KPMG FINAL.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 seaborn as sb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_customer_list = pd.read_excel(\"C:/Users/My/Desktop/KPMG/KPMG_VI_New_raw_data_update_final.xlsx\",sheet_name = 'NewCustomerList')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "customer_address = pd.read_excel(\"C:/Users/My/Desktop/KPMG/KPMG_VI_New_raw_data_update_final.xlsx\",sheet_name = 'CustomerAddress')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "customer_transaction = pd.read_excel(\"C:/Users/My/Desktop/KPMG/KPMG_VI_New_raw_data_update_final.xlsx\",sheet_name = 'Transactions')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "customer_demographics = pd.read_excel(\"C:/Users/My/Desktop/KPMG/KPMG_VI_New_raw_data_update_final.xlsx\",sheet_name = 'CustomerDemographic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.merge(customer_demographics,customer_transaction,how = 'left',on='customer_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.merge(df,customer_address,how = 'left',on = 'customer_id' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>customer_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>past_3_years_bike_related_purchases</th>\n",
       "      <th>Age</th>\n",
       "      <th>DOB</th>\n",
       "      <th>job_title</th>\n",
       "      <th>job_industry_category</th>\n",
       "      <th>wealth_segment</th>\n",
       "      <th>...</th>\n",
       "      <th>product_class</th>\n",
       "      <th>product_size</th>\n",
       "      <th>list_price</th>\n",
       "      <th>standard_cost</th>\n",
       "      <th>product_first_sold_date</th>\n",
       "      <th>address</th>\n",
       "      <th>postcode</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>property_valuation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Laraine</td>\n",
       "      <td>Medendorp</td>\n",
       "      <td>F</td>\n",
       "      <td>93</td>\n",
       "      <td>67.415808</td>\n",
       "      <td>1953-10-12</td>\n",
       "      <td>Executive Secretary</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>...</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>235.63</td>\n",
       "      <td>125.07</td>\n",
       "      <td>2005-05-10</td>\n",
       "      <td>060 Morning Avenue</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Laraine</td>\n",
       "      <td>Medendorp</td>\n",
       "      <td>F</td>\n",
       "      <td>93</td>\n",
       "      <td>67.415808</td>\n",
       "      <td>1953-10-12</td>\n",
       "      <td>Executive Secretary</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>...</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>1577.53</td>\n",
       "      <td>826.51</td>\n",
       "      <td>2008-03-19</td>\n",
       "      <td>060 Morning Avenue</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Laraine</td>\n",
       "      <td>Medendorp</td>\n",
       "      <td>F</td>\n",
       "      <td>93</td>\n",
       "      <td>67.415808</td>\n",
       "      <td>1953-10-12</td>\n",
       "      <td>Executive Secretary</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>...</td>\n",
       "      <td>low</td>\n",
       "      <td>small</td>\n",
       "      <td>1720.70</td>\n",
       "      <td>1531.42</td>\n",
       "      <td>2003-07-21</td>\n",
       "      <td>060 Morning Avenue</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Laraine</td>\n",
       "      <td>Medendorp</td>\n",
       "      <td>F</td>\n",
       "      <td>93</td>\n",
       "      <td>67.415808</td>\n",
       "      <td>1953-10-12</td>\n",
       "      <td>Executive Secretary</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>...</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>360.40</td>\n",
       "      <td>270.30</td>\n",
       "      <td>2003-09-09</td>\n",
       "      <td>060 Morning Avenue</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Laraine</td>\n",
       "      <td>Medendorp</td>\n",
       "      <td>F</td>\n",
       "      <td>93</td>\n",
       "      <td>67.415808</td>\n",
       "      <td>1953-10-12</td>\n",
       "      <td>Executive Secretary</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>...</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>71.49</td>\n",
       "      <td>53.62</td>\n",
       "      <td>2005-08-09</td>\n",
       "      <td>060 Morning Avenue</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_id first_name  last_name gender  \\\n",
       "0            1    Laraine  Medendorp      F   \n",
       "1            1    Laraine  Medendorp      F   \n",
       "2            1    Laraine  Medendorp      F   \n",
       "3            1    Laraine  Medendorp      F   \n",
       "4            1    Laraine  Medendorp      F   \n",
       "\n",
       "   past_3_years_bike_related_purchases        Age        DOB  \\\n",
       "0                                   93  67.415808 1953-10-12   \n",
       "1                                   93  67.415808 1953-10-12   \n",
       "2                                   93  67.415808 1953-10-12   \n",
       "3                                   93  67.415808 1953-10-12   \n",
       "4                                   93  67.415808 1953-10-12   \n",
       "\n",
       "             job_title job_industry_category wealth_segment  ...  \\\n",
       "0  Executive Secretary                Health  Mass Customer  ...   \n",
       "1  Executive Secretary                Health  Mass Customer  ...   \n",
       "2  Executive Secretary                Health  Mass Customer  ...   \n",
       "3  Executive Secretary                Health  Mass Customer  ...   \n",
       "4  Executive Secretary                Health  Mass Customer  ...   \n",
       "\n",
       "  product_class product_size list_price  standard_cost  \\\n",
       "0        medium       medium     235.63         125.07   \n",
       "1        medium       medium    1577.53         826.51   \n",
       "2           low        small    1720.70        1531.42   \n",
       "3        medium       medium     360.40         270.30   \n",
       "4        medium       medium      71.49          53.62   \n",
       "\n",
       "   product_first_sold_date             address postcode            state  \\\n",
       "0               2005-05-10  060 Morning Avenue   2016.0  New South Wales   \n",
       "1               2008-03-19  060 Morning Avenue   2016.0  New South Wales   \n",
       "2               2003-07-21  060 Morning Avenue   2016.0  New South Wales   \n",
       "3               2003-09-09  060 Morning Avenue   2016.0  New South Wales   \n",
       "4               2005-08-09  060 Morning Avenue   2016.0  New South Wales   \n",
       "\n",
       "     country property_valuation  \n",
       "0  Australia               10.0  \n",
       "1  Australia               10.0  \n",
       "2  Australia               10.0  \n",
       "3  Australia               10.0  \n",
       "4  Australia               10.0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['customer_id'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['first_name', 'last_name', 'gender',\n",
       "       'past_3_years_bike_related_purchases', 'DOB', 'job_title',\n",
       "       'job_industry_category', 'wealth_segment', 'deceased_indicator',\n",
       "       'owns_car', 'tenure', 'address', 'postcode', 'state', 'country',\n",
       "       'property_valuation', 'Unnamed: 16', 'Unnamed: 17', 'Unnamed: 18',\n",
       "       'Unnamed: 19', 'Unnamed: 20', 'Rank', 'Value'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_customer_list.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['customer_id', 'address', 'postcode', 'state', 'country',\n",
       "       'property_valuation'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_address.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['transaction_id', 'product_id', 'customer_id', 'transaction_date',\n",
       "       'online_order', 'order_status', 'brand', 'product_line',\n",
       "       'product_class', 'product_size', 'list_price', 'standard_cost',\n",
       "       'product_first_sold_date'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_transaction.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_customer_list.insert(0, 'customer_id', range(4001, 4001 + len(new_customer_list)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = pd.concat([df2, new_customer_list], ignore_index=True, sort=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>past_3_years_bike_related_purchases</th>\n",
       "      <th>Age</th>\n",
       "      <th>DOB</th>\n",
       "      <th>job_title</th>\n",
       "      <th>job_industry_category</th>\n",
       "      <th>wealth_segment</th>\n",
       "      <th>...</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>property_valuation</th>\n",
       "      <th>Unnamed: 16</th>\n",
       "      <th>Unnamed: 17</th>\n",
       "      <th>Unnamed: 18</th>\n",
       "      <th>Unnamed: 19</th>\n",
       "      <th>Unnamed: 20</th>\n",
       "      <th>Rank</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Laraine</td>\n",
       "      <td>Medendorp</td>\n",
       "      <td>F</td>\n",
       "      <td>93</td>\n",
       "      <td>67.415808</td>\n",
       "      <td>1953-10-12</td>\n",
       "      <td>Executive Secretary</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>...</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Laraine</td>\n",
       "      <td>Medendorp</td>\n",
       "      <td>F</td>\n",
       "      <td>93</td>\n",
       "      <td>67.415808</td>\n",
       "      <td>1953-10-12</td>\n",
       "      <td>Executive Secretary</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>...</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Laraine</td>\n",
       "      <td>Medendorp</td>\n",
       "      <td>F</td>\n",
       "      <td>93</td>\n",
       "      <td>67.415808</td>\n",
       "      <td>1953-10-12</td>\n",
       "      <td>Executive Secretary</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>...</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Laraine</td>\n",
       "      <td>Medendorp</td>\n",
       "      <td>F</td>\n",
       "      <td>93</td>\n",
       "      <td>67.415808</td>\n",
       "      <td>1953-10-12</td>\n",
       "      <td>Executive Secretary</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>...</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Laraine</td>\n",
       "      <td>Medendorp</td>\n",
       "      <td>F</td>\n",
       "      <td>93</td>\n",
       "      <td>67.415808</td>\n",
       "      <td>1953-10-12</td>\n",
       "      <td>Executive Secretary</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>...</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_id first_name  last_name gender  \\\n",
       "0            1    Laraine  Medendorp      F   \n",
       "1            1    Laraine  Medendorp      F   \n",
       "2            1    Laraine  Medendorp      F   \n",
       "3            1    Laraine  Medendorp      F   \n",
       "4            1    Laraine  Medendorp      F   \n",
       "\n",
       "   past_3_years_bike_related_purchases        Age        DOB  \\\n",
       "0                                   93  67.415808 1953-10-12   \n",
       "1                                   93  67.415808 1953-10-12   \n",
       "2                                   93  67.415808 1953-10-12   \n",
       "3                                   93  67.415808 1953-10-12   \n",
       "4                                   93  67.415808 1953-10-12   \n",
       "\n",
       "             job_title job_industry_category wealth_segment  ...  \\\n",
       "0  Executive Secretary                Health  Mass Customer  ...   \n",
       "1  Executive Secretary                Health  Mass Customer  ...   \n",
       "2  Executive Secretary                Health  Mass Customer  ...   \n",
       "3  Executive Secretary                Health  Mass Customer  ...   \n",
       "4  Executive Secretary                Health  Mass Customer  ...   \n",
       "\n",
       "             state    country property_valuation  Unnamed: 16  Unnamed: 17  \\\n",
       "0  New South Wales  Australia               10.0          NaN          NaN   \n",
       "1  New South Wales  Australia               10.0          NaN          NaN   \n",
       "2  New South Wales  Australia               10.0          NaN          NaN   \n",
       "3  New South Wales  Australia               10.0          NaN          NaN   \n",
       "4  New South Wales  Australia               10.0          NaN          NaN   \n",
       "\n",
       "   Unnamed: 18 Unnamed: 19  Unnamed: 20 Rank Value  \n",
       "0          NaN         NaN          NaN  NaN   NaN  \n",
       "1          NaN         NaN          NaN  NaN   NaN  \n",
       "2          NaN         NaN          NaN  NaN   NaN  \n",
       "3          NaN         NaN          NaN  NaN   NaN  \n",
       "4          NaN         NaN          NaN  NaN   NaN  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21504, 38)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['customer_id', 'first_name', 'last_name', 'gender',\n",
       "       'past_3_years_bike_related_purchases', 'Age', 'DOB', 'job_title',\n",
       "       'job_industry_category', 'wealth_segment', 'deceased_indicator',\n",
       "       'default', 'owns_car', 'tenure', 'transaction_id', 'product_id',\n",
       "       'transaction_date', 'online_order', 'order_status', 'brand',\n",
       "       'product_line', 'product_class', 'product_size', 'list_price',\n",
       "       'standard_cost', 'product_first_sold_date', 'address', 'postcode',\n",
       "       'state', 'country', 'property_valuation', 'Unnamed: 16', 'Unnamed: 17',\n",
       "       'Unnamed: 18', 'Unnamed: 19', 'Unnamed: 20', 'Rank', 'Value'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_id                                0\n",
       "first_name                                 0\n",
       "last_name                                684\n",
       "gender                                     0\n",
       "past_3_years_bike_related_purchases        0\n",
       "Age                                     1009\n",
       "DOB                                      474\n",
       "job_title                               2583\n",
       "job_industry_category                   3489\n",
       "wealth_segment                             0\n",
       "deceased_indicator                         0\n",
       "default                                 2499\n",
       "owns_car                                   0\n",
       "tenure                                   457\n",
       "transaction_id                          1507\n",
       "product_id                              1507\n",
       "transaction_date                        1507\n",
       "online_order                            1867\n",
       "order_status                            1507\n",
       "brand                                   1704\n",
       "product_line                            1704\n",
       "product_class                           1704\n",
       "product_size                            1704\n",
       "list_price                              1507\n",
       "standard_cost                           1704\n",
       "product_first_sold_date                 1704\n",
       "address                                   29\n",
       "postcode                                  29\n",
       "state                                     29\n",
       "country                                   29\n",
       "property_valuation                        29\n",
       "Unnamed: 16                            20504\n",
       "Unnamed: 17                            20504\n",
       "Unnamed: 18                            20504\n",
       "Unnamed: 19                            20504\n",
       "Unnamed: 20                            20504\n",
       "Rank                                   20504\n",
       "Value                                  20504\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['first_name','last_name','Age','default','Unnamed: 16','Unnamed: 17','Unnamed: 18','Unnamed: 19','Unnamed: 20','Rank','Value']\n",
    "new_df.drop(cols,axis = 1,inplace =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['customer_id', 'gender', 'past_3_years_bike_related_purchases', 'DOB',\n",
       "       'job_title', 'job_industry_category', 'wealth_segment',\n",
       "       'deceased_indicator', 'owns_car', 'tenure', 'transaction_id',\n",
       "       'product_id', 'transaction_date', 'online_order', 'order_status',\n",
       "       'brand', 'product_line', 'product_class', 'product_size', 'list_price',\n",
       "       'standard_cost', 'product_first_sold_date', 'address', 'postcode',\n",
       "       'state', 'country', 'property_valuation'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21504, 27)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_id                             0.000000\n",
       "gender                                  0.000000\n",
       "past_3_years_bike_related_purchases     0.000000\n",
       "DOB                                     2.204241\n",
       "job_title                              12.011719\n",
       "job_industry_category                  16.224888\n",
       "wealth_segment                          0.000000\n",
       "deceased_indicator                      0.000000\n",
       "owns_car                                0.000000\n",
       "tenure                                  2.125186\n",
       "transaction_id                          7.007999\n",
       "product_id                              7.007999\n",
       "transaction_date                        7.007999\n",
       "online_order                            8.682106\n",
       "order_status                            7.007999\n",
       "brand                                   7.924107\n",
       "product_line                            7.924107\n",
       "product_class                           7.924107\n",
       "product_size                            7.924107\n",
       "list_price                              7.007999\n",
       "standard_cost                           7.924107\n",
       "product_first_sold_date                 7.924107\n",
       "address                                 0.134859\n",
       "postcode                                0.134859\n",
       "state                                   0.134859\n",
       "country                                 0.134859\n",
       "property_valuation                      0.134859\n",
       "dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.isnull().sum()/new_df.shape[0]*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>transaction_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.513987e+18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.491437e+18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.494461e+18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.483574e+18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.487635e+18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21499</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21500</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21501</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21502</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21503</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21504 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       transaction_date\n",
       "0          1.513987e+18\n",
       "1          1.491437e+18\n",
       "2          1.494461e+18\n",
       "3          1.483574e+18\n",
       "4          1.487635e+18\n",
       "...                 ...\n",
       "21499      0.000000e+00\n",
       "21500      0.000000e+00\n",
       "21501      0.000000e+00\n",
       "21502      0.000000e+00\n",
       "21503      0.000000e+00\n",
       "\n",
       "[21504 rows x 1 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df[\"transaction_date\"] = new_df[\"transaction_date\"].replace(np.nan,0,regex=True)\n",
    "new_df['year'] = pd.DatetimeIndex(new_df['DOB']).year\n",
    "new_df['month'] = pd.DatetimeIndex(new_df['DOB']).month\n",
    "new_df['age'] = 2021- new_df['year']\n",
    "new_df[\"profit\"] = new_df[\"list_price\"] - new_df[\"standard_cost\"]\n",
    "new_df['recency'] = 0\n",
    "new_df[['transaction_date']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 21504 entries, 0 to 21503\n",
      "Data columns (total 32 columns):\n",
      "customer_id                            21504 non-null int64\n",
      "gender                                 21504 non-null object\n",
      "past_3_years_bike_related_purchases    21504 non-null int64\n",
      "DOB                                    21030 non-null datetime64[ns]\n",
      "job_title                              18921 non-null object\n",
      "job_industry_category                  18015 non-null object\n",
      "wealth_segment                         21504 non-null object\n",
      "deceased_indicator                     21504 non-null object\n",
      "owns_car                               21504 non-null object\n",
      "tenure                                 21047 non-null float64\n",
      "transaction_id                         19997 non-null float64\n",
      "product_id                             19997 non-null float64\n",
      "transaction_date                       21504 non-null float64\n",
      "online_order                           19637 non-null float64\n",
      "order_status                           19997 non-null object\n",
      "brand                                  19800 non-null object\n",
      "product_line                           19800 non-null object\n",
      "product_class                          19800 non-null object\n",
      "product_size                           19800 non-null object\n",
      "list_price                             19997 non-null float64\n",
      "standard_cost                          19800 non-null float64\n",
      "product_first_sold_date                19800 non-null datetime64[ns]\n",
      "address                                21475 non-null object\n",
      "postcode                               21475 non-null float64\n",
      "state                                  21475 non-null object\n",
      "country                                21475 non-null object\n",
      "property_valuation                     21475 non-null float64\n",
      "year                                   21030 non-null float64\n",
      "month                                  21030 non-null float64\n",
      "age                                    21030 non-null float64\n",
      "profit                                 19800 non-null float64\n",
      "recency                                21504 non-null int64\n",
      "dtypes: datetime64[ns](2), float64(13), int64(3), object(14)\n",
      "memory usage: 5.3+ MB\n"
     ]
    }
   ],
   "source": [
    "new_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df['transaction_date'] = pd.to_datetime(new_df['transaction_date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\My\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    }
   ],
   "source": [
    "import datetime\n",
    "\n",
    "comparison_date = datetime.datetime(2017, 12, 30)\n",
    "\n",
    "for i in range(21504):\n",
    "    if new_df['transaction_date'][i] == 0:\n",
    "        pass\n",
    "    else:\n",
    "        x = comparison_date - new_df['transaction_date'][i]\n",
    "        new_df[\"recency\"][i] = x.days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      7\n",
       "1    268\n",
       "2    233\n",
       "3    359\n",
       "4    312\n",
       "Name: recency, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['recency'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['list_price','standard_cost','year','month','DOB']\n",
    "new_df.drop(cols,axis = 1,inplace =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21504, 27)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_id                               0\n",
       "gender                                    0\n",
       "past_3_years_bike_related_purchases       0\n",
