code.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir(r'C:\\Users\\lwhieldon\\OneDrive - SC&H Group\\Administrative\\School Materials\\DATA 602 - Into to Data Analysis and ML\\OnlineRetailCustomerSegmentation\\images')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = 'https://archive.ics.uci.edu/ml/machine-learning-databases/00352/Online%20Retail.xlsx'"
   ]
  },
  {
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "          InvoiceDate  UnitPrice CustomerID         Country  \n",
       "0 2010-12-01 08:26:00       2.55      17850  United Kingdom  \n",
       "1 2010-12-01 08:26:00       3.39      17850  United Kingdom  \n",
       "2 2010-12-01 08:26:00       2.75      17850  United Kingdom  \n",
       "3 2010-12-01 08:26:00       3.39      17850  United Kingdom  \n",
       "4 2010-12-01 08:26:00       3.39      17850  United Kingdom  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_initial = pd.read_excel(dataset,encoding=\"ISO-8859-1\",converters={'CustomerID':str,'InvoiceNo':str})\n",
    "df_initial.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 541909 entries, 0 to 541908\n",
      "Data columns (total 8 columns):\n",
      " #   Column       Non-Null Count   Dtype         \n",
      "---  ------       --------------   -----         \n",
      " 0   InvoiceNo    541909 non-null  object        \n",
      " 1   StockCode    541909 non-null  object        \n",
      " 2   Description  540455 non-null  object        \n",
      " 3   Quantity     541909 non-null  int64         \n",
      " 4   InvoiceDate  541909 non-null  datetime64[ns]\n",
      " 5   UnitPrice    541909 non-null  float64       \n",
      " 6   CustomerID   406829 non-null  object        \n",
      " 7   Country      541909 non-null  object        \n",
      "dtypes: datetime64[ns](1), float64(1), int64(1), object(5)\n",
      "memory usage: 33.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df_initial.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
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       "\n",
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       "        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>Quantity</th>\n",
       "      <th>UnitPrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>541909.000000</td>\n",
       "      <td>541909.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.552250</td>\n",
       "      <td>4.611114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>218.081158</td>\n",
       "      <td>96.759853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-80995.000000</td>\n",
       "      <td>-11062.060000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.080000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>4.130000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80995.000000</td>\n",
       "      <td>38970.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Quantity      UnitPrice\n",
       "count  541909.000000  541909.000000\n",
       "mean        9.552250       4.611114\n",
       "std       218.081158      96.759853\n",
       "min    -80995.000000  -11062.060000\n",
       "25%         1.000000       1.250000\n",
       "50%         3.000000       2.080000\n",
       "75%        10.000000       4.130000\n",
       "max     80995.000000   38970.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_initial.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this purpose of the exercise, let's first check to see if invoices beginning with 'c' (i.e. cancelled purchases) are in the dataset. Giftware shop is interested in determining customer segmentation based on customers who purchased products, not cancelled purchases, so we can remove all cancelled purchases from the dataset.\n",
    "\n",
    "Let's also remove null records, especially where a customer id is not assigned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_initial = df_initial[~df_initial.InvoiceNo.str.startswith('C', na=False)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "InvoiceNo           0\n",
       "StockCode           0\n",
       "Description      1454\n",
       "Quantity            0\n",
       "InvoiceDate         0\n",
       "UnitPrice           0\n",
       "CustomerID     134697\n",
       "Country             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_initial.isnull().sum()\n",
    "#Description & Customer ID contain null records. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of Customer IDs missing is 33.849931%\n"
     ]
    }
   ],
   "source": [
    "missing_customerIDs = df_initial['CustomerID'].isnull().sum()/df_initial['CustomerID'].count()\n",
    "\n",
    "print('Percentage of Customer IDs missing is {:0%}'.format(missing_customerIDs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "InvoiceNo      0\n",
       "StockCode      0\n",
       "Description    0\n",
       "Quantity       0\n",
       "InvoiceDate    0\n",
       "UnitPrice      0\n",
       "CustomerID     0\n",
       "Country        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Since ~34% of the records are missing and still leaves us with a good portion of data, let's remove these records for now\n",
    "#Now we are left with fully populated data. \n",
    "df_initial.dropna(inplace=True)\n",
    "df_initial.isnull().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Add a new Total Invoice Amt Column\n",
    "df_initial['Total Line Amount']= df_initial['Quantity'] * df_initial['UnitPrice']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3, 4, 6, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add a Day of Week Purchase Column\n",
    "df_initial['PurchaseDayofWeek'] = pd.to_datetime(df_initial['InvoiceDate']).dt.dayofweek\n",
    "df_initial['PurchaseDayofWeek'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 8,  9, 10, 11, 12, 13, 14, 15, 16, 17,  7, 18, 19, 20,  6],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add a Time of Day Purchase Column\n",
    "df_initial['PurchaseTimeofDay'] = pd.to_datetime(df_initial['InvoiceDate'],format='%H:%m').dt.hour\n",
    "df_initial['PurchaseTimeofDay'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Add a Date Only Column from Invoice Date (no timestamp)\n",
    "df_initial['InvoiceDate_noTime'] = pd.to_datetime(df_initial['InvoiceDate']).dt.date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total no. of customers:  4339\n",
      "Total transactions :  18536\n",
      "Products sold are :  3665\n"
     ]
    }
   ],
   "source": [
    "print('Total no. of customers: ',df_initial['CustomerID'].nunique())\n",
    "print('Total transactions : ',df_initial['InvoiceNo'].nunique())\n",
    "print('Products sold are : ',df_initial['StockCode'].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_initial.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 397924 entries, 0 to 397923\n",
      "Data columns (total 12 columns):\n",
      " #   Column              Non-Null Count   Dtype         \n",
      "---  ------              --------------   -----         \n",
      " 0   InvoiceNo           397924 non-null  object        \n",
      " 1   StockCode           397924 non-null  object        \n",
      " 2   Description         397924 non-null  object        \n",
      " 3   Quantity            397924 non-null  int64         \n",
      " 4   InvoiceDate         397924 non-null  datetime64[ns]\n",
      " 5   UnitPrice           397924 non-null  float64       \n",
      " 6   CustomerID          397924 non-null  object        \n",
      " 7   Country             397924 non-null  object        \n",
      " 8   Total Line Amount   397924 non-null  float64       \n",
      " 9   PurchaseDayofWeek   397924 non-null  int64         \n",
      " 10  PurchaseTimeofDay   397924 non-null  int64         \n",
      " 11  InvoiceDate_noTime  397924 non-null  object        \n",
      "dtypes: datetime64[ns](1), float64(2), int64(3), object(6)\n",
      "memory usage: 36.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df_initial.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we are looking at the data on a customer level, let's aggregate & perform feature engineering on the dataset:\n",
    "\n",
    "1. Total Purchase Amount by Customer \n",
    "1. Number of Transactons by Customer\n",
    "1. Distinct Products Types Purchased by Customer\n",
    "1. Tenure (in days) of being a customer (based on last transaction in the dataset)\n",
    "1. Apply getdummies to country feature; 1 = UK, 0 = Non-UK\n",
    "1. Customers who, on average, purchase more products during the business week (Monday - Friday). Will denote with 1\n",
    "1. Customers who, on average, purchase more products during normal business hours (To keep our analysis consistent, let's assume the dates and times are collected in British time; therefore, we will check to see times between 9a-5pm British time). Also, we will also factor in when a customer calls during the weekend as I want to make this indicator mutually exclusive from the workweek attribute above. Will denote with 1\n",
    "\n",
    "Once we have aggregated our measures, let's create a new dataframe that groups all features by customer \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Total Purchase Amount by Customer\n",
    "total_amount = df_initial['Total Line Amount'].groupby(df_initial['CustomerID']).sum().reset_index()\\\n",
    "        .rename(columns={\"Total Line Amount\": \"TotlAmtbyCust\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Number of Transactions by Customer\n",
    "transactions = df_initial['InvoiceNo'].groupby(df_initial['CustomerID']).count().reset_index()\\\n",
    "        .rename(columns={\"InvoiceNo\": \"TotalCustTransactions\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Distinct Products Types Purchased by Customer\n",
    "product_types = df_initial['StockCode'].groupby(df_initial['CustomerID']).nunique().reset_index()\\\n",
    "                .rename(columns={\"StockCode\":\"DistProductsbyCust\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Tenure (in days) of being a customer (based on last transaction in the dataset)\n",
