customer.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Customer Behaviour Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image](image.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## a) Business Understanding\n",
    "\n",
    "In the competitive landscape of retail and e-commerce, understanding customer shopping preferences is pivotal for businesses striving to enhance their customer experience, optimize product offerings, and tailor their marketing strategies. By gaining insights into consumer behavior, businesses can make informed decisions that drive customer satisfaction and loyalty, ultimately leading to increased sales and market share.\n",
    "\n",
    "---------------------------------------------------------\n",
    "## b) Problem  statement\n",
    "\n",
    "In the dynamic and highly competitive retail and e-commerce landscape, businesses must continuously adapt to changing consumer behaviors and preferences to remain relevant and profitable. Traditional approaches to understanding customer needs often fall short in providing the granular insights necessary for personalized marketing and optimized product offerings. As a result, businesses risk losing market share to more agile competitors who leverage data-driven strategies.\n",
    "\n",
    "#### **Prevailing Circumstances?**\n",
    "The retail and e-commerce sector is highly competitive, requiring businesses to continuously adapt to evolving consumer behaviors and preferences to remain relevant and profitable.\n",
    "\n",
    "#### **Problem Being Addressed?**\n",
    "The business lacks deep, actionable insights into customer shopping preferences, hindering effective customer segmentation, identification of purchase drivers, optimization of product offerings, and enhancement of customer satisfaction.\n",
    "\n",
    "#### **How the Project Aims to Solve the Problem?**\n",
    "The project aims to analyze the Customer Shopping Preferences Dataset to:\n",
    "\n",
    "i) Segment customers based on their shopping behaviors.<br>\n",
    "ii) Identify key drivers of customer purchases.<br>\n",
    "iii) Evaluate the impact of promotions on customer behavior.<br>\n",
    "iv) Determine factors influencing customer satisfaction.\n",
    "\n",
    "-----------------------------------------------------------------------------\n",
    "## c) Objectives\n",
    "#### Main Objective\n",
    "To gain actionable insights into customer shopping preferences to inform business strategies, enhance customer satisfaction, and optimize product offerings.\n",
    "\n",
    "#### Specific Objectives\n",
    "i) Segment Customers: Group customers based on their shopping behaviors to enable targeted marketing.<br>\n",
    "ii) Identify Purchase Drivers: Determine factors influencing customer purchasing decisions.<br>\n",
    "iii) Evaluate Promotions: Assess the impact of discounts and promotional offers on customer behavior.<br>\n",
    "iv) Enhance Satisfaction: Identify key factors contributing to customer satisfaction and positive review ratings.\n",
    "\n",
    "## d) Data Understanding\n",
    "\n",
    "The data used in this project was obtained from: [Kaggle](https://www.kaggle.com/datasets/iamsouravbanerjee/customer-shopping-trends-dataset).\n",
    "\n",
    "This dataset includes features such as age, gender, purchase amount, preferred payment methods, frequency of purchases, feedback ratings, item types, shopping frequency, preferred shopping seasons, and interactions with promotional offers. It contains 3900 records, providing essential information for businesses to enhance their understanding of customer behavior, enabling data-driven decision-making and customer-centric strategies."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder \n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "\n",
    "from xgboost import XGBRegressor\n",
    "\n",
    "import xgboost as xgb\n",
    "from tensorflow.keras.wrappers.scikit_learn import KerasRegressor\n",
    "\n",
    "\n",
    "from warnings import filterwarnings\n",
    "filterwarnings(action='ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Customer ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Item Purchased</th>\n",
       "      <th>Category</th>\n",
       "      <th>Purchase Amount (USD)</th>\n",
       "      <th>Location</th>\n",
       "      <th>Size</th>\n",
       "      <th>Color</th>\n",
       "      <th>Season</th>\n",
       "      <th>Review Rating</th>\n",
       "      <th>Subscription Status</th>\n",
       "      <th>Shipping Type</th>\n",
       "      <th>Discount Applied</th>\n",
       "      <th>Promo Code Used</th>\n",
       "      <th>Previous Purchases</th>\n",
       "      <th>Payment Method</th>\n",
       "      <th>Frequency of Purchases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "      <td>Male</td>\n",
       "      <td>Blouse</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>53</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>L</td>\n",
       "      <td>Gray</td>\n",
       "      <td>Winter</td>\n",
       "      <td>3.1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Express</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>14</td>\n",
       "      <td>Venmo</td>\n",
       "      <td>Fortnightly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>Sweater</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>64</td>\n",
       "      <td>Maine</td>\n",
       "      <td>L</td>\n",
       "      <td>Maroon</td>\n",
       "      <td>Winter</td>\n",
       "      <td>3.1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Express</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Cash</td>\n",
       "      <td>Fortnightly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>50</td>\n",
       "      <td>Male</td>\n",
       "      <td>Jeans</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>73</td>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>S</td>\n",
       "      <td>Maroon</td>\n",
       "      <td>Spring</td>\n",
       "      <td>3.1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Free Shipping</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>23</td>\n",
       "      <td>Credit Card</td>\n",
       "      <td>Weekly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "      <td>Male</td>\n",
       "      <td>Sandals</td>\n",
       "      <td>Footwear</td>\n",
       "      <td>90</td>\n",
       "      <td>Rhode Island</td>\n",
       "      <td>M</td>\n",
       "      <td>Maroon</td>\n",
       "      <td>Spring</td>\n",
       "      <td>3.5</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Next Day Air</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>49</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>Weekly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>45</td>\n",
       "      <td>Male</td>\n",
       "      <td>Blouse</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>49</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>M</td>\n",
       "      <td>Turquoise</td>\n",
       "      <td>Spring</td>\n",
       "      <td>2.7</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Free Shipping</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>31</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>Annually</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>46</td>\n",
       "      <td>Male</td>\n",
       "      <td>Sneakers</td>\n",
       "      <td>Footwear</td>\n",
       "      <td>20</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>M</td>\n",
       "      <td>White</td>\n",
       "      <td>Summer</td>\n",
       "      <td>2.9</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>14</td>\n",
       "      <td>Venmo</td>\n",
       "      <td>Weekly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>63</td>\n",
       "      <td>Male</td>\n",
       "      <td>Shirt</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>85</td>\n",
       "      <td>Montana</td>\n",
       "      <td>M</td>\n",
       "      <td>Gray</td>\n",
       "      <td>Fall</td>\n",
       "      <td>3.2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Free Shipping</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>49</td>\n",
       "      <td>Cash</td>\n",
       "      <td>Quarterly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>27</td>\n",
       "      <td>Male</td>\n",
       "      <td>Shorts</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>34</td>\n",
       "      <td>Louisiana</td>\n",
       "      <td>L</td>\n",
       "      <td>Charcoal</td>\n",
       "      <td>Winter</td>\n",
       "      <td>3.2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Free Shipping</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>19</td>\n",
       "      <td>Credit Card</td>\n",
       "      <td>Weekly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>26</td>\n",
       "      <td>Male</td>\n",
       "      <td>Coat</td>\n",
       "      <td>Outerwear</td>\n",
       "      <td>97</td>\n",
       "      <td>West Virginia</td>\n",
       "      <td>L</td>\n",
       "      <td>Silver</td>\n",
       "      <td>Summer</td>\n",
       "      <td>2.6</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Express</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>8</td>\n",
       "      <td>Venmo</td>\n",
       "      <td>Annually</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>57</td>\n",
       "      <td>Male</td>\n",
       "      <td>Handbag</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>31</td>\n",
       "      <td>Missouri</td>\n",
       "      <td>M</td>\n",
       "      <td>Pink</td>\n",
       "      <td>Spring</td>\n",
       "      <td>4.8</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2-Day Shipping</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4</td>\n",
       "      <td>Cash</td>\n",
       "      <td>Quarterly</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer ID  Age Gender Item Purchased     Category  Purchase Amount (USD)  \\\n",
       "0            1   55   Male         Blouse     Clothing                     53   \n",
       "1            2   19   Male        Sweater     Clothing                     64   \n",
       "2            3   50   Male          Jeans     Clothing                     73   \n",
       "3            4   21   Male        Sandals     Footwear                     90   \n",
       "4            5   45   Male         Blouse     Clothing                     49   \n",
       "5            6   46   Male       Sneakers     Footwear                     20   \n",
       "6            7   63   Male          Shirt     Clothing                     85   \n",
       "7            8   27   Male         Shorts     Clothing                     34   \n",
       "8            9   26   Male           Coat    Outerwear                     97   \n",
       "9           10   57   Male        Handbag  Accessories                     31   \n",
       "\n",
       "        Location Size      Color  Season  Review Rating Subscription Status  \\\n",
       "0       Kentucky    L       Gray  Winter            3.1                 Yes   \n",
       "1          Maine    L     Maroon  Winter            3.1                 Yes   \n",
       "2  Massachusetts    S     Maroon  Spring            3.1                 Yes   \n",
       "3   Rhode Island    M     Maroon  Spring            3.5                 Yes   \n",
       "4         Oregon    M  Turquoise  Spring            2.7                 Yes   \n",
       "5        Wyoming    M      White  Summer            2.9                 Yes   \n",
       "6        Montana    M       Gray    Fall            3.2                 Yes   \n",
       "7      Louisiana    L   Charcoal  Winter            3.2                 Yes   \n",
       "8  West Virginia    L     Silver  Summer            2.6                 Yes   \n",
       "9       Missouri    M       Pink  Spring            4.8                 Yes   \n",