       "job_title                              2583\n",
       "job_industry_category                  3489\n",
       "wealth_segment                            0\n",
       "deceased_indicator                        0\n",
       "owns_car                                  0\n",
       "tenure                                  457\n",
       "transaction_id                         1507\n",
       "product_id                             1507\n",
       "transaction_date                          0\n",
       "online_order                           1867\n",
       "order_status                           1507\n",
       "brand                                  1704\n",
       "product_line                           1704\n",
       "product_class                          1704\n",
       "product_size                           1704\n",
       "product_first_sold_date                1704\n",
       "address                                  29\n",
       "postcode                                 29\n",
       "state                                    29\n",
       "country                                  29\n",
       "property_valuation                       29\n",
       "age                                     474\n",
       "profit                                 1704\n",
       "recency                                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = new_df[-new_df['job_title'].isnull()]\n",
    "new_df = new_df[-new_df['job_industry_category'].isnull()]\n",
    "new_df = new_df[-new_df['online_order'].isnull()]\n",
    "new_df = new_df[-new_df['address'].isnull()]\n",
    "new_df = new_df[-new_df['brand'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['customer_id', 'gender', 'past_3_years_bike_related_purchases',\n",
       "       'job_title', 'job_industry_category', 'wealth_segment',\n",
       "       'deceased_indicator', 'owns_car', 'tenure', 'transaction_id',\n",
       "       'product_id', 'transaction_date', 'online_order', 'order_status',\n",
       "       'brand', 'product_line', 'product_class', 'product_size',\n",
       "       'product_first_sold_date', 'address', 'postcode', 'state', 'country',\n",
       "       'property_valuation', 'age', 'profit', 'recency'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_id                             2593\n",
       "gender                                     5\n",
       "past_3_years_bike_related_purchases      100\n",
       "job_title                                195\n",
       "job_industry_category                      9\n",
       "wealth_segment                             3\n",
       "deceased_indicator                         2\n",
       "owns_car                                   2\n",
       "tenure                                    22\n",
       "transaction_id                         14454\n",
       "product_id                               101\n",
       "transaction_date                         364\n",
       "online_order                               2\n",
       "order_status                               2\n",
       "brand                                      6\n",
       "product_line                               4\n",
       "product_class                              3\n",
       "product_size                               3\n",
       "product_first_sold_date                  100\n",
       "address                                 2592\n",
       "postcode                                 768\n",
       "state                                      5\n",
       "country                                    1\n",
       "property_valuation                        12\n",
       "age                                       55\n",
       "profit                                   100\n",
       "recency                                  364\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female    7117\n",
       "Male      6919\n",
       "U          400\n",
       "F           11\n",
       "Femal        7\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>past_3_years_bike_related_purchases</th>\n",
       "      <th>job_title</th>\n",
       "      <th>job_industry_category</th>\n",
       "      <th>wealth_segment</th>\n",
       "      <th>deceased_indicator</th>\n",
       "      <th>owns_car</th>\n",
       "      <th>tenure</th>\n",
       "      <th>transaction_id</th>\n",
       "      <th>...</th>\n",
       "      <th>product_size</th>\n",
       "      <th>product_first_sold_date</th>\n",
       "      <th>address</th>\n",
       "      <th>postcode</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>property_valuation</th>\n",
       "      <th>age</th>\n",
       "      <th>profit</th>\n",
       "      <th>recency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>200</td>\n",
       "      <td>34</td>\n",
       "      <td>U</td>\n",
       "      <td>59</td>\n",
       "      <td>Legal Assistant</td>\n",
       "      <td>IT</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1039.0</td>\n",
       "      <td>...</td>\n",
       "      <td>small</td>\n",
       "      <td>2011-04-16</td>\n",
       "      <td>833 Luster Way</td>\n",
       "      <td>4005.0</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>8.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>187.39</td>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>201</td>\n",
       "      <td>34</td>\n",
       "      <td>U</td>\n",
       "      <td>59</td>\n",
       "      <td>Legal Assistant</td>\n",
       "      <td>IT</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1107.0</td>\n",
       "      <td>...</td>\n",
       "      <td>medium</td>\n",
       "      <td>2005-12-07</td>\n",
       "      <td>833 Luster Way</td>\n",
       "      <td>4005.0</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>8.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>209.84</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>202</td>\n",
       "      <td>34</td>\n",
       "      <td>U</td>\n",
       "      <td>59</td>\n",
       "      <td>Legal Assistant</td>\n",
       "      <td>IT</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2638.0</td>\n",
       "      <td>...</td>\n",
       "      <td>large</td>\n",
       "      <td>2013-09-16</td>\n",
       "      <td>833 Luster Way</td>\n",
       "      <td>4005.0</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>8.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>1408.91</td>\n",
       "      <td>267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>203</td>\n",
       "      <td>34</td>\n",
       "      <td>U</td>\n",
       "      <td>59</td>\n",
       "      <td>Legal Assistant</td>\n",
       "      <td>IT</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>20.0</td>\n",
       "      <td>9044.0</td>\n",
       "      <td>...</td>\n",
       "      <td>medium</td>\n",
       "      <td>2004-08-17</td>\n",
       "      <td>833 Luster Way</td>\n",
       "      <td>4005.0</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>8.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>1069.55</td>\n",
       "      <td>320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>204</td>\n",
       "      <td>34</td>\n",
       "      <td>U</td>\n",
       "      <td>59</td>\n",
       "      <td>Legal Assistant</td>\n",
       "      <td>IT</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>20.0</td>\n",
       "      <td>9792.0</td>\n",
       "      <td>...</td>\n",
       "      <td>small</td>\n",
       "      <td>2011-08-24</td>\n",
       "      <td>833 Luster Way</td>\n",
       "      <td>4005.0</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>8.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>217.51</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19263</td>\n",
       "      <td>3365</td>\n",
       "      <td>U</td>\n",
       "      <td>29</td>\n",
       "      <td>Engineer III</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19229.0</td>\n",
       "      <td>...</td>\n",
       "      <td>medium</td>\n",
       "      <td>2004-12-18</td>\n",
       "      <td>297 Kinsman Park</td>\n",
       "      <td>2766.0</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75.48</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19861</td>\n",
       "      <td>3473</td>\n",
       "      <td>U</td>\n",
       "      <td>34</td>\n",
       "      <td>Analog Circuit Design manager</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60.0</td>\n",
       "      <td>...</td>\n",
       "      <td>small</td>\n",
       "      <td>2014-07-28</td>\n",
       "      <td>7279 Steensland Circle</td>\n",
       "      <td>3803.0</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Australia</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>217.51</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19862</td>\n",
       "      <td>3473</td>\n",
       "      <td>U</td>\n",
       "      <td>34</td>\n",
       "      <td>Analog Circuit Design manager</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11079.0</td>\n",
       "      <td>...</td>\n",
       "      <td>medium</td>\n",
       "      <td>1997-08-25</td>\n",
       "      <td>7279 Steensland Circle</td>\n",
       "      <td>3803.0</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Australia</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>448.68</td>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19863</td>\n",
       "      <td>3473</td>\n",
       "      <td>U</td>\n",
       "      <td>34</td>\n",
       "      <td>Analog Circuit Design manager</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16206.0</td>\n",
       "      <td>...</td>\n",
       "      <td>small</td>\n",
       "      <td>1999-12-04</td>\n",
       "      <td>7279 Steensland Circle</td>\n",
       "      <td>3803.0</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Australia</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>144.26</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19864</td>\n",
       "      <td>3473</td>\n",
       "      <td>U</td>\n",
       "      <td>34</td>\n",
       "      <td>Analog Circuit Design manager</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17845.0</td>\n",
       "      <td>...</td>\n",
       "      <td>medium</td>\n",
       "      <td>2005-10-22</td>\n",
       "      <td>7279 Steensland Circle</td>\n",
       "      <td>3803.0</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Australia</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>693.76</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>400 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       customer_id gender  past_3_years_bike_related_purchases  \\\n",
       "200             34      U                                   59   \n",
       "201             34      U                                   59   \n",
       "202             34      U                                   59   \n",
       "203             34      U                                   59   \n",
       "204             34      U                                   59   \n",
       "...            ...    ...                                  ...   \n",
       "19263         3365      U                                   29   \n",
       "19861         3473      U                                   34   \n",
       "19862         3473      U                                   34   \n",
       "19863         3473      U                                   34   \n",
       "19864         3473      U                                   34   \n",
       "\n",
       "                           job_title job_industry_category     wealth_segment  \\\n",
       "200                  Legal Assistant                    IT  Affluent Customer   \n",
       "201                  Legal Assistant                    IT  Affluent Customer   \n",
       "202                  Legal Assistant                    IT  Affluent Customer   \n",
       "203                  Legal Assistant                    IT  Affluent Customer   \n",
       "204                  Legal Assistant                    IT  Affluent Customer   \n",
       "...                              ...                   ...                ...   \n",
       "19263                   Engineer III                    IT      Mass Customer   \n",
       "19861  Analog Circuit Design manager                    IT      Mass Customer   \n",
       "19862  Analog Circuit Design manager                    IT      Mass Customer   \n",
       "19863  Analog Circuit Design manager                    IT      Mass Customer   \n",
       "19864  Analog Circuit Design manager                    IT      Mass Customer   \n",
       "\n",
       "      deceased_indicator owns_car  tenure  transaction_id  ...  product_size  \\\n",
       "200                    N       No    20.0          1039.0  ...         small   \n",
       "201                    N       No    20.0          1107.0  ...        medium   \n",
       "202                    N       No    20.0          2638.0  ...         large   \n",
       "203                    N       No    20.0          9044.0  ...        medium   \n",
       "204                    N       No    20.0          9792.0  ...         small   \n",
       "...                  ...      ...     ...             ...  ...           ...   \n",
       "19263                  N       No     NaN         19229.0  ...        medium   \n",
       "19861                  N       No     NaN            60.0  ...         small   \n",
       "19862                  N       No     NaN         11079.0  ...        medium   \n",
       "19863                  N       No     NaN         16206.0  ...         small   \n",
       "19864                  N       No     NaN         17845.0  ...        medium   \n",
       "\n",
       "      product_first_sold_date                 address postcode state  \\\n",
       "200                2011-04-16          833 Luster Way   4005.0   QLD   \n",
       "201                2005-12-07          833 Luster Way   4005.0   QLD   \n",
       "202                2013-09-16          833 Luster Way   4005.0   QLD   \n",
       "203                2004-08-17          833 Luster Way   4005.0   QLD   \n",
       "204                2011-08-24          833 Luster Way   4005.0   QLD   \n",
       "...                       ...                     ...      ...   ...   \n",
       "19263              2004-12-18        297 Kinsman Park   2766.0   NSW   \n",
       "19861              2014-07-28  7279 Steensland Circle   3803.0   VIC   \n",
       "19862              1997-08-25  7279 Steensland Circle   3803.0   VIC   \n",
       "19863              1999-12-04  7279 Steensland Circle   3803.0   VIC   \n",
       "19864              2005-10-22  7279 Steensland Circle   3803.0   VIC   \n",
       "\n",
       "         country property_valuation    age   profit recency  \n",
       "200    Australia                8.0  178.0   187.39     182  \n",
       "201    Australia                8.0  178.0   209.84     130  \n",
       "202    Australia                8.0  178.0  1408.91     267  \n",
       "203    Australia                8.0  178.0  1069.55     320  \n",
       "204    Australia                8.0  178.0   217.51     188  \n",
       "...          ...                ...    ...      ...     ...  \n",
       "19263  Australia                9.0    NaN    75.48     166  \n",
       "19861  Australia                7.0    NaN   217.51      12  \n",
       "19862  Australia                7.0    NaN   448.68     148  \n",
       "19863  Australia                7.0    NaN   144.26      84  \n",
       "19864  Australia                7.0    NaN   693.76     225  \n",
       "\n",
       "[400 rows x 27 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df[new_df.gender == \"U\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df.drop(new_df.index[new_df['gender'] == 'U'],axis = 0,  inplace = True)\n",
    "#Report_Card.drop(Report_Card.index[(Report_Card[\"Lectures\"] == \"German\")],axis=0,inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#new_df[\"gender\"] = new_df[\"gender\"].replace([\"F\"],\"Female\")\n",
    "#new_df[\"gender\"] = new_df[\"gender\"].replace([\"Femal\"],\"Female\")\n",
    "new_df['gender'].replace('F','Female',True)\n",
    "new_df['gender'].replace('Femal','Female',True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female    7135\n",
       "Male      6919\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>past_3_years_bike_related_purchases</th>\n",
       "      <th>job_title</th>\n",
       "      <th>job_industry_category</th>\n",
       "      <th>wealth_segment</th>\n",
       "      <th>deceased_indicator</th>\n",
       "      <th>owns_car</th>\n",
       "      <th>tenure</th>\n",
       "      <th>transaction_id</th>\n",
       "      <th>...</th>\n",
       "      <th>product_size</th>\n",
       "      <th>product_first_sold_date</th>\n",
       "      <th>address</th>\n",
       "      <th>postcode</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>property_valuation</th>\n",
       "      <th>age</th>\n",
       "      <th>profit</th>\n",
       "      <th>recency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>93</td>\n",
       "      <td>Executive Secretary</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>...</td>\n",
       "      <td>medium</td>\n",
       "      <td>2005-05-10</td>\n",
       "      <td>060 Morning Avenue</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>110.56</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>93</td>\n",
       "      <td>Executive Secretary</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3765.0</td>\n",
       "      <td>...</td>\n",
       "      <td>medium</td>\n",
       "      <td>2008-03-19</td>\n",
       "      <td>060 Morning Avenue</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>751.02</td>\n",
       "      <td>268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>93</td>\n",
       "      <td>Executive Secretary</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5157.0</td>\n",
       "      <td>...</td>\n",
       "      <td>small</td>\n",
       "      <td>2003-07-21</td>\n",
       "      <td>060 Morning Avenue</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>189.28</td>\n",
       "      <td>233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>93</td>\n",
       "      <td>Executive Secretary</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9785.0</td>\n",
       "      <td>...</td>\n",
       "      <td>medium</td>\n",
       "      <td>2003-09-09</td>\n",
       "      <td>060 Morning Avenue</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>90.10</td>\n",
       "      <td>359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>93</td>\n",
       "      <td>Executive Secretary</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11.0</td>\n",
       "      <td>13424.0</td>\n",
       "      <td>...</td>\n",
       "      <td>medium</td>\n",
       "      <td>2005-08-09</td>\n",
       "      <td>060 Morning Avenue</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>17.87</td>\n",
       "      <td>312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19980</td>\n",
       "      <td>3496</td>\n",
       "      <td>Male</td>\n",
       "      <td>99</td>\n",
       "      <td>Editor</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>19.0</td>\n",
       "      <td>9769.0</td>\n",
       "      <td>...</td>\n",
       "      <td>medium</td>\n",
       "      <td>2003-02-07</td>\n",
       "      <td>2565 Caliangt Point</td>\n",
       "      <td>2171.0</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>9.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>114.93</td>\n",
       "      <td>298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19981</td>\n",
       "      <td>3496</td>\n",
       "      <td>Male</td>\n",
       "      <td>99</td>\n",
       "      <td>Editor</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>19.0</td>\n",
       "      <td>18797.0</td>\n",
       "      <td>...</td>\n",
       "      <td>small</td>\n",
       "      <td>2010-05-05</td>\n",
       "      <td>2565 Caliangt Point</td>\n",
       "      <td>2171.0</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>9.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>182.81</td>\n",
       "      <td>272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19982</td>\n",
       "      <td>3497</td>\n",
       "      <td>Female</td>\n",
       "      <td>73</td>\n",
       "      <td>Administrative Assistant IV</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6926.0</td>\n",
       "      <td>...</td>\n",
       "      <td>medium</td>\n",
       "      <td>2016-11-14</td>\n",
       "      <td>96 Delladonna Trail</td>\n",
       "      <td>3976.0</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Australia</td>\n",
       "      <td>5.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>448.68</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19983</td>\n",
       "      <td>3497</td>\n",
       "      <td>Female</td>\n",
       "      <td>73</td>\n",
       "      <td>Administrative Assistant IV</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>18.0</td>\n",
       "      <td>8276.0</td>\n",
       "      <td>...</td>\n",
       "      <td>medium</td>\n",
       "      <td>1996-04-05</td>\n",
       "      <td>96 Delladonna Trail</td>\n",
       "      <td>3976.0</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Australia</td>\n",
       "      <td>5.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>143.82</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19984</td>\n",
       "      <td>3497</td>\n",
       "      <td>Female</td>\n",
       "      <td>73</td>\n",
       "      <td>Administrative Assistant IV</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>18.0</td>\n",
       "      <td>10493.0</td>\n",
       "      <td>...</td>\n",
       "      <td>large</td>\n",
       "      <td>2004-07-25</td>\n",
       "      <td>96 Delladonna Trail</td>\n",
       "      <td>3976.0</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Australia</td>\n",
       "      <td>5.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1055.82</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14054 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       customer_id  gender  past_3_years_bike_related_purchases  \\\n",
       "0                1  Female                                   93   \n",
       "1                1  Female                                   93   \n",
       "2                1  Female                                   93   \n",
       "3                1  Female                                   93   \n",
       "4                1  Female                                   93   \n",
       "...            ...     ...                                  ...   \n",
       "19980         3496    Male                                   99   \n",
       "19981         3496    Male                                   99   \n",
       "19982         3497  Female                                   73   \n",
       "19983         3497  Female                                   73   \n",
       "19984         3497  Female                                   73   \n",
       "\n",
       "                         job_title job_industry_category     wealth_segment  \\\n",
       "0              Executive Secretary                Health      Mass Customer   \n",
       "1              Executive Secretary                Health      Mass Customer   \n",
       "2              Executive Secretary                Health      Mass Customer   \n",
       "3              Executive Secretary                Health      Mass Customer   \n",
       "4              Executive Secretary                Health      Mass Customer   \n",
       "...                            ...                   ...                ...   \n",
       "19980                       Editor         Manufacturing      Mass Customer   \n",
       "19981                       Editor         Manufacturing      Mass Customer   \n",
       "19982  Administrative Assistant IV         Manufacturing  Affluent Customer   \n",