    "final = df_initial['InvoiceDate_noTime'].max()\n",
    "df_initial['TenureofCustomer'] = final - df_initial['InvoiceDate_noTime']\n",
    "CustomerTenure = df_initial['TenureofCustomer'].groupby(df_initial['CustomerID']).min().dt.days                         "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "United Kingdom          354345\n",
       "Germany                   9042\n",
       "France                    8342\n",
       "EIRE                      7238\n",
       "Spain                     2485\n",
       "Netherlands               2363\n",
       "Belgium                   2031\n",
       "Switzerland               1842\n",
       "Portugal                  1462\n",
       "Australia                 1185\n",
       "Norway                    1072\n",
       "Italy                      758\n",
       "Channel Islands            748\n",
       "Finland                    685\n",
       "Cyprus                     614\n",
       "Sweden                     451\n",
       "Austria                    398\n",
       "Denmark                    380\n",
       "Poland                     330\n",
       "Japan                      321\n",
       "Israel                     248\n",
       "Unspecified                244\n",
       "Singapore                  222\n",
       "Iceland                    182\n",
       "USA                        179\n",
       "Canada                     151\n",
       "Greece                     145\n",
       "Malta                      112\n",
       "United Arab Emirates        68\n",
       "European Community          60\n",
       "RSA                         58\n",
       "Lebanon                     45\n",
       "Lithuania                   35\n",
       "Brazil                      32\n",
       "Czech Republic              25\n",
       "Bahrain                     17\n",
       "Saudi Arabia                 9\n",
       "Name: Country, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_initial['Country'].value_counts()\n",
    "#Biggest customer is from the UK."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since a good portion of the online retailer's customers are from the UK, let's create a new feature where we assign a customer from the UK with 1 and everyone else 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#Apply getdummies to country feature\n",
    "Countries = pd.concat([df_initial['CustomerID'],pd.get_dummies(df_initial['Country']=='United Kingdom', prefix='IsUK')],1)\n",
    "Countries = Countries.loc[1:].groupby(Countries['CustomerID']).max()\n",
    "Countries = pd.DataFrame(Countries).reset_index(drop=True)\n",
    "Countries.drop('IsUK_False',axis='columns', inplace=True)\n",
    "Countries=Countries.rename(columns={'IsUK_True':'IsUK'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Customers who, on average, purchase more products during the business week (Monday - Friday). Will denote with 1\n",
    "IsWeekday = pd.concat([df_initial['CustomerID'].groupby(df_initial['CustomerID'])\\\n",
    "                       .max(),round(df_initial['PurchaseDayofWeek'].groupby(df_initial['CustomerID']).sum()\\\n",
    "                                    /df_initial['PurchaseDayofWeek'].groupby(df_initial['CustomerID']).count())],1)\n",
    "IsWeekday = pd.DataFrame(IsWeekday).reset_index(drop=True)\n",
    "IsWeekday['IsWeekDay'] = (IsWeekday['PurchaseDayofWeek']==0) & (IsWeekday['PurchaseDayofWeek']==6).any()\n",
    "IsWeekday['IsWeekDay']=pd.get_dummies(IsWeekday['IsWeekDay'])\n",
    "IsWeekday.drop('PurchaseDayofWeek',axis='columns', inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Customers who, on average, purchase more products during normal business hours (9a-5pm British time). Will denote with 1\n",
    "IsWorkDay = pd.concat([df_initial['CustomerID'].groupby(df_initial['CustomerID'])\\\n",
    "                       .max(),round(df_initial['PurchaseTimeofDay'].groupby(df_initial['CustomerID']).sum()\\\n",
    "                                    /df_initial['PurchaseTimeofDay'].groupby(df_initial['CustomerID']).count())],1)\n",
    "IsWorkDay = pd.DataFrame(IsWorkDay).reset_index(drop=True)\n",
    "\n",
    "IsWorkDay['IsWorkDay'] = np.where((IsWorkDay['PurchaseTimeofDay']>= 9) & (IsWorkDay['PurchaseTimeofDay']<= 14), False, True)\n",
    "IsWorkDay['IsWorkDay']=pd.get_dummies(IsWorkDay['IsWorkDay'])\n",
    "IsWorkDay.drop('PurchaseTimeofDay',axis='columns', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new = pd.merge(total_amount,transactions,how='inner',on='CustomerID')\n",
    "df_new = pd.merge(df_new,product_types,how='inner',on='CustomerID')\n",
    "df_new = pd.merge(df_new,CustomerTenure,how='inner',on='CustomerID')\n",
    "df_new = pd.merge(df_new,Countries,how='inner',on='CustomerID')\n",
    "df_new = pd.merge(df_new,IsWeekday,how='inner',on='CustomerID')\n",
    "df_new = pd.merge(df_new,IsWorkDay,how='inner',on='CustomerID')\n",
    "df_new.drop('CustomerID',axis='columns', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Attributes')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Outlier Analysis of Amount #ofTransactions, and Disinct Products & Tenure\n",
    "\n",
    "attributes = ['TotlAmtbyCust','TotalCustTransactions','DistProductsbyCust','TenureofCustomer']\n",
    "plt.rcParams['figure.figsize'] = [10,8]\n",
    "sns.boxplot(data = df_new[attributes], orient=\"v\", palette=\"Set2\" ,whis=1.5,saturation=1, width=0.7)\n",
    "plt.title(\"Outliers Variable Distribution\", fontsize = 14, fontweight = 'bold')\n",
    "plt.ylabel(\"Range\", fontweight = 'bold')\n",
    "plt.xlabel(\"Attributes\", fontweight = 'bold')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Whoa! BIG outliers in TotlAmtbyCust; this could throw off our model. Let's reduce the dataset to only include prices & customer transactions that fall with the 95% percentile to help reduce the <b>outliers</b>. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>TotlAmtbyCust</th>\n",
       "      <th>TotalCustTransactions</th>\n",
       "      <th>DistProductsbyCust</th>\n",
       "      <th>TenureofCustomer</th>\n",
       "      <th>IsUK</th>\n",
       "      <th>IsWeekDay</th>\n",
       "      <th>IsWorkDay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "      <td>3573.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1027.136629</td>\n",
       "      <td>59.933389</td>\n",
       "      <td>48.597537</td>\n",
       "      <td>93.115029</td>\n",
       "      <td>0.902323</td>\n",
       "      <td>0.915477</td>\n",
       "      <td>0.837392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1026.018189</td>\n",
       "      <td>53.587022</td>\n",
       "      <td>39.822855</td>\n",
       "      <td>97.554484</td>\n",
       "      <td>0.296919</td>\n",
       "      <td>0.278209</td>\n",
       "      <td>0.369060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>103.300000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>320.720000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>645.960000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1351.630000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5756.890000</td>\n",
       "      <td>247.000000</td>\n",
       "      <td>219.000000</td>\n",
       "      <td>373.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       TotlAmtbyCust  TotalCustTransactions  DistProductsbyCust  \\\n",
       "count    3573.000000            3573.000000         3573.000000   \n",
       "mean     1027.136629              59.933389           48.597537   \n",
       "std      1026.018189              53.587022           39.822855   \n",
       "min       103.300000               6.000000            2.000000   \n",
       "25%       320.720000              20.000000           18.000000   \n",
       "50%       645.960000              41.000000           35.000000   \n",
       "75%      1351.630000              85.000000           68.000000   \n",
       "max      5756.890000             247.000000          219.000000   \n",
       "\n",
       "       TenureofCustomer         IsUK    IsWeekDay    IsWorkDay  \n",
       "count       3573.000000  3573.000000  3573.000000  3573.000000  \n",
       "mean          93.115029     0.902323     0.915477     0.837392  \n",
       "std           97.554484     0.296919     0.278209     0.369060  \n",
       "min            0.000000     0.000000     0.000000     0.000000  \n",
       "25%           21.000000     1.000000     1.000000     1.000000  \n",
       "50%           53.000000     1.000000     1.000000     1.000000  \n",
       "75%          140.000000     1.000000     1.000000     1.000000  \n",
       "max          373.000000     1.000000     1.000000     1.000000  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q_low = df_new[\"TotlAmtbyCust\"].quantile(0.04)\n",
    "q_hi  = df_new[\"TotlAmtbyCust\"].quantile(0.95)\n",
    "df_new = df_new[(df_new[\"TotlAmtbyCust\"] < q_hi) & (df_new[\"TotlAmtbyCust\"] > q_low)]\n",
    "q_low = df_new[\"TotalCustTransactions\"].quantile(0.04)\n",
    "q_hi  = df_new[\"TotalCustTransactions\"].quantile(0.95)\n",
    "df_new = df_new[(df_new[\"TotalCustTransactions\"] < q_hi) & (df_new[\"TotalCustTransactions\"] > q_low)]\n",
    "df_new.reset_index(drop=True, inplace=True)\n",
    "df_new.describe()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have an aggregated dataset with features important to customers & removed outliers, let's perform some EDA & visualize the dataset.\n",
    "\n",
    "Let's first look at the continuous features to see if we can obtain any details related to customer spending habits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1 , figsize = (10,15))\n",
    "n = 0 \n",
    "for x in ['TotlAmtbyCust' , 'TotalCustTransactions' ,'DistProductsbyCust','TenureofCustomer']:\n",
    "    n += 1\n",
    "    plt.subplot(3,2,n)\n",
    "    plt.hist(df_new[x] , bins = 100)\n",
    "    plt.title('Histogram of {}'.format(x))\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1 , figsize = (15,6))\n",
    "n = 0 \n",