       "\n",
       "    Shipping Type Discount Applied Promo Code Used  Previous Purchases  \\\n",
       "0         Express              Yes             Yes                  14   \n",
       "1         Express              Yes             Yes                   2   \n",
       "2   Free Shipping              Yes             Yes                  23   \n",
       "3    Next Day Air              Yes             Yes                  49   \n",
       "4   Free Shipping              Yes             Yes                  31   \n",
       "5        Standard              Yes             Yes                  14   \n",
       "6   Free Shipping              Yes             Yes                  49   \n",
       "7   Free Shipping              Yes             Yes                  19   \n",
       "8         Express              Yes             Yes                   8   \n",
       "9  2-Day Shipping              Yes             Yes                   4   \n",
       "\n",
       "  Payment Method Frequency of Purchases  \n",
       "0          Venmo            Fortnightly  \n",
       "1           Cash            Fortnightly  \n",
       "2    Credit Card                 Weekly  \n",
       "3         PayPal                 Weekly  \n",
       "4         PayPal               Annually  \n",
       "5          Venmo                 Weekly  \n",
       "6           Cash              Quarterly  \n",
       "7    Credit Card                 Weekly  \n",
       "8          Venmo               Annually  \n",
       "9           Cash              Quarterly  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer = pd.read_csv('shopping_data.csv')\n",
    "customer.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Customer ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Item Purchased</th>\n",
       "      <th>Category</th>\n",
       "      <th>Purchase Amount (USD)</th>\n",
       "      <th>Location</th>\n",
       "      <th>Size</th>\n",
       "      <th>Color</th>\n",
       "      <th>Season</th>\n",
       "      <th>Review Rating</th>\n",
       "      <th>Subscription Status</th>\n",
       "      <th>Shipping Type</th>\n",
       "      <th>Discount Applied</th>\n",
       "      <th>Promo Code Used</th>\n",
       "      <th>Previous Purchases</th>\n",
       "      <th>Payment Method</th>\n",
       "      <th>Frequency of Purchases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3890</th>\n",
       "      <td>3891</td>\n",
       "      <td>35</td>\n",
       "      <td>Female</td>\n",
       "      <td>Shirt</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>81</td>\n",
       "      <td>Nebraska</td>\n",
       "      <td>XL</td>\n",
       "      <td>Green</td>\n",
       "      <td>Winter</td>\n",
       "      <td>2.6</td>\n",
       "      <td>No</td>\n",
       "      <td>Standard</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>33</td>\n",
       "      <td>Debit Card</td>\n",
       "      <td>Annually</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3891</th>\n",
       "      <td>3892</td>\n",
       "      <td>36</td>\n",
       "      <td>Female</td>\n",
       "      <td>Dress</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>30</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>L</td>\n",
       "      <td>Peach</td>\n",
       "      <td>Winter</td>\n",
       "      <td>4.7</td>\n",
       "      <td>No</td>\n",
       "      <td>Free Shipping</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>6</td>\n",
       "      <td>Bank Transfer</td>\n",
       "      <td>Quarterly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3892</th>\n",
       "      <td>3893</td>\n",
       "      <td>35</td>\n",
       "      <td>Female</td>\n",
       "      <td>Jewelry</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>86</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>L</td>\n",
       "      <td>Indigo</td>\n",
       "      <td>Summer</td>\n",
       "      <td>3.5</td>\n",
       "      <td>No</td>\n",
       "      <td>Standard</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>5</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>Fortnightly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3893</th>\n",
       "      <td>3894</td>\n",
       "      <td>21</td>\n",
       "      <td>Female</td>\n",
       "      <td>Hat</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>64</td>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>L</td>\n",
       "      <td>White</td>\n",
       "      <td>Fall</td>\n",
       "      <td>3.3</td>\n",
       "      <td>No</td>\n",
       "      <td>Store Pickup</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>29</td>\n",
       "      <td>Bank Transfer</td>\n",
       "      <td>Bi-Weekly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3894</th>\n",
       "      <td>3895</td>\n",
       "      <td>66</td>\n",
       "      <td>Female</td>\n",
       "      <td>Skirt</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>78</td>\n",
       "      <td>Connecticut</td>\n",
       "      <td>L</td>\n",
       "      <td>White</td>\n",
       "      <td>Spring</td>\n",
       "      <td>3.9</td>\n",
       "      <td>No</td>\n",
       "      <td>2-Day Shipping</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>44</td>\n",
       "      <td>Credit Card</td>\n",
       "      <td>Every 3 Months</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3895</th>\n",
       "      <td>3896</td>\n",
       "      <td>40</td>\n",
       "      <td>Female</td>\n",
       "      <td>Hoodie</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>28</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>L</td>\n",
       "      <td>Turquoise</td>\n",
       "      <td>Summer</td>\n",
       "      <td>4.2</td>\n",
       "      <td>No</td>\n",
       "      <td>2-Day Shipping</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>32</td>\n",
       "      <td>Venmo</td>\n",
       "      <td>Weekly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3896</th>\n",
       "      <td>3897</td>\n",
       "      <td>52</td>\n",
       "      <td>Female</td>\n",
       "      <td>Backpack</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>49</td>\n",
       "      <td>Iowa</td>\n",
       "      <td>L</td>\n",
       "      <td>White</td>\n",
       "      <td>Spring</td>\n",
       "      <td>4.5</td>\n",
       "      <td>No</td>\n",
       "      <td>Store Pickup</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>41</td>\n",
       "      <td>Bank Transfer</td>\n",
       "      <td>Bi-Weekly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3897</th>\n",
       "      <td>3898</td>\n",
       "      <td>46</td>\n",
       "      <td>Female</td>\n",
       "      <td>Belt</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>33</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>L</td>\n",
       "      <td>Green</td>\n",
       "      <td>Spring</td>\n",
       "      <td>2.9</td>\n",
       "      <td>No</td>\n",
       "      <td>Standard</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>24</td>\n",
       "      <td>Venmo</td>\n",
       "      <td>Quarterly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3898</th>\n",
       "      <td>3899</td>\n",
       "      <td>44</td>\n",
       "      <td>Female</td>\n",
       "      <td>Shoes</td>\n",
       "      <td>Footwear</td>\n",
       "      <td>77</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>S</td>\n",
       "      <td>Brown</td>\n",
       "      <td>Summer</td>\n",
       "      <td>3.8</td>\n",
       "      <td>No</td>\n",
       "      <td>Express</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>24</td>\n",
       "      <td>Venmo</td>\n",
       "      <td>Weekly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3899</th>\n",
       "      <td>3900</td>\n",
       "      <td>52</td>\n",
       "      <td>Female</td>\n",
       "      <td>Handbag</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>81</td>\n",
       "      <td>California</td>\n",
       "      <td>M</td>\n",
       "      <td>Beige</td>\n",
       "      <td>Spring</td>\n",
       "      <td>3.1</td>\n",
       "      <td>No</td>\n",
       "      <td>Store Pickup</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>33</td>\n",
       "      <td>Venmo</td>\n",
       "      <td>Quarterly</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Customer ID  Age  Gender Item Purchased     Category  \\\n",
       "3890         3891   35  Female          Shirt     Clothing   \n",
       "3891         3892   36  Female          Dress     Clothing   \n",
       "3892         3893   35  Female        Jewelry  Accessories   \n",
       "3893         3894   21  Female            Hat  Accessories   \n",
       "3894         3895   66  Female          Skirt     Clothing   \n",
       "3895         3896   40  Female         Hoodie     Clothing   \n",
       "3896         3897   52  Female       Backpack  Accessories   \n",
       "3897         3898   46  Female           Belt  Accessories   \n",
       "3898         3899   44  Female          Shoes     Footwear   \n",
       "3899         3900   52  Female        Handbag  Accessories   \n",
       "\n",
       "      Purchase Amount (USD)       Location Size      Color  Season  \\\n",
       "3890                     81       Nebraska   XL      Green  Winter   \n",
       "3891                     30       Colorado    L      Peach  Winter   \n",
       "3892                     86       Michigan    L     Indigo  Summer   \n",
       "3893                     64  Massachusetts    L      White    Fall   \n",
       "3894                     78    Connecticut    L      White  Spring   \n",
       "3895                     28       Virginia    L  Turquoise  Summer   \n",
       "3896                     49           Iowa    L      White  Spring   \n",
       "3897                     33     New Jersey    L      Green  Spring   \n",
       "3898                     77      Minnesota    S      Brown  Summer   \n",
       "3899                     81     California    M      Beige  Spring   \n",
       "\n",
       "      Review Rating Subscription Status   Shipping Type Discount Applied  \\\n",
       "3890            2.6                  No        Standard               No   \n",
       "3891            4.7                  No   Free Shipping               No   \n",
       "3892            3.5                  No        Standard               No   \n",
       "3893            3.3                  No    Store Pickup               No   \n",
       "3894            3.9                  No  2-Day Shipping               No   \n",
       "3895            4.2                  No  2-Day Shipping               No   \n",
       "3896            4.5                  No    Store Pickup               No   \n",
       "3897            2.9                  No        Standard               No   \n",
       "3898            3.8                  No         Express               No   \n",
       "3899            3.1                  No    Store Pickup               No   \n",
       "\n",
       "     Promo Code Used  Previous Purchases Payment Method Frequency of Purchases  \n",
       "3890              No                  33     Debit Card               Annually  \n",
       "3891              No                   6  Bank Transfer              Quarterly  \n",
       "3892              No                   5         PayPal            Fortnightly  \n",
       "3893              No                  29  Bank Transfer              Bi-Weekly  \n",
       "3894              No                  44    Credit Card         Every 3 Months  \n",
       "3895              No                  32          Venmo                 Weekly  \n",
       "3896              No                  41  Bank Transfer              Bi-Weekly  \n",
       "3897              No                  24          Venmo              Quarterly  \n",
