtlgtCN7ZV2pVCI1NbVQ+40bN2Bvby9DRERERERE+kH2ZL1nz56YN28ecnJyAACSJOHOnTuYMWMG+vbtK3N0RERERFSWhNDNo6KQPVn/7LPPkJaWhipVqiAjIwNt2rSBt7c3LCwssHDhQrnDIyIiIiKSjexj1q2srPD777/j+PHjuHDhAtLS0tCoUSP4+/vLHRoRERERlbGKVPXWBdkq6xkZGdi9e7f6+e7duxEREYGHDx9i7969mD59OjIzM+UKj4iIiIhIdrJV1jdt2oQ9e/age/fuAIDly5ejbt26MDU1BQBcv34dTk5OmDJlilwhEhEREVEZUwnOs64N2SrrW7duxdixYzXavv32W4SFhSEsLAyLFy/GDz/8IFN0RERERETyky1Zj4iIQP369dXPTUxMoFD8E06TJk1w9epVOUIjIiIionLC2WC0I9swmOTkZGRlZamfx8fHayxXqVQay4mIiIiIXjayVdarVq2Ky5cvF7v84sWLqFq1qg4jIiIiIqLyxsq6dmRL1rt27YqgoKAiZ3zJyMhAcHAwunXrJkNkRERERET6QbZhMLNmzcIPP/yAmjVrYsKECahRowYAIDw8HMuXL0dubi5mzZolV3hEREREVA5UFajqrQuyJesODg74448/MG7cOLz//vsQf39fIUkSOnbsiJUrV8LBwUGu8IiIiIiIZCfrHUw9PDywf/9+JCUlISIiAgDg7e0NW1tbOcMiIiIionIiOM+6VmRN1gvY2tqiSZMmcodBRERERKRX9CJZJyIiIqKXQ0WaqUUXZJsNhoiIiIhIn6xYsQLu7u4wMTFB06ZNcerUqWL77ty5E35+frC2toa5uTl8fX2xZcsWjT5CCAQFBcHJyQmmpqbw9/fHzZs3tYqJyToRERERvfS2bduGwMBAzJkzB+fOnUODBg0QEBCAuLi4Ivvb2trigw8+wIkTJ3Dx4kWMHDkSI0eOxK+//qru88knn+CLL77AqlWrcPLkSZibmyMgIKDIqcuLw2SdiIiIiHRGJXTz0NaSJUswZswYjBw5EnXq1MGqVatgZmaG9evXF9m/bdu2eO2111C7dm14eXlh0qRJ8PHxwbFjxwDkV9WXLVuGDz/8EL169YKPjw82b96M+/fvY9euXSWOi8k6EREREb3UsrOzcfbsWfj7+6vbFAoF/P39ceLEieeuL4RAaGgowsPD8eqrrwIAoqKi8PDhQ41tWllZoWnTpiXaZgFeYEpEREREOqOrC0yzsrKQlZWl0aZUKqFUKgv1TUhIQF5eXqF7/Dg4OOD69evF7iMlJQUuLi7IysqCgYEBVq5ciY4dOwIAHj58qN7Gv7dZsKwkWFknIiIiogonJCQEVlZWGo+QkJAy3UelSpVw/vx5nD59GgsXLkRgYCAOHTpUpvtgZZ2IiIiIdEZXlfWZM2ciMDBQo62oqjoA2NnZwcDAALGxsRrtsbGxcHR0LHYfCoUC3t7eAABfX19cu3YNISEhaNu2rXq92NhYODk5aWzT19e3xMfByjoRERERVThKpRKWlpYaj+KSdWNjYzRu3BihoaHqNpVKhdDQUDRv3rzE+1SpVOqhNx4eHnB0dNTYZmpqKk6ePKnVNllZJyIiIiKdKc1MLboQGBiI4cOHw8/PD02aNMGyZcuQnp6OkSNHAgCGDRsGFxcX9VCakJAQ+Pn5wcvLC1lZWdi7dy+2bNmCr776CgAgSRImT56MBQsWoHr16vDw8MDs2bPh7OyM3r17lzguJutERERE9NIbMGAA4uPjERQUhIcPH8LX1xf79+9XXyB6584dKBT/DEpJT0/H+PHjce/ePZiamqJWrVr45ptvMGDAAHWf6dOnIz09HWPHjkVycjJatWqF/fv3w8TEpMRxSUJUzJu+dht9We4QiGS1Z2097DGqKXcYRLLqlhOOeVtz5Q6DSFZBb+hXbXbNAd3sZ4z/8/v8F3DMOhERERGRntKvP7WIiIiIqEJTqeSO4L+FlXUiIiIiIj3FyjoRERER6UzFvFqy/LCyTkRERESkp1hZJyIiIiKdYWVdO6ysExERERHpKVbWiYiIiEhn9PUOpvqKlXUiIiIiIj3FyjoRERER6YzQ2aB1SUf7KV+srBMRERER6SlW1omIiIhIZzgbjHZYWSciIiIi0lOsrBMRERGRzqhUckfw38LKOhERERGRnmJlnYiIiIh0hmPWtcPKOhERERGRnmJlnYiIiIh0hncw1Q4r60REREREekoSuruNFBERERG95D7bpZvUc2rvinEH0wo7DObV147JHQKRrI781Apn2jSXOwwiWfkdPoHvjrMmRS+3QS0rRtL6sqqwyToRERER6R+hs0HrFeOPFI5ZJyIiIiLSU6ysExEREZHOcDYY7bCyTkRERESkp1hZJyIiIiKd4TyE2mFlnYiIiIhIT7GyTkREREQ6o+Kgda2wsk5EREREpKdYWSciIiIineGYde3oTbIeFxeHuLg4qFQqjXYfHx+ZIiIiIiIikpfsyfrZs2cxfPhwXLt2DeLvP7UkSYIQApIkIS8vT+YIiYiIiKissLKuHdmT9TfffBM1atTAunXr4ODgAEmqGLeGJSIiIiJ6UbIn67du3cKOHTvg7e0tdyhERERERHpF9tlgOnTogAsXLsgdBhERERHpgEoInTwqCtkr62vXrsXw4cNx+fJl1KtXD0ZGRhrLe/bsKVNkRERERETykj1ZP3HiBI4fP459+/YVWsYLTImIiIgqFqF6fh/6h+zDYN59910MGTIEDx48gEql0ngwUSciIiKil5nslfXExERMmTIFDg4OcodCREREROVMVKDx5Loge2W9T58+CAsLkzsMIiIiIiK9I3tlvUaNGpg5cyaOHTuG+vXrF7rAdOLEiTJFRkRERERlTcUx61qRPVlfu3YtLCwscPjwYRw+fFhjmSRJTNaJiIiI6KUle7IeFRUldwhEREREpCMcs64d2cesP00IwReQiIiIiOhvepGsb968GfXr14epqSlMTU3h4+ODLVu2yB0WEREREZUxldDNo6KQfRjMkiVLMHv2bEyYMAEtW7YEABw7dgxvv/02EhISMGXKFJkjJCIiIiKSh+zJ+pdffomvvvoKw4YNU7f17NkTdevWxdy5c5msExEREVUgoiKVvXVA9mEwDx48QIsWLQq1t2jRAg8ePJAhIiIiIiIi/SB7su7t7Y0ffvihUPu2bdtQvXp1GSIiIiIiovIihG4eFYXsw2CCg4MxYMAAHDlyRD1m/fjx4wgNDS0yiSciIiIielnInqz37dsXJ0+exNKlS7Fr1y4AQO3atXHq1Ck0bNhQ3uCIiIiIqEypOGZdK7In6wDQuHFjfPPNN3KHQURERESkV2RJ1lNTU0vc19LSshwjISIiIiJd4g0wtSNLsm5tbQ1JkkrUNy8vr5yjISIiIiLST7Ik62FhYer/R0dH4/3338eIESPQvHlzAMCJEyewadMmhISEyBEeEREREZUToZI7gv8WWZL1Nm3aqP8/b948LFmyBIMGDVK39ezZE/Xr18fq1asxfPhwOUIkIiIiIpKd7POsnzhxAn5+foXa/fz8cOrUKRkiIiIiIqLyohJCJ4+KQvZk3dXVFWvWrCnUvnbtWri6usoQERERERGRfpB96salS5eib9++2LdvH5o2bQoAOHXqFG7evIkdO3bIHB0RERERlSXOBqMd2SvrXbt2xY0bN9CjRw8kJSUhKSkJPXr0wI0bN9C1a1e5wyMiIiIiko3slXUgfyjMRx99JHcYRERERFTOeAdT7cierF+8eLHIdkmSYGJigmrVqkGpVOo4KiIiIiIi+cmerPv6