    "for x in ['IsUK' , 'IsWeekDay' ,'IsWorkDay']:\n",
    "    n += 1\n",
    "    plt.subplot(1,3,n)\n",
    "    plt.subplots_adjust(hspace = 0.5 , wspace = 0.5)\n",
    "    sns.countplot(df_new[x] )\n",
    "    plt.title('Histogram of {}'.format(x))\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Majority of customers have purchased between \\\\$0-\\\\$1000 in total from Giftware Shop\n",
    "1. Majority of customers have purchased between 0-50 or so times\n",
    "1. Customers tend to only purchase less than 50 distinct products from Giftware Shop in total\n",
    "1. The tenure is a little bit more spread out - But the majority of Giftware Shop's customer base fall between 0 (new) to 100 days of transactions. This might be a good item to note to Giftware Shop that they should focus on retention of customers.\n",
    "1. As expected, the majority of the customers are from the UK\n",
    "1. Most customers purchase products during the week\n",
    "1. Most customers purchase products during the work day"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we know that most of the customers are in the UK, let's go ahead and remove anyone outside of the UK as a customer. There's not a ton of data to delineate customers from outside of the UK so let's focus this to reflect only UK customers.\n",
    "\n",
    "Let's keep customers who purchase during the week & workday for now since they do not appear to have much presence in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3224 entries, 0 to 3223\n",
      "Data columns (total 7 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   TotlAmtbyCust          3224 non-null   float64\n",
      " 1   TotalCustTransactions  3224 non-null   int64  \n",
      " 2   DistProductsbyCust     3224 non-null   int64  \n",
      " 3   TenureofCustomer       3224 non-null   int64  \n",
      " 4   IsUK                   3224 non-null   uint8  \n",
      " 5   IsWeekDay              3224 non-null   uint8  \n",
      " 6   IsWorkDay              3224 non-null   uint8  \n",
      "dtypes: float64(1), int64(3), uint8(3)\n",
      "memory usage: 110.3 KB\n"
     ]
    }
   ],
   "source": [
    "df_new = df_new[(df_new[\"IsUK\"] == 1) ]\n",
    "df_new.reset_index(drop=True, inplace=True)\n",
    "df_new.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_scaled = sc.fit_transform(df_new[['TotlAmtbyCust' , 'TotalCustTransactions' ,'DistProductsbyCust','TenureofCustomer']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:143: FutureWarning: The sklearn.metrics.classification module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.metrics. Anything that cannot be imported from sklearn.metrics is now part of the private API.\n",
      "  warnings.warn(message, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "import sys\n",
    "from yellowbrick.cluster import KElbowVisualizer\n",
    "kmeans = KMeans(random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\yellowbrick\\utils\\kneed.py:182: YellowbrickWarning: No \"knee\" or \"elbow point\" detected This could be due to bad clustering, no actual clusters being formed etc.\n",
      "  warnings.warn(warning_message, YellowbrickWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\yellowbrick\\utils\\kneed.py:140: YellowbrickWarning: No 'knee' or 'elbow point' detected This could be due to bad clustering, no actual clusters being formed etc.\n",
      "  warnings.warn(warning_message, YellowbrickWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\yellowbrick\\cluster\\elbow.py:343: YellowbrickWarning: No 'knee' or 'elbow' point detected, pass `locate_elbow=False` to remove the warning\n",
      "  warnings.warn(warning_message, YellowbrickWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\yellowbrick\\utils\\kneed.py:182: YellowbrickWarning: No \"knee\" or \"elbow point\" detected This could be due to bad clustering, no actual clusters being formed etc.\n",
      "  warnings.warn(warning_message, YellowbrickWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\yellowbrick\\utils\\kneed.py:140: YellowbrickWarning: No 'knee' or 'elbow point' detected This could be due to bad clustering, no actual clusters being formed etc.\n",
      "  warnings.warn(warning_message, YellowbrickWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\yellowbrick\\cluster\\elbow.py:343: YellowbrickWarning: No 'knee' or 'elbow' point detected, pass `locate_elbow=False` to remove the warning\n",
      "  warnings.warn(warning_message, YellowbrickWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ce53a8fc70>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visualizer = KElbowVisualizer(kmeans, k=(2,10), metric = 'calinski_harabasz', timings=False)\n",
    "visualizer.fit(X_scaled) \n",
    "visualizer.show()        \n",
    "visualizer = KElbowVisualizer(kmeans, k=(2,10), metric = 'silhouette', timings=False,locate_elbow=True)\n",
    "visualizer.fit(X_scaled)   \n",
    "visualizer.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "k_means = KMeans(n_clusters= 3, init='k-means++', random_state=0).fit(X_scaled)\n",
    "clusters = k_means.predict(X_scaled)\n",
    "\n",
    "cl_labels_k = k_means.labels_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use PCA to reduce our dimensionality to see how KMeans clusters our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=2)\n",
    "\n",
    "X_pca = pca.fit_transform(X_scaled)\n",
    "\n",
    "\n",
    "plt.scatter(X_pca[cl_labels_k==0,0],X_pca[cl_labels_k==0,1],label='Cluster 1')\n",
    "plt.scatter(X_pca[cl_labels_k==1,0],X_pca[cl_labels_k==1,1],label='Cluster 2')\n",
    "plt.scatter(X_pca[cl_labels_k==2,0],X_pca[cl_labels_k==2,1],label='Cluster 3')\n",
    "\n",
    "plt.xlabel('Feature-1')\n",
    "plt.ylabel('Feature-2')\n",
    "plt.legend()\n",
    "plt.title('PCA KMeans Clustering Scatter Plot')\n",
    "plt.savefig('KMEANS.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_labels = pd.DataFrame(k_means.labels_ , columns = list(['labels']))\n",
    "df_labels.astype({'labels': 'object'}).dtypes\n",
    "df_labels.loc[(df_labels.labels == 0)] = 'Cluster 1'\n",
    "df_labels.loc[(df_labels.labels == 1)] = 'Cluster 2'\n",
    "df_labels.loc[(df_labels.labels == 2)] = 'Cluster 3'\n",
    "df_new['Kmeans_labels'] = df_labels['labels'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cluster 1     733\n",
       "Cluster 2     753\n",
       "Cluster 3    1738\n",
       "Name: Kmeans_labels, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new['Kmeans_labels'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(1 , figsize = (10,10))\n",
    "n = 0 \n",
    "for var in ['TotlAmtbyCust' , 'TotalCustTransactions' ,'DistProductsbyCust','TenureofCustomer']:\n",
    "    n += 1\n",
    "    label = df_new['Kmeans_labels'].unique()                   \n",
    "\n",
    "    plt.subplot(2,2,n)\n",
    "    plt.hist([df_new.loc[df_new['Kmeans_labels'] == x, var] for x in label], label=label)\n",
    "\n",
    "    plt.title('K Means Histogram of {}'.format(var))\n",
    "    plt.legend()\n",
    "plt.suptitle('K Means Cluster Results')\n",
    "plt.savefig('KMEANSHistograms.png')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusions on K-Means Clustering with k=3\n",
    "\n",
    "K-Means Clustering identified 3 primary clusters in the dataset:\n",
    "\n",
    "- Customers tagged to __Cluster Id 2__ have a high amount of transactions as compared to the other customer clusters.\n",
    "- Customers tagged to __Cluster Id 2__ are also frequent buyers compared to __Cluster 1__ and __Cluster 3__ & tend to buy a wider array of products.\n",
    "- Customers tagged to __Cluster Id 3__ are newer buyers and, while they buy more than cluster 1, they don't really spend that much from a total amount & transaction perspective.\n",
    "- Customers tagged to __Cluster Id 1__ are not recent buyers and tend to purchase less (in total amount spent, transactions, as well as the type of products they buy).\n",
    "\n",
    "But based on the dimensionality reduction we perform to visualize our clusters, does this really reflect our customer base? It looks like one big cluster, which gives the indication that KMeans doesn't do the best job at fitting our model. Let's try DBSCAN & Spectral Clustering to see if they do a better job at identifying our customer segmentation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import DBSCAN\n",
    "db = DBSCAN().fit(X_scaled)\n",
    "cl_labels_d = db.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=2)\n",
    "\n",
    "X_pca = pca.fit_transform(X_scaled)\n",
    "# X_pca_centers_dbscan = pca.transform(cl_centers)\n",
    "\n",
    "plt.scatter(X_pca[cl_labels_d==0,0],X_pca[cl_labels_d==0,1],label='Cluster 1')\n",
    "plt.scatter(X_pca[cl_labels_d==1,0],X_pca[cl_labels_d==1,1],label='Cluster 2')\n",
    "plt.scatter(X_pca[cl_labels_d==2,0],X_pca[cl_labels_d==2,1],label='Cluster 3')\n",
    "plt.scatter(X_pca[cl_labels_d==-1,0],X_pca[cl_labels_d==-1,1],s=6,label='Noisy')\n",
    "# plt.scatter(X_pca_centers_kmeans[:, 0], X_pca_centers_kmeans[:, 1], c='black', s = 75)\n",
    "plt.xlabel('Feature-1')\n",
    "plt.ylabel('Feature-2')\n",
    "plt.legend()\n",
    "plt.title('PCA DBScan Scatter Plot')\n",
    "plt.savefig('DBSCAN.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_labels = pd.DataFrame(cl_labels_d, columns = list(['labels']))\n",
    "df_labels.astype({'labels': 'object'}).dtypes\n",
    "df_labels.loc[(df_labels.labels == -1)] = 'Noisy'\n",
    "df_labels.loc[(df_labels.labels == 0)] = 'Cluster 1'\n",
    "df_labels.loc[(df_labels.labels == 1)] = 'Cluster 2'\n",
    "df_labels.loc[(df_labels.labels == 2)] = 'Cluster 3'\n",
    "df_new['DBSCAN_labels'] = df_labels['labels'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cluster 1    3054\n",
       "Cluster 2      13\n",
       "Cluster 3       6\n",
       "Noisy         151\n",