       "3898              No                  24          Venmo                 Weekly  \n",
       "3899              No                  33          Venmo              Quarterly  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3900 entries, 0 to 3899\n",
      "Data columns (total 18 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   Customer ID             3900 non-null   int64  \n",
      " 1   Age                     3900 non-null   int64  \n",
      " 2   Gender                  3900 non-null   object \n",
      " 3   Item Purchased          3900 non-null   object \n",
      " 4   Category                3900 non-null   object \n",
      " 5   Purchase Amount (USD)   3900 non-null   int64  \n",
      " 6   Location                3900 non-null   object \n",
      " 7   Size                    3900 non-null   object \n",
      " 8   Color                   3900 non-null   object \n",
      " 9   Season                  3900 non-null   object \n",
      " 10  Review Rating           3900 non-null   float64\n",
      " 11  Subscription Status     3900 non-null   object \n",
      " 12  Shipping Type           3900 non-null   object \n",
      " 13  Discount Applied        3900 non-null   object \n",
      " 14  Promo Code Used         3900 non-null   object \n",
      " 15  Previous Purchases      3900 non-null   int64  \n",
      " 16  Payment Method          3900 non-null   object \n",
      " 17  Frequency of Purchases  3900 non-null   object \n",
      "dtypes: float64(1), int64(4), object(13)\n",
      "memory usage: 548.6+ KB\n"
     ]
    }
   ],
   "source": [
    "customer.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Customer ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Purchase Amount (USD)</th>\n",
       "      <th>Review Rating</th>\n",
       "      <th>Previous Purchases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3900.000000</td>\n",
       "      <td>3900.000000</td>\n",
       "      <td>3900.000000</td>\n",
       "      <td>3900.000000</td>\n",
       "      <td>3900.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1950.500000</td>\n",
       "      <td>44.068462</td>\n",
       "      <td>59.764359</td>\n",
       "      <td>3.749949</td>\n",
       "      <td>25.351538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1125.977353</td>\n",
       "      <td>15.207589</td>\n",
       "      <td>23.685392</td>\n",
       "      <td>0.716223</td>\n",
       "      <td>14.447125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>975.750000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>3.100000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1950.500000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2925.250000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>38.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3900.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Customer ID          Age  Purchase Amount (USD)  Review Rating  \\\n",
       "count  3900.000000  3900.000000            3900.000000    3900.000000   \n",
       "mean   1950.500000    44.068462              59.764359       3.749949   \n",
       "std    1125.977353    15.207589              23.685392       0.716223   \n",
       "min       1.000000    18.000000              20.000000       2.500000   \n",
       "25%     975.750000    31.000000              39.000000       3.100000   \n",
       "50%    1950.500000    44.000000              60.000000       3.700000   \n",
       "75%    2925.250000    57.000000              81.000000       4.400000   \n",
       "max    3900.000000    70.000000             100.000000       5.000000   \n",
       "\n",
       "       Previous Purchases  \n",
       "count         3900.000000  \n",
       "mean            25.351538  \n",
       "std             14.447125  \n",
       "min              1.000000  \n",
       "25%             13.000000  \n",
       "50%             25.000000  \n",
       "75%             38.000000  \n",
       "max             50.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## e) Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Age Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  figure and axes\n",
    "plt.figure(figsize=(8, 4))\n",
    "\n",
    "# Plot the histogram\n",
    "sns.histplot(customer['Age'], bins=20, kde=True, color='skyblue', edgecolor='black')\n",
    "\n",
    "# labels and title\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Frequency of purchase')\n",
    "plt.title('Age Distribution of Customers')\n",
    "\n",
    "# Show plot\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribution of review ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#  figure and axes\n",
    "plt.figure(figsize=(8, 4))\n",
    "\n",
    "# Plot the count plot\n",
    "sns.countplot(x='Review Rating', data=customer, palette='viridis')\n",
    "\n",
    "# Customize labels and title\n",
    "plt.xlabel('Review Rating')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Distribution of Review Ratings')\n",
    "\n",
    "# Show plot\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Distribution of customer satisfaction levels based on their review ratings."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Most reviews and items with fewer reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Group by 'Item Purchased' and count reviews\n",
    "item_review_counts = customer.groupby('Item Purchased')['Review Rating'].count().sort_values(ascending=False)\n",
    "\n",
    "# Plotting items with most reviews\n",
    "plt.figure(figsize=(10, 5))\n",
    "item_review_counts.head(10).plot(kind='bar', color='skyblue')\n",
    "plt.xlabel('Item Purchased')\n",
    "plt.ylabel('Number of Reviews')\n",
    "plt.title('Items with Most Reviews')\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Plotting items with less reviews\n",
    "plt.figure(figsize=(10, 5))\n",
    "item_review_counts.tail(10).plot(kind='bar', color='lightgreen')\n",
    "plt.xlabel('Item Purchased')\n",
    "plt.ylabel('Number of Reviews')\n",
    "plt.title('Items with Fewest Reviews')\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The first plot shows the top 10 items that have received the most reviews, indicating which products are most popular or have generated the most feedback.\n",
    "- The second plot displays the 10 items with the fewest reviews, highlighting products that may have lower customer engagement or visibility."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Purchases made for each item category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Group by 'Category' and count purchases\n",
    "category_purchase_counts = customer['Category'].value_counts().sort_values(ascending=False)\n",
    "\n",
    "# Plotting\n",
    "plt.figure(figsize=(10, 5))\n",
    "category_purchase_counts.plot(kind='bar', color='skyblue')\n",
    "plt.xlabel('Item Category')\n",
    "plt.ylabel('Number of Purchases')\n",
    "plt.title('Most Frequently Purchased Item Categories')\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " The visualization provides insights into customer preferences and allows businesses to focus their marketing and inventory strategies accordingly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Purchases made during each season."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Group by 'Season' and count purchases\n",
    "season_purchase_counts = customer['Season'].value_counts().sort_values(ascending=False)\n",
    "\n",
    "# Plotting\n",
    "plt.figure(figsize=(10, 5))\n",
    "season_purchase_counts.plot(kind='bar', color='skyblue')\n",
    "plt.xlabel('Season')\n",
    "plt.ylabel('Number of Purchases')\n",
    "plt.title('Most Popular Shopping Seasons')\n",
    "plt.xticks(rotation=0)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Businesses can use this information to plan marketing campaigns, adjust inventory levels, and tailor promotions to match peak shopping seasons."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribution of payment methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Group by 'Payment Method' and count transactions\n",
    "payment_method_counts = customer['Payment Method'].value_counts()\n",
    "\n",
    "# Plotting a pie chart\n",
    "plt.figure(figsize=(6, 6))\n",
    "plt.pie(payment_method_counts, labels=payment_method_counts.index, autopct='%1.1f%%', startangle=140, colors=plt.cm.tab20.colors)\n",
    "plt.title('Distribution of Payment Methods')\n",
    "plt.axis('equal')  \n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- This visualization helps identify which payment methods are most preferred by customers, indicating their payment preferences.\n",
    "- Businesses can use this information to optimize their checkout processes, offer preferred payment options, and enhance customer satisfaction."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Correlations between numerical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvQAAAJNCAYAAABeGeGrAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAACFFUlEQVR4nOzdd1gU1xoG8HfpRUCQbkFsFLGAFYxi77ErlqCo0aiJRtFEsQRLFDVqjL0RsUXRWGPBXgOxgsSIigZjg4BSrPS5f3jZuO6Cu7gDLLy/55nn3j1zZuabCYMf3545IxEEQQAREREREWkkreIOgIiIiIiICo8JPRERERGRBmNCT0RERESkwZjQExERERFpMCb0REREREQajAk9EREREZEGY0JPRERERKTBmNATEREREWkwJvRERERERBqMCT0ViejoaAwdOhSOjo4wMDBAuXLl4OHhgYULFyI5Obm4w5Nx5swZSCQSnDlzRuVtb968iZkzZ+L+/fty6/z8/FC1atWPjq8wJBIJvvrqK4Xrfv3110Kfr7Jev36NmTNninqMkiQ5ORn9+/eHtbU1JBIJevTokW/fli1bQiKRoGPHjnLr7t+/D4lEgkWLFokYrbhCQkIgkUgU3hPqoOz9mtdP0dKnTx9RYnvy5AlmzpyJqKgoUfZPRJRHp7gDoNJv/fr1GDNmDJycnPDNN9/A1dUVWVlZuHLlCtasWYOIiAjs3bu3uMNUi5s3b2LWrFlo2bKlXPI+Y8YMfP3118UTWDF7/fo1Zs2aBeBtAlvazZkzB3v37sXPP/+M6tWrw8LC4oPbHD16FKdOnULr1q2LIMKi06VLF0RERMDOzq64QwEAzJs3D61atZJpq1ChgijHevLkCWbNmoWqVauifv36ohyDiAhgQk8ii4iIwOjRo9GuXTvs27cP+vr60nXt2rXDxIkTERYWppZjvX79GkZGRnLtOTk5yM7Oljl2cahevXqxHp+Kzo0bN1C9enUMGjRIqf61atVCdnY2vv32W1y+fBkSiUTkCMX35s0bGBgYwMrKClZWVsUdjlTNmjXRtGnT4g7jo5SU32lEVHJwyA2Jat68eZBIJFi3bp3Cf3z09PTQrVs36efc3FwsXLgQzs7O0NfXh7W1NQYPHoxHjx7JbNeyZUu4ubnh3Llz8PLygpGREYYNGyYdorBw4UJ8//33cHR0hL6+Pk6fPg0AuHLlCrp16wYLCwsYGBjA3d0dO3fu/OB5XLlyBf3790fVqlVhaGiIqlWrYsCAAfjnn3+kfUJCQtC3b18AQKtWraRf54eEhABQPOQmPT0dAQEBcHR0hJ6eHipWrIgvv/wSqampMv2qVq2Krl27IiwsDB4eHjA0NISzszN+/vnnD8ZeWMpcq6SkJIwZMwaurq4oV64crK2t0bp1a5w/f17a5/79+9KEbtasWdLr4ufnBwCYOXMmJBIJoqOj0bdvX5iZmcHCwgL+/v7Izs7G7du30bFjR5iYmKBq1apYuHChTAzp6emYOHEi6tevL93W09MT+/fvlzunvKFHa9euRa1ataCvrw9XV1fs2LFDqWuSnJyMMWPGoGLFitDT00O1atUwbdo0ZGRkSM9VIpHgxIkTiImJkZ7rh4aD6OrqYu7cubh69SpCQ0ML7Jt3vd6naGhL3s/NwYMH4e7uDkNDQ7i4uODgwYPSbVxcXGBsbIzGjRvjypUrcvtV5ucg79jHjh3DsGHDYGVlBSMjI2RkZOQ75CYsLAxt2rSBmZkZjIyM4OLigqCgIJnjfuieE8OJEyfQpk0bmJqawsjICM2aNcPJkydl+ty9exdDhw5FzZo1YWRkhIoVK+LTTz/Fn3/+Ke1z5swZNGrUCAAwdOhQ6c/CzJkzAbz9Habo26r3f0+o43fa69evMWnSJOmQRwsLCzRs2BDbt29XwxUjopKAFXoSTU5ODk6dOoUGDRqgcuXKSm0zevRorFu3Dl999RW6du2K+/fvY8aMGThz5gyuXbsGS0tLad/4+Hh89tln+PbbbzFv3jxoaf339+myZctQq1YtLFq0CKampqhZsyZOnz6Njh07okmTJlizZg3MzMywY8cO+Pj44PXr19IEU5H79+/DyckJ/fv3h4WFBeLj47F69Wo0atQIN2/ehKWlJbp06YJ58+Zh6tSpWLlyJTw8PADkX5kXBAE9evTAyZMnERAQgObNmyM6OhqBgYGIiIhARESEzB9B169fx8SJEzFlyhTY2Nhgw4YNGD58OGrUqIEWLVp88NoKgoDs7Gy59tzcXLk2Za9V3vMPgYGBsLW1xcuXL7F37160bNkSJ0+eRMuWLWFnZ4ewsDB07NgRw4cPx+effw4AclXbfv364bPPPsMXX3yB48ePY+HChcjKysKJEycwZswYTJo0Cb/88gsmT56MGjVqoFevXgCAjIwMJCcnY9KkSahYsSIyMzNx4sQJ9OrVCxs3bsTgwYNljnPgwAGcPn0as2fPhrGxMVatWoUBAwZAR0enwLHU6enpaNWqFe7du4dZs2ahbt26OH/+PIKCghAVFYVDhw7Bzs4OERERGDNmDNLS0rBt2zYAgKur6wf/+/j4+GDRokWYPn06evfuDV1d3Q9uo4zr168jICAA06ZNg5mZGWbNmoVevXohICAAJ0+elP7RPXnyZHTt2hVxcXEwNDQEoPzPQZ5hw4ahS5cu2LJlC169epXvOQQHB2PEiBHw9vbGmjVrYG1tjTt37uDGjRvSPsrcc4WRm5srdx/o6Lz9p3Dr1q0YPHgwunfvjk2bNkFXVxdr165Fhw4dcPToUbRp0wbA26E0FSpUwPz582FlZYXk5GRs2rQJTZo0QWRkJJycnODh4YGNGzdi6NChmD59Orp06QIAqFSpUqHi/pjfaf7+/tiyZQu+//57uLu749WrV7hx4waePXtWqFiIqAQSiESSkJAgABD69++vVP+YmBgBgDBmzBiZ9osXLwoAhKlTp0rbvL29BQDCyZMnZfrGxcUJAITq1asLmZmZMuucnZ0Fd3d3ISsrS6a9a9eugp2dnZCTkyMIgiCcPn1aACCcPn0631izs7OFly9fCsbGxsJPP/0kbd+1a1e+2w4ZMkRwcHCQfg4LCxMACAsXLpTpFxoaKgAQ1q1bJ21zcHAQDAwMhH/++Ufa9ubNG8HCwkL44osv8o0zD4APLu/GrOy1UnRdsrKyhDZt2gg9e/aUticlJQkAhMDAQLltAgMDBQDC4sWLZdrr168vABD27NkjbcvKyhKsrKyEXr165XuueTEMHz5ccHd3l7sOhoaGQkJCgkx/Z2dnoUaNGvnuUxAEYc2aNQIAYefOnTLtCxYsEAAIx44dk7Z5e3sLtWvXLnB/ivqeOHFCACAsX75cEIT/fp5/+OEHaf+86/W+jRs3CgCEuLg4aZuDg4NgaGgoPHr0SNoWFRUlABDs7OyEV69eSdv37dsnABAOHDggbVP25yDv2IMHD/5gXC9evBBMTU2FTz75RMjNzVXqGglC/vecMvfru/0ULbGxscKrV68ECwsL4dNPP5XZLicnR6hXr57QuHHjAmPLzMwUatasKUyYMEHafvnyZQGAsHHjRrltvL29BW9vb7n2939PqON3mpubm9CjR4984ycizcchN1Ri5H2F/H7Vr3HjxnBxcZH72tvc3DzfBwi7desmUx28e/cubt26JR3TnJ2dLV06d+6M+Ph43L59O9/YXr58Ka0M6+joQEdHB+XKlcOrV68QExNTmNPFqVOnAMifb9++fWFsbCx3vvXr10eVKlWknw0MDFCrVi2lhyD069cPly9fllsWLFgg00/Va7VmzRp4eHjAwMAAOjo60NXVxcmTJ1W+Ll27dpX57OLiAolEgk6dOknbdHR0UKNGDblz3rVrF5o1a4Zy5cpJYwgODlYYQ5s2bWBjYyP9rK2tDR8fH9y9e1duaNe7Tp06BWNjY7kqft5/v/f/exVGmzZt0L59e8yePRsvXrz46P0Bb39uKlasKP3s4uIC4O2Qj3efOclrz7u2hblnevfu/cF4wsPD8fz5c4wZM6bAZwXEuOcAYMGCBXL3QOXKlREeHo7k5GQMGTJE5lxzc3PRsWNHXL58Ga9evZJei3nz5sHV1RV6enrQ0dGBnp4eYmNjPyq2gnzM77TGjRvjyJEjmDJlCs6cOYM3b96IEiMRFR8OuSHRWFpawsjICHFxcUr1z/v6V9FsGPb29nJJXEGzZry/7t9//wUATJo0CZMmTVK4zdOnT/Pd38CBA3Hy5EnMmDEDjRo1gqmpKSQSCTp37lzofxyfPXsGHR0duaEnEokEtra2cl+HK5qJQ19fX+njW1lZoWHDhnLt749tVuVaLVmyBBMnTsSoUaMwZ84cWFpaQltbGzNmzFA5sXl/Jhg9PT0YGRnBwMBArv358+fSz3v27EG/fv3Qt29ffPPNN7C1tYWOjg5Wr16t8BkDW1vbfNuePXuW75CIZ8+ewdbWVi4Jtba2ho6OjtqGLyxYsAAeHh5YtGgRhg4d+tH7U3RdC2pPT08HULh7RpmZbJKSkgB8eOiJGPccAFSrVk3hfZB3vgUNu0pOToaxsTH8/f2xcuVKTJ48Gd7e3jA3N4eWlhY+//xz0ZLlj/mdtmzZMlSqVAmhoaFYsGABDAwM0KFDB/zwww+oWbOmKPESUdFiQk+i0dbWRps2bXDkyBE8evTog/+A5yWs8fHxcn2fPHkiN2a2oOre++vytg0ICJCOvX6fk5OTwva0tDQcPHgQgYGBmDJlirQ9b+x2YVWoUAHZ2dlISkqSSeoFQUBCQoL0gbqipsq12rp1K1q2bInVq1fLrFdXdVkZW7duhaOjI0JDQ2X+u+c9qPq+hISEfNsKmr6wQoUKuHjxIgRBkDlOYmIisrOzCz2m+33169fHgAEDsGTJEnTu3Flufd4fOBkZGTLPWBT0B2lhFOaeUWZ2nryf9YK+DRHrnitI3vkuX74831lw8r7ZyRtrP2/ePJn1T58+Rfny5ZU6noGBAdLS0uTa8/vv+DG/04yNjTFr1izMmjUL//77r7Ra/+mnn+LWrVtKxUtEJRuH3JCoAgICIAgCRowYgczMTLn1WVlZ+O233wBAOnxm69atMn0uX76MmJgY6QNpheHk5ISaNWvi+vXraNiwocLFxMRE4bYSiQSCIMjN0rNhwwbk5OTItOX1UaZKl3c+75/v7t278erVq48634+hyrWSSCRy1yU6OhoREREybapcF1VJJBLo6enJJDwJCQkKZ7kB3g6NyatuAm8f3g4NDUX16tUL/KOzTZs2ePnyJfbt2yfTvnnzZul6dfn++++RmZkpnbv/XXkzoERHR8u0591H6vIx90xBvLy8YGZmhjVr1kAQBIV9VLnn1KVZs2YoX748bt68me/55n2Loejn/tChQ3j8+LFMW0E/91WrVsWdO3dk/vB89uwZwsPDlYq3sP99bGxs4OfnhwEDBuD27dt4/fq1UscjopKNFXoSlaenJ1avXo0xY8agQYMGGD16NGrXro2srCxERkZi3bp1cHNzw6effgonJyeMHDkSy5cvh5aWFjp16iSd5aZy5cqYMGHCR8Wydu1adOrUCR06dICfnx8qVqyI5ORkxMTE4Nq1a9i1a5fC7UxNTdGiRQv88MMPsLS0RNWqVXH27FkEBwfLVePc3NwAAOvWrYOJiQkMDAzg6OiosPLbrl07dOjQAZMnT8bz58/RrFkz6Sw37u7u8PX1/ajz/RjKXquuXbtizpw5CAwMhLe3N27fvo3Zs2fD0dFRZiYRExMTODg4YP/+/WjTpg0sLCyk1/Jjde3aFXv27MGYMWPQp08fPHz4EHPmzIGdnR1iY2Pl+ltaWqJ169aYMWOGdJabW7dufXDqysGDB2PlypUYMmQI7t+/jzp16uDChQuYN28eOnfujLZt2370ueRxdHTE6NGj8dNPP8mt69y5MywsLDB8+HDMnj0bOjo6CAkJwcOHD9V2/DyFvWcKUq5cOSxevBiff/452rZtixEjRsDGxgZ3797F9evXsWLFCpXuOXUpV64cli9fjiFDhiA5ORl9+vSBtbU1kpKScP36dSQlJUm/ieratStCQkLg7OyMunXr4urVq/jhhx/k/iCsXr06DA0NsW3bNri4uKBcuXKwt7eHvb09fH19sXbtWnz22WcYMWIEnj17hoULF8LU1FTpmJX979OkSRN07doVdevWhbm5OWJiYrBlyxZ4enoqfHcHEWmg4nwil8qOqKgoYciQIUKVKlUEPT09wdjYWHB3dxe+++47ITExUdovJydHWLBggVCrVi1BV1dXsLS0FD777DPh4cOHMvvLbxYRRbOCvOv69etCv379BGtra0FXV1ewtbUVWrduLaxZs0baR9GsGY8ePRJ69+4tmJubCyYmJkLHjh2FGzduCA4ODsKQIUNkjrF06VLB0dFR0NbWlpnh4v3ZKwTh7Uw1kydPFhwcHARdXV3Bzs5OGD16tJCSkiLTz8HBQejSpYvc+eQ3U8b7AAhffvmlwnX5zcyjzLXKyMgQJk2aJFSsWFEwMDAQPDw8hH379ik81xMnTgju7u6Cvr6+AEB63fJmbUlKSpLpP2TIEMHY2FjhOb//337+/PlC1apVBX19fcHFxUVYv369wtlg8q7DqlWrhOrVqwu6urqCs7OzsG3btoIun9SzZ8+EUaNGCXZ2doKOjo7g4OAgBAQECOnp6R+MMT/59U1KShJMTU0V/jxfunRJ8PLyEoyNjYWKFSsKgYGBwoYNGxTOcqPo50bRz0N+944yPwd5M9lcvnxZ7liKZt8RBEE4fPiw4O3tLRgbGwtGRkaCq6ursGDBAul6Ze85VWe52bVrV4H9zp49K3Tp0kWwsLAQdHV1hYoVKwpdunSR2S4lJUUYPny4YG1tLRgZGQmffPKJcP78eYX34/bt2wVnZ2dBV1dXbqanTZs2CS4uLoKBgYHg6uoqhIaG5jvLzcf8TpsyZYrQsGFDwdzcXNDX1xeqVasmTJgwQXj69GmB14KINIdEEPL5zpOIqJSRSCT48ssvsWLFiuIOhYiISG04hp6IiIiISIMxoSciIiIi0mB8KJaIygyOMCQiotKIFXoiIiIiKpPOnTuHTz/9FPb29pBIJHJTEyty9uxZNGjQAAYGBqhWrRrWrFkj12f37t1wdXWFvr4+XF1dsXfvXhGi/w8TeiIiIiIqk169eoV69eopPVlCXFwcOnfujObNmyMyMhJTp07FuHHjsHv3bmmfiIgI+Pj4wNfXF9evX4evry/69euHixcvinUa4Cw3RERERFQqZGRkyL0pXF9fX+5lcIpIJBLs3bsXPXr0yLfP5MmTceDAAcTExEjbRo0ahevXr0tfqujj44Pnz5/jyJEj0j4dO3aEubk5tm/fruIZKafEjKE/pOv04U5EZZDTrbDiDoGoRBL4JTORQjWrOxR3CHKKKs+7PG2A3Fu2AwMDMXPmTLXsPyIiAu3bt5dp69ChA4KDg5GVlQVdXV1ERETIvQyzQ4cOWLp0qVpiUKTEJPRERERERB8jICAA/v7+Mm3KVOeVlZCQABsbG5k2GxsbZGdn4+nTp7Czs8u3T0JCgtrieB8TeiIiIiISlURXUiTHUXZ4zceQSGTPJW/0+rvtivq836ZO/L6SiIiIiEgJtra2cpX2xMRE6OjooEKFCgX2eb9qr05M6ImIiIhIVFo6kiJZxObp6Ynjx4/LtB07dgwNGzaErq5ugX28vLxEi4tDboiIiIioTHr58iXu3r0r/RwXF4eoqChYWFigSpUqCAgIwOPHj7F582YAb2e0WbFiBfz9/TFixAhEREQgODhYZvaar7/+Gi1atMCCBQvQvXt37N+/HydOnMCFCxdEOw8m9EREREQkKoluyRwUcuXKFbRq1Ur6Oe+B2iFDhiAkJATx8fF48OCBdL2joyMOHz6MCRMmYOXKlbC3t8eyZcvQu3dvaR8vLy/s2LED06dPx4wZM1C9enWEhoaiSZMmop1HiZmHntNWEinGaSuJFOO0lUSKlcRpK49WqF0kx+nw7K8iOU5Jw9+GREREREQajENuiIiIiEhURfHAalnGCj0RERERkQZjhZ6IiIiIRFVUL5Yqq1ihJyIiIiLSYKzQExEREZGoOIZeXKzQExERERFpMFboiYiIiEhUHEMvLlboiYiIiIg0GCv0RERERCQqjqEXFyv0REREREQajBV6IiIiIhKVRJsVejGxQk9EREREpMFYoSciIiIiUWmxQi8qVuiJiIiIiDQYK/REREREJCqJFiv0YmKFnoiIiIhIg7FCT0RERESikmizhiwmXl0iIiIiIg3GCj0RERERiYqz3IiLFXoiIiIiIg3GCj0RERERiYqz3IiLFXoiIiIiIg3GCj0RERERiYpj6MXFCj0RERERkQZjhZ6IiIiIRCVhhV5UrNATEREREWkwVuiJiIiISFQSLdaQxcSrS0RERESkwVihJyIiIiJRcR56cbFCT0RERESkwVihJyIiIiJRcR56cbFCT0RERESkwVihJyIiIiJRcQy9uFihJyIiIiLSYKzQExEREZGoOA+9uHh1iYiIiIg0GBN6IiIiIiINxiE3RERERCQqPhQrLlboiYiIiIg0GCv0RERERCQqvlhKXKzQExERERFpMFboiYiIiEhUHEMvLlboiYiIiIg0GCv0RERERCQqvlhKXLy6REREREQajBV6IiIiIhIVx9CLixV6IiIiIiINxgo9EREREYmKFXpxsUJPRERERKTBWKEnIiIiIlGxQi8uVuiJiIiIiDQYK/REREREJCrOQy8uXl0iIiIiIg3GCj0RERERiUpLm2PoxcQKPRERERGVWatWrYKjoyMMDAzQoEEDnD9/Pt++fn5+kEgkckvt2rWlfUJCQhT2SU9PF+0cmNATERERkagkWpIiWVQVGhqK8ePHY9q0aYiMjETz5s3RqVMnPHjwQGH/n376CfHx8dLl4cOHsLCwQN++fWX6mZqayvSLj4+HgYFBoa6dMjjkhoiIiIhKhYyMDGRkZMi06evrQ19fX2H/JUuWYPjw4fj8888BAEuXLsXRo0exevVqBAUFyfU3MzODmZmZ9PO+ffuQkpKCoUOHyvSTSCSwtbX92NNRGiv0RERERCQqiZZWkSxBQUHSpDtvUZSYA0BmZiauXr2K9u3by7S3b98e4eHhSp1XcHAw2rZtCwcHB5n2ly9fwsHBAZUqVULXrl0RGRlZuAunJFboiYiIiKhUCAgIgL+/v0xbftX5p0+fIicnBzY2NjLtNjY2SEhI+OCx4uPjceTIEfzyyy8y7c7OzggJCUGdOnXw/Plz/PTTT2jWrBmuX7+OmjVrqnhGymFCT0RERESiKqo3xRY0vCY/EolsbIIgyLUpEhISgvLly6NHjx4y7U2bNkXTpk2ln5s1awYPDw8sX74cy5YtUyk2ZXHIDRERERGVOZaWltDW1parxicmJspV7d8nCAJ+/vln+Pr6Qk9Pr8C+WlpaaNSoEWJjYz865nyPIdqeiYiIiIhQMme50dPTQ4MGDXD8+HGZ9uPHj8PLy6vAbc+ePYu7d+9i+PDhHzyOIAiIioqCnZ2dSvGpgkNuiIiIiKhM8vf3h6+vLxo2bAhPT0+sW7cODx48wKhRowC8HZP/+PFjbN68WWa74OBgNGnSBG5ubnL7nDVrFpo2bYqaNWvi+fPnWLZsGaKiorBy5UrRzoMJPRERERGJSqJVMgeF+Pj44NmzZ5g9ezbi4+Ph5uaGw4cPS2etiY+Pl5uTPi0tDbt378ZPP/2kcJ+pqakYOXIkEhISYGZmBnd3d5w7dw6NGzcW7TwkgiAIou1dBYd0nYo7BKISyelWWHGHQFQiCRw1SqRQzeoOH+5UxB6M6lUkx6myZk+RHKekYYWeiIiIiERVVLPclFUsbxARERERaTBW6ImIiIhIVCV1DH1pUeirm5mZidu3byM7O1ud8RARERERkQpUTuhfv36N4cOHw8jICLVr15Y++Ttu3DjMnz9f7QESERERkYaTSIpmKaNUTugDAgJw/fp1nDlzBgYGBtL2tm3bIjQ0VK3BERERERFRwVQeQ79v3z6EhoaiadOmkLzzl5Crqyvu3bun1uCIiIiISPNxlhtxqVyhT0pKgrW1tVz7q1evZBJ8IiIiIiISn8oJfaNGjXDo0CHp57wkfv369fD09FRfZERERERE9EEqD7kJCgpCx44dcfPmTWRnZ+Onn37CX3/9hYiICJw9e1aMGKkIWHzSENUmDoeZhxsM7K1xpfcY/HvgZHGHRSQqQRDwy7atCAs7gpcvX8LJyQmjx3wJB4eqBW73+4UL2LJlM+Lj42FnZ4fBQ4bAy6uZdP2NP//E7t2/4u7dWCQnJ2P69O/g6eUl8tkQFc7b+2ALjoYdxsuXL1HLyRmjx3ylxH1wHlu3bJLeB75D/ODl9Yl0/c7Q7YgI/x2PHj2Enp4eXFxc4Tfsc1SqVBkAkJ2djS2bQ3Dl8iUkJMTD2NgY9ep7wG/ocFSoUEHMU6ZiwGkrxaXy1fXy8sLvv/+O169fo3r16jh27BhsbGwQERGBBg0aiBEjFQFtYyM8j76Nv76eXdyhEBWZX3/dhb1792LU6DH4cekymJtbYPq0qXj9+nW+28TE3MT8+fPQunVrrFi5Cq1bt8b8oHm4deuWtE96ejocHR0xavSYojgNoo+y+9ed2Ld3D0aN/gpLli6Hubk5Zkyb8sH7YMH8uWjVug2Wr1yNVq3bYEHQXNy+FSPtc+PGn+jStRsWLfkJc+bOR05OLmZMC0B6+hsAQEZGBu7djUX/AYPw0/JVmDo9EE8eP8KcWd+Jfs5EpU2hXixVp04dbNq0Sd2xUDFKOnoOSUfPFXcYREVGEATs37cXPv37o1mzt1VF/4kTMWjgAJw9cxqdOndRuN3+ffvg7u6Bfj79AQCVffrjzxt/Yv/+vXB2DgAANGzUCA0bNSqaEyH6CP/dBwPgJb0PvsFnA31w9swpdOrcVeF2B/bt/f99MAAAUNmnCm78/z741tkFADB7zjyZbcb7T8SgAf1wNzYWbnXqwtjYGN/PWyDT54vRX8J//FgkJiYqfF6PNBcfihWXyhX658+fK1xevHiBzMxMMWIkIlK7hIQEpKSkwMPDQ9qmq6sHtzp1EBMTk+92t27FwP2dbQDAw6MBYm7mvw1RSfVvQgJSUpLh7vHfN+xv74O6iIm5me92t27dlNkGyLsP8t/m1atXAIByJib59nn9/wk2ypUzVvYUiAiFqNCXL1++wNlsKlWqBD8/PwQGBkIrn/FSGRkZyMjIkGnLEnKhK+H4KiIqGikpKQCA8uXNZdrLlzdHUuK/BW5n/t425uXNpfsj0iQpKckAFN0H5ZGYmFjAdikK75387gNBELBh/Vq41nZD1aqOCvtkZmYiZGMwvFu2gpERE/rShmPoxaVyQh8SEoJp06bBz88PjRs3hiAIuHz5MjZt2oTp06cjKSkJixYtgr6+PqZOnapwH0FBQZg1a5ZM2wCJBQZpWxbuLIiIPuD06VNYsXyZ9PPMWW+fF5GrTwjCh982+N5qAUJZfkEhaZDTp09i5fKfpJ8DZ30PQP5H/u1tUPAP9fvrC7oP1qxagftxcVi4aInC9dnZ2Vg4fy4EQcCYL8d+4CyI6H0qJ/SbNm3C4sWL0a9fP2lbt27dUKdOHaxduxYnT55ElSpVMHfu3HwT+oCAAPj7+8u0nbLgA7VEJJ4mTZrCyclZ+jkr6+0QwZSUFFhY/DejRmpaqlwF/l3m5vJVyNTUVLlqJVFJ1KSJ53v3QRYA+fsgLS0V5cuXz3c/b++DZJm2tHzugzWrV+LixQjMX7gYlpZWcuuzs7MxP+h7JPz7L+YFLWR1vpTiGHpxqfz9R0REBNzd3eXa3d3dERERAQD45JNP8ODBg3z3oa+vD1NTU5mFw22ISExGRkawt7eXLlWqOMDc3ByR1yKlfbKysnDjzz/h4uKS736cnV0QFRkp0xZ57RpcXPPfhqikeHsfVJQub+8DC0Reuybt8/Y+iIaLi2u++3F2dkVk5DWZtshrV+Hi+t82giBg9aoVCA+/gLlBP8DW1k5uP3nJ/JMnjzF33nyYmpqq4SyJyh6Vs+hKlSohODhYrj04OBiVK7+dW/bZs2cwN2e1SpNoGxvBtJ4zTOu9rdwYOVaCaT1nGFSW/wVMVBpIJBJ079ETO3fuQHj477h//z5+XLIY+vr68G7ZStpv8aIfELLxZ+nnbt174Nq1q9i1aycePnyIXbt2IioqEt2795T2efPmDe7du4d79+4BABL+TcC9e/cKHJNMVBzy7oNdO7cjPPwC7t+Pw9Ili/5/H7SW9lu8aCFCNv73b3+37j0Qee0qft0ViocPH+DXXaFy98HqVctx5vRJfPNtAIwMDZGSnIyU5GTpM3Q5OTkImjcHd2PvYNI3U5Cbkyvtk/fNAZUeEi1JkSxllcpDbhYtWoS+ffviyJEjaNSoESQSCS5fvoyYmBjs3r0bAHD58mX4+PioPVgSj1kDN3ie3CL97Lro7XCph5v3IHp4QHGFRSSqPn36IjMjA6tWrvj/i6WcMef7eTAyMpL2SUpKlPlHwtXVFZOnBGDL5k3YumUzbO3sMHlKAJyd/xvGEBt7BwFTJks/b1i/DgDQpm1b+PtPKoIzI1Je7z79kJGRgdUrV+DlyxdwcnLG7O+D5O4DrXfuAxfX2vh2ylRs3RyCrVs2/f8+mAYn5/++qTp86CAAIGCy7M/8+AmT0LZdezx9moSLf7z9Zn/cV6Nl+syb/wPq1q2n9nMlKq0kgiAIqm70zz//YPXq1bhz5w4EQYCzszO++OILpKamon79+oUK5JCuU6G2IyrtnG6FFXcIRCWSoPqXzERlQs3qDsUdgpzEaX5FchzruSFFcpySplAvlnJwcMD8+fMBvH0YbNu2bejduzeioqKQk5Oj1gCJiIiIiCh/hS5vnDp1Cp999hns7e2xYsUKdOrUCVeuXFFnbERERERUCkgkkiJZyiqVKvSPHj1CSEgIfv75Z7x69Qr9+vVDVlYWdu/eDVfX/J+GJyIiIiIicShdoe/cuTNcXV1x8+ZNLF++HE+ePMHy5cvFjI2IiIiISgGJllaRLGWV0hX6Y8eOYdy4cRg9ejRq1qwpZkxERERERKQkpf+UOX/+PF68eIGGDRuiSZMmWLFiBZKSksSMjYiIiIhKAc5DLy6lE3pPT0+sX78e8fHx+OKLL7Bjxw5UrFgRubm5OH78OF68eCFmnEREREREpIDKg42MjIwwbNgwXLhwAX/++ScmTpyI+fPnw9raGt26dRMjRiIiIiLSZFpaRbOUUR915k5OTli4cCEePXqE7du3qysmIiIiIiJSUqFeLPU+bW1t9OjRAz169FDH7oiIiIioFCnL49uLQtn9boKIiIiIqBRQS4WeiIiIiCg/EglryGLi1SUiIiIi0mCs0BMRERGRuDiGXlSs0BMRERERaTBW6ImIiIhIVJIyPEd8UeDVJSIiIiLSYKzQExEREZGoOA+9uFihJyIiIiLSYKzQExEREZG4OA+9qHh1iYiIiIg0GCv0RERERCQqjqEXFyv0REREREQajBV6IiIiIhIX56EXFa8uEREREZEGY4WeiIiIiEQlkXAMvZhYoSciIiIi0mBM6ImIiIiINBiH3BARERGRuPhQrKh4dYmIiIiINBgr9EREREQkKr5YSlys0BMRERERaTBW6ImIiIhIXBLWkMXEq0tEREREpMGY0BMRERGRuLQkRbMUwqpVq+Do6AgDAwM0aNAA58+fz7fvmTNnIJFI5JZbt27J9Nu9ezdcXV2hr68PV1dX7N27t1CxKYsJPRERERGVSaGhoRg/fjymTZuGyMhING/eHJ06dcKDBw8K3O727duIj4+XLjVr1pSui4iIgI+PD3x9fXH9+nX4+vqiX79+uHjxomjnIREEQRBt7yo4pOtU3CEQlUhOt8KKOwSiEklgTYpIoZrVHYo7BDmv1k4rkuMYfzFXpf5NmjSBh4cHVq9eLW1zcXFBjx49EBQUJNf/zJkzaNWqFVJSUlC+fHmF+/Tx8cHz589x5MgRaVvHjh1hbm6O7du3qxSfsvjbkIiIiIhKhYyMDDx//lxmycjIUNg3MzMTV69eRfv27WXa27dvj/Dw8AKP4+7uDjs7O7Rp0wanT5+WWRcRESG3zw4dOnxwnx+DCT0RERERiauIxtAHBQXBzMxMZlFUaQeAp0+fIicnBzY2NjLtNjY2SEhIULiNnZ0d1q1bh927d2PPnj1wcnJCmzZtcO7cOWmfhIQElfapDpy2koiIiIhKhYCAAPj7+8u06evrF7iNRCL7MK0gCHJteZycnODk9N8wcU9PTzx8+BCLFi1CixYtCrVPdWBCT0RERESikmgVzaAQfX39DybweSwtLaGtrS1XOU9MTJSrsBekadOm2Lp1q/Szra3tR+9TVRxyQ0RERERljp6eHho0aIDjx4/LtB8/fhxeXl5K7ycyMhJ2dnbSz56ennL7PHbsmEr7VBUr9EREREQkLhGHm3wMf39/+Pr6omHDhvD09MS6devw4MEDjBo1CsDbITyPHz/G5s2bAQBLly5F1apVUbt2bWRmZmLr1q3YvXs3du/eLd3n119/jRYtWmDBggXo3r079u/fjxMnTuDChQuinQcTeiIiIiIqk3x8fPDs2TPMnj0b8fHxcHNzw+HDh+Hg8Hbqz/j4eJk56TMzMzFp0iQ8fvwYhoaGqF27Ng4dOoTOnTtL+3h5eWHHjh2YPn06ZsyYgerVqyM0NBRNmjQR7Tw4Dz1RCcd56IkU4zz0RIqVxHnoX4fMKpLjGPkFFslxShr+NiQiIiIi0mAcckNERERE4iqhY+hLC1boiYiIiIg0GCv0RERERCSqopqHvqzi1SUiIiIi0mCs0BMRERGRuCSsIYuJV5eIiIiISIOxQk9ERERE4tLiLDdiYoWeiIiIiEiDsUJPRERERKKScAy9qHh1iYiIiIg0GCv0RERERCQujqEXFSv0REREREQajBV6IiIiIhIXx9CLileXiIiIiEiDsUJPREREROKScAy9mFihJyIiIiLSYKzQExEREZG4tFhDFhOvLhERERGRBmNCT0RERESkwTjkhoiIiIjExWkrRcWrS0RERESkwVihJyIiIiJxaXHaSjGxQk9EREREpMFYoSciIiIicXEMvah4dYmIiIiINBgr9EREREQkLgnH0IuJFXoiIiIiIg3GCj0RERERiUuLNWQx8eoSEREREWkwVuiJiIiISFwcQy8qVuiJiIiIiDQYK/REREREJC7OQy8qXl0iIiIiIg3GCj0RERERiYuz3IiKV5eIiIiISIOxQk9ERERE4uIsN6IqMQm9062w4g6BqES67dyxuEMgKpHcYg4UdwhERCVCiUnoiYiIiKiU4iw3ouLVJSIiIiLSYKzQExEREZG4OIZeVKzQExERERFpMFboiYiIiEhcnIdeVLy6REREREQaTKUKfVpaGvbu3Yvz58/j/v37eP36NaysrODu7o4OHTrAy8tLrDiJiIiISEMJHEMvKqUq9PHx8RgxYgTs7Owwe/ZsvHr1CvXr10ebNm1QqVIlnD59Gu3atYOrqytCQ0PFjpmIiIiIiP5PqQp9vXr1MHjwYFy6dAlubm4K+7x58wb79u3DkiVL8PDhQ0yaNEmtgRIRERGRhuI89KJSKqH/66+/YGVlVWAfQ0NDDBgwAAMGDEBSUpJagiMiIiIiooIpldB/KJn/2P5EREREVIqxQi8qlaetPHXqFPbs2YP79+9DIpHA0dERffr0QYsWLcSIj4iIiIiICqDSn0ujRo1C27ZtsX37djx79gxJSUnYtm0bWrVqhbFjx4oVIxERERFpMEEiKZKlrFI6od+7dy82btyIn3/+GU+fPkVERAT++OMPJCUlYf369Vi3bh0OHDggZqxERERERPQepYfcbNy4Ef7+/vDz85Np19LSwrBhw3D79m0EBwejW7du6o6RiIiIiDQZx9CLSumre+3aNfTs2TPf9b1798bVq1fVEhQRERERESlH6Qr906dPUbFixXzXV6xYEc+ePVNLUERERERUipTh8e1FQekKfWZmJvT09PJdr6Ojg8zMTLUERURERERUFFatWgVHR0cYGBigQYMGOH/+fL599+zZg3bt2sHKygqmpqbw9PTE0aNHZfqEhIRAIpHILenp6aKdg0rTVs6YMQNGRkYK171+/VotARERERERFYXQ0FCMHz8eq1atQrNmzbB27Vp06tQJN2/eRJUqVeT6nzt3Du3atcO8efNQvnx5bNy4EZ9++ikuXrwId3d3aT9TU1Pcvn1bZlsDAwPRzkMiCIKgTMeWLVtCosTXJadPny5UIHfvxRVqO6LS7rZzx+IOgahEcovhzGpEijjUcCruEOS8/n13kRzHqFlvlfo3adIEHh4eWL16tbTNxcUFPXr0QFBQkFL7qF27Nnx8fPDdd98BeFuhHz9+PFJTU1WK5WMoXaE/c+aMiGEQEREREX2cjIwMZGRkyLTp6+tDX19frm9mZiauXr2KKVOmyLS3b98e4eHhSh0vNzcXL168gIWFhUz7y5cv4eDggJycHNSvXx9z5syRqeCr20fPIZSdnY2XL1+qIxYiIiIiKoWK6sVSQUFBMDMzk1nyq7Q/ffoUOTk5sLGxkWm3sbFBQkKCUue1ePFivHr1Cv369ZO2OTs7IyQkBAcOHMD27dthYGCAZs2aITY2tvAX8AOUTugPHz6MLVu2yLTNnTsX5cqVQ/ny5dG+fXukpKSoPUAiIiIiImUEBAQgLS1NZgkICChwm/eHlAuCoNQw8+3bt2PmzJkIDQ2FtbW1tL1p06b47LPPUK9ePTRv3hw7d+5ErVq1sHz58sKdlBKUTugXLVqE58+fSz+Hh4fju+++w4wZM7Bz5048fPgQc+bMESVIIiIiItJgEq0iWfT19WFqaiqzKBpuAwCWlpbQ1taWq8YnJibKVe3fFxoaiuHDh2Pnzp1o27ZtgX21tLTQqFGjklGhv3HjBry8vKSff/31V7Rr1w7Tpk1Dr169sHjxYvz222+iBElEREREpE56enpo0KABjh8/LtN+/PhxmZz3fdu3b4efnx9++eUXdOnS5YPHEQQBUVFRsLOz++iY86P0Q7EvXrxAhQoVpJ8vXLiAPn36SD/Xrl0bT548UW90RERERKTxBMlHP7YpCn9/f/j6+qJhw4bw9PTEunXr8ODBA4waNQrA2yE8jx8/xubNmwG8TeYHDx6Mn376CU2bNpVW9w0NDWFmZgYAmDVrFpo2bYqaNWvi+fPnWLZsGaKiorBy5UrRzkPpq2tvb4+YmBgAb5/cvX79Opo1ayZd/+zZs3znqCciIiIiKml8fHywdOlSzJ49G/Xr18e5c+dw+PBhODg4AADi4+Px4MEDaf+1a9ciOzsbX375Jezs7KTL119/Le2TmpqKkSNHwsXFBe3bt8fjx49x7tw5NG7cWLTzUHoe+smTJ+PAgQOYOnUqDh8+jPDwcPz999/Q1tYGAKxbtw6bN2/GhQsXChUI56EnUozz0BMpxnnoiRQrifPQv7xYNMOyyzX5tEiOU9IoPeQmMDAQT548wbhx42Bra4utW7dKk3ng7VcQn35aNi8iEREREVFxUTqhNzIykpu28l2FfUMsEREREZVuJXUMfWnBq0tEREREpMGUrtA7OjoqnGTfzMwMTk5OmDRpEho2bKjW4IiIiIioFFDiRU1UeEon9OPHj1fYnpqaisuXL8PT0xPHjh1Dq1at1BUbERERERF9gNIJ/bvT8SgyZ84czJw5kwk9EREREcniGHpRqe3q9unTB3/99Ze6dkdEREREREpQukJPRERERFQYAsfQi0ptFfpff/0Vbm5u6todEREREREpQekK/bJlyxS2p6Wl4fLlyzhy5AiOHj2qtsCIiIiIqJTgGHpRKZ3Q//jjjwrbTU1N4ezsjAsXLqBJkyZqC4yIiIiIiD5M6YQ+Li5OzDiIiIiIqJQSwDH0YuL3H0REREREGkyphH7+/Pl49eqVUju8ePEiDh069FFBEREREVHpIUi0imQpq5Q685s3b8LBwQGjR4/GkSNHkJSUJF2XnZ2N6OhorFq1Cl5eXujfvz9MTU1FC5iIiIiIiP6j1Bj6zZs3Izo6GitXrsSgQYOQlpYGbW1t6Ovr4/Xr1wAAd3d3jBw5EkOGDIG+vr6oQRMRERGRBinD1fOioPRDsXXr1sXatWuxZs0aREdH4/79+3jz5g0sLS1Rv359WFpaihknEREREREpoPKbYiUSCerVq4d69eqJEQ8RERERlTJ8U6y4+P0HEREREZEGU7lCT0RERESkirI8A01R4NUlIiIiItJgrNATERERkbg4hl5UKlfohw0bhhcvXsi1v3r1CsOGDVNLUEREREREpByVE/pNmzbhzZs3cu1v3rzB5s2b1RIUEREREZUefFOsuJQecvP8+XMIggBBEPDixQsYGBhI1+Xk5ODw4cOwtrYWJUgiIiIiIlJM6YS+fPnykEgkkEgkqFWrltx6iUSCWbNmqTU4IiIiItJ8AjiGXkxKJ/SnT5+GIAho3bo1du/eDQsLC+k6PT09ODg4wN7eXpQgiYiIiIhIMaUTem9vbwBAXFwcKleuDC2tsjtOiYiIiIiUV5bHtxcFlaetdHBwQGpqKi5duoTExETk5ubKrB88eLDagiMiIiIiooKpnND/9ttvGDRoEF69egUTExNI3plXVCKRMKEnIiIiIipCKn//MXHiROlc9KmpqUhJSZEuycnJYsRIRERERJpMIimapYxSOaF//Pgxxo0bByMjIzHiISIiIiIiFaic0Hfo0AFXrlwRIxYiIiIiKoUEaBXJUlapPIa+S5cu+Oabb3Dz5k3UqVMHurq6Muu7deumtuCIiIiIiKhgKif0I0aMAADMnj1bbp1EIkFOTs7HR0VEREREpYZQhse3FwWVE/r3p6kkIiIiIqLio3JCT0RERESkCr5YSlwqJ/SKhtq867vvvit0MEREREREpBqVE/q9e/fKfM7KykJcXBx0dHRQvXp1JvREREREJEMAx9CLSeWEPjIyUq7t+fPn8PPzQ8+ePdUSFBERERERKUctA5pMTU0xe/ZszJgxQx27IyIiIqJSRJBoFclSVqntzFNTU5GWlqau3RERERERkRJUHnKzbNkymc+CICA+Ph5btmxBx44d1RYYEREREZUOnIdeXCon9D/++KPMZy0tLVhZWWHIkCEICAhQW2BERERERPRhKif0cXFxYsRBRERERKUUZ7kR10eNoX/06BEeP36srliIiIiIiEhFKif0ubm5mD17NszMzODg4IAqVaqgfPnymDNnDnJzc8WIkYiIiIg0GGe5EZfKQ26mTZuG4OBgzJ8/H82aNYMgCPj9998xc+ZMpKenY+7cuWLESURERERECqic0G/atAkbNmxAt27dpG316tVDxYoVMWbMGCb0RERERCSDY+jFpfJ3E8nJyXB2dpZrd3Z2RnJyslqCIiIiIiIi5aic0NerVw8rVqyQa1+xYgXq1aunlqCIiIiIqPTgGHpxqTzkZuHChejSpQtOnDgBT09PSCQShIeH4+HDhzh8+LAYMRIRERERUT5U/lPG29sbd+7cQc+ePZGamork5GT06tULt2/fRvPmzcWIkYiIiIg0mABJkSxllcoVegCwt7fnw68aRBAE/LJtK8LCjuDly5dwcnLC6DFfwsGhaoHb/X7hArZs2Yz4+HjY2dlh8JAh8PJqJl1/488/sXv3r7h7NxbJycmYPv07eHp5iXw2REXL4pOGqDZxOMw83GBgb40rvcfg3wMnizssIrU5cPAwdu3Zg+TkFDhUqYLRIz9HHbfa+faP/vMG1qwPxj8PHqCChQX69emFrp07Sddf+D0c23f+iifx8cjOzkZFe3v06dUDbVu3+m8fN25g1+69iL17D8nJyQicPhXNPJuKep5EpVmhEvr09HRER0cjMTFRbu75d2e/oZLh1193Ye/evZjg74+KFSshdMd2TJ82FWvXbYCRkZHCbWJibmL+/Hnw9R0MT69miAj/HfOD5mHhD4ulD0Wnp6fD0dERbdu1w7y53xflKREVGW1jIzyPvo1Hm/agwS7554eINNmZc+exZv0GjB0zCrVdXHAoLAzTAmdhw+qVsLa2kusfn5CAaYGz0Llje0yZ5I+/YmKwfNUamJmZoXmztwUdExMTDPDpiyqVKkFHVwcXL13Goh9/QnkzMzRs4AEASE/PQDVHR3Ro2waz580v0nOm4lGWx7cXBZWvblhYGKpUqYKmTZuiW7du6NGjh3Tp2bOnGDHSRxAEAfv37YVP//5o1uwTVK1aFf4TJyIjIwNnz5zOd7v9+/bB3d0D/Xz6o3Llyujn0x/16tfH/v17pX0aNmqEwUP80KzZJ0VxKkTFIunoOdwJXIqEfceLOxQitdu9dz86tm+LTh3ao0qVyhg9cgSsLC3xWz7PxB06HAZrKyuMHjkCVapURqcO7dGhXVv8uue/fxvq1a2DT7w8UaVKZdjb2aFn926o5lgVN27elPZp3LABhg7+DJ8047e6VPxWrVoFR0dHGBgYoEGDBjh//nyB/c+ePYsGDRrAwMAA1apVw5o1a+T67N69G66urtDX14erqyv27t2rYE/qo3JC/9VXX6Fv376Ij49Hbm6uzJKTkyNGjPQREhISkJKSAg8PD2mbrq4e3OrUQUxMTL7b3boVA/d3tgEAD48GiLmZ/zZERKQ5srKyEHv3Ljzc3WXaG3i442bMLYXb3Lx1Cw085Pvfib2L7Oxsuf6CICAy6joePnpc4DAeKv1K6hj60NBQjB8/HtOmTUNkZCSaN2+OTp064cGDBwr7x8XFoXPnzmjevDkiIyMxdepUjBs3Drt375b2iYiIgI+PD3x9fXH9+nX4+vqiX79+uHjxYqGv34eoPOQmMTER/v7+sLGxKfRBMzIykJGRIdemr69f6H2SYikpKQCA8uXNZdrLlzdHUuK/BW5n/t425uXNpfsjIiLN9vz5c+Tm5sK8fHmZdvPyZkhJSVW4TUpKKszLm73XvzxycnKQ9vw5KlhYAABevXqFAYOHIisrC1paWhg7ZhQavPeHA5EYFOWY+vr6+eaYS5YswfDhw/H5558DAJYuXYqjR49i9erVCAoKkuu/Zs0aVKlSBUuXLgUAuLi44MqVK1i0aBF69+4t3Ue7du0QEBAAAAgICMDZs2exdOlSbN++XV2nKkPlCn2fPn1w5syZjzpoUFAQzMzMZJa1a1Z/1D7prdOnT6F3rx7SJSfnbcVE8v4frYKgoPE9760WIHxwEyIi0iyS936xCwLkfv+/t8F7DcLb5nc2MjQ0xOrlS7Hix8UYOvgzrN3wM65H/6megEkjCRJJkSyKckxFiTkAZGZm4urVq2jfvr1Me/v27REeHq5wm4iICLn+HTp0wJUrV5CVlVVgn/z2qQ4qV+hXrFiBvn374vz586hTpw50dXVl1o8bN+6D+wgICIC/v79M28NHT1QNhRRo0qQpnJz+e5NvVlYmgLcVdwuLCtL21LRUuQr8u8zN5avxqampcpV+IiLSTKamptDS0kLy+7/r09LkqvZ5zM3Ly1XvU1LToK2tDVNTE2mblpYWKtrbAwCqV6+GBw8fYceuX1Gvbh21ngPR+xTlmPlV558+fYqcnBy5USc2NjZISEhQuE1CQoLC/tnZ2Xj69Cns7Ozy7ZPfPtVB5YT+l19+wdGjR2FoaIgzZ87I/GUvkUiUSugVffWhr/9M1VBIASMjI5mZawRBgLm5OSKvRaJ69RoA3o6bvPHnnxg6dFi++3F2dkFUZCR69uwlbYu8dg0uri7iBU9EREVGV1cXNWvUwLXIKHzi5SltvxYZBc+mjRVu4+rsjD8uXZZpuxYZiVo1a0BHJ/+UQhAEafWSyiZBKJqv+AsaXpMf+W+pBLm2D/V/v13VfX4slRP66dOnY/bs2ZgyZQq0tDgFUUknkUjQvUdP7Ny5A/YV7WFvXxE7Q3dAX18f3i3/mxN48aIfUKFCBfj9P8nv1r0HJn87Cbt27UTTpp74448IREVFYuEPi6XbvHnzBk+e/PfNSsK/Cbh37x5MTExgbW1ddCdJJCJtYyMY16gi/WzkWAmm9ZyRmZyG9IfxxRgZ0cfr3bM7Fi7+EbVq1oCrszMOhR1FYlKSdF754JBNePYsGd9OnAAA6NK5I/YfPIQ164PRuUN73Lx1C2HHTiDg20nSfW7fuQu1ataAva0dsrKzcenKFZw4dRrjvhwt7fP234//7p+EhH9x797f///3Q366TCIxWFpaQltbW65ynpiYmO+zora2tgr76+jooEKFCgX2+ZjnTz9E5YQ+MzMTPj4+TOY1SJ8+fZGZkYFVK1f8/8VSzpjz/TyZSn5SUiIkWv/95ejq6orJUwKwZfMmbN2yGbZ2dpg8JUA6Bz0AxMbeQcCUydLPG9avAwC0adsW/v7//XIn0mRmDdzgeXKL9LProqkAgIeb9yB6eEBxhUWkFi1bNMfz5y+wbXsokpOT4eDggO9nfQeb/xdlkpNTkJiUJO1vZ2uLubMCsWb9Bvx28BAsKlhgzBcjpHPQA2/nmF++ag2ePn0GfT09VK5UCZMn+aNli//eJn8n9i6+CZgm/bx2QzAAoF2b1vjGf7zIZ03FQVD9sU3R6enpoUGDBjh+/LjM1OvHjx9H9+7dFW7j6emJ3377Tabt2LFjaNiwoXQYuqenJ44fP44JEybI9PES8eWbEiHvewIlTZgwAVZWVpg6dapaA7l7L06t+yMqLW47dyzuEIhKJLeYA8UdAlGJ5FDDqbhDkBN7758iOU7N6g4q9Q8NDYWvry/WrFkDT09PrFu3DuvXr8dff/0FBwcHBAQE4PHjx9i8eTOAt9NWurm54YsvvsCIESMQERGBUaNGYfv27dJZbsLDw9GiRQvMnTsX3bt3x/79+zF9+nRcuHABTZo0Ufs5A4Wo0Ofk5GDhwoU4evQo6tatK/dQ7JIlS9QWHBERERFpvsLMEV8UfHx88OzZM8yePRvx8fFwc3PD4cOH4eDw9g+D+Ph4mTnpHR0dcfjwYUyYMAErV66Evb09li1bJk3mAcDLyws7duzA9OnTMWPGDFSvXh2hoaGiJfNAISr0rVq1KnD96dP5v320IKzQEynGCj2RYqzQEylWEiv0d+4pflGTutWqXuXDnUohlSv0hU3YiYiIiIhI/dTyhEJubi5+++039OjRQx27IyIiIqJSRICkSJay6qMS+tjYWAQEBKBSpUro16+fumIiIiIiIiIlqTzk5s2bN9i5cyeCg4Pxxx9/ICcnBz/++COGDRuGcuXKiREjEREREWmwslw9LwpKV+gvXbqEkSNHwtbWFitWrEDv3r3x8OFDaGlpoW3btkzmiYiIiIiKgdIVei8vL4wdOxaXLl2Ck1PJe3qaiIiIiEomVujFpXRC37p1awQHByMxMRG+vr7o0KEDJBL+xyEiIiIiKk5KJ/THjh3Dw4cPsXHjRowePRpv3ryBj48PADCxJyIiIqJ8CQJzRTGpNMtN5cqV8d133yEuLg5btmxBYmIidHR00L17d0ydOhXXrl0TK04iIiIiIlKg0NNWtmvXDtu3b8eTJ08wduxYHDlyBI0aNVJnbERERERUCnAeenF99IulzM3NMXbsWERGRuLy5cvqiImIiIiIiJSk8jz0BfHw8FDn7oiIiIioFCjL1fOi8NEVeiIiIiIiKj5qrdATEREREb2PFXpxsUJPRERERKTBCpXQZ2dn48SJE1i7di1evHgBAHjy5Alevnyp1uCIiIiISPMJgqRIlrJK5SE3//zzDzp27IgHDx4gIyMD7dq1g4mJCRYuXIj09HSsWbNGjDiJiIiIiEgBlSv0X3/9NRo2bIiUlBQYGhpK23v27ImTJ0+qNTgiIiIi0ny5kBTJUlapXKG/cOECfv/9d+jp6cm0Ozg44PHjx2oLjIiIiIiIPkzlhD43Nxc5OTly7Y8ePYKJiYlagiIiIiKi0oOz3IhL5SE37dq1w9KlS6WfJRIJXr58icDAQHTu3FmdsRERERER0QeoXKH/8ccf0apVK7i6uiI9PR0DBw5EbGwsLC0tsX37djFiJCIiIiINVpZnoCkKKif09vb2iIqKwo4dO3D16lXk5uZi+PDhGDRokMxDskREREREJL5CvSnW0NAQQ4cOxdChQ9UdDxERERGVMhxDLy6Vx9Bv2rQJhw4dkn7+9ttvUb58eXh5eeGff/5Ra3BERERERFQwlRP6efPmSYfWREREYMWKFVi4cCEsLS0xYcIEtQdIRERERJqNb4oVl8pDbh4+fIgaNWoAAPbt24c+ffpg5MiRaNasGVq2bKnu+IiIiIiIqAAqV+jLlSuHZ8+eAQCOHTuGtm3bAgAMDAzw5s0b9UZHRERERBpPgKRIlrJK5Qp9u3bt8Pnnn8Pd3R137txBly5dAAB//fUXqlatqu74iIiIiIioACpX6FeuXAlPT08kJSVh9+7dqFChAgDg6tWrGDBggNoDJCIiIiLNxjH04pIIgiAUdxAAcPdeXHGHQFQi3XbuWNwhEJVIbjEHijsEohLJoYZTcYcg59KttCI5TmNnsyI5TklTqHnoAeD169d48OABMjMzZdrr1q370UERERERUemRW9wBlHIqJ/RJSUnw8/NDWFiYwvU5OTkfHRQRERERESlH5TH048ePR2pqKv744w8YGhoiLCwMmzZtQs2aNXHgAL/+JCIiIiJZHEMvLpUr9KdOncL+/fvRqFEjaGlpwcHBAe3atYOpqSmCgoKks94QEREREZH4VK7Qv3r1CtbW1gAACwsLJCUlAQDq1KmDa9euqTc6IiIiItJ4nIdeXCon9E5OTrh9+zYAoH79+li7di0eP36MNWvWwM7OTu0BEhERERFR/lQecjN+/HjEx8cDAAIDA9GhQwds27YNenp6CAkJUXd8RERERERUAJUT+kGDBkn/v7u7O+7fv49bt26hSpUqsLS0VGtwRERERKT5yvIDq0Wh0PPQ5zEyMoKHh4c6YiEiIiIiIhWpnNDn5OQgJCQEJ0+eRGJiInJzZV8VcOrUKbUFR0RERESaryw/sFoUVE7ov/76a4SEhKBLly5wc3ODRML/QERERERExUXlhH7Hjh3YuXMnOnfuLEY8RERERFTK5ArFHUHppvK0lXp6eqhRo4YYsRARERERkYpUTugnTpyIn376CYLAP7WIiIiI6MP4YilxKTXkplevXjKfT506hSNHjqB27drQ1dWVWbdnzx71RUdERERERAVSKqE3MzOT+dyzZ09RgiEiIiKi0ofz0ItLqYR+48aNYsdBRERERESFoPIsN3FxccjOzkbNmjVl2mNjY6Grq4uqVauqKzYiIiIiKgX46KW4VH4o1s/PD+Hh4XLtFy9ehJ+fnzpiIiIiIiIiJamc0EdGRqJZs2Zy7U2bNkVUVJQ6YiIiIiKiUiQXkiJZyiqVE3qJRIIXL17ItaelpSEnJ0ctQRERERERkXJUTuibN2+OoKAgmeQ9JycHQUFB+OSTT9QaHBERERFpPkGQFMlSVqn8UOyCBQvg7e0NJycnNG/eHABw/vx5PH/+HKdOnVJ7gERERERElD+VK/S1a9dGdHQ0+vXrh8TERLx48QKDBw/GrVu34ObmJkaMRERERKTBBKFolrJKpYQ+KysLrVq1wsuXLzFv3jwcOnQIv/76K7777jtYWFiIFSMRERERUbFKSUmBr68vzMzMYGZmBl9fX6SmpubbPysrC5MnT0adOnVgbGwMe3t7DB48GE+ePJHp17JlS0gkEpmlf//+KsWmUkKvq6uLGzduQCIpu2OUiIiIiEg1AiRFsohp4MCBiIqKQlhYGMLCwhAVFQVfX998+79+/RrXrl3DjBkzcO3aNezZswd37txBt27d5PqOGDEC8fHx0mXt2rUqxabyGPrBgwcjODgY8+fPV3VTIiIiIiLRZGRkICMjQ6ZNX18f+vr6H7XfmJgYhIWF4Y8//kCTJk0AAOvXr4enpydu374NJycnuW3MzMxw/Phxmbbly5ejcePGePDgAapUqSJtNzIygq2tbaHjUzmhz8zMxIYNG3D8+HE0bNgQxsbGMuuXLFlS6GCIiIiIqPTJLaLx7UFBQZg1a5ZMW2BgIGbOnPlR+42IiICZmZk0mQfevoPJzMwM4eHhChN6RdLS0iCRSFC+fHmZ9m3btmHr1q2wsbFBp06dEBgYCBMTE6XjUzmhv3HjBjw8PAAAd+7ckVnHoThEREREVFwCAgLg7+8v0/ax1XkASEhIgLW1tVy7tbU1EhISlNpHeno6pkyZgoEDB8LU1FTaPmjQIDg6OsLW1hY3btxAQEAArl+/LlfdL4jKCf3p06dV3YSIiIiIyrCimiNeX19PpQR+5syZchX9912+fBmA4sK1IAhKFbSzsrLQv39/5ObmYtWqVTLrRowYIf3/bm5uqFmzJho2bIhr165Ji+gfonJCT0RERERUGnz11VcfnFGmatWqiI6Oxr///iu3LikpCTY2NgVun5WVhX79+iEuLg6nTp2Sqc4r4uHhAV1dXcTGxoqX0Ldq1arAv0T4cikiIiIieldJnSPe0tISlpaWH+zn6emJtLQ0XLp0CY0bNwYAXLx4EWlpafDy8sp3u7xkPjY2FqdPn0aFChU+eKy//voLWVlZsLOzU/o8VE7o69evLxdoVFQUbty4gSFDhqi6OyIiIiKiEs3FxQUdO3bEiBEjpFNKjhw5El27dpV5INbZ2RlBQUHo2bMnsrOz0adPH1y7dg0HDx5ETk6OdLy9hYUF9PT0cO/ePWzbtg2dO3eGpaUlbt68iYkTJ8Ld3R3NmjVTOj6VE/off/xRYfvMmTPx8uVLVXdHRERERKVcrshzxBeFbdu2Ydy4cWjfvj0AoFu3blixYoVMn9u3byMtLQ0A8OjRIxw4cACAfEH89OnTaNmyJfT09HDy5En89NNPePnyJSpXrowuXbogMDAQ2traSscmEQT1fAly9+5dNG7cGMnJyYXb/l6cOsIgKnVuO3cs7hCISiS3mAPFHQJRieRQQ7kpFIvSwWvZRXKcrh5l8/FQtZ11REQEDAwM1LU7IiIiIiolSuoY+tJC