+qpvkFQwhunpGyYZGRlhwIAB+Prrr2FiYiJLjERERERUNjhkXTuyj1n/6aefUL16daxevRoXLlzAhQsXsHr1atSsWRPffvst1q1bh4MHD+LDDz+UO1QiIiIiIp2SvbK+cOFCfP755wgICFC31a9fH1WrVsXs2bNx6tQpmJubY+rUqfj0009ljJSIiIiIXpTgmHWtyF5Zv3TpEtzc3Aq1u7m54dKlSwDyh8o8ePBA16EREREREclK9mS9Vq1a+Pjjj5Gdna1uy8nJwccff4xatWoBAGJiYuDg4CBXiERERERURngHU+3IPgxmxYoV6NmzJ6pWrQofHx8A+dX2vLw87N69GwBw69YtjB8/Xs4wiYiIiIh0TvZkvUWLFoiKisLWrVtx48YNAEC/fv0wePBgVKpUCQAwdOhQOUMkIiIiojLCMevakT1ZB4BKlSrh7bffljsMIiIiIiK9ohfJ+s2bNxEWFoa4uDioVCqNZUFBQTJFRURERERljZV17cierK9Zswbjxo2DnZ0dHB0dNW6IJEkSk3UiIiIiemnJnqwvWLAACxcuxIwZM+QOhYiIiIjKGQvr2pF96sZHjx6hX79+codBRERERKR3ZE/W+/Xrh99++03uMIiIiIhIB4RK6ORRUcg+DMbb2xuzZ8/Gn3/+ifr168PIyEhj+cSJE2WKjIiIiIhIXrIn66tXr4aFhQUOHz6Mw4cPayyTJInJOhEREVEFIirQ3UV1QfZkPSoqSu4QiIiIiIj0kuxj1omIiIiIqGiyVNYDAwMxf/58mJubIzAw8Jl9lyxZoqOo6Fle6+KEgb1dYGttjMjodHy+NhLXbqYV2bd7RwcEtK0Cz2rmAIDwyDSs2Rqt7m9gIGHMYDc0a2wDJwcTpD/JxZkLKfh6SzQSH2Xr7JiItGXfuy8cB74BI1tbPImMwN3PlyD9+tXnrmfT3h9ec+bj0dHDiPzwfXW73+ETRfa/+9VyxH6/tcziJiorp0K34vj+dUhLSYCjay10eeNDVPX0KbLv1bO/4ejur5EUdweqvFzYOrihRcBINGjRS6PPmUPf40H0FWSkp+CtuT/BqVptXR0OyURVgS7+1AVZkvW//voLOTk56v+Tfmvf0g7vjPTAZ6sicPXGY/Tr4YJPg+rhjQlnkZySU6h/w7pWCD0aj8+v30J2jgqDX6uKT+fUw/CJ55CQlA0TpQLVPc2x6Ye7iIhORyULQ0wc5YmQWbUx9r0LMhwh0fPZtOsA13cm4vaST5B+9Qoc+g1A9U+X4vKQgchNflTsesaOjnAd9y4eXyj8s+78a900nls1bQ736bPw6HBYmcdP9KIun9qLX7d9jO5D58LFswH+/H0TvlkyGhM+2gcLy8qF+puaW+HV7m/DzskTBoZGuHHhEHatnwVzS1t412sNAMjJykC16o1R95Uu+GXjbF0fEtF/gizJelhYWJH//7fHjx/rIhx6jv49XbD794fYdzAOAPDZqgg0b2yDbh0csHXnvUL95y+7ofH8k5U30aZ5ZTT2scavh+KQ/iQPU4OvaPRZtiYSqxf7ooqdEnEJWeV3MESl5NB/EBJ2/4zEfXsAALc/+wRWzVrCrmt3PPx2S9ErKRTw/DAY9zeshYVPAxhYWGgszk1K0nhu3bI1Hv91DtkP7pfLMRC9iBO/bkSjV/uhYeu+AIDuw4Jx8+Jh/HV0B1p3G1uov0etphrPm3UchvPHd+HOjXPqZL2gyv4oofDvEqq4eIGpdmQbs7506dJnLn/8+DE6d+6so2ioOIaGEmp4WeDMhWR1mxDA2YvJqFuzUom2oTQ2gKGBhNS0wlX4AuZmBlCpBNLSc180ZKIyJxkawrxGTaSePf1PoxBIPXsa5nXrFbue8/A3kZP8CAl7f3nuPgxtbGDVvGWJ+hLpWm5uNu7fvgLPOi3UbQqFAp51muNe5Pnnri+EwK2rJ5D4MApuNf3KMVKiike22WBmzZqFypUrY9iwYYWWpaWloXPnzkhMTJQhMnqaVSUjGBpIePSv4S5JyTmo5mJWom28PcwdCY+ycfaphP9pxkYS3h7mgdCj8XiSkfeiIROVOUMra0iGhsh5pFkJz32UBJNqbkWuY1HfB3Zde+Dq6MI/44pi17krVE+e4NGRQy8aLlGZe/L4EYQqr9BwF3NLOyQ8KH5Wt8wnj/HZ1DbIy82GJCnQbegceNVtWd7hkp6rSDcs0gXZkvUtW7Zg6NChsLa2Rs+ePdXt6enp6Ny5M+Lj4wvNu16UrKwsZGVpDptQKpVlHi+Vzht9qqJDKztMnH0J2TmFP5wGBhKCp9WCBOCzryN1HyBROVCYmsHjgzmI/jQEuSkpJVqncpceSDzwK0Q2L7KmisPYxBxvz/0J2VlPEHX1BH79/mPY2FctNESGiIonW7L++uuvIzk5GYMGDcKePXvQtm1bdaIeGxuLw4cPw8nJ6bnbCQkJQXBwsEbbnDlzAPiXU+Qvl5THOcjNE7Cx0ryzrK21EZKSn51UDOzlgsF9qiJwzmXcuv2k0PKCRN3B3gST51xiVZ30Vm5KMkRuLoxsbDXaDW1skZNU+BtApYsLlE7OqP7R4n8aFfmjDhuHHsXloQORdT9GvcjCpwFM3dxwK/jD8jkAohdkVskGksIAaama7/f01ARYWNkVu55CoUBlh/xvn5yq1Ub8g1s4tmc1k/WXHCvr2pH1pkijR49GUlISevXqhf/9738ICgrC/fv3cfjwYTg7O5doGzNnziw0/aNSqcTBgaeLWYO0kZsrcCMyDY19rHHsVP4QAEkCGtW3xk/7HhS73qDeLhj6uiumzbuC8MjCUzwWJOpVnU0wafYlpD7mWHXSXyI3F+k3wlGpsR+Sjx3Jb5QkWDbyQ9xP2wv1z7xzG5dHvKHR5jJqLAzMzHHny6XIjovVWGbXtQfSr19DRmREuR0D0YswNDSGs1tdRF07gdqN8othKpUKt679iSbt33jO2v8QQoXcXH57RKQN2W+KNH36dIwbNw4dOnRATEwMDh06hKpVq5Z4faVSCUtLS40Hh8GUrR9+jkH3jo7o3K4K3KqaYupbXjA1McDe0PyEY9bEGhg75J9xu4Nfc8GowW5YtPwmHsZlwtbaCLbWRjA1yX+7GRhImD+9Fmp5W2D+0hswUEjqPoaGkizHSPQ8sT98B/tuPVE5oCtM3NzgFjgdClMTJOzbDQBwnxUElzHjAAAiOxuZUbc0Hnlpach7ko7MqFsQuf/8caowM4NN2/ZI2MMLS0m/NQ8YgbOHf8T54z8h/n4k9myZi5ysDDRs1QcAsHPNDBzY/pm6/9E9XyPyynEkxd1F/P1I/LF/PS6e+Bk+zf8Z+vokLRkP7lxD/P38YZCJD6Pw4M41PE6J1+3BkU6phNDJozRWrFgBd3d3mJiYoGnTpjh16lSxfdesWYPWrVvDxsYGNjY28Pf3L9R/xIgRkCRJ46HtBCqyVdb79Omj8dzIyAh2dnaYNGmSRvvOnTt1GRYV4eDxBFhbGuHNgdVga2OMiKh0TJt3WX3RqYO9UmMapl6dnWBspMD8GZo3ttjw/R1s2HYH9rbGaNUk/yKlDUsbavSZ+OElnL9SsjG+RLr0KCwUhtY2cH5zNIxsK+NJxE3cfG8Kch/lz7GurOIAqFRab9e2Q0dAkpAU+ltZh0xUpuo16Yr0x0kI2/Ul0lLi4ehaG0OmrFEPg0lJug9J8U/BJTsrA3u2zEPqo4cwNDaBnaMH+oz5BPWadFX3CT9/EP9bP0v9fPuq/G/K2/R8B+16v6ujIyPKt23bNgQGBmLVqlVo2rQpli1bhoCAAISHh6NKlSqF+h86dAiDBg1CixYtYGJigkWLFqFTp064cuUKXFxc1P06d+6MDRs2qJ9rW1SWhEyTXY4cObJE/Z4+OG28+tqxUq1HVFEc+akVzrRpLncYRLLyO3wC3x3n+Fh6uQ1qqV/fWg8PeqiT/Wya56hV/6ZNm+KVV17B8uXLAeQP9XJ1dcW7776L999//zlrA3l5ebCxscHy5cvVsx2OGDECycnJ2LVrl9bxF5Ctsl7aJJyIiIiI6HmKmzGwqMp2dnY2zp49i5kzZ6rbFAoF/P39ceLEiRLt78mTJ8jJyYGtreZkBIcOHUKVKlVgY2OD9u3bY8GCBahcufBdf4sj+5h1IiIiInp5CCF08ggJCYGVlZXGIyQkpMiYEhISkJeXBwcHB412BwcHPHxYsm8CZsyYAWdnZ/j7/zMjYefOnbF582aEhoZi0aJFOHz4MLp06YK8vJLPgCfrbDBEREREROWhuBkDy8PHH3+M77//HocOHYKJiYm6feDAger/169fHz4+PvDy8sKhQ4fQoUOHEm2byToRERER6YxKR/OsFzfkpSh2dnYwMDBAbKzm1LqxsbFwdHz22PdPP/0UH3/8MQ4cOAAfH59n9vX09ISdnR0iIiJKnKxzGAwRERERvdSMjY3RuHFjhIaGqttUKhVCQ0PRvHnxkzV88sknmD9/Pvbv3w8/P7/n7ufevXtITEws0Y0/C7CyTkREREQ6o693MA0MDMTw4cPh5+eHJk2aYNmyZUhPT1fPYDhs2DC4uLiox70vWrQIQUFB+Pbbb+Hu7q4e225hYQELCwukpaUhODgYffv2haOjIyIjIzF9+nR4e3sjICCgxHHJkqz//PPPJe7bs2fP53ciIiIiInoBAwYMQHx8PIKCgvDw4UP4+vpi//796otO79y5A4Xin0EpX331FbKzs/H6669rbGfOnDmYO3cuDAwMcPHiRWzatAnJyclwdnZGp06dMH/+fK3GzsuSrPfu3VvjuSRJGjfVkaR/5gPV5mpZIiIiItJvMt3ip0QmTJiACRMmFLns0KFDGs+jo6OfuS1TU1P8+uuvLxyTLGPWVSqV+vHbb7/B19cX+/btQ3JyMpKTk7F37140atQI+/fvlyM8IiIiIiK9IPuY9cmTJ2PVqlVo1aqVui0gIABmZmYYO3Ysrl27JmN0RERERFSWhEoldwj/KbLPBhMZGQlra+tC7VZWVs/9eoGIiIiIqCKTPVl/5ZVXEBgYqDGvZWxsLN577z00adJExsiIiIiIqKypVEInj4pC9mR9/fr1ePDgAapVqwZvb294e3ujWrVqiImJwbp16+QOj4iIiIhINrKPWff29sbFixfx+++/4/r16wCA2rVrw9/fX2NWGCIiIiL679Pn2WD0kezJOpA/VWOnTp3w6quvQqlUMkknIiIiIoIeDINRqVSYP38+XFxcYGFhgaioKADA7NmzOQyGiIiIqIIRKqGTR0Uhe7K+YMECbNy4EZ988gmMjY3V7fXq1cPatWtljIyIiIiISF6yJ+ubN2/G6tWr8cYbb8DAwEDd3qBBA/UYdiIiIiKqGFhZ147syXpMTAy8vb0LtatUKuTk5MgQERERERGRfpD9AtM6derg6NGjcHNz02jfvn07GjZsKFNURERERFQeVIJ3MNWG7Ml6UFAQhg8fjpiYGKhUKuzcuRPh4eHYvHkzdu/eLXd4RERERESykX0YTK9evfDLL7/gwIEDMDc3R1BQEK5du4ZffvkFHTt2lDs8IiIiIipDHLOuHdkr6wDQunVr/P7773KHQURERESkV2SvrHt6eiIxMbFQe3JyMjw9PWWIiIiIiIjKCyvr2pE9WY+OjkZeXl6h9qysLMTExMgQERERERGRfpBtGMzPP/+s/v+vv/4KKysr9fO8vDyEhobC3d1dhsiIiIiIqLwIUXGq3rogW7Leu3dvAIAkSRg+fLjGMiMjI7i7u+Ozzz6TITIiIiIiIv0gW7KuUuXPsenh4YHTp0/Dzs5OrlCIiIiISEcKckAqGdlng4mKipI7BCIiIiIivSRLsv7FF19g7NixMDExwRdffPHMvhMnTtRRVERERERU3irSTC26IEuyvnTpUrzxxhswMTHB0qVLi+0nSRKTdSIiIiJ6acmSrD899IXDYIiIiIheHkJwzLo2ZJ9nnYiIiIiIiib7BaZ5eXnYuHEjQkNDERcXV+gK4YMHD8oUGRERERGRvGRP1idNmoSNGzeiW7duqFevHiRJkjskIiIiIionvMBUO7In699//z1++OEHdO3aVe5QiIiIiIj0iuzJurGxMby9veUOg4iIiIh0gJV17ch+genUqVPx+eefQwi+cERERERET5O9sn7s2DGEhYVh3759qFu3LoyMjDSW79y5U6bIiIiIiKisqTh1o1ZkT9atra3x2muvyR0GEREREZHekT1Z37Bhg9whEBEREZGOcMy6dmQfs05EREREREWTrbJuY2NT5JzqVlZWqFGjBqZNm4aOHTvKEBkRERERlReh4ph1bciWrC9btqzI9uTkZJw9exbdu3fH9u3b0aNHD90GRkRERESkJ2RL1ocPH/7M5b6+vggJCWGyTkRERFSBcMy6dvR2zHr37t1x/fp1ucMgIiIiIpKN7LPBFCcrKwvGxsZyh0FEREREZUhwnnWt6G1lfd26dfD19ZU7DCIiIiIi2chWWQ8MDCyyPSUlBefOncONGzdw5MgRHUdFREREROVJxTHrWpEtWf/rr7+KbLe0tETHjh2xc+dOeHh46DgqIiIiIiL9IVuyHhYWJteuiYiIiEgmnGddO3o7Zp2IiIiI6GWnt7PBEBEREVHFw3nWtcPKOhERERGRnmJlnYiIiIh0hvOsa4eVdSIiIiIiPcXKOhERERHpDMesa4eVdSIiIiIiPcXKOhERERHpDOdZ1w4r60REREREekoSQnDgEJWprKwshISEYObMmVAqlXKHQyQLfg6I+DkgKgtM1qnMpaamwsrKCikpKbC0tJQ7HCJZ8HNAxM8BUVngMBgiIiIiIj3FZJ2IiIiISE8xWSciIiIi0lNM1qnMKZVKzJkzhxcT0UuNnwMifg6IygIvMCUiIiIi0lOsrBMRERER6Skm60REREREeorJOhERERGRnmKyXo6io6MhSRLOnz8vdyh6Ze7cufD19ZU7jDLl7u6OZcuWyR2Gzly/fh3NmjWDiYmJ+rUsqk3X2rZti8mTJz+zz3/5tRoxYgR69+4tdxhlSpIk7Nq1S+4wKpyKfF75O4ReNkzW6T/v0KFDkCQJycnJ5b6vjRs3wtraulD76dOnMXbs2HLfv76YM2cOzM3NER4ejtDQ0GLbSou/uHSjuPdzeSguwXrw4AG6dOmikxheJjyvJcffIaTvmKxTqWRnZ8sdQpl60eOxt7eHmZlZGUWj/yIjI9GqVSu4ubmhcuXKxbZpq6K9r4pS0Y7xRY/H0dGR0/qVA30/r/wcaHrZfoeQdpislwGVSoVPPvkE3t7eUCqVqFatGhYuXKhefuvWLbRr1w5mZmZo0KABTpw4obH+sWPH0Lp1a5iamsLV1RUTJ05Eenq6evmWLVvg5+eHSpUqwdHREYMHD0ZcXJx6eUFVYM+ePfDx8YGJiQmaNWuGy5cvlyj+gr/0d+3aherVq8PExAQBAQG4e/euuk9BVWzt2rXw8PCAiYkJAODOnTvo1asXLCwsYGlpif79+yM2NlZj+x9//DEcHBxQqVIljBo1CpmZmRrLixq60Lt3b4wYMUL9PCsrCzNmzICrqyuUSiW8vb2xbt06REdHo127dgAAGxsbSJKksV5x2rZtiwkTJmDy5Mmws7NDQEAAAGDJkiWoX78+zM3N4