       "19983  Administrative Assistant IV         Manufacturing  Affluent Customer   \n",
       "19984  Administrative Assistant IV         Manufacturing  Affluent Customer   \n",
       "\n",
       "      deceased_indicator owns_car  tenure  transaction_id  ...  product_size  \\\n",
       "0                      N      Yes    11.0            94.0  ...        medium   \n",
       "1                      N      Yes    11.0          3765.0  ...        medium   \n",
       "2                      N      Yes    11.0          5157.0  ...         small   \n",
       "3                      N      Yes    11.0          9785.0  ...        medium   \n",
       "4                      N      Yes    11.0         13424.0  ...        medium   \n",
       "...                  ...      ...     ...             ...  ...           ...   \n",
       "19980                  N      Yes    19.0          9769.0  ...        medium   \n",
       "19981                  N      Yes    19.0         18797.0  ...         small   \n",
       "19982                  N      Yes    18.0          6926.0  ...        medium   \n",
       "19983                  N      Yes    18.0          8276.0  ...        medium   \n",
       "19984                  N      Yes    18.0         10493.0  ...         large   \n",
       "\n",
       "      product_first_sold_date              address postcode            state  \\\n",
       "0                  2005-05-10   060 Morning Avenue   2016.0  New South Wales   \n",
       "1                  2008-03-19   060 Morning Avenue   2016.0  New South Wales   \n",
       "2                  2003-07-21   060 Morning Avenue   2016.0  New South Wales   \n",
       "3                  2003-09-09   060 Morning Avenue   2016.0  New South Wales   \n",
       "4                  2005-08-09   060 Morning Avenue   2016.0  New South Wales   \n",
       "...                       ...                  ...      ...              ...   \n",
       "19980              2003-02-07  2565 Caliangt Point   2171.0              NSW   \n",
       "19981              2010-05-05  2565 Caliangt Point   2171.0              NSW   \n",
       "19982              2016-11-14  96 Delladonna Trail   3976.0              VIC   \n",
       "19983              1996-04-05  96 Delladonna Trail   3976.0              VIC   \n",
       "19984              2004-07-25  96 Delladonna Trail   3976.0              VIC   \n",
       "\n",
       "         country property_valuation   age   profit recency  \n",
       "0      Australia               10.0  68.0   110.56       7  \n",
       "1      Australia               10.0  68.0   751.02     268  \n",
       "2      Australia               10.0  68.0   189.28     233  \n",
       "3      Australia               10.0  68.0    90.10     359  \n",
       "4      Australia               10.0  68.0    17.87     312  \n",
       "...          ...                ...   ...      ...     ...  \n",
       "19980  Australia                9.0  35.0   114.93     298  \n",
       "19981  Australia                9.0  35.0   182.81     272  \n",
       "19982  Australia                5.0  35.0   448.68      52  \n",
       "19983  Australia                5.0  35.0   143.82     120  \n",
       "19984  Australia                5.0  35.0  1055.82     109  \n",
       "\n",
       "[14054 rows x 27 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NSW                7074\n",
       "VIC                3171\n",
       "QLD                3110\n",
       "New South Wales     364\n",
       "Victoria            335\n",
       "Name: state, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['state'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df['state'].replace('New South Wales','NSW',True)\n",
    "new_df['state'].replace('Victoria','VIC',True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NSW    7438\n",
       "VIC    3506\n",
       "QLD    3110\n",
       "Name: state, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['state'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14054, 27)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_id                            0\n",
       "gender                                 0\n",
       "past_3_years_bike_related_purchases    0\n",
       "job_title                              0\n",
       "job_industry_category                  0\n",
       "wealth_segment                         0\n",
       "deceased_indicator                     0\n",
       "owns_car                               0\n",
       "tenure                                 0\n",
       "transaction_id                         0\n",
       "product_id                             0\n",
       "transaction_date                       0\n",
       "online_order                           0\n",
       "order_status                           0\n",
       "brand                                  0\n",
       "product_line                           0\n",
       "product_class                          0\n",
       "product_size                           0\n",
       "product_first_sold_date                0\n",
       "address                                0\n",
       "postcode                               0\n",
       "state                                  0\n",
       "country                                0\n",
       "property_valuation                     0\n",
       "age                                    0\n",
       "profit                                 0\n",
       "recency                                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfmTable = new_df.groupby('customer_id').agg({'recency': lambda x:x.min(), \n",
    "                                              'product_id': lambda x: len(x), \n",
    "                                              'profit': lambda x: x.sum()})\n",
    "\n",
    "rfmTable.rename(columns={'recency': 'recency', \n",
    "                         'product_id': 'frequency', \n",
    "                         'profit': 'monetary_value'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary_value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3018.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>128</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2226.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>78</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2353.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>67</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3540.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>27</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4337.38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             recency  frequency  monetary_value\n",
       "customer_id                                    \n",
       "1                  7       11.0         3018.09\n",
       "2                128        3.0         2226.26\n",
       "9                 78        6.0         2353.11\n",
       "12                67        7.0         3540.03\n",
       "13                27        7.0         4337.38"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfmTable.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfmTable[\"monetary_value\"] = rfmTable[\"monetary_value\"].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      recency  frequency  monetary_value\n",
      "0.00     0.00       1.00            15.0\n",
      "0.25    18.00       4.00          1785.5\n",
      "0.50    45.00       5.00          2799.0\n",
      "0.75    87.00       7.00          4096.0\n",
      "0.99   267.79      11.79          8252.0 <class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "quartiles = rfmTable.quantile(q=[0,0.25,0.50,0.75,0.99])\n",
    "print(quartiles, type(quartiles))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfmTable[\"R_Score\"] = 0\n",
    "rfmTable[\"F_Score\"] = 0\n",
    "rfmTable[\"M_Score\"] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary_value</th>\n",
       "      <th>R_Score</th>\n",
       "      <th>F_Score</th>\n",
       "      <th>M_Score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3018</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>128</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2226</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>78</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2353</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>67</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3540</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>27</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4337</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3493</td>\n",
       "      <td>93</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3728</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3494</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2755</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3495</td>\n",
       "      <td>13</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3847</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3496</td>\n",
       "      <td>256</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2045</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3497</td>\n",
       "      <td>52</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1648</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2522 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             recency  frequency  monetary_value  R_Score  F_Score  M_Score\n",
       "customer_id                                                               \n",
       "1                  7       11.0            3018        0        0        0\n",
       "2                128        3.0            2226        0        0        0\n",
       "9                 78        6.0            2353        0        0        0\n",
       "12                67        7.0            3540        0        0        0\n",
       "13                27        7.0            4337        0        0        0\n",
       "...              ...        ...             ...      ...      ...      ...\n",
       "3493              93        6.0            3728        0        0        0\n",
       "3494               4        4.0            2755        0        0        0\n",
       "3495              13        7.0            3847        0        0        0\n",
       "3496             256        4.0            2045        0        0        0\n",
       "3497              52        3.0            1648        0        0        0\n",
       "\n",
       "[2522 rows x 6 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfmTable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### R_Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([   1,    2,    9,   12,   13,   14,   15,   19,   20,   21,\n",
       "            ...\n",
       "            3488, 3489, 3490, 3491, 3492, 3493, 3494, 3495, 3496, 3497],\n",
       "           dtype='int64', name='customer_id', length=2522)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfmTable.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\My\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  if __name__ == '__main__':\n",
      "C:\\Users\\My\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "C:\\Users\\My\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n",
      "C:\\Users\\My\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  import sys\n"
     ]
    }
   ],
   "source": [
    "for i in rfmTable.index:\n",
    "    if (int(rfmTable[\"recency\"][i])) > 76:\n",
    "        rfmTable[\"R_Score\"][i] = 1\n",
    "    elif ((int(rfmTable[\"recency\"][i])) <=76) & ((int(rfmTable[\"recency\"][i])) > 35):\n",
    "        rfmTable[\"R_Score\"][i] = 2\n",
    "    elif ((int(rfmTable[\"recency\"][i])) <=35) & ((int(rfmTable[\"recency\"][i])) > 8):\n",
    "        rfmTable[\"R_Score\"][i] = 3\n",
    "    else:\n",
    "        rfmTable[\"R_Score\"][i] = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary_value</th>\n",
       "      <th>R_Score</th>\n",
       "      <th>F_Score</th>\n",
       "      <th>M_Score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3018</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>128</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2226</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>78</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2353</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>67</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3540</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>27</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4337</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3493</td>\n",
       "      <td>93</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3728</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3494</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2755</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3495</td>\n",
       "      <td>13</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3847</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3496</td>\n",
       "      <td>256</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2045</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3497</td>\n",
       "      <td>52</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1648</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2522 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             recency  frequency  monetary_value  R_Score  F_Score  M_Score\n",
       "customer_id                                                               \n",
       "1                  7       11.0            3018        4        0        0\n",
       "2                128        3.0            2226        1        0        0\n",
       "9                 78        6.0            2353        1        0        0\n",
       "12                67        7.0            3540        2        0        0\n",
       "13                27        7.0            4337        3        0        0\n",
       "...              ...        ...             ...      ...      ...      ...\n",
       "3493              93        6.0            3728        1        0        0\n",
       "3494               4        4.0            2755        4        0        0\n",
       "3495              13        7.0            3847        3        0        0\n",
       "3496             256        4.0            2045        1        0        0\n",
       "3497              52        3.0            1648        2        0        0\n",
       "\n",
       "[2522 rows x 6 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfmTable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculating F_score and M_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\My\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "C:\\Users\\My\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  if __name__ == '__main__':\n",
      "C:\\Users\\My\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n",
      "C:\\Users\\My\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  import sys\n"
     ]
    }
   ],
   "source": [
    "for i in rfmTable.index:\n",
    "    if (int(rfmTable[\"frequency\"][i])) > 7:\n",
    "        rfmTable[\"F_Score\"][i] = 4\n",
    "    elif ((int(rfmTable[\"frequency\"][i])) <=7) & ((int(rfmTable[\"frequency\"][i])) > 5):\n",
    "        rfmTable[\"F_Score\"][i] = 3\n",
    "    elif ((int(rfmTable[\"frequency\"][i])) <=5) & ((int(rfmTable[\"frequency\"][i])) > 4):\n",
    "        rfmTable[\"F_Score\"][i] = 2\n",
    "    else:\n",
    "        rfmTable[\"F_Score\"][i] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary_value</th>\n",
       "      <th>R_Score</th>\n",
       "      <th>F_Score</th>\n",
       "      <th>M_Score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3018</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>128</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2226</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>78</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2353</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>67</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3540</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>27</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4337</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3493</td>\n",
       "      <td>93</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3728</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3494</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2755</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3495</td>\n",
       "      <td>13</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3847</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3496</td>\n",
       "      <td>256</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2045</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3497</td>\n",
       "      <td>52</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1648</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2522 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             recency  frequency  monetary_value  R_Score  F_Score  M_Score\n",
       "customer_id                                                               \n",
       "1                  7       11.0            3018        4        4        0\n",
       "2                128        3.0            2226        1        1        0\n",
       "9                 78        6.0            2353        1        3        0\n",
       "12                67        7.0            3540        2        3        0\n",
       "13                27        7.0            4337        3        3        0\n",
       "...              ...        ...             ...      ...      ...      ...\n",
       "3493              93        6.0            3728        1        3        0\n",
       "3494               4        4.0            2755        4        1        0\n",
       "3495              13        7.0            3847        3        3        0\n",
       "3496             256        4.0            2045        1        1        0\n",
       "3497              52        3.0            1648        2        1        0\n",
       "\n",
       "[2522 rows x 6 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfmTable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\My\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n",
      "C:\\Users\\My\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  import sys\n",
      "C:\\Users\\My\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "C:\\Users\\My\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "for i in rfmTable.index:\n",
    "    if (int(rfmTable[\"monetary_value\"][i])) > 4096:\n",
    "        rfmTable[\"M_Score\"][i] = 4\n",
    "    elif ((int(rfmTable[\"monetary_value\"][i])) <=4096) & ((int(rfmTable[\"monetary_value\"][i])) > 2799):\n",
    "        rfmTable[\"M_Score\"][i] = 3\n",
    "    elif ((int(rfmTable[\"monetary_value\"][i])) <=2799) & ((int(rfmTable[\"monetary_value\"][i])) > 1785):\n",
    "        rfmTable[\"M_Score\"][i] = 2\n",
    "    else:\n",
    "        rfmTable[\"M_Score\"][i] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary_value</th>\n",
       "      <th>R_Score</th>\n",
       "      <th>F_Score</th>\n",
       "      <th>M_Score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3018</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>128</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2226</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>78</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2353</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>67</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3540</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>27</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4337</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3493</td>\n",
       "      <td>93</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3728</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3494</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2755</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3495</td>\n",
       "      <td>13</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3847</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3496</td>\n",
       "      <td>256</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2045</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3497</td>\n",
       "      <td>52</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1648</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2522 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             recency  frequency  monetary_value  R_Score  F_Score  M_Score\n",
       "customer_id                                                               \n",
       "1                  7       11.0            3018        4        4        3\n",
       "2                128        3.0            2226        1        1        2\n",
       "9                 78        6.0            2353        1        3        2\n",
       "12                67        7.0            3540        2        3        3\n",
       "13                27        7.0            4337        3        3        4\n",
       "...              ...        ...             ...      ...      ...      ...\n",
       "3493              93        6.0            3728        1        3        3\n",
       "3494               4        4.0            2755        4        1        2\n",
       "3495              13        7.0            3847        3        3        3\n",
       "3496             256        4.0            2045        1        1        2\n",
       "3497              52        3.0            1648        2        1        1\n",
       "\n",
       "[2522 rows x 6 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfmTable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\My\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "rfmTable[\"RFM_Value\"] = 0\n",
    "\n",
    "for i in rfmTable.index:\n",
    "    rfmTable[\"RFM_Value\"][i] = (100*rfmTable[\"R_Score\"][i]) + (10*rfmTable[\"F_Score\"][i]) + rfmTable[\"M_Score\"][i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.00    111.0\n",
      "0.25    133.0\n",
      "0.50    234.0\n",
      "0.75    333.0\n",
      "0.99    444.0\n",
      "Name: RFM_Value, dtype: float64 <class 'pandas.core.series.Series'>\n"
     ]
    }
   ],
   "source": [
    "quartiles = rfmTable[\"RFM_Value\"].quantile(q=[0,0.25,0.50,0.75,0.99])\n",
    "print(quartiles, type(quartiles))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfmTable[\"Customer_Profile\"] = \" \""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\My\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "C:\\Users\\My\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  if __name__ == '__main__':\n",
      "C:\\Users\\My\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  import sys\n",
      "C:\\Users\\My\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n"
     ]
    }
   ],
   "source": [
    "for i in rfmTable.index:\n",
    "    if (int(rfmTable[\"RFM_Value\"][i])) > 411:\n",
    "        rfmTable[\"Customer_Profile\"][i] = \"Platinum Customer\"\n",
    "    elif ((int(rfmTable[\"RFM_Value\"][i])) <=411) & ((int(rfmTable[\"RFM_Value\"][i])) > 311):\n",
    "        rfmTable[\"Customer_Profile\"][i] = \"Silver Customer\"\n",
    "    elif ((int(rfmTable[\"RFM_Value\"][i])) <=311) & ((int(rfmTable[\"RFM_Value\"][i])) > 160):\n",
    "        rfmTable[\"Customer_Profile\"][i] = \"Gold Customer\"\n",
    "    else:\n",
    "        rfmTable[\"Customer_Profile\"][i] = \"Bronze Customer\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary_value</th>\n",
       "      <th>R_Score</th>\n",
       "      <th>F_Score</th>\n",
       "      <th>M_Score</th>\n",
       "      <th>RFM_Value</th>\n",
       "      <th>Customer_Profile</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3018</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>443</td>\n",
       "      <td>Platinum Customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>128</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2226</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>112</td>\n",