       "Name: DBSCAN_labels, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new['DBSCAN_labels'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LU0EmIiIiYmEqyEREREQsTAWZiIiIiIWpIBMRERGxMBVkIiIiIhamgkxERETEwlSQiYiIiFiYCjIRERERC1NBJiIiImJhKshERERELEwFmYiIiIiFqSATERERsTAVZCIiIiIWpoJMRERExMJUkImIiIhYmAoyEREREQtTQSYiIiJiYSrIRERERCxMBZmIiIiIhakgExEREbEwFWQiIiIiFqaCTERERMTCVJCJiIiIWJgKMhERERELU0EmIiIiYmEqyEREREQsTAWZiIiIiIWpIBMRERGxMBVkIiIiIhamgkxERETEwlSQiYiIiFiYCjIRERERC1NBJiIiImJhKshERERELEwFmYiIiIiFqSATERERsbBSW5CdOXOGevXq0blzZzp37oyvry8BAQGsX7/e1CYqKopnnnmGzp0789JLL/HCCy8wfPhwUlNTTW2io6NNz3fq1IkZM2aQkZFhev7cuXOEhYXh6+vLSy+9RPfu3fnPf/6TJ57Bgwfj5eXFH3/8Yba9bdu2jBgxwmzbDz/8QNu2bfMdV9u2bfnhhx+K9F4EBwfz1VdfFek1CQkJvPHGG3m2nzlzhkaNGuXZHhUVxaRJkwAYO3Ys27dvv23/48aN4+DBg0WKqaTYunUr3t7edOvWjevXr5u2DxkyxPTzVrduXXx9fencuTPBwcEF9rVy5UqWLVsGmL+HuaZPn467uztnz569q1jv1H9h7Nu3j759+5p+j/r378/Ro0fvKh6A06dPM3jw4Lt+fUmlnPMn5Zzida9yzu3c/LlnZ2ezaNEiunTpQufOnXnxxReZNWuW2c9lUd0cR0BAgKnfm3+Hhg8fftf93ys3x718+XIWLFhw3/Zte9/2dA+UKVOGNWvWmB4nJyfTp08fbGxs8PHxAeDFF19kwoQJwI0fukGDBhETE8OAAQP48ssv+c9//sOKFSsoU6YM6enpDBkyhPfff59hw4Zx6dIlAgIC+Oc//8k777yDlZUVR44c4dVXX6Vs2bK0aNECuJFA9+zZQ8OGDVm9ejWBgYFmcX711Ve0bNmSzp0736d35t6aOnXqHdts376dHj163Idoit+6devo3r07AwcONNseGRlp+n/dunVZvHgxlSpVum1fSUlJPPHEE/k+l56ezurVq/Hx8WHp0qWEhoYWOdbb9V8Ye/bsYcSIEbz//vu4u7sD8PnnnxMcHMyXX355x/Hl57///S+//PLLXcdUkinnWIZyTvHknIKEh4dz5coVFi9ejLOzM9euXSM0NJSxY8cya9asIvWVXxxxcXHAjeLb19fX7HeopLk57lt/r+61Ul2Q3apatWoMGTKEhQsXmpLjzdLT07l27RqVK1cG4MKFC2RnZ3P9+nXKlCmDg4MD48eP59KlSwDExsbSuHFj/Pz8TH08+eSTREZG4uLiYtoWHx9P8+bN8fHx4b333iMgIAArKyvT8yEhIUyZMoXGjRtTo0aNuxrbtWvXCA8P5+TJk/z+++84Ojoye/ZsateuDcC///1vFixYwPXr1/H19WXAgAHAjdmP2bNn88cff2Btbc1bb72Ft7f3XcWQKzg4mJ49e9KuXTsmT57Mvn37sLOzo3r16rzzzjssWLCA8+fPExoaysyZM3n00UcJDw8nOTkZwzDw8/OjX79+wI1vzQsWLKBMmTI888wzLFmyhB9//JGoqCi+//57zp8/T926dQkLC2PChAlcvHiRCxcuUK1aNd59910eeeQR2rZtS6dOndi5cydXrlyhX79+7Nu3j0OHDmFra8u8efN49NFHzcaQmZnJ9OnT2bFjBzY2Nnh4eDB69Gji4uLYuHEjDg4OpKSkMGrUqDu+HwX1tWPHDjZt2sS2bdsoU6ZMntetW7cOV1dX+vTpQ9++fRk0aBBly5YFKNSYDhw4kKf/EydO0LNnT65cuUK9evWYOHEiP/30E8OHD2fTpk1YW1vzxx9/0LZtW9atW0dkZCQDBw40FWMAL730Eg4ODmRnZ7Nr1y4mT57MF198AWD2+MSJE4wdO5aMjAwMw6Bbt24EBAQwbtw4zp07R9++fVm4cOHd/ZCVEso5yjklOef4+PgUOIZcZ86cYe3atWzduhUnJycAypUrx9tvv82+ffsACAsL44knnqBv3755HsfGxhIXF4ednR0ODg5MmjSJX375xSyOnj173vazLV++PD///DOBgYE8/fTTptm5Cxcu8OyzzzJt2jTOnDlDnz59eO6559i/fz9Xr15lxIgRtG/fPt9c1LNnT/73v/8VOP5ffvmFCRMmcOnSJaytrRkwYAB2dnZmcV+6dInLly8zYcIEjh07xqRJk/j999+xsrLitddew8/Pj127dvGvf/2LGjVqcOzYMbKysnj77bfx9PRk7969TJ8+nZycHADeeOONfPNErlJ7yLIgTz75pNnhlvXr15sOL7Rq1YrLly/z/PPPA/Dyyy/j4uJCy5Yt6dGjB9OnT+e3337Dw8MDgIMHD9K4ceM8+2jatCl169YFICsri/j4eF566SXatm3LxYsXSUxMzNM+KCiI0NBQsrKy7mpciYmJuLi4sGLFCjZs2IC7u7vZtHRaWhrx8fHEx8fz+eefs3nzZq5cucLo0aOZOXMmq1atYu7cuYSHh/Pf//73tvu6fv26aUo591/uN5ybff/99+zevZvPP/+chIQEatSowU8//URISAhVqlRh9uzZNGjQgNDQULy8vFi7di3Lly/n888/Z926dRw/fpzZs2fzySefsHr1apycnMjOzjb1n5yczKpVq5g9ezbr1q2jYcOGrFixgo0bN+aZqUhPTyc+Pp5//vOfTJgwgd69e/P555/z2GOPsWrVqjyxz5s3j/Pnz7NmzRrWrFlDTk4OM2fOpF+/frRt25Y+ffoUKjHerq/27dub+sovIcXGxvLSSy/x9NNPU7ly5Txx3mlM+fV/6tQpoqKiWLt2LYZhMG/ePDw9PSlfvjxbtmwBbhSCzZs3p1KlSgX+jPv4+JiKiIIsXLiQtm3bmv7A7d27FysrK6ZMmYKrq+sDX4zlUs5RzimpOedOYwA4dOgQjz/+uKkYy1W5cuXbFg9wYwZ42rRpfPTRR3z22Wf4+/uTlJR0x9x3KxcXF9avX09wcDBLlixhyJAhrFy5knXr1rFp0ybToejTp0/TsmVLPv30U4YPH860adOA/HNRTk7Obcc/bNgwOnTowLp161iwYAERERE0b94837izsrIYMGAAwcHBrF27lujoaCIiIvjuu+8AOHDgAK+99hqrV6+mS5cu/Otf/wJuHHZ/9dVXSUhIYNq0aezcufO278MDNUMGYGVlZTYbcfPhg8zMTCZNmkRISAgLFy7E2dmZjz/+mNOnT7Nz5052795N//79CQoKYsSIEVhZWWEYxm33t3HjRnJycmjVqhW2tra8+OKLLFmyhOeee86s3eDBg9mxYwdRUVG0a9euyOPq0KEDNWrUICYmhpMnT7J7926zdRfdunXD1tYWJycnfHx8TOstLly4wKBBg8zen59++um2+8rvlzYqKorLly+bbXNzc8PGxobu3bvTsmVLfHx8TH9Ycl27do19+/bx8ccfA+Ds7EyXLl1ITEzkwoULtGjRgv/7v/8DoFevXkRFRZle27BhQ2xtb/yI9u7dm71797Jo0SJ+/fVXjh07RoMGDUxtc//g1ahRg7/97W88+eSTALi6unLlypU8Y0xMTCQkJAQ7Ozvgxre0m9+noribvg4dOsSRI0fo2LEjAH5+fixZsoTAwEDTTEdRxwTQvn170yGNrl27MnPmTAB69uxJfHw8zz33HCtWrGDkyJEAWFtbm769FVX79u0ZNWoUBw4coHnz5owbNw5r6wfuO94dKeco55TUnHOnMcBfywE2NjZ06NCBgIAA2rRpQ8uWLfP8HBZGkyZNTP+fPn06iYmJzJ8/n59//tk0y1yhQgXs7OxM/devX5/ff/8dKDgXFTT+33//nSNHjtC9e3cAHnvssXzXaeb69ddfSU9PN33mjz76KM8//zxbtmzBy8uLqlWrUq9ePVNcuQX5Cy+8wKRJk9i0aRPPPvssw4YNu+378MAVZD/88ANubm75PmdnZ0dQUJBpnUF0dDSenp6maf3u3buzd+9eXn/9dUaMGEHDhg35/vvv6dWrl1k/cXFx/PHHH7z66qvExsZy/fp10weVO8167Ngxs+P4tra2zJkzhy5dulChQoUijys2Npb4+Hh69uyJr68vFSpU4MyZM6bnbWxsTP83DANbW1uys7OpU6cOK1euND137tw5KlWqxNq1a4scw61cXFxYs2YN+/btY+fOnQwdOpS+ffuafbPIycnJ8wcmJyeHrKwsbGxszJ67eQxwY9o816xZszhw4ABdu3bFy8uLrKwss9fa29ub/p+bpG4nJyfH7BBPTk4OmZmZhRh18fS1bNkybG1t6dq1K3DjG9j58+dJTEw0JZyijgnM38OcnBzTHxdfX18iIiLYuXMn165do2nTpsCNP0D79+/P8zvz9ttv0759e2xtbc3e55vH5e3tzYYNG9i+fTs7duzggw8+ICEhoVBxPkiUc5RzSmrOudMYADw8PPj5559JTU01myU7d+4c48ePJzIyMs8XhZv3NXv2bI4ePcr27dtZsGABa9as4b333ivSeG5+33v16kXdunVp1aoVL7zwAvv37zft287OzvSl7+bxF5SLYmJi8h1/bl68uY+ff/6ZqlWr5htfdna2WVu48TOfO/t88xeym9+rgIAAvL292bZtG1u2bOH999/nq6++wsHBId/9PFBfZ3/55Rfmzp3La6+9VmCbb7/91vSN6vr168yZM8dUZQMcPXqU+vXrA9CjRw/T9HjuG3zw4EEiIyNxc3Pjl19+Yc+ePSQkJLBp0yY2bdrE1q1badq0KUuWLMmz7xo1ajB27FgiIiKKPLatW7fy8ssv0717d2rVqsWmTZvMptpXr16NYRhcuXKFL7/8klatWtGwYUNOnjzJnj17ADh8+DA+Pj6cO3euyPvPzzfffEOfPn1o1KgRgwcPxs/PzzS1bGNjQ1ZWFk5OTjRo0MB0qCMlJYXVq1fz7LPP0rJlS3bs2GGK5+Yknt/4e/fujZ+fH4888gjbt283G39RtWrViuXLl5OZmUlOTg7Lli0zLZguzr5y34ebXb16lfXr1zN//nzTz01iYiIvvfQSixcvLtK+b+1/06ZNXLlyhezsbOLj42ndujUAZcuW5aWXXmLMmDEEBASY2g8YMID333/f7Oy0hIQENmzYgJubG5UqVeK///0vFy9exDAM1q1bZ2o3fPhw1q9fT8eOHZk4cSJOTk6cOnUKGxubu/5DU9oo5yjnFJYlck5hxvDoo4/i6+vLmDFjTGcDp6amEh4eToUKFShTpgwVK1Y0vc/nzp1j9+7dAFy6dInnnnuOChUq0KdPH4YOHWo6czO/3HcnV69e5YcffiA0NJTnn3+es2fPcurUqTvO4BWUiwoav5OTE0899RSrV68G4LfffiMwMJCUlJR8465duza2trZ8/fXXpvdgw4YNPPvss7eNKyAggMOHD9OlSxcmT57M1atXuXDhQoHtS/UMWe66A7gx7erg4MCwYcNo06aNqc369etJSkrCysqK9PR0atSowYwZMwAYOHAgVlZWpgWxOTk5uLu78+677wJQoUIFYmJimDVrFh9++CHW1taULVuWqVOn0qJFC6ZNm0a7du34+9//bhbXoEGDeOONNwgJCckTs5+fH1u3bjUtlsxPr169zA79hIaG8tprrzFhwgQ+/fRT4MbMxs3rVnKn5a9fv06vXr145plngBtn6cycOZP09HQMw2DmzJlUr17d9Av1V7Ru3ZrExEQ6depEuXLlKF++PJMnTwZuTCGPGDGC8PBwZs+ezaRJk0hISCAjIwNfX1+6dOmClZUVo0ePpm/fvtjb21OvXj3TovZbDRo0iJkzZ/Lee+9hZ2dH48aNOXXq1F3HPmDAAGbMmIGfnx9ZWVl4eHgwfvz4Yu+rdevWTJ8+3az9qlWrqFOnjukzurmfjh07FumSE7f2X6dOHd544w2uXr2Kp6cn/fv3Nz3XpUsX4uPjzRaMN2nShClTpjB16lSuXbtGZmYmrq6uLFmyhL/97W/87W9/I6rXJj4AACAASURBVCAggK5du1K5cmXatGljSrgDBw5k7NixrFixAhsbG9q1a0fTpk25cuUKDg4OdOvWjZUrV+b5ZlmaKeco59wtS+Scwo5h4sSJzJ07l4CAAGxsbMjIyKBdu3amy9cEBwcTGhqKj48P1atXN33WlSpVYsCAAfTp04cyZcpgY2PDlClT8sSR3+VO8uPi4kL//v15+eWXKVeuHI8++iiNGzfm5MmTtz05paBcdLvxz5kzh7fffpuYmBisrKyYOnUqlStXzjdn29nZMXfuXKZMmUJUVJTp7OlnnnmGXbt2FRhXaGgo06ZN491338XKyoq33nqL6tWrF9jeyrjTggWRe+T06dOsWbOGgQMHYm1tzddff010dPRtv7XK3TEMg+joaJKTk3n77bctHY6IRSjnSElWqmfIpHT7v//7P86fP4+vry82NjY4OzubzpqR4vWPf/yDKlWqMHfuXEuHImIxyjlSkmmGTERERMTCHqhF/SIiIiKlUYk7ZJmTk0NaWhp2dnYP1GJgEcmfYRhkZmbi6OhY6q9jpvwl8vAprhxW4gqytLS0v3RjYxEpndzc3HB2drZ0GH+J8pfIw+uv5rASV5DlXmDPzc3N7MJ7Bw8eNLvfXklUGmKE0hFnaYgRFGdxyMjI4OjRo4W++G1Jdmv+Ksnve2FpDCWDxmB5BcVfXDmsxBVkudP89vb2ea5mW9DVbUuS0hAjlI44S0OMoDiLy4NwiC+//FXS3/fC0BhKBo3B8m4X/1/NYaV7wYaIiIjIA0AFmYiIiIiFqSATERERsTAVZCIiIiIWpoJMRERExMJUkImIiIhYWIm77IVIaWczPKZY+8ueE1ys/YmIFET5y3I0QybyADh27Bj9+/cnODiYrl27EhkZiWEY7Nq1i5CQkCL3t3Tp0r8Uz7Rp01i+fPlf6kNEHg7KXzeoIBMp5a5evcqwYcMYM2YMMTExxMfHc/ToUeLi4u66z3nz5t3V6y5dukS/fv3YtGnTXe9bRB4eyl9/KvWHLPObXtUUqTxMNm7ciJeXFzVr1gTAxsaGGTNmYGdnx3fffWdq16JFC7Zt2wZASEgIAQEBVKlShdGjR2Nra4uNjQ0zZ84kISGBK1euEB4eztixY5k4cSInT54kJyeHoUOH4uXlRadOnahZsyb29vZERESY9pGWlsbgwYNJTEy8r+9BaXZrDlP+koeJ8tefNEMmUsqdP3+eGjVqmG1zdHQ0uxdsQbZv385TTz3FokWLePPNN7ly5QoDBgygfPnyhIeHs3LlSipWrMiyZcuYO3cukyZNAuDatWsMHDjQLJkB1KhRgwYNGhTf4ETkgab89adSP0Mm8rCrWrUqP/74o9m206dPc/bs2QJfYxgGAN26dSM6Opp+/frh7OycZ73G0aNHSUpK4sCBAwBkZWVx+fJlAGrVqlWcwxCRh5Dy1580QyZSynl7e7NlyxZOnToFQGZmJtOnT+fo0aNm7bKyskhLSyMjI4Pjx48DNw4XeHp6snjxYjp06MBHH30E/JnwateuTceOHYmJiSE6OpoOHTpQvnx5AKytlT5E5K9R/vqTZshEitn9XgPk5OTE9OnTGTduHIZhkJaWhre3N0FBQezevdvU7pVXXqFHjx5Ur16dqlWrAuDu7s6IESOIiorC2tqa0aNHA1CnTh1CQ0OZNm0a48aNo1evXqSmphIUFFQiE5mIFA/lL8uxMnJLyRIiPT2dgwcP4u7ujoODg2l7UlISnp6eedqXpEX9BcVY0pSGOEtDjKA4i0NBv/Ol0a1jKcz7XtIX9Zfkn53C0hhKhtI+hoLiL64cVnJLRREREZGHhAoyEREREQtTQSYiIiJiYSrIRERERCxMBZmIiIiIhRXqshf79+9n9uzZxMTEcPHiRcaNG8fVq1fJzs5m5syZuLq6Eh8fT1xcHLa2tgwYMABvb2+uX7/OiBEjuHjxIo6OjsyYMYNKlSrd6zGJWNQnW8OKtb8+LacXa38iIgVR/rKcO86QRUdHM27cONLT0wGYNWsWvr6+LFu2jKFDh/Lzzz9z4cIFYmJiiIuLY+HChURERJCRkcHy5ctxc3MjNjYWPz8/5s6de88HJPIwOnbsGP379yc4OJiuXbsSGRmJYRjs2rUrz9WrC2Pp0qV3Fcfhw4cJCgoiODiYvn378r///e+u+hGRh4fy1w13LMhcXV2JiooyPd63bx/nzp2jT58+rF27lmbNmnHgwAEaNWqEvb09zs7OuLq6cuTIEZKSkmjVqhUArVu3ZseOHfduJCIPqatXrzJs2DDGjBlDTEwM8fHxHD16lLi4uLvuc968eXf1uqlTpzJ+/HhiYmJo37490dHRdx2DiDz4lL/+dMdDlj4+Ppw5c8b0ODk5GRcXFz755BPef/99oqOjqVmzJs7OzqY2jo6OpKamkpqaatru6OhISkpKoQM7ePBgnm1JSUmFem1h290Lltx3UZSGOEtDjHDv47xT/4mJidSqVYuLFy9y8eJFAAIDA7G1teXo0aNcunQJgGbNmpkSVWRkJO3ataNixYrMnz8fGxsbbGxsGDBgAImJiVy+fJlBgwbxyiuv8PHHH3P27FlycnLw9/enfv36jBw5ksceewxbW1sGDx5siqVPnz5cu3aNpKQkfvnlFy5dulRqPkcRuf82btyIl5cXNWvWBMDGxoYZM2ZgZ2fHd999Z2rXokULtm3bBkBISAgBAQFUqVKF0aNHY2tri42NDTNnziQhIYErV64QHh7O2LFjmThxIidPniQnJ4ehQ4fi5eVFp06dqFmzJvb29mY3GI+IiKBKlSoAZGdn3/cLVRf51kkVKlSgbdu2ALRt25Z//etfuLu7k5aWZmqTlpaGs7MzTk5Opu1paWm4uLgUej+FvVI/sT/m2WSpKwGXlqsQl4Y4S0OMkH+cP2xdWaz7uNP7sHfvXjw9PQtst2/fPgDs7OxMbSpVqoSbmxvHjx/Hy8uLsLAw9u7dyyOPPMLkyZPZtGkTH3zwAbGxsTz++ON8+OGHXL58mV69erFu3ToMw2DMmDHUr1+/wH0mJiaybNmyO64bzb3KtYg8fM6fP0+NGjXMtjk6Ohbqtdu3b+epp54y5a8rV64wYMAAli5dSnh4OLGxsVSsWJFp06aZ5a9r164xcODAPPkrtxjbt28fS5cuZdmyZcUzyEIq8lmWnp6ebN68GYA9e/bw+OOP4+HhQVJSEunp6aSkpHDixAnc3Nxo3LixqW1iYmKp+AMrUtpUrVqVs2fPmm07ffo0e/bsKfA1uXdM69atGxUrVqRfv34sW7YMGxsbs3ZHjx4lMTGR4OBghgwZQlZWFpcvXwagVq1a+fa9fv16Jk6cyIIFC0rMSTz79+8nOPjGLYkKWicSHx9Ply5d8Pf355tvvgHg+vXrDB48mKCgIF5//XXTbKOIFA/lrz8VuSAbNWoUa9asISAggC1btvDmm29SuXJlgoODCQoKonfv3oSEhODg4EBgYCDHjh0jMDCQFStW8NZbb92LMYg81Ly9vdmyZQunTp0CIDMzk+nTp3P06FGzdllZWaSlpZGRkcHx48eBG4cLPD09Wbx4MR06dOCjjz4C/kx4tWvXpmPHjsTExBAdHU2HDh0oX748QL436V2zZg1Lly4lJiYmz7deS7n1xKT81onoxCQRy1D++lOhDllWr16d+Ph4AKpVq8aiRYvytPH398ff399sW9myZYmMjCyGMEVKj/t9mreTkxPTp09n3LhxGIZBWloa3t7eBAUFsXv3blO7V155hR49elC9enWqVq0K3FgaMGLECKKiorC2tmb06NEA1KlTh9DQUKZNm8a4cePo1asXqampBAUF5ZvI4Maai6lTp/LYY4+Z1pU1bdqUIUOG3ON34PZyT0waOXIkkP86kZtPTLK3tzc7Malfv37AjROTVJDJg075y3L5y8rILSVLiILuml7QmiKb4TF5tmXPCb6nMRakNK97KmlKQ4ygOItDQb/zxenMmTMMGzbM9MUSbqwTGTt2LMuWLWPLli0cPXqUESNGADBy5Ej8/PxYsGAB48ePp06dOuTk5NCmTRsSExPvOJaiaHbLOtjdQfmvyxORku2v5rAiL+oXESnt1