5YS+V69eMp8FQUB8fDyuXLmCGTNmqC0wIiIiIiL6MJUTelNTU5lZbrS0tODk5ITZs2dLxxQREREREeUpqnnoyyqVE/qQkBARwiAiIiIiosLQUrbj69ev8eWXX6JixYqwtrbGwIED8fTpUzFjIyIiIqJSIFcomqWsUjqhDwwMREhICLp06YL+/fvj+PHjGD16tJixERERERHRByg95GbPnj0IDg6Wvh73s88+Q7NmzZCTk6PSPJlEREREVLZwlhtxKV2hf/jwIZo3by793LhxY+jo6ODJkyeiBEZERERERB+mdIU+JycHenp6shvr6CA7u2heFEBEREREmkkoBW+KLcmUTugFQYCfnx/09fWlbenp6Rg1ahSMjY2lbXv27FFvhERERERElC+lE/ohQ4bItX322WdqDYaIiIiIiFSjdEK/ceNGMeMgIiIiolKqLE8pWRSUfiiWiIiIiIhKHpXfFEtEREREpApOWymuEpPQC/yygEght5gDxR0CUYl0w6VbcYdAVCI5ZN0u7hCoiJWYhJ6IiIiISidW6MXFsjgRERERkQZjhZ6IiIiIRJUr8MVSYmKFnoiIiIhIg7FCT0RERESi4hh6cbFCT0RERESkwVihJyIiIiJRsUIvLlboiYiIiIg0GCv0RERERCSqXFboRcUKPRERERGRBmOFnoiIiIhEJXAeelGxQk9EREREpMFYoSciIiIiUXGWG3GxQk9EREREpMFYoSciIiIiUXGWG3GxQk9EREREpMFYoSciIiIiUXEMvbhYoSciIiIi0mCs0BMRERGRqFihFxcr9EREREREGowVeiIiIiISFWe5ERcr9EREREREGowVeiIiIiISFcfQi4sVeiIiIiIiDcYKPRERERGJKje3uCMo3VihJyIiIiLSYKzQExEREZGoOIZeXKzQExERERFpMFboiYiIiEhUrNCLixV6IiIiIiINxgo9EREREYmKb4oVFyv0REREREQajAk9EREREZEG45AbIiIiIhKVUGRPxUqK6DglCyv0REREREQajBV6IiIiIhIVp60UFyv0REREREQajBV6IiIiIhJVbm5xR1C6sUJPRERERKTBmNATERERkagEoWgWMaWkpMDX1xdmZmYwMzODr68vUlNTC9zGz88PEolEZmnatKlMn4yMDIwdOxaWlpYwNjZGt27d8OjRI5ViU3nIjbu7OyQS+SmBJBIJDAwMUKNGDfj5+aFVq1aq7pqIiIiIqEQaOHAgHj16hLCwMADAyJEj4evri99++63A7Tp27IiNGzdKP+vp6cmsHz9+PH777Tfs2LEDFSpUwMSJE9G1a1dcvXoV2traSsWmcoW+Y8eO+Pvvv2FsbIxWrVqhZcuWKFeuHO7du4dGjRohPj4ebdu2xf79+1XdNRERERGVQrlC0SxiiYmJQVhYGDZs2ABPT094enpi/fr1OHjwIG7fvl3gtvr6+rC1tZUuFhYW0nVpaWkIDg7G4sWL0bZtW7i7u2Pr1q34888/ceLECaXjUzmhf/r0KSZOnIjz589j8eLFWLJkCc6dO4dJkybh1atXOHbsGKZPn445c+aoumsiIiIiokLLyMjA8+fPZZaMjIyP3m9ERATMzMzQpEkTaVvTpk1hZmaG8PDwArc9c+YMrK2tUatWLYwYMQKJiYnSdVevXkVWVhbat28vbbO3t4ebm9sH9/sulRP6nTt3YsCAAXLt/fv3x86dOwEAAwYM+OBfK0RERERUNhTVGPqgoCDpGPe8JSgo6KPjT0hIgLW1tVy7tbU1EhIS8t2uU6dO2LZtG06dOoXFixfj8uXLaN26tfSPjISEBOjp6cHc3FxmOxsbmwL3+z6Vx9AbGBggPDwcNWrUkGkPDw+HgYEBACA3Nxf6+vqq7pqIiIiIqNACAgLg7+8v01ZQTjpz5kzMmjWrwH1evnwZABQ+QyoIgsL2PD4+PtL/7+bmhoYNG8LBwQGHDh1Cr1698t3uQ/t9n8oJ/dixYzFq1ChcvXoVjRo1gkQiwaVLl7BhwwZMnToVAHD06FG4u7urumsiIiIiKoUEMQe4v0NfX1+lovJXX32F/v37F9inatWqiI6Oxr///iu3LikpCTY2Nkofz87ODg4ODoiNjQUA2NraIjMzEykpKTJV+sTERHh5eSm9X5UT+unTp8PR0RErVqzAli1bAABOTk5Yv349Bg4cCAAYNWoURo8erequiYiIiIiKjKWlJSwtLT/Yz9PTE2lpabh06RIaN24MALh48SLS0tJUSryfPXuGhw8fws7ODgDQoEED6Orq4vjx4+jXrx8AID4+Hjdu3MDChQuV3q9EEMSetVM5sff+Ke4QiEokPSG9uEMgKpFuuHQr7hCISqQuWSXvOcaFu4vmVbHf9hbvFUudOnXCkydPsHbtWgBvp610cHCQmbbS2dkZQUFB6NmzJ16+fImZM2eid+/esLOzw/379zF16lQ8ePAAMTExMDExAQCMHj0aBw8eREhICCwsLDBp0iQ8e/ZMpWkrVa7Q58nMzERiYiJy33uXb5UqVQq7SyIiIiKiEmnbtm0YN26cdEaabt26YcWKFTJ9bt++jbS0NACAtrY2/vzzT2zevBmpqamws7NDq1atEBoaKk3mAeDHH3+Ejo4O+vXrhzdv3qBNmzYICQlROpkHClGhj42NxbBhw+Sm0skbvJ+Tk6PK7v7bLyv0RAqxQk+kGCv0RIqVxAr9gl+LpkI/uY94FfqSTOUKvZ+fH3R0dHDw4EHY2dmp9AQuERERERGpl8oJfVRUFK5evQpnZ2cx4iEiIiKiUia3iGa5KatU/l7C1dUVT58+FSMWIiIiIiJSkcoJ/YIFC/Dtt9/izJkzePbsmdzrdYmIiIiI3lVUb4otq1QectO2bVsAQJs2bWTaP/ahWCIiIiIiUp3KCf3p06fFiIOIiIiISqmyXD0vCion9N7e3mLEQUREREREhaBUQh8dHQ03NzdoaWkhOjq6wL5169ZVS2BEREREVDrkskQvKqUS+vr16yMhIQHW1taoX78+JBIJFL2PimPoiYiIiIiKllIJfVxcHKysrKT/n4iIiIhIWULRvCi2zFIqoXdwcJD+/3/++QdeXl7Q0ZHdNDs7G+Hh4TJ9iYiIiIhIXCrPQ9+qVSskJyfLtaelpaFVq1ZqCYqIiIiISg9BEIpkKatUTujz5pt/37Nnz2BsbKyWoIiIiIiISDlKT1vZq1cvAG8ffPXz84O+vr50XU5ODqKjo+Hl5aX+CImIiIhIo+VyDL2olE7ozczMALyt0JuYmMDQ0FC6Tk9PD02bNsWIESPUHyEREREREeVL6YR+48aNAICqVati0qRJHF5DREREREopy+Pbi4LKb4oNDAwUIw4iIiIiIioElRN6APj111+xc+dOPHjwAJmZmTLrrl27ppbAiIiIiKh0yGWBXlQqz3KzbNkyDB06FNbW1oiMjETjxo1RoUIF/P333+jUqZMYMRIRERERUT5UTuhXrVqFdevWYcWKFdDT08O3336L48ePY9y4cUhLSxMjRiIiIiLSYEKuUCRLWaVyQv/gwQPp9JSGhoZ48eIFAMDX1xfbt29Xb3RERERERFQglRN6W1tbPHv2DADg4OCAP/74AwAQFxfHJ5iJiIiIiIqYygl969at8dtvvwEAhg8fjgkTJqBdu3bw8fFBz5491R4gEREREWk2QSiapaxSeZabdevWIff/r/saNWoULCwscOHCBXz66acYNWqU2gMkIiIiIqL8qZzQa2lpQUvrv8J+v3790K9fPwDA48ePUbFiRfVFR0REREQaL7cMP7BaFFQecqNIQkICxo4dixo1aqhjd0REREREpCSlE/rU1FQMGjQIVlZWsLe3x7Jly5Cbm4vvvvsO1apVwx9//IGff/5ZzFiJiIiISAMJglAkS1ml9JCbqVOn4ty5cxgyZAjCwsIwYcIEhIWFIT09HUeOHIG3t7eYcRIRERERkQJKJ/SHDh3Cxo0b0bZtW4wZMwY1atRArVq1sHTpUhHDIyIiIiJNJ+QWdwSlm9JDbp48eQJXV1cAQLVq1WBgYIDPP/9ctMCIiIiIiOjDlK7Q5+bmQldXV/pZW1sbxsbGogRFRERERKVHbhke314UlE7oBUGAn58f9PX1AQDp6ekYNWqUXFK/Z88e9UZIRERERET5UjqhHzJkiMznzz77TO3B0McTBAG/bNuCo2GH8fLlS9RycsboMV/BwaFqgdv9fuE8tm7ZhPj4eNjZ2cF3iB+8vD6Rrt8Zuh0R4b/j0aOH0NPTg4uLK/yGfY5KlSoDALKzs7FlcwiuXL6EhIR4GBsbo159D/gNHY4KFSqIecpESjlw8DB27dmD5OQUOFSpgtEjP0cdt9r59o/+8wbWrA/GPw8eoIKFBfr16YWunTtJ11/4PRzbd/6KJ/HxyM7ORkV7e/Tp1QNtW7f6bx83bmDX7r2IvXsPycnJCJw+Fc08m4p6nkRFxeKThqg2cTjMPNxgYG+NK73H4N8DJ4s7LCqhyvIMNEVB6YR+48aNYsZBarL7153Yt3cPJvhPgn3Figjd8QtmTJuCNet+hpGRkcJtYmJuYsH8ufjMdwg8vZohIvx3LAiai4U/LIGTswsA4MaNP9GlazfUrFULOTk52LIpBDOmBWD12vUwMDBERkYG7t2NRf8Bg+BYrRpevnyJ9WtXY86s77B02cqivAREcs6cO4816zdg7JhRqO3igkNhYZgWOAsbVq+EtbWVXP/4hARMC5yFzh3bY8okf/wVE4Plq9bAzMwMzZt5AQBMTEwwwKcvqlSqBB1dHVy8dBmLfvwJ5c3M0LCBBwAgPT0D1Rwd0aFtG8yeN79Iz5lIbNrGRngefRuPNu1Bg10rijscojJN5TfFUsklCAL279sLn/4D4NXsbXXdf+I3+GygD86eOYVOnbsq3O7Avr1wd/dAP58BAIDKPlVw48af2L9/L779f0I/e848mW3G+0/EoAH9cDc2Fm516sLY2Bjfz1sg0+eL0V/Cf/xYJCYmwtraWt2nS6S03Xv3o2P7tujUoT0AYPTIEbhyNRK/HT6M4X5D5PofOhwGaysrjB45AgBQpUpl3Im9i1/37JUm9PXq1pHZpmf3bjh+8hRu3LwpTegbN2yAxg0biHlqRMUm6eg5JB09V9xhkIbgm2LFpZY3xVLJ8G9CAlJSkuHu8V8CoaurB7c6dRETczPf7W7duimzDQB4eDRAzM38t3n16hUAoJyJSb59Xr96BYlEgnLl+PA0FZ+srCzE3r0LD3d3mfYGHu64GXNL4TY3b91CAw/5/ndi7yI7O1uuvyAIiIy6joePHhc4jIeIiEgMxVKhz8jIQEZGhkxbZkYG9P7/wC0VTkpKMgCgfHlzmfby5csjMTGxgO1SFGxjjpSUFIX9BUHAhvVr4VrbDVWrOirsk5mZiZCNwfBu2QpGRkzoqfg8f/4cubm5MC9fXqbdvLwZUlJSFW6TkpIK8/Jm7/Uvj5ycHKQ9f44KFhYA3v5hO2DwUGRlZUFLSwtjx4xCg/f+cCAiIoBD6MVVLBX6oKAgmJmZySxr1qwqjlA02unTJ9GnVzfpkp2TAwCQSGT7CQIgeb/xPe+vFyDI7SfPmlUrcD8uDt9ODlC4Pjs7Gwvnz4UgCBjz5VjlToZIZHI/4wKAgm4LuRvg7b9Gknc2MjQ0xOrlS7Hix8UYOvgzrN3wM65H/6megImIiJRULBX6gIAA+Pv7y7Q9fJRQHKFotCZNPOHk5Cz9nJWVBeBtxd3C4r+ZZdLSUlH+verku8zNzaXVfek2qalyVXsAWLN6JS5ejMD8hYthaSn/MGF2djbmB32PhH//xbyghazOU7EzNTWFlpYWkt/7xik1LU2uap/H3Ly8XPU+JTUN2traMDX9b5iZlpYWKtrbAwCqV6+GBw8fYceuX+XG1xMRlXUCx9CLSuUKvb29PQYOHIh169bh9u3bhTqovr4+TE1NZRYOt1GdkZER7O0rSpcqVRxgbm6ByGvXpH2ysrJw489ouLi45rsfZ2dXREZek2mLvHYVLq7/bSMIAlavWoHw8AuYG/QDbG3t5PaTl8w/efIYc+fNh6mpqRrOkujj6OrqomaNGrgWGSXTfi0yCq4uzgq3cXV2VtA/ErVq1oCOTv51EEEQpH9YExERFRWVE/rFixfD1NQUS5YsgYuLC+zs7NC/f3+sWbMGMTExYsRISpJIJOjeoyd27dyO8PALuH8/DkuXLIK+vj68W7aW9lu8aCFCNgZLP3fr3gOR167i112hePjwAX7dFYqoqEh0795T2mf1quU4c/okvvk2AEaGhkhJTkZKcrL0WYicnBwEzZuDu7F3MOmbKcjNyZX2YYJDxa13z+4IO3YcYceO48GDh1i9bgMSk5Kk88oHh2zCwsU/Svt36dwR/yYmYs36YDx48PD/255An17/3RPbd+7C1chIxMcn4MHDR/h17z6cOHUabVq1lPZ58+YN7t37G/fu/Q0ASEj4F/fu/Y3ExKQiOW8iMWkbG8G0njNM6739w9jIsRJM6znDoLJ8wYcoVxCKZCmrJMJHzPT/77//4vTp0zh48CBCQ0ORm5uLnP+P41ZV7L1/ChsGvSPvxVJhRw7j5csXcHJyxqgxX8k8vDpl8iTY2Nhggv830rYLF85h6+YQJCQkwNbODoMHD5VOfQkAXTu3V3i88RMmoW279vj33wQMHzpYYZ95839A3br11HSGZY+ekF7cIZQKBw4exq7de5CcnAwHBweMGjkcdd3cAAA/LFmKfxMTsWj+f9Ozvn2x1Ab8888DWFSwgE+f3jIvltq4eSvOnj+Pp0+fQV9PD5UrVUKP7p+iZYvm0j7Xo//ENwHT5GJp16Y1vvEfL97JlhE3XLoVdwhlmkWLxvA8uUWu/eHmPYgervgZKyoaXbIKN4JCTGOXPi+S4ywfXzZHBxQqoX/58iUuXLiAs2fP4syZM4iMjISrqyu8vb3x448/fngHCjChJ1KMCT2RYkzoiRQriQn9V0vSiuQ4K/zNPtypFFL5odgmTZogOjoabm5uaNmyJaZOnYrmzZsX+NAlERERERGJQ+WEPjY2FkZGRqhWrRqqVauGGjVqMJknIiIionxxlhtxqfxQbHJyMk6fPo1mzZrhxIkT8Pb2hq2tLXx8fLBmzRoxYiQiIiIionx81EOxAHD16lWsWLECW7du5UOxRCLgGHoixTiGnkixkjiGfvQPqUVynNXflC+S45Q0Kg+5iYyMxJkzZ3DmzBmcP38eL168QL169fD111+jVatWYsRIRERERET5UDmhb9SoEdzd3eHt7Y0RI0agRYsWfIEQEREREeWLY+jFpXJCn5yczASeiIiIiKiEUPmhWFNTU6SmpmLDhg0ICAhAcnIyAODatWt4/Pix2gMkIiIiIs0mCEKRLGWVyhX66OhotGnTBuXLl8f9+/cxYsQIWFhYYO/evfjnn3+wefNmMeIkIiIiIiIFVK7Q+/v7Y+jQoYiNjYWBgYG0vVOnTjh37pxagyMiIiIizZebKxTJUlapnNBfvnwZX3zxhVx7xYoVkZCQoJagiIiIiIhIOSoPuTEwMMDz58/l2m/fvg0rKyu1BEVEREREpUdZHt9eFFSu0Hfv3h2zZ89GVlYWAEAikeDBgweYMmUKevfurfYAiYiIiIiKW0pKCnx9fWFmZgYzMzP4+voiNTW1wG0kEonC5YcffpD2admypdz6/v37qxSbygn9okWLkJSUBGtra7x58wbe3t6oUaMGTExMMHfuXFV3R0RERESlnJArFMkipoEDByIqKgphYWEICwtDVFQUfH19C9wmPj5eZvn5558hkUjkiuAjRoyQ6bd27VqVYlN5yI2pqSkuXLiAU6dO4dq1a8jNzYWHhwfatm2r6q6IiIiIiEq8mJgYhIWF4Y8//kCTJk0AAOvXr4enpydu374NJycnhdvZ2trKfN6/fz9atWqFatWqybQbGRnJ9VWFygl9ntatW6N169aFPjARERERkTplZGQgIyNDpk1fXx/6+voftd+IiAiYmZlJk3kAaNq0KczMzBAeHp5vQv+uf//9F4cOHcKmTZvk1m3btg1bt26FjY0NOnXqhMDAQJiYmCgdn1IJ/bJlyzBy5EgYGBhg2bJlBfYdN26c0gcnIiIiotJP7OEweYKCgjBr1iyZtsDAQMycOfOj9puQkABra2u5dmtra6Vnedy0aRNMTEzQq1cvmfZBgwbB0dERtra2uHHjBgICAnD9+nUcP35c6fiUSuh//PFHDBo0CAYGBvjxxx/z7SeRSJjQExEREVGxCAgIgL+/v0xbQdX5mTNnyv0B8L7Lly8DeJvnvk8QBIXtivz888/SfPpdI0aMkP5/Nzc31KxZEw0bNsS1a9fg4eGh1L6VSujj4uIU/n8iIiIiog/JLaJpK1UdXvPVV199cEaZqlWrIjo6Gv/++6/cuqSkJNjY2HzwOOfPn8ft27cRGhr6wb4eHh7Q1dVFbGysehP6d509exbe3t6qbkZEREREVKJYWlrC0tLyg/08PT2RlpaGS5cuoXHjxgCAixcvIi0tDV5eXh/cPjg4GA0aNEC9evU+2Pevv/5CVlYW7OzsPnwC/6fytJXt2rVDlSpVMGXKFPz555+qbk5EREREZYymT1vp4uKCjh07YsSIEfjjjz/wxx9/YMSIEejatavMA7HOzs7Yu3evzLbPnz/Hrl278Pnnn8vt9969e5g9ezauXLmC+/fv4/Dhw+jbty/c3d3RrFkzpeNTOaF/8uQJvv32W5w/fx716tVD3bp1sXDhQjx69EjVXRERERERaYRt27ahTp06aN++Pdq3b4+6detiy5YtMn1u376NtLQ0mbYdO3ZAEAQMGDBAbp96eno4efIkOnToACcnJ4wbNw7t27fHiRMnoK2trXRsEuEj3sUbFxeHX375Bdu3b8etW7fQokULnDp1qlD7ir33T2HDICrV9IT04g6BqES64dKtuEMgKpG6ZN0u7hDkDJ4RXyTH2TxH+WEqpYnKFfp3OTo6YsqUKZg/fz7q1KmDs2fPqisuIiIiIiJSQqET+t9//x1jxoyBnZ0dBg4ciNq1a+PgwYPqjI2IiIiISoHcXKFIlrJK5Vlupk6diu3bt+PJkydo27Ytli5dih49esDIyEiM+IiIiIiIqAAqJ/RnzpzBpEmT4OPjo9Q0P0RERERUthXVm2LLKpUT+vDwcDHiICIiIiKiQijUGPotW7agWbNmsLe3xz//vJ2dZunSpdi/f79agyMiIiIizScIQpEsZZXKCf3q1avh7++Pzp07IzU1FTk5OQCA8uXLY+nSpeqOj4iIiIiICqByQr98+XKsX78e06ZNk5nwvmHDhnxzLBERERHJEXJzi2Qpq1RO6OPi4uDu7i7Xrq+vj1evXqklKCIiIiIiUo7KD8U6OjoiKioKDg4OMu1HjhyBq6ur2gIjIiIiotKhLM8RXxRUTui/+eYbfPnll0hPT4cgCLh06RK2b9+OoKAgbNiwQYwYiYiIiIgoHyon9EOHDkV2dja+/fZbvH79GgMHDkTFihXx008/oX///mLESEREREQarCzPQFMUVE7oAWDEiBEYMWIEnj59itzcXFhbWwMAHj9+jIoVK6o1QCIiIiIiyl+h5qHPY2lpCWtrayQkJGDs2LGoUaOGuuIiIiIiolJCyBWKZCmrlE7oU1NTMWjQIFhZWcHe3h7Lli1Dbm4uvvvuO1SrVg1//PEHfv75ZzFjJSIiIiKi9yg95Gbq1Kk4d+4chgwZgrCwMEyYMAFhYWFIT0/HkSNH4O3tLWacRERERKShynL1vCgondAfOnQIGzduRNu2bTFmzBjUqFEDtWrV4tthiYiIiIiKkdIJ/ZMnT6TzzFerVg0GBgb4/PPPRQuMiIiIiEqHXKHsvsW1KCg9hj43Nxe6urrSz9ra2jA2NhYlKCIiIiIiUo7SFXpBEODn5wd9fX0AQHp6OkaNGiWX1O/Zs0e9ERIRERGRRuMYenEpndAPGTJE5vNnn32m9mCIiIiIiEg1Sif0GzduFDMOIiIiIiqlWKEX10e9WIqIiIiIiIqX0hV6IiIiIqLCEARW6MXECj0RERERkQZjhZ6IiIiIRJWby3noxcQKPRERERGRBmOFnoiIiIhExVluxMUKPRERERGRBmNCT0RERESkwTjkhoiIiIhEJQh8KFZMrNATEREREWkwVuiJiIiISFR8KFZcrNATEREREWkwVuiJiIiISFSs0IuLFXoiIiIiIg3GCj0RERERiSqXs9yIihV6IiIiIiINxgo9EREREYmKY+jFxQo9EREREZEGY4WeiIiIiEQl5HIMvZhYoSciIiIi0mCs0BMRERGRqDiGXlys0BMRERERaTBW6ImIiIhIVALnoRcVK/RERERERBqMFXoiIiIiElUux9CLihV6IiIiIiINxgo9EREREYmK89CLixV6IiIiIiINxgo9EREREYmK89CLixV6IiIiIiINxgo9EREREYmK89CLixV6IiIiIiINxgo9EREREYmKY+jFxQo9EREREdEHzJ07F15eXjAyMkL58uWV2kYQBMycORP29vYwNDREy5Yt8ddff8n0ycjIwNixY2FpaQljY2N069YNjx49Uik2JvREREREJCohN7dIFjFlZmaib9++GD16tNLbLFy4EEuWLMGKFStw+fJl2Nraol27dnjx4oW0z/jx47F3717s2LEDFy5cwMuXL9G1a1fk5OQofRwOuSEiIiKiUiEjIwMZGRkybfr6+tDX1//ofc+aNQsAEBISolR/QRCwdOlSTJs2Db169QIAbNq0CTY2Nvjll1/wxRdfIC0tDcHBwdiyZQvatm0LANi6dSsqV66MEydOoEOHDkodq8Qk9DWrOxR3CIS3N0JQUBACAgLU8sNPVFrw3ih5HLJuF3cIBN4bpJwLv3kXyXFmzpwpTbzzBAYGYubMmUVy/HfFxcUhISEB7du3l7bp6+vD29sb4eHh+OKLL3D16lVkZWXJ9LG3t4ebmxvCw8OVTug55IZkZGRkYNasWXJ/3RKVdbw3iBTjvUElSUBAANLS0mSWgICAYoklISEBAGBjYyPTbmNjI12XkJAAPT09mJub59tHGUzoiYiIiKhU0NfXh6mpqcxS0DdHM2fOhEQiKXC5cuXKR8UkkUhkPguCINf2PmX6vKvEDLkhIiIiIipKX331Ffr3719gn6pVqxZq37a2tgDeVuHt7Oyk7YmJidKqva2tLTIzM5GSkiJTpU9MTISXl5fSx2JCT0RERERlkqWlJSwtLUXZt6OjI2xtbXH8+HG4u7sDeDtTztmzZ7FgwQIAQIMGDaCrq4vjx4+jX79+AID4+HjcuHEDCxcuVPpYTOhJhr6+PgIDA/lgE9F7eG8QKcZ7g8qKBw8eIDk5GQ8ePEBOTg6ioqIAADVq1EC5cuUAAM7OzggKCkLPnj0hkUgwfvx4zJs3DzVr1kTNmjUxb948GBkZYeDAgQAAMzMzDB8+HBMnTkSFChVgYWGBSZMmoU6dOtJZb5QhEQSBr+4iIiIiIiqAn58fNm3aJNd++vRptGzZEsDb8fIbN26En58fgLdj4WfNmoW1a9ciJSUFTZo0wcqVK+Hm5ibdPj09Hd988w1++eUXvHnzBm3atMGqVatQuXJlpWNjQk9EREREpME4yw0RERERkQZjQk9EREREpMGY0BMRERERaTAm9EQkGj8/P/To0aO4wyjzfH19MW/evGKNITExEVZWVnj8+HGxxlHWVa1aFUuXLi3uMNTi/v37kEgk0plGiMoyJvRlSHh4OLS1tdGxY8fiDoWKmJ+fn/SNd7q6uqhWrRomTZqEV69eFXdoxap9+/bQ1tbGH3/8UdyhqOTMmTOQSCRITU39YN/o6GgcOnQIY8eOlbbll9QtXbpU5gUqr169wuTJk1GtWjUYGBjAysoKLVu2xMGDB6V9WrZsKf3Z0tfXR8WKFfHpp59iz549Mvu2traGr68vAgMDVT7fsuDde1RHRwdVqlTB6NGjkZKSotbjXL58GSNHjlTrPguirvNSVByoXLky