erqivHjxyMtLU19nkeOHImUlBRIkgRJkjB37lwAhSvBJTkv+mz//v1o1aoVrK2tUblyZXTv3h2RkZEA8r9eP3v2LObNm6c+B0W1AcClS5fQvn17mJqaonLlyhg7dqz6fAL/DOtYuHAhnJ2dUbNmTbRt2xa3b9/GlClT1OcZABITEzFo0CC4uLjAzMwM9evXx3fffVco9tzcXEyYMAFWVlaws7PD7Nmz8awZapOTkzF69GjY29vD0tIS7du3x4ULF0p0ngo+F19//TVcXV1hZmaG/v37IyUl5ZnHWJJzk5eXh8DAQPVrMH369ELHUdQ3EL6+vurzX3B8b731FhwcHGBiYoJ69eph9+7dz3w/P4u7uzvmz5+PYcOGwdLSUl0NnDFjBmrUqAEzMzN4enpi9uzZyMnJAZD/MyY4OBgXLlxQ72vjxo0ACg/XeN550QWVSoWQkBB4eHjA1NQUDRo0wPbt2yGEgL+/PwICAtSvRVJSEqpWrYqgoCAAJf95/Lyf++7u7vjoo4/w5ptvolKlSqhWrRpWr16tXp6dnY0JEybAyckJJiYmcHNzQ0hIiHr50+e1RYsWmDFjhsb+4+PjYWRkhCNHjgDI/xk7bdo0uLi4wNzcHE2bNsWhQ4dKdL74O4S/Q6icCXph06dPFzY2NmLjxo0iIiJCHD16VKxZs0ZERUUJAKJWrVpi9+7dIjw8XLz++uvCzc1N5OTkCCGEiIiIEObm5mLp0qXixo0b4vjx46Jhw4ZixIgR6u2vW7dO7N27V0RGRooTJ06I5s2biy5duqiXh4WFCQCidu3a4rfffhMXL14U3bt3F+7u7iI7O/u58W/YsEEYGRkJPz8/8ccff4gzZ86IJk2aiBYtWqj7zJkzR5ibm4vOnTuLc+fOiQsXLoi8vDzh6+srWrVqJc6cOSP+/PNP0bhxY9GmTRv1etu2bRNKpVKsXbtWXL9+XXzwwQeiUqVKokGDBuo+bdq0EZMmTdKIqVevXmL48OHq5/379xeurq5i586dIjIyUhw4cEB8//33Ijc3V+zYsUMAEOHh4eLBgwciOTn5ucfcpk0bYWFhId577z1x/fp1cf36dSGEEEuXLhUHDx4UUVFRIjQ0VNSsWVOMGzdOCCFEVlaWWLZsmbC0tBQPHjwQDx48EI8fPxZCCOHm5iaWLl0qhBAlOi/6bvv27WLHjh3i5s2b4q+//hI9evQQ9evXF3l5eeLBgweibt26YurUqepzUFRbWlqacHJyEn369BGXLl0SoaGhwsPDQ+N1HT58uLCwsBBDhw4Vly9fFpcvXxaJiYmiatWqYt68eerzLIQQ9+7dE4sXLxZ//fWXiIyMFF988YUwMDAQJ0+eVG+v4HWdNGmSuH79uvjmm2+EmZmZWL16tbrP06+VEEL4+/uLHj16iNOnT4sbN26IqVOnisqVK4vExMTnnqeCz0X79u3FX3/9JQ4fPiy8vb3F4MGDn3mMJTk3ixYtEjY2NmLHjh3i6tWrYtSoUaJSpUqiV69exR6LEEI0aNBAzJkzRwiR/15s1qyZqFu3rvjtt99EZGSk+OWXX8TevXuf+X5+Fjc3N2FpaSk+/fRTERERISIiIoQQQsyfP18cP35cREVFiZ9//lk4ODiIRYsWCSGEePLkiZg6daqoW7euel9PnjwRQggBQPz0009CCFGi86ILCxYsELVq1RL79+8XkZGRYsOGDUKpVIpDhw6Je/fuCRsbG7Fs2TIhhBD9+vUTTZo0Uf9ML8nP45L83HdzcxO2trZixYoV4ubNmyIkJEQoFAr1z6rFixcLV1dXceTIEREdHS2OHj0qvv32W/X6T5/X5cuXi2rVqgmVSqVe/uWXX2q0jR49WrRo0UIcOXJEREREiMWLFwulUilu3Ljx3PPF3yH8HULli8n6C0pNTRVKpVKsWbOm0LKCZH3t2rXqtitXrggA4tq1a0IIIUaNGiXGjh2rsd7Ro0eFQqEQGRkZRe7z9OnTAoD6Q17wy+H7779X90lMTBSmpqZi27Ztzz2GDRs2CADizz//VLddu3ZNAFAnQnPmzBFGRkYiLi5O3ee3334TBgYG4s6dO4WO79SpU0IIIZo3by7Gjx+vsb+mTZtq9YM2PDxcABC///57kfEXHP+jR4+ee6xP77Nhw4bP7ffjjz+KypUrq59v2LBBWFlZFer39A/akpyX/5r4+HgBQFy6dEkIoZkQFvh32+rVq4WNjY1IS/t/e/ce0+T1xgH8S6FQ11K1yKUqtGGAFi1u4pzETCKXNcvm0Dg1pmrdzKIYAS9TtunmpvOSOZ2TZBfNFDVeNzfn7LzgBWXiJcMV5ywXXaGTaZyIGqaIts/vD9L3x7sWWi8o0+eTNGnfy3nPOZz3nNO35xzqhW1ms5kkEgldvHiRiJo6suHh4XTr1i1RWJ46oZ68/PLLNGPGDOFzcnIy6XQ6UackNzeXdDqdx7CLiopIqVRSQ0ODKNynn36avvrqK6/Xnzt3Lvn7+9P58+eFbbt27SKJRCJ8yfCURl/yRq1W08cffyzsv337NnXv3v2uOut79uwhiURC5eXlHuPfUnlujUajoaFDh3o9bsmSJZSYmCh8njt3rui+d2neqfQlX9paQ0MDPfXUU1RcXCzaPmHCBBo9ejQREW3dupVkMhm9/fbbJJfLRR1aX+pjX+p9jUZDY8aMEfY7nU4KCwujL774goiIsrKyKCUlRVTWm2uer5cuXaKAgAA6fPiwsD8pKYlyc3OJiKi6upr8/f2ppqZGFEZqaiq98847XnKM25DWcBvCHgQeBnOfrFYrbt26hdTU1BaPSUhIEN6r1WoAEIaxlJaWIj8/HwqFQngZDAY4nU7YbDYAQElJCYYMGYKoqCgEBwcjOTkZQNPPZM0lJSUJ71UqFXr06AGr1epTOgICAvDcc88Jn3v27IlOnTqJztdoNAgNDRWlPTIyEpGRkcK2+Ph40XlWqxXPP/98i/H0hcVigb+/v5DuByUxMdFt2759+5Camopu3bohODgYY8eORW1tLW7cuOFzuL7kS3tXWVmJ0aNHIzo6GkqlElqtFoB7mWuN1WpFnz59IJfLhW0DBw6E0+lEeXm5sE2v1yMwMNBreA6HA/Pnz4der4dKpYJCocCePXvc4jRgwABh6AzQVN4qKyvhcDjcwiwtLUV9fT1CQkJE96DNZhOG/XgTFRWFbt26ia7nLY3e8ubatWu4cOGC6N4JCAhAv379fIqTi8ViQffu3REXF3dX53njKR5btmzBwIEDERERAYVCgTlz5txVeQF8LzNt6ezZs7hx4wbS09NFZWLdunVCmRgxYgSGDRuGxYsX45NPPkFsbKxbOK3Vx77U+4C47fDz80NERITQdowfPx4WiwU9evRAdnY29u7d22KaQkND8eKLL2LDhg0AAJvNhqNHj8JoNAJoGnrkcDgQFxcnitOhQ4d8vg+4DWnCbQhrCwGPOgL/dR06dPB6jFQqFd67OhFOpxMAUF9fj4kTJyI7O9vtvKioKPzzzz8wGAwwGAzYsGEDQkNDYbfbYTAYHvoEneYN6IMkkUjcxuK6xroCvuXxvfh3eqqqqvDKK68gMzMTCxYsgEqlws8//4wJEyagsbHxiZr8M2TIEGg0GqxatQpdu3aF0+lE796926TM+VqulixZgs8++wzLly8XxoROnTr1vuJUX18PtVrtcWzug1wl5XG/d1wdvw8//BAGgwEdO3bE5s2bsXTp0ja5fltyjS82m82iL2EAhAmbN27cQElJCfz9/VFZWXlP12it3ndp3nYATe2Hq+3o27cvbDYbdu3ahX379mHkyJFIS0vDt99