q9fz7x580zrRG4+AQn++olJuYm5UIXwLQVZSSucS3IxX1gaQ8lQ2sdQUPzFdWKSbp0kIg+V/NaJ6MQkEbE0zZCJyEPjdutEck9MMgzD7MSkUaNGERgYiJ2dHXPmzLHwCETkQaWCTEQeeDefmHTzQuGb6cQkEbEkHbIUERERsTDNkIkUsz1Oxftr1TQ1q1j7ExEpiPKX5WiGTOQBsGvXLpo0acJvv/1m2jZ79mwSEhLybZ+QkMDGjRvvV3giIgVS/rpBBZnIA8LOzo7Ro0dTmEsLdunShX/84x/3ISoRkTtT/lJBJvLAeOaZZyhfvnyeG+J+/PHHjBs3jh49ejBr1iwAoqKiWL58OZcuXeKVV14hODiYgIAAfvrpJ1asWMGMGTOAG2cl+vr6kpGRcd/HIyIPj9vlr65duz4U+UsFmcgDJDw8nE8++YRff/0VuHEx0y+//JLw8HDi4uI4efKk6cbZAAcOHMDZ2dl0v8fU1FQ6duzIxo0byc7OZsuWLXh5eWFvb2+hEYnIw6Kg/BUXF/dQ5C8VZCIPkIoVKzJmzBjCwsLIyckhPT2dBg0aYGtri5WVFU2aNOHYsWOm9q1bt6Zp06YMHDiQyMhIrK2tcXJyomnTpmzdupWEhAS6detmwRGJyMOioPxlZ2f3UOQvFWQiD5i2bdtSq1YtVq1aZbpxdnZ2NoZhsGfPHmrVqmVqu2vXLqpUqcLHH3/MgAEDiIiIAG5ck2vlypVcvHiRJ5980lJDEZGHTH75Kysr66HIX7rshUgxKwmneY8dO5adO3fi6OjICy+8QHh4OGXLlsXT05N27dpx5MgRAJ588klCQkJYvHgx1tbWDBo0CIAGDRpw8uRJevbsaclhiMh9VhLzV2BgIDk5OQ98/lJBJvIA8PLywsvLy/TYycnJbK2Fh4eH2X0Yc28bBPDJJ5/k6S8nJ4dy5crRqVOnexOwiMj/d6f89eqrr5q1f1Dzlw5ZioiZ06dP8/LLL9O5c2ecnJwsHY6ISKGV5vylGTIRMVOjRg3WrFlj6TBERIqsNOcvzZCJiIiIWJgKMhERERELU0EmIiIiYmGFWkO2f/9+Zs+eTUxMjGnb2rVrWbp0KStWrAAgPj6euLg4bG1tGTBgAN7e3ly/fp0RI0Zw8eJFHB0dmTFjBpUqVbo3IxEpIWJtAou1v6Ds5cXan4hIQZS/LOeOM2S5tyRIT083bTt8+DCffvqp6SagFy5cICYmhri4OBYuXEhERAQZGRksX74cNzc3YmNj8fPzY+7cufduJCIPsWPHjtG/f3+Cg4Pp2rUrkZGRGIbBrl27CAkJKXJ/S5cuvas4jh8/TmBgIAEBAYSHh5OdnX1X/YjIw0P564Y7zpC5uroSFRXFyJEjAbh8+TKzZ89mzJgxjB8/HrhxP6lGjRphb2+Pvb09rq6uHDlyhKSkJPr16wfcuMVBUQqygwcP5tmWlJRUqNcWtt29YMl9F0VpiLM0xAj3Ps479Z+WlsakSZMYOnQojz32GDk5Obz33nvMnDmTqlWrcunSpSLHGRkZSb169Yoc65w5c+jYsSP16tVj/vz5REdH07Rp0yL3IyIPh6tXrzJs2DCioqKoWbMm2dnZ/POf/yQuLo7atWvfVZ/z5s2jV69eRX5dREQEw4YNo2nTpoSFhbFp0ybat29/VzHcjTsWZD4+Ppw5cwa4cef0sWPHMmbMGBwcHExtUlNTcXZ2Nj12dHQkNTXVbLujoyMpKSmFDszd3d1sH0lJSWYXtjSJ/THPpnzb3QcFxljClIY4S0OMkH+cPxXzPu70PqxatYo2bdqYXQTxww8/xM7Oju+++459+/YBMGTIELZt2wZASEgIAQEBVKlShdGjR2Nra4uNjQ0zZ84kISGBa9eusXbtWsaOHcvEiRM5efIkOTk5DB06FC8vLzp16kTNmjWxt7c33a4EICYmBhsbGzIyMsjJyaFZs2Y0btz4tvGnp6fn+wVMRB58GzduxMvLi5o1awJgY2PDjBkzTPkrV4sWLQqdv65cuUJ4eHiR81dUVJQpf124cIFHHnnkvr4XRboO2aFDhzh58iTh4eGkp6dz/Phxpk6dyjPPPENaWpqpXVpaGs7Ozjg5OZm2p6Wl4eLiUrzRiwjnz5+nRo0aZtscHR0L9drt27fz1FNPERYWxt69e7ly5QoDBgxg6dKlhIeHExsbS8WKFZk2bRqXL1+mV69erFu3jmvXrjFw4EDq169v1p+NjQ3Jycm8+uqrODk5md13TkTkVspffyrSWZYeHh6sW7eOmJgYIiIiePzxxxk7diweHh4kJSWRnp5OSkoKJ06cwM3NjcaNG7N582YAEhMTS8WMh0hpU7VqVc6ePWu27fTp0+zZs6fA1+Su/+zWrRsVK1akX79+LFu2DBsbG7N2R48eJTExkeDgYIYMGUJWVhaXL18GKDBZVatWja+//prAwECmT5/+V4YmIg845a8/FctlLypXrkxwcDBBQUH07t2bkJAQHBwcCAwM5NixYwQGBrJixQreeuut4tidiNzE29ubLVu2cOrUKQAyMzOZPn06R48eNWuXlZVFWloaGRkZHD9+HLhxuMDT05PFixfToUMHPvroI+DPhFe7dm06duxITEwM0dHRdOjQgfLlywNgbZ03fbz55pv8+uuvwI1vufm1ERHJpfz1p0IdsqxevTrx8fG33ebv74+/v79Zm7JlyxIZGVkMYYqUHvf7NG8nJyemT5/OuHHjMAyDtLQ0vL29CQoKYvfu3aZ2r7zyCj169KB69epUrVoVuLFWc8SIEURFRWFtbc3o0aMBqFOnDqGhoUybNo1x48bRq1cvUlNTCQoKum2S6t+/P2FhYdjZ2VG2bFmmTJlybwcvIsVK+cty+cvKyC0lS4jcBb6FXdRvMzwmz7bsOcH3NMaClOaF6CVNaYgRFGdxKOh3vjS6dSyFed9vzWGWyl8FKck/O4WlMZQMpX0MBcVfXDlMxxNERERELEwFmYiIiIiFqSATERERsTAVZCIiIiIWpoJMRB54+/fvJzj4xmL5kydPEhgYSFBQEBMnTiQnJweA+Ph4unTpgr+/P9988w0A169fZ/DgwQQFBfH666+bbkMlIlLcVJCJyAMtOjqacePGkZ6eDsA777zD0KFDiY2NxTAMNm7cyIULF4iJiSEuLo6FCxcSERFBRkYGy5cvx83NjdjYWPz8/Ip0P14RkaJQQSYiDzRXV1eioqJMjw8dOkSzZs0AaN26Ndu3b+fAgQM0atQIe3t7nJ2dcXV15ciRIyQlJdGqVStT2x07dlhkDCLy4CvSvSxFREobHx8fzpw5Y3psGAZWVlbAjatxp6SkkJqairOzs6mNo6MjqampZttz2xbGzTdLT0pKKlK8RW1/P5TEmIpKYygZSvsY7mX8KshE5KFy85W609LScHFxwcnJibS0NLPtzs7OZttz2xZGUS4MS+yPZg9L2oUzS/vFPEFjKClK+xjudGHYv0qHLEXkoVK/fn127doFQGJiIk2aNMHDw4OkpCTS09NJSUnhxIkTuLm50bhxYzZv3mxqW5r/mIhIyaYZMhF5qIwaNYrx48cTERFB7dq18fHxwcbGhuDgYIKCgjAMg5CQEBwcHAgMDGTUqFEEBgZiZ2fHnDlzLB2+iDygVJCJyAOvevXqxMfHA1CrVi2WLl2ap42/vz/+/v5m28qWLUtkZOR9iVFEHm46ZCkiIiJiYSrIRERERCxMBZmIiIiIhakgExEREbEwFWQiIiIiFqaCTERERMTCVJCJiIiIWFihCrL9+/cTHBwMwOHDhwkKCiI4OJi+ffvyv//9D4D4+Hi6dOmCv78/33zzDQDXr19n8ODBBAUF8frrr3Pp0qV7NAwRERGR0uuOBVl0dDTjxo0jPT0dgKlTpzJ+/HhiYmJo37490dHRXLhwgZiYGOLi4li4cCERERFkZGSwfPly3NzciI2Nxc/Pj7lz597zAYmIiIiUNne8Ur+rqytRUVGMHDkSgIiICKpUqQJAdnY2Dg4OHDhwgEaNGmFvb4+9vT2urq4cOXKEpKQk+vXrB0Dr1q2LVJDld6POwt5l3ZJ3ky8td7IvDXGWhhhBcYqIyF93x4LMx8eHM2fOmB7nFmP79u1j6dKlLFu2jC1btuDs7Gxq4+joSGpqKqmpqabtjo6OpKSkFDowd3d3HBwcTI8LvEt87I95NlnqBsCl5U72pSHO0hAjKM7ikJ6enu8XMBGRh8ld3cty/fr1zJs3jwULFlCpUiWcnJxIS0szPZ+Wloazs7PZ9rS0NFxcXIonahEREZEHSJHPslyzZg1Lly4lJiaGGjVqAODh4UFSUhLp6emkpKRw4sQJ3NzcaNy4MZs3bwYgMTGxxH5DFxEREbGkIs2QZWdnM3XqVB577DEGDx4MQNOmTRkyZAjBwcEEBQVhGAYhISE4ODgQGBjIqFGjCAwMxM7Ojjlz5tyTQYiIiIiUZoUqyKpXr058fDwAu3fvzreNv78//v7+ZtvKli1LZGTkXwxRRERE5MGmC8OKiIiIWJgKMhERERELU0EmIiIiYmEqyEREREQsTAWZiIiIiIWpIBMRERGxMBVkIiIiIhamgkxERETEwlSQiYiIiFiYCjIRERERC1NBJiIiImJhKshERERELEwFmYiIiIiFqSATERERsTBbSwcgInK/ZWZmEhYWRnJyMtbW1kyePBlbW1vCwsKwsrLiiSeeYOLEiVhbWxMfH09cXBy2trYMGDAAb29vS4cvIg8gFWQi8tDZvHkzWVlZxMXFsW3bNt59910yMzMZOnQoXl5eTJgwgY0bN9KwYUNiYmL47LPPSE9PJygoiBYtWmBvb2/pIYjIA0YFmYg8dGrVqkV2djY5OTmkpqZia2vL999/T7NmzQBo3bo127Ztw9ramkaNGmFvb4+9vT2urq4cOXIEDw+P2/Z/8OBB0/+TkpKKFFtR298PJTGmotIYSobSPoZ7Gb8KMhF56JQrV47k5GReeOEFLl++zPz589mzZw9WVlYAODo6kpKSQmpqKs7OzqbXOTo6kpqaesf+3d3dcXBwICkpCU9Pz9s3jv3R7OEd299nhRpDCacxlAylfQwFxZ+enm72JexuqSATkYfOJ598QsuWLRk+fDi//fYbvXv3JjMz0/R8WloaLi4uODk5kZaWZrb95gJNRKS4FOosy/379xMcHAzAyZMnCQwMJCgoiIkTJ5KTkwNAfHw8Xbp0wd/fn2+++QaA69evM3jwYIKCgnj99de5dOnSPRqGiEjhubi4mAqr8uXLk5WVRf369dm1axcAiYmJNGnSBA8PD5KSkkhPTyclJYUTJ07g5uZmydBF5AF1x4IsOjqacePGkZ6eDsA777zD0KFDiY2NxTAMNm7cyIULF4iJiSEuLo6FCxcSERFBRkYGy5cvx83NjdjYWPz8/Jg7d+49H5CIyJ306dOHQ4cOERQURO/evQkJCWHChAlERUXRo0cPMjMz8fHxoXLlygQHB5u1c3BwsHT4IvIAuuMhS1dXV6Kiohg5ciQAhw4dKvTC16SkJPr162dqq4JMREoCR0dH3nvvvTzbly5dmmebv78//v7+9yMsEXmI3bEg8/Hx4cyZM6bHhmEUeuHrzdtz2xZWfgvkCnt2gyXP4igtZ5CUhjhLQ4ygOEVE5K8r8qJ+a+s/j3LeaeHrzdtz2xZW7llKuQo8O+OWM5TAcmcplZYzSEpDnKUhRlCcxaG4zlASESnNinzrpKIsfG3cuDGbN282tS2pfxBERERELKnIM2SjRo1i/PjxREREULt2bXx8fLCxsTEtfDUMw7TwNTAwkFGjRhEYGIidnR1z5sy5F2MQERERKdUKVZBVr16d+Ph44MYVrgu78LVs2bJERkYWQ5giIiIiD64iH7IUERERkeKlgkxERETEwlSQiYiIiFiYCjIRERERC1NBJiIiImJhKshERERELEwFmYiIiIiFqSATERERsTAVZCIiIiIWpoJMRERExMJUkImIiIhYmAoyEREREQtTQSYiIiJiYSrIRERERCxMBZmIiIiIhakgExEREbEwFWQiIiIiFqaCTERERMTCVJCJiIiIWJgKMhERERELs72bF2VmZhIWFkZycjLW1tZMnjwZW1tbwsLCsLKy4oknnmDixIlYW1sTHx9PXFwctra2DBgwAG9v7+Ieg4iI5MNmeEyebdlzgi0QiYjcyV0VZJs3byYrK4u4uDi2bdvGu+++S2ZmJkOHDsXLy4sJEyawceNGGjZsSExMDJ999hnp6ekEBQXRokUL7O3ti3scIiIiIqXWXR2yrFWrFtnZ2eTk5JCamoqtrS2HDh2iWbNmALRu3Zrt27dz4MABGjVqhL29Pc7Ozri6unLkyJFiHYCIiIhIaXdXM2TlypUjOTmZF154gcuXLzN//nz27NmDlZUVAI6OjqSkpJCamoqzs7PpdY6OjqSmphZqHwcPHsyzLSkpqVCvLWy7e8GS+y6K0hBnaYgRFGdp9eGHH7Jp0yYyMzMJDAykWbNmWnYhIhZzVwXZJ598QsuWLRk+fDi//fYbvXv3JjMz0/R8WloaLi4uODk5kZaWZrb95gLtdtzd3XFwcDA9TkpKwtPTM2/D2B/zbMq33X1QYIwlTGmIszTECIqzOKSnp+f7Bexe2rVrF9999x3Lly/njz/+4OOPP+add97RsgsRsZi7KshcXFyws7MDoHz58mRlZVG/fn127dqFl5cXiYmJPPPMM3h4ePDuu++Snp5ORkYGJ06cwM3NrVgHICJSVFu3bsXNzY1BgwaRmprKyJEjiY+PN1t2sW3bNqytrU3LLuzt7U3LLjw8PG7b/80FZlFnJu/1TObd9P8gzK5qQxZvEQAAIABJREFUDCVDaR/DvYz/rgqyPn36MGbMGIKCgsjMzCQkJAR3d3fGjx9PREQEtWvXxsfHBxsbG4KDgwkKCsIwDEJCQsxmvURELOHy5cv897//Zf78+Zw5c4YBAwZgGEaxLbvIneEv1MzkLbP8xTqTWQxHEEry7GphaQwlQ2kfQ0HxF9cs/10VZI6Ojrz33nt5ti9dujTPNn9/f/z9/e9mNyIi90SFChWoXbs29vb21K5dGwcHB86ePWt6vjiWXYiIFIUuDCsiDx1PT0+2bNmCYRicO3eOP/74g+bNm7Nr1y4AEhMTadKkCR4eHiQlJZGenk5KSoqWXYjIPXNXM2QiIqWZt7c3e/bsoVu3bhiGwYQJE6hevbqWXYiIxaggE5GH0siRI/Ns07ILEbEUHbIUERERsTAVZCIiIiIWpoJMRET+H3t3HxdVmf9//DUMNxo3mtvanZpSoplLKioZatG2Ua2urikKSTdmLmS5YN6LN6smYkalfbV07W4UEYt0t5utFitSC13UNS0zfRSr5l1qxqAMd+f3Bz8nR0ARgTPA+/l49Mg558w1n+vMzIfPnHOd64iIyVSQiYiIiJhMBZmIiIiIyVSQiYiIiJhMBZmIiIiIyVSQiYiIiJhMBZmIiIiIyVSQiYiIiJhMBZmIiIiIyVSQiYiIiJhMBZmIiIiIyVSQiYiIiJjM0+wA3J31aZvL45LnYkyKRERERBoqHSETERERMZkKMhERERGTVfuU5SuvvML69espKioiKiqKnj17MmnSJCwWC+3bt2fGjBl4eHiQnp5OWloanp6exMXFER4eXpPxi4iIiNR71TpClp2dzbZt21i1ahU2m43Dhw+TlJREfHw8qampGIZBZmYmx44dw2azkZaWxvLly0lJSaGwsLCm+yAiIiJSr1XrCNmGDRsICgpi9OjR2O12JkyYQHp6Oj179gSgb9++bNy4EQ8PD7p27Yq3tzfe3t60adOG3bt3ExwcfNHX2LlzZ7llOTk5VYqvqttVx8Xars3Xrkn1Ic76ECMoThERuXzVKshOnjzJjz/+yMsvv8yBAweIi4vDMAwsFgsAvr6+5OXlYbfb8ff3dz7P19cXu91epdfo3LkzPj4+zsc5OTmEhISU3zD163KLKtyuus5r/0JtVxqjm6kPcdaHGEFx1gSHw1HhDzARkcakWgVZ8+bNCQwMxNvbm8DAQHx8fDh8+LBzfX5+PgEBAfj5+ZGfn++y/NwCTURERESqOYYsJCSEzz//HMMwOHLkCGfOnKFXr15kZ2cDkJWVRffu3QkODiYnJweHw0FeXh779u0jKCioRjsgIiIiUt9V6whZeHg4W7ZsYfDgwRiGwfTp02nVqhXTpk0jJSWFwMBAIiIisFqtxMTEEB0djWEYJCQkuJyGFBEREZHLmPZiwoQJ5ZatWLGi3LLIyEgiIyOr+zIiIiIiDZ4mhhURERExmQoyEREREZOpIBMRERExmQoyEWmUjh8/zh133MG+ffvIzc0lKiqK6OhoZsyYQWlpKQDp6ekMGjSIyMhIPvnkE5MjFpGGTAWZiDQ6RUVFTJ8+nSZNmgDo1m8iYrpqX2UpIlJfJScnM2zYMJYuXQrArl27au3Wb5d6y6ravsVVddpvCLfdUh/cQ33vQ23Gr4JMRBqVjIwMWrRoQZ8+fZwFWW3d+q1Kt6y6hNuzXbIauLWcO992q6rUB/dQ3/tQWfw1dfs3FWQi0qi8/fbbWCwWvvjiC7755hsmTpzIiRMnnOt16zcRMYPGkIlIo7Jy5UpWrFiBzWbj5ptvJjk5mb59++rWbyJiKh0hE5FGb+LEibr1WzVYn7a5PC55LsakSETqPxVkItJo2Wy/FhS69ZuImEmnLEVERERMpoJMRERExGQqyERERERMpoJMRERExGQqyERERERMpoJMRERExGQqyERERERMpoJMRERExGSXVZAdP36cO+64g3379pGbm0tUVBTR0dHMmDGD0tJSANLT0xk0aBCRkZF88sknNRK0iIiISENS7YKsqKiI6dOn06RJEwCSkpKIj48nNTUVwzDIzMzk2LFj2Gw20tLSWL58OSkpKRQWFtZY8CIiIiINQbULsuTkZIYNG0bLli0B2LVrFz179gSgb9++bNq0iR07dtC1a1e8vb3x9/enTZs27N69u2YiFxEREWkgqnUvy4yMDFq0aEGfPn1YunQpAIZhYLFYAPD19SUvLw+73Y6/v7/zeb6+vtjt9iq9xs6dO8sty8nJqdJzq7pddVys7dp87ZpUH+KsDzGC4hQRkctXrYLs7bffxmKx8MUXX/DNN98wceJETpw44Vyfn59PQEAAfn5+5Ofnuyw/t0C7kM6dO+Pj4+N8nJOTQ0hISPkNU78ut6jC7arrvPYv1HalMbqZ+hBnfYgRFGdNcDgcFf4AExFpTKp1ynLlypWsWLECm83GzTffTHJyMn379iU7OxuArKwsunfvTnBwMDk5OTgcDvLy8ti3bx9BQUE12gERERGR+q5aR8gqMnHiRKZNm0ZKSgqBgYFERERgtVqJiYkhOjoawzBISEhwOeolIiIiIjVQkNlsNue/V6xYUW59ZGQkkZGRl/syIiIiIg2WJoYVERERMZkKMhERERGT1dgYMhERkZpifdpWblnJczEmRCJSN3SETERERMRkKshERERETKZTlibSIXkREREBHSETERERMZ0KMhERERGTqSATERERMZkKMhERERGTqSATERERMZkKMhERERGTadoLEWlUioqKmDJlCgcPHqSwsJC4uDhuuukmJk2ahMVioX379syYMQMPDw/S09NJS0vD09OTuLg4wsPDzQ5fRBooFWQi0qj84x//oHnz5jz77LOcPHmSP//5z3Ts2JH4+HhCQ0OZPn06mZmZdOnSBZvNxttvv43D4SA6OpqwsDC8vb3N7oKINEAqyESkUbn33nuJiIhwPrZarezatYuePXsC0LdvXzZu3IiHhwddu3bF29sbb29v2rRpw+7duwkODr7oa+zcudP575ycnEuK71K3v1TVab+qz3HH2Gviue5CfTBfbcavgkxEGhVfX18A7HY7Y8aMIT4+nuTkZCwWi3N9Xl4edrsdf39/l+fZ7fYqvUbnzp3x8fEhJyeHkJCQC2+c+rXLw4tufynOa7s67V+wD24e+1lVeh/cnPpgvsridzgcLj/CqkuD+kWk0Tl06BAPPfQQAwYMoH///nh4/JoK8/PzCQgIwM/Pj/z8fJfl5xZoIiI1SQWZiDQqP/30EyNGjGD8+PEMHjwYgE6dOpGdnQ1AVlYW3bt3Jzg4mJycHBwOB3l5eezbt4+goCAzQxeRBkynLEWkUXn55Zf55ZdfWLx4MYsXLwZg6tSpzJkzh5SUFAIDA4mIiMBqtRITE0N0dDSGYZCQkICPj4/J0UtNsT5tc3lc8lyMSZGIlFFBJiKNSmJiIomJieWWr1ixotyyyMhIIiMj6yIsEWnkqlWQaR4fERERkZpTrYJM8/iIiIiI1JxqFWR1MY+PiIiISGNRrYKsLubxqWhOD3eYnPBibV/ua9fVpHn1YXK++hAjKE4REbl81R7Uf+jQIUaPHk10dDT9+/fn2Wefda6riXl8zk6seFalE8rV4OSBFbqEiQ8vedK72o69EvVhcr76ECMozppQU5MqiojUZ9UqyM7O4zN9+nR69eoF/DqPT2hoKFlZWdx2220EBwfzwgsv4HA4KCws1Dw+dez8y7pBl3aLiIi4o2oVZJrHR0RERKTmVKsg0zw+IiIiIjVHE8OKiIjUIA0XkerQvSxFRERETKaCTERERMRkKshERERETKaCTERERMRkKshERERETKaCTERERMRkmvZCqkWXdYuIiNQcFWQiIiL1yPk/iPVjuGHQKUsRERERk6kgExERETGZCjIRERERk6kgExERETGZCjIRERERk+kqS3FLPVO/htSvXZbpSiIRkdqlKY3MoyNkIiIiIiZTQSYiIiJiMp2yFBERkTqhU6KVU0EmjY4SgoiI+9riV7406WEvrrX2a7Lty6GCTKQBqiih4SZJR0TK1GZh0FiLmvqs1guy0tJSZs6cybfffou3tzdz5szhhhtuqO2XrVPnfjC3/P//15cPfm1/aWtTXSecmmxf+73+cIcc1tj2ubvQfq9f6vvZj1ovyP79739TWFjI6tWr2b59O/PmzWPJkiWVbm8YBgCFhYXl1jkcjnLLrvX1qtJ2O265yeVx8K69F429ovYraptrrq1SDBdru6rtV6Xti7XvbKOWY6+p/V5h+zUY+8qNs8otC6pi++cvu/GZd8ptkxyxp1ptV1tttl/DbZ/9rp/97rubS8lhFeWvi+0bd8gxF/ueVvZaVYn9/LYrar8qbVe1/craPv+57rDfLzW3n11fH2O/UPvn59/q5sfajr2ybWsqh1mMWs6CSUlJBAcH88c//hGAPn368Pnnn1e6fV5eHnv27Kl0vYg0TEFBQfj7+5sdRjmXksOUv0Qar8vNYbV+hMxut+Pn5+d8bLVaKS4uxtOz4pf29fUlKCgILy8vLBZLbYcnIiYzDIOioiJ8fX3NDqVCl5LDlL9EGp+aymG1XpD5+fmRn5/vfFxaWlppMQbg4eHhlr+SRaT2NGnSxOwQKnUpOUz5S6RxqokcVusTw3br1o2srCwAtm/fTlBQUG2/pIhIjVEOE5G6UOtjyM5eobRnzx4Mw2Du3LnceOONtfmSIiI1RjlMROpCrRdkIiIiInJhupeliIiIiMlUkImIiIiYzK1vneQOM2Sf9d///pcFCxZgs9nIzc1l0qRJWCwW2rdvz4wZM/Dw8CA9PZ20tDQ8PT2Ji4sjPDycgoICxo8fz/Hjx/H19SU5OZkWLVrUeHxFRUVMmTKFgwcPUlhYSFxcHDfddJPbxVlSUkJiYiLff/89VquVpKQkDMNwuzgBjh8/zqBBg3j11Vfx9PR0yxgHDhzovKqvVatWxMbGumWcjY075a5LVdXPlDuqbp52J+f2YdeuXcTGxtK2bVsAoqKiuP/++92yD5f7N8gdVNSHa665pu7eA8ONffjhh8bEiRMNwzCMbdu2GbGxsabEsXTpUqNfv37GkCFDDMMwjL/85S/Gl19+aRiGYUybNs346KOPjKNHjxr9+vUzHA6H8csvvzj//eqrrxoLFy40DMMw3n33XWP27Nm1EuNbb71lzJkzxzAMwzhx4oRxxx13uGWcH3/8sTFp0iTDMAzjyy+/NGJjY90yzsLCQuOJJ54w7rnnHmPv3r1uGWNBQYExYMAAl2XuGGdj5C6561JV9TPlji4nT7uL8/uQnp5uLF++3GUbd+3D5f4NcgcV9aEu3wP3/Jnz/+Xk5NCnTx8AunTpws6dO02Jo02bNixatMj5eNeuXfTs2ROAvn37smnTJnbs2EHXrl3x9vbG39+fNm3asHv3bpc+9O3bly+++KJWYrz33nv561//6nxstVrdMs67776b2bNnA/Djjz9y1VVXuWWcycnJDBs2jJYtWwLu+Z7v3r2bM2fOMGLECB566CG2b9/ulnE2Ru6Suy5VVT9T7uhy8rS7OL8PO3fu5NNPP+XBBx9kypQp2O12t+3D5f4NcgcV9aEu3wO3LsgqmyG7rkVERLhMBGkYhnMWbl9fX/Ly8rDb7S4TQvr6+mK3212Wn922Nvj6+uLn54fdbmfMmDHEx8e7ZZwAnp6eTJw4kdmzZxMREeF2cWZkZNCiRQvnH1Rwz/e8SZMmPPbYYyxfvpy//e1vjBs3zi3jbIzcJXddqqp+ptzR5eRpd3F+H4KDg5kwYQIrV66kdevW/N///Z/b9uFy/wa5g4r6UJfvgVsXZJc6y39dOXf8RH5+PgEBAeVizc/Px9/f32X52W1ry6FDh3jooYcYMGAA/fv3d9s4oewI1Icffsi0adNcbtbqDnG+/fbbbNq0iZiYGL755hsmTpzIiRMn3CpGgHbt2vGnP/0Ji8VCu3btaN68OcePH3e7OBsjd81dF1PVz1R9cCn5z1394Q9/oHPnzs5/f/31127dh8v5G+Quzu9DXb4Hbl2QuesM2Z06dSI7OxuArKwsunfvTnBwMDk5OTgcDvLy8ti3bx9BQUF069aNzz77zLltSEhIrcT0008/MWLECMaPH8/gwYPdNs61a9fyyiuvANC0aVMsFgudO3d2qzhXrlzJihUrsNls3HzzzSQnJ9O3b1+3ihHgrbfeYt68eQAcOXIEu91OWFiY28XZGLlr7rqYqn6m6oNLyX/u6rHHHmPHjh0AfPHFF9xyyy1u24fL/RvkDirqQ12+B249Maw7zZB94MABxo4dS3p6Ot9//z3Tpk2jqKiIwMBA5syZg9VqJT09ndWrV2MYBn/5y1+IiIjgzJkzTJw4kWPHjuHl5cVzzz3Hb3/72xqPb86cOXzwwQcEBgY6l02dOpU5c+a4VZynT59m8uTJ/PTTTxQXF/P4449z4403ut3+PCsmJoaZM2fi4eHhdjEWFhYyefJkfvzxRywWC+PGjePKK690uzgbI3fKXZfiUj5T7qi6edqdnNuHXbt2MXv2bLy8vLjqqquYPXs2fn5+btmHy/0b5A4q6kN8fDzPPvtsnbwHbl2QiYiIiDQGbn3KUkRERKQxUEEmIiIiYjIVZCIiIiImU0EmIiIiYjIVZCIiIiImU0EmIiIiYjIVZCIiIiImU0EmIiIiYjIVZCIiIiImU0EmIiIiYjIVZCIiIiImU0EmIiIiYjIVZCIiIiImU0EmIiIiYjIVZCIiIiImU0EmIiIiYjIVZCIiIiImU0EmIiIiYjIVZCIiIiImU0EmIiIiYjIVZCIiIiImM70gO3DgADfffDMDBgxgwIAB9O/fn2HDhvH+++87t1m0aBG33XYbAwYM4E9/+hP33XcfTz/9NHa73bnNsmXLnOv79etHcnIyhYWFzvVHjhxh0qRJ9O/fnz/96U8MGTKEf//73+XieeqppwgNDeXMmTMuy++66y7Gjx/vsuyrr77irrvuqrBfd911F1999dUl7YuYmBj+9a9/XdJzMjIy+Mtf/lJu+YEDB+jatWu55YsWLWLWrFkATJ06lU2bNl2w/cTERHbu3HlJMbmLDRs2EB4ezuDBgykoKHBZ16FDB/r37+/8zAwaNIiVK1c6169atYqlS5desP01a9Y4n5ORkUFISAgDBgxg4MCBDBgwgGHDhrFt27bL6sOJEyfo0KFDtZ+/Y8cOpk+ffsFtsrOz6dev3yW3XdXv1KUYMWIEJ06cuKw2zKR89ivls5pVWT4bM2aM8/N2bl6LiYkxMdrKZWRkcOedd/LYY48ByiPn8jQ7AIAmTZqwbt065+ODBw/yyCOPYLVaiYiIAOD+++93/mEpKSlh9OjR2Gw24uLi+OCDD/j3v//N6tWradKkCQ6HgzFjxvDSSy8xduxYTpw4wbBhw/jrX/9KUlISFouF3bt38+ijj9K0aVPCwsKAsg/Gli1b6NKlC2vXriUqKsolzn/961/07t2bAQMG1NGeqV3PPPPMRbfZtGkTQ4cOrYNoat57773HkCFDeOKJJypc/8Ybb9CiRQugrPCJjY3F4XAwYsSIcu99RXJycmjfvr3zcffu3XnllVecj9evX89TTz3Fp59+iqenOV+1vXv3cuTIkRpvt6rfqUu1cePGGo607imfmaOx5rOFCxc6/92hQweXvOaO1q5dS0JCAgMGDFAeOY9bFGTnu/766xkzZgzLly93JrBzORwOTp8+zW9/+1sAjh07RklJCQUFBTRp0gQfHx+mTZvmrJBTU1Pp1q0bAwcOdLbRsWNHFi5cSEBAgHNZeno6vXr1IiIighdffJFhw4ZhsVic6xMSEpgzZw7dunWjdevW1erb6dOnmTlzJrm5ufz888/4+vqyYMECAgMDAfj4449ZunQpBQUF9O/fn7i4OAC2bt3KggULOHPmDB4eHjz55JOEh4dXK4azYmJiePDBB7n77ruZPXs2W7duxcvLi1atWpGUlMTSpUs5evQo48aNY/78+Vx99dXMnDmTgwcPYhgGAwcOZOTIkUDZr56lS5fSpEkTbrvtNt58802+/vprFi1axPbt2zl69CgdOnRg0qRJTJ8+nePHj3Ps2DGuv/56XnjhBX7zm99w11130a9fP7788ktOnTrFyJEj2bp1K7t27cLT05MlS5Zw9dVXu/ShqKiIefPm8cUXX2C1WgkODmby5MmkpaWRmZmJj48PeXl5TJw48YL7okWLFkyaNIkxY8bw6KOP8tJLL3Hy5EmmT59OamoqaWlpeHl54ePjw6xZs/j+++9Zv349GzdupEmTJjRt2rRcm7169eLYsWP88ssvzJ8/n59//pn9+/dz5513Ehsby9/+9jd2796NxWKhT58+jB07Fk9PTz766COef/55mjZtSufOnZ3tZWRk8OGHHzqLvnMf5+fnM2fOHLZu3YrVauXuu+8mKiqKhQsXkpeXx+TJk0lMTGTy5Mnk5ubi4eHBLbfc4jy6cPr0acaMGUNubi4BAQHMmjWLq6++mjvuuIP09HTatWsHwCOPPMLw4cPZvXv3Rb9THTp04IsvvnD+cTj72MfHp8I4pk6dCsDDDz/M0qVLufbaa6v+YXZjymfKZ3Wdz85asmQJH330EaWlpVx//fXMmDGDq6++mpiYGLp06cLWrVs5dOgQvXr1Yvbs2fz444/079/feWT/wIEDzscZGRm89dZbnDlzBj8/P2w2G2vWrGHVqlWUlpbSvHlzpk2bxo033kheXl6F+W3+/Pl89dVXHDhwgJMnT2K322s9j9jtdmbNmsXPP/+MxWJhxIgRDBw4kOzsbFJSUrj22mv5/vvvadq0KaNGjcJms/H9999zzz33MGXKFKDsx/WSJUsoKiqiSZMmTJw4ka5du5b7PCxYsOAyPsFuWpBB2ZuyZ88e5+P333+fnJwcSktL+fHHH7nuuuu45557APjzn//Mp59+Su/evbnlllvo2rUrv//97+nRowcAO3fupE+fPuVe4+x6gOLiYtLT05k1axZhYWFMnz6drKws7rjjDpftT506xbhx41xOb12KrKwsAgICWL16NQDTp09n5cqVTJs2DYD8/HzS09MpKChgyJAhdOrUiS5dujB58mSWL19Oq1atOHLkCJGRkRc9lVVQUFDu1+9PP/1U7o/C9u3b2bx5M++//z4Wi4Vnn32Wb7/9loSEBP75z3+yYMECfve73zF8+HB+//vf8+ijj5KXl8eDDz7Itdde6/wgZmRkcM011/DSSy9RUlLibP/gwYO8++67eHp68sYbb9ClSxdGjRqFYRiMGjWKdevWMWLECKDsj1N6ejrvv/8+Tz/9NO+88w4dO3Zk9OjRvPPOO8TGxrrEvmTJEo4ePcq6deuwWq1MnTqV+fPnM2vWLPbu3Uv79u2dh8YvpmPHjhw7doyTJ086l5WUlDB37lzWr19Py5YtWbt2LTk5OQwdOpTMzEzat2/Pgw8+SEZGhktbhmGwevVqgoKCnImkoKCA9957D4CJEyfSvHlz/vnPf1JUVERcXByvvvoqgwYNYsqUKaSlpXHTTTe5HHG7kIULF+JwOHj//fcpKSlhxIgRhIWFMWbMGD788EOSkpJYu3Yt+fn5rFu3jpKSEmbMmMH+/fsBOHToEAsWLKBbt26sXr2aCRMmsGbNGgYOHMiaNWuYMGEC//vf//jhhx8IDw/nrbfeuuh3qjIff/xxhXEkJSWRkZHh9r/wq0P5TPmsrvPZ2rVr2bNnD2vWrMHT05PVq1eTmJjIsmXLAPjf//6HzWbj9OnT3HfffWzevJlWrVpdsM29e/eyfv16/Pz82Lx5M2vXrmXlypU0bdqUDRs28OSTT/LBBx8wZ86cCvPblClT+Oabb3jwwQe59957iY2NrdU8EhAQQExMDBMmTOCee+7hyJEjDBkyhBtuuAEoO00/Y8YMOnXqxMiRI1m6dClvvvkmdrudvn378thjj3HmzBmef/553nzzTa688kq+++47Hn30UT766CPA9fNwudy2ILNYLDRp0sT5+NxD/EVFRcyaNYuEhASWL1+Ov78/r776Kvv37+fLL79k8+bNjBo1iujoaMaPH4/FYsEwjAu+XmZmJqWlpfTp0wdPT0/uv/9+3nzzTZcEBmVjMr744gsWLVrE3Xfffcn9uvfee2ndujU2m43c3Fw2b97sMjZi8ODBeHp64ufnR0REhHNMxLFjxxg9erTL/vn2228v+FrnnzqBsjEX5xYcAEFBQVitVoYMGULv3r2JiIggODjYZZvTp0+zdetWXn31VQD8/f0ZNGgQWVlZHDt2jLCwMK655hoAhg8fzqJFi5zP7dKli/PD+vDDD/Of//yH1157jR9++IHvvvuOW2+91bnt2T9KrVu35qqrrqJjx44AtGnThlOnTpXrY1ZWFgkJCXh5eQFlv5LP3U+X4uzRAx8fH+cyq9XKvffey7Bhw7jzzjvp3bt3uc/EWf/5z38YMGAAFouFwsJCAgMDXU4nhISEuMS9atUqLBYL3t7eDBs2jDfeeIMbbriBoKAgbrrpJgCGDh1KSkrKRWPftGkTkydPxmq1YrVaWbFiBYBLoRgSEsLzzz9PTEwMt99+Ow8//DA33HADhw8fpkOHDnTr1g0oKwhmzpxJXl4e0dHRDB8+nISEBFavXs3gwYOxWq1V+k5VprI4GjLlM+Wzus5nn3zyCV999RUPPPAAAKWlpS5jCcPDw/Hw8MDPz48bbriBU6dOXbQg69ChA35+fgB8+umn5ObmMmzYMOf6X375hZ9//rnS/DZq1CiX9mo7j/zwww84HA7n+3D11Vdzzz338PnnnxMaGkqrVq3o1KkTUPae+Pv74+3tTYsWLfD19eXUqVNs2bKFo0eP8sgjj7jE/b///Q9w/TxcLrctyL766iuCgoIqXOfl5UV0dLRzLMCyZcsICQlxHnofMmQI//nPf3j88ccZP35Dve01AAAgAElEQVQ8Xbp0Yfv27QwfPtylnbS0NM6cOcOjjz5KamoqBQUFzjeusLCQY8eO8d1337mME/L09OS5555j0KBBNG/e/JL7lZqaSnp6Og8++CD9+/enefPmHDhwwLnearU6/20YBp6enpSUlHDjjTeyZs0a57ojR47QokUL/vnPf15yDOcLCAhg3bp1bN26lS+//JL4+Hgee+wxHnzwQec2paWl5b44paWlFBcXY7VaXdad2weAK664wvnvZ599lh07dvDAAw8QGhpKcXGxy3O9vb2d/z6blC6ktLTU5TRMaWkpRUVFVeh1eV999RWtWrXC19fXZfmCBQvYs2cPmzZtYunSpaxbt44XX3yx3PPPH0N2vnP3Q0VxFxcXA7jsj3O/6Ocnr3P76enp6dLeoUOHXAoAKPuj8PHHH5Odnc2XX37Jo48+yqxZs/D19cXDw/X6HovFgqenJ+3ataNDhw5kZmby7rvvkp6eDlCl79S5zh2QXlkclQ0obwiUz5TP6jqflZaWMnLkSKKjo4Gyz8C5ReC5+eFsbrlQjoHyOWzAgAHOi0NKS0s5evQozZo1u2B+O1dt55GSkhKXOKDsc3g2lnPfH6DCwqq0tJRevXrxwgsvOJcdOnSIli1b8vHHH7vsk8tl+lWWFfn+++9ZvHix87BvRT799FPnr56CggKee+45fv75Z+f6PXv2OCvfoUOHsnnzZv7xj384P2w7d+5k4cKFBAUF8f3337NlyxYyMjJYv34969evZ8OGDfTo0YM333yz3Gu3bt2aqVOnVunIxfk2bNjAn//8Z4YMGUK7du1Yv369y+HwtWvXYhgGp06d4oMPPqBPnz506dKF3NxctmzZAsA333xDREREjQ3W/uSTT3jkkUfo2rUrTz31FAMHDnReiWS1WikuLsbPz49bb73VeWojLy+PtWvXcvvtt9O7d2+++OILZzznJtqK+v/www8zcOBAfvOb37Bp0yaX/l+qPn36sGrVKoqKiigtLWXlypXVGgh65MgRFixYUO4zd+LECe644w6aN2/OI488Qnx8vPNqs7P7pjp69+7NihUrMAyDwsJC0tPTuf322+nRowd79+5l9+7dgOsRrhYtWvDdd9/hcDgoKiriww8/dK7r1asX77zzDqWlpRQWFjJmzBi2bNniEmNqaiqTJ0+md+/ejB8/nt69e/P1118D8O233/LNN98AsHr1akJCQpzj4qKjo5k/fz7BwcHOMS8X+06djffsvnr33XedsV4ojsvZp+5K+Uz5rKpqKp9BWY556623nFfvvvjii0yYMOGCzwkICKCoqIi9e/cCOIdYVNb+e++9x9GjR4GyK9Mffvhh57qK8tv5ajuPBAYGOsflQlme//DDDyuMpTK9evVi48aN7Nu3D4DPPvuMP/3pT+Wu3K8JbnGE7NyxAR4eHvj4+DB27FjuvPNO5zZnx1xYLBYcDgetW7cmOTkZgCeeeAKLxeIctFpaWkrnzp2dFW3z5s2x2Ww8++yzvPLKK3h4eNC0aVOeeeYZwsLCmDt3LnfffXe5w52jR4/mL3/5CwkJCeViHjhwIBs2bGDr1q2V9mv48OEuRx7GjRvHiBEjmD59Om+99RZQ9gvh3LElZw+dFxQUMHz4cG677TagbIzQ/PnzcTgcGIbB/PnzadWqFZs3b76UXV2hvn37kpWVRb9+/bjiiito1qwZs2fPBuAPf/gD48ePZ+bMmSxYsIBZs2aRkZFBYWEh/fv3Z9CgQVgsFiZPnsxjjz2Gt7c3N998c4WD3KFsn86fP58XX3wRLy8vunXr5jz0Wx1xcXEkJyczcOBAiouLCQ4Odo5fuZiHH34YDw8P5y/gBx54wOVXNJQlg7i4OB555BGaNGmC1Wplzpw5QNl+mzdvHoBzQHZVJSYmMmfOHPr3709RURF9+vQhNjYWb29vFixYwLhx4/Dy8nIZSxEWFkaPHj247777+O1vf0toaKjzNM+TTz7JM888w4ABAygpKeH+++/nnnvuITc3l//7v//jySefZP78+WzevJn777+fpk2bcu211xITE8Pu3bsJDAzkpZdeYv/+/fzmN79x9gvKTm0kJia6nJq42HfqbB9nzZpFQEAAt99+u3MfDRw4sMI4oOwUWExMDIsWLar0iJK7Uz5TPquuy8ln5xsyZIhzfJ7FYuHaa691+V5XxN/fn/Hjx/P444/TokUL7r333kq37d27N48//jgjRozAYrHg5+fHSy+9hMViqTS/na8u8sjixYuZM2cOixYtcl7RfNttt5GdnV2l/XjTTTcxa9Ysxo4d6zzKu2TJknJnUmqCxajuCVyR/2///v2sW7eOJ554Ag8PDz766COWLVt2wV+WUn9s27aNxMRE3n333XKH/0UaGuUzMYtbHCGT+u2aa67h6NGj9O/fH6vVir+/P3PnzjU7LKkBEydOZPPmzTz//PMqxqRRUD4Ts+gImYiIiIjJ3HJQv4iIiEhj4nanLEtLS8nPz8fLy0unSEQaAcMwKCoqqnD6jfpG+Uuk8ampHOZ2BVl+fr7LVToi0jgEBQXh7+9vdhiXRflLpPG63BzmdgXZ2cnzgoKCyk3aBmVzlJx7f7+GRv2r39S/S1dYWMiePXuqNHGmu7tY/jqroX9OLkR9V98bmprKYW5XkJ09zO/t7e1yC5tzVba8oVD/6jf1r3oawim+quSvsxr65+RC1PfGqaH3/XJzWP0esCEiIiLSAKggExERETGZCjIRERERk6kgExERETGZCjIRERERk6kgExERETGZ2017IVLfWZ+21Wh7Jc/F1Gh7IiKVUf4yj46QiTQA3333HaNGjSImJoYHHniAhQsXYhgG2dnZJCQkXHJ7K1asuKx45s6dy6pVqy6rDRFpHJS/ytT7I2QVVfOqyKUx+eWXXxg7diyLFi2ibdu2lJSU8Ne//pW0tDQCAwOr1eaSJUsYPnz4JT/vxIkTTJgwgR9++IHHHnusWq/d2Jyfw5S/pDFR/vpVvS/IRBq7zMxMQkNDadu2LQBWq5Xk5GS8vLzYtm2bc7uwsDA2btwIQEJCAsOGDaNly5ZMnjwZT09PrFYr8+fPJyMjg1OnTjFz5kymTp3KjBkzyM3NpbS0lPvvv5+QkBD69etH27Zt8fb2JiUlxfka+fn5PPXUU2RlZdXpPhCR+qku81d8fDyhoaFum7+qdMryv//9LzExZb/acnNziYqKIjo6mhkzZlBaWgpAeno6gwYNIjIykk8++QSAgoICnnrqKaKjo3n88cc5ceJELXVDpPE6evQorVu3dlnm6+t7wXspnrVp0yZuueUWXnvtNWJjYzl16hRxcXE0a9aMmTNnsmbNGq688kpWrlzJ4sWLee211wA4ffo0TzzxhEsyA2jdujW33nprzXVORBq0usxfs2bNAtw3f120IFu2bBmJiYk4HA4AkpKSiI+PJzU1FcMwyMzM5NixY9hsNtLS0li+fDkpKSkUFhayatUqgoKCSE1NZeDAgSxevLjWOyTS2Fx33XUcPnzYZdn+/fvZsmVLpc8xDAOAwYMHc+WVVzJy5EhWrlyJ1Wp12W7Pnj1kZWURExPDmDFjKC0t5eTJkwC0a9euhnsiIo1NXeav4uJit85fFz1l2aZNGxYtWsSECRMA2LVrFz179gSgb9++bNy4EQ8PD7p27Yq3tzfe3t60adOG3bt3k5OTw8iRI53bXkpBtnPnzkrX5eTkXPC5F1vv7up7/BfT0PtX0y62v5o3b85HH33E7373O66++mqKi4tZuHAhv/vd77j++us5ceIEOTk5FBQUsHHjRjw9Pfnqq68ICQlhy5YtBAQEMGbMGDZt2sS8efOIjY2lsLCQnJwcvLy86NKlCwMHDqSwsJC1a9eyd+9eHA4H27dvr/RX7I8//sjp06f1XovIBYWHh/PKK68QFRVFmzZtKCoqYt68edx+++3cdNNNzu2Ki4vJz8/Hy8uLvXv3AmWnO0NCQnjyySd59913+fvf/05SUpKzYAsMDOSaa64hNjaWgoIClixZQrNmzQDw8HC/axovWpBFRERw4MAB52PDMJx3NPf19SUvLw+73Y6/v79zG19fX+x2u8vys9tWVefOnSu8M3xOTg4hISG/Lkj9utw2LuvrmXL9a2AaQ//MGJT94osvMn/+fAzDID8/n/DwcJ588kk2b97M1q1bCQkJYcSIESQlJdGqVStuvPFGgoKCuPbaaxk/fjz/+te/8PDwYPLkydxyyy107NiRVatWMXfuXBITE3n++eex2+2EhYXRo0cPfHx86NatW4XfUSg7lXDVVVdV6b12OBwX/AFWE/773/+yYMECbDYb33zzDbNnz8ZqteLt7U1ycjJXXXUV6enppKWl4enpSVxcHOHh4RQUFDB+/HiOHz+Or68vycnJtGjRolZjFTFTXecvPz8/5s2bR2Jiokv+io6OZvPmzc7tHnroIYYOHUqrVq247rrrgLI6Yfz48SxatMiZvwBuvPFGxo0b58xfw4cPx263Ex0d7ZaF2FmXPKj/3M7k5+cTEBCAn58f+fn5Lsv9/f1dlp/dVkRqXufOnXnzzTfLLQ8NDSU0NBSA0aNHM3r06HLbrF69utwym+3XK//mz5/v/PfZI17r16+/YDxPPfVU1QKvA8uWLeMf//gHTZs2BeCZZ55h2rRp3HzzzaSlpbFs2TJGjhyJzWbj7bffxuFwEB0dTVhYmHPYxVNPPcV7773H4sWLSUxMNLlHIg1LXeWvs9w1f11yQdapUyeys7MJDQ0lKyuL2267jeDgYF544QUcDgeFhYXs27ePoKAgunXrxmeffUZwcDBZWVkN+siIiLin84ddpKSk0LJlSwBKSkrw8fFhx44dNTrsoipH/Co7ndsYTvM2hj5WRn2XylxyQTZx4kSmTZtGSkoKgYGBREREYLVaiYmJITo6GsMwSEhIwMfHh6ioKCZOnEhUVBReXl4899xztdEHEZFKnT/s4mwxtnXrVlasWMHKlSv5/PPPa3TYRWVDLs5yOXV/3rCLhv7DtaEPW7gQ9b1h9r2mhl1UqSBr1aoV6enpQNmVCRXNghsZGUlkZKTLsqZNm7Jw4cLLDlJEpCa9//77LFmyhKVLl9KiRQsNuxAR07nv6DYRkVqwbt06VqxYgc1mc85/FBwcTE5ODg6Hg7y8vHLDLgANuxCRWqWZ+kWk0SgpKeGZZ57h2muvdQ7c7dGjB2PGjNGwCxExlQoykRr2+oZJNdreI73n1Wh7jdG5wy7OvZT+XBp2IaL8ZSadshRpAL777jtGjRpFTEwMDzzwAAsXLsQwDLKzs0lISLjk9ioaJ1oV33zzDdHR0cTExPDYY4/x008/VasdEWk8lL/KqCATqed++eUXxo4dy5QpU7DZbKSnp7Nnzx7S0tKq3eaSJUuq9byzc3zZbDb+8Ic/sGzZsmrHICINn/LXr3TKUqSey8zMJDQ0lLZt2wJgtVpJTk7Gy8uLbdu2ObcLCwtj48aNACQkJDBs2DBatmzJ5MmT8fT0xGq1Mn/+fDIyMjh16hQzZ85k6tSpzJgxg9zcXEpLS7n//vsJCQmhX79+tG3bFm9vb5cb9FY0x5eISGXqMn/Fx8cTGhrqtvlLBZlIPXf06FHn1YJn+fr6Vum5mzZt4pZbbmHSpEn85z//4dSpU8TFxbFixQpmzpxJamoqV155JXPnzuXkyZM88MADxMTEcPr0aZ544gk6derk0l5Fc3yJiFSmLvPX8OHDee+999w2f6kgE6nnrrvuOr7+2nVy0f3793P48OFKn3P25ruDBw923jrI39+/3HiNPXv2kJOTw44dOwAoLS3l5MmTQNmchBU5f44vEZHK1GX+Ki4uduv8pTFkIvVceHg4n3/+Of/73/8AKCoqYt68eezZs8dlu+LiYvLz8yksLGTv3r1A2emCkJAQ3njjDe69917+/ve/A78mvMDAQP74xz9is9lYtmwZoaGhNGvWDKDCm/RWNMeXiEhl6jJ/3XvvvW6dv3SETKSG1fVl3n5+fsybN4/ExEQMwyA/P5/w8HCio6Ndpnh46KGHGDp0KK1ateK6664Dym7xM378eBYtWoSHhweTJ08G4MYbb2TcuHHMnTuXxMREhg8fjt1uJywsrMJEBhee40tE6oeGnL+io6PdOn9ZjLOlpJs4e0+oyu4Fd/79sKxP28ptU/JcTK3GWJsa8v2+QP2r72qjfxf7ztcnVe3Lufvx/BxWn/NXVTT078iFqO8Ns+81lcN0ylJERETEZCrIREREREymgkxERETEZCrIREREREymgkxERETEZJr2QqSGbfGr2a9VD3txjbYnIlIZ5S/z6AiZSAOQnZ1N9+7dOXTokHPZggULyMjIqHD7jIwMMjMz6yo8EZFKKX+VUUEm0kB4eXkxefJkqjK14KBBg/j9739fB1GJiFyc8pcKMpEG47bbbqNZs2blboj76quv8sADDzB06FCeffZZABYtWsSqVas4ceIEDz30EDExMQwbNoxvv/2W1atXk5ycDJTNXt2/f38KCwvrvD8i0ngof6kgE2lQZs6cyeuvv84PP/wAQH5+Ph988AFpaWmkpaWRm5vLJ5984tx+x44d+Pv7s2zZMhITE7Hb7fzxj38kMzOTkpISPv/8c0JDQ/H29japRyLSWDT2/KWCTKQBufLKK5kyZQqTJk2itLQUh8PBrbfeipeXFxaLhe7du/Pdd985t+/bty89evTgiSeeYOHChXh4eODn50ePHj3YsGEDGRkZDB482MQeiUhj0djzlwoykQbmrrvuol27drzzzjv4+PiwY8cOiouLMQyDLVu20K5dO+e22dnZtGzZkldffZW4uDhSUlIAiIyMZM2aNRw/fpyOHTua1RURaWQac/7StBciNcwdLvOeOnUqX375Jb6+vtx3331ERUVRWlpKSEgId999N7t37wagY8eOJCQk8MYbb+Dh4cHo0aMBuPXWW8nNzeXBBx80sxsiUseUv8yjgkykAQgNDSU0NNT52M/Pz2WsxaOPPuqy/VNPPeX89+uvv16uvdLSUq644gr69etX88GKiJxD+auMTlmKiIv9+/fz5z//mQEDBuDn52d2OCIiVVaf85eOkImIi9atW7Nu3TqzwxARuWT1OX/pCJmIiIiIyVSQiYiIiJhMBZmIiIiIyTSGTKSGpVqjarS96JJVNdqeiEhllL/MoyNkIg3Ad999x6hRo4iJieGBBx5g4cKFGIZBdnY2CQkJl9zeihUrqhXH3r17iYqKYtiwYcycOZOSkpJqtSMijYfyVxkVZBdhfdrm8p+Iu/nll18YO3YsU6ZMwWazkZ6ezp49e0hLS6t2m0uWLKnW81JSUhg7dixpaWkUFBSwfv36ascgIg2f8tevdMpSpJ7LzMwkNDSUtm3bAmC1WklOTsbLy4tt27Y5twsLC2Pjxo0AJCQkMGzYMFq2bMnkyZPx9PTEarUyf/58MjIyOHXqFDNnzmTq1KnMmDGD3NxcSktLuf/++wkJCaFfv360bdsWb29v5+1KABYtWoTVaqWwsJBjx47xm9/8pk73RWX++9//smDBAmw2G7m5uUyaNAmLxUL79u2ZMWMGHh4epKenk5aWhqenJ3FxcYSHh1NQUMD48eM5fvw4vr6+JCcn06JFC7O7I9Jg1GX+io+PJzQ01G3zl46QidRzR48epXXr1i7LfH198fb2vuhzN23axC233MJrr71GbGwsp06dIi4ujmbNmjFz5kzWrFnDlVdeycqVK1m8eDGvvfYaAKdPn+aJJ55wSWZQlkwPHjxIv379OHnypMt958yybNkyEhMTcTgcACQlJREfH09qaiqGYZCZmcmxY8ew2WykpaWxfPlyUlJSKCwsZNWqVQQFBZGamsrAgQNZvHixyb0RaVjqMn/NmjULcN/8pSNkIvXcddddx9dff+2ybP/+/Rw+fLjS5xiGAcDgwYNZtmwZI0eOxN/fv9x4jT179pCTk8OOHTuAsluSnDx5EqDSZHX99dfz0UcfsWbNGubNm0dycnK1+1YT2rRpw6JFi5gwYQIAu3btomfPngD07duXjRs34uHhQdeuXfH29sbb25s2bdqwe/ducnJyGDlypHPbqhZkO3fuvOg2OTk5l7S8IWkMfaxMY+v7uf2tqO8Oh4Nvv/3WZd3Ro0c5fvw4hmFw4sQJcnJyKCoqcm5z4sQJ9uzZw0033cSuXbsYOnQoV1xxBZGRkeTl5Tm33bhxI99++63zyFp+fj6ffvopDoeDn3/+udL3IikpiU8++YTx48cTFxdXk7vjglSQidRz4eHhvPLKK0RFRdGmTRuKioqYN28et99+OzfddJNzu+LiYvLz8/Hy8mLv3r1A2emCkJAQnnzySd59913+/ve/k5SU5CzYAgMDueaaa4iNjaWgoIAZM2bQrFkzADw8yh9gj42NZdKkSbRt2xZfX98Kt6lrERERHDhwwPnYMAwsFgtQ9ks8Ly8Pu92Ov7+/cxtfX1/sdrvL8rPbVkXnzp3x8fGpdH1OTg4hISFlD1Jdi2nn8gbKpe+NTH3o+7c13N7Z/lbW9w4dOjBs2DB++9vfOvNXfHw8t99+O+3bt2fr1q2EhIRgsVjo2LEjXl5eHD9+nKCgII4fP07//v2ZO3cu7777Lhs3biQpKQlPT09CQkLYtWsXv/vd75z5a8mSJfTt2xcfHx9CQkLKfUfPzV9Hjhzh+PHjVXq/HA5HlX6EXYwKMpEaVteXefv5+TFv3jwSExMxDIP8/HzCw8OJjo5m8+bNzu0eeughhg4dSqtWrbjuuuuAssJh/PjxLFq0CA8PDyZPngzAjTfeyLhx45g7dy6JiYkMHz4cu91OWFjYBYusUaNGMWnSJLy8vGjatClz5syp3c5Xw7nx5+fnExAQgJ+fH/n5+S7L/f39XZaf3VakIWvI+Ss6Otqt81e1CrKioiImTZrEwYMH8fDwYPbs2Xh6elZ5oKyI1KzOnTvz5ptvllseGhpKaGgoAKNHj2b06NHltlm9enW5ZTbbr1cUz58/3/nvs4f4K7v6qFu3bpd1dVRd6NSpE9nZ2YSGhpKVlcVtt91GcHAwL7zwAg6Hg8LCQvbt20dQUBDdunXjs88+Izg4mKysLLc/uiFSH9VV/jrLXfNXtQqyzz77jOLiYtLS0ti4cSMvvPCC8zBjaGgo06dPJzMzky5dumCz2Xj77bdxOBxER0cTFhZWpcF6IiK1YeLEiUybNo2UlBQCAwOJiIjAarUSExNDdHQ0hmGQkJCAj48PUVFRTJw4kaioKLy8vHjuuefMDl9EGqhqFWTt2rWjpKSE0tJS7HY7np6ebN++vcoDZYODg2u0EyIiF9KqVSvS09OBsvxV0cSRkZGRREZGuixr2rQpCxcurJMYRaRxq1ZBdsUVV3Dw4EHuu+8+Tp48ycsvv8yWLVuqPFC2Ki40QO5iV6nU5lUsdXGFTEO/Ckf9q98aev9ERMxQrYLs9ddfp3fv3jz99NMcOnSIhx9+mKKiIuf6iw2UrYrKrlIqd6XGeVcoQQ1fpVTHV0DVh6twLof6V7/VRv9q6golEZH6rFrXpAcEBDgLq2bNmlFcXOwcKAuQlZVF9+7dCQ4OJicnB4fDQV5ennOgrIiIiIj8qlpHyB555BGmTJlCdHQ0RUVFJCQk0Llz5yoPlBURERGRX1WrIPP19eXFF18st7yqA2VFRERE5FfmT6MtIiIi0sipIBMRERExmQoyEREREZOpIBMRERExmQoyEREREZOpIBMRERExmQoyEREREZOpIBMRERExmQoyEREREZOpIBMRERExmQoyEREREZOpIBMRERExmQoyEREREZOpIBMRERExmQoyEREREZOpIBMRERExmQoyEREREZOpIBMRERExmQoyEREREZOpIBMRERExmQoyEREREZOpIBMRERExmQoyEREREZOpIBMRERExmQoyEREREZN5mh2AiEhdKyoqYtKkSRw8eBAPDw9mz56Np6cnkyZNwmKx0L59e2bMmIGHhwfp6emkpaXh6elJXFwc4eHhZocvIg2QCjIRaXQ+++wziouLSUtLY+PGjbzwwgsUFRURHx9PaGgo06dPJzMzky5dumCz2Xj77bdxOBxER0cTFhaGt7e32V0QkQZGBZmINDrt2rWjpKSE0tJS7HY7np6ebN++nZ49ewLQt29fNm7ciIeHB127dsXb2xtvb2/atGnD7t27CQ4OvmD7O3fuvGgMOTk5l7S8IWkMfayM+i6VUUEmIo3OFVdcwcGDB7nvvvs4efIkL7/8Mlu2bMFisQDg6+tLXl4edrsdf39/5/N8fX2x2+0Xbb9z5874+PhUuj4nJ4eQkJCyB6lfu6xzLm+gXPreyKjvDbPvDoejSj/CLkYFmYg0Oq+//jq9e/fm6aef5tChQzz88MMUFRU51+fn5xMQEICfnx/5+fkuy88t0EREaoqushSRRicgIMBZWDVr1ozi4mI6depEdnY2AFlZWXTv3p3g4GBycnJwOBzk5eWxb98+goKCzAxdRBooHSETkUbnkUceYcqUKURHR1NUVERCQgKdO3dm2rRppKSkEBgYSEREBFarlZiYGKKjozEMg4SEhAueihQRqS4VZCLS6Pj6+vLiiy+WW75ixYpyyyIjI4mMjKyLsESkEdMpSxERERGTqSATERERMZkKMhERERGTqSATERERMZkKMhERERGTVfsqy1deeYX169dTVFREVFQUPXv21I15RURERKqhWkfIsrOz2bZtG6tWrcJms3H48GGSkpKIj48nNTUVwzDIzMzk2LFj2Gw20tLSWL58OSkpKRQWFtZ0H0RERETqtWoVZBs2bCAoKIjRo0cTGxvLnXfeya5du1xuzLtp0yZ27NjhvDGvv7+/88a8IiIiIvKrap2yPHnyJD/++CMvv/wyBw4cIC4uDsMwauzGvMAFb9R5sTvG1+Yd5evibvV18RpmUv/qtxh8OBgAABLxSURBVIbePxERM1SrIGvevDmBgYF4e3sTGBiIj48Phw8fdq6viRvzdu7cucJblJS7Y3zq1+W2qdE7yp/Xfm3frb5c/xoY9a9+q43+ORyOC/4AExFpDKp1yjIkJITPP/8cwzA4cuQIZ86coVevXroxr4iIiEg1VOsIWXh4OFu2bGHw4MEYhsH06dNp1aqVbswrIiIiUg3VnvZiwoQJ5ZbpxrwiIiIil04Tw4qIiIiYTAWZiIiIiMlUkImIiIiYTAWZiIiIiMlUkImIiIiYTAWZiIiIiMlUkImIiIiYTAWZiIiIiMlUkImIiIiYTAWZiIiIiMlUkImIiIiYTAWZiIiIiMlUkImIiIiYzNPsAEREpHZYn7aVW1byXIwJkYjIxegImYiIiIjJVJCJiIiImEwFmYiIiIjJNIZMRBqlV155hfXr11NUVERUVBQ9e/Zk0qRJWCwW2rdvz4wZM/Dw8CA9PZ20tDQ8PT2Ji4sjPDzc7NBFpAHSETIRaXSys7PZtm0bq1atwmazcfjwYZKSkoiPjyc1NRXDMMjMzOTYsWPYbDbS0tJYvnw5KSkpFBYWmh2+iDRAKshEpNHZsGEDQUFBjB49mtjYWO6880527dpFz549Aejbty+bNm1ix44ddO3aFW9vb/z9/WnTpg27d+82OXoRaYh0ylJEGp2TJ0/y448/8vLLL3PgwAHi4uIwDAOLxQKAr68veXl52O12/P39nc/z9fXFbrdftP2dO3dedJucnJxLWl5Tarv9+hKDWdR3qYwKMhFpdJo3b05gYCDe3t4EBgbi4+PD4cOHnevz8/MJCAjAz8+P/Px8l+XnFmiV6dy5Mz4+PpWuz8nJISQkpOxB6tcu65zLa8J5bdd4+9Xg0vdGRn1vmH13OBxV+hF2MTplKSKNTkhICJ9//jmGYXDkyBHOnDlDr169yM7OBiArK4vu3bsTHBxMTk4ODoeDvLw89u3bR1BQkMnRi0hDpCNkItLohIeHs2XLFgYPHoxhGEyfPp1WrVoxbdo0UlJSCAwMJCIiAqvVSkxMDNHR0RiGQUJCwgWPfImIVJcKMhFplCZMmFBu2YoVK8oti4yMJDIysi5CEpFGTKcsRUREREz2/9q7/9im6n+P46+uo8zbbUFCzP0mOmXAIrg0DHbnH4RNEnWEgCjBBmrGHxDDFhMcCo5fY3q3CAu6P64RlWTJTTrIaIAY/zEoBL/L5cfuTSMuG0xyF0IiEOWHCW3VbrBz/7ihsp9sXbvP2vN8/LWe037O583p+fDqOe3nEMgAAAAMI5ABAAAYRiADAAAwjC/1G+R83z9k2X/7FhjoCQAAMIkzZAAAAIYRyAAAAAwjkAEAABhGIAMAADCMQAYAAGAYgQwAAMAwAhkAAIBhBDIAAADDCGQAAACGTSiQ3blzR2VlZerp6dG1a9e0fv16+Xw+1dXVqb+/X5IUCAS0Zs0aeb1enTlzJiGdBgAASCdxB7K+vj7t3btXWVlZkqR9+/apurpaR44ckWVZOn36tG7duiW/36/W1lY1NzerqalJvb29Ces8AABAOoj7XpaNjY1at26dDh06JEnq6upSSUmJJKm0tFRnz55VRkaGioqK5HK55HK5lJeXp+7ubnk8nse239nZOeK6YDA46msft34iktn2ZG7DJOpLbeleHwCYEFcgO3HihGbOnKmlS5fGApllWXI4HJIkt9utUCikcDisnJyc2OvcbrfC4fCYtlFYWKjp06cPWR4MBrV48eK/Fxy5NOQ5A9ZP1KD2k9l2UrYxxQzZf2mG+sYvGo2O+gEMAOwgrkB2/PhxORwOnT9/XpcvX1ZNTY3u3r0bWx+JRJSbm6vs7GxFIpEByx8NaAAAAIjzO2SHDx9WS0uL/H6/5s+fr8bGRpWWlqq9vV2S1NbWpuLiYnk8HgWDQUWjUYVCIfX09KigoCChBQAAAKS6uL9DNlhNTY1qa2vV1NSk/Px8lZeXy+l0qqKiQj6fT5ZlaevWrcNehgQAALCzCQcyv98f+7ulpWXIeq/XK6/XO9HNAAAApC0mhgUAADCMQAYAAGAYgQwAAMAwAhkAAIBhBDIAAADDCGQAAACGEcgAAAAMI5ABAAAYRiADYEt37txRWVmZenp6dO3aNa1fv14+n091dXXq7++XJAUCAa1Zs0Zer1dnzpwx3GMA6YxABsB2+vr6tHfvXmVlZUmS9u3bp+rqah05ckSWZen06dO6deuW/H6/Wltb1dzcrKamJvX29hruOYB0RSADYDuNjY1at26dnnrqKUlSV1eXSkpKJEmlpaU6d+6cOjo6VFRUJJfLpZycHOXl5am7u9tktwGksYTdXBwAUsGJEyc0c+ZMLV26VIcOHZIkWZYlh8MhSXK73QqFQgqHw8rJyYm9zu12KxwOj2kbnZ2dj31OMBgc1/JESXb7qdIHU6gdIyGQAbCV48ePy+Fw6Pz587p8+bJqamp09+7d2PpIJKLc3FxlZ2crEokMWP5oQBtNYWGhpk+fPuL6YDCoxYsX//+DI5cGrIstT4RBbSe8/TgMqN1mqD09a49Go2P6EPY4XLIEYCuHDx9WS0uL/H6/5s+fr8bGRpWWlqq9vV2S1NbWpuLiYnk8HgWDQUWjUYVCIfX09KigoMBw7wGkK86QAbC9mpoa1dbWqqmpSfn5+SovL5fT6VRFRYV8Pp8sy9LWrVtHPesFABNBIANgW36/P/Z3S0vLkPVer1der3cyuwTAprhkCQAAYBhnyNKY833/kGUPPq0w0BMAADAazpABAAAYRiADAAAwjEAGAABgGIEMAADAMAIZAACAYQQyAAAAwwhkAAAAhhHIAAAADCOQAQAAGEYgAwAAMIxABgAAYBiBDAAAwDACGQAAgGEEMgAAAMMIZAAAAIZlmu4AACA1Od/3D3j84NMKQz0BUh9nyAAAAAwjkAEAABhGIAMAADCMQAYAAGBYXF/q7+vr065du3T9+nX19vaqqqpKc+fO1Y4dO+RwODRv3jzV1dUpIyNDgUBAra2tyszMVFVVlZYtW5boGgAAAFJaXIHsm2++0YwZM3TgwAH9/vvveuONN/T888+rurpaL774ovbu3avTp09r4cKF8vv9On78uKLRqHw+n5YsWSKXy5XoOjDJBv+6SuIXVgAAxCuuQLZ8+XKVl5fHHjudTnV1damkpESSVFpaqrNnzyojI0NFRUVyuVxyuVzKy8tTd3e3PB7PY7fR2dk54rpgMDjqax+3fiKS2fZkbGMqtD0Z/4YmUR8AYLziCmRut1uSFA6HtWXLFlVXV6uxsVEOhyO2PhQKKRwOKycnZ8DrwuHwmLZRWFio6dOnD1keDAa1ePHivxccuTTkOQPWT9Sg9pPZdsK3kcx/mzjbHrL/0gz1jV80Gh31AxgA2EHcX+q/efOmNmzYoNWrV2vVqlXKyPi7qUgkotzcXGVnZysSiQxY/mhAAwAAQJyB7Pbt29q4caO2b9+utWvXSpIWLFig9vZ2SVJbW5uKi4vl8XgUDAYVjUYVCoXU09OjgoKCxPUeAAAgDcR1yfLLL7/UvXv3dPDgQR08eFCStHv3bjU0NKipqUn5+fkqLy+X0+lURUWFfD6fLMvS1q1bh70MCQAAYGdxBbI9e/Zoz549Q5a3tLQMWeb1euX1euPZDAAAgC1wc3EAtsI8igCmIgIZAFthHkUAUxGBDICtTMY8igAwXgQyALYyGfMojmVetZEm2E32xLtMDm0WtWMkBDIAtnPz5k2988478vl8WrVqlQ4cOBBbl4h5FEea2PqhARPsTvLk06Ynzk73yZNHQ+3pWXuiJreOe2JYAEhFzKMIYCriDBkAW2EeRQBTEYEMgK0wjyKAqYhABgCYcpzv+4cse/BphYGeAJODQAYAsJ3BgY+wB9MIZJiS+HQMALATfmUJAABgGIEMAADAMAIZAACAYQQyAAAAw/hSPwAACZTsHyXxC9H0xBkyAAAAwwhkAAAAhhHIAAAADOM7ZAAAQBKTcpvEGTIAAADDCGQAAACGEcgAAAAM4ztkAABgUvAdtZFxhgwAAMAwAhkAAIBhXLKE7XDKHADST6qP7ZwhAwAAMIxABgAAYBiXLAFgivuf7KFD9b+F7xvoCYBk4QwZAACAYQQyAAAAw7hkCSRYqv/SBwAw+ThDBgAAYBiBDAAAwDAuWSJlJfuXZ/yyDQAwWQhkKWBwMEilUDC47xn/bDfUEwAjSeUxBkgXtglkDDgAAGCqSnog6+/v14cffqiff/5ZLpdLDQ0NevbZZ5O9WYwRl+XM4HJr6mAMm5jh3otK4nud9zlSVdID2alTp9Tb26ujR4/q4sWL2r9/v7744otkbxaYUv7zv3YMePxCnO0MN6VG8xtdQ5bF2/5Y2C3sTfYYNvi9IiV3f2Jkk/3BKZ2PIzxe0gNZMBjU0qVLJUkLFy5UZ2fnqM+3LEuS1NvbO+JzotFo7O9/uKeNuj7mX//x+OcMY3D7w72u44W5Q5Z5uv533G2PuI0E9X0sbY+1/Xj/3RPavoG+B57ePODx6p7/GFP70xz/Mu62h2t/TG2P0P5Y3zePleC2Hx7rD4/9qWY8Y9hYxq+HHv6bDd6nY92f8b4XD5/99yHLCpI4Pk72cTrV+z7g9Sny/9JY2h9L29FodEztT8W+jyZRY5jDSvIouHv3br366qsqKyuTJL300ks6deqUMjOHz4KhUEhXrlxJZpcATEEFBQXKyckx3Y0hxjOGMX4B9jXRMSzpZ8iys7MViURij/v7+0cMY5LkdrtVUFCgadOmyeFwJLt7AAyzLEt9fX1yu92muzKs8YxhjF+A/SRqDEt6IFu0aJHOnDmjFStW6OLFiyooKBj1+RkZGVPyUzKA5MnKyjLdhRGNZwxj/ALsKRFjWNIvWT78hdKVK1dkWZY+/vhjzZkzJ5mbBICEYQwDMBmSHsgAAAAwOu5lCQAAYBiBDAAAwDACGQAAgGEpcS/LdL11yeuvvx77RdbTTz+tyspK7dixQw6HQ/PmzVNdXZ0yMlIvM//000/65JNP5Pf7de3atWFrCgQCam1tVWZmpqqqqrRs2TLT3R6zR+vr6upSZWWlnnvuOUnS+vXrtWLFipStr6+vT7t27dL169fV29urqqoqzZ07N+32oSnpOpYNJ93HgeHY+fh58OCB9uzZo6tXr8rpdGrfvn2yLMsWtSeMlQJOnjxp1dTUWJZlWT/++KNVWVlpuEcT99dff1mrV68esGzz5s3WhQsXLMuyrNraWuu7774z0bUJOXTokLVy5UrrzTfftCxr+Jp+++03a+XKlVY0GrXu3bsX+zsVDK4vEAhYzc3NA56TyvUdO3bMamhosCzLsu7evWuVlZWl3T40KR3HsuGk+zgwEjsfP99//721Y8cOy7Is68KFC1ZlZaVtak+UlDj9Mt7bL6WC7u5u/fnnn9q4caM2bNigixcvqqurSyUlJZKk0tJSnTt3znAvxy8vL0+fffZZ7PFwNXV0dKioqEgul0s5OTnKy8tTd3e3qS6Py+D6Ojs79cMPP+itt97Srl27FA6HU7q+5cuX69133409djqdabcPTUrHsWw46T4OjMTOx8/LL7+s+vp6SdKNGzc0a9Ys29SeKCkRyMLhsLKzs2OPnU6n7t9P7ZuwZmVladOmTWpubtZHH32kbdu2ybKs2OzebrdboVDIcC/Hr7y8fMAs5sPVFA6HB0ye6Xa7FQ6HJ72v8Rhcn8fj0QcffKDDhw/rmWee0eeff57S9bndbmVnZyscDmvLli2qrq5Ou31oUjqOZcNJ93FgJHY/fjIzM1VTU6P6+nqVl5fbqvZESIlANt7bL6WC2bNn67XXXpPD4dDs2bM1Y8YM3blzJ7Y+EokoNzfXYA8T49HvwD2safD+jEQiKTu7+SuvvKLCwsLY35cuXUr5+m7evKkNGzZo9erVWrVqVdrvw8mUjmPZWNjpPWT346exsVEnT55UbW3tgBt726H2iUqJQLZo0SK1tbVJ0phuv5QKjh07pv3790uSfv31V4XDYS1ZskTt7e2SpLa2NhUXF5vsYkIsWLBgSE0ej0fBYFDRaFShUEg9PT0pu083bdqkjo4OSdL58+f1wgsvpHR9t2/f1saNG7V9+3atXbtWUvrvw8mUjmPZWNjlPWTn4+frr7/WV199JUl64okn5HA4VFhYaIvaEyUlZupPx1uX9Pb2aufOnbpx44YcDoe2bdumJ598UrW1terr61N+fr4aGhrkdDpNd3XcfvnlF7333nsKBAK6evXqsDUFAgEdPXpUlmVp8+bNKi8vN93tMXu0vq6uLtXX12vatGmaNWuW6uvrlZ2dnbL1NTQ06Ntvv1V+fn5s2e7du9XQ0JBW+9CUdBzLRpLu48Bw7Hz8/PHHH9q5c6du376t+/fv6+2339acOXNssd8TJSUCGQAAQDpLiUuWAAAA6YxABgAAYBiBDAAAwDACGQAAgGEEMgAAAMMIZAAAAIYRyAAAAAz7PxDczjvgyAnDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1 , figsize = (10,10))\n",
    "n = 0 \n",
    "for var in ['TotlAmtbyCust' , 'TotalCustTransactions' ,'DistProductsbyCust','TenureofCustomer']:\n",
    "    n += 1\n",
    "    label = df_new['DBSCAN_labels'].unique()                   \n",
    "\n",
    "    plt.subplot(2,2,n)\n",
    "    plt.hist([df_new.loc[df_new['DBSCAN_labels'] == x, var] for x in label], label=label)\n",
    "\n",
    "    plt.title('DBSCAN Label Histogram of {}'.format(var))\n",
    "    plt.legend()\n",
    "plt.suptitle('DBSCAN Cluster Results')\n",
    "plt.savefig('DBSCANHistograms.png')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusions on DBSCAN Clustering\n",
    "\n",
    "This algorithm looks promising! It was able to identify that the majority of our customers fit in the same cluster based on their behaviors.\n",
    "\n",
    "DBSCAN Clustering identified 3 primary cluster & 1 noisy label:\n",
    "\n",
    "- DBSCAN grouped the majority of our customers in __Cluster Id 1__: It recognized that most of the customers spend and transact relatively in the same manner. This aligns with the 'single' cluster presented in the lower dimensionality visualization.\n",
    "- Customers tagged to __Cluster Id 2__ & __Cluster Id 3__ are more frequent buyers & tend to buy a wider array of products.\n",
    "- We can disregard the __Noisy__ label as the algorithm detected noise in our dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import SpectralClustering\n",
    "sc = SpectralClustering(n_clusters=3).fit(X_scaled)\n",
    "cl_labels_s = sc.labels_\n",
    "np.unique(cl_labels_s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=2)\n",
    "\n",
    "X_pca = pca.fit_transform(X_scaled)\n",
    "# X_pca_centers_dbscan = pca.transform(cl_centers)\n",
    "\n",
    "plt.scatter(X_pca[cl_labels_s==0,0],X_pca[cl_labels_s==0,1],label='Cluster 1')\n",
    "plt.scatter(X_pca[cl_labels_s==1,0],X_pca[cl_labels_s==1,1],label='Cluster 2')\n",
    "plt.scatter(X_pca[cl_labels_s==2,0],X_pca[cl_labels_s==2,1],label='Cluster 3')\n",
    "# plt.scatter(X_pca_centers_kmeans[:, 0], X_pca_centers_kmeans[:, 1], c='black', s = 75)\n",
    "plt.xlabel('Feature-1')\n",
    "plt.ylabel('Feature-2')\n",
    "plt.title('PCA Spectral Clustering Scatter Plot')\n",
    "plt.legend()\n",
    "plt.savefig('SPECTRALCLUSTER.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_labels = pd.DataFrame(cl_labels_s , columns = list(['labels']))\n",
    "df_labels.astype({'labels': 'object'}).dtypes\n",
    "df_labels.loc[(df_labels.labels == 0)] = 'Cluster 1'\n",
    "df_labels.loc[(df_labels.labels == 1)] = 'Cluster 2'\n",
    "df_labels.loc[(df_labels.labels == 2)] = 'Cluster 3'\n",
    "df_new['SpectralClustering_labels'] = df_labels['labels'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cluster 1     370\n",
       "Cluster 2     195\n",
       "Cluster 3    2659\n",
       "Name: SpectralClustering_labels, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new['SpectralClustering_labels'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1 , figsize = (10,10))\n",
    "n = 0 \n",
    "for var in ['TotlAmtbyCust' , 'TotalCustTransactions' ,'DistProductsbyCust','TenureofCustomer']:\n",
    "    n += 1\n",
    "    label = df_new['SpectralClustering_labels'].unique()                   \n",
    "\n",
    "    plt.subplot(2,2,n)\n",
    "    plt.hist([df_new.loc[df_new['SpectralClustering_labels'] == x, var] for x in label], label=label)\n",
    "\n",
    "    plt.title('Spectral Clustering Histogram of {}'.format(var))\n",
    "    plt.legend()\n",
    "plt.suptitle('Spectral Clustering Cluster Results')\n",
    "plt.savefig('SCHistograms.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusions on Spectral Clustering\n",
    "\n",
    "Spectral Clustering appears to be the 'middle ground' between KMeans & DBSCAN:\n",
    "- Similar to DBSCAN, Spectral Clustering identified that majority of customers have similar behaviors and grouped them into a single cluster\n",
    "- Like KMeans, it still segregated customers into __Cluster 2__ & __Cluster 3__ for those groups that tend to be more frequent buyers & tend to buy a wider array of products. \n",
    "- Another interesting observation is __Cluster 1__ tends to have less transactions, product selection, and purchases than that of __Cluster 2__ & __Cluster 3__. Seems like Giftware could really benefit from a marketing campaign to bring it new clients!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Final Observations\n",
    "\n",
    "When we reduce the dimensionality of the data to visualize the clusters, DBSCAN identified a single cluster, aligning to the majority of customers transacting in the same manner. Another important consideration is that all 3 models identified that a good portion of customers tend to only purchase minimally & not buy a ton of products. Perhaps Giftware Shop should ignite customer engagement by conducting additional marketing compaigns to bring more traffic (and thereby purchases) to the online retail site. "
   ]
  },
  {
   "source": [],
   "cell_type": "markdown",
   "metadata": {}
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3-final"
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 },
 "nbformat": 4,
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}