4uPjZWYLISqrmNCXIT///DPGjh2LCxcu4MGDB8UdDhWxjh07Ij4+Hn///Te+//57rFq1CpMmTSr0/jIzM9UYXdF78OABIiIi8NVXXyE4OLi4wxHNihUr0LdvX5iYmKi87ahRo7Bv3z6sWLECt27dQlhYGHr37o1nz57J9BsxYgTi4+Nx9+5d7N69G66urujfv79c4jh06FBs27ZN7UlqaZF3j96/fx8bNmzAb7/9hjFjxqj1GFZWVjAyMlLrPj9ErPPS1taGra0tdHT4Sh0iJvRlxKtXr7Bz506MHj0aXbt2RUhIiMz6AwcOoGbNmjA0NESrVq2wadMmuQpgeHg4WrRoAUNDQ1SuXBnjxo0r8xVeTaKvrw9bW1tUrlwZAwcOxKBBg7Bv3z4Aiqtf48ePl86rC7ytxH711Vfw9/eHpaUl2rVrBwD466+/0KVLF5iamsLExATNmzfHvXv3ZPa1aNEi2NnZoUKFCvjyyy+RlZUlXbd161Y0bNgQJiYmsLW1xcCBA5GYmChdn5KSgkGDBsHKygqGhoaoWbMmNm7cKF3/+PFj+Pj4wNzcHBUqVED37t1x//79D16PjRs3omvXrhg9ejRCQ0PlfpZbtmyJsWPHYvz48TA3N4eNjQ3WrVuHV69eYejQoTAxMUH16tVx5MgRme3Onj2Lxo0bQ19fH3Z2dpgyZQqys7Ol6xVVx+vXr4+ZM2dKP0skEmzYsAE9e/aEkZERatasiQMHDgB4O8ygVatWAABzc3NIJBLpfMfvy83Nxa5du9CtW7cPXg9FfvvtN0ydOhWdO3dG1apV0aBBA4wdOxZDhgyR6WdkZCT92WratCkWLFiAtWvXYv369Thx4oS0X506dWBra4u9e/cWKp7SLu8erVSpEtq3bw8fHx8cO3ZMps/GjRvh4uICAwMDODs7Y9WqVdJ1np6emDJlikz/pKQk6Orq4vTp0wDkf/7S0tIwcuRIWFtbw9TUFK1bt8b169el67S1tXH16lUAb+fTtrCwQKNGjaTbb9++XeaV9oU5r5ycHAwfPhyOjo4wNDSEk5MTfvrpJ+n6mTNnYtOmTdi/f7+02n/mzBm5ITd531ydPHkSDRs2hJGREby8vHD79m2ZeL7//ntYW1vDxMQEn3/+OaZMmYL69esXeA5EJR0T+jIiNDQUTk5OcHJywmeffYaNGzci7xUE9+/fR58+fdCjRw9ERUXhiy++wLRp02S2//PPP9GhQwf06tUL0dHRCA0NxYULF/DVV18Vx+mQGhgaGsok1srYtGkTdHR08Pvvv2Pt2rV4/PgxWrRoAQMDA5w6dQpXr17FsGHDZBLY06dP4969ezh9+jQ2bdqEkJAQmT8oMzMzMWfOHFy/fh379u1DXFycTII6Y8YM3Lx5E0eOHEFMTAxWr14tfU3369ev0apVK5QrVw7nzp3DhQsXUK5cOXTs2LHAbxAEQcDGjRvx2WefwdnZGbVq1cLOnTsVnq+lpSUuXbqEsWPHYvTo0ejbty+8vLxw7do1dOjQAb6+vnj9+jWAt39cdO7cGY0aNcL169exevVqBAcH4/vvv1fpOgPArFmz0K9fP0RHR6Nz584YNGgQkpOTUblyZezevRsAcPv2bcTHx8skP++Kjo5GamoqGjZsqPLxAcDW1haHDx/GixcvVN52yJAhMDc3lxt607hxY5w/f75Q8ZQlf//9N8LCwqCrqyttW79+PaZNm4a5c+ciJiYG8+bNw4wZM6Qvuhk0aBC2b9+Od18vExoaChsbG3h7e8sdQxAEdOnSBQkJCTh8+DCuXr0KDw8PtGnTBsnJyTAzM0P9+vVx5swZAG9/nvL+9/nz5wDeJtGK9q3KeeXm5qJSpUrYuXMnbt68ie+++w5Tp06V3pOTJk1Cv379pJX++Ph4eHl55XuMadOmYfHixbhy5Qp0dHQwbNgw6bpt27Zh7ty5WLBgAa5evYoqVapg9erVSsdPVGIJVCZ4eXkJS5cuFQRBELKysgRLS0vh+PHjgiAIwuTJkwU3NzeZ/tOmTRMACCkpKYIgCIKvr68wcuRImT7nz58XtLS0hDdv3oh/AvRRhgwZInTv3l36+eLFi0KFChWEfv36KVwvCILw9ddfC97e3tLP3t7eQv369WX6BAQECI6OjkJmZma+x3VwcBCys7OlbX379hV8fHzyjfXSpUsCAOHFixeCIAjCp59+KgwdOlRh3+DgYMHJyUnIzc2VtmVkZAiGhobC0aNH8z3GsWPHBCsrKyErK0sQBEH48ccfhWbNmsn08fb2Fj755BPp5+zsbMHY2Fjw9fWVtsXHxwsAhIiICEEQBGHq1Kly8axcuVIoV66ckJOTIwiCIDg4OAg//vijzLHq1asnBAYGSj8DEKZPny79/PLlS0EikQhHjhwRBEEQTp8+LXN/5mfv3r2Ctra2TDz5xZB3HRwcHKSfz549K1SqVEnQ1dUVGjZsKIwfP164cOGCzDbe3t7C119/rfD4TZo0ETp16iTTNmHCBKFly5YFxl0WDRkyRNDW1haMjY0FAwMDAYAAQFiyZIm0T+XKlYVffvlFZrs5c+YInp6egiAIQmJioqCjoyOcO3dOut7T01P45ptvpJ/f/W9/8uRJwdTUVEhPT5fZZ/Xq1YW1a9cKgiAI/v7+QteuXQVBEISlS5cKffr0ETw8PIRDhw4JgiAItWrVElavXv1R56XImDFjhN69e8vs5/3fUXFxcQIAITIyUhCE/+6LEydOSPscOnRIACD9d6pJkybCl19+KbOfZs2aCfXq1SswHqKSjhX6MuD27du4dOkS+vfvDwDQ0dGBj48Pfv75Z+n6d79CBd5W0d519epVhISEoFy5ctKlQ4cOyM3NRVxcXNGcCH2UgwcPoly5cjAwMICnpydatGiB5cuXq7SP9yu9UVFRaN68uUy17X21a9eGtra29LOdnZ3MkJrIyEh0794dDg4OMDExkQ7zyXvOY/To0dixYwfq16+Pb7/9FuHh4dJtr169irt378LExET6c2lhYYH09HS5YT/vCg4Oho+Pj3Ts7YABA3Dx4kW5r+br1q0r/f/a2tqoUKEC6tSpI22zsbEBAOn5xMTEwNPTExKJRNqnWbNmePnyJR49epRvPIq8e2xjY2OYmJjIXDdlvHnzBvr6+jLxqKJFixb4+++/cfLkSfTu3Rt//fUXmjdvjjlz5ii1vSAIcsc2NDSUfqNBslq1aoWoqChcvHgRY8eORYcOHaQPMyclJeHhw4cYPny4zO/h77//XvqzbmVlhXbt2mHbtm0AgLi4OERERGDQoEEKj3f16lW8fPkSFSpUkNlnXFycdJ8tW7bE+fPnkZubi7Nnz6Jly5Zo2bIlzp49i4SEBNy5c+eDFfqCzivPmjVr0LBhQ1hZWaFcuXJYv359oZ/1evfeyRsOlHfv3L59W+7ft/c/E2kiPklSBgQHByM7OxsVK1aUtgmCAF1dXaSkpCj8R1d45ytb4O1Xol988QXGjRsnt/8qVaqIEzipVatWrbB69Wro6urC3t5eJgnX0tKS+2+uaDiOsbGxzGdDQ8MPHvf9ZF8ikSA3NxfA22c72rdvj/bt22Pr1q2wsrLCgwcP0KFDB+mQmU6dOuGff/7BoUOHcOLECbRp0wZffvklFi1ahNzcXDRo0ECawLzLyspKYTzJycnYt28fsrKyZL5qz8nJwc8//4wFCxYUGPu7bXn3Td75FHQv5bUre60Lum7KsrS0xOvXr5GZmQk9PT1pu6mpKdLS0uT6p6amwszMTC6O5s2bo3nz5pgyZQq+//57zJ49G5MnT5bZ5/tycnIQGxsrVyxITk7O979NWWdsbIwaNWoAAJYtW4ZWrVph1qxZmDNnjvS//fr169GkSROZ7d79g3nQoEH4+uuvsXz5cvzyyy+oXbs26tWrp/B4ubm5sLOzkw6peVf58uUBvP2j7sWLF7h27RrOnz+POXPmoHLlypg3bx7q168Pa2truLi4FPq8AGDnzp2YMGECFi9eDE9PT5iYmOCHH37AxYsXP3zRFCjoHn23Lc/79yORJmJCX8plZ2dj8+bNWLx4Mdq3by+zrnfv3ti2bRucnZ1x+PBhmXVXrlyR+ezh4YG//vpL+kuZNM+7/6i+z8rKCjdu3JBpi4qKKrDyDrythG3atAlZWVkf7KvIrVu38PTpU8yfPx+VK1cGIP+zlxefn58f/Pz80Lx5c3zzzTdYtGgRPDw8EBoaKn2gTxnbtm1DpUqVpA8E5zl58iSCgoIwd+7cQs+a4erqit27d8sk9uHh4TAxMZH+QW1lZYX4+HjpNs+fP1f5W668RDonJ6fAfnkP+t28eVPmoT9nZ2dcvnxZrv/ly5fh5ORU4D5dXV2RnZ2N9PT0AhP6TZs2ISUlBb1795Zpv3HjhszD1pS/wMBAdOrUCaNHj4a9vT0qVqyIv//+O9+KOwD06NEDX3zxBcLCwvDLL7/A19c3374eHh5ISEiAjo6OzHSl78obR79ixQpIJBK4urrC3t4ekZGROHjwoErj5/M7r/Pnz8PLy0tm5pv3v2HT09P74M+7MpycnHDp0iWZ66Lodw6RpuGQm1Lu4MGDSElJwfDhw+Hm5iaz9OnTB8HBwfjiiy9w69YtTJ48GXfu3MHOnTulDy3mJSWTJ09GREQEvvzyS0RFRSE2NhYHDhyQ+9qUNFPr1q1x5coVbN68GbGxsQgMDJRL8BX56quv8Pz5c/Tv3x9XrlxBbGwstmzZIjd0JT9VqlSBnp4eli9fjr///hsHDhyQG87x3XffYf/+/bh79y7++usvHDx4UFoRHDRoECwtLdG9e3ecP38ecXFxOHv2LL7++ut8h7gEBwejT58+cvfDsGHDkJqaikOHDikVuyJjxozBw4cPMXbsWNy6dQv79+9HYGAg/P39oaX19tdt69atsWXLFpw/fx43btzAkCFDZCqsynBwcIBEIsHBgweRlJSEly9fKuxnZWUFDw8PXLhwQabd398fR44cwezZs3Hz5k3cvHkTc+bMQVhYGCZOnCjt17JlS6xduxZXr17F/fv3cfjwYUydOhWtWrWS+QPq9evXSEhIwKNHj3Dx4kVMnjwZo0aNwujRo6Uz8uT1u3r1qlxxgRRr2bIlateuLX0p2MyZMxEUFISffvoJd+7cwZ9//omNGzdiyZIl0m2MjY3RvXt3zJgxAzExMRg4cGC++2/bti08PT3Ro0cPHD16FPfv30d4eDimT58uk+S2bNkSW7duhbe3NyQSCczNzeHq6orQ0NBC/XH2/nnVqFEDV65cwdGjR3Hnzh3MmDFD7g/OqlWrIjo6Grdv38bTp09VfqA/z9ixYxEcHIxNmzYhNjYW33//PaKjows9LI2opGBCX8oFBwejbdu2cl+jA28r9FFRUUhJScGvv/6KPXv2oG7duli9erV0lht9fX0AbyuxZ8+eRWxsLJo3bw53d3fMmDHjg9OVkWbo0KEDZsyYgW+//RaNGjXCixcvMHjw4A9uV6FCBZw6dQovX76Et7c3GjRogPXr1ytdrbeyskJISAh27doFV1dXzJ8/H4sWLZLpo6enh4CAANStWxctWrSAtrY2duzYAeDtdInnzp1DlSpV0KtXL7i4uGDYsGF48+aNwor91atXcf36dbmqMQCYmJigffv2HzUnfcWKFXH48GFcunQJ9erVw6hRozB8+HBMnz5d2icgIAAtWrRA165d0blzZ/To0QPVq1dX+TizZs3ClClTYGNjU+BsUyNHjpQbktS0aVMcPXoUJ06cwCeffIJPPvkEx44dw9GjR2WGc3To0AGbNm1C+/bt4eLiIh3//P6MQOvXr4ednR2qV6+Onj174ubNmwgNDZWZUhEA9u/fjypVqqB58+YqnW9Z5u/vj/Xr1+Phw4f4/PPPsWHDBoSEhKBOnTrw9vZGSEgIHB0dZbYZNGgQrl+/jubNmxc4JFIikeDw4cNo0aIFhg0bhlq1aqF///64f/++9PkQ4O1wvZycHJnk3dvbGzk5OYWq0L9/XqNGjUKvXr3g4+ODJk2a4NmzZ3Lz1I8YMQJOTk7Scfa///57oY47aNAgBAQEYNKkSfDw8JDOqmVgYFCo/RGVFBKBg8dIgblz52LNmjV4+PBhcYdCRB8hPT0dTk5O2LFjBzw9PYs1lsaNG2P8+PEFVo2Jilq7/7V3h7YKBAEURV+CwWNWYamAEsgqDMGsoAgkCR3gCRoEoQaw2C2EECQERw8fsRn+ORU8eTOZ2Z1MUlVVDodD11Pgz9yhJ0my3W4zHo8zGAxyvV6z2Wx8Yx5+QL/fz36/z/1+73TH7XbLfD5P0zSd7uB/ez6f2e12qes6vV4vx+Mxl8sl5/O562nwFSf0JEmWy2VOp1Mej0eGw2EWi0VWq5VfagPwM16vV6bTadq2zfv9zmg0ynq9zmw263oafEXQAwBAwTyKBQCAggl6AAAomKAHAICCCXoAACiYoAcAgIIJegAAKJigBwCAggl6AAAo2AdYQiGPUEnjtwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#  numerical columns for correlation analysis\n",
    "numerical_columns = ['Age', 'Purchase Amount (USD)', 'Review Rating']\n",
    "\n",
    "# Compute the correlation matrix\n",
    "correlation_matrix = customer[numerical_columns].corr()\n",
    "\n",
    "# Plotting the heatmap\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', vmin=-1, vmax=1)\n",
    "plt.title('Correlation Heatmap of Numerical Features')\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## f) Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### encoding of categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Label encoding for binary categories\n",
    "le = LabelEncoder()\n",
    "customer['Gender'] = le.fit_transform(customer['Gender'])\n",
    "customer['Subscription Status'] = le.fit_transform(customer['Subscription Status'])\n",
    "customer['Discount Applied'] = le.fit_transform(customer['Discount Applied'])\n",
    "customer['Promo Code Used'] = le.fit_transform(customer['Promo Code Used'])\n",
    "\n",
    "# One-hot encoding for multi-class categories\n",
    "customer = pd.get_dummies(customer, columns=['Item Purchased', 'Category', 'Location', 'Size', 'Color', 'Season', 'Payment Method', 'Frequency of Purchases', 'Shipping Type'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting remaining non-numeric columns to numeric\n",
    "non_numeric_columns = customer.select_dtypes(exclude=['number']).columns\n",
    "if len(non_numeric_columns) > 0:\n",
    "    customer[non_numeric_columns] = customer[non_numeric_columns].apply(pd.to_numeric, errors='coerce')\n",
    "\n",
    "# Ensuring all data is of type float32\n",
    "customer = customer.astype('float32')\n",
    "\n",
    "# Fill NaNs with zeros or appropriate values\n",
    "customer = customer.fillna(0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Standardization of Numerical Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "numerical_features = ['Age', 'Purchase Amount (USD)', 'Previous Purchases']\n",
    "scaler = StandardScaler()\n",
    "customer[numerical_features] = scaler.fit_transform(customer[numerical_features])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- This ensures that each feature contributes equally to the model and prevents any single feature from dominating due to its scale."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting Dataset into Features and Target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = customer.drop(['Customer ID', 'Review Rating'], axis=1)\n",
    "y = customer['Review Rating']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train-Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### i) Baseline Model: Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Regression Mean Squared Error: 0.5705653429031372\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Initializing the linear regression model\n",
    "model_lr = LinearRegression()\n",
    "\n",
    "# Fit the model to the training data\n",
    "model_lr.fit(X_train, y_train)\n",
    "\n",
    "# Predictions on the test set\n",
    "y_pred_lr = model_lr.predict(X_test)\n",
    "\n",
    "# Evaluate the model\n",
    "mse_lr = mean_squared_error(y_test, y_pred_lr)\n",
    "\n",
    "\n",
    "print(f\"Linear Regression Mean Squared Error: {mse_lr}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ii) Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree Mean Squared Error: 1.0780769524207685\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Initializing the decision tree regressor model\n",
    "model_dt = DecisionTreeRegressor(random_state=42)\n",
    "\n",
    "# Fit the model to the training data\n",
    "model_dt.fit(X_train, y_train)\n",
    "\n",
    "# Predictions on the test set\n",
    "y_pred_dt = model_dt.predict(X_test)\n",
    "\n",
    "# Evaluate the model\n",
    "mse_dt = mean_squared_error(y_test, y_pred_dt)\n",
    "print(f\"Decision Tree Mean Squared Error: {mse_dt}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using GridSearchCV for Decision tree  Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 108 candidates, totalling 540 fits\n",
      "Best parameters for Decision Tree: {'max_depth': 10, 'max_features': 'log2', 'min_samples_leaf': 4, 'min_samples_split': 10}\n",
      "Best MSE for Decision Tree: 0.5332328833587315\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#  Decision Tree Regressor tuning with GridSearchCV\n",
    "param_grid = {\n",
    "    'max_depth': [None, 10, 20, 30],\n",
    "    'min_samples_split': [2, 5, 10],\n",
    "    'min_samples_leaf': [1, 2, 4],\n",
    "    'max_features': ['auto', 'sqrt', 'log2']\n",
    "}\n",
    "\n",
    "grid_dt = GridSearchCV(estimator=DecisionTreeRegressor(random_state=42),\n",
    "                       param_grid=param_grid,\n",
    "                       scoring='neg_mean_squared_error',\n",
    "                       cv=5,\n",
    "                       verbose=1,\n",
    "                       n_jobs=-1)\n",
    "\n",
    "grid_dt.fit(X_train, y_train)\n",
    "\n",
    "print(\"Best parameters for Decision Tree:\", grid_dt.best_params_)\n",
    "print(\"Best MSE for Decision Tree:\", -grid_dt.best_score_)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### iii) XG BOOST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost Mean Squared Error: 0.6459589004516602\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Initializing the XGBoost model\n",