+6/GaRqMR2dnZyMvLw8aNG6HX66HX64X4+Pv7C2lqTqFQ3HX6WvK43wfchrC2wk/W71NsbCw6dOhwz0vV9e3bF2fOnEFMTIzbKzAwEGVlZaitrcXixYvxwgsvoGfPnqLJpc0dO3ZMeF9XV4eKigrodDqf4nHnzh388ssvwufy8nJcvXq11fN1Oh3+/PNP0SSiM2fO4OrVq4iPjxeOOX78eIvxBJqe+ly4cEH47HA4RJOx9Ho9nE4nDh065DEerieWnp6c3o2SkhI4nU4sXboUAwYMQFxcHP766y+3a3m7ji/50p7V1taivLwcc+bMQWpqKnQ6Herq6u46HJ1Oh9LSUtGkuSNHjkAikQiTLFviKZ+PHDmCjIwMjBkzBn369EF0dDQqKirczvVU3mJjY906IUDT/Xfx4kUEBAS43X9dunTxKZ12u11UTo4dO+Y1jd7ypmPHjlCr1aK03LlzByUlJaJw/n3vXL9+3e3J7Pnz5z3mE+BbefZFcXExNBoNZs+ejX79+iE2NhbV1dV3fa37KTMPSnx8PIKCgmC3293KhOtJ54wZMyCRSLBr1y6sWLECBw4ccAuntfrYW73vK6VSiVGjRmHVqlXYsmULtm3bhitXrng8NiMjAw0NDdi9ezc2btwoPFUHgGeffRYOhwOXLl1yi09ERIRPceE2hNsQ1na4s36fZDIZcnNzMWvWLOFn0mPHjuHrr7/26fzc3FwUFxdjypQpsFgsqKysxA8//IApU6YAaHrKEhgYiLy8PPzxxx/YsWMH5s+f7zGsefPmYf/+/Th9+jTGjx+PLl26+PwPVKRSKbKysnD8+HGUlJRg/PjxGDBgAPr379/iOWlpadDr9TAajTh58iROnDiBcePGITk5WfiZPCcnB6tXr8aaNWtQUVGBuXPn4vfffxeFk5KSArPZDLPZjLKyMmRmZorWu9VqtTCZTHjjjTewfft22Gw2FBYWYuvWrQCaflr18/PDzp078ffff9/zyhExMTG4ffu2kNfr16/Hl19+KTpGq9Wivr4e+/fvx+XLlz3+tOlLvrRnnTt3RkhICFauXImzZ8/iwIEDmD59+l2HYzQaIZPJYDKZcPr0aRw8eBBZWVkYO3YswsPDWz1Xq9Xi8OHDqKmpweXLlwE0fTEuKChAcXExrFYrJk6c6HF1BLvdjunTp6O8vBybNm1CXl4ecnJyPF4nLS0NSUlJGDp0KPbu3YuqqioUFxdj9uzZoo5Ha1xpLC0tRVFREbKzszFy5MhWOzm+5E1OTg4WL16M7du3o6ysDJMnT3ZbBzolJQXr169HUVERfvvtN5hMJtGXkuTkZAwaNAjDhw9HQUGB8CR29+7dQj57K8++iI2Nhd1ux+bNm3Hu3DmsWLEC33//vegYrVYLm80Gi8WCy5cv49atW/eUL20tODgYb731FqZNm4a1a9fi3LlzOHnyJPLy8rB27VqYzWasXr0aGzZsQHp6OmbOnAmTyeT2hba1+thbve+LZcuWYdOmTSgrK0NFRQW++eYbREREtPiLkFwux9ChQ/Hee+/BarVi9OjRwr64uDgYjUaMGzcO3333HWw2G06cOIFFixbBbDb7FB9uQ7gNYW3oEY+Zfyw4HA766KOPSKPRkFQqpaioKFq4cKEwwfTXX38Vjq2rqyMAdPDgQWHbiRMnKD09nRQKBcnlckpISKAFCxYI+zdu3EharZaCgoIoKSmJduzYIQrXNTnmxx9/pF69elFgYCD179+fSktLfYq/a8LLtm3bKDo6moKCgigtLY2qq6uFY1qaHFZdXU2vvvoqyeVyCg4OphEjRrhNBFuwYAF16dKFFAoFmUwmmjVrliisxsZGyszMJJVKRWFhYbRo0SK3mfw3b96kadOmkVqtpsDAQIqJiaHVq1cL++fNm0cRERHk5+fn08oRniYkEREtW7aM1Go1dejQgQwGA61bt85t4tGkSZMoJCSEAAgT+f490c+XfGnPCgoKSKfTUVBQECUkJFBhYaFowpovE0yJiE6dOkWDBw8mmUxGKpWK3nzzTdGKIyaTSTRh0uXo0aOUkJBAQUFB5KqmamtrKSMjgxQKBYWFhdGcOXNo3LhxovOTk5Np8uTJNGnSJFIqldS5c2d69913RZPw/v23un79OmVlZVHXrl1JKpVSZGQkGY1G0eSulrjui88//5y6du1KMpmMXnvtNbpy5YrXNHrLm9u3b1NOTg4plUrq1KkTTZ8+3S29165do1GjRpFSqaTIyEjKz893+zvU1tbS66+/TiEhISSTyah37960c+dOYb+n8tyalib/zpw5k0JCQkihUNCoUaPo008/FU2ka2hooOHDh1OnTp0IAK1Zs4aIxBMhfcmXh8HpdNLy5cupR48eJJVKKTQ0lAwGAxUWFlJ4eDgtXLhQOLaxsZESExNp5MiRROR7feyt3vc2eXjlypX0zDPPkFwuJ6VSSampqXTy5Enh2H/nKxHRTz/9RABo0KBBbmlubGyk999/n7RaLUmlUlKr1TRs2DA6deqU1/ziNuT/uA1hbcGPqJUFiNl/QmFhIQYPHoy6urp7Gmebn5+PqVOnPpT/3sbY4+SDDz7A9u3bYbFYHnVUWDtxv/XxfxG3IYy1LR4GwxhjjDHGWDvFnfUnwEsvvSRajqv5a+HChY86eg+c3W5vMb0KheKul5NjT65evXq1WI5cy+A9ToqKilq9d9iTidsQbkPYo8XDYJ4ANTU1uHnzpsd9KpUKKpXqIceobd25cwdVVVUt7tdqtQgI4FVLmXfV1dWiJeCac/3788fJzZs3UVNT0+L+mJiYhxgb1l5wGyLGbQh72LizzhhjjDHGWDvFw2AYY4wxxhhrp7izzhhjjDHGWDvFnXXGGGOMMcbaKe6sM8YYY4wx1k5xZ50xxhhjjLF2ijvrjDHGGGOMtVPcWWeMMcYYY6yd4s46Y4wxxhhj7dT/AJXH27tIF3nYAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the heatmap\n",
    "heatmap_data = ratio.pivot_table(index='Country', \n",
    "                                 values=['cheap_product_ratio', 'affortable_product_ratio', 'expensive_product_ratio'], \n",
    "                                 aggfunc='mean')\n",
    "\n",
    "heatmap_data = heatmap_data[['cheap_product_ratio', 'affortable_product_ratio', 'expensive_product_ratio']]\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(heatmap_data, annot=True, cmap=\"coolwarm\", linewidths=.5)\n",
    "plt.title('Heatmap of Average Product Ratios by Country')\n",
    "plt.xticks(rotation=0)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "________\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>CustomerID</th>\n",
       "      <th>Recency</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>MonetaryValue</th>\n",