    "model_xgb = xgb.XGBRegressor(random_state=42)\n",
    "\n",
    "# Fit the model to the training data\n",
    "model_xgb.fit(X_train, y_train)\n",
    "\n",
    "# Predictions on the test set\n",
    "y_pred_xgb = model_xgb.predict(X_test)\n",
    "\n",
    "# Evaluate the model\n",
    "mse_xgb = mean_squared_error(y_test, y_pred_xgb)\n",
    "print(f\"XGBoost Mean Squared Error: {mse_xgb}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using GridSearchCV for XGBoost Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 729 candidates, totalling 3645 fits\n",
      "Best parameters for XGBoost: {'colsample_bytree': 1.0, 'gamma': 0, 'learning_rate': 0.01, 'max_depth': 3, 'n_estimators': 100, 'subsample': 1.0}\n",
      "Best cross-validation negative mean squared error: -0.5052288472652435\n",
      "Tuned XGBoost Mean Squared Error: 0.5456407070159912\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Define the parameter grid for XGBoost\n",
    "param_grid = {\n",
    "    'n_estimators': [100, 200, 300],  # Number of boosting rounds\n",
    "    'learning_rate': [0.01, 0.1, 0.2],  # Step size shrinkage to prevent overfitting\n",
    "    'max_depth': [3, 5, 7],  # Maximum depth of a tree\n",
    "    'subsample': [0.6, 0.8, 1.0],  # Subsample ratio of training instances\n",
    "    'colsample_bytree': [0.6, 0.8, 1.0],  # Subsample ratio of columns when constructing each tree\n",
    "    'gamma': [0, 0.1, 0.2]  # Minimum loss reduction required to make a further partition on a leaf node of the tree\n",
    "}\n",
    "\n",
    "# Initialize XGBoost model\n",
    "model_xgb = XGBRegressor(random_state=42)\n",
    "\n",
    "# Setup grid search\n",
    "grid_xgb = GridSearchCV(estimator=model_xgb,\n",
    "                        param_grid=param_grid,\n",
    "                        scoring='neg_mean_squared_error',  # Use negative mean squared error as the evaluation metric\n",
    "                        cv=5,  # 5-fold cross-validation\n",
    "                        verbose=1,\n",
    "                        n_jobs=-1)\n",
    "\n",
    "# Fit grid search\n",
    "grid_xgb.fit(X_train, y_train)\n",
    "\n",
    "# Print best parameters and best score\n",
    "print(\"Best parameters for XGBoost:\", grid_xgb.best_params_)\n",
    "print(\"Best cross-validation negative mean squared error:\", grid_xgb.best_score_)\n",
    "\n",
    "# Predictions on the test set with the best model\n",
    "y_pred_xgb_tuned = grid_xgb.best_estimator_.predict(X_test)\n",
    "\n",
    "# Evaluate performance on the test set\n",
    "mse_xgb_tuned = mean_squared_error(y_test, y_pred_xgb_tuned)\n",
    "print(f\"Tuned XGBoost Mean Squared Error: {mse_xgb_tuned}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### iv) Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "78/78 [==============================] - 2s 12ms/step - loss: 2.1516 - val_loss: 0.6013\n",
      "Epoch 2/100\n",
      "78/78 [==============================] - 1s 7ms/step - loss: 0.5207 - val_loss: 0.5760\n",
      "Epoch 3/100\n",
      "78/78 [==============================] - 0s 5ms/step - loss: 0.4784 - val_loss: 0.5735\n",
      "Epoch 4/100\n",
      "78/78 [==============================] - 0s 5ms/step - loss: 0.4538 - val_loss: 0.5705\n",
      "Epoch 5/100\n",
      "78/78 [==============================] - 0s 5ms/step - loss: 0.4332 - val_loss: 0.5937\n",
      "Epoch 6/100\n",
      "78/78 [==============================] - 0s 5ms/step - loss: 0.4061 - val_loss: 0.5966\n",
      "Epoch 7/100\n",
      "78/78 [==============================] - 0s 4ms/step - loss: 0.3712 - val_loss: 0.5818\n",
      "Epoch 8/100\n",
      "78/78 [==============================] - 0s 5ms/step - loss: 0.3245 - val_loss: 0.6250\n",
      "Epoch 9/100\n",
      "78/78 [==============================] - 0s 5ms/step - loss: 0.2868 - val_loss: 0.6395\n",
      "Epoch 10/100\n",
      "78/78 [==============================] - 0s 4ms/step - loss: 0.2376 - val_loss: 0.6462\n",
      "Epoch 11/100\n",
      "78/78 [==============================] - 0s 6ms/step - loss: 0.1963 - val_loss: 0.6584\n",
      "Epoch 12/100\n",
      "78/78 [==============================] - 0s 4ms/step - loss: 0.1609 - val_loss: 0.6940\n",
      "Epoch 13/100\n",
      "78/78 [==============================] - 0s 5ms/step - loss: 0.1196 - val_loss: 0.7432\n",
      "Epoch 14/100\n",
      "78/78 [==============================] - 0s 5ms/step - loss: 0.0937 - val_loss: 0.7590\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 0.5887\n",
      "Neural Network Mean Squared Error: 0.5886560082435608\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Define the neural network model\n",
    "model_nn = Sequential()\n",
    "model_nn.add(Dense(128, input_dim=X_train.shape[1], activation='relu'))\n",
    "model_nn.add(Dense(64, activation='relu'))\n",
    "model_nn.add(Dense(32, activation='relu'))\n",
    "model_nn.add(Dense(1))  # Regression output layer\n",
    "\n",
    "# Compile the model\n",
    "model_nn.compile(optimizer='adam', loss='mean_squared_error')\n",
    "\n",
    "# Early stopping callback\n",
    "early_stop = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)\n",
    "\n",
    "# Fit the model to the training data\n",
    "history = model_nn.fit(X_train, y_train, epochs=100, validation_split=0.2, callbacks=[early_stop])\n",
    "\n",
    "# Evaluate the model on the test set\n",
    "mse_nn = model_nn.evaluate(X_test, y_test)\n",
    "print(f\"Neural Network Mean Squared Error: {mse_nn}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GridSearchCV for Neural Network Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 24 candidates, totalling 120 fits\n",
      "Best parameters for Neural Network: {'activation': 'tanh', 'layers': 3, 'optimizer': 'rmsprop', 'units': 32}\n",
      "Best cross-validation negative mean squared error: -0.5254148840904236\n",
      "Tuned Neural Network Mean Squared Error: 0.5632502436637878\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Function to create the neural network model\n",
    "def create_model(layers=2, units=64, activation='relu', optimizer='adam'):\n",
    "    model = Sequential()\n",
    "    model.add(Dense(units, input_dim=X_train.shape[1], activation=activation))\n",
    "    for _ in range(layers - 1):\n",
    "        model.add(Dense(units, activation=activation))\n",
    "    model.add(Dense(1))  # Output layer for regression\n",
    "    model.compile(optimizer=optimizer, loss='mean_squared_error')\n",
    "    return model\n",
    "\n",
    "# Wrap Keras model so it can be used by GridSearchCV\n",
    "model = KerasRegressor(build_fn=create_model, verbose=0)\n",
    "\n",
    "# Define the parameter grid for GridSearchCV\n",
    "param_grid = {\n",
    "    'layers': [2, 3],  # Number of hidden layers\n",
    "    'units': [32, 64, 128],  # Number of units in each hidden layer\n",
    "    'activation': ['relu', 'tanh'],  # Activation function\n",
    "    'optimizer': ['adam', 'rmsprop']  # Optimizer\n",
    "}\n",
    "\n",
    "# Setup grid search\n",
    "grid_nn = GridSearchCV(estimator=model,\n",
    "                       param_grid=param_grid,\n",
    "                       scoring='neg_mean_squared_error',  \n",
    "                       cv=5,  # 5-fold cross-validation\n",
    "                       verbose=1,\n",
    "                       n_jobs=-1)\n",
    "\n",
    "# Fit grid search\n",
    "grid_nn.fit(X_train, y_train, epochs=100, validation_split=0.2, callbacks=[EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)])\n",
    "\n",
    "# Print best parameters and best score\n",
    "print(\"Best parameters for Neural Network:\", grid_nn.best_params_)\n",
    "print(\"Best cross-validation negative mean squared error:\", grid_nn.best_score_)\n",
    "\n",
    "# Predictions on the test set with the best model\n",
    "y_pred_nn_tuned = grid_nn.best_estimator_.predict(X_test)\n",
    "\n",
    "# Evaluate performance on the test set\n",
    "mse_nn_tuned = mean_squared_error(y_test, y_pred_nn_tuned)\n",
    "print(f\"Tuned Neural Network Mean Squared Error: {mse_nn_tuned}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Importance Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#  feature_importances\n",
    "feature_importances = pd.Series(grid_dt.best_estimator_.feature_importances_, index=X_train.columns)\n",
    "feature_importances = feature_importances.sort_values(ascending=False)\n",
    "\n",
    "\n",
    "top_10_features = feature_importances.head(10)\n",
    "\n",
    "# Plot top 10 feature importances\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x=top_10_features.values, y=top_10_features.index)\n",
    "plt.xlabel('Feature Importance')\n",
    "plt.ylabel('Feature')\n",
    "plt.title('Top 10 Feature Importances from Decision Tree Regressor')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Importance Analysis for Predicting Customer Behavior Using a Decision Tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Item Purchased_Coat: Most important feature, indicating that buying a coat strongly influences customer behavior.\n",
    "- Location_Ohio: Customers from Ohio have distinct preferences, making this a significant factor.\n",
    "- Item Purchased_Sandals: Key factor in predicting behavior, highlighting the impact of buying sandals.\n",
    "- Frequency of Purchases_Bi-Weekly: Bi-weekly purchase frequency is crucial for understanding customer behavior.\n",
    "- Color_Lavender: The color lavender affects customer preferences, suggesting its popularity.\n",
    "- Shipping Type_Store Pickup: Preference for store pickup is an important predictor.\n",
    "- Shipping Type_Standard: Standard shipping impacts behavior, but less so than store pickup.\n",
    "- Payment Method_Bank Transfer: The choice of bank transfer influences customer behavior.\n",
    "- Size_S: Smaller sizes (Size 'S') are relevant in predicting behavior.\n",
    "- Age: Age demographics are important in shaping customer preferences."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Insights for Customer Behavior and Preferences Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "- Product Preferences: Coats and sandals are key drivers of customer behavior.\n",
    "- Geographic Influence: Location, especially Ohio, impacts behavior, useful for targeted marketing.\n",
    "- Purchase Frequency: Bi-weekly purchasing patterns are significant for promotions.\n",
    "- Color Preference: Lavender is a popular color, valuable for product design and marketing.\n",
    "- Shipping and Payment Methods: Preferences for store pickup, standard shipping, and bank transfers highlight the need for diverse options.\n",
    "- Demographic Factors: Size and age demographics are crucial for understanding customer segments and tailoring strategies."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Leveraging Data Insights for Enhanced Customer Behavior and Preferences Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Summary**\n",
    "\n",
    "i) Product Focus: Prioritize coats and sandals in inventory and marketing to boost sales.<br>\n",
    "ii) Targeted Marketing: Develop region-specific campaigns, particularly for Ohio, to cater to local preferences.<br>\n",
    "iii) Purchase Patterns: Create promotions and loyalty programs aligned with bi-weekly purchase habits to encourage repeat business.<br>\n",
    "iv) Product Design and Marketing: Incorporate popular colors like lavender in product design and marketing to attract customers.<br>\n",
    "v) Shipping and Payment Options: Offer diverse and convenient shipping and payment options, emphasizing store pickup, standard shipping, and bank transfers.<br>\n",
    "vi) Customer Segmentation: Tailor product offerings and marketing strategies based on size preferences and age demographics to better meet customer needs."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.9.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}