       "      <th>Country</th>\n",
       "      <th>one_dollar_product</th>\n",
       "      <th>two_dollar_product</th>\n",
       "      <th>three_dollar_product</th>\n",
       "      <th>above_3_product</th>\n",
       "      <th>last_purchase_date</th>\n",
       "      <th>Churn_Status</th>\n",
       "      <th>Active</th>\n",
       "      <th>Churn_30days</th>\n",
       "      <th>Churn_60days</th>\n",
       "      <th>Churn_89days</th>\n",
       "      <th>total_products</th>\n",
       "      <th>cheap_product_ratio</th>\n",
       "      <th>affortable_product_ratio</th>\n",
       "      <th>expensive_product_ratio</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12413.0</td>\n",
       "      <td>66</td>\n",
       "      <td>37</td>\n",
       "      <td>676.15</td>\n",
       "      <td>France</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>2011-10-04 09:00:00</td>\n",
       "      <td>Churn_60days</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>0.243243</td>\n",
       "      <td>0.378378</td>\n",
       "      <td>0.378378</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12426.0</td>\n",
       "      <td>194</td>\n",
       "      <td>29</td>\n",
       "      <td>510.73</td>\n",
       "      <td>Germany</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>2011-05-29 12:26:00</td>\n",
       "      <td>Churn_89days</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>0.068966</td>\n",
       "      <td>0.655172</td>\n",
       "      <td>0.275862</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12427.0</td>\n",
       "      <td>11</td>\n",
       "      <td>32</td>\n",
       "      <td>582.37</td>\n",
       "      <td>Germany</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>2011-11-28 10:48:00</td>\n",
       "      <td>Active</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.312500</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12437.0</td>\n",
       "      <td>1</td>\n",
       "      <td>185</td>\n",
       "      <td>4392.66</td>\n",
       "      <td>France</td>\n",
       "      <td>59</td>\n",
       "      <td>60</td>\n",
       "      <td>27</td>\n",
       "      <td>39</td>\n",
       "      <td>2011-12-08 11:35:00</td>\n",
       "      <td>Active</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>185</td>\n",
       "      <td>0.318919</td>\n",
       "      <td>0.470270</td>\n",
       "      <td>0.210811</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12441.0</td>\n",
       "      <td>366</td>\n",
       "      <td>10</td>\n",
       "      <td>155.55</td>\n",
       "      <td>France</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2010-12-08 10:45:00</td>\n",
       "      <td>Churn_89days</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID  Recency  Frequency  MonetaryValue  Country  one_dollar_product  \\\n",
       "0     12413.0       66         37         676.15   France                   9   \n",
       "1     12426.0      194         29         510.73  Germany                   2   \n",
       "2     12427.0       11         32         582.37  Germany                   2   \n",
       "3     12437.0        1        185        4392.66   France                  59   \n",
       "4     12441.0      366         10         155.55   France                   3   \n",
       "\n",
       "   two_dollar_product  three_dollar_product  above_3_product  \\\n",
       "0                   3                    11               14   \n",
       "1                  12                     7                8   \n",
       "2                  12                     8               10   \n",
       "3                  60                    27               39   \n",
       "4                   1                     4                2   \n",
       "\n",
       "   last_purchase_date  Churn_Status  Active  Churn_30days  Churn_60days  \\\n",
       "0 2011-10-04 09:00:00  Churn_60days       0             0             1   \n",
       "1 2011-05-29 12:26:00  Churn_89days       0             0             0   \n",
       "2 2011-11-28 10:48:00        Active       1             0             0   \n",
       "3 2011-12-08 11:35:00        Active       1             0             0   \n",
       "4 2010-12-08 10:45:00  Churn_89days       0             0             0   \n",
       "\n",
       "   Churn_89days  total_products  cheap_product_ratio  \\\n",
       "0             0              37             0.243243   \n",
       "1             1              29             0.068966   \n",
       "2             0              32             0.062500   \n",
       "3             0             185             0.318919   \n",
       "4             1              10             0.300000   \n",
       "\n",
       "   affortable_product_ratio  expensive_product_ratio  Cluster  \n",
       "0                  0.378378                 0.378378        2  \n",
       "1                  0.655172                 0.275862        2  \n",
       "2                  0.625000                 0.312500        2  \n",
       "3                  0.470270                 0.210811        2  \n",
       "4                  0.500000                 0.200000        1  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_behaviour.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/x_/08j42z3j22377xv4cbjvxw880000gn/T/ipykernel_49911/3971461504.py:17: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"bo-\" (-> color='b'). The keyword argument will take precedence.\n",
      "  plt.plot(K, inertia, 'bo-', color='blue')\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "features = customer_behaviour[['Recency', 'Frequency', 'MonetaryValue', 'total_products', \n",
    "                               'cheap_product_ratio', 'affortable_product_ratio', 'expensive_product_ratio']]\n",
    "\n",
    "scaler = StandardScaler()\n",
    "scaled_features = scaler.fit_transform(features)\n",
    "\n",
    "inertia = []\n",
    "K = range(1, 12)\n",
    "\n",
    "for k in K:\n",
    "    kmeans = KMeans(n_clusters=k, random_state=42)\n",
    "    kmeans.fit(scaled_features)\n",
    "    inertia.append(kmeans.inertia_)\n",
    "\n",
    "# Plotting the elbow method result\n",
    "plt.figure(figsize=(6, 4))\n",
    "plt.plot(K, inertia, 'bo-', color='blue')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('Inertia')\n",
    "plt.title('Elbow Method For Optimal Number of Clusters')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
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        "title": {
         "text": "Customer Clusters by Frequency, Monetary Value, and Recency"
        },
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         "anchor": "y",
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          0,
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          "text": "Frequency"
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     },
     "metadata": {},
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   ],
   "source": [
    "kmeans = KMeans(n_clusters=4, random_state=42)\n",
    "customer_behaviour['Cluster'] = kmeans.fit_predict(scaled_features)\n",
    "\n",
    "fig = px.scatter(customer_behaviour, \n",
    "                 x='Frequency', \n",
    "                 y='MonetaryValue', \n",
    "                 color='Cluster', \n",
    "                 size='Recency',\n",
    "                 hover_data=['CustomerID'],\n",
    "                 title='Customer Clusters by Frequency, Monetary Value, and Recency')\n",
    "\n",
    "# Show the plot\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Recency</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>MonetaryValue</th>\n",
       "      <th>cheap_product_ratio</th>\n",
       "      <th>affortable_product_ratio</th>\n",
       "      <th>expensive_product_ratio</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cluster</th>\n",
       "      <th></th>\n",
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       "      <th>0</th>\n",
       "      <td>138.748252</td>\n",
       "      <td>37.608392</td>\n",
       "      <td>970.043487</td>\n",
       "      <td>0.103022</td>\n",
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       "      <td>98.543558</td>\n",
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       "      <td>802.097902</td>\n",
       "      <td>0.487295</td>\n",
       "      <td>0.349232</td>\n",
       "      <td>0.163473</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>63.839136</td>\n",
       "      <td>124.433671</td>\n",
       "      <td>2459.590031</td>\n",
       "      <td>0.182843</td>\n",
       "      <td>0.580946</td>\n",
       "      <td>0.236211</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>5896.750000</td>\n",
       "      <td>64271.327500</td>\n",
       "      <td>0.222294</td>\n",
       "      <td>0.454492</td>\n",
       "      <td>0.323214</td>\n",
       "      <td>3.0</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "            Recency    Frequency  MonetaryValue  cheap_product_ratio  \\\n",
       "Cluster                                                                \n",
       "0        138.748252    37.608392     970.043487             0.103022   \n",
       "1         98.543558    51.771779     802.097902             0.487295   \n",
       "2         63.839136   124.433671    2459.590031             0.182843   \n",
       "3          1.000000  5896.750000   64271.327500             0.222294   \n",
       "\n",
       "         affortable_product_ratio  expensive_product_ratio  Cluster  \n",
       "Cluster                                                              \n",
       "0                        0.322383                 0.574596      0.0  \n",
       "1                        0.349232                 0.163473      1.0  \n",
       "2                        0.580946                 0.236211      2.0  \n",
       "3                        0.454492                 0.323214      3.0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_columns = customer_behaviour.select_dtypes(include=['number']).columns.drop(['CustomerID', 'Active', \n",
    "                                                                                     'Churn_30days', 'Churn_60days', 'Churn_89days',\n",
    "                                                                                     'one_dollar_product', 'two_dollar_product', \n",
    "                                                                                     'three_dollar_product', 'above_3_product', 'total_products'])\n",
    "\n",
    "cluster_summary = customer_behaviour.groupby('Cluster')[numeric_columns].mean()\n",
    "cluster_summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cluster 0: High-value but disengaged shoppers. Focus on reactivation.\n",
    "\n",
    "Cluster 1: Budget-conscious, moderately engaged shoppers. Focus on promoting affordable products.\n",
    "\n",
    "Cluster 2: Loyal, frequent shoppers with moderate-high spending. Focus on loyalty programs.\n",
    "\n",
    "Cluster 3: Elite, extremely high-value customers. Focus on VIP treatment and exclusivity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "customer_behaviour.to_csv('customer_behaviour.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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