       "      <td>Bronze Customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>78</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2353</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>132</td>\n",
       "      <td>Bronze Customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>67</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3540</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>233</td>\n",
       "      <td>Gold Customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>27</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4337</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>334</td>\n",
       "      <td>Silver Customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <td>3493</td>\n",
       "      <td>93</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3728</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>133</td>\n",
       "      <td>Bronze Customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3494</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2755</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>412</td>\n",
       "      <td>Platinum Customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3495</td>\n",
       "      <td>13</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3847</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>333</td>\n",
       "      <td>Silver Customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3496</td>\n",
       "      <td>256</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2045</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>112</td>\n",
       "      <td>Bronze Customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3497</td>\n",
       "      <td>52</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1648</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>211</td>\n",
       "      <td>Gold Customer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2522 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             recency  frequency  monetary_value  R_Score  F_Score  M_Score  \\\n",
       "customer_id                                                                  \n",
       "1                  7       11.0            3018        4        4        3   \n",
       "2                128        3.0            2226        1        1        2   \n",
       "9                 78        6.0            2353        1        3        2   \n",
       "12                67        7.0            3540        2        3        3   \n",
       "13                27        7.0            4337        3        3        4   \n",
       "...              ...        ...             ...      ...      ...      ...   \n",
       "3493              93        6.0            3728        1        3        3   \n",
       "3494               4        4.0            2755        4        1        2   \n",
       "3495              13        7.0            3847        3        3        3   \n",
       "3496             256        4.0            2045        1        1        2   \n",
       "3497              52        3.0            1648        2        1        1   \n",
       "\n",
       "             RFM_Value   Customer_Profile  \n",
       "customer_id                                \n",
       "1                  443  Platinum Customer  \n",
       "2                  112    Bronze Customer  \n",
       "9                  132    Bronze Customer  \n",
       "12                 233      Gold Customer  \n",
       "13                 334    Silver Customer  \n",
       "...                ...                ...  \n",
       "3493               133    Bronze Customer  \n",
       "3494               412  Platinum Customer  \n",
       "3495               333    Silver Customer  \n",
       "3496               112    Bronze Customer  \n",
       "3497               211      Gold Customer  \n",
       "\n",
       "[2522 rows x 8 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfmTable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Customer_Profile\n",
       "Bronze Customer       91221\n",
       "Gold Customer        190803\n",
       "Platinum Customer    123054\n",
       "Silver Customer      225884\n",
       "Name: RFM_Value, dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfmTable.groupby(['Customer_Profile'])['RFM_Value'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Customer_Profile\n",
       "Bronze Customer       91221\n",
       "Gold Customer        190803\n",
       "Platinum Customer    123054\n",
       "Silver Customer      225884\n",
       "Name: RFM_Value, dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_customerprofile = rfmTable.groupby(['Customer_Profile'])['RFM_Value'].sum()\n",
    "df_customerprofile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Bronze Customer', 'Gold Customer', 'Platinum Customer',\n",
       "       'Silver Customer'],\n",
       "      dtype='object', name='Customer_Profile')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_customerprofile.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 91221, 190803, 123054, 225884], dtype=int64)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_customerprofile.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [10,6])\n",
    "plt.style.use('ggplot')\n",
    "plt.title('Customer Profile vs RFM Value\\n',fontsize = 18)\n",
    "rfmTable.groupby(['Customer_Profile'])['RFM_Value'].sum().plot(kind = 'bar',width = 0.7,color =['blue','orange','red','green'])\n",
    "plt.xlabel('\\nCustomer Profile',fontsize = 18,color = 'black')\n",
    "plt.ylabel('Customer Value\\n',fontsize = 18,color = 'black')\n",
    "label = df_customerprofile.values\n",
    "for i in range(4):\n",
    "    plt.text(x = i, y = label[i], s = label[i],horizontalalignment='center',rotation = 360, color = \"black\", fontsize=14)\n",
    "\n",
    "plt.xticks(rotation=0)    \n",
    "\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df[\"age_group\"] = pd.cut(new_df.age, [0,30,40,50,60,9999], labels=[\"<30\",\"30-40\",\"40-50\",\"50-60\",\"60+\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>wealth_segment</th>\n",
       "      <th>Affluent Customer</th>\n",
       "      <th>High Net Worth</th>\n",
       "      <th>Mass Customer</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>&lt;30</td>\n",
       "      <td>396007.92</td>\n",
       "      <td>335764.77</td>\n",
       "      <td>692526.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30-40</td>\n",
       "      <td>334178.62</td>\n",
       "      <td>372241.23</td>\n",
       "      <td>681114.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40-50</td>\n",
       "      <td>626030.45</td>\n",
       "      <td>681495.37</td>\n",
       "      <td>1356499.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50-60</td>\n",
       "      <td>340137.99</td>\n",
       "      <td>360956.51</td>\n",
       "      <td>676747.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60+</td>\n",
       "      <td>233502.02</td>\n",
       "      <td>215472.67</td>\n",
       "      <td>449430.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "wealth_segment  Affluent Customer  High Net Worth  Mass Customer\n",
       "age_group                                                       \n",
       "<30                     396007.92       335764.77      692526.75\n",
       "30-40                   334178.62       372241.23      681114.78\n",
       "40-50                   626030.45       681495.37     1356499.08\n",
       "50-60                   340137.99       360956.51      676747.74\n",
       "60+                     233502.02       215472.67      449430.01"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rss1 = pd.pivot_table(data = new_df,index = 'age_group',columns = 'wealth_segment',values = 'profit',aggfunc=np.sum)\n",
    "rss1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "rss1[\"Affluent Customer\"] = rss1[\"Affluent Customer\"].astype(int)\n",
    "rss1[\"High Net Worth\"] = rss1[\"High Net Worth\"].astype(int)\n",
    "rss1[\"Mass Customer\"] = rss1[\"Mass Customer\"].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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>wealth_segment</th>\n",
       "      <th>Affluent Customer</th>\n",
       "      <th>High Net Worth</th>\n",
       "      <th>Mass Customer</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>&lt;30</td>\n",
       "      <td>396007</td>\n",
       "      <td>335764</td>\n",
       "      <td>692526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30-40</td>\n",
       "      <td>334178</td>\n",
       "      <td>372241</td>\n",
       "      <td>681114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40-50</td>\n",
       "      <td>626030</td>\n",
       "      <td>681495</td>\n",
       "      <td>1356499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50-60</td>\n",
       "      <td>340137</td>\n",
       "      <td>360956</td>\n",
       "      <td>676747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60+</td>\n",
       "      <td>233502</td>\n",
       "      <td>215472</td>\n",
       "      <td>449430</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "wealth_segment  Affluent Customer  High Net Worth  Mass Customer\n",
       "age_group                                                       \n",
       "<30                        396007          335764         692526\n",
       "30-40                      334178          372241         681114\n",
       "40-50                      626030          681495        1356499\n",
       "50-60                      340137          360956         676747\n",
       "60+                        233502          215472         449430"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rss1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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P2hisIknqJjB69Ghee+01Dh8+TGFhIfPmzaO4uJgBAwZUa/vGjRtjY2NDWlpalevee++9eHp6MmzYMO6++27+8Y9/aMuSkpL4/vvvKSgowN7eHmdn52oPd96wYQM2Njb88ssvpKamav9Wr17NRx99xPnz5xk4cCDZ2dksWbKEoqIi9u3bR0xMjLYPLy8vHn30USIiIrRfkFlZWWzZsoWcnJwK650zZw4XLlxg6NCh7N+/n6KiIoqLi/n++++rHJI8YMAA8vPzmT9/PoWFhRw6dIhXX31VGxrs6empfXmUlJSwf/9+1qxZU24/b731FikpKRQVFfHf//6XvLy8ar93NeXt7U1JSYnZj5qBAwdSVFTE/Pnzyc7ORinF6dOnKxzWfT0OHTrEl19+SV5eHg0aNMDV1RWdTlfhF/mdd95J27ZtmT59Onl5efz555+88MILjBkzpkZf/N26dUOv17Nw4UJCQ0O18t69e7NixQouXLhgVh4REcH777/Pp59+qn0WDh48SEJCAlD6Y8re3h4PDw8KCwuZM2dOuRGvV/L29ubEiRMUFhZWO+ar/fbbb8ybN4+0tDRKSkrIz8/njTfeICsri549ewIwdepUXnrpJVJTU1FKcenSJb7//nt+++03AEaOHMnq1avZt28fRUVFLFmypMIfTDURGRnJ0qVL+e677ygpKaGwsJA9e/bU6OyJt7d3tb53KiNJ6ibw9NNP88gjj9C3b1+aNGlCXFwc27dvx8XFpVrbN2rUiLlz5/LII4/g5ubGvHnzLK5rY2PD+PHjOXbsWLleVHZ2NlOmTMHT0xMPDw+2b9/O+++/ry3X6/Vs3Lixwv2uXr2a8ePH4+fnp/WkvL29GT16NN7e3qxfvx43Nze2bdvGxo0bMRgMTJo0idGjR2Nvb6/tZ82aNdx2222EhITg7OxM586d+fDDDy3+YmvevDnJyck0b96cgQMH4uLigq+vL8899xwxMTHal0BFXF1d2b59Ozt27KBJkybce++9PP7440ybNk1b5+233+azzz7D1dWVadOmVXhvy4QJE5gyZQoGg4FNmzaxbds2XF1dLdZ7Pfz9/YmIiODOO+/Ezc2Nd999F0dHR7755hsOHjxI+/btcXV1JSwsTPuVXlsKCwt5+eWXadq0KW5ubkRHR/PRRx/h4OBQbl07Ozs+++wzTp06ha+vL3feeSd33XUXixYtqlGddnZ2BAcHc/bs2XJJ6q+//iIwMBAPDw+t/B//+AefffYZS5cupWnTpnh5eTF69GjtdN60adNwc3PDx8eHNm3a4OjoWOFp0DIPPvggLVq0wNvbGzc3N7MfB9Xl7OzMwYMH6du3L66urjRr1ox3332XDz74gLvuuguA8ePH88wzzzBmzBgMBgO+vr7MnTtXO0Pw+OOPM2nSJPr370+TJk04deoU3bt3N/u/U1N9+/Zl9erVPP3003h6etK0aVOmTp1q8QdhRebNm8eLL76IwWBg4sSJ1xSHTlX1c1KIejRz5kz27Nlj8dqGtWvVqhWvvPIKjz32WH2HIm4hJpMJX19fXnvtNR599NH6Due6SE9KWJWvv/6aM2fOYDKZ+O6771i9erXF601CiMs2bdpEfn4+eXl5zJ49m9zcXPr371/fYV03SVLCquzfv5/AwED0ej1jxozh6aefZtSoUfUdlhBW7/XXX6dJkyY0bdqUuLg4Pv/8cwwGQ32Hdd3kdJ8QQgirJT0pIYQQVkuSlBBCCKslSUoIIYTVkiQlhBDCakmSEkIIYbUkSQkhhLBakqSEEEJYLUlSQgghrJYkKSGEEFZLkpQQQgirJUlKCCGE1ZIkJYQQwmpJkhJCCGG1JEkJIYSwWpKkhBBCWC1JUkIIIayWJCkhhBBWy66+AyizcuVKUlJScHV1ZfHixWbLtm7dyoYNG1i7di0uLi4opYiJiWHv3r3Y29sTERGBn58fAPHx8Xz88ccADB06lJCQEACOHj3KihUrKCwsJDAwkDFjxqDT6cjJySEqKopz587RuHFjpk6dil6vr7QOIYQQdcNqklRISAj9+vVjxYoVZuXnz59n//79eHp6amV79+7l7NmzREdHk5aWxtq1a5k/fz45OTls3ryZhQsXAjBjxgyCgoLQ6/WsWbOGiRMn0q5dOxYsWEBqaiqBgYHExsbSuXNnhgwZQmxsLLGxsTz22GMW66iOP//8s/Ya5hp4enpy/vz5eo3BWkhbXCZtcZm0xWXW0hY+Pj4VllvN6b6OHTui1+vLlb/99tuMGDECnU6nlSUnJ9OrVy90Oh3+/v7k5uaSmZlJamoqXbp0Qa/Xo9fr6dKlC6mpqWRmZnLp0iX8/f3R6XT06tWLpKQkAJKSkggODgYgODhYK7dUhxBCiLpjNT2piiQnJ+Pu7k6rVq3Myo1Go1nPysPDA6PRiNFoxMPDQyt3d3evsLxsfYALFy5gMBgAMBgMXLx4sdI6yta90o4dO9ixYwcACxcuNNuuPtjZ2dV7DNZC2uIyaYvLpC0us/a2sNokVVBQwMcff8zzzz9fbplSqlzZlT2tq8srWr8qNakjPDyc8PBw7XV9d52tpftuDaQtLpO2uEza4jJraQtLp/usNkn99ddfpKen8/TTTwOQkZHBs88+y4IFC/Dw8DBr1IyMDAwGA+7u7hw8eFArNxqNdOzYEQ8PDzIyMszWd3d3B8DV1ZXMzEwMBgOZmZm4uLgAWKxDCHH9lFLk5+djMpks/vi7kf766y8KCgrqvF5rVJdtoZTCxsYGBweHar/vVpukfH19Wbt2rfZ60qRJLFiwABcXF4KCgvjyyy/p2bMnaWlpODo6YjAYCAgI4L333iMnJweAffv28eijj6LX62nUqBGHDx+mXbt2JCYm0q9fPwCCgoJISEhgyJAhJCQk0K1bN628ojqEENcvPz+fBg0aYGdXP19BdnZ22Nra1kvd1qau26K4uJj8/HwaNWpUrfWtJkktXbqUgwcPkp2dzb///W8eeughQkNDK1w3MDCQlJQUpkyZQsOGDYmIiABAr9czbNgwZs6cCcDw4cO1wRjjxo1j5cqVFBYWEhAQQGBgIABDhgwhKiqKuLg4PD09mTZtWqV1CCGun8lkqrcEJeqXnZ1djXpuOnUtF2xEpWQIuvWQtrjMmtoiLy8PR0fHeqvfzs6O4uLieqvfmtRHW1T0/lv9EHQhhKhrX3zxBc2aNePIkSNm5XPnzqV3797MnTuXjIwMBg4cSN++ffnxxx+56667tNHBteXkyZNs2bLF4vLff/+dkSNH0rNnT4KDg5k4cSLnzp2rcT2bNm3i7Nmz1xNqnZP+thC3uE83ZVV73UEPu92QGErGD67V/dmu2Vqt9WJjY7nzzjv55JNPmD59ula+YcMGfv75Z+zt7fnkk09o06YNy5Ytq9UYr1SWpB544IFyy/Lz83n88ceZPXs2ffv2BWDnzp1kZGTQuHHjGtXz4Ycf0r59e7y9vWsl7qoopbTBEtdKelJCiFtSbm4uycnJLFq0iE8++UQrHz16NHl5eQwcOJAVK1bwyiuvEBcXR58+fbh06ZK23smTJ82um69atUp7pNsff/zBiBEj6NevHw888IDWU4uMjOSFF15g8ODB3H333Xz22WcAzJ8/n59++ok+ffqwevVqszhjY2Pp2rWrlqAAevbsSfv27dm0aRPPPfecVv7444+za9cuSkpKiIyMJDQ0lLCwMFavXs1nn33Gvn37eOqpp7Rj+e677wgLCyMsLIxp06Zp14ruuusuFixYwKBBg+jfvz/79+/n0UcfpUePHrzzzjtafW+88Qb33Xcf4eHhLFq0SGuX4OBgZs6cyb333nvdlz+kJyWEuCV9+eWXhISE0KZNG9zc3Ni/fz+dO3dm/fr1tGvXjq+//hoovZb3888/M2/evGrv+5lnnmHhwoX4+fmRkpLCzJkz+fDDD4HSId+xsbEcOXKEMWPGMHDgQGbNmsWqVavMEkCZ3377jS5dutTo2A4cOMDZs2eJi4sDSh9a4Orqyvr163nhhRe4/fbbyc/PZ+rUqXz00Ue0bNmSKVOm8M477zB+/Hig9BrRp59+yuzZs5k6dSqxsbEUFBTQu3dvHn/8cRISEjh27Bjbtm1DKcXo0aPZvXs3zZo14/fff2fJkiUsWLCgRnFXRJKUEOKWFBsbq30h33///dpzPK9Xbm4ue/bsYeLEiVpZYWGh9ne/fv2wsbHB39//mq4rVYevry8nTpzg+eefJywsTHv025V+//13fH19adOmDcXFxTz44IO8/fbbWpuU9dw6dOhAXl6e9rg5e3t7Lly4QEJCAgkJCdp6eXl5HDt2jGbNmtG8eXO6du1aK8ciSUoIccsxGo3s2rWLQ4cOodPpKCkpQafT8fzzz1f7JlNbW1tMJpP2Oj8/HygdXu/i4qL1xK7WsGFD7e/qDK6+7bbb+OGHHypcZmdnZxZD2ek6Nzc3vv76a+Lj41m/fj2ffvopS5YsMdu2qrrt7e2B0iftXBmzjY0NJSUlKKV46qmnGDlypNl2J0+erNWRm3JNSghxy/nss88YNmwYP/30Ez/++CPJycn4+vry008/VXsfjRs35vz58xiNRgoKCrTndzo7O9OiRQs+/fRToDQZHDhwoNJ96fV6cnNzK1w2ZMgQ9uzZo+0f4Ntvv+XXX3+lRYsWHDhwAJPJxOnTp0lNTQVKk7DJZGLAgAE8/fTT7N+/HwAnJyftYQdt27bl5MmTHDt2DICPPvqI7t27V/v4Q0JC2LRpkxb3mTNnbsgtDtKTEkLccrZs2VLuBv377ruPLVu2cNddd1VrHw0aNGDq1KkMGjSIFi1a0LZtW23Z8uXLmTlzJsuWLaO4uJj777+fTp06WdxXhw4dsLW1JTw8nIceeogJEyZoyxo1asTbb7/N7NmzmT17Ng0aNKBDhw7MmTOH9u3b4+vrS1hYGLfddpt2uvLMmTNMmzZN62WVPeDgoYceYsaMGTg4OLB161aWLFnCuHHjKC4u5vbbby/XK6pMcHAwaWlpDB5cOjLT0dGR119/vdafXiE3894AcjOv9ZC2uMxSW9THEHS5mdd6yM28QgghxDWSJCWEEMJqSZISQghhtSRJCSGEsFqSpIQQQlgtSVJCCCGsliQpIcQtqV27dmavr3xY6zvvvKM9a8+Sqx/uasnw4cPp37+/9nrfvn0MHz680m0qm7pj7NixfPnll9rrf/7znyxdulR7PX78eD7//PMq4ypz4sQJs7qqe1x1RW7mFULUu/s3/lar+/tkRPvr2v7xxx+vpUhKnT9/nri4OIuzjV+tsqk7goKCSE5Opl+/fhiNRpycnNizZ4+2fM+ePdV+GG5xcXGldVkD6UkJIcRVFi9ezKpVqwBITU0lPDycQYMGMXfuXLNE89dffzFixAh69uzJK6+8YnF/Tz75ZIXzUZWUlDB37lxtuot3330XqHzqjrIkBZCcnEx4eDhGoxGlFCdOnMDBwQEvLy/tKedhYWH07duXnTt3AqU9pQkTJjBq1CgeeeQRXnnllXJ1Vfe46oL0pIQQt6T8/Hz69Omjvc7KyjKbs6nMtGnTePXVV+nWrRvz5883W3bgwAG++uorGjZsSK9evRgzZgzNmjUrt4+uXbvyxRdfsHPnTvR6vVb+3nvv4ezszOeff05BQQFDhgwhODi40qk7unTpwqFDhygsLGTPnj10796d48ePk5aWxi+//EJQUBAA69evB+Cbb77hyJEjPPLII3z33XcA2rMADQYDP/74IytWrNDq2rRpU7WPqy5IT0oIcUtycHDg66+/1v795z//KbfOhQsXyMnJoVu3bkDpw16vdM899+Di4oKDgwP+/v6cPn3aYn3/93//V643lZCQwObNm+nTpw8DBw4kMzNTe+CrJfb29vj7+7N//3727NlDYGAgXbt2JTk5meTkZC1JJSUlMWzYMKD0YbLNmzfn6NGjAPTq1QuDwWCxjpoc140mSUoIISyo6tGmV09hUdkz8O655x4KCgpISUkxK3/llVe0RLl79+4K5366WlBQED/++CO5ubm4ubmZJamyhFpZ7FU9N7Emx3WjWc3pvpUrV5KSkoKrq6s2BfO7777Lnj17sLOzo0mTJkRERODk5ASUPsU4Lh8tg+cAACAASURBVC4OGxsbxowZQ0BAAFB6/jgmJgaTyURYWJj2yyc9PZ2lS5eSk5ND69atmTx5MnZ2dhQVFbF8+XKOHj2Ks7MzkZGReHl5VVqHEOLW4Obmhl6vZ8+ePXTt2tVsmvlrMWXKFGbMmEHLli2B0ieJv/POO/Ts2ZMGDRrw+++/07Rp00qn7gDo1q0bc+bM4e677wZKn6KekpLCuXPnuO2224DSKeC3bNnCPffcw++//87p06dp06aNNm1Hmarqqm9W05MKCQlh1qxZZmVdunRh8eLFLFq0iKZNm2rDJE+dOsWuXbtYsmQJzz33HOvWrcNkMmEymVi3bh2zZs0iKiqKnTt3curUKQA2bNjAgAEDiI6OxsnJSZtWOS4uDicnJ15//XUGDBjAxo0bK61DCHFrWbRoEc8++yyDBg0CSueLulZhYWF4eHhorx999FHatWtHv379CA0N5dlnn6W4uNhs6o6rB05AaU/q+PHj2uy3dnZ2eHp6cvvtt2NjU/q1PmrUKEpKSggLC+PJJ58kKipKm8jwSh07dqy0rvpmVVN1pKen8+qrr2o9qSv99NNP7N69mylTpmjJqmzI5Lx583jwwQcB+PDDD7Ux/mXrDRkyhHHjxrF69WpsbW05fPiwtl7Ztv7+/pSUlDBhwgTWrl1LbGxshXX4+/tXeRwyVYf1kLa4TKbquKwm01Pk5uZqZ3CWL19Oeno6c+bMuZHh1Slrn6rDak73VSUuLo4ePXoApbNOXnkjnru7O0ajEcDsV4qHhwdpaWlkZ2fj6OioTcZ15fpGo1HbxtbWFkdHR7KzsyutQwhx69ixYwfLly+npKSEZs2amd04K268myJJffzxx9ja2vLPf/4TsHxBsKJynU5X6b4tbVOTDuaOHTu0qZ0XLlyIp6dntbe9Ecq6/kLa4kqW26L6Panaasu//voLO7v6/fqpbv3Dhg3TRsn9XdX1e2Fvb1/tz5LVJ6n4+Hj27NnDiy++qCUcDw8PMjIytHWMRiPu7u4AZuUZGRkYDAacnZ3Jy8ujpKQEW1tbs/XL9uXh4UFJSQl5eXno9fpK67haeHg44eHh2uv6Pr0kp7guk7a4rDbaorbasqCgoNanGa8JmZn3svpoi4KCgnKfpZtyZt7U1FQ++eQTnn32WbMLfkFBQezatYuioiLS09M5c+YMbdu2pU2bNpw5c4b09HSKi4vZtWsXQUFB6HQ6OnXqxO7du4HSxFd2L0HXrl2Jj48HYPfu3XTq1AmdTmexDiGEEHXHagZOLF26lIMHD5KdnY2rqysPPfQQW7Zsobi4WLtDu127dkyYMAEoPQX47bffYmNjw+jRowkMDAQgJSWFt99+G5PJRO/evRk6dChQenrh6iHoDRo0oLCwkOXLl3Ps2DH0ej2RkZE0adKk0jqqIgMnrIe0xWUycOIy6UldZu0DJ6wmSf2dSJKyHtIWl0mSukyS1GXWnqSs+nSfEELcKM2aNWPy5Mna6+LiYjp37lzrT0C/0qpVq+jVqxehoaGEh4dXOR1IRSqbxuPvyOoHTggh/v5q0purjur0+BwdHTl06BCXLl2iUaNGJCYm4u3tXatxXOmdd94hMTGRbdu24ezszMWLF83mhaqu+phao7i4uN5GY0pPSghxy+rduzfffPMNALGxsWYPkN27dy+DBw+mb9++DB48mCNHjgBw6NAhBgwYQJ8+fQgPD+fo0aPk5eUxcuRIwsPDCQ0NrfDxSa+//jrz58/Xnljh4uLCQw89BJQ+wqjsPswrJ0X84Ycf6NOnD3369KFv377k5OSUm8ajsik5nnjiCUaNGkX37t2JiYnhzTffpG/fvtrDbAH++OMPRowYQb9+/XjggQe044yMjOSll15i+PDh1Z6f6kaQnpQQ4pZ1//33ExUVRXh4OL/++iv/+te/+PHHH4HSJ4d//PHH2NnZkZiYyKuvvsqaNWt49913GTt2LEOHDqWwsJCSkhLi4uLw9vbW5oO6ePGiWT05OTnk5ubSqlWrGsW3atUq5s+fT7du3cjNzcXe3r7cNB5l815VNCXHoUOH+OqrrygoKKBnz57MmjWL7du3M3v2bDZv3sz48eOZPn06CxYswM/Pj5SUFGbOnKmdhjx69CibNm2q39sF6q1mIYSoZx07duTUqVN88skn5WbNvXjxIpGRkRw7dgydTkdRURFQettKdHQ0Z86coX///vj5+dG+fXvmzp3LvHnzCA8P56677jLbl1KqygcLVKRbt268/PLLPPDAA/Tv37/CwQVJSUmMGTMGKD8lR48ePdDr9ej1epydnbX5szp06MDBgwfJzc0lOTmZiRMnavsrLCzU/h44cGC9JiiQ031CiFtc3759mTNnTrm5ov773//So0cP4uLiWL9+PQUFBUDp8zxjYmJwcHBgxIgRfP/997Rp04YvvviC9u3bs2DBAqKiosz25ezsTKNGjTh+/HiFMdjZ2WkPsC6rB+Cpp57iv//9L/n5+QwaNEg7FXelygZoXz3lRtn9pjqdjpKSEkwmEy4uLmbzaiUkJGjb1OcIzDKSpIQQt7SHH36YqVOn0qFDB7Py7OxsbSDFBx98oJUfP36cli1bMnbsWPr06cOvv/7K2bNnadSoEcOGDePf//53uekwoDThPPfcc2RnZ2v737BhAwDNmzfn559/BmDbtm3aNn/88QcdOnRg0qRJ3H777Rw5cqTc1BplU3IAZlNyVIezszO+vr58+umnQGnCO3DgQLW2rStyuk8IcUvz8fFh3Lhx5cqffPJJIiMjWb16NT179tTKt27dql2r8vLyYurUqezbt49XXnkFnU5HgwYNWLBgQbn9jRo1iry8PO677z4aNGiAnZ2ddppt2rRpTJ8+nddff93soQFr165l165d2NjY4O/vT+/evbGxsdGm1njooYcYNWoUM2bMICwsDFtbW4tTcliycuVKnnnmGZYtW0ZxcTH3338/nTp1qkkT3lByM+8NIDfzWg9pi8vkZt7L5Gbey+RmXiGEEOIaSZISQghhtSRJCSGEsFqSpIQQdU4uhd/aavL+S5ISQtQ5GxsbGbhwiyouLsbGpvqpR4agCyHqnIODA/n5+RQUFFzTkxiul729vdlNs7eyumwLpRQ2NjY4ODhUextJUkKIOqfT6WjUqFG91S+3Jlxm7W0hp/uEEEJYLUlSQgghrJYkKSGEEFZLkpQQQgirJUlKCCGE1ZIkJYQQwmpZzRD0lStXkpKSgqurK4sXLwZKp1yOiori3LlzNG7cmKlTp6LX61FKERMTw969e7G3tyciIgI/Pz8A4uPj+fjjjwEYOnQoISEhQOk0yCtWrKCwsJDAwEDGjBmDTqe7pjqEEELUDavpSYWEhDBr1iyzstjYWDp37kx0dDSdO3cmNjYWgL1793L27Fmio6OZMGECa9euBUqT2ubNm5k/fz7z589n8+bN5OTkALBmzRomTpxIdHQ0Z8+eJTU19ZrqEEIIUXesJkl17NgRvV5vVpaUlERwcDAAwcHBJCUlAZCcnEyvXr3Q6XT4+/uTm5tLZmYmqampdOnSBb1ej16vp0uXLqSmppKZmcmlS5fw9/dHp9PRq1cvbV81rUMIIUTdsZokVZELFy5gMBgAMBgMXLx4EQCj0Yinp6e2noeHB0ajEaPRiIeHh1bu7u5eYXnZ+tdShxBCiLpjNdekaqKiJ+haev6XTqe7picu16SOHTt2sGPHDgAWLlxoltzqg52dXb3HYC2kLS6z3BbVn5n379KW8rm4zNrbwqqTlKurK5mZmRgMBjIzM3FxcQFKezVXPmsqIyMDg8GAu7s7Bw8e1MqNRiMdO3bEw8ODjIwMs/Xd3d2vqY6KhIeHEx4err2u7+dgWfuzuOqStMVltdEWf5e2lM/FZdbSFjfl9PFBQUEkJCQAkJCQQLdu3bTyxMRElFIcPnwYR0dHDAYDAQEB7Nu3j5ycHHJycti3bx8BAQEYDAYaNWrE4cOHUUqRmJhIUFDQNdUhhBCi7lhNT2rp0qUcPHiQ7Oxs/v3vf/PQQw8xZMgQoqKiiIuLw9PTk2nTpgEQGBhISkoKU6ZMoWHDhkRERACg1+sZNmwYM2fOBGD48OHaYIxx48axcuVKCgsLCQgIIDAwEKDGdQghhKg7OiVTZNa6P//8s17rt5buuzWQtrjMUlt8uqn616QGPexWmyHVG/lcXGYtbXFTnu4TQghxa6sySf3+++/arI3x8fFER0eTlVX9X15CCCHEtaoySQ0bNgxbW1uOHDnC2LFjOXbsGI8++mhdxCaEEOIWV2WSsrGxwc7Oji1bthAZGUlUVBRnzpypi9iEEELc4qpMUg0aNOC9997j7bffZuDAgQAUFRXd8MCEEEKIKpNUTEwMP/zwA8899xytW7fm2LFjPPbYY3URmxBCiFtcpfdJlZSUMH/+fDZs2KCVtW7dmhkzZtzwwIQQQohKe1K2tracO3eOwsLCuopHCCGE0FT5xIlWrVrRs2dPBg8ejJOTk1Ze9mQGIYQQ4kapMkn5+Pjg4+ODyWQiOzu7LmISQgghgGokqdmzZ9dFHEIIIUQ5VSapc+fO8dprr3HgwAHy8/O18ri4uBsamBBCCFHlEPQRI0bQvn17jh07xuzZs2nVqpU2nYUQQghxI1WZpDIyMhg7diwNGjQgODiYt956i927d9dFbEIIIW5xVZ7ua9CgAQBNmzZl27Zt+Pj4cOrUqRsemBBCCFFlknr++ee5cOECixcvZvLkyVy8eJGoqKi6iE0IIcQtrsokVfa8PldXV7799tsbHpAQQghRxmKSmjx5MjqdzuKG0dHRNyQgIYQQoozFJBUUFFSXcQghhBDlWExSo0aNqss4hBBCiHIsJqlBgwZVerpv69atNyQgIYQQoozFJPWf//ynLuMQQgghyrGYpIKDg7W/CwsLOXz4MAC33Xabdu9UXfnss8+Ii4tDp9PRokULIiIiyMrKYunSpeTk5NC6dWsmT56MnZ0dRUVFLF++nKNHj+Ls7ExkZCReXl4AbNmyhbi4OGxsbBgzZgwBAQEApKamEhMTg8lkIiwsjCFDhgCQnp5eYR1CCCHqRpVPnIiPj6ddu3ZMmjSJiIgI/P39SUxMrIvYADAajXzxxRcsXLiQxYsXYzKZ2LVrFxs2bGDAgAFER0fj5OSkPUswLi4OJycnXn/9dQYMGMDGjRsBOHXqFLt27WLJkiU899xzrFu3DpPJhMlkYt26dcyaNYuoqCh27typ3axsqQ4hhBB1o8okNX36dLZv305CQgKJiYl89dVXTJ06tS5i05hMJgoLCykpKaGwsBA3NzcOHDhA9+7dAQgJCSEpKQmA5ORkQkJCAOjevTu//PILSimSkpLo0aMHDRo0wMvLC29vb44cOcKRI0fw9vamSZMm2NnZ0aNHD5KSklBKWaxDCCFE3ajy3FVRURG33Xab9trf35+ioqIbGtSV3N3dGTRoEE8++SQNGzbk9ttvx8/PD0dHR2xtbbV1jEYjUNrz8vDwAEpnFnZ0dCQ7Oxuj0Ui7du3M9lu2Tdn6ZX+npaWRnZ1tsY6r7dixgx07dgCwcOFCPD09a7kVasbOzq7eY7AW0haXWW6LrGrv4+/SlvK5uMza26LKJBUUFMTYsWMZOXIkABs3bqRr1643PLAyOTk5JCUlsWLFChwdHVmyZAmpqakW11dKlSvT6XQVlle2fk2Eh4cTHh6uvT5//nyNtq9tnp6e9R6DtZC2uKw22uLv0pbyubjMWtrCx8enwvIqk9Qbb7zBihUriI6ORilFr169iIiIqPUALdm/fz9eXl64uLgAcNddd3Ho0CHy8vIoKSnB1tYWo9GIu7s7UNoTysjIwMPDg5KSEvLy8tDr9Vp5mSu3ubI8IyMDg8GAs7OzxTqEEELUjSqvSdnb2zNy5EjefPNNtmzZwtSpU7G3t6+L2IDSLJ+WlkZBQQFKKfbv30/z5s3p1KmTNmVIfHy89oSMrl27Eh8fD8Du3bvp1KkTOp2OoKAgdu3aRVFREenp6Zw5c4a2bdvSpk0bzpw5Q3p6OsXFxezatYugoCB0Op3FOoQQQtQNiz0ppRQvv/wyy5cvRymFUgpbW1smT57Miy++WGcBtmvXju7du/Pss89ia2tLq1atCA8P54477mDp0qW8//77tG7dmtDQUABCQ0NZvnw5kydPRq/XExkZCUCLFi24++67mTZtGjY2NowdOxYbm9Ic/cQTTzBv3jxMJhO9e/emRYsWQOmEjxXVIYQQom7olIWLNVFRUXz++eesXr2a1q1bA3D06FGefPJJ+vXrV+cj/G4mf/75Z73Wby3nmK2BtMVlltri003VHzgx6GG32gyp3sjn4jJraQtL16Qsnu575513eO+997QEBeDn58eGDRt45513aj9CIYQQ4ioWk1RRUVGFwxIbN25cp0PQhRBC3LosJqmGDRta3KiyZUIIIURtsThwYt++fdqw7ysppcjPz7+hQQkhhBBQSZIqKSmpyziEEEKIcqq8T0oIIYSoL5KkhBBCWC1JUkIIIayWJCkhhBBWq8ok9fHHH9OuXTtcXV1xcXHB2dm5wlF/QgghRG2r8inozzzzDJ9++ikdOnSoi3jEDXQrPv5GCHFzq7In1aRJE0lQQggh6kW1Jj18+OGHGTJkiNkUHUOHDr2hgQlxI/3de5X3b/yt2uuOs/O+gZEIcX2qTFIXL17E0dGR7du3a2U6nU6SlBDib+fv/uPlZlRlkoqJiamLOIQQQohyLCap1157jWeeeYbJkyej0+nKLY+Ojr6hgQkhhBAWk1TZYAmZMl0IIUR9sZikBg0aBMCoUaPqLBghhBDiSvLECSGEEFZLkpQQQgirVWWS2rlzZ7XKhBBCiNpW5RD0yZMnk5KSUmXZjZSbm8uqVas4efIkOp2OJ598Eh8fH6Kiojh37hyNGzdm6tSp6PV6lFLExMSwd+9e7O3tiYiIwM/PD4D4+Hg+/vhjoPRm5JCQEACOHj3KihUrKCwsJDAwkDFjxqDT6cjJyamwDiHqWsn4wTXbIOS1GxOIEHXMYpL64Ycf2LVrF+fOnWPJkiVa+cWLF+t81t6YmBgCAgKYPn06xcXFFBQUsGXLFjp37syQIUOIjY0lNjaWxx57jL1793L27Fmio6NJS0tj7dq1zJ8/n5ycHDZv3szChQsBmDFjBkFBQej1etasWcPEiRNp164dCxYsIDU1lcDAQGJjYyusQwghRN2weLqvsLCQnJwciouLyc7O1v65uLiwefPmOgswLy+PX3/9ldDQUADs7OxwcnIiKSmJ4OBgAIKDg0lKSgIgOTmZXr16odPp8Pf3Jzc3l8zMTFJTU+nSpQt6vR69Xk+XLl1ITU0lMzOTS5cu4e/vj06no1evXtq+LNUhhBCibljsSQUHBxMcHMzo0aNp2bJlXcZkJj09HRcXF1auXMnx48fx8/Nj9OjRXLhwAYPBAIDBYODixYsAGI1GPD09te09PDwwGo0YjUY8PDy0cnd39wrLy9YHLNYhhBCiblhMUpGRkSxdupSnnnqqwidObN269YYGVqakpIRjx47xxBNP0K5dO2JiYoiNjbW4vlKqXFlF8ZeVV7R+Te3YsYMdO3YAsHDhQrMkWR/s7OwsxFD955LV9zHUlr9LW/xV3wH8f9bQFrXh7/K5qA2W28I6WExSjz/+OAD/+c9/6iyYinh4eODh4UG7du0A6N69O7Gxsbi6upKZmYnBYCAzM1ObiNHDw4Pz589r22dkZGAwGHB3d+fgwYNaudFopGPHjnh4eJCRkWG2vru7O4DFOq4WHh5OeHi49vrK+uuDp6fndcdQ38dQW6QtatffpS3kc3FZbbRFbfDx8amw3OI1qaeffhqAzz//XDv1d+W/uuLm5oaHhwd//vknAPv376d58+YEBQWRkJAAQEJCAt26dQNKH+OUmJiIUorDhw/j6OiIwWAgICCAffv2kZOTQ05ODvv27SMgIACDwUCjRo04fPgwSikSExO1R0FZqkMIIUTdsNiTOnPmDAkJCWzdupV//etf5U6L3XHHHTc8uDJPPPEE0dHRFBcX4+XlRUREBEopoqKiiIuLw9PTk2nTpgEQGBhISkoKU6ZMoWHDhkRERACg1+sZNmwYM2fOBGD48OHacPJx48axcuVKCgsLCQgIIDAwEIAhQ4ZUWIcQQoi6oVMWLsps3ryZdevW8f3335d7yKxOpyMuLq5OArwZlfX66oul7vutOFfO36Utanqf1NAa3CdVk0kPraEtasPf5XNRG6z9dJ/FntTw4cMZPnw4c+fO5YUXXrhhgQkhhBCWVPnEiRdeeIGtW7eSmJgIQEhICAMHDrzhgQkhhBBVPrtv5syZLFu2jI4dO9KxY0eWLVumXdcRQgghbqQqe1Lbtm0jNTUVG5vSfDZq1CgCAwNZsGDBDQ9OCCHEra1aU3VkZV2+mHjhwoUbFowQQghxpSp7UjNnziQwMJDevXtr9xFJL0oIIURdqDRJKaW455572L17N0lJSSilePXVV/H2rv6QVSGEEOJaVZqkdDodQ4YMYc+ePQweXMP5bIQQQojrVOU1qe7du8sUFUIIIepFldekvv32W1atWkWrVq1wcnJCKYVOp+Pnn3+ui/iEEELcwqpMUl988UVdxCGEEEKUYzFJ5efns2rVKo4cOULnzp0ZO3YsdnZV5jQhhBCi1li8JjVq1CiSk5Pp3LkzX3zxBdOnT6/LuIQQQgjLPamDBw+yf/9+AMaOHcudd95ZZ0EJIYQQUElPqkGDBtrfcppPCCFEfbCYffbt26dNl66U4tKlS7i4uGij+y5evFhnQQohhLg1WUxSJSUldRmHEEIIUU61HjArhBBC1Ae52CSEEKKcTzdlVb3S/zfoYbcbFof0pIQQQlgtSVJCCCGsliQpIYQQVuumuSZlMpmYMWMG7u7uzJgxg/T0dJYuXUpOTg6tW7dm8uTJ2NnZUVRUxPLlyzl69CjOzs5ERkbi5eUFwJYtW4iLi8PGxoYxY8YQEBAAQGpqKjExMZhMJsLCwhgyZAiAxTqEEELUjZumJ/X555/TrFkz7fWGDRsYMGAA0dHRODk5ERcXB0BcXBxOTk68/vrrDBgwgI0bNwJw6tQpdu3axZIlS3juuedYt24dJpMJk8nEunXrmDVrFlFRUezcuZNTp05VWocQQoi6cVMkqYyMDFJSUggLCwNKby4+cOAA3bt3ByAkJESb8yo5OZmQkBCgdC6sX375BaUUSUlJ9OjRgwYNGuDl5YW3tzdHjhzhyJEjeHt706RJE+zs7OjRo4c2C7GlOoQQQtSNm+Lc1fr163nssce4dOkSANnZ2Tg6OmJrawuAu7s7RqMRAKPRiIeHBwC2trY4OjqSnZ2N0WikXbt22j6v3KZs/bK/09LSKq3jajt27GDHjh0ALFy4EE9Pz9o8/Bqzs7OzEEP1h5TW9zHUlr9LW/xV3wH8f9bQFrXh7/K5qA3W3hZWn6T27NmDq6srfn5+HDhwoMr1lVLlynQ6XYXlla1fE+Hh4YSHh2uvz58/X6Ptr1YyfnC117Vds7Vcmaen53XHcL3bWwtpi9r1d2kL+VxcZi1t4ePjU2G51SepQ4cOkZyczN69eyksLOTSpUusX7+evLw8SkpKsLW1xWg04u7uDpT2hDIyMvDw8KCkpIS8vDz0er1WXubKba4sz8jIwGAw4OzsbLEOIYQQdcPqr0k9+uijrFq1ihUrVhAZGck//vEPpkyZQqdOndi9ezcA8fHxBAUFAdC1a1fi4+MB2L17N506dUKn0xEUFMSuXbsoKioiPT2dM2fO0LZtW9q0acOZM2dIT0+nuLiYXbt2ERQUhE6ns1iHEEKIumH1PSlLRowYwdKlS3n//fdp3bo1oaGhAISGhrJ8+XImT56MXq8nMjISgBYtWnD33Xczbdo0bGxsGDt2LDY2pTn6iSeeYN68eZhMJnr37k2LFi0qrUNYp/s3/lbtdcfZed/ASIQQteWmSlKdOnWiU6dOADRp0oQFCxaUW6dhw4ZMmzatwu2HDh3K0KFDy5Xfcccd3HHHHeXKLdUhhBCibtxUSUoIIWpKetg3N6u/JiWEEOLWJUlKCCGE1ZLTfUKIm0pN7iMEIOS1GxOIqBOSpG5yf/fz7TX6QpIvIyH+duR0nxBCCKslSUoIIYTVkiQlhBDCakmSEkIIYbUkSQkhhLBakqSEEEJYLUlSQgghrJYkKSGEEFZLkpQQQgirJUlKCCGE1ZIkJYQQwmpJkhJCCGG1JEkJIYSwWpKkhBBCWC1JUkIIIayWzCclhBA3qZpOAGm7ZusNiuTGsfokdf78eVasWEFWVhY6nY7w8HDuu+8+cnJyiIqK4ty5czRu3JipU6ei1+tRShETE8PevXuxt7cnIiICPz8/AOLj4/n4448BGDp0KCEhIQAcPXqUFStWUFhYSGBgIGPGjEGn01msQwghRN2w+iRla2vLyJEj8fPz49KlS8yYMYMuXboQHx9P586dGTJkCLGxscTGxvLYY4+xd+9ezp49S3R0NGlpaaxdu5b58+eTk5PD5s2bWbhwIQAzZswgKCgIvV7PmjVrmDhxIu3atWPBggWkpqYSGBhIbGxshXUIIcTN6Gacydvqr0kZDAatJ9SoUSOaNWuG0WgkKSmJ4OBgAIKDg0lKSgIgOTmZXr16odPp8Pf3Jzc3l8zMTFJTU+nSpQt6vR69Xk+XLl1ITU0lMzOTS5cu4e/vj06no1evXtq+LNUhhBCiblh9krpSeno6x44do23btly4cAGDwQCUJrKLFy8CYDQa8fT01Lbx8PDAaDRiNBrx8PDQyt3d3SssL1sfsFiHEEKIumH1p/vK5Ofns3jxYkaPHo2jo6PF9ZRS5cp0Ol2F6+p0ugrXr6kdO3awY8cOABYuXGiWJK/FX9cd0fW73mOoLdIWpayhHUDa4krSFpfdyLa4KZJUcXExixcv5p//OI9+awAAD2hJREFU/Cd33XUXAK6urmRmZmIwGMjMzMTFxQUo7QmdP39e2zYjIwODwYC7uzsHDx7Uyo1GIx07dsTDw4OMjAyz9d3d3Sut42rh4eGEh4drr6+s/2b1dziG2iJtcZm0xWXSFpfVRlv4+PhUWG71p/uUUqxatYpmzZoxcOBArTwoKIiEhAQAEhIS6Natm1aemJiIUorDhw/j6OiIwWAgICCAffv2kZOTQ05ODvv27SMgIACDwUCjRo04fPgwSikSExMJCgqqtA4hhBB1w+p7UocOHSIxMRFfX1+efvppAB555BGGDBlCVFQUcXFxeHp6Mm3aNAACAwNJSUlhypQpNGzYkIiICAD0ej3Dhg1j5syZAAwfPlwbTj5u3DhWrlxJYWEhAQEBBAYGAlisQwghRN2w+iTVvn17PvjggwqXvfjii+XKdDod48aNq3D90NBQQkNDy5W3adOGxYsXlyt3dnausA4hhBB1w+pP9wkhhLh1SZISQghhtSRJCSGEsFqSpIQQQlgtSVJCCCGsliQpIYQQVkuSlBBCCKslSUoIIYTVkiQlhBDCakmSEkIIYbUkSQkhhLBakqSEEEJYLUlSQgghrJYkKSGEEFZLkpQQQgirJUlKCCGE1ZIkJYQQwmpJkhJCCGG1JEkJIYSwWpKkhBBCWC1JUkIIIayWJCkhhBBWy66+A7B2qampxMTEYDKZCAsLY8iQIfUdkhBC3DKkJ1UJk8nEunXrmDVrFlFRUezcufP/tXP/MVXVfxzHn/eCxg/jyr3+wB8VwsStK8oSlYR1wSDN2Sw1da7UyCZhmjm3cNYgk0LDH0v8tWVSZNikZNmWlTF/oJICkkDJZIrTUpCf8nPAvZ/vH8yrTFH8inBuvh//cTjn3Pd5nw/3dT+Hcw+XL1/u6bKEEOKRISF1F8XFxXh5eTFw4ECcnZ2ZMGECp06d6umyhBDikaFTSqmeLkKrsrKyyMvLIyoqCoAjR45w7tw53nzzzXbrHTx4kIMHDwKQkJDQ7XUKIcR/lcyk7uJO+a3T6W5bFh4eTkJCgmYCKiYmpqdL0AzpxU3Si5ukFzdpvRcSUndhMpmoqKiw/1xRUYGnp2cPViSEEI8WCam78PX15cqVK5SVldHa2srx48cJDAzs6bKEEOKRIbeg34WTkxORkZHEx8djs9kICwvjiSee6Omy7ik8PLynS9AM6cVN0oubpBc3ab0XcuOEEEIIzZLLfUIIITRLQkoIIYRmyf+k/mO2bdvG+fPnUUoxaNAgFi9ejIuLCy0tLSQlJXH+/Hkef/xxli1bxoABA3q63PvS3NxMbGwsra2tWK1WgoKCmDVrFmVlZWzatIm6ujqGDRvGkiVLcHbueGivXbuWsrIy1q9fD0BdXR0bN27k2rVr9O/fn/fee48+ffp012H932w2GzExMRiNRmJiYjrdh7i4OKqqqujduzcAH3zwAQaDwaHHyI1xrtfrcXJyIiEhodPnVSnFnj17yMrKQq/XExERwZQpU1BKsWvXLk6fPs1jjz1GdHQ0Pj4+PXB0/7/6+nq2b9/OpUuX0Ol0vP322wwePNixxrsSDq+lpUU1NjYqpZSqr6+3L09OTlb79u1TSil14MABtWPHDqWUUpmZmWrDhg3dX+gDstls9uNsaWlRK1euVEVFRWr9+vUqMzNTKaXUjh071C+//NLhPrKystSmTZvU8uXL7ctSUlLsfdq3b59KSUl5iEfRdfbv3682bdqkPv30U6WU6nQfYmNjVXFx8W3LHXmMREdHq5qamnbLOnteMzIy1ObNm5XValVKKVVdXa2UUionJ0fFx8crm82mioqK1MqVKx/iETwcmzdvVgcPHlRKtf3N1NXVdaovSUlJqqCgoFtr7Yhc7nNgly9f5uuvv+bdd9/lypUrALi5uQFtnw6bm5vt62ZnZxMaGgpAUFAQBQUFd/yyspbpdDpcXFwAsFqtWK1WdDodhYWFBAUFARAaGtrho6uampr46aefmDFjRrvlp06dwmKxAGCxWBzi0VcVFRXk5uby/PPPA23nu7N96Mh/YYzcqrPn9ddff2XmzJno9W1vhwaDAWjrx3PPPYdOp8PPz4/6+nqqqqq6p/gu0NDQwN9//83EiRMBcHZ2xt3d3eHGu1zuczBNTU2cOHGCjIwMoO3NKDExEVdXV/s6W7du5fTp0wwdOpR58+YBUFlZiclkAtpurXdzc6O2thYPD4/uP4gHYLPZeP/997l69SqTJk1i4MCBuLm54eTkBIDRaKSysvKO2+7Zs4eXXnrJfpnrhpqaGvuXtD09Pbl+/frDPYgukJyczGuvvUZjYyMAtbW1ne4DtI0RvV7P+PHjmTFjBjqdzuHHSHx8PAARERGEh4d3+ryWlpZy/PhxTp48iYeHB2+88QaDBg2isrKSfv362dczmUxUVlY6zBf6y8rK8PDwYOvWrVy8eBEfHx8WLFjgcONdQsrBLFq0iCeffJKoqCiGDBlyx3Wio6Ox2Wx8+eWXHD9+nLCwsE4/4knr9Ho9n332GfX19SQmJvLPP/90aruSkhKuXr3KggULKCsre8hVPlw5OTkYDAZ8fHwoLCy87+2XLl2K0WiksbGR9evXc+TIESwWi0OPkY8//hij0UhNTQ1r1qxh8ODBnd62paWFXr16kZCQwB9//MG2bdtYvXq1Q/cD2q42XLhwgcjISIYPH86uXbtIT0/vcP28vDx2794NQHl5OWfPnsXFxYVevXrxySefdFfZt5GQcjDLly8nIyODxMREgoODsVgs9O/f/7b19Ho9EyZM4McffyQsLMz+iCeTyYTVaqWhoUHb/yy9B3d3d55++mnOnTtHQ0MDVqsVJycnKisrMRqN9hkXQGBgIJ6enly4cIHFixdjtVqpqakhLi6OuLg4DAYDVVVVeHp6UlVVpfmZQ1FREdnZ2Zw+fZrm5mYaGxtJTk7uVB9mz56N0WgEwNXVlZCQEIqLi7FYLA49Rm4ck8FgYOzYsRQXF3d4XuPj46mursbX15eoqChMJhPjx48HYNy4cWzduhVomzmVl5fbX8PRHotmMpkwmUwMHz4caLuEm56e3mFfAgICCAgIAGDLli2EhoZiNpt7rP4bJKQczOjRoxk9ejS1tbUcPXqUdevW4eHhwaJFi+jfvz+lpaV4eXmhlCI7O9v+iXLMmDEcOnQIPz8/srKyMJvNDvWpEOD69es4OTnh7u5Oc3Mz+fn5TJs2DbPZTFZWFsHBwRw6dIjAwED7jOtWL7zwAtB2GWTt2rXExcUBbW/ehw8f5uWXX+bw4cOMHTu2uw/tvsydO5e5c+cCUFhYyP79+1m6dCkbNmy4Zx+sViv19fV4eHjQ2tpKTk4O/v7+gOOOkaamJpRSuLq60tTUxJkzZ5g5c2aH53XVqlXtth87diwFBQVMnDiRv/76y/43ExgYyIEDBwgODubcuXO4ubk5VEj17dsXk8nEv//+y+DBg8nPz2fo0KEMHTrUoca7PHHiP6C4uJi+fftiNBqJjY2loaEBgKeeeoqFCxfi5uZGc3MzSUlJXLhwgT59+rBs2TIGDhzYw5Xfn4sXL7JlyxZsNhtKKZ599llmzpxJaWnpbbde9+rVq8P93AipG7eg19bWsnHjRsrLy+nXrx/Lly93mBnEjZCKiYnpVB+ampqIjY3FarVis9nw9/dn/vz56PV6hx0jpaWlJCYmAm0hHBISwvTp0zt9Xuvr6/n8888pLy/HxcWFt956C29vb5RS7Ny5kz///JPevXsTHR2Nr69vdx/eAykpKWH79u20trYyYMAAoqOjUUrdsy9amklJSAkhhNAsuQVdCCGEZklICSGE0CwJKSGEEJolISWEEEKzJKSEEEJoloSUEEIIzZKQEkIIoVkSUkIIITRLQkoIIYRmSUgJIYTQLAkpIYQQmiUhJYQQQrMkpIQQQmiWhJQQQgjNkpASQgihWRJSQgghNEtCSgiN2LdvHzqdjrNnz3bpfr/55htGjRqF2Wxm9OjRLFy4kOrq6i59DSEeFgkpITQiNTWVkJAQ9uzZ02X7PHDgABs3buTnn3+msLCQ3NxcJkyYQGlp6W3rWq3WLntdIbqKhJQQGlBXV8exY8fYuXNnu5Cy2WxER0djNpuZOnUqU6ZMIS0tDYCcnBwsFgtjxoxh0qRJXLly5bb9xsfHk5iYyJAhQwBwcnIiMjKSESNGAODt7c3q1asJCQlh79695OXlERQUxKhRo3jllVeoqqoCIDQ0lOzsbADKy8vx9vYGIDk5mWnTpjF58mRGjBjBRx999NB6JB5NElJCaEB6ejqTJ0/Gz88Po9FIbm4uAD/88AMlJSXk5+fzxRdfcOLECQBaWlpYsmQJaWlp5OTkEBkZyapVq27bb2FhIc8888xdX9vFxYXMzEzmzJnDvHnzWLt2LWfOnMHf379ToXPy5El2795NXl4ee/futYeZEF1BQkoIDUhNTWXOnDkAzJkzh9TUVAAyMzN59dVX0ev1eHl5ERYWBkBRUREFBQVEREQQEBDAmjVruHz58l1fIz8/n4CAAHx9ffnuu+/sy2fPng1ATU0N1dXVWCwWAObPn8+RI0fuWXtERAQmkwlXV1emT59OZmbm/TdAiA4493QBQjzqKioqyMjIoKCgAJ1Oh9VqRafTsW7dOpRSd9xGKYXZbLbPrDpiNpvJzc0lLCwMf39/8vLyeOedd2hsbLSv4+7ufs8anZ2dsdlsADQ1NbX7nU6nu+vPQjwImUkJ0cPS0tKYN28eFy9epKSkhEuXLjFs2DAyMzMJCQnh+++/x2azUVpayqFDhwAYMWIE165da3f5r7Cw8LZ9r1y5khUrVrSbZd0aULcyGAx4enpy9OhRAFJSUuyzKm9vb3Jycuz13uq3336jsrKSxsZG0tPTCQ4OfrCGCHELmUkJ0cNSU1OJiYlpt2zGjBl8++23bNmyhd9//52RI0fi5+fH+PHjMRgM9O7dm7S0NJYuXUpNTQ2tra0sW7YMs9ncbj9Tpkzh2rVrvPjii1itVvr27cvIkSOZNGnSHWv56quviIqKoqGhAR8fH3bt2gXAihUrmDVrFikpKUycOLHdNiEhIbz++usUFxczd+5cAgMDu7A74lGnUx1dTxBCaEJdXR19+vShoqKCcePGcezYMby8vHq6LKDt7r7s7GySkpJ6uhTxHyUzKSE0burUqVRXV9Pc3MyHH36omYASojvITEoIIYRmyY0TQgghNEtCSgghhGZJSAkhhNAsCSkhhBCaJSElhBBCsySkhBBCaNb/AKvsExziT348AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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aWho+Pj5ygrsUKKKwcRrpsRIXF4e/v7/aAyhJOp0OW1tbIP+qppiYGHXeoiJSFAWdTlfgsJb0cO79LKxatYrg4GB+++23cq6VVBIe6TkLqWIYP348hw4dIjs7Gw8PD3VoSKp8Fi5cyA8//EBubi6Ojo4sX768vKsklRDZs5AkSZKMqhRzFpIkSdLDkcFCkiRJMkoGC0mSJMkoGSwkSZIko2SwkCRJkoySwUKSJEkySgYLSZIkySgZLCRJkiSjZLCQJEmSjJLBQpIkSTJK3htKkh5zKSkpTJ48mfPnz6MoCtOnT8fS0pIpU6aQnp5O9erVmT17NjY2Nhw6dIg5c+aQk5ODqakp77//vrog04ABA0hISFDXk1i5ciVOTk6sWrWKH374Aa1Wi6OjI9OnT1fXZ7l69SqTJ09Wb2v/9ddfqwuASY+YclidT5KkMvTBBx+IjRs3CiGEyMrKErdv3xZ9+vQRf/zxhxBCiB9++EHMmzdPCJG/7HB8fLwQQohz586JNm3aqPm88sor4sSJE/fl//vvv4v09HQhhBAbNmwQI0eONNjnt99+E0IIkZqaqm4nPXrkMJQkPcZSU1OJiIigb9++QP7SunZ2dsTGxhIQEADkL1O6a9cuIH/Z4TtL3np6epKdnU12dvYDy2jZsiWWlpYA+Pr6Eh8fD0BMTAy5ubm0bt0aAGtra3U76dFT4YJFamoqqamp5V0NSXos/P333zg6OjJhwgR69+7NpEmTSE9Pp169euzduxeAnTt3cu3atfv2/fXXX2nYsKHBAlkTJ07k2WefZcmSJQUuWfzjjz+qa67HxcVhZ2fHu+++S+/evfn888/VVSOlR0+FuEX5zZs3Wb9+PSdPnsTa2hohBBkZGTRu3JiXXnqpwFXM4vu2LPN6KlY2VPngc3TBc1AsrbAd8C6YmJJ9/DDmzQJJfP81bAe/h2ndhiRPfRfMzHGavoLk6WMwqe2JuU9LUr6aDoBFu26YPulF6o/BOE1bzs3h/QDQOLngMHEuie+9opZr3XsAGkcXdCvnlFlbb18s++UpNTa2VJ+ziOtfTCP7QvQ/iRpcPphM5umTpGzdjMa+CiI9DZGTg/3z/8O2SzcuDxmo5qGt6kzt77dwoWsQ/LM2s+dvR7j51WKS16/GpuNTOLz8Kn8PHsATn88jaf1qMk8ex+3j6aQeDCV1764yaWu9/xteJuWcPHudF4Z+zzeL++PTyI1pi/ZjY2VGr871+WzRfm6lZNIxsA7rNh3nj61D1P2iYxMZOmkbwbN6U7N6FQCu30jF1dmG1PRsRnz0fzzzVAN6d22o7rN111k2bD7OugXPY2Zmws7QaCbP2sPm5S9RzcWW0Z/8QlCL2vTt0bjU261UG/rwebxTtO8Y8WX4Q5f1KKgQE9zz5s2jR48ejBgxAo0mv7Oj1+v5/fffWbBgAdOmTSvnGuYT6alk/3kUM7+WpG/9hqQP3wbAzKc52ify19PWJyaQrbuNyMqErEyyTx/DpLYn+sQEtFXvBj2tkwv65BuIlFtorG1BowV93j/phqvZWbTuTMqK2WXX0HKiT9WRHnkY68A2d4OFXo9u1w4cXxtEytbN6G/fXWb29uYfcB7xnkEetk91I3XfHjVQAORcv47unyCQGrIbtyn5nyfzRo15YkZ+ANZWqYJ1m3aQl0dq6N7SbGaZcnO2wdXZBp9GbgB0DXqS5d8cYeTgVqyc/RwAsX8nsz88Tt0nPkHHux9u5/MJXdRAAeDqnL+yoI2VGT071efEmXg1WIQd+Yuv1h9m3YK+mJmZqGU3fNIZ9yfyl9nt3KYux0/Hl3qbpdJRIYahdDodgYGBaqAA0Gg0tG7dGp1OV441A8WuCorVP8tvmplj7h1A3pVLaOwc8tNMTLHuPYCMXZsByIw4iGlDn/wvfzNzTD29yLscR07MGbTV3NG6VAMTEyxaP0VWxEEAsv+MxKJVBwAs23cn8590AO0TNdFY25Fz7mTZNboMaas4oLHJX4ZTMTfHqkUrsi/FYupeU93Gpl17smMv5m9f9e7a6TZBHcmOu2iQn123HqTs/D+DtNTQvVg1zz9LtGwWQPZfcQDE9nqKiz07c7FnZ3R7dnF9xiePVaAAcHayppqLLRf/Sgbg98i/qVvLkcTkdAD0esFX6w7zwjNNAEjRZTFkwlbGvBlI0yZPqPnk5upJvpUBQE5uHqG/x1KvTv7a36ejE5gyN4Sl03vh5HB3ve0mDVxJSc0i6VZ+WeFH88t+VGhMNEV6VBYVomfh4eHBihUrCAoKUhefT0xMZP/+/dSuXbtc66Z1qIr9ux/mf/krCplhe8mKPITtgHcxb9YGFIX0XZvIPhUJQN6VOLKOhVN1znqE0JOxdyu5f+d/oaWsmI3D5AWg0ZARsp3cy7EA6NYtwX70p9i8MITcuPNk7N2qlm/ZpgsZh3aXfcPLiImzM25TZ6BotaBo0O3eSdrB/dQMXo/G2gYUhazzZ7k+YyoADi+8gk1QR0ReLvrbt4mfMuFuXtWewMTVjYzICIMyklYtp9q0WTi+9Br6jHSuf/JhmbaxvE0e0Z73P9tJTm4e7tXsmT7+KX7+9QwbtpwAoEvbuvR5uhEAGzYf568rt/hy7WG+XHsYgODZz2FpYcrgD7aQm5uHXi9o1awm/XrmDyfN+vI30jOyGTXlFwCqudry5fRn0Go1fPBOG14fswkhwKuei7rPo0DRKOVdhQqlQsxZ5ObmEhISQkREBElJSQA4Ojri7+9Px44dMTU1vW+f8pizqEzKY86iMimrOYvKqiTmLExHtynSdjnzfnvosh4FFaJnYWJiQpcuXejSpUt5V0WSJAkARZE9i3tViGCRlZXFzp07URSFbt26ERYWxh9//EH16tXp27ev+otRSZKksiKHoQxViNmZJUuWcPv2bRISEpg5cyYXLlygV69eCCFYvnx5eVdPkqRKSNEoRXpUFhWiZ3Ht2jXGjBmDEIK33nqLDz/8EEVRaNiwIe+//355V0+SpEqoMl3pVBQVIljcoSgKfn5+6lihoihy3FCSpHJRmXoNRVEhgkXdunXJzMzEwsKCoUPvXsUQHx8v5yskSSoXMlgYqhDB4u233y4w3c3NjU8++aSMayNJkiSDxb9VmEG5rKws4uLiDNJu3rxJcnJy+VRIkqRK7c4wuLFHZVFhgoVWq2XOnDlkZmaqaV999ZUMFpIklQt5uw9DFaalJiYmBAQEEBYWBuT3KlJSUqhbt24510ySpMpIXjprqMIEC4BOnToRGhoKwP79+2nfvn251keSpMpLBgtDFSpY3Ltu76FDh9RFVCRJksqaDBaGKsTVUPfq2LEjy5Yto2bNmtjY2JR3dSRJqqQqUyAoigrVswBo1aoVcXFxdOzYsbyrIklSJSZ7FoYqXM/C3NycNWvWlHc1JEmq5DTaCncuXa4qXLCQJEmqCCpTr6EoZLCQJEkqgAwWhmSwkCRJKoAMFoZksJAkSSqARk5ZGJDBQpIkqQDaSnTfp6KQwUKSJKkAZvJqKAMyWEiSJBVAxgpDj2ywuH3xdnlX4bFm5WJZ3lV4vOVml3cNJCNKchhq6dKlHD16FHt7e+bMmQPAunXriIyMxMTEBFdXV4YOHYq1tTUAmzdvJiQkBI1Gw8CBA/H19QUgKiqKVatWodfr6dSpE7179wYgISGB+fPnk5qaSp06dRg+fDgmJibk5OSwePFiLl68iK2tLaNGjcLFxeWBZRRGxk5JkqQCaBWlSI+iaN++PRMnTjRI8/b2Zs6cOcyePZtq1aqxefNmAC5fvkxYWBhz585l0qRJBAcHo9fr0ev1BAcHM3HiRObNm8ehQ4e4fPkyAOvXr6dHjx4sXLgQa2trQkJCAAgJCcHa2ppFixbRo0cPNmzY8MAyHkQGC0mSpAJoNUqRHkXRqFGj++515+Pjg1arBaBevXokJSUBEBERQWBgIKampri4uODm5kZMTAwxMTG4ubnh6uqKiYkJgYGBREREIITgzz//pGXLlkB+YIqIiADgyJEj6t27W7ZsyalTpxBCFFrGgzyyw1CSJEmlyUxbtECwZ88e9uzZoz7v3LkznTt3/k9lhYSEEBgYCEBSUhKenp7qa46OjmogcXJyUtOdnJyIjo5Gp9NhZWWlBp57t09KSlL30Wq1WFlZodPpHlhGYWSwkCRJKkBRh5iKExzutWnTJrRaLW3btgVACFHgdgWlG1vWtbB9CivjQeQwlCRJUgFKchiqMKGhoURGRjJixAj1i9/JyYnExER1m6SkJBwdHe9LT0xMxMHBAVtbW9LT08nLyzPY/t955eXlkZ6ejo2NTaFlPIgMFpIkSQXQKkV7FFdUVBQ///wz48aNw9zcXE339/cnLCyMnJwcEhISuHbtGk8++SR169bl2rVrJCQkkJubS1hYGP7+/iiKgpeXF+Hh4UB+APL39wegWbNm6uqj4eHheHl5oShKoWU8iCKK0x+pAM41bVjeVXisyUtnS1eN5a+WdxUea4r7qIfOo9NPLxdpu73PbzC6zfz58zl9+jQ6nQ57e3v69+/P5s2byc3NVSe+PT09eeutt4D8oal9+/ah0Wh4/fXX8fPzA+Do0aOsWbMGvV5Phw4d6NOnDwDXr1+/79JZU1NTsrOzWbx4MbGxsdjY2DBq1ChcXV0fWEZhZLCQCiSDRemSwaJ0lUSw6LLplSJtt6vP+ocu61EgJ7glSZIKUNSroSoLGSwkSZIKIG8kaEgGC0mSpAI87JVOjxsZLCRJkgogexaGZLCQJEkqgLzrrCEZLCRJkgogexaGZLCQJEkqgFz8yJAMFpIkSQWQscKQDBaSJEkFkMNQhmSwkCRJKoD8TZ4hGSwkSZIKoJE9CwMyWEiSJBXAVM5ZGJDBQpIkqQByGMqQDBaSJEkF0MjbfRiQwcIIxcwM9xXrUMzMULQm6Pb+SuJXi3H96DMsGuUvJJJ9KY5rUyYiMtLV/Ww6daH6rAXEvdyXrDN/orGvQvUv5mPh1Zjb27aQ8Pln+flbWVEz+O4tjk1c3EjZsY0bs2dg4laNalNnoLG1RdFqubFwLmmHDpT5MShVpma4zF6BYmqGotWSfnAvKeu/wmV2MIqlFQDaKo5knTtF4ifvYdXhaWz7vw6AyEgnedF0cmKj0VZ1xfH9T9A6VAWhJ/WXTaT+/K1BUbbPD6DKm6O50r8j+pRbmNSojeN7H2NWtwG31yxB99O6sm59mUhJzWLynFCi45JQFJg2tgMWZiZ8PH8/WTl5aLUapoxoi3cDVy7+lcyEWfs4HXODUQNbMLi/r5rPxFn7CP0jDqcqlmxb8YKavnP/BRavjeDCX8lsXPw8Teq7GJR/9bqOnoO/Y9irAQb5VXSyZ2FIBgsjRHY2fw8ZmB8ITEyoGbyetEMHuTFnBvq0NACcx4zD4X8vkbR6BZAfABxeHEDGyeN388nK4uaXCzGr64n5k3cXShfp6Vx6sY/6vNaGH0kN2Q2A0xtvo9u9k1s/fodZnbrUWLSMiz2Lv9ZvhZSTzY1xQxCZGaA1wWVOMJlHDpEwdrC6idPkWWT8HgpAbvwVEt5/A5Gqw8I/EIeRk0kY9RpCn8et5fPIiTmLYmmF66INZB4LJ/evWAC0VV0xb9qS3OvX1Hz1utvc+vILLFt1KNMml7VpS36jbYA7C6d0JTsnj8ysXEZ9uothr/rTrnkt9v9xiVlfh7Nu7rPY25ozeVgb9oTF3pfPc13r83Lvxoz/fK9BumdtRxZ+3JUp8wo+kZnx5SHaNq9ZKm0rTbJjYUhO4RTBnR6DYmKCYmIKQqiBAkBjbgH3LCFVdehIktYEI7Ky7uaRmUFG1FFE9t20fzN1r4XWwZGMo0f+2Umgsc5fRUtja0vujYQSbFXFITIzgDvH1wTuWY9LsbTCwidADRbZZ04gUnUAZJ09ibZq/qpf+qSb5MSczc8vI53cv2PROt09w60y5D1ur5jPvW+U/nYy2edPI/JyS7N55So1LZsjJ6/R9+n8xcLMTLXY2ZijoJCalgOALi0bF6f8XpyTgxVNGrhgUsAv0gK8n8De1vy+9Lq1HPBwdyiw/D2HYnGvZseTtR68vnNFVNrLqj5qZM+iKDQaam34ETP3miRv/JbMUycAcPt4Gtat25F98YvI0k0AACAASURBVAIJ8z4HwLx+Q0xd3Ug7GIrjgIH/qRi7bj3Q7dqhPr+5bAnuS1ZQ5YWX0Vha8vc7g0quTRWJRoProg2YPOFO6raNZJ87pb5kGdiBzKjDiPS0+3az6dqbzCOH7kvXulbDtG59NR+Llu3IS0wgJza69NpQQf19LQVHe0smzNrHuQuJeNWrysShbZg4tDVvjN/OF1+HodfDtwufK/Gy0zNyWP7dMVZ+0YuVG6NKPP/SZiq7FgYqRLBIT09n8+bNREREkJKSAoC9vT3+/v707t0ba2vr8q2gXs+lF/ugsbGl+pxFmNX1JPtCNPEfTwKNBpcPJmPb5WlStm3B5b3xXJsyoVjF2HZ9mmsfjlOf23Xtzu1tm0levxoLb1+qffo5cf2eMTjzfizo9Vwf9iKKtQ1VP5qDaa265Fy6AIBV+26k7dx83y7m3v5Yd+1NwnuGAVSxsKTq5NncWjYHkZ6GYm6B3QuDuTFxWJk0paLJzdNzOvoGk99tg09DV6Yt+Y3l3x1Dl5bN+HcC6dquLjtCY5g8ex+rZj1TomUvWhvB6897Y21pWqL5lhUZKwxViGGoefPmYW1tzccff8zKlStZuXIlU6ZMwdramrlz55Z39VT6VB3pkYexDmxzT6Ie3a4d2HbqgsbaGrO6ntRcvhaP7XuwaOJDjflLMW/oZTRvc8/6KFoTss6cVtPse/dFt3snAJknolDMzNFWKbi7/zgQaalknYjEwj8QAI2tPWb1vcg4/JvBdqZ1PHEc9SE3p45Gr7t99wWtCU4fziZt3y9kHAoBwKRaDUzcquP25XdUW7MdbVUXXBdvQOPgVGbtKk9uzja4Otvg0zB/uK5rOw9OR99gy65zdGnrAUC3oLqcOFfyQ5wnzlxn1vJwOr68nrWbTvD1t0dZv+VkiZdTWrSKUqRHZVEhehYJCQlMmjTJIK1KlSr07t2bffv2lVOt8mmrOCByc9Gn6lDMzbFq0YqkNcGYutck5++/ALBp157s2IvoU1O50ClQ3df96zUkzPuCrDN/Gi3HtlsPUn79P4O0nPirWDVvScq2LZjV8UBjbk5eclLJNrCcaeyrIHJzEWmpKGbmWPi1IGXjagAs23Ym84+DkJOtbq91dsPpw9kkzvqQ3Ct/GeTlOPojcv+KJXXTBjUtJy6Gqy/cvSig2prtXB/+CvqUW6XbsArC2dGKas7WXPw7GQ93B34/eoW6tRz4Oz6Fw8ev0sK3OuHHrlCrun2Jl71h/t2hrUVrIrCyNOWV3k1KvJzSInsWhipEsHB2dubnn38mKCiIKlWqAHDr1i1CQ0OpWrVqudbNxNkZt6kzULRaUDTodu8k7eB+agavz598VhSyzp/l+oypRvPy2L4HjbU1iqkpNu07cXnoG2TH5g+32D7VjSsjhhhsf2PuF7h9+AkOL78GQhR7eKsi0zo64/jeVNBqURSF9AO7yTx8EACr9l3Rfb/aYHu7l99Ea2uPw7v/HIu8PK6PeAUzL1+sO/ckOzYa1yX5l8zeXr2YzIj75zTu0Dg44bpwPRoraxACm94vET+kb4HzI4+yye+25f0Ze8nJycO9mh3T3+9Ip8A6TFv6G3l5AnMzLZ+Mbg/AjaR0+g79kdT0bDSKwtpNJ/i/4BewsTZjzLTdRBy/SvLtTIJeWMvw1wLo+3RDdv92kc8W/0bS7QzenvQLDepWJfjznuXb6BJQmSavi0IRovwHwFNTU9myZQtHjhzh9u38YYUqVarQrFkzevfujY2NzX37nGvasKyrWalYuViWdxUeazWWv1reVXisKe6jHjqPeVFvF2m70b5fPXRZj4IK0bOwsbHhlVde4ZVXXinvqkiSJAHyaqh/qxAT3AWZOtX4sI4kSVJpkb+zMFQhehZjx441eC6E4Nq1a2r67Nmzy6NakiRVYrJjYahCBAtnZ2csLS15/vnnMTMzQwjBlClTGDdunPGdJUmSSkFluiy2KCrEMNS4ceNo2bIlX3/9NZcuXcLFxQWtVouzszPOzs7lXT1JkiohjVK0R2VRIXoWAM2bN8fb25vvv/+evXv3kpv7+N6vR5Kkik9OcBuqMMECwMLCgtdee424uDjOnz9f3tWRJKkSK8nJ66VLl3L06FHs7e2ZM2cOkP+TgXnz5nHjxg2cnZ0ZPXo0NjY2CCFYtWoVx44dw9zcnKFDh+Lhkf9r+9DQUDZt2gRAnz59aN++PQAXL15kyZIlZGdn4+fnx8CBA1EUpVhlFKZCDEP9W+3atbl1q3L8wlaSpIpJoyhFehRF+/btmThxokHali1baNKkCQsXLqRJkyZs2bIFgGPHjhEfH8/ChQt56623WLEif+mD1NRUfvzxR6ZPn8706dP58ccfSU1NBWD58uUMGTKEhQsXEh8fT1RUVLHKeODxKNphK3uRkZHlXQVJkiqxkgwWjRo1uu/HxREREQQFBQEQFBREREQEAEeOHKFdu3YoikK9evVIS0sjOTmZqKgovL29sbGxwcbGBm9vb6KiokhOTiYjI4N69eqhKArt2rVT8/qvZTxIhRqGulcF+GG5JEmVWFEDwZ49e9izZ4/6vHPnznTubHyRstu3b+PgkH9jUAcHB/WO20lJSQa3OXJyciIpKYmkpCScnO7eANPR0bHA9DvbF6eMO9sWxGiw6NSpE3v37jWaVtJmzpxZqvlLkiQ9iEYp2sBLUYNDURV0oqwUErgURSnWifV/KeOOQoNFZmYm6enp3Lx5k+TkZDXzlJQUrl69+p8r9yB5eXmEhIRw+PBhkpOTURQFBwcH/P396dixIyYmFbYDJEnSY8pEU7qj9Pb29iQnJ+Pg4EBycjJ2dnZA/ln+zZs31e0SExNxcHDA0dGR06fvLmGQlJREo0aNcHJyIjEx0WB7R0fHYpXxIIUejWXLltGsWTPOnj1Ls2bN1Mezzz7LsGElu5DMokWLiIuLo1+/fkyYMIHx48fTt29fLl26xKJFi0q0LEmSpKIoyTmLgvj7+7N//34A9u/fT0BAgJp+4MABhBCcP38eKysrHBwc8PX15fjx46SmppKamsrx48fx9fXFwcEBS0tLzp8/jxCCAwcO4O/vX6wyHsToXWcXLVrE8OHDi31AimLkyJEsWLDgP70m7zpbuuRdZ0uXvOts6SqJu87+Evd+kbbrXnuW0W3mz5/P6dOn0el02Nvb079/fwICApg3bx43b96katWqjBkzRr2sNTg4mOPHj2NmZsbQoUOpW7cuACEhIWzenL9yZJ8+fejQoQMAFy5cYOnSpWRnZ+Pr68ugQYNQFAWdTvefyyhMkW5RHhYWRlxcnMEP5V59teQ+7JMmTaJnz560aNECzT9dP71eT3h4ONu3b2f69On37SODRemSwaJ0yWBRukoiWOy89EGRtutW64uHLutRYHQyYMCAAVy4cAFfX1+0Wi2QPxFSksFi5MiRbNiwgeDgYHW97bS0NLy8vBg16uHfdEmSpP/qYYaYHkdGg8WRI0c4ffq00Znyh+Hi4kLPnj3p1asXrq6uXLlyhfPnz1OjRg1cXFxKrVxJkqTCFPVqqMrCaLBo3Lgx8fHxVKtWrdQq8cMPPxAVFUVeXh7e3t7ExMTQqFEjfv75Z+Li4ujTp0+plS1JklSQ0r4a6lFjNFjcvHmTRo0a0bx5c8zNzdX0rVu3llglwsPDmTVrFjk5Obz11lt8+eWXWFlZ8cwzzzBx4kQZLCRJKnNyGMqQ0WDx8ccfl3oltFotGo0Gc3NzXF1dsbKyAsDMzKxUh78kSZIKI4ehDBkNFkFBQVy6dIno6Gg6d+5Meno6eXl5JVsJExOysrIwNzc3+OV2enq6enWUJElSWdIgT1TvZfSbePny5fTt25chQ4YAcOXKFXr37l2ilZg6dao6xHVvcMjNzS3xHwBKkiQVRWn/KO9RY7RnsWTJEg4fPkyLFi0A8PT0JCEhoUQrYWpqWmC6nZ2d+vP0f6u3/PkSrYP0Ly5VjW8jFZtm+nflXYXHmvjy4S+5N9FoS6Amjw+jwcLc3BwzMzP1eW5urpxHkCTpsVeZeg1FYXQYKigoiOnTp5ORkcHu3bvp168fvXr1Kou6SZIklRuNoinSo7Iw2tKZM2fi7OxMkyZNWLZsGd27d+ezzz4ri7pJkiSVGzlnYcjoMJRGo+HNN9/kzTffLIv6SJIkVQjyaihDRnsW27dvx8/PD0dHR+zs7LC1tS100lmSJOlxIXsWhoz2LEaNGsWmTZto0qSJnNiWJKnSkFdDGTIaLNzd3WncuLEMFJIkVSpKJZq8LgqjweKLL76ge/fuBAUFGdwbasyYMaVaMUmSpPKkMT5KX6kYDRaTJk3CxsaGzMxMsrOzy6JOkiRJ5U72LAwZDRZJSUns2rWrLOoiSZJUYVSm31AUhdGj0blzZxksJEmqdLSKSZEelYXRYLFkyRK6deuGpaWlvHRWkqRKQ0FTpEdlYTQs6nS6sqiHJElShSKHoQwZDRZHjx69L83e3p5atWphYlJ5umCSJFUucoLbkNFv+6FDh3L06FGaNGkCwMmTJ/Hx8SExMZGvvvqKLl26lHolJUmSypq8dNaQ0aNRu3Ztjh07RmRkJJGRkURFRdG4cWP27NnDBx98UBZ1lCRJKnOKoinSo7Iw2rM4e/YsXl5e6vNGjRpx7NgxPDw8SrVikiRJ5UmrFLwoW2VlNFjUr1+fd955hxdeeAGA77//nnr16pGVlVXoCneSJEmPOjnBbchosFi9ejVLly5l/vz5CCFo06YNs2fPxtTUlH379pVFHSVJkspcZbostiiMBgtLS0uGDh1Kz549qV+/vsFrNjY2pVYxSZKk8iR7FoaMHo2tW7fi6+tLt27dAIiKiuKZZ54p9YpJkiSVJznBbchoz2Lq1KkcPnyY9u3bA+Dr60tcXFwpV0uSJKl8leSls9u3byckJARFUXB3d2fo0KHcunWL+fPnk5qaSp06dRg+fDgmJibk5OSwePFiLl68iK2tLaNGjcLFxQWAzZs3ExISgkajYeDAgfj6+gL5J/GrVq1Cr9fTqVMnevfuDUBCQkKBZRSH0aNhYmKCvb19sTKXJEl6VGk1JkV6GJOUlMSOHTuYOXMmc+bMQa/XExYWxvr16+nRowcLFy7E2tqakJAQAEJCQrC2tmbRokX06NGDDRs2AHD58mXCwsKYO3cukyZNIjg4GL1ej16vJzg4mIkTJzJv3jwOHTrE5cuXAQotoziMtrRx48Z888035OXlER0dzcKFCwkMDCx2gY+ilLRsJi8/QvTfKSgKTHsrgN0Rl9l39BqmJhpqulozfUgAdtZmAJz76xYfrYgkLSMXRQM/ftoZczMtpy4mM2HZYbKy82jnW41Jr/qiKAoLNp5ib+RVNBpwtLNgxtsBuDpYIoRg2tooDkRdw8LMhBlvB+BVx6Gcj0bJS0nNYvKcUKLjkvKP79gOWJiZ8PH8/WTl5KHVapgyoi3eDVy5+FcyE2bt43TMDUYNbMHg/r4GeeXl6ek79CdcqlqzbFp3AMKPXeaLZb+Tk5tHI09npo3tgIlWwx9RVxj20U5qVLMF4Kk2Hgwb4F/m7S9pwQMm0bNJaxJ0yTT59GUAPun1Fs96t0Mv9CToknl97adcu32TIM+m/PzOF8TevArApqhQPv1lJQD2ljaseGUijZ/wQAgYtO4zwmNPqeW81/klZj8/gqpju5KYdpsqVrasHDCJulVrkJmbxaB10/jz6kUARnToz5ttnkVBYfmhn1kQ8n0ZH5X/riQnuPV6PdnZ2Wi1WrKzs6lSpQp//vknI0eOBKB9+/b88MMPdOnShSNHjtCvXz8AWrZsycqVKxFCEBERQWBgIKampri4uODm5kZMTAwAbm5uuLq6AhAYGEhERATVq1cvtIziMBosFi1axLRp0zA3N+fFF1+ka9eufPjhh8Uq7FE1bW0UbX3cWDgqkOxcPZlZuaRlujLmhSaYaDXM/vYEX289y9gXvcnN0/P+ksN8MbQ5DWpVIVmXhYlJ/odu6spIPhnsj6+nI2998RsHj8fTzrcag3vWZ2T/xgCs3RnN0k2nmTq4GQei4rkUn8qvc5/meEwSU1ceZeOnncrzUJSKaUt+o22AOwundCU7J4/MrFxGfbqLYa/60655Lfb/cYlZX4ezbu6z2NuaM3lYG/aExRaY19rNJ/GoWYXU9BwA9HrB+C9CWDXrGerUqMLC1YfZsuscfZ9uCECzJtXUoPK4WP37/7E49EfWvv6RmjZr93o+2vY1AMM79Oej7oN459svADgYE0WvpWPvy2dB/9HsPB1Ov+UTMdWaYGVmob5Ww8GFpxo251LiNTVtYrfXiLocTZ9l46nvWoslL4yl84LheD3hwZttnqX5zEFk5+Wyc/h8/u9kGDE3/i6tQ1AiijrBvWfPHvbs2aM+79y5M507d1afOzo60qtXL9555x3MzMzw8fHBw8MDKysrtFqtuk1SUhKQ3xNxcnICQKvVYmVlhU6nIykpCU9PT4N87+xzZ/s7f0dHR6PT6QotoziMHg0rKyumTZtGREQEu3fv5rPPPsPCwsLYbsWSmppKampqqeRdXKnpORw5e4O+7esAYGaiwc7ajDbebpho8w+fz5NOxCdmAHDoxHXq17SnQa0qADjYmqPVKCQkZ5CakYtfPScUReHZtrXYcyT/bM7G6u7vVTKycrmzgO3eyKs827YWiqLg6+lESno2CckZZdTyspGals2Rk9fUL28zUy12NuYoKKSm5X/h69KycXGyAsDJwYomDVzUY3+v+Bup7P/jEv26N1TTbqVkYmaqpU6N/PcjsFkNdh28WNrNKlcHY6JISksxSNNlpqt/W5tZIIzkYWthRbsn/Qg+tBWAnLxcbmfc/b85r+8oPti02CCfRm512Hv2CADnrl+itlM1XGwdaehWm/DYP8nIySJPn8f+80d5zjfoodpYFop619nOnTszc+ZM9XFvoID877WIiAiWLFnCsmXLyMzMJCoqqtByhbj/3VEUpcD0B21f0grtWXzyySf079+fBg0akJWVxdNPP83x48fRarV888039x2Q4rp58ybr16/n5MmTWFtbI4QgIyODxo0b89JLL6kTO+Xl74Q0HG3NmbAsgnOXbuNVx4GJr/piZXH30P0UGkv3Vu4AxMXrUBQYPOMAybosurdy541eDbienIGbo6W6j5ujJdfv+eKf9/1Jfj54CVsrU9ZMbg/A9eQMqjla3bOPFdeTM3BxuJvPo+7vayk42lsyYdY+zl1IxKteVSYObcPEoa15Y/x2vvg6DL0evl34nNG8pi89xNg3W5GWfndFRwd7C3Jz9Zw8l0CT+i78euAi1xLufulFnY7n2bc24uJkxQdDAvGs7Vgq7awIPnvmbV5t8TS3M1PpMG+Ymt6qThOiJq3j6u2bjP1pIaevxeJRtTo3UpNZ9eqH+NR4ksi/zjFy41zSszPp5d2WK7ducOJKjEH+x69E08e3PYcuHCegViNqObpRw8GZU1cvMu2Zt3G0tiMjO4vujQM5culsWTf/PyupS2dPnjyJi4uLurRDixYtOHfuHOnp6eTl5aHVaklKSsLRMf+z5+TkRGJiIk5OTuTl5ZGeno6NjY2afse9+9ybnpiYiIODA7a2toWWURyFHo3vv/9e/V3FmjVr0Ov1JCQksH//fiZOnFjsAv9t3rx5NG/enOXLl7Nw4UIWLVrE119/TUBAAAsWLCixcoorV6/ndNwtXuxcl80znsLSXMvyrXc/6F9tOYOJVqFX65r52+cJIs/dZPawFmyY0oHdEVf4/dR1CjqVuzf2j/5fE0IX96Rn65qs3/XPf8KCzhgo+TOG8pSbp+d09A1e7OXF5mX9sLQwZfl3x/h225+MfyeQ0G9fZcI7gUye/eAfgO4Lj8OpiiWN6zkbpCuKwpzJTzHzy0P0G/YT1pamaq/Ey9OZkG8G8PPX/XmldxPenbKz1NpZEUze+hU1Jz3LhsO/8m77vgAc/fsstSb3xnfaABbt28iWt/OHpkw0Wpq61+fLA5toOv010rIyGN/1VSxNzZnU7XV1SOteM39di4OVLccmrmV4h34c+/s8uXl5nI2P4/Nd69g9YhE7h8/n+OVocvW5Zdr24tAU8Z8xVatWJTo6mqysLIQQnDx5kho1auDl5UV4eDgAoaGh+Pvnz5c1a9aM0NBQAMLDw/Hy8kJRFPz9/QkLCyMnJ4eEhASuXbvGk08+Sd26dbl27RoJCQnk5uYSFhaGv78/iqIUWkbxjkchzMzM1K7Mr7/+yosvvohWq6Vhw4bk5pbcG63T6QgMDESjuVsVjUZD69atK8RaGm6OVrg6WuLzZP6YYNcWNTgdlwzA5gNx7Dt6lVnDWqjHys3RkoCGzjjYmWNpbkKQbzVOx97C1dGS+KS7PYn4pIJ7CD0Da7L7cP6VDK6OVlxLSr9nn3RcHEpnCLC8uDnb4Opsg0/D/Mm5ru08OB19gy27ztGlbf79x7oF1eXEuYQH5nP0VDwhv8fR8eX1vDdtN39EXeH9GfnjyH6N3Ngw/zl+WPI8/t5PUKt6/tV9NtZmWFvmDwEGtahFTq6e5NuP1zBfQb6J2MXzfh2A/OGptKz8Nu/483dMtSY4Wdtz+VYCl2/d4HDcnwD8eCyEpu71qetcgzpVq3F88npiP9tMjSrOHJ24Blc7R3SZ6Qxa9xl+01/l1dVTcbZ1IDYxf6h1Zdg2ms14jaC575CUnkJ0wuXyafx/IfRFexjh6elJy5YtGTduHGPHjkUIQefOnXn55ZfZvn07w4cPJzU1lY4dOwLQsWNHUlNTGT58ONu3b+fll/MvUnB3d6dVq1aMGTOGadOmMXjwYDQaDVqtlkGDBjFt2jRGjx5Nq1atcHfPH+korIziKHQYytzcnFOnTuHq6sq+ffuYPXu2+lp6enphu/1nHh4erFixgqCgIHWSJjExkf3791O7du0SK6e4nKtYUM3JiotXdXg8YcvvpxKoW92Og8fjWbHtLOs+7ICl+d3D2MbbjeDt58jIysXUREPEmRu81r0eLg6WWFuaEBWdiM+Tjvx88BKvdHkSgLhrOmr/c0VOyNGr1Hki/++OzZ5gw64YerRy53hMEraWpo/VEBSAs6MV1Zytufh3Mh7uDvx+9Ap1aznwd3wKh49fpYVvdcKPXVG/4Avz3hstee+NlgD8EXWFlT8cZ9aE/KHSxOR0nBysyM7OY8X3x3j7paYA3EhKp6qDJYqicOLsdYReUMXu8QrGdzzp7K5OKD/j3Zaz8ZcAcLVz5HpK/qRnQK1GaBSFxLTbAPydfJ16rjU5f/0vOtUP4HR8LKeuXsD1g7sXBMR+thn/Ga+TmHYbe0sb0rMzycnL5Y3Wz3Ig+pg6V+Js68ANXTLuDq708W1Pq1lvlmXzi6cIgaCo+vfvT//+/Q3SXF1dmTFjxn3bmpmZMWbMmALz6dOnD3369LkvvWnTpjRt2vS+9MLKKI5Cg8WCBQvo27cvN27cYPTo0dSpkz/B+8svv+Dn51cihQO8++67hISEsHHjRnWm3tHREX9//4eKgiVp8mt+vL/kD3Jy9bi75F8m2+/DPWTn6Bk0Yz+QP8k9dXAz7G3MeL17PfpN3ouiQDvfarT3qwbAlEFNmfhVBJnZebT1caOdrxsAc747Sdw1HYqi8ERVK6YObgZAkK8bB6Ku0WX0DizMtUwfElA+B6CUTX63Le/P2EtOTh7u1eyY/n5HOgXWYdrS38jLE5ibaflkdHsg/wu+79AfSU3PRqMorN10gv8LfgGbfy5bLkjwxihC/7iEXi94sZcXLf1qAPDrgQt8t+1PtFoNFmZa5kx+qlQmBsvaN4M+oX29plS1qcLf07cyZftyujcOpL5rTfR6waWkeN7+5nMA+vp15J12fcjV55GRk8ULwXevdBz+/Rw2DJyKmdaUizevMHDdZw8st6Fbbda+PoU8fR6nr8UxeP009bWf3pqBk7U9OXm5DPtuNrfSy3/UwKgSDBaPA0UUNsVewYnIyeVdhcebS9XyrsFjTTP9u/KuwmNNfBn+8Jnk7CjadqZPP3xZj4ByXxc1JSVFvUoA4MCBA8TExFCzZk06der0WJzpSZL0CNLLnsW9yv0uWNOm3dNV/eknDh48iIeHBydOnGDNmjXlWDNJkio1fW7RHpVEufcs7h0FO3z4MFOnTsXCwoI2bdowbty4cqyZJEmVmpyzMFCkYBEWFkZcXJzBJbOvvvpqiVQgOzub2NhYhBDo9Xr11+EmJiYGl9NKkiSVKTkMZcBosBgwYAAXLlzA19dXvceIoiglFiwcHBxYu3YtkL+YUnJyMg4ODuh0OrU8SZKkMid7FgaMBosjR45w+vTpUptonjJlSoHp1tbWTJ06tVTKlCRJMkoGCwNGx3kaN25MfHx8WdRFtXHjRjQaDebm5mVariRJkqqEfsH9uCi0Z9GrVy8URUGn09GoUSOaN29u8OW9devWUqtUZGTkfb92lCRJKksiL6dI21WWi/sLDRZjx95/f/uy8oj+TlCSpMdJJeo1FEWhwSIoKP9+8+PGjePzzz83eG3cuHHq66Vh5syZpZa3JElSkchgYcDoBPfu3bvvCxY7duy4L6248vLyCAkJ4fDhwyQnJ6MoCg4ODuq9oYq7uLgkSdJDkcHCQKHfxF9++SVLly7l4sWLeHt7q+k6nY7WrVuXWAUWLVqEtbU1/fr1u++us4sWLWL06NElVpYkSVKRyWBhoNBg8dJLL/H0008zYcIEg2EhW1vbh1pt6d9iY2PvW+TIycmJevXqqQuNS5Iklbm8ynMrj6Io9NLZvLw87OzsWLJkCba2tuoDeKhFv//NxsaG33//Hf09v5bU6/WEhYVhbW1dYuVIkiT9J/LSWQOF9iyaNWum/hDv31cnKYrCxYsls+j9yJEj2bBhAytWrMDGxgYhBOnp6Xh5eTFq1KgSKUOSJOk/q0SBoCgKDRaxsbFlUgEXFxd1XkKnHrpSUAAAIABJREFU0yGEYPXq1YwYMaJMypckSSqQvDeUgSJdapScnEx0dDSZmZlqWrt27UqkAgVdVXXq1Ck1Xd55VpKkciF7FgaMBosVK1awYMECLl++jK+vL+Hh4bRq1YqQkJASqUBSUhLVq1dXFzoSQnDhwgV69epVIvlLkiQViwwWBozeG2rBggVERERQq1Yt9u3bx7Fjx3B2di6xCsyYMQMPDw82bdqElZUVXl5emJmZ0ahRIxo1alRi5UiSJP0nuXlFe1QSRnsWFhYW/9/encdFVfUPHP/MDDvDvogouC+JCyIpKam4ZalFaj79ynxSn8osy1zKpUUrcyvtsVBLTUvNejKl3dQUFdBEERc0FIFERVbZ95n5/TF5dWLVQCi/79drXi/mzrn3nHuHud97zrn3HGWOiZKSEjp27EhcXFydFUCtVjN8+HDuuecePv30UxwcHNDp7pwvQAjRSEmfhYkag0Xz5s3Jzs4mODiYwYMH4+TkhKenZ50XxMXFhWnTphEdHY21tXWdb18IIW6KBAsTNQaL7du3AzBv3jyCgoLIyclh6NCh9VYgPz8//Pz86m37QghRK3oZ0PRGtbobKjw8nHPnzjF+/HjS09O5dOkSrVq1qu+yVevcpG0Nmv8/nbnWvKGL8I+m//TfDV0EUROpWZioMVjMnz+fI0eOEBcXx/jx4ykrK2Ps2LFERETcjvIJIUTDkGBholbNUMeOHVOahjw9PcnLy6v3ggkhRIO6g+50qo0ag4WFhQUqlUoZ+qOgoKDeCyWEEA1O+ixM1PicxZgxY3jmmWfIzs5mzZo1DBo0iKeeeup2lE0IIRqOXl+71x2ixprFjBkz2LVrF/b29sTFxfHmm28yePDg21E2IYRoOHUYCAoKCli9ejXJycmoVCqeffZZPD09Wb58Oenp6bi5ufHSSy8pg6muX7+eY8eOYWlpyeTJk2ndujUAYWFhbNtmvLln5MiR9O/fH4CEhARCQkIoLS2le/fujB8/HpVKRX5+fqV53Ioqg8X7779Pnz596N69O4MHD5YAIYS4o/x5tO2qqGqRZv369fj6+jJ9+nTKy8spKSlh+/btdOnSheDgYEJDQwkNDWXs2LEcO3aMK1eusGLFCs6dO8fatWt55513yM/PZ+vWrcr8QrNmzcLf3x+tVsuaNWt45plnaNeuHQsXLiQmJobu3bsTGhpaaR63ospmqIsXL/Liiy/i7u5O//79mTNnDj/88EOdzmUhhBCNVnl57V41KCws5MyZMwwYMAAAMzMzbG1tiYqKol+/fgD069ePqKgoAI4cOULfvn1RqVS0b9+egoICrl69SkxMDF27dkWr1aLVaunatSsxMTFcvXqVoqIi2rdvj0qlom/fvsq2qsrjVlRZs3j33XcBKC0t5ciRI0RGRvLJJ5/w1FNP4ejoyOnTp285UyGEaPRq2cG9e/dudu/erbwfNGgQgwYNUt6npaVhb2/PypUr+f3332ndujVPPvkkOTk5ODk5AeDk5ERubi5gHFzV1dVVWd/FxYWsrCyysrKUqacBnJ2dK11+LT1QZR63osY+i6KiInJzc8nJySEnJwdPT0+6dOlyyxkKIcTfQi37LP4cHP5Mp9ORmJjIhAkTaNeuHevXryc0NLTK9JU1f127G7Wy5bVtLvurqgwWTz/9NLGxsdjZ2dGrVy969+7NtGnTlCglhBD/aHXUwe3i4oKLiwvt2rUDICAggNDQUBwcHLh69SpOTk5cvXoVe3t7JX1GRoayfmZmJk5OTjg7O5u06GRlZdGpUydcXFzIzMw0Se/s7AxQZR63oso+iwsXLlBSUoKHhwfNmjWjefPmODo63nJGQgjxt6I31O5VA0dHR1xcXLh8+TIAJ0+epHnz5vj7+7Nv3z4A9u3bx9133w2Av78/+/fvx2AwcPbsWWxsbHBycsLX15fjx4+Tn59Pfn4+x48fx9fXFycnJ6ytrTl79iwGg4H9+/fj7++vbKuyPG6FylBNHcZgMBAbG0tkZCSRkZGcOnUKZ2dn7rnnHubPn3/LmdaFs3fLXBf1ScaGql8tZWyoeqXynvaXt6Hf+Vyt0qmHhNSYJikpidWrV1NeXo67uzuTJ0/GYDCwfPlyMjIycHV1Zdq0acqts+vWreP48eNYWFgwefJk2rRpA8CePXuUwV1HjhxJUFAQAOfPn2flypWUlpbi6+vLhAkTUKlU5OXlVZrHrag2WFxz8eJFIiIiiIyM5PvvvyczM5Ps7OxbyrCuSLCoXxIs6pcEi/pVJ8Hix0m1Sqd+YPVfzuvvoMo+ixUrVhAZGUlERATm5ub06dOHe+65hwkTJkgHtxDin+8Oejq7NqoMFklJSYwePZrly5fTtGnT21kmIYRoeBIsTFQZLJYtW3Y7yyGEEI2LDCRoolaTHwkhxB1HahYmJFgIIUQlDGUSLG4kwUIIISqjk2aoG0mwEEKIShikz8KEBAshhKiM1CxMSLAQQojK6KTP4kYSLGqgsrDA6+PPUJlbgJkZ+b/sJPPjD5XP3WbMxWHEw8T3M47FojI3x2P+Iiw7+qDLySZlzjTKUy6jdnDAc9H7WHXqQu7320lbukDZRrMVH2Hm4gZmZhQdO0rakrdAr6fpO+9h3qIVABqtHbr8PC48PvL2HoB6pjK3oOl/14OFOSqNGQX7dpG9YRUAThOfx7bfEAx6HXnffkXuts8BcJ7yCja9AjEUF5O++DVKz/1mTP/0VGwC7gUge+PHFOz9GQDXV97Eqps/+oI8ADIWvU7p+TgArLr54/z8TFRm5uhyrnJl6sTbuv+3Q25+Ca8u28e5pKuogAUz+vHZtpMkJucYPy8owd7WktCPRgMQl5DJ6+/vp6CwDJVKxdaQhykr1zP2pW+VbV7JKODBgW2ZM7mPsmzH/gSmvrWLrz4cSZcObnz3yznW/e+48nlcYibbVo7irrbXh99uzKQZypQEixoYSktJfnYChqJC0JjhtXYTBZH7KT51Asu7fNDY2Zmkt39oFLrcXJJGDsVu8P24TZlOypzpGEpKyVj9AZZt2mHZpq3JOimzp6EvKACg6eL3sRt4H3m7fiJlznQljevUl9Hn59X/Dt9mhrJSUqb9B0NxEWjMaPrBBop+Dce8RWs07h5c/PdDYDCgdjSOomndKxDzZt5cHDsCy7u64PLSq6RMHot1wL1YtuvIpf+MQWVhQdP311H4aziGQuNxzVq9jML9u03yVtva4TJ1DldemYwu7YqSxz/NgpWR3OvvxYrXh1BapqO4pJzlr16f+XLR6oPY2VoAUK7TM3PRHpa8MoCObVy4mluMmUaNpYWZEkwARk7+msGBrZT3+YWlbAo9SbeO7sqyEQPbMWKgcaTVuMRMnnv9579NoABA7oYyUeWos+I6Q1EhACozM1RmZmAA1GrcXphB+op3TdJq+w4g9wfjWPV5e3Zic3eAcRvFRRQfj8ZQWlJh+9cCBRozVObmGKh4RWM36D7yfv6xDveq8TAUFwF/HF+N8frF/sExZH/6EfwxdJk+2ziZi02fIPJ3fgdAyZmTqG3t0Di7YtGiNcXHj4Jeh6G4iNLzZ7Hp2aeS3K6zHXQ/hQd+QZd2xSSPf5L8glKOnExh9P0dAbAw12CvtVQ+NxgM7Nh/nmFBxguYiCMX6dDamY5tjJPpONlbodGYniaSLuaQlV2Ef5frIzus2BDFxDG+WFhoKi3HD3vilTz+Lgw6Q61ed4pGFSyys7NJSEggMTGxwQcqNKFW4715G212hlP4ayTFsSdwHPMY+fv3osvMMElq5t6E8lTjyQedDl1+HmqHmod2b7biY9rsPIC+oID8X3aafGbdvQe6zEzKkn+vs11qVNRqPNd8iff2vRQdPUTJmZOYeTbHNug+PFd/TpNFIZg18wbAzNWd8rRUZVVdRioaV3dKz5/FulcfVJZWqO0dsfK9G42bh5LOaeIUmq39CufJM8DcOEiiefMWqO3s8Vi+Fs+PtqAdMvz27vdtkJySi7ODFbOXhvHwpK28+t4+CovKlM+PnEzBxdGals0dAEi6lI0KFRNn/cDIZ79m7ZcxFbb5w9547u/XRpmQ53R8BinpBQQFtKiyHD/tS/jbBQv0+tq97hCNohkqKSmJNWvWUFhYqEzakZmZia2tLRMnTqR169YNW0C9nguPj0SttcNz6Qqsu/fAbuB9JE96smLaSme0qvnq49ILT6OysMDjrSXY+Pei8PBB5TO7IcPI2/nPrFUAoNdz+al/oba1w/2t5Zi3bIvKwgJDaSmXJz2Gzb0DcXt5PikvjodKJwwzUHTkIBYdfGj64afos69Scvo46I3zI19dswJdVgaYm+M6/XUc/28C2Z99hEpjhkX7TlyZ/jQqC0s8Qz6j+PRJyi/+c4Jyuc7A6XMZvPpcH7rd1YQFIRGs+TKGF580zmvww97zJifxcp2Bo7FX2Prhw1hZmvHky9/j086Ve/yaK2l+DItn8SvG+aT1egMLV0WycGZQlWU4fiYVK0sz2rf6mzXz3UG1htpoFMEiJCSEp59+WplJ6pqzZ8+yatUqli5d2kAlM6XPz6PwaBTWPXph7tWCVtt2AKCysqLlth0kjRxKeeoVzJp4GK9+NRo0Wjv0OTm12r6htJSC/XvR9htwPVhoNGiDBnFh3CP1tVuNhr4gj+KYKKx79qY8PVXpYyg88AtuLxvnTylPT8PMvQnXGvM0rk3QZaQDkLN5LTmb1wLg9upCyi5eADAGCoCyMvJ/+gaHf/37j22losu5iqG4yNhMeCIaizbt/1HBwsPNliZutnS7qwkA9/VtzZovjLWFcp2eXeGJfL3y+k0THq623N2lKU4O1gD06+nN6fgMJVj8dj6Tcp2Bzu3dACgoKuVc0lXGzTB2fmdkFTH59R2sfHMoXToY0/wYdp5hQW1uzw7XIengNtUomqFKSkoqBAqA9u3bU1xc3AAluk7j6IRaa+zEVllaYtPzHkp+iyVhaF8SHxpM4kODMRQXkzRyKAD5B/ZiPywYALsBQyiM+rXa7ausbdC4/NHpp9Fg26cvpUmJyuc2Pe+h9PdEk6aXfxK1gxNq2z+Or4Ul1j0CKLuQRGH4Xqz8egLGO5bK/jiBF0aGoR0yAgDLu7pgKMg3BgO1GrW9sSnFvHU7LFq3pyjKGHA1ztc7VW0CgyhNjDduK2IvVl39QK1BZWmF5V1dKPv9+rH/J3BztqGpm5aEZGOz7sFjl2jTwtgsejD6Iq28HPFwuz4ZTqC/F2cTsygqLqNcpyfqRAptWlyfSvmHvfEmJ347W0sOff1v9mx6nD2bHqfbXe4mgUKvN7Bj/9+wCQqMNYvavO4QjaJm4evry8KFC+nXrx8uLsaOtczMTPbt24evr2+Dlk3j6obHvIWo1GpQq8nbvYOC8H1Vps/95ms85i+m5bYd6HOzSZk7Q/ms1Te7UNtqUZmbY9tvIJemPIUuJ5tmy0KMt+ZqNBRG/Ur2ti+VdeyG3P+P7dgG0Li44jbrbeX4FoTtpOjQfkpOHsPt1XdwGD0WfVEhGe8aaxZFhw5g0yuQ5pu+x1BSTPri1wFQacyMt+AC+sIC0hfMAb0OALe5C9E4OoFKRWl8HBnL3gKg7EIiRYcjaLbuKzAYyPthG2VJ8Q1wFOrXq8/1YebCXygr1+PV1J53ZvQHjE1Qw/90Enews+TJUV145PntqFTQt6c3/Xtd74v4ad95Pl5wf63zjjqZgoerLV5Nb33u54YiY0OZqtVMebfDsWPHiIqKIivLeEeKs7Mz/v7++Pn5VZpeZsqrXzJTXv2SmfLqV13MlFf85ohapbN6/bu/nNffQaOoWQB0796d7t27N3QxhBACkD6LP2sUwWLHjh307t0be3t7rly5wqpVq7hw4QJNmzZl0qRJeHt7N3QRhRB3mjuoP6I2GkUH986dO7G3N7Zprl+/nmHDhrF+/Xoef/xx1qxZ08ClE0LckfSG2r3uEI2iZqHT6ZS/c3Nz6dnTeBeMj48PRUVFDVUsIcQdTDq4TTWKmkVAQAAhISGkpqZy991388MPP5CRkcHevXtxdf0bjSUjhPjn0Olr97pDNIqaxf/93/8RFhbGf//7X1JTUykrK2P37t3cfffdvPDCCw1dPCHEHUg6uE01imAB0L9/f/r379/QxRBCCCPp4DbRKJqhKvPRRx81dBGEEHcwg95Qq9edotHULP4sISGhoYsghLiD3UnDj9dGow0W126lFUKIhqCTu6FMNNpgMXfu3IYughDiDnYnNTHVRqMIFoWFhWzfvp2oqChyc3MBcHBwwN/fn+DgYGxtbRu4hEKIO42hjic20uv1zJo1C2dnZ2bNmkVaWhrvv/8++fn5tGrViilTpmBmZkZZWRkffvghCQkJ2NnZMXXqVNzdjdPVbt++nT179qBWqxk/frwy0GpMTAzr169Hr9czcOBAgoONI19XlcetaBQd3MuXL8fW1pZ58+bxySef8Mknn/DGG29ga2vLsmXLGrp4Qog7UF1Pq/rjjz/SrFkz5f2mTZsYNmwYK1aswNbWlj179gCwZ88ebG1t+eCDDxg2bBibN28G4OLFi0RGRrJs2TLmzp3LunXr0Ov16PV61q1bx5w5c1i+fDkRERFcvHix2jxuRaMIFmlpaQQHB+PoeH36UUdHR4KDg8nIyKhmTSGEqB91eTdUZmYm0dHRDBw40Lhtg4HY2FgCAgIA46MDUVFRABw5ckR5jCAgIIBTp05hMBiIioqid+/emJub4+7ujoeHB/Hx8cTHx+Ph4UGTJk0wMzOjd+/eREVFVZvHrWgUwcLNzY1vvvnGZN7t7OxsQkND5QluIUSDqMuaxYYNGxg7dqwyb3leXh42NjZoNBrAOCXDtekZsrKylHl9NBoNNjY25OXlmSy/cZ0/L3dxcSErK6vaPG5Fo+izmDp1KqGhocybN4+cP6YgdXR0pEePHrz00ksNXDohxJ1IV167Povdu3eze/du5f2gQYMYNGiQ8v7o0aM4ODjQunVrYmNja9xeZVMMqVSqSpdXl76uNYpgodVqGTt2LGPHjgXgzJkzxMfH4+3tjVarrWFtIYSoe7WtNfw5OPxZXFwcR44c4dixY5SWllJUVMSGDRsoLCxEp9Oh0WjIysrC2dkZMNYMMjMzcXFxQafTUVhYiFarVZZfc+M6Ny7PzMzEyckJOzu7KvO4FY2iGWr27NnK37/88gvr16+nuLiYrVu3Ehoa2oAlE0Lcqeqqz+Kxxx5j9erVhISEMHXqVDp37swLL7yAj48Phw4dAiAsLAx/f38AevToQVhYGACHDh3Cx8cHlUqFv78/kZGRlJWVkZaWRkpKCm3btqVNmzakpKSQlpZGeXk5kZGR+Pv7o1KpqszjVjSKmsWNQ5Tv3r2bV199FXt7e0aMGMHcuXOV28CEEOJ2qe/nLB5//HHef/99vvjiC1q1asWAAQMAGDBgAB9++CFTpkxBq9UydepUALy8vLjnnnuYNm0aarWaiRMnolYbr/cnTJjAggUL0Ov1BAUF4eXlVW0et6JRBAuDwUB+fj4GgwGDwaA8vW1lZaV0zgghxO1UH8N9+Pj44OPjA0CTJk1YuHBhhTQWFhZMm1b5HOIjR45k5MiRFZb7+fnh5+dXYXlVedyKRhEsCgsLmTVrFgaDAZVKRXZ2No6OjhQXF1fZqSOEEPVJX6arOdEdpFEEi5CQkEqXq1QqZs6ceZtLI4QQMtzHnzWKYFEVS0tL5TH3Pzt1SqJ+fSouluNbn9yX/9TQRfhHs11eeTPOzdBLsDDRqIOFEEI0FBmi3JQECyGEqIQ0Q5mSYCGEEJWQmoUpCRZCCFEJfS2H+7hTSLAQQohKSM3ClAQLIYSohNwNZUqChRBCVKKOJ8r725NgIYQQlZBgYUqChRBCVEKChSkJFkIIUYlyGcTAhAQLIYSohNQsTEmwEEKISkiwMCXBQgghKiHBwpQECyGEqIQEC1MSLIQQohLl5Q1dgsZFgoUQQlRCahamJFgIIUQlZEpnUxIshBCiElKzMCXBQgghKiHBwpQECyGEqIQEC1MSLIQQohJyN5QpCRZCCFEJqVmYkmBRA+vmHvivXYJlE1fQ60n85H+cD/mMTq+/SNPhAzHo9ZSkZ3L06dkUp6TRdPhAOr3+Iga9HkO5jhMvv0Nm5FEAHs4/Tc6pswAUJadw8JFnAbBp0ZyeG5dh4eRAdsxpoia8jKGsjLYvPEnLJx/BUK6jJCOLo5PmUHThcoMdi/pg09yDez5bgpWHKwa9nvMf/4+4FZ/hNXooXeY9j8Ndbfi55yNkHT0FgG2LZgw78yN5cYkAZBw6TtSzb5hss+83q9C2bs6PXUYA0OeL5dh3aAWAuaMdZdl5/NQ9GI9BvfFdNB21hTn60jKOzVxK6t5Dt3HvbyOVGqtpIRhyMihZ+5qy2GLkc5j1vI/CWQ8akzm5Y/noDFRaBwyFeZRsWoQhJ+P6dixtsJ61Dt3JCEq3fWhc9PQ7qOydUWk06BJOUbr1AzDowcYOq3FzUTl7YMi6QvGnb0NR/m3d7b9CgoUpCRY1MJTrODlrEdkxpzHT2hIU+TVpv0RwdvlaTr/5XwDaTH6CjrOfI+aFN0jbe5CU738BwL5zB3ptep9dvvcDoCsqZk9AcIU8Oi+YQfwHG7j41Y/4rphPyydHk7hmC9kxZ9jbZxS6omJaPfV/dFkwk8NPvHT7dv420JfriJ6+iKvHjMd36NGvSdkVQc6psxwYOYWeH82vsE7++Qv81L3icQRo/vBgyvMLTJZFPHr9mHV/9xXKcownrJKMq+wb8SxFKWk4+LQj6Od1hDbvW4d713iY9X0YQ+oFsLJRlqm92qOy1pqks3jwGcqP7KI8ahfqtr5YDJ9IyebF1z9/4En050+YrFPy6dtQUgiA5ZOvo/Hti+5YGOYD/4Xu3DHKfvkS84H/wnzgo5R9v7Ye97JuyUR5ptQNXYDGrvhKOtkxpwEozy8g77cErD2bUJ53/YSksbGGP+7J1hUUKsvNbK1rda+2W78ALm37GYALm7fjOWIgABn7f0VXVAxA1uEYrJt51M1ONSLFV9K5euz68c09k4BNsybk/pZA3tnEm9qWma0NHaeN59Tbq6pM4z3mfn7f8j0AV2POUJSSBkBO7Dk0VhaoLcxvcU8aL5WDK2adelF26KcbFqqxGPEUpd+tMUmr9vBGd/YYAPr4GDSd77n+WfN2qLSO6OKOmmbwR6BArQEzM+W3YNa5N+VRuwAoj9qFWZfedbxn9Uuvr93rTiE1i5tg490MR9+7yIo6DkCneVPxfjyYspw8Dgwdp6TzfHAQPm9Ox9LNmciRzyjL1VaWBIV/jUFXTty7H5Py3S9YuDhRlpOLQWccPL/o0hWsPJtUyLvlk6O58vP+et7DhmXbohlO3e8i49fj1abTtmrO0OjtlOXmc+LV90kPN568ur71Ir+99wm6wuJK13O715/i1Ezy4n+v8JnXqPu4euwM+tKyv74jjYzFw88ag4KltbLM7N6HKI89iCE3yySt/lICmm73Ur5/O5ougaisbMHGDorysXjoGUo2L0bTrnuFPCyfWYjGuwO6M1Hojh8AQGXnpGzfkJuFSutYj3tZ96SD21SjrFnMnTu3oYtQgcbWhl5bVnBi5jtKreL0vPfZ0a4/yV98R5tJY5W0l7/dzS7f+zk45jk6vf6isnxH+yD2Bo7i8L+n03XpHGxbeYGqksz+VBvxevRBnPw6c27536cKf7PMbG249+sVHJ36jkmt7c+KUtII9Q5ih9/DRE9bRO/P38PMzhbHbh2xa+vNxdDdVa7b8v+GK7WKGzl0aovv4hkcfub1OtmXxkTTqReGvGz0F88py1T2Lph160v5gdAK6Uu//RhNm65YTV+Fpm1X9NnpoNdh1udBdGcOY8hOrzSfko9mU/jGv8DMHHU733rbn9tJahamVIZG+Ez7yy+/zJIlSxq6GEIIIf7QaJqhTp8+rfxdUlJi8r5Tp04NUSQhhBB/aDTBIiwsTPk7Ly/P5L0ECyGEaFiNshnqlVdeYfHixTUnFEIIcVs0yg7u2savw4cPM2bMGC5dumSyfOPGjUybNo2NGzeSm5vLnDlzePnllzlz5gzPPfccubm5dVretLQ0wsPDq/z88uXLLFy4kClTpvDSSy+xbNkysrOzbzqfsLAwsrKyak5Yz5544gmT92FhYaxbtw6AnTt3sm/fvmrXvzF9debNm8esWbOU9+fPn2fevHnVrlPdd7F06VIOHz6svH/xxRf5+uuvlffvvvsuv/76a43lqiqv2u5XTcaMGcMHH3ygvNfpdEycOJFFixb95W1X5dtvv2Xq1KlMnz6dmTNn1vgdVqam34H4e2s0zVA3evzxx2uVLjw8nI4dOxIREcGYMWOU5bt372bt2rWYm5sTERGBp6cnzz//fH0Vl/T0dMLDwwkMDKzwWWlpKYsWLWLcuHH4+/sDcOrUKXJzc3F0vLlbCcPCwvDy8sLZ2blOyl0Tg8GAwWBAra79NcWQIUPqtAw5OTkcO3aM7t0r3q5Zmeq+iw4dOnD27Fl69uxJXl4eVlZWnD17Vvn83Llz/Oc//6lVPjqdrtq8/gpLS0uSk5MpLS3FwsKCEydO1Ot3vnPnTk6ePMk777yDjY0NhYWFJkG1turreFRHp9Oh0WhuW353skYZLLp161ZjmuLiYuLi4njjjTdYsmSJEiwWL15McXExc+bMoU+fPvz888+UlpYyc+ZMFixYoKyflpbG4sWLee+99wDjlVVxcTFjxozhypUrrFu3jtzcXCwtLXnmmWdo1qwZISEhWFtbk5CQQHZ2NmPHjiUgIIDPP/+cixcvMnPmTPr168fw4cOVfMLDw2nfvr0SKAA6d+4MGE/+58+fZ+LEiQAsWrSIESNGcNddd7Fq1SoSEhIACAoKwtXVlfPnz7NixQosLCxYsGABcXFxbNy4EZ1OR5s2bXjqqacwNzfnuecBvQ+CAAAPN0lEQVSeo0+fPsTGxqLT6Xj66afZsmULV65cYcSIEcoJ/dtvv+XgwYOUlZXRs2dPxowZQ1paGgsXLsTHx4ezZ88yc+ZM3Nzcav3d/e9//8PKyooHH3yQ+Ph4Vq9ejaWlJR07diQmJkY53levXmXBggWkpqbSs2dPxo4dW+n2HnzwQbZt21YhWOj1ejZv3szp06cpKyvjvvvuY/DgwdV+Fx06dGDTpk0AxMXF0aNHD44dO4bBYCA9PR0LCwscHR0pLS1l7dq1nD9/Ho1Gw7hx4+jcuTNhYWFER0dTWlpKSUkJpaWlJnlptdpa71dNfH19iY6OJiAggIiICPr06cNvv/0GQHx8PBs2bFCCyeTJk/H09CQ5OZmVK1dSXl6OwWBg+vTpODk5sXz5crKystDr9YwaNYrevU0fjtu+fTtvvPEGNjbGp7ttbGzo378/AM899xwLFy7E3t6e8+fPs3HjRubNm8fp06dZv349ACqVivnz51c49kOGDKnyOB4+fBi9Xk9ycjIjRoygvLyc/fv3Y25uzuzZs9FqtdX+DrVaLUlJSbRq1Ypx48Yh6l+jCBaFhYVs376dqKgopYnIwcEBf39/goODsbW1rbDO4cOH8fX1xdPTE61WS0JCAq1bt+aVV17hiSeeYOnSpQA4OjqanJBr4+OPP+app56iadOmnDt3jrVr1/LGG8bxh7Kzs3nzzTe5fPkyixcvJiAggMcee4zvvvvOpMnkmuTkZFq3bn1TxyMpKYmsrCzlxFpQUICtrS07duzgiSeeoE2bNpSWlrJy5Upee+01PD09+fDDD9m5cyfDhg0DwNXVlQULFrBhwwZWrlzJW2+9RVlZGdOmTWPIkCEcP36clJQU3nnnHQwGA0uWLOH06dO4urpy+fJlnn322Sqvsq8F32vy8/NNguE1q1at4umnn6ZDhw5s3ry5wj4uWbIEMzMzpk6dytChQ3F1da2wjfbt23P48GFOnTqFtfX1h8r27NmDjY0NCxcupKysjNdee41u3bpV+120bt2a5ORkysvLOXv2LJ06dSI1NZVLly6RmJhIhw4dAPj5Z+PT9O+99x6XLl3i7bff5r//NQ7tcvbsWd599120Wi2xsbEmeYWFhdV6v2rSp08ftm7dip+fH7///jtBQUFKsPD09GT+/PloNBpOnDjB559/zowZM9i1axcPPPAA9957L+Xl5ej1eqKjo3FycmL27NmA8bd2o6KiIoqLi/HwuLnRAb799lsmTpxIx44dKS4uxtzcvMKx/+6776o8jsnJySxZsoSysjKmTJnC448/zpIlS9iwYQP79u1j2LBh1f4OU1JSeO21126q1iv+mkYRLJYvX46Pjw/z5s1Tmmays7MJCwtj2bJlvPbaaxXWiYiIUE6MvXv3JiIi4qZPypW5VmNZtmyZsqz8hkc57777btRqNc2bNycnJ+cv51cZd3d30tLS+OSTT/Dz86Nr164V0ly+fBl3d3c8PT0B6NevHz///LNyTK6dvL29vSkuLsba2hpra2vMzc0pKCjg+PHjnDhxgpdfflnZ7ytXruDq6oqrqyvt27evsnwWFhZKMIbrNaQbFRQUUFRUpJyAAwMDiY6OVj7v3LmzciXbvHlzMjIyqjypjho1im3btpk0Tx4/fpwLFy5w6JBx4L/CwkJSUlIwM6v6X9rc3BwvLy8SEhI4d+4cDz74IKmpqcTFxZGYmKjs82+//cb99xvH82rWrBlubm6kpKQA0LVrV7RabZV53Mx+VadFixakp6cTERFRoVZVWFhISEgIV65cAYxNMWAMrNu2bSMzM5NevXrRtGlTvL292bhxI5s2baJHjx7cddddJtu61ftbOnbsyGeffUZgYCC9evXCxcWlQprqjqOPj4/yP2ljY2Py/3rhwoUaf4cBAQESKG6zRhEs0tLSKjy17ejoSHBwMHv37q2QPi8vj1OnTpGcnIxKpUL/x2OUY8eORaWq7JHoijQajbIeQFmZcZgHvV6Pra2tycnwRubm18cOqs0PzcvLy+SZkRup1WqTbVwrg1arZenSpcTExLBjxw4iIyOZPHlyzTt1g2snTbVabVJmtVqtnFyCg4MZPHiwyXppaWlYWVndVF6VqenYVFWmynTu3Jkvv/ySc+euP4VsMBgYP348vr6mTwvHxsZWm2/79u05c+YMRUVFaLVa2rVrx44dO0hKSlKa56oru6WlZZ3tV038/f2VZp+8vDxl+ZdffomPjw8zZ84kLS2N+fONgy0GBgbStm1boqOjWbBgAZMmTaJz584sXryY6OhoPv/8c7p168bo0aOVbdnY2GBlZUVqaipNmlQcZubG/9Fr/59g/N/x8/MjOjqauXPnVnpBV91x/PNxuvH/VafT1fg7rIv/UXFzGkVodnNz45tvvjG5Qyg7O5vQ0NBKr8oOHTpEv379WLlyJSEhIaxatQp3d3elml4bDg4O5ObmkpeXR1lZmXLVa2Njg7u7OwcPHgSM//BJSUnVbsva2pqioqJKPwsMDCQuLs7kqjomJoYLFy7g7u5OUlISer2ejIwM4uPjAcjNzUWv1xMQEMCjjz5KYqJxQD0rKyslH09PT9LS0pSry/3799/U8yjdunVj7969FBf/MVBhVlad1pS0Wi3W1tZKB3JERMRf2t7DDz/MN998o7z39fVl586dytXm5cuXlRpUVd8FGPstdu/eTYsWLQDjFfy5c+fIyMigefPmgPG5ngMHDijbzcjIUGpwN6opr78qKCiI0aNH4+3tbbK8sLBQ6fC+8Xmkayf8Bx54AH9/f37//XeysrKwsLCgb9++jBgxQukHu1FwcDDr1q1TmqgKCwvZvds4bIq7u7uyzrVaHMCVK1fw9vYmODiY1q1bc+nSpQrHo7bHsTK38jsU9atR1CymTp1KaGgo8+bNU05Yjo6O9OjRg5deqjgkd0REBMHBpkNU9+rVi/Dw8ArV7KqYmZkxatQo5syZY9KcA/DCCy+wZs0atm3bRnl5OX369KFly5ZVbsvb2xuNRlNpp6qFhQWzZs1iw4YNbNiwAY1GQ4sWLXjyySfx8vLC3d2dGTNm4OXlRatWxjkXsrKyWLVqlVLzeeyxxwDo378/a9asUTq4J0+ezLJly5QO7j/XEqrTrVs3Ll26pNTorKysmDJlSp1W7SdNmsRHH32EpaUlPj4+SvPMrfDz88Pe3l55P2DAANLS0njllVcAsLe3Z+bMmdV+F2AMFqmpqcr/j0ajwcHBAVdXV2XfhwwZwpo1a5g+fToajYbJkyebXAlf8+e8qmueuhUuLi488MADFZY/9NBDhISE8MMPP+Dj46Msj4yM5MCBA2g0GhwdHRk9ejTx8fFs2rQJlUqFmZlZpf1QQ4YMobi4mNmzZ2NmZoZGo1GO2+jRo1m9ejXbt2+nbdu2yjo//vgjsbGxqNVqmjVrRvfu3VGpVCbHo7bHsSo3+zsU9atRPJR37tw5mjVrho2NDSUlJYSGhpKYmEjz5s0ZOXLkXzrJiIZTXFysNBeEhoZy9epVxo8f38ClEkLcikbRDLVq1SqlLXjDhg0UFRURHByMpaUlK1eubODSiVsVHR3NzJkzmT59OmfOnGHUqFENXSQhxC1qFM1QBoNBebAmISFBGeqjY8eOJrdoir+X3r17V7inXwjx99QoahZeXl7KXU8tWrRQbsO8fPlytbdCCiGEuD0aRZ9FYWEh69ev57fffsPOzo7ExERcXFxwcXFh/Pjx0qklhBANrFEEi2uKiopITU1Fr9fj7Ox802MnCSGEqB+NKlgIIYRonBpFn4UQQojGTYKFEEKIGkmwEEIIUSMJFkIIIWokwUIIIUSNJFgIIYSokQQLIYQQNZJgIYQQokYSLIQQQtRIgoUQQogaSbAQt8X27dtRqVQ3NfVtbWzatImuXbvi4+NDt27d+M9//mMyPa8Qom5IsBC3xZYtWwgMDOSLL76os23u2LGD5cuX89NPPxEbG0t0dDS9e/cmNTW1QlqdTldn+QpxJ5JgIepdfn4+ERERrFu3ziRY6PV6Jk+ejI+PD8OHD+eBBx5g69atABw9epR+/frRo0cP7rvvPlJSUipsd8GCBbz77rs0a9YMMM6nPWHCBDp06ABAy5YtefPNNwkMDOSrr74iJiaGgIAAunbtysMPP8zVq1cB49zmR44cASAjI0MZEn/Dhg089NBDDB06lA4dOjB//vx6O0ZCNHYSLES9Cw0NZejQobRv3x5nZ2eio6MB2LZtG0lJSZw8eZK1a9dy8OBBAMrKypgyZQpbt27l6NGjTJgwgblz51bYbmxsLH5+ftXmbWVlRXh4OI8++ijjxo1j8eLFnDhxgi5dutTq5H/48GE2b95MTEwMX331lRJUhLjTSLAQ9W7Lli08+uijADz66KNs2bIFgPDwcB555BHUajUeHh4EBQUBEBcXx6lTpxg8eDC+vr68/fbbXLx4sdo8Tp48ia+vL23atOHLL79Ulv/rX/8CICcnh+zsbPr16wfAv//9b/bv319j2QcPHoyLiwvW1taMHDmS8PDwmz8AQvwDyJylol5lZmayZ88eTp06hUqlQqfToVKpWLJkCVVNpWIwGPDx8VFqGlXx8fEhOjqaoKAgunTpQkxMDM8//zxFRUVKGltb2xrLaGZmhl6vB6C4uNjkM5VKVe17Ie4UUrMQ9Wrr1q2MGzeO33//naSkJJKTk2nVqhXh4eEEBgby9ddfo9frSU1NJSwsDIAOHTqQnp5u0iwVGxtbYduzZ89mxowZJrWOGwPFjRwcHHBycuLAgQMAbNy4UalltGzZkqNHjyrlvdGuXbvIysqiqKiI0NBQ+vTp89cOiBB/U1KzEPVqy5YtzJo1y2TZqFGj+PzzzwkJCeGXX36hc+fOtG/fnl69euHg4ICFhQVbt27lhRdeICcnh/LycqZOnYqPj4/Jdh544AHS09O5//770el0ODo60rlzZ+67775Ky/Lpp58yadIkCgsLad26NevXrwdgxowZjBkzho0bNzJgwACTdQIDA3niiSeIj4/nsccew9/fvw6PjhB/HzKtqmhQ+fn5aLVaMjMz6dmzJxEREXh4eDR0sQDj3VBHjhzhww8/bOiiCNHgpGYhGtTw4cPJzs6mtLSU1157rdEECiGEKalZCCGEqJF0cAshhKiRBAshhBA1kmAhhBCiRhIshBBC1EiChRBCiBpJsBBCCFGj/wdET5XLqwQQYQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 936x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.pivot_table(data=new_df, index=\"age_group\", columns=\"wealth_segment\", values=\"profit\",aggfunc=np.sum).plot(kind='bar')\n",
    "plt.title(\"Profit Vs. Age Group in terms of Wealth Segment\\n\", fontsize=13)\n",
    "plt.xlabel(\"\\nAge Group\", fontsize=10,color=\"black\")\n",
    "plt.ylabel(\"Profit in Dollars\\n\",fontsize=10, color=\"black\")\n",
    "plt.legend(loc='upper right',fontsize=10)\n",
    "plt.xticks(rotation=0)\n",
    "\n",
    "plt.figure(figsize=[13,4])\n",
    "plt.subplot(1,2,1)\n",
    "\n",
    "plt.title(\"Profit Heatmap- Age Group Vs. Wealth Segment\\n\", fontsize=11)\n",
    "ax = sb.heatmap(rss1, annot=True, cmap=\"RdYlGn\", fmt='d')\n",
    "ax.tick_params(axis='both', which='both', length=0)\n",
    "plt.xlabel(\"\\nAge Group\", fontsize=10,color=\"black\")\n",
    "plt.ylabel(\"Wealth Segment\\n\",fontsize=10,color=\"black\")\n",
    "plt.show()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['customer_id', 'gender', 'past_3_years_bike_related_purchases',\n",
       "       'job_title', 'job_industry_category', 'wealth_segment',\n",
       "       'deceased_indicator', 'owns_car', 'tenure', 'transaction_id',\n",
       "       'product_id', 'transaction_date', 'online_order', 'order_status',\n",
       "       'brand', 'product_line', 'product_class', 'product_size',\n",
       "       'product_first_sold_date', 'address', 'postcode', 'state', 'country',\n",
       "       'property_valuation', 'age', 'profit', 'recency', 'age_group'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Yes    7111\n",
       "No     6943\n",
       "Name: owns_car, dtype: int64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df[\"owns_car\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df[\"owns_car_flag\"]=np.where(new_df[\"owns_car\"]==\"Yes\",1,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state  owns_car\n",
       "NSW    No          3647\n",
       "       Yes         3791\n",
       "QLD    No          1555\n",
       "       Yes         1555\n",
       "VIC    No          1741\n",
       "       Yes         1765\n",
       "Name: owns_car_flag, dtype: int64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.groupby([\"state\",\"owns_car\"])[\"owns_car_flag\"].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JFyig4cOHq0qVKrYzd//sxskcN5ZUJencuXM5zhgsLP/617/sHn/88ceyWCy6++67JV1fzjx48KCqV6+uiIiIHF9lypS55fcsXbq07rnnHn366ad2S6EnT55UfHy82rVrd8uvee+996pjx46aPXt2nhdYvnFZlsuXL0uS3YxHampqgS/bUqNGDY0dO1ZhYWF237e/qlKlip588kktX75cCQkJOZ5funSp7cSbP7sxm7dp0yZ16NDB1pacnKylS5eqWrVqqlOnzi3X3blzZ125ckXnz5/P9Xt7I/C1bNlSv/32m7Zt22bbNjs7O8fYKU5y+x5nZGTkmFWPioqS2Wx22hmrFy9e1IULF3K0Z2Vl6ciRI3azx97e3kpPT8/R9/Llyzlm51atWpWjX17bO+NnGCULM3xAAXl7e2vq1Km5Hjf00EMPqVy5cho6dKhmzJihq1evau7cuXkuu92ud999V9nZ2WrevLk2bNig5cuXa/r06Spfvryk60vQn3zyie6//36NHj1a9evX16VLl3Tw4EF9//33BQ5Jr776qrp06aKuXbtq+PDhSktL07Rp01SuXLmbztDl5YMPPlBUVJSaN2+ukSNH2i68fPDgQa1cuVIZGRl69NFH1apVK5UtW1bPP/+8ZsyYoUuXLmnmzJkKCgrS+fPnHXqvli1bqlu3bgoLC5PFYtGWLVu0Z8+em14/7/XXX9f+/fvVoUMHjRs3znbh5c8++0wrV67U008/rcGDB9tt06FDBy1YsECnT5+2zeRVqFBBjRo10nfffacBAwYU6POKjIxUnz591KtXL40ZM0b33nuvzGazTpw4oX//+9967bXXVK9ePQ0cOFAxMTHq0aOHZs+erYoVK+qtt97KNcgUlYsXL9oF0Bv8/f1Vv3593XXXXapRo4YmT54sDw8PeXp6atGiRTn6165dW6NHj9bChQt18eJFdevWTR4eHtq+fbsaNGigJ5544rbqPHTokNq3b6/+/furY8eOqlixohITE7V8+XL9/PPPdhelbtiwod5880198sknql27tsqUKaP69eurc+fOeu211zR79mzde++92rhxo9asWZPjvRo2bCir1aply5YpIiJCPj4+CgsLc9rPMEoQ154zAtwZ/nyW7p9lZGQYdevWzXGWrmEYxvfff29EREQYvr6+Rt26dY0PPvggz7N03333Xbttb5x9+9cz72rUqGH07ds3R7+9e/cakZGRho+Pj1GpUiVjypQptjMYb7BarUZ0dLRRs2ZNw9PT06hQoYLRpk0bY9GiRTfdz/z85z//MVq0aGH4+PgYZcuWNbp165bjDFZHz9K94eLFi8asWbOM8PBww8/Pz/Dy8jLq1atnjBo1yvjll19s/b777jsjPDzc8PHxMUJDQ40lS5bkeoa0JGPy5Mk53mf8+PFGeHi4UbZsWcPPz8+4++67jSVLljhU46VLl4xZs2YZd999t+Hj42NYLBajdevWdmdW/9mFCxeMUqVKGZUqVbJrHzVqlCHJWLVqlV37jbNg/9//+3927Te+R38+qzYrK8tYvHix0bhxY8Pb29soW7as0bhxY+PFF180zp07Z+v3yy+/GA899JDh6+trBAUFGaNGjTLeeuut2zpL15H68no95XGWbpcuXWz9du3aZbRu3drw9fU1qlSpYrz88svGu+++m+t7LFu2zAgLCzO8vLwMf39/o127dkZ8fLxhGHn/rN3Yj02bNuVZa2pqqjFjxgzj/vvvN4KDg41SpUoZ5cuXNyIjI3OcUZyYmGg89NBDhsViMSTZPrPLly8bw4YNM4KCggyLxWJ06dLFOHbsWI7fHWlpacbf/vY3o3z58oYku98XjvwMA3kxGYYDp5wBAADgjsUxfAAAAG6OwAcAAODmCHwAAABujsAHAADg5gh8AAAAbo7ABwAA4OYIfAAAAG6OwAcAAODmCHwAAABujsAHAADg5gh8AAAAbo7ABwAA4OYIfAAAAG6OwAcAAODmCHwAAABujsAHAADg5gh8AAAAbo7ABwAA4OYIfAAAAG6OwAcAAODmCHwAAABujsAHAADg5gh8AAAAbq6Uqwso7k6fPu3qEkqUoKAgJScnu7oMwKkY5ygJGOdFLyQkJM/nmOEDAABwcwQ+AAAAN0fgAwAAcHMEPgAAADdH4AMAAHBznKULAACKhStXrigrK0smk8nVpRQ7hmHIw8NDPj4+BdqewAcAAFwuIyNDklS6dGkXV1J8XblyRRkZGfL09LzlbVnSBQAALnft2jV5e3u7uoxizdvbW9euXSvQtgQ+AABQLLCUm7/b+XwIfAAAwOUIe44p6OdE4AMAAHBznLSBYqX1kv+6uoQC+6JvA1eXAABArgh8AACgWMoa2q1I38/j3S9veZvo6Gh9+umnevHFFxUdHW1rj4+P1+OPP669e/cqICCgMMssEJZ0AQAAboOPj4+WLVumlJQUV5eSJwIfAADAbWjVqpWqVaumxYsX59ln27Zt6tq1q0JDQ9WkSRNNmzatwJdYKQgCHwAAwG0wmUyaOHGiPvjgA504cSLH84mJierXr58aNWqkDRs2aP78+friiy80Z86cIquRwAcAAHCbOnbsqIiICL322ms5nnvvvfdUqVIlzZkzR3Xr1lWnTp00ceJErV69Wunp6UVSH4EPAACgEEyZMkVfffWV9uzZY9d+9OhRNWvWTGbz/8Wue++9V9euXdPx48eLpDYCHwAAQCEIDw/Xww8/rFmzZtm1G4aR5wWTi+qC0wQ+AACAQjJhwgRt375dmzdvtrXVrVtX//vf/5SdnW1r2759u7y8vFSzZs0iqYvABwAAUEhq1aqlvn37asWKFba2gQMH6o8//tDEiRN15MgRxcbGas6cORo0aJB8fX2LpC4uvOymivpilYUmcq6rKwAA4LaMHj1an376qe1x5cqV9eGHH2rmzJl64IEHVLZsWXXv3l0TJkwospoIfAAAoFgqyJ0vilpu194LCgrS4cOH7dpatGihr776qqjKyoElXQAAADdH4AMAAHBzBD4AAAA3R+ADAABwc8X+pI1r165p2rRpyszMVFZWllq0aKHevXvrjTfe0P79++Xn5ydJev7551WzZk0ZhqFVq1Zp165d8vb21vDhwxUaGipJ2rx5s9auXStJ6tGjhyIjI121WwAAAEWm2Ac+T09PTZs2TT4+PsrMzNTUqVMVHh4uSerfv79atGhh13/Xrl06c+aMli5dqiNHjmj58uWaPXu20tLStGbNGsXExEi6fmHEiIgIWSyWIt8nAACAolTsl3RNJpN8fHwkSVlZWcrKysr3NiQ7duxQ27ZtZTKZVK9ePV26dEmpqanavXu3GjduLIvFIovFosaNG2v37t1FtRsAAAAuU+wDnyRlZ2frxRdf1JAhQxQWFqa6detKkv75z39q3LhxWr16tTIyMiRJVqtVQUFBtm0DAwNltVpltVoVGBhoaw8ICJDVai3aHQEAAHCBYr+kK0lms1nz5s3TpUuXNH/+fP3666968sknVb58eWVmZurtt9/WF198oV69eskwjBzb38oNi2NjYxUbGytJiomJsQuPd5I/XF1ACXSnjhUUvVKlSjFe4PZudZz/8ccfKlXqjoglLuXt7V2g3x931CdbunRpNWzYULt371a3btdvHebp6an27dtr/fr1kq7P6CUnJ9u2SUlJkb+/vwICArR//35bu9VqVcOGDXO8R1RUlKKiomyP//xaQH4YK3BUUFAQ4wVu71bH+dWrV+Xh4eHEitzD1atX8/xcQ0JC8tyu2Ae+CxcuyMPDQ6VLl9a1a9e0d+9ePfroo0pNTZW/v78Mw1BCQoKqVasmSYqIiNA333yj1q1b68iRI/Lz85O/v7/Cw8P1z3/+U2lpaZKkPXv26Mknn3TlrgEAgHw8+o+DRfp+X/Rt4HDfkSNH6uDBg/r666/l5eVla//+++/Vr18/rVmzRs2bN3dGmQVS7ANfamqq3njjDWVnZ8swDLVs2VL33HOPZsyYoQsXLkiSatSooWeeeUaS1LRpU+3cuVOjRo2Sl5eXhg8fLkmyWCzq2bOnJk6cKEnq1asXZ+gCAIACmTlzpjp27KiFCxdqwoQJkqSLFy9q7NixGjZsWLEKe5JkMnI76A02p0+fdnUJBZI1tJurSyiQHpFzXV1Cgd3KX4Yo2VjSRUlwq+P88uXLtmvr3lCcZ/gkKS4uTv3799e6devUtGlTjR07Vj/99JNt1u/gwYOaOXOmtm/fLh8fH7Vt21bTpk1ThQoVJEn79u3T9OnT9dNPP8kwDNWoUUOvvPKKWrZsmed75vY53ZDfku4dcZYuAABAcdO2bVsNGDBAL7zwgr766it9/vnnWrp0qby8vJSYmKiePXuqUaNG+ve//61//vOfOn/+vJ5++mnbCabDhw9XSEiIvv76a23YsEGjR4+Wt7e3U2ot9ku6AAAAxdWkSZO0ZcsWPffcc5o0aZLuuusuSdLq1avVpEkT26FkkrRkyRKFhYXp559/VlhYmH7//Xe98MILqlOnjiSpVq1aTquTGT4AAIAC8vX11bBhw+Tl5aVnn33W1r53717Fx8erbt26tq/77rtPknTixAlJ0jPPPKPRo0friSee0NKlS/XLL784rU5m+AAAAG6Dh4eHzGazzOb/m0fLzs5Wp06dNGnSpBz9K1asKEkaP368evbsqY0bNyouLk4LFy7U3Llz1bt370KvkcAHAABQyMLCwrRhwwZVq1Yt3wtK165dW7Vr19bQoUP14osv6uOPP3ZK4GNJFwAAoJANHjxYVqtVw4cP165du3Ty5EnFxcVp3LhxSk9P16VLlzRlyhT98MMPOnXqlP73v/8pISHBdvvYwsYMHwAAQCELCQnRunXrFBMTo759++rq1asKCQlRZGSkPD09lZmZKavVqhdeeEFnz56Vv7+/oqKiNHXqVKfUw3X4boLr8BUtrsOHkoDr8KEkKIzr8CEnrsMHAACAXBH4AAAA3ByBDwAAwM0R+AAAANwcgQ8AALgc55A6pqCfE4EPAAAUC4S+/N3O50PgAwAALufl5aWrV6+6uoxi7erVq/Ly8irQtlx4GQAAuJynp6eysrJ06dIlmUwmV5dT7BiGIQ8PD3l6ehZoewIfAAAoFnx8fFxdgttiSRcAAMDNEfgAAADcHIEPAADAzRH4AAAA3ByBDwAAwM0R+AAAANwcgQ8AAMDNEfgAAADcHIEPAADAzRX7O21cu3ZN06ZNU2ZmprKystSiRQv17t1bSUlJWrx4sdLS0lSrVi2NHDlSpUqVUkZGhv7+97/r2LFjKlOmjKKjo1WxYkVJ0ueff66NGzfKbDZr8ODBCg8Pd/HeAQAAOF+xn+Hz9PTUtGnTNG/ePM2dO1e7d+/W4cOH9eGHH6pLly5aunSpSpcurY0bN0qSNm7cqNKlS+v1119Xly5d9I9//EOSdOrUKcXHx2vhwoWaPHmyVqxYoezsbFfuGgAAQJEo9oHPZDLZ7q2XlZWlrKwsmUwm7du3Ty1atJAkRUZGKiEhQZK0Y8cORUZGSpJatGihn3/+WYZhKCEhQa1atZKnp6cqVqyo4OBgHT161CX7BAAAUJSK/ZKuJGVnZ+ull17SmTNn9OCDD6pSpUry8/OTh4eHJCkgIEBWq1WSZLVaFRgYKEny8PCQn5+fLl68KKvVqrp169pe88/b/FlsbKxiY2MlSTExMQoKCnL27jnFH64uoAS6U8cKil6pUqUYL3B7jPPi5Y4IfGazWfPmzdOlS5c0f/58/f7773n2NQwjR5vJZMq1PTdRUVGKioqyPU5OTr71glEiMVbgqKCgIMYL3B7jvOiFhITk+VyxX9L9s9KlS6thw4Y6cuSILl++rKysLEnXZ/UCAgIkSYGBgUpJSZF0fQn48uXLslgsdu1/3QYAAMCdFfvAd+HCBV26dEnS9TN29+7dqypVqqhRo0batm2bJGnz5s2KiIiQJN1zzz3avHmzJGnbtm1q1KiRTCaTIiIiFB8fr4yMDCUlJSkxMVF16tRxyT4BAAAUpWK/pJuamqo33nhD2dnZMgxDLVu21D333KOqVatq8eLF+vjjj1WrVi116NBBktShQwf9/e9/18iRI2WxWBQdHS1Jqlatmlq2bJGs7GkAACAASURBVKkxY8bIbDbr6aefltlc7PMuAADAbTMZjh7cVkKdPn3a1SUUSNbQbq4uoUB6RM51dQkF9kXfBq4uAXcIjm1CScA4L3pucwwfAAAAbh2BDwAAwM0R+AAAANwcgQ8AAMDNEfgAAADcHIEPAADAzRH4AAAA3ByBDwAAwM0R+AAAANwcgQ8AAMDNEfgAAADcHIEPAADAzRH4AAAA3ByBDwAAwM05FPi2bt2qkydPSpKSkpI0aNAgDRkyRMnJyU4tDgAAALfPocD33HPPKSMjQ5L00ksv6ffff9cff/yh559/3qnFAQAA4PaVcqTTb7/9pjp16sgwDH311Vfat2+f/Pz8FBoa6uz6AAAAcJscCnweHh5KT0/XgQMHFBwcrIoVKyo7O1vp6enOrg8AAAC3yaHA1759e/Xu3VspKSnq3r27JOnw4cMKDg52anEAAAC4fQ4dw/fuu++qcePGevDBBzVp0iRJ0tGjRzVixAinFgcAAIDbd9MZvszMTE2ePFkLFiyQj4+Prb1r165OLQwAAACF46YzfKVKldLHH38sb2/voqgHAAAAhcyhJd1HH31Un332mbNrAQAAgBM4dNLGtWvX1K9fP7311luqWbOmzOb/y4nvvPOO04oDAADA7XMo8Hl6eqpPnz6SpKysLGVlZTm1KAAAABQehwLfqlWrnF1HnpKTk/XGG2/o3LlzMplMioqK0sMPP6x//etf+u6771S2bFlJUp8+fdSsWTNJ0ueff66NGzfKbDZr8ODBCg8PlyTt3r1bq1atUnZ2tjp27Gi7xAwAAIA7cyjw3ZCenq7k5GQZhmFrq169eqEX9WceHh7q37+/QkNDlZ6ergkTJqhx48aSpC5duqhbt252/U+dOqX4+HgtXLhQqampevXVV7VkyRJJ0ooVKzRlyhQFBgZq4sSJioiIUNWqVZ1aPwAAgKs5FPiOHTumfv366ccff8zxnLOXd/39/eXv7y9J8vX1VZUqVWS1WvPsn5CQoFatWsnT01MVK1ZUcHCwjh49KkkKDg5WpUqVJEmtWrVSQkICgQ8AALg9hwLfiBEjVK1aNb3zzjtq06aNtm7dqqlTp+qRRx5xdn12kpKSdPz4cdWpU0cHDx7Uhg0bFBcXp9DQUA0YMEAWi0VWq1V169a1bRMQEGALiIGBgbb2wMBAHTlyJMd7xMbGKjY2VpIUExOjoKAgJ++Vc/zh6gJKoDt1rKDolSpVivECt8c4L14cCnw//vijTpw4oTJlykiSGjVqpLffflvt2rXToEGDnFmfzZUrV7RgwQINGjRIfn5+euCBB9SrVy9J0ieffKL3339fw4cPt1tu/rPc2k0mU462qKgoRUVF2R4nJycX0h7A3TFW4KigoCDGC9we47zohYSE5PmcQ9fhM5vN8vX1lSRZLBadO3dOAQEB+vXXXwunwpvIzMzUggULdP/99+u+++6TJJUvX15ms1lms1kdO3bUL7/8Iun6zF1KSoptW6vVqoCAgBztKSkptqViAAAAd+ZQ4GvUqJG2bt0qSbrvvvs0evRojRo1SrVq1XJqcdL1mbm33npLVapUsbudW2pqqu3f27dvV7Vq1SRJERERio+PV0ZGhpKSkpSYmKg6deqodu3aSkxMVFJSkjIzMxUfH6+IiAin1w8AAOBqDi3pLl261Lb8OW/ePA0bNkwXLlzQ22+/7dTiJOnQoUOKi4tT9erV9eKLL0q6fgmWrVu36sSJEzKZTKpQoYKeeeYZSVK1atXUsmVLjRkzRmazWU8//bTtQtFPPfWUZs2apezsbLVv394WEgEAANyZycjroDdIkk6fPu3qEgoka2i3m3cqhnpEznV1CQX2Rd8Gri4BdwiObUJJwDgvegU+hm/nzp16+eWXc31u6tSp2rNnz+1VBgAAAKfLN/DNnTtXDRrkPmvRsGFDxcTEOKUoAAAAFJ58A9+2bdv02GOP5fpct27d9MMPPzilKAAAABSefANfSkqK7XIsf+Xj42N3mRMAAAAUT/kGvsDAQB06dCjX5w4fPsx17AAAAO4A+Qa+hx56SOPHj89xv9ysrCxNnDjR7rp4AAAAKJ7yvQ7ftGnT1Lx5c9199916/PHHVaVKFf3+++9as2aN0tLSlJCQUFR1AgAAoIDyDXzBwcHavn27Xn75Za1YsUJnz55VhQoV9PDDD2vGjBmqVKlSUdUJAACAArrpnTYqV66s5cuXF0UtAAAAcAKH7qULAACAOxeBDwAAwM0R+AAAANxcnoHv888/t/07IyOjSIoBAABA4csz8A0cOND278DAwCIpBgAAAIUvz7N0y5Urpw0bNigsLEzZ2dlKTEyUYRg5+oWEhDi1QAAAANyePAPfzJkz1bNnT6Wnp0uSqlatave8YRgymUw57sIBAACA4iXPwDdw4ED169dPiYmJatCggfbt21eUdQEAAKCQ5HvhZQ8PD1WtWlWxsbGqUaNGUdUEAACAQnTTO21IUosWLZSQkKCVK1fqt99+U7Vq1fTUU0+pefPmzq4PAAAAt8mh6/CtW7dObdu21fnz59W0aVNduHBB7dq1s7t0CwAAAIonh2b4ZsyYoc8++0wPP/ywre0///mPJkyYoMcee8xpxQEAAOD2OTTDd+LECXXu3Nmu7cEHH9TJkyedUhQAAAAKj0OBr0aNGoqNjbVr++6771S9enWnFAUAAIDC49CS7ssvv6xHH31UvXr1UmhoqI4fP67PPvtM7733nrPrAwAAwG1yaIavZ8+e2rhxo/z8/JSQkCBfX1/FxsaqV69ezq4PAAAAt8mhGT5JatmypVq2bOnMWnKVnJysN954Q+fOnZPJZFJUVJQefvhhpaWladGiRTp79qwqVKig0aNHy2KxyDAMrVq1Srt27ZK3t7eGDx+u0NBQSdLmzZu1du1aSVKPHj0UGRlZ5PsDAABQ1BwOfK7i4eGh/v37KzQ0VOnp6ZowYYIaN26szZs3KywsTN27d9e6deu0bt069evXT7t27dKZM2e0dOlSHTlyRMuXL9fs2bOVlpamNWvWKCYmRpI0YcIERUREyGKxuHgPAQAAnMuhJV1X8vf3t83Q+fr6qkqVKrJarUpISFC7du0kSe3atVNCQoIkaceOHWrbtq1MJpPq1aunS5cuKTU1Vbt371bjxo1lsVhksVjUuHFj7d6922X7BQAAUFSK/QzfnyUlJen48eOqU6eOzp8/L39/f0nXQ+GFCxckSVarVUFBQbZtAgMDZbVaZbVaFRgYaGsPCAiQ1WrN8R6xsbG2M5JjYmLsXutO8oerCyiB7tSxgqJXqlQpxgvcHuO8eLlp4MvMzNTatWv16KOPytvbuyhqytWVK1e0YMECDRo0SH5+fnn2MwwjR5vJZMq1b27tUVFRioqKsj1OTk4uQLUoiRgrcFRQUBDjBW6PcV70QkJC8nzupku6pUqV0tNPP+3SsJeZmakFCxbo/vvv13333SdJKleunFJTUyVJqampKlu2rKTrM3p/HmApKSny9/dXQECAUlJSbO1Wq9U2QwgAAODOHDqGr3nz5vrpp5+cXUuuDMPQW2+9pSpVqqhr16629oiICG3ZskWStGXLFjVv3tzWHhcXJ8MwdPjwYfn5+cnf31/h4eHas2eP0tLSlJaWpj179ig8PNwl+wQAAFCUHDqGr3379nrkkUf0zDPPqEaNGjKb/y8nPvnkk04rTpIOHTqkuLg4Va9eXS+++KIkqU+fPurevbsWLVqkjRs3KigoSGPGjJEkNW3aVDt37tSoUaPk5eWl4cOHS5IsFot69uypiRMnSpJ69erFGboAAKBEMBm5HfT2F7Vq1cp9Y5NJx44dK/SiipPTp0+7uoQCyRrazdUlFEiPyLmuLqHAvujbwNUl4A7BsU0oCRjnRS+/Y/gcmuE7fvx4oRUDAACAonVL1+E7ffq0tm3b5qxaAAAA4AQOBb6kpCRFRUWpatWqtkuWfPLJJ7bj4wAAAFB8ORT4Ro0apVq1auns2bPy9PSUJHXo0EHffvutU4sDAADA7XPoGL5Nmzbp5MmT8vHxsV2suEKFCjp79qxTiwMAAMDtc2iGz9vbW5mZmXZtVqtVAQEBTikKAAAAhcehwPfAAw9o7NixysjIsLVNnz5dXbp0cVphAAAAKBwOLenOnTtX3bt3l7+/v65cuaLy5csrPDxc69atc3Z9AAAAuE0OBb6AgADFxcVpx44dOnHihGrUqKGIiAjb8XwAAAAovhwKfDdERESoZs2aCgoKclY9AAAAKGQOBb7Lly9rzJgxev/993X16lV5e3trwIABWrBggUqXLu3sGgEAwB2m9ZL/urqEAnPHW2U6dNLG888/r71792r9+vU6fPiwvvzyS+3bt08jRoxwdn0AAAC4TQ7N8K1fv14HDhxQhQoVJEm1a9dW48aNdddddzm1OAAASrqsod1cXULBRM51dQX4E4dm+CwWi3x9fe3afH19VaZMGacUBQAAgMLjUOB75ZVX9NRTT+nEiRPKzs7W8ePHNXToUL366qvOrg8AAAC3Kc8lXU9PT7vLrmRmZuqzzz6zPTYMQ2vXrlX//v2dWyEAAABuS56BLzY2tijrAAAAgJPkGfjatWtXlHUAAADASRy+8PK2bduUkJCgixcv2rVPmjSp0IsCAABA4XEo8E2ZMkXz589XkyZN5OfnZ2s3mUwEPgAAgGLOocD39ttva/v27WrcuLGz6wEAAEAhc+iyLL6+vmrYsKGzawEAAIATOBT4xowZo5kzZzq7FgAAADiBQ0u6jz/+uDp06KDFixerYsWKds8dPnzYKYUBAACgcDgU+J544glVrVpV0dHRdidtAAAAoPhzKPDt3r1bycnJ8vHxcXY9Obz55pvauXOnypUrpwULFkiS/vWvf+m7775T2bJlJUl9+vRRs2bNJEmff/65Nm7cKLPZrMGDBys8PNy2D6tWrVJ2drY6duyo7t27F/m+AChcd+pN5VvfwTeV/6JvA1eXAKAAHAp8d911l1JTU1W5cmVn15NDZGSkOnfurDfeeMOuvUuXLurWzf6X/alTpxQfH6+FCxcqNTVVr776qpYsWSJJWrFihaZMmaLAwEBNnDhRERERqlq1apHtBwAAgKs4FPgGDRqknj17aty4cQoODrZ7rlWrVk4p7IaGDRsqKSnJob4JCQlq1aqVPD09VbFiRQUHB+vo0aOSpODgYFWqVEnS9ZoTEhIIfAAAoERwKPCNHDlSktSrVy+7dpPJpKysrMKvygEbNmxQXFycQkNDNWDAAFksFlmtVtWtW9fWJyAgQFarVZIUGBhoaw8MDNSRI0dyfd3Y2FjbfYRjYmIUFBTkxL1wnj9cXUAJdKeOlTsZ47zoMc6LHuO86LnjOHco8GVnZzu7jlvywAMP2MLnJ598ovfff1/Dhw+XYRi59s+t3WQy5do3KipKUVFRtsfJycmFUDFKAsYKSgLGOUqCO3Wch4SE5PmcQ9fhK27Kly8vs9kss9msjh076pdffpF0feYuJSXF1s9qtSogICBHe0pKivz9/Yu8bgAAAFdwaIavU6dOec6Iffvtt4VakCNSU1NtgW379u2qVq2aJCkiIkJLly5V165dlZqaqsTERNWpU0eGYSgxMVFJSUkKCAhQfHy8Ro0aVeR1AwAAuIJDga9NmzZ2j0+fPq01a9Zo0KBBzqjJzuLFi7V//35dvHhRw4YNU+/evbVv3z6dOHFCJpNJFSpU0DPPPCNJqlatmlq2bKkxY8bIbDbr6aefltl8fRLzqaee0qxZs5Sdna327dvbQiIAAIC7cyjwTZs2LUdb//799frrrxd6QX8VHR2do61Dhw559u/Ro4d69OiRo71Zs2a2a/UBAACUJAU+hq9169b65ptvCrMWAAAAOIFDM3x/lZGRoeXLl7vlacsAAADuxqHA5+npaXfSRlZWliwWi1atWuW0wgAAAFA4HAp8Ny5EfIPFYlH9+vVlsVicUhQAAAAKj0OBr127ds6uAwAAAE6Sb+B7//33b/oCAwYMKLRiAAAAUPjyDXyvvvpqru0mk0lnz57VhQsXCHwAAADFXL6B78iRIznarFarXnnlFb3zzjsaOHCg0woDAABA4XD4OnwZGRmaP3++6tSpo4MHD2rbtm1auXKlM2sDAABAIXDopI2PP/5YEydOVJkyZfTRRx+pc+fOzq4LAAAAhSTfwPff//5X48aN06lTp/TKK69o0KBBtnvTAgAA4M6Qb+Br27atgoKCNHToUJ05c0YxMTE5+kyaNMlpxQEAAOD23TTwmUwmxcfH5/q8yWQi8AEAABRz+Qa+zZs3F1EZAAAAcBYOyAMAAHBzBD4AAAA3R+ADAABwcwQ+AAAAN0fgAwAAcHMEPgAAADdH4AMAAHBzBD4AAAA3R+ADAABwcwQ+AAAAN0fgAwAAcHP53ku3OHjzzTe1c+dOlStXTgsWLJAkpaWladGiRTp79qwqVKig0aNHy2KxyDAMrVq1Srt27ZK3t7eGDx+u0NBQSdfvC7x27VpJUo8ePRQZGemqXQIAAChSxX6GLzIyUpMmTbJrW7duncLCwrR06VKFhYVp3bp1kqRdu3bpzJkzWrp0qZ555hktX75c0vWAuGbNGs2ePVuzZ8/WmjVrlJaWVuT7AgAA4ArFPvA1bNhQFovFri0hIUHt2rWTJLVr104JCQmSpB07dqht27YymUyqV6+eLl26pNTUVO3evVuNGzeWxWKRxWJR48aNtXv37iLfFwAAAFco9oEvN+fPn5e/v78kyd/fXxcuXJAkWa1WBQUF2foFBgbKarXKarUqMDDQ1h4QECCr1Vq0RQMAALhIsT+G71YYhpGjzWQy5do3r/bY2FjFxsZKkmJiYuwC5J3kD1cXUALdqWPlTsY4L3qM86LHOC967jjO78jAV65cOaWmpsrf31+pqakqW7aspOszesnJybZ+KSkp8vf3V0BAgPbv329rt1qtatiwYa6vHRUVpaioKNvjP78ekB/GCkoCxjlKgjt1nIeEhOT53B25pBsREaEtW7ZIkrZs2aLmzZvb2uPi4mQYhg4fPiw/Pz/5+/srPDxce/bsUVpamtLS0rRnzx6Fh4e7chcAAACKTLGf4Vu8eLH279+vixcvatiwYerdu7e6d++uRYsWaePGjQoKCtKYMWMkSU2bNtXOnTs1atQoeXl5afjw4ZIki8Winj17auLEiZKkXr165TgRBAAAwF0V+8AXHR2da/vUqVNztJlMJg0ZMiTX/h06dFCHDh0KtTYAAIA7wR25pAsAAADHEfgAAADcHIEPAADAzRH4AAAA3ByBDwAAwM0R+AAAANwcgQ8AAMDNEfgAAADcHIEPAADAzRH4AAAA3ByBDwAAwM0R+AAAANwcgQ8AAMDNEfgAAADcHIEPAADAzRH4AAAA3ByBDwAAwM0R+AAAANwcgQ8AAMDNEfgAAADcHIEPAADAzRH4AAAA3ByBDwAAwM0R+AAAANwcgQ8AAMDNlXJ1Abfj+eefl4+Pj8xmszw8PBQTE6O0tDQtWrRIZ8+eVYUKFTR69GhZLBYZhqFVq1Zp165d8vb21vDhwxUaGurqXQAAAHC6OzrwSdK0adNUtmxZ2+N169YpLCxM3bt317p167Ru3Tr169dPu3bt0pkzZ7R06VIdOXJEy5cv1+zZs11YOQAAQNFwuyXdhIQEtWvXTpLUrl07JSQkSJJ27Nihtm3bymQyqV69erp06ZJSU1NdWSoAAECRuONn+GbNmiVJ6tSpk6KionT+/Hn5+/tLkvz9/XXhwgVJktVqVVBQkG27wMBAWa1WW18AAAB3dUcHvldffVUBAQE6f/68Zs6cqZCQkDz7GoaRo81kMuVoi42NVWxsrCQpJibGLiTeSf5wdQEl0J06Vu5kjPOixzgveozzoueO4/yODnwBAQGSpHLlyql58+Y6evSoypUrp9TUVPn7+ys1NdV2fF9gYKCSk5Nt26akpOQ6uxcVFaWoqCjb4z9vA+SHsYKSgHGOkuBOHef5TXzdscfwXblyRenp6bZ///TTT6pevboiIiK0ZcsWSdKWLVvUvHlzSVJERITi4uJkGIYOHz4sPz8/lnMBAECJcMfO8J0/f17z58+XJGVlZalNmzYKDw9X7dq1tWjRIm3cuFFBQUEaM2aMJKlp06bauXOnRo0aJS8vLw0fPtyV5QMAABSZOzbwVapUSfPmzcvRXqZMGU2dOjVHu8lk0pAhQ4qiNAAAgGLljl3SBQAAgGMIfAAAAG6OwAcAAODmCHwAAABujsAHAADg5gh8AAAAbo7ABwAA4OYIfAAAAG6OwAcAAODmCHwAAABujsAHAADg5gh8AAAAbo7ABwAA4OYIfAAAAG6OwAcAAODmCHwAAABujsAHAADg5gh8AAAAbo7ABwAA4OYIfAAAAG6OwAcAAODmCHwAAABujsAHAADg5gh8AAAAbo7ABwAA4OZKubqAorZ7926tWrVK2dnZ6tixo7p37+7qkgAAAJyqRM3wZWdna8WKFZo0aZIWLVqkrVu36tSpU64uCwAAwKlKVOA7evSogoODValSJZUqVUqtWrVSQkKCq8sCAABwqhIV+KxWqwIDA22PAwMDZbVaXVgRAACA85WoY/gMw8jRZjKZ7B7HxsYqNjZWkhQTE6OQkJAiqa3Qfb3D1RUUCPOtuCWMc5QEjHMUghI1wxcYGKiUlBTb45SUFPn7+9v1iYqKUkxMjGJiYoq6PEiaMGGCq0sAnI5xjpKAcV68lKjAV7t2bSUmJiopKUmZmZmKj49XRESEq8sCAABwqhK1pOvh4aGnnnpKs2bNUnZ2ttq3b69q1aq5uiwAAACnKlGBT5KaNWumZs2auboM5CEqKsrVJQBOxzhHScA4L15MRm5nMgAAAMBtlKhj+AAAAEqiErekC9fo3bu3unbtqgEDBkiSvvzyS125ckW9e/fW6dOn9c477+jSpUvKzMxUgwYN9Oyzz2r8+PEaPny4atasqaysLA0aNEhDhw5V27ZtJUkvvfSSnn32WYWGhrpy1wA7KSkpWrFihU6dOqXs7Gw1bdpUAwYM0OHDh7V+/focZy5Onz5dqamp8vT0VGZmpsLCwvS3v/1NpUuXdtEeAI6ZPn26unfvrvDwcFvb119/rZ9++knJyclasGCBpOs3Pfjggw907tw5mUwmNWjQQIMHD5a3t7erSi+RmOFDkfD09NSPP/6oCxcu5Hhu1apV6tKli+bNm6dFixbpoYcekiTVq1dPhw4dkiSdPHlSISEhOnz4sCTpypUrSkpKUs2aNYtsH4CbMQxD8+fPV/PmzbV06VItXbpU165d04cffpjvdqNGjdL8+fM1f/58eXp6au7cuUVUMVBwrVu3Vnx8vF1bfHy83T3qz507p4ULF6pv375asmSJFi1apPDwcKWnpxd1uSUegQ9Fwmw2KyoqSl9//XWO51JTU+3ugFK9enVJUoMGDWyB79ChQ+rUqZNOnDgh6fpfjLVq1ZLZzBBG8fHzzz/Ly8tL7du3l3R93A8cOFBxcXG6cuXKTbcvVaqU+vXrp+TkZNtYB4qrFi1a6H//+58yMjIkSUlJSTnuaLVhwwa1a9dO9erVk3T9ZgctWrRQ+fLlXVJzScb/ligyDz74oP773//q8uXLdu1dunTRjBkzNHv2bH311Ve6dOmSpOszfDdm9A4dOqS77rpLnp6eSk9P1+HDh1W/fv0i3wcgP7/99ptq1apl1+bn56cKFSrozJkzDr2G2WxWjRo1dPr0aWeUCBSaMmXKqE6dOtq9e7ek67N7rVq1suvz22+/cdhNMUHgQ5Hx8/NT27Zt9e9//9uuvX379lq0aJFatGih/fv3a/LkycrIyFDFihWVmZmpc+fO6fTp0woJCVHt2rV15MgRHTp0iMCHYumvt2uUcr+tI+AOWrdura1bt0qStm7dqtatW7u4IuSFwIci1aVLF23atElXr161aw8ICFCHDh00fvx4eXh46LfffpN0fZZv27Zt8vf3l8lkUt26dXXo0CEdPXpUdevWdcUuAHmqWrWqjh07Ztd2+fJlnT9/3uH7cmdnZ+vXX39VlSpVnFEiUKjuvfde/fzzzzp27JiuXbuWYzYvt58JuAaBD0XKYrGoZcuW2rhxo61t9+7dyszMlHT9AN+LFy8qICBAklS/fn19/fXXtnBXr149xcXFqXz58pzFiGInLCxMV69e1ZYtWyRdD2/vv/++OnfuLC8vr5tun5mZqY8++kiBgYGqUaOGs8sFbpuPj48aNmyoZcuW5Tq717lzZ23ZskVHjhyxtcXFxencuXNFWSbEZVngAl27dtU333xje7xnzx6tWrXK9h9iv379bAf01q9fX++9957tgF9/f39lZ2eznItiyWQyady4cVqxYoU+++wzXbhwQa1atVKPHj20b98+7d27V8OGDbP1HzNmjCRp6dKl8vT0VEZGhsLCwjR+/HhX7QJwy9q0aaP58+crOjo6x3Ply5dXdHS0PvjgA50/f15ms1l33XWX7rvvPhdUWrJxpw0AcJJDhw5pyZIlGjduHAeuA3ApAh8AAICb4xg+AAAAN0fgAwAAcHMEPgAAADdH4AMAAHBzBD4AAAA3R+ADAABwcwQ+AAAAN0fgAwAAcHMEPgAAADdH4AMAAHBzBD4AAAA3R+ADAABwcwQ+AAAAN0fgAwAAcHMEPgAAADdH4AMAAHBzBD4AAAA3R+ADAABwcwQ+AAAAN0fgA4D/387dx1Rd/QEcf18QLpfLwwWURxEfybgbC1tkLh6ECzEHa4LkMjZMw1Er7WE021oZFmtJgPWPa2sZllmYtkxqyZMQKSOcObjN6koEKPEU3suDAdfz+8N59+MXF/Mnzv34fV4bf9zvOedzPt9zB/ezc76XGzAajXz66ad3Oo075rvvvkOj0dzpNIQQt0AKPiHErLtw4QLZ2dkEBwfj5eVFeHg469evZ3x8HID9+/ezfPnym4pZgr6B1wAACalJREFUV1fHvHnzbimvvXv3/m3ed955B41GwzfffOO4NjY2hoeHB8eOHQOgra2NjRs33tLczly9epWSkhKMRiOenp4EBASQlZWF2Wy+LfPdLpcuXeLJJ58kIiICvV7PokWLeOSRR2hpabnTqQkhkIJPCHEbrFu3jpCQEM6fP4/NZuPUqVM89NBDKKXuaF4mkwmLxUJHR4fjWk1NDUajkerqase1xsZG7HY7iYmJtz2nxx9/nJKSEkpLSxkaGqK1tZWgoCDuv/9+zp07d9vnnw0XL17kvvvuo7Ozk8rKSqxWK2azmYyMDI4cOfJfxZyYmJjlLIX4P6eEEGIW9ff3K0C1tbVN2/79998rrVarNBqN0uv1Sq/Xq9raWjUyMqLWr1+vgoKClLe3t4qJiVHffvutUkqp7u5u5eHhoQDHmP379yullOro6FBZWVkqODhYBQcHq7y8PGW1Wp3mFxISot5//32llFKTk5PKYDCoiooKFRMT4+izc+dOtWbNGsfriIgIdeDAAaWUUoODg2rDhg3K399f+fj4KKPRqOrr6x19jx49qlatWqV8fX3VypUr1UcffeQ0l4aGBgWourq6v7UlJiaq5ORkpZRSxcXFKjU11dGWk5OjtFqtGh0dVUopdejQIRUVFaWUUuqDDz5Qy5YtU3v37lVhYWHKYDCobdu2qcnJScf4G63Zzz//rBISEpSXl5eKjo5WpaWlaqaPi61bt6rIyEg1Pj7utM/Zs2dVfHy8CggIUAaDQaWlpalff/3V0Z6bm6s2bdqkNm/erPz8/FR+fr7TWEKImyc7fEKIWRUQEIDRaOSJJ56gvLwcs9k8ZWfvgQceYN++fSxdupTh4WGGh4dJTEzk6tWrZGZm8ssvvzAwMMCjjz5KVlYWfX19hIaG8vXXX+Pq6uoYk5uby5UrV0hKSiIqKooLFy5gNpvp6upix44dTvNLSkpy7Oa1tLQQHBzMww8/jMViYWBgAIDq6mpMJtO04/fs2cPo6CgdHR0MDQ1x5MgRFi5cCMCJEyfYunUrZWVlDA4O8uGHH/L0009TX18/bazKykoWLlxIQkLC39pycnKoq6tjbGwMk8lEQ0MDf/31F3BtVzI8PJyGhgYAqqqqpuTb0dHBH3/8gcViobm5mYqKCg4dOgRwwzWbnJwkIyMDo9FIb28vhw8fZt++fU7X8/p9ZGdn4+bm5rSPRqNh165ddHd389tvv+Hl5UVOTs6UPhUVFaSlpdHX18fbb78945xCiJsjBZ8QYtbV1dWRmJhIWVkZ99xzD0FBQezevXvGI93rBYC3tzdubm4UFBTg7u5Oc3Oz0zFfffUVSikKCwvR6XT4+fmxe/duPv74Y+x2+7RjTCYTNTU1wLXCLikpCTc3N9asWUNtbS2XL1/mzJkzTgs+d3d3BgYGOH/+PEopIiMjWbJkCXDtGcEdO3YQFxeHi4sLsbGx5OTkUF5ePm2svr4+wsLCpm0LDQ3FbrczODhIdHQ03t7eNDY20tbWhlarZcuWLZw4ccJxH/+er06no7CwEK1Wy/Lly0lOTuaHH374R2vW1NREe3s7e/bsQafTsWLFCl544QWn78GN7uO66Oho1q5di1arxdfXl1dffZXTp08zMjLi6PPggw+yceNGXF1d8fT0nDGeEOLm3NoT0EIIMY358+dTVFREUVERo6OjfPbZZ+Tl5REWFsaWLVumHTM2NsaLL77I8ePH6e/vx8XFBZvNRl9fn9N52tvb+f333zEYDFOuazQaenp6pi1CkpOT6enpwWw2U1NTQ35+PgBr166lpqYGNzc3dDodq1evnnbOgoICJiYmyM3N5dKlS6Snp/PWW28RFBREe3s7tbW1lJSUOPrb7Xbi4uKmjbVgwQK6u7unbbt48SKurq74+/uj0WhISkqiqqqKoKAgUlJSMJlM5OXlYbFY6OzsnPK8YWBgIK6uro7Xer0em832j9asq6uLwMDAKQXX9YLWmZnu4zqLxUJBQQFNTU3YbDbHt377+/vR6/UALF68eMYYQoj/nuzwCSFuK09PTzZv3kx0dDRnz54FwMXl7396SkpKOHnyJNXV1Vy+fJmhoSH8/Pwcu4LTjYmIiCAyMpKhoaEpP1euXHG64xQeHk5kZCTHjx/n1KlTjkLp+lFvdXU18fHxTo8n9Xo9b7zxBq2trbS1tdHd3U1BQYEjn127dk3JxWazUVlZOW2stLQ0urq6HEez/+7gwYMkJCSg0+mAazuTVVVVVFVVkZKSwr333ktnZycHDx4kNjYWb2/vaee42TULCwujt7eX0dFRx5j29vYZY65bt47Dhw/P+EWL/Px8vL29OXfuHFarlcbGRoApu77TvcdCiNkhv11CiFn1559/8tJLL9Ha2srExASTk5N8/vnntLa2Ona6goOD6e3txWq1OsZZrVa0Wi0BAQGMj49TWFjI0NCQoz04OBi73T6l+EhPT2diYoKioiJsNhtKKbq7uzl69OiMOSYnJ1NSUsKKFSsICAgAYNWqVfT29lJRUeH0OBfg2LFj/PTTT9jtdry8vPDw8HD8u5hnn32WsrIyGhoasNvtjI+P09LS4jhO/U/x8fFs2rSJxx57jKqqKsbHx+np6eGZZ56hqamJ4uJiR9+UlBTOnDlDfX09SUlJuLi4kJCQQHFx8Yz5/qcbrdnq1auJiIhg586djI2NYbFYKC0tnTHma6+9xvDwMBs2bHCszcjICJ988gkvv/wycO391ev1GAwG+vv7eeWVV/5xzkKIWycFnxBiVrm7u9Pb20tmZib+/v4sWLCA119/nXfffZfs7Gzg2m5aSkoKS5YswWAwcPLkSZ5//nkMBgOhoaEsW7YMT0/PKUd8kZGRPPXUU8TGxmIwGDhw4ACenp5UV1djNptZuXIlvr6+JCcnO3YSnTGZTPT09JCUlOS45uLiQnx8PD09PTMWUBaLhYyMDHx8fFi8eDE6nY4333wTgNTUVN577z0KCgqYP38+ISEhPPfccwwPDzuNV15ezvbt29m+fTsGg4GoqCi6uro4ffo0MTExjn6LFi1i6dKl3HXXXfj7+zvuw2q13lTBd6M1mzdvHl9++SU//vgjgYGBZGZmsm3bthljhoWF0dzcTEhICKmpqfj4+HD33XfzxRdfkJWVBUBpaSkNDQ34+PgQFxdHenr6P85ZCHHrNGqmp6iFEEIIIcT/PNnhE0IIIYSY46TgE0IIIYSY46TgE0IIIYSY46TgE0IIIYSY46TgE0IIIYSY46TgE0IIIYSY46TgE0IIIYSY46TgE0IIIYSY46TgE0IIIYSY4/4Fv+FyxIscXmMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.pivot_table(data=new_df, index=\"state\", columns=\"owns_car\", values=\"owns_car_flag\",aggfunc='count').plot(kind='bar',figsize=[10,5])\n",
    "plt.title(\"Number of Cars Owned in Each State\\n\", fontsize=16)\n",
    "plt.xlabel(\"\\nState Wise Owned Car\", fontsize=13,color=\"black\")\n",
    "plt.ylabel(\"Number of Cars\\n\",fontsize=13, color=\"black\")\n",
    "plt.legend(loc='upper right',fontsize=14)\n",
    "plt.xticks(rotation=0)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['customer_id', 'gender', 'past_3_years_bike_related_purchases',\n",
       "       'job_title', 'job_industry_category', 'wealth_segment',\n",
       "       'deceased_indicator', 'owns_car', 'tenure', 'transaction_id',\n",
       "       'product_id', 'transaction_date', 'online_order', 'order_status',\n",
       "       'brand', 'product_line', 'product_class', 'product_size',\n",
       "       'product_first_sold_date', 'address', 'postcode', 'state', 'country',\n",
       "       'property_valuation', 'age', 'profit', 'recency', 'age_group',\n",
       "       'owns_car_flag'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "job_industry_category\n",
       "Argiculture            485\n",
       "Entertainment          625\n",
       "Financial Services    3341\n",
       "Health                2733\n",
       "IT                     584\n",
       "Manufacturing         3432\n",
       "Property              1091\n",
       "Retail                1451\n",
       "Telecommunications     312\n",
       "Name: past_3_years_bike_related_purchases, dtype: int64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.groupby(\"job_industry_category\")[\"past_3_years_bike_related_purchases\"].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HR9asWYOTk5O1PTEx0ebMTm68vb1xdXXNdfKfihUrAtf/UM1pMpWbJ7q5layiJzeurq4A2c7M5Va45zTJko+PT54nTLqZvbn18PBg5syZzJw5kz/++INPP/2UqKgonJ2dmT17tt05dnJyYty4cYwbN47Tp08TExPD6NGjuXLlCqtXr77j+OvWrQtg/RwcOXKE+Ph4Vq5cSe/eva39vvzyy2zrRkZGEhkZSVJSElu3bmXcuHF06NCBP//80+7PRVZxc+bMGWssWc/t4eXlxenTp7O1nz59OseCryD5+Pjg5OSU47GcOXOGgIAA63N739cmk4nBgwczePBgEhMT2bBhA2PGjKFnz578+OOPdsWV02fCycmJZ555hjfffJMXXniBjz76iDFjxlhnnhaR+5vOUIqI5ODKlSscOXIk24QVOTGZTCxcuJChQ4fyxBNP8Mknn1iXPfDAA1SpUoUDBw5Yz/7c+C9rUpY74e3tzeTJk9m/f791MhZ3d3dat27N3r17adSoUY77utWxOjg42PwhuXnz5mxD/bJuH3Dj8Ee4fhuTq1evcvHixRz3m1XsNG/enJMnT9rMcJqZmcnHH398xznITdYf4TcXhF9//bXd2wgPD+f06dM5Fkl3yt7c3iggIIAxY8ZQv35963HYm+MblS9fnmeeeYawsLA8F8hZhWTW5yDrjOmNxXFaWhoffvhhrtvw8PCgc+fODB48mFOnTnH+/Hm7PxcNGjTA3d0923vko48+siv+4OBgvvrqKy5fvmxtu3z5Ml9++SXBwcF2bSO/ODg40KRJEz799FOb0QM//vhjtjOKOb2v09PT2bBhQ67b9/T0pGfPnvTo0cNmPRcXl2yfYXtk/XD2+OOPc+3aNU3GIyJW+mlJRATYs2cP586dwzAMTp06xcKFC7FYLAwbNszubcybNw8HBweeeuopMjMz6dmzJyaTiUWLFtGtWzdSU1Pp0aMHPj4+nDlzhtjYWCpXrszo0aPvON7Bgwfz6quvMn36dCIjIzGZTMydO5c2bdrQvn17Bg4cSIUKFTh37hy7du0iIyPDOgPnzTp06MC8efPo168f/fv35+DBg0ybNi3bsM86deoAsGjRIvr27YuTkxMNGjSgbdu2PPnkk0RGRjJ69Gj+9a9/YTabOX78OF9//TWzZ8+mVq1a9O3bl1mzZvHYY48xY8YMfH19Wbx4MZcuXbrj489NhQoVCA4OZubMmfj4+ODr68sHH3zAkSNH7N5G7969Wbp0KU8++STjx4+nadOmXL58mW+++YaRI0cSGBho97bszW3z5s3p2rUr9evXx8PDg23btrF371769u0LYHeOu3XrxoMPPkijRo3w9PRk9+7drF+/3q5r3VJTU63Ffnp6OgcPHuTll1+mZMmS1ltL1K5dm4CAACZOnIiDgwNOTk68/vrr2bY1efJkzpw5w8MPP0zFihX5888/eeONN2jYsKG1OLXnc1GmTBlGjRpljSM8PJy4uDiWLVtmV/5ffPFFYmJiCA0NZdy4cZhMJmbPns2VK1eYPHmyXdvIT1OnTiU8PJxHHnmEwYMHc/bsWaKjo7Nd19ukSROqV6/O888/T2ZmJi4uLrz55pvZbrkzaNAgSpYsSfPmzfH19eXgwYOsXLnSeq0tXP8cf/fdd8TExFC+fHl8fHzsumbTz8+PLl26sHbtWrp06ZKn2alFpJi6x5MCiYjcUznN8lq2bFnj4YcfNtavX2/T91azvN7ohRdeMBwcHIxVq1ZZ22JjY42IiAijTJkyhouLixEQEGD07NnTiI2NvWV8WftMS0vLtmzJkiUGYKxZs8ba9r///c/o2bOnUbZsWcPZ2dnw8/MzunTpYnz11VfWPjnN8vrGG28YVapUMVxdXY2goCBj48aNRnBwsM1Mk4ZhGFOmTDEqVqxomM1mm1knMzIyjHnz5hkNGjQwXFxcjFKlShkNGjQwnn/+eePChQvW9Y8cOWJ07NjRKFGihOHj42MMHz7cWLx48R3N8no7J0+eNDp37myULl3aKFeunDF+/Hhj6dKlOc7ymtNsl4ZhGJcvXzbGjh1rVK5c2XBycjLKly9vdO/e3Thz5oxhGLnPeJvTe8Se3L7wwgtGw4YNjVKlShlubm5GvXr1jPnz59tsx54cv/baa0bTpk0NLy8vw9XV1ahVq5YRHR1tpKam3jJnN8/y6uDgYFSuXNl44oknjF9//dWm7+7du42WLVsaJUqUMPz8/IwXX3wxW35jYmKM8PBwo3z58oazs7NRqVIlY8CAAcZff/1lsy17Phfp6enGxIkTjXLlyhmurq5GcHCwceDAAbtmeTWM67OmhoaGGu7u7oabm5sREhJi/Pjjj9mOP6f3Vk6fgZvlNstry5Yts/UNCAgw+vbta9O2atUqo1atWoazs7NRp04dY82aNTnud//+/UZwcLDh7u5u+Pv7G3PmzMn2fluxYoURHBxs/fxXqVLFGDlypHHx4kVrn19//dVo1aqVUaJECQOwxnOr75obYwWMmJiYW+ZERO4vJsMwjAKsX0VERESkCOrVqxc//PADR48ezfNEWCJS/GjIq4iIiIjkaufOnezZs4fVq1czd+5cFZMiYkNnKEVEREQkVyaTCQ8PD3r06MGSJUs0u6uI2NA3goiIiIjkSuceRORWNGZBRERERERE8kQFpYiIiIiIiOSJCkoRERERERHJExWUIiIiIiIikicqKEVERERERCRPVFCKiIiIiIhInqigFBERERERkTxRQSkiIiIiIiJ5ooJSRERERERE8kQFpYiIiIiIiOSJCkoRERERERHJExWUIiIiIiIikicqKEVERERERCRPVFCKiIiIiIhInqigFBERERERkTxRQSkiIiIiIiJ54nivA7BXZmYmUVFReHl5ERUVRUJCAvPmzSMpKYmqVasybNgwHB0dSUtLY+HChRw9epSSJUsycuRIfH19AVi7di2bN2/GbDbTv39/GjZseI+PSkREREREpOgqMmcov/76a/z8/KzPP/jgAyIiInjjjTdwd3dn8+bNAGzevBl3d3cWLFhAREQEH374IQB//vknsbGxzJ07l4kTJ7Js2TIyMzPvybGIiIiIiIgUB0WioDx//jy7du0iNDQUAMMwOHDgAM2aNQOgbdu2xMXFARAfH0/btm0BaNasGfv378cwDOLi4mjRogVOTk74+vpSvnx5Dh8+fE+OR0REREREpDgoEkNeV6xYQe/evUlJSQHg8uXLuLm54eDgAICXlxcWiwUAi8WCt7c3AA4ODri5uXH58mUsFgs1a9a0bvPGdW60adMmNm3aBMCsWbPy9bhERERERESKskJfUP7888+ULl2aatWqceDAgdv2NwwjW5vJZMqxPSdhYWGEhYVZn//999/2B1uI+fj4cO7cuXsdxn1D+S5YynfBUr4LlvJdsJTvgqV8Fyzlu2AVl3xXrFjxlssLfUH5+++/Ex8fz+7du0lNTSUlJYUVK1Zw5coVMjIycHBwwGKx4OXlBYC3tzfnz5/H29ubjIwMrly5goeHh7U9y43riIiIiIiIyJ0r9NdQPvXUUyxevJhFixYxcuRI6tWrx/Dhw6lbty47d+4EYOvWrQQFBQHQuHFjtm7dCsDOnTupW7cuJpOJoKAgYmNjSUtLIyEhgVOnTlGjRo17dVgiIiIiIiJFXqE/Q5mbXr16MW/ePD766COqVq1KSEgIACEhISxcuJBhw4bh4eHByJEjAfD396d58+aMHj0as9nMwIEDMZsLfT0tIiIiIiJSaJkMey8uvE/pGkrJC+W7YCnfBUv5LljKd8FSvguW8l2wlO+CVVzyXeSvoRQRERGRwsMwDK5evUpmZiYmk+kfbevMmTNcu3btLkUmt6N8F6yilG/DMDCbzbi6ut7x51oFpYiIiIjY7erVqzg5OeHo+M//jHR0dLTeBk7yn/JdsIpavtPT07l69SolSpS4o/V0EaGIiIiI2C0zM/OuFJMiUrg4OjqSmZl5x+upoBQRERERu/3TYa4iUnjl5fOtn5dEREREJM8ynu2a93Vvscxh6Rd53q6IFBydoRQRERERAWJjY9m2bdtt+61evZqwsDDatWtHaGgoy5YtK4Do7p6TJ09Sr169HJedPn2ayMhI63M/Pz+Sk5MLKjSryMhINm7ceNt+S5cuzfNMqiNHjmT58uV5WvdumTNnDi+99NI9jeGf0hlKERERERFgx44dJCcnExwcfMt+nTp1okePHphMJpKSkggJCaF58+bUqVOngCK1lZ6efteuay1fvjyffvrpXdnWrWRkZNyVCWveeecdWrdujY+Pz12IKn/crWMtrHSGUkRERESKLD8/P+bMmUPXrl1p3bo1X331lXXZc889R8eOHQkNDWXgwIFcuHABgMOHD9OlSxfCwsIICQlh8eLF/Prrr6xcuZJPP/2Udu3asXDhwlz3WbJkSeu1ZikpKaSnp9/y2rPx48ezePFi6/P9+/fTunVrDMPg8uXLjB07loiICMLCwpg8eTIZGdcHAy9evJhOnToRHh5Oly5d2L9/v81xv/XWW0RGRjJ37lzi4uJo37497dq14+GHH2bdunW3zd1LL71EREQEoaGh/Pjjj0DuZy8zMzOJjo5myJAhXLt27ZZx5yQ2NpZ27doxadIkOnfuzObNm+3extq1a+ncuTPh4eGEh4fz3XffATB//nzOnDnDoEGDaNeuHQcPHiQ1NZVp06YRERFBu3btGDZsmPUM66lTp+jRowdhYWEMGDAAi8Vyy/xk5SKnPMXGxtKxY0eb48t6nnWs48ePtx7rpUuXGD16NKGhoYSFhTFx4kTruqdPn6ZPnz60adOGPn36kJKSAsB3331Hly5dCA8PJzQ0lM8//9y6zty5c2nTpg3t2rUjPDycixcvArBr1y4iIyPp0KEDHTp0YNOmTQCcO3eOnj17EhoaSmhoKNHR0bc89juhM5QiIiIiUqSZzWa++OILDh8+TLdu3WjatCk+Pj689NJLeHl5ATB79mzefPNNJkyYwPvvv09ISAijRo0C4MKFC5QpU4Y+ffqQnJzM5MmTb7vPDRs2MHPmTP744w+ioqKoXbt2rn0HDBhAv379GDx4MCaTieXLl9O3b19MJhNTp06lWbNmvPbaa2RmZvLcc8/x0Ucf0atXLx5//HH+/e9/A7B9+3aioqKIiYmxbjczM9N6NrF///48++yzREZGYhgGly5dumX8iYmJ1K5dm8mTJ7Njxw6GDBlCbGxsjn2vXbvGyJEj8ff3Z9GiRZhMJiZOnJhr3Ln57bffmDlzJtOnTwdg7Nixdm0jODiYRx55BJPJxOHDh+nZsyc///wzI0aMYNWqVbz99tsEBgYCMG/ePEqWLGn9YeHll19mwYIFREVFMXnyZJo1a8bo0aP5448/rMX33crTzcf6yiuvMG3aNABGjRqFu7s7GzduxGw22xSze/fu5euvv6ZUqVI89dRTrFmzhl69elG/fn3WrVuHg4MDZ8+epUOHDtaz54sXL2bv3r2UKFGCpKQkXF1duXjxIlFRUaxcuZJy5cpx5swZOnXqxObNm1mzZg2VKlVi9erVANYfV+4GFZQiIiIiUqQ98cQTANSoUYP69euza9cuwsPD+eSTT1i7di1paWlcuXKFatWqAdC0aVOmTZtGWloaLVq0oGXLlne8z6yzZX/99RcDBgwgJCSEGjVq5Ni3Zs2aVK5cmS1bttCoUSM2bNhgPUO0YcMG9uzZw5IlS4DrZzwrVKgAwC+//MKCBQu4cOECZrOZo0eP2mz38ccftz5u0aIFCxcu5M8//6RNmzY0atTolvE7OzvTvXt3AJo3b46rqytHjhyhZMmS2fr26tWLbt26WYvb28Wdm6pVqxIUFHTH2/jjjz8YOnQop0+fxtHRkbNnz5KQkICvr2+2vhs3buTy5cvWgjI1NdU6FDk2NtZ6vWJAQACtWrW6ZbyQe55up2rVqjRp0oT09HQANm3axPr16zGbrw8QzfqhA6Bt27aULl0agIceeog//vgDgPPnzzNmzBiOHTuGg4MDFy5c4MiRIzRs2JDq1aszfPhw2rZtS1hYGB4eHsTHx3Py5El69+5t3bbJZOL48eM0atSIt99+m2nTptGsWTPatm17289B85AAACAASURBVGOwlwpKERERESk2DMMA4Mcff+T999/niy++wNvbm7Vr1/Lhhx8CEBERQePGjdm2bRuLFi1i9erVLFiwIE/78/Pzo2HDhmzatCnXghKun6V87733OHjwIB07dqRUqVLWeJctW0ZAQIBN/9TUVAYNGsSaNWuoX78+p0+fpnHjxjZ93N3drY+fffZZ2rVrx3fffceLL75ImzZtGDdu3B0dS27Ddlu0aMGWLVt4+umncXNzu2Xct3JjvHeyjaFDhzJ58mQ6dOhAZmYmNWrU4Nq1azn2NQyDGTNm2FUs5pXJZMp2z8ab47n5WG/FxcXF+tjBwYGrV68C14dKh4eH884772AymWjVqhXXrl3DwcGBL7/8kri4OH744Qc6duzIBx98gGEY1K5dmzVr1uS4nw0bNvDdd9/x2WefsWjRIruGRdtD11CKiIiISJGWNYzv6NGjHDhwgEaNGnHx4kVKlSqFp6cn165d46OPPrL2P3bsGL6+vvTs2ZPRo0eze/duADw8PLh8+fJt93f48GHrY4vFQmxs7C2HvAKEhoZy5MgRli5dSt++fa3t4eHhLFq0yHrtoMVi4cSJE1y7do2MjAwqVqwIwHvvvXfL7R85coQqVarQp08fBg4cyJ49e27ZPzU1lbVr1wLXi++rV69SvXr1HPuOHj2aNm3a0KtXL2t+cov7Tti7jYsXL+Lv7w/Af/7zH5virWTJkjavWbt27Xj77bet1yEmJSVx6NAh4HphnPVeOXHiBN9///1tY8wtT/7+/pw4cYILFy5gGMZti7OwsDDeeust6w8et7t+E+DSpUv4+/tjMpnYvn07x48ftx7T+fPnad68OWPHjuWBBx7g999/JygoiGPHjvHDDz9Yt7Fnzx4Mw+DEiROULFmSbt26ER0dzb59+2wK4n9CZyhFREREJM/+yf0iHR0drUMC/wkXFxe6deuGxWJh9uzZ+Pj4EBISwpo1a2jTpg0VKlTgwQcftBZZX375JWvXrsXJyQmTyWQdBtmxY0eeeeYZ2rVrR7du3Xjuuedy3N/KlSvZvn07jo6OGIZB//79bzszrNls5vHHH2fLli3UrVvX2j516lSmT59Ou3btMJlMODs7M3XqVCpXrszYsWPp1KkTfn5+t73W79133yU2NhYnJyecnZ2t1ynmxtPTk2PHjtG5c2dSUlJ48803cXZ2zrX/0KFDcXV15YknnuCDDz64Zdz2sncbU6dOZeDAgZQvX55mzZrh6elpXTZgwABGjRpFiRIlWLRoEc899xxz5swhIiLCOrx09OjR1KxZk5deeokRI0YQExND9erVadOmzW1jzC1PFSpUYNCgQXTs2BF/f38aNmzIwYMHc93OlClTiI6OJiQkBAcHB5o3b269vjI3EyZMYPz48SxcuJDatWtbf7S4dOkSgwYN4urVq2RmZlKvXj06duyIq6sry5cvZ/r06URHR5OWlkblypV577332LFjB0uWLMHBwQHDMJg5c6Y1P/+UycgqkyVHf//9970O4a7w8fHJ8z165M4p3wVL+S5YynfBUr4LlvJ9e1euXLEOe/yn7kZB6efnx8GDB+9oiOG98sQTT9CrVy+6dOlyT/Z/twr4+8XJkyfp2LGjzey6d6Io5junz3fWWfLcaMiriIiIFFsDBgwgLCyM8PBwHn300Wx/GM6dOxc/Pz9+++034PqsmVm3kwgLC6NXr16cPHkSuD6kMDIykjZt2lhnCM0aVidyK3v37qVFixaULFmSiIiIex2OyF2lIa8iIiJSbM2bN886+ck333zDmDFj+OabbwDYt28fu3btws/Pz9rfbDbz4YcfWtd55513mDp1Ku+88w7Ozs5MmTKFevXqkZmZyZAhQ1i8eLH11hNyb/z111/5st39+/fn+Nr279+fp556Ksd1xo0bx65du2zaHB0d+e9//2vXrSbutpvjMZlMODg48N///jff9tmvX79sr4mfnx8rVqzIt33+U7d63fJ6dvJ+ooJSREREiq2swhCuX3eUdc3QtWvXmDBhAosWLbK59cLN61y+fNk686W/v791YhCz2UzDhg1tJmeR4qVevXps3LjxjtaZPXt2PkWTNzfHUxBDMAtz4Zibwva6FTUqKEVERKRYGzt2LNu2bcMwDOttI1577TW6d++e6wQiffr0Yd++fXh5ebFq1apsy1NSUli9ejXjx4/P19gLI02/IVJ85eXzrWsoRUREpFh77bXXiIuLIyoqiunTpxMfH8+ePXtsbt1ws5UrV7Jr1y66devG/PnzbZalp6czZMgQWrZsSXh4eH6HX+iYzeYiN9GIiNxeenp6nmZ+1Syvt6FZXiUvlO+CpXwXLOW7YCnfd1f16tUZPnw47733Hk5OTgCcOnWKsmXLMnfuXLp3726T74SEBFq1amW9HUBGRob19gmvv/56rjeCL84Mw7DeruCfHr+Li0uuN6iXu0/5LlhFKd+GYWA2m3F1dc32ub7dLK8a8ioihc6AAQM4ceIEZrMZd3d3pk2bRr169XJtv9HcuXOZM2cO3377LYGBgQA899xzxMbGcubMmSIztbyI/HPJyclcuHDBOunOhg0bKFOmDMOHD2fEiBHWfk2bNuW9994jMDCQs2fPkpiYiJeXFwAxMTHW75LMzExGjhyJg4MDc+bMuS+LSbg+sUuJEiXuyrb0g0nBUr4L1v2SbxWUIlLo5DYr461ma4ScZ2yE6/f9mjJlCg8++GDBHYSI3HNXrlxh8ODBpKSkYDabKVOmDCtWrLhlIXjmzBn69etHeno6hmHg7+/PggULANi8eTNr1qwhMDCQDh06ANCkSRNmzJhRIMcjIlIYqaAUkUInt1kZc2uHW8/Y2KpVq3yOWEQKo7JlyxITE3Pbfj/++KP1cb169Vi/fn2O/cLCwvLtFhUiIkWVCkoRKZRympXxVu23m7FRRERERO4+zfIqIoXSzbMy3qp9586dt52xUURERETuvkJ/hjI1NZXo6GjS09PJyMigWbNm9OjRg0WLFvG///0PNzc3AIYOHUqVKlUwDIPly5eze/duXFxcGDJkCNWqVQNg69atrFmzBoDHHnuMtm3b3qvDEhE7RUZGMm7cOCwWi3WSjJvbv/vuO44cOUKzZs2A6zM29urVi7lz5xIcHHyvQhcREREp9gp9Qenk5ER0dDSurq6kp6czefJkGjZsCFy/6XDWH5BZdu/ezenTp3njjTc4dOgQ77zzDjNmzCApKYlPP/2UWbNmARAVFUVQUBAeHh4FfkwikrvcZmV0dnbmr7/+ytbu6enJ888/T//+/a3buHHGRhERERHJP7kWlGaz2a7psDMyMu5qQDczmUy4urpa95WRkXHLuOLj42nTpg0mk4latWqRnJxMYmIiBw4coEGDBtYCskGDBuzZs0eTdYgUMrnNypiSknLHszVmeeaZZ9i9ezcAbdq04YEHHmDVqlX5fSgiIiIixV6uBeV3331nfRwfH8/ixYsZM2YMVatW5ejRo8ybN4/BgwcXSJCZmZmMGzeO06dP0759e2rWrMmGDRv4z3/+w6effkq9evXo1asXTk5OWCwWfHx8rOt6e3tjsViwWCx4e3tb2728vLBYLNn2tWnTJjZt2gTArFmzbLZVlDk6OhabYykKlO+88/HxYefOnTkuy6395nwfOXLEZvm6devuXoCi93cBU74LlvJdsJTvgqV8F6z7Jd+5FpQtW7a0Pn7uueeIiYmhevXqAISGhhISEkJkZCTDhw/P9yDNZjOvvvoqycnJvPbaa5w4cYKnnnqKMmXKkJ6ezpIlS/j888+JjIzEMIxs6+d2BiOn9rCwMMLCwqzPi8vNSO+XG6sWFsp3wVK+C5byXbCU74KlfBcs5btgKd8Fq7jku2LFirdcbtcsr0eOHMHf39+mzc/Pj6NHj+Y9sjxwd3enTp067NmzB09PT0wmE05OTjz88MMcPnwYuH5G8sYX7vz583h6euLl5cX58+et7RaLBU9PzwKNX0REREREpDixq6Bs3LgxY8eO5erVqwBcvXqVqKgoHnrooXwNDq7fvDw5ORm4PuPrvn378PPzIzExEQDDMIiLi7MWvEFBQWzfvh3DMDh48CBubm54enrSsGFD9u7dS1JSEklJSezdu9c6uY+IiIiIiIjcObtmeV26dCldunTB09MTX19fEhISCAgI4Isvvsjv+EhMTGTRokVkZmZiGAbNmzencePGTJ06lUuXLgEQEBDAoEGDAHjooYfYtWsXw4cPx9nZmSFDhgDg4eFB9+7dGT9+PHD9lgOa4VVERERERCTvTEZOFx3mICMjg507d1qn7W/WrBkODg75Hd899/fff9/rEO6K4jKGu6hQvguW8l2wlO+Cdb/nO+PZrvc6hHzlsDT/f5wvzO7393dBU74LVnHJ9+2uobT7PpQODg60aNGC06dPU6FChX8cmIiIiIiIiBRtdl1DmZSUxMCBAylRogQ1atQArk/DP3Xq1HwNTkRERERERAovuwrKMWPGcObMGX744QecnZ0BaNKkCatXr87X4ERERERERKTwsmvIa0xMDP/73/8oXbq09d6Nfn5+xeb6QhEREREREblzdp2hNAyDEiVK2LQlJSVpllQREREREZH7mF1nKFu2bMnMmTOJjo62ti1YsICHH3443wITkaKroGdlPFOge9OsjCIiIiJZ7Coo586dS0hICB988AFJSUnUr1+ftLQ0vv322/yOT0RERERERAopuwpKf39/9u/fT0xMDMeOHSMgIIDOnTtnGwYrIiIiIiIi9w+770Pp4uJC9+7dAbh69Spms12XX4qIiIiIiEgxZVdVOGnSJH766ScANm7ciJeXF15eXmzYsCFfgxMREREREZHCy66C8r333iMwMBCAadOmMXv2bBYtWsTEiRPzNTgREREREREpvOwa8nrp0iVKlSpFcnIye/fuZfPmzTg6OjJy5Mj8jk9EREREREQKKbsKSm9vb3777Tf2799P06ZNcXR0JCUlJb9jExERERERkULMroJy5MiRNG7cGIAPP/wQgO3bt1O7du38i0xEREREREQKNbsKyuHDh9OxY0ccHR2pWrUqAFWrVuXtt9/O1+BERERERESk8LL7tiE1a9a0eV6rVq27HoyIiIiIiIgUHXYVlCkpKUyfPp1vv/2Ws2fPYhiGddnRo0fzLTgREREREREpvOy6bcioUaNYt24dffr04cyZM4wZMwYXFxcGDBiQ3/GJiIiIiIhIIWVXQfnll1/y5ZdfMnToUBwdHRk6dCifffYZW7Zsye/4REREREREpJCyq6BMSkqiWrVqADg7O5OamkqdOnWIi4vL1+BERERERESk8LLrGsqqVavy66+/Urt2bQIDA3n33XcpU6YMpUuXzu/4REREREREpJCyq6AcP348J06coHbt2rz44os8+uijXLt2jbfeeiu/4xMREREREZFCyq6CsmfPntbH7dq1IzExkdTUVNzd3fMtMBERERERESnc7L4P5Y2cnJxwcnK627GIiIiIiIhIEWLXpDwHDx6kffv2eHt74+zsbPNPRERERERE7k92naHs168flSpVYuXKlQU+zDU1NZXo6GjS09PJyMigWbNm9OjRg4SEBObNm0dSUhJVq1Zl2LBhODo6kpaWxsKFCzl69CglS5Zk5MiR+Pr6ArB27Vo2b96M2Wymf//+NGzYsECPRUREREREpDixq6Dcv38/27ZtuyfDXJ2cnIiOjsbV1ZX09HQmT55Mw4YNiYmJISIigpYtW/L222+zefNmwsPD2bx5M+7u7ixYsIAffviBDz/8kFGjRvHnn38SGxvL3LlzSUxMZNq0acyfPx+z2a6TtCIiIiIiInITuwrKwMBAEhIS8PPzy+94sjGZTLi6ugKQkZFBRkYGJpOJAwcOMGLECADatm3LJ598Qnh4OPHx8Tz++OMANGvWjHfffRfDMIiLi6NFixY4OTnh6+tL+fLlOXz4MLVq1SrwYxIRKSwsFgsjRozg+PHjuLi4UKVKFWbPns3Ro0eZMGGCtd/58+cpW7Ys33zzDZmZmXTp0oWUlBQAypUrx6xZs/D39yczM5Nu3brluExERESKn1wLytjYWOvj/v370717d1544QXKly9v069Fixb5F93/k5mZybhx4zh9+jTt27enXLlyuLm54eDgAICXlxcWiwW4/seRt7c3AA4ODri5uXH58mUsFgs1a9a0bvPGdW60adMmNm3aBMCsWbPw8fHJ78MrEI6OjsXmWIqC+z3fZ+51APmsOL22ZrOZqKgogoODAYiKimLu3LksWbKEjh07WvtFRkbSokULfHx8cHR0ZP369dZ7ES9YsICZM2fy8ccfA9xymdw5fZ8Ub/fzawt6fxc05btg3S/5zrWgbNWqVba2yMhIm+cmk4mMjIy7H9VNzGYzr776KsnJybz22mv89ddfufY1DCNbm8lkyrE9J2FhYYSFhVmfnzt37s4DLoR8fHyKzbEUBcp38VbcXtu6detaj6l27dq8//77Nsd47tw5Nm7cyLRp0zh37hw+Pj6kpaVZ+5w+fdrmedY6uS2TO6Pvk+Ltfn9t9f4uWMp3wSou+a5YseItl+daUGZmZt71YP4pd3d36tSpw6FDh7hy5QoZGRk4ODhgsVjw8vICwNvbm/Pnz+Pt7U1GRgZXrlzBw8PD2p7lxnVEROT69/77779PeHi4Tfunn35KcHAwZcuWtWnv06cP+/btw8vLi1WrVtm9TERERIoPu2akSUxMJDk52aYtOTmZCxcu5EtQN7p06ZJ136mpqezbtw8/Pz/q1q3Lzp07Adi6dStBQUEANG7cmK1btwKwc+dO6tati8lkIigoiNjYWNLS0khISODUqVPUqFEj3+MXESkqJk2ahLu7O/3797dpX716NU888US2/itXrmTXrl1069aN+fPn271MREREig+7CsquXbuyf/9+m7b9+/fTrVu3fAnqRomJiUydOpWxY8cyfvx4GjRoQOPGjenVqxcxMTEMGzaMpKQkQkJCAAgJCSEpKYlhw4YRExNDr169APD396d58+aMHj2al19+mYEDB2qGVxGR/+ell17i2LFjvPXWWzbfjT///DOJiYnW79ibmc1mnnzyST777LM7WiYiIiLFg12zvB44cIAmTZrYtDVp0oR9+/blS1A3CggI4JVXXsnWXq5cOWbOnJmt3dnZmdGjR+e4rccee4zHHnvsrscoIlKUzZo1i19++YWVK1fi4uJis2z16tVERkbi6Pj//7s4e/YsiYmJ1ssGYmJiCAwMBK7PBmsymXJcJiIiIsWPXQWlq6ur9VrELElJSffkvpQiInL3/P777yxYsIBq1arRtWtXACpXrsyyZctISUnhyy+/5IsvvrBZ58yZM/Tr14/09HQMw8Df358FCxYAkJCQwKhRo3JcJiIiIsWPXQVlq1atmDBhAvPmzcNsNmMYBpMnT6Zly5b5HZ+IiOSjBx54INeZs0uUKMGvv/6arb1evXqsX78+x3Vq166d6zIREREpfuwqKF955RVCQ0P57LPPqFatGseOHcPZ2ZnNmzfnd3wiIiIiIiJSSNlVUFapUoX9+/cTExPD8ePHqVKlChEREbi5ueV3fCIiIiIiIlJI3bagTE9Pp1u3bnz22Wc8/vjjBRGTiIiIiIiIFAG3vW+Go6MjP//8s80MfyIiIiIiIiJ23YixT58+LFy4ML9jERERERERkSLErtOOu3btYv78+SxcuJAqVarY3PR6w4YN+RaciIiIiIiIFF52FZRt2rShTZs2+R2LiIiIiIiIFCF2FZTR0dH5HYeIiIiIiIgUMXYVlLGxsbkua9GixV0LRkRERERERIoOuwrKVq1aZWszmUwAZGRk3N2IREREREREpEiwq6DMzMy0ef73338zadIkOnfunC9BiYiI/TKe7Vqg+ztToHsDh6VfFPAeRURExF523TbkZhUrVmT+/PmMGzfubscjIiIiIiIiRUSeCkqAa9eukZCQcDdjERERERERkSLEriGvM2bMsHmenJzM559/Trt27fIlKBERERERESn87CooN27caPPcw8ODxx9/nFGjRuVLUCIiIiIiIlL42VVQbtmyJb/jEBERERERkSLGroISwDAMfvzxR/7880/8/f3517/+Zb11iIiIiIiIiNx/7Coojxw5QteuXTly5Ahly5bl7NmzVK9enc8//5waNWrkd4wiIiIiIiJSCNk1y+ugQYMICQkhMTGRkydPYrFYCA0N5dlnn83v+ERERERERKSQsusMZVxcHF9//TUuLi4AuLm58corr1CuXLl8DU5EREREREQKL7vOUFavXp2///7bpu3vv/+matWq+RKUiIiIiIiIFH52naHs168fXbp0YezYsQQEBHD8+HFef/11nnnmGWJjY639WrRokW+BioiIiIiISOFiV0GZdb/JAQMG2LSPHDnS+thkMpGRkXEXQxMREREREZHCzK6CMjMzM7/jyNW5c+dYtGgRFy5cwGQyERYWRqdOnfj444/59ttvKVWqFABPPvkkjRo1AmDt2rVs3rwZs9lM//79adiwIQB79uxh+fLlZGZmEhoayiOPPHLPjktERERERKSos/s+lPeKg4MDffr0oVq1aqSkpBAVFUWDBg0AiIiIoGvXrjb9//zzT2JjY5k7dy6JiYlMmzaN+fPnA7Bs2TImTZqEt7c348ePJygoiEqVKhX4MYmIiIiIiBQHhb6g9PT0xNPTE4ASJUrg5+eHxWLJtX9cXBwtWrTAyckJX19fypcvz+HDhwEoX768dWbaFi1aEBcXp4JSREREREQkjwp9QXmjhIQEjh07Ro0aNfjtt9/45ptv2L59O9WqVePpp5/Gw8MDi8VCzZo1ret4eXlZC1Bvb29ru7e3N4cOHcq2j02bNrFp0yYAZs2ahY+PTz4fVcFwdHQsNsdSFNzv+T5zrwPIZ4XttVW+izd9nxRv9/NrC3p/FzTlu2DdL/kuMgXl1atXmTNnDv369cPNzY3w8HAiIyMBWL16Ne+//z5DhgzBMIwc18+p3WQyZWsLCwsjLCzM+vzcuXN36QjuLR8fn2JzLEWB8l286bUtWPd7vvV9Urzd76+t3t8FS/kuWMUl3xUrVrzlcrvuQ3mvpaenM2fOHFq3bk3Tpk0BKFOmDGazGbPZTGhoKEeOHAGun3k8f/68dV2LxYKXl1e29vPnz1uH0oqIiIiIiMids6ugzMjIYPr06dSsWZPSpUsD8M0337B48eJ8DQ6un1lcvHgxfn5+dO7c2dqemJhoffzTTz/h7+8PQFBQELGxsaSlpZGQkMCpU6eoUaMG1atX59SpUyQkJJCenk5sbCxBQUH5Hr+IiIiIiEhxZdeQ1xdffJFNmzYxe/Zs670oa9WqRVRUFP/+97/zNcDff/+d7du3U7lyZZ5//nng+i1CfvjhB44fP47JZKJs2bIMGjQIAH9/f5o3b87o0aMxm80MHDgQs/l63TxgwABefvllMjMzefjhh61FqIiIiIiIiNw5uwrKVatWsWPHDipUqMAzzzwDQJUqVTh+/Hh+xgZAYGAgH3/8cbb2rHtO5uSxxx7jsccey3GdW60nIiIiIiIi9rNryGtycjK+vr42bampqbi6uuZLUCIiIiIiIlL42VVQNm7cmOXLl9u0rVq1in/961/5EpSIiIiIiIgUfnYNeX3ttddo27YtH330EVeuXKFLly7Ex8ezZcuW/I5PRERERERECim7Csp69erx66+/8v777xMYGEhAQADvvPMO5cqVy+/4REREREREpJCyq6A8d+4cZcuWZcyYMTbt+/bto379+vkSmIiIiIiIiBRudl1D2blzZ65evWrTdujQIdq3b58vQYmIiIiIiEjhZ1dB2bBhQ5588knr85MnT9KuXTvGjRuXb4GJiIiIiIhI4WZXQfnmm2+SmprKqFGjSEhIICwsjGeeeYYRI0bkd3wiIiIiIiJSSNlVUJrNZj7++GO2bt3Kgw8+yCOPPMKkSZPyOzYREREREREpxHKdlGfGjBnZ2po3b87atWspXbq0dfmECRPyLzoREREREREptHItKDdu3Jhje2BgoHWZyWRSQSkiIiIiInKfyrWg3LJlS0HGISIiIiIiIkWMXddQioiIiIiIiNws1zOUDz30ELt37wagZs2amEymHPsdPHgwfyITERERERGRQi3XgvL555+3PtaMriIiIiIiInKzXAvKp556yvq4b9++BRKMiIiIiIiIFB25FpQ3O3HiBKtWreLPP/+kUqVKPPnkkwQEBORnbCIiIiIiIlKI2TUpz/r163nggQf46quvuHjxIl999RWBgYGsX78+v+MTERERERGRQsquM5TPP/88y5YtsxkG+5///IcxY8bQoUOHfAtORERERERECi+7zlAeP36cJ554wqatZ8+enDhxIl+CEhERERERkcLProKybdu2bN261aZt27ZtBAcH50dMIiIiIiIiUgTkOuR1xowZ1sc1atTg0Ucf5ZFHHqFKlSocP36cdevWMXDgwAIJUkRERERERAqfXAvKjRs32jxv1KgRJ06csA5zbdSoEXv27Mnf6ERERERERKTQyrWg3LJlS0HGISIiIiIiIkWM3fehvFfOnTvHokWLuHDhAiaTibCwMDp16kRSUhKvv/46Z8+epWzZsowaNQoPDw8Mw2D58uXs3r0bFxcXhgwZQrVq1QDYunUra9asAeCxxx6jbdu29/DIREREREREirZCX1A6ODjQp08fqlWrRkpKClFRUTRo0ICtW7dSv359HnnkEdatW8e6devo3bs3u3fv5vTp07zxxhscOnSId955hxkzZpCUlMSnn37KrFmzAIiKiiIoKAgPD497fIQiIiIiIiJFk12zvN5Lnp6e1jOMJUqUwM/PD4vFQlxcnHWW2eDgYOLi4gCIj4+nTZs2mEwmatWqRXJyMomJiezZs4cGDRrg4eGB6QqkDgAAIABJREFUh4cHDRo00DWgIiIiIiIi/0ChLyhvlJCQwLFjx6hRowYXL17E09MTuF50Xrp0CQCLxYKPj491HW9vbywWCxaLBW9vb2u7l5cXFoulYA9ARERERESkGMnTkNetW7fi6OhIq1at7nY8ubp69Spz5syhX79+uLm55drPMIxsbSaT6f+yd+fRNZ37H8ffJ7MIkdEQCRJztIIYLkoR2kurbqlb1V7UUKVXFaG0hhqDGlqlo6GD9mpVaYuWGFtjSElRU4gxJJEQEUFy8vsjK+cnMjhSOZk+r7WyVs4ev/vZw9nf/Tz7OTlOm9Pw0NBQQkNDAQgJCcmSnBZnNjY2JWZbioPSXt6XCzuAAlbU9q3Ku2TT9aRkK837FnR8W5rK27JKS3mblVB26tSJt956i7Zt2/Lee+8xduxYrK2tmTx5Mm+88UZBx0hqaipz5szhscceo3nz5gA4OzuTkJCAi4sLCQkJlC9fHsiokYyLizPNe+XKFVxcXHB1deXIkSOm4fHx8dSvXz/buoKCgggKCjJ9vntZxZm7u3uJ2ZbiQOVdsmnfWlZpL29dT0q20r5vdXxblsrbskpKeVepUiXP8WY1eT1w4AAtW7YE4NNPP2XDhg3s2rWLhQsX/v0I7yM9PZ2PPvoILy8vnnrqKdPwwMBAtm3bBsC2bdto2rSpafj27dtJT0/n+PHjODo64uLiQkBAAAcPHiQpKYmkpCQOHjxIQEBAgccvIiIiIiJSUplVQ3n79m1sbW25fPkyMTExpqauMTExBRocwLFjx9i+fTs+Pj4EBwcD0KtXL7p168a8efPYvHkz7u7ujBgxAoBGjRoRHh7OsGHDsLOzY8iQIQA4OTnRvXt3xo4dC0CPHj3Uw6uIiIiIiMjfYFZC6evry+eff05kZCTt27cHMpqSOjg4FGhwAHXr1uXbb7/NcdyECROyDTMYDAwYMCDH6du3b2+KX0RERERERP4esxLK2bNn85///Ad7e3vWrFkDwNq1a03NTEVERERERKT0uW9CmZaWRrly5Th9+jR2dnam4b169aJXr14FGpyIiIiIiIgUXfftlMfa2pp27dpha2ubZbitrW22YSIiIiIiIlJ6mNXLa/369Tlz5kxBxyIiIiIiIiLFiFnvUL700kt069aN4OBgqlWrhpXV/+ehmT8nIiIiIiIiIqWLWQnl8OHDgYzE8m4Gg4G0tLSHH5WIiIiIiIgUeWYllEajsaDjEBERERERkWLGrHcoRURERERERO5lVg1leno6n332GZs2bSI2Npb09HTTuM2bNxdYcCIiIiIiIlJ0mVVD+dZbbzF+/Hi8vb3ZvXs3TZo04ciRIwQEBBR0fCIiIiIiIlJEmZVQfv311/z666/Mnj0bOzs7Zs+ezerVq4mKiirg8ERERERERKSoMiuhjI+Pp2HDhgBYW1uTlpZGixYt2LJlS4EGJyIiIiIiIkWXWe9Qenl5cfbsWXx8fPD19WX9+vW4u7tja2tb0PGJiIiIiIhIEWVWDeWrr77K/v37AXjjjTfo1q0brVq1YtiwYQUanIiIiIiIZDd58mRatGiBl5cXR48ezTZ+7ty52cbZ29sTFBREx44d6dixI3/99RcAt27donfv3jRo0IAGDRpYbBukZDCrhvLuxLFXr1489thjJCUlUbdu3QILTEREREREcvbkk0/Sv39/nn322Wzj/vzzT8LDw/Hy8so2bs2aNZQtWzbLMGtra1555RVcXV15/vnnCyxmKZke6HcoL168yO7du6lataqSSRERERGRQtKsWbMcE8Zbt24xbtw4pk+fjsFgMGtZNjY2tGnTBmdn54cdppQCZiWUMTExBAUFUbVqVYKCggBYsWIFQ4YMKdDgRERERETEfO+++y7du3fHx8cnx/E9evSgY8eOzJgxg1u3blk4OimJzEoohw0bRo0aNYiNjTV1xNO+fXs2bNhQoMGJiIiIiIh59u3bx4EDB+jTp0+O40+ePMn69etZtWoVx48fZ/78+RaOUEoisxLKLVu2sGDBAtzc3ExV5x4eHsTGxhZocCIiIiIiYp7du3cTGRlJixYtaN68OdHR0fTu3Ztt27YB4O3tDUC5cuXo1asXYWFhhRmulBBmdcpjb29PampqlmHx8fG4uroWSFAiIiIiIvJgXnvtNV577TXT5+bNm/P5559Tt25drl69auqMJzU1lbVr1+Lv719YoUoJYlYNZadOnRg5ciR37twxDZs0aRJdunQpsMBERERERCRn48ePp0mTJkRHR/P888/Trl27PKc/efIkrVu3JigoiKCgIGxtbRk9erRpfOfOnenatSvXrl2jSZMmjBo1qqA3QUoIs2ooZ82aRbdu3XBxcSElJYUKFSoQEBDA6tWrCzo+ERERERG5x5QpU5gyZUqe0+zZs8f0f2BgIPv37ycuLi7HadetW/dQ45PSw6yE0tXVle3bt7Nv3z6ioqKoVq0agYGBZndFLCIiIiIiIiWPWQnljh07aNWqFYGBgQQGBhZ0TCIiIiIiIlIMmPUO5ZNPPom/vz/z588nPj6+oGMSERERERGRYsCsGsro6Gi+/vprlixZwtixY+nWrRsDBw6kffv2BR0fixYtIjw8HGdnZ+bMmQPAt99+y6ZNmyhfvjwAvXr1onHjxgD88MMPbN68GSsrK/r160dAQAAABw4cYOnSpRiNRjp06EC3bt0KPHYREREREZGSzKwaSicnJwYNGsTu3bsJCwujUqVKPP/889SqVYuZM2dy5cqVAgvw8ccfZ9y4cdmGd+nShdmzZzN79mxTMnn+/Hl27tzJ3Llzeeutt1i8eDFGoxGj0cjixYsZN24c8+bNY8eOHZw/f77AYhYRERERESkNzEoo71ahQgVcXFxwdHQkLS2NX375herVq/P5558XRHzUr18fJycns6YNCwujZcuW2Nra4unpSaVKlTh58iQnT56kUqVKVKxYERsbG1q2bKkfchUREREREfmbzEooU1NT+f777/nnP/9JrVq1iIiI4OOPPyYyMpItW7awdu1aRowYUdCxZvHrr78yatQoFi1aRFJSEgDx8fG4ubmZpnF1dSU+Pj7bcDc3N70LKiIiIiIi8jeZ9Q5llSpVcHJyon///ixdupRKlSplGd+mTRv8/PwKJMCcdOrUiR49egCwYsUKvvjiC4YMGUJ6enqO0+c0PLefPAkNDSU0NBSAkJAQ3N3dH1LUhcvGxqbEbEtxUNrL+3JhB1DAitq+VXmXbLqelGyled+Cjm9LU3lbVmkpb7MSyi+++IInnngiz9+d3Lt370ML6n4qVKhg+r9Dhw7MnDkTyKh5vPt9zvj4eFxdXQGyDL9y5QouLi45LjsoKIigoCDT59x+/LW4cXd3LzHbUhyovEs27VvLKu3lretJyVba962Ob8tSeVtWSSnvKlWq5Dne7J8NySuZtLSEhATT/3v37sXb2xuAwMBAdu7cyZ07d4iJiSE6OpqaNWvi5+dHdHQ0MTExpKamsnPnTv2epoiIiIiIyN9kVg1lYZo/fz5Hjhzh+vXrDB48mJ49e3L48GGioqIwGAx4eHgwaNAgALy9vfnHP/7BiBEjsLKyon///lhZZeTML7/8MtOmTcNoNNKuXTtTEioiIiIiUhjSBna16Pos2YTc+tMfLbg2KUxFPqEcPnx4tmF5/f7ls88+y7PPPptteOPGjU0/LyIiIiIiIiJ/3wP/bIiIiIiIiIgIPGBCefHiRXbv3l1QsYiIiIiIiEgxYlZCGRMTQ1BQEFWrVjX1gLpixQqGDBlSoMGJiIiIiIhI0WVWQjls2DBq1KhBbGwstra2QMZ7jBs2bCjQ4ERERERERKToMiuh3LJlCwsWLMDNzc308yEeHh7ExsYWaHAiIiIiUjxMnjyZFi1a4OXlxdGjR03DIyMjefrpp2ndujVPP/00p06dMo1bt24dTzzxBB06dKB79+6cPXv2vssTkaLFrITS3t6e1NTULMPi4+NxdXUtkKBEREREpHh58skn+f7776latWqW4W+++SZ9+/bl999/p2/fvowZMwaAq1evMmDAABYtWsSmTZt44YUXGDt27H2XJyJFi1kJZadOnRg5ciR37twxDZs0aRJdunQpsMBEREREpPho1qwZXl5eWYbFxcVx6NAhunXrBkC3bt04dOgQV65cISoqCk9PT/z8/ICM16m2bt1KfHx8rssTkaLHrIRy1qxZ/PXXX7i4uJCYmEiFChWIiIhg6tSpBR2fiIiIiBRTFy9epFKlSlhbWwNgbW1NxYoVuXjxIr6+vly+fJkDBw4A8MMPPwBw4cKFQotXRB6cjTkTubq6sn37dvbt20dUVBTVqlUjMDDQ9D6liIiIiMiDKF++PF999RUTJ07k1q1btGvXDmdnZ2xszLo9FZEi4oHO2CZNmuDl5UXlypULKh4RERERKSGqVKnCpUuXSEtLw9ramrS0NC5fvkyVKlUA6NChAw0bNgQgNjaWjz76iGrVqhVmyCLygMxq8pqUlET//v0pU6YMNWvWBGD16tW88847BRqciIiIiBRf7u7u+Pv7s3r1aiDj/tHf3x83NzcALl26BIDRaCQkJIQXX3wRR0fHQotXRB6cWQnlyJEjuXz5Mjt27MDOzg6Apk2bsmLFigINTkRERESKh/Hjx9OkSROio6N5/vnnadeuHQAhISEsXbqU1q1bs3TpUkJCQkzzTJo0ibZt29K6dWtsbW0ZN27cfZcnIkWLIT09Pf1+E3l5eXHkyBGcnZ1xdXU19b5VoUIFrl69WuBBFqaLFy8WdggPhbu7O3FxcYUdRqlR2ss7bWDXwg6hQFl/+mNhh5CFyrtk0/VEx3dJpuO75B7fpf3YhpJzfGc2Uc+NWTWU6enplClTJsuwpKQknJyc8h+ZiIiIiIiIFGtmJZStWrVixowZWYYtWLBATQ9ERERERERKMbN6eZ0zZw4dOnTgq6++IikpiUceeYQ7d+6wadOmgo5PREREREREiiizEkofHx8OHTrEzz//zOnTp6lWrRpPPfVUtmawIiIiIiIiUnrcN6FMTU3lmWee4fvvv6d79+6WiElERERERESKgfu+Q2ljY8P+/fuxsTGrMlNERERERERKCbM65XnppZf44IMPCjoWERERERERKUbMqnYMDw/nvffe44MPPqB69epYWf1/Hrphw4YCC05ERERERESKLrMSyjZt2tCmTZuCjkVERERERESKEbMSyokTJxZ0HCIiIiIiIlLMmN3TTkpKCidOnOD69etZhrds2fKhByUiIiIilpc2sKtF13fZguuy/vRHC65NpPQwK6H88ccf6dOnD9euXcsy3GAwkJaWViCBiYiIiIiISNFmVkI5cuRIJk6cyKBBg3B0dCzomLJYtGgR4eHhODs7M2fOHACSkpKYN28esbGxeHh48MYbb+Dk5ER6ejpLly7ljz/+wN7eniFDhuDr6wvA1q1bWbVqFQDPPvssjz/+uEW3Q0REREREpKQx62dDLl++zPDhwy2eTAI8/vjjjBs3Lsuw1atX88gjj/D+++/zyCOPsHr1agD++OMPLl26xPvvv8+gQYP47LPPgIwEdOXKlUyfPp3p06ezcuVKkpKSLL4tIiIiIiIiJYlZCWWnTp3YvXt3QceSo/r16+Pk5JRlWFhYGG3btgWgbdu2hIWFAbBv3z7atGmDwWCgdu3a3Lhxg4SEBA4cOMCjjz6Kk5MTTk5OPProoxw4cMDi2yIiIiIiIlKS5Nrkdfr06ab/q1evTteuXfn3v/9N5cqVs0x3b+2hJVy7dg0XFxcAXFxcSExMBCA+Ph53d3fTdG5ubsTHxxMfH4+bm5tpuKurK/Hx8ZYNWkREREREpITJNaHcuHFjls/+/v4cOnSIQ4cOmYYZDIZCSShzk56enm2YwWDIcdrchoeGhhIaGgpASEhIlgS1OLOxsSkx21IclPbytmSvfYWhqO1blXfJputJyVbU9m1JLu+iVtag8i7pSsv1O9eEcsuWLZaM44E4OzuTkJCAi4sLCQkJlC9fHsiokYyLizNNd+XKFVxcXHB1deXIkSOm4fHx8dSvXz/HZQcFBREUFGT6fPfyijN3d/cSsy3Fgcq7ZNO+tazSXt66npRs2reWo7K2LJV3ybl+V6lSJc/xZr1D+eSTT+Y4vEuXLg8e0UMQGBjItm3bANi2bRtNmzY1Dd++fTvp6ekcP34cR0dHXFxcCAgI4ODBgyQlJZGUlMTBgwcJCAgolNhFRERERERKCrN+NmTnzp05DrdERz3z58/nyJEjXL9+ncGDB9OzZ0+6devGvHnz2Lx5M+7u7owYMQKARo0aER4ezrBhw7Czs2PIkCEAODk50b17d8aOHQtAjx49snX0IyIiIiIiIg8mz4Ty66+/BiA1NZVvvvkmyzuKJ06cMHWMU5CGDx+e4/AJEyZkG2YwGBgwYECO07dv35727ds/1NhERERERERKszwTyrfeeguAW7duZel8x8rKikqVKrFgwYKCjU5ERERERESKrDwTytOnTwPQtWtXfvzxR4sEJFLUpaSkMGnSJH777TccHBxo0qQJb775Jq+//jpRUVHY29tTu3ZtpkyZYvq5Gi8vL+rVq2fqXfj999+nXr16hbkZIiIiIiJ/m1nvUCqZFPl/06ZNw97ent9//x2DwUBsbCwGg4FXX32Vli1bAvDuu+8yffp05syZY5pvzZo1lC1btrDCFhERERF56MxKKEUkw40bN1i5ciX79u0z1TZ6eHgAmJJJgObNm/PBBx8USowiIiIiIpZi1s+GiEiGqKgoXFxcmDt3Lv/85z/p0aMHe/fuzTKN0Wjk448/plOnTlmG9+jRg44dOzJjxgxu3bplybBFpIhq3rw5bdq0oWPHjnTs2JGtW7cC8L///Y8OHTrQsWNH/vGPf7Bnz55s844YMQIvLy9u3Lhh4ahFRET+n2ooRR5AWloaZ86coUGDBowfP57w8HD69u3Ljh07KFeuHABvv/02Tk5O9OvXzzTf3r178fLy4vr16wwbNoz58+czZsyYwtoMESlCPvnkE+rWrWv6HB8fz8SJE/n999/x8PBg165djBkzxpRsAmzYsMHUSkJERKQwmVVD+eWXX+Y4fPny5Q81GJGirmrVqtjY2NCtWzcAGjdujKurK6dOnQJg8uTJnD59muXLl2Nl9f+nl5eXFwDlypWjV69ehIWFWT54ESlWkpKSALh27RqVK1c2DY+Pj2fevHlMnDixsEITERExMSuhHDp0aI7D//vf/z7UYESKOldXV1q2bMn27dsBiIyMJC4ujurVqxMSEkJERARLlizB3t7eNM/Vq1e5efMmkPGbrmvXrsXf379Q4heRoue1114jKCiIsWPHcu3aNVxdXZkxYwZPPPEETZs2ZcKECUyfPt00/VtvvcWIESMoX758IUYtIiKSwayEMj09PduwqKgobGzUYlZKn5CQEBYsWECHDh0YMmQI77//PpcuXWLBggVcvnyZrl270rRpU/r37w/AyZMnefrppwkKCiIoKAhbW1tGjx5dyFshIkXBqlWrCA0NZe3ataSnp/P2229z/fp1li1bxrp16wgLC2PWrFkMGDCA9PR0fvrpJ2xtbenYsWNhhy4iIgLc5x1KW1tbDAYDaWlp2NnZZRmXlpbGkCFDCjQ4kaKoWrVqrFy5MtvwCxcumP53d3cnLi4OgMDAQEJDQy0Wn4gUH5nN4e3t7enTpw/9+vVj27ZtODs7U7NmTSCjQ6/+/fsTHx/Pzp072bFjB82bNzcto127dnz11VfUrl27ULZBRERKtzwTytDQUNLT0+ncuTPr1683DbeysqJSpUrUqlWrwAMUEREpiZKTk0lNTaV8+fKkp6ezZs0a/P398fHx4dChQ8TFxeHu7s7WrVspV66cqSnsjBkzTMvw8vJiy5Yt+o1bEREpNHkmlG3btgUy3hO7u0MAKZrmzp3LnDlz2LRpE3Xr1uV///sfn376KVZWVjg4OPD222+bnmp7eXlRr149Uy+B77//PvXq1SvM8EVESpXY2FgGDhyI0WgkLS2NWrVqMX36dCpWrMjgwYPp3r07tra2lC1blk8++US9uoqISJFk1kuQu3bton79+tStW5fIyEj69++PjY0NH3/8MX5+fgUdo5jhzz//JDw83NR8ypxu59esWaOn2iIihaRatWps2LAhx3GvvPIKr7zyCpC1Cf297m5qLyIiUhjM6pRn3Lhxpt/YGzNmDN7e3tSsWVO9vBYRt27dYty4cUyfPj3bE+zcup0XERERERH5u8yqobx06RJeXl6kpaURGhrK2bNnsbe3N9WGSeF699136d69Oz4+PqZhd3c77+zsjMFgYMWKFVnm69GjB6mpqbRv354RI0Zk+akLERERERGR+zGrhtLe3p6rV6+ya9cuatWqRfny5bG2tub27dsFHZ/cx759+zhw4AB9+vTJMjyvbucB9u7dy/r161m1ahXHjx9n/vz5hRG+iIiIiIgUY2YllM888wwdOnSgf//+9OzZE4CIiAi8vb0LNDi5v927dxMZGUmLFi1o3rw50dHR9O7dmy1btmTrdj4qKor4+Hjg/7uqL1euHL169SIsLKzQtkFERERERIons5q8Lly4kGXLlmFnZ8eLL74IZLyTN378+AINTu7vtdde47XXXjN9bt68OZ9//jm3b9/Otdv5q1evYm9vT5kyZUhNTWXt2rX4+/sX4laIiIiIiEhxZFZCaWtry8CBA7MMa9euXYEEJA/Ho48+mmu38ydPnuTNN98EIDU1lcDAQEaPHl3IEYuIiIiISHFjVkIJsHHjRjZt2kRsbKzpPTyAJUuWFEhgkj979uwx/Z9bt/OBgYGEhoYWSnwiIiIiIlJymJVQvvfee4wdO5YuXbrw888/89RTT7F+/XqeffbZgo5P5KFIG9jVouu7bNG1gfWnP1p4jSIiIiIiZnbK88EHH7Bu3Tq+++47HBwc+O6771ixYgW2trYFHZ+IiIiIiIgUUWYllJcuXeLxxx8HwGAwANC5c2fWrFlTYIGJiIiIiIhI0WZWk1dPT08uX75MxYoVqVq1Knv27MHd3R2j0VjQ8YmIiBQpakIvIiIvv/wyZ8+excrKirJlyzJlyhQaNGjA5MmTWbduHefOnSM8PJyKFSsCEB8fz+uvv05UVBT29vZUr16dmTNn4ubmVshb8veZVUP5/PPPs2nTJgAGDBhAu3btaNSoEb169SrQ4ERERERERIqa+fPnExoayoYNGxg8eDAjR44E4Mknn+T777+natWqWaY3GAy8+uqr/Pbbb4SGhlKtWjWmT59eGKE/dGbVUE6bNs30/7BhwwgMDCQxMZEnnniiwAIzx9ChQ3FwcMDKygpra2tCQkJISkpi3rx5xMbG4uHhwRtvvIGTkxPp6eksXbqUP/74A3t7e4YMGYKvr2+hxi8iIiIiIsVP+fLlTf8nJiZiZZVRT9esWbMcp3dxcaFly5amz40bN+aLL74o2CAt5L4J5cmTJ/nzzz9p2LChKQG7uzAK28SJE7Ps0NWrV/PII4/QrVs3Vq9ezerVq3nxxRf5448/uHTpEu+//z4nTpzgs88+KzFPBURERERExLJGjRrFtm3bSE9PZ/ny5WbPZzQa+eKLL+jUqVMBRmc5eTZ5XbVqFfXq1aN79+7Ur1+fdevWWSqufAsLC6Nt27YAtG3blrCwMAD27dtHmzZtMBgM1K5dmxs3bpCQkFCYoYqIiIiISDH17rvvEhYWxptvvsnUqVPNnu/tt9+mbNmy9OvXrwCjs5w8E8qpU6cyffp0rl+/zjvvvFMka/SmTZvGmDFjCA0NBeDatWu4uLgAGVXLiYmJQMaLsO7u7qb53NzciI+Pt3zAIiIiIiJSYvTo0YOdO3ealVtMnjyZ06dP8+GHH5qayRZ3eTZ5PX36NCNHjsTKyooRI0Ywb948S8VllilTpuDq6sq1a9eYOnUqVapUyXXa9PT0bMMyfwLlbqGhoabkNCQkJEsSWpzZ2NiUmG3JD0v3kmhpRW3fqrwtS+VtWSpvy1J5W1ZJLu+iVtag8i6ukpKSSEhIwNvbG4Cff/4ZV1dXatWqZcovrK2tsba2zlIOEyZM4OjRo6xZswZHR8dCib0g5JlQpqWlmTJnW1tbbt++bZGgzOXq6gqAs7MzTZs25eTJkzg7O5OQkICLiwsJCQmm9yvd3NyIi4szzXvlyhVTTebdgoKCCAoKMn2+e56HydLdzluaup23rII6TiVnKm/LUnlblsrbslTelqOytqySXN6xsbH069ePmzdvYmVlRYUKFViyZAlXrlxh/PjxrFu3jtjYWDp16oSzszNbtmzh2LFjzJw5E19fX1q1agWAj48PixcvLuStub+8Ku3gPgnl7du3szRzTUlJydbsddy4cX8jvPxLSUkhPT2dMmXKkJKSQkREBD169CAwMJBt27bRrVs3tm3bRtOmTQEIDAzkl19+oVWrVpw4cQJHR8ccE0oREREREZHceHh48PPPP+c4bsqUKUyZMgXIqKXNTKzr1KnDhQsXLBajJeWZULZo0YKNGzeaPjdv3jzLZ4PBUGgJ5bVr13j33XeBjJrU1q1bExAQgJ+fH/PmzWPz5s24u7szYsQIABo1akR4eDjDhg3Dzs6OIUOGFErcIiIiIiIiJUWeCeXWrVstFMaDq1ixIrNnz842vFy5ckyYMCHbcIPBwIABAywRmoiIiIiISKlQMroWEhEREREREYtTQikiIiIiIiL5ooRSRERERERE8kUJpYiIiIiIiOSLEkoRERERERHJFyWUIiIiIiIiki9KKEVERERERCRflFCKiIiIiIhIvtgUdgAiIiIiIiIFLW1gV4uu77IF12X96Y8WXFtWqqEUERERERGRfFFCKSIiIiIiIvmihFJERERERETyRQmliIiIiIiI5IsSShEREREREckXJZQiIiIiIiKSL0ooRUThico4AAAgAElEQVREREREJF+UUIqIiIiIiEi+KKEUERERERGRfFFCKSIiIiIiIvmihFJERERERETyRQmliIiIiIiI5IsSShEREREREckXJZQiIiIiIiKSL0ooRUREREREJF+UUIqIiIiIiEi+2BR2AJZ24MABli5ditFopEOHDnTr1q2wQxIRERERESmWSlUNpdFoZPHixYwbN4558+axY8cOzp8/X9hhiYiIiIiIFEulKqE8efIklSpVomLFitjY2NCyZUvCwsIKOywREREREZFiqVQllPHx8bi5uZk+u7m5ER8fX4gRiYiIiIiIFF+G9PT09MIOwlJ27drFwYMHGTx4MADbt2/n5MmTvPzyy6ZpQkNDCQ0NBSAkJKRQ4hQRERERESkOSlUNpZubG1euXDF9vnLlCi4uLlmmCQoKIiQkpMQlk2+++WZhh1CqqLwtS+VtWSpvy1J5W5bK27JU3pal8ras0lLepSqh9PPzIzo6mpiYGFJTU9m5cyeBgYGFHZaIiIiIiEixVKp+NsTa2pqXX36ZadOmYTQaadeuHd7e3oUdloiIiIiISLFkPWnSpEmFHYQlVa5cmX/+85907tyZevXqFXY4FuXr61vYIZQqKm/LUnlblsrbslTelqXytiyVt2WpvC2rNJR3qeqUR0RERERERB6eUvUOpYiIiIiIiDw8SigL2d69e+nZsycXLlx44HlXrFhBREREvtY7dOhQEhMTuXHjBr/++mu+llHU/Pvf/yY4ONj0t3r16jynP3z4MMeOHXvg9URFRREeHn7f6SIjI1myZMkDL/9hWbVqVb7nvbcsY2JiLL49b7/99n2neemll3IcvmrVKkaMGMGoUaMIDg7mxIkTDyWmv3POFTX3lt3WrVtZvHhxvpZ1+PBhU8/Y955XCxcuZPfu3fkPtJR66aWXOHv2rOkc7NevH0OHDiU4OJgpU6YUdngPXc+ePVmwYIHpc1paGv379y+QHtcTExMZN24co0eP5q+//nqgec29/udkxowZ3LhxI1/zFiWZ3w8jR45k7ty53Lp1y6Lrz+93d0l0974ICQm57/H1IPd8md/BMTExjBw58m/Haq7r16+brnsDBw7klVdeMX1OTU3NcZ7BgweXiHMr0969e/nxxx8feD6j0Zjl3tdoNDJhwoSHGVquSlWnPEXR77//Tt26ddmxYwc9e/bMMs5oNGJllXvO/+9///tvr//GjRts2LCBJ5544oHmu19shcHOzo7Zs2ebPf3hw4dxcHCgTp06Zs+TlpZGVFQUkZGRNG7cOM9p/fz88PPzM3vZD9sPP/zAs88+m695cypLT09Pi27P1KlT8zXf8ePH2b9/PzNnzsTW1pbExMRcv4RykpaWhrW1dY7jHsY5V9Ll57ySnPn4+JjOw4ULF9KkSRNatGhRyFEVDHt7e86dO8ft27exs7MjIiICV1fXAlnXn3/+SZUqVXjttdceeF5zr/93S09PJz09nbFjxz7w+oqiu78f3n//fTZu3MhTTz1lGp+5vQVxj5CWlqZrzF3u3hcffPABv/76a57f+w9yz5ff7+C/q1y5cqZt+vbbb3FwcKBr166FEkthadasWb7mS09PZ/Xq1XTr1g0AKysrJk+e/DBDy5USykKUkpLCsWPHmDhxIrNmzaJnz54cPnyYlStXUqFCBaKiopg3bx4rV67k999/x83NjXLlyuHr60vXrl2z3GCcPHmSZcuWcevWLWxsbJgwYQJ79uwhMjKS/v37AxASEsLTTz+Nv7+/KYavv/6aS5cuERwczKOPPkrjxo356aefTL+bs3jxYvz8/Hj88ccZOnQo7dq14+DBgzz55JP4+fmxePFiEhMTsbe355VXXsHLy6tQyjIvQ4cOpW3btuzfv5/U1FRGjBiBra0tGzduxMrKit9++42XX34ZLy8vPvnkE9Nvlfbp04e6devy7bffkpCQQGxsLOXKlePo0aPcvn2bo0eP8q9//QtPT0+WLVtmuhEaMmQIVapU4fDhw6ay/Pbbb4mLiyMmJoa4uDg6d+5M586diYmJYfr06dStW5cTJ05QrVo1Hn/8cb777juuXbvGsGHDqFmzJikpKSxZsoRz586RlpbGc889R9OmTdm6dSv79u3j1q1bXL58mWbNmvHiiy+yfPlybt++TXBwMN7e3gwbNuxvl6M52wMwa9Ysrly5wp07d+jcuTNBQUFARo1L586dCQ8Px87OjuDgYCpUqMDVq1f59NNPiYmJAWDAgAHUqVOHl156iS+//JKUlBRmzZrFjRs3SE1N5fnnn6dp06a5xpmQkEC5cuWwtbUFoHz58qZxp06d4vPPPyclJYXy5cszZMgQXFxcmDRpErVr1+bYsWM0aNCArVu3smDBAqysrLh16xbDhw9nwYIFfPzxx3mec/b29ixfvpwjR45w584dnnjiCTp27EhCQgLz588nOTkZo9HIgAEDinSnYImJiTmeC5nbfO+xnikmJibbeQVw5MgRfv75Z65evcqLL75YYpMi+XsCAgIIDw+nRYsW7Nixg1atWnH06FGAXI+93K6BgOkaArB79272799Ply5d+Oqrr0zXx2nTpvH5558TGRnJ7du3adGihenh7r3n+Pjx41mxYkWW6//58+ez3PCOHDmSMWPGABk1kv7+/hw/fpzg4GAmTZrEjBkzSElJYcaMGdSpU4fjx4/j6urK6NGjsbOz4+TJk3z00UfY29tTt25dDhw4wJw5cyy9K8xWt25dzp49S0xMTLbtPXbsGD/88AMAjRo1yrJfOnbsyOHDhylbtizDhw+nfPnyXLp0Kcd7ioULF+Lk5ERUVBRly5bl2LFjWa4xH3zwAe+99x42NjYkJycTHBxs+lya1K5dm7Nnz5o+//jjj+zatYs7d+7QrFkzevbsme2e77nnnsv1+/Xu86eo2Lp1K7/++iupqanUqVOHl19+OduDi9ymCQ8PZ8WKFRiNRpydnXn77bdJTEzkww8/JDY2FgcHBwYNGoSPjw//+9//uHLlCgkJCURHR9OnTx+OHj3KwYMHcXd3Z/To0VhbWzN48GDatm3LoUOHMBqNDBw4kK+//prLly/zzDPPEBQUREREBL/88gujR48G4JNPPqFu3bq0adOGwYMH0759e/bt24fRaGTEiBFUqVKFTZs2ce7cOfr27cvVq1f55JNPiImJwWAwMGjQIGrVqkVISAgJCQncuXOHLl260KFDB5YvX87NmzcJDg6mWrVqvPrqq/Tv359ly5ZhNBr58ssvTa2snnvuOVq0aEFERASrV6/G0dGRc+fOUatWLdPDti+//JLw8HCsra0JCAgwncM5KV1nWxGzd+9eAgICqFKlCk5OTpw6dQrI+BKbM2cOnp6eREZGsmfPHmbNmkVaWhpjxozJ1ltUamoq8+fPZ/jw4dSsWZPk5GTs7OzMiuGFF17g3LlzpqdBhw8fznN6W1tbU3OryZMnM3DgQCpXrsyJEyf47LPPmDhx4oMWw0OTeYOQ6V//+hctW7YEMp54zZw5k19//ZWffvqJwYMH07Fjxyw3Au+99x5PPfUUdevWJS4ujmnTpjFv3jwgIxGZMmUKdnZ2bN26NUuinpyczDvvvIO1tTURERF8/fXXjBo1Klt8Fy9eZOLEidy8eZPhw4fTqVMnAC5dusSIESOoWrUqY8eO5ffff2fy5Mns27ePVatWMXr0aFatWkWDBg0YMmQIN27cYNy4cTzyyCNAxhPzWbNmYWNjw/Dhw3nyySfp3bs3v/zyywPV2OZWlp6enlnKNa/tsbGxYciQITg5OXH79m3Gjh1L8+bNKVeuHLdu3aJWrVr06tWLr776ik2bNtG9e3eWLl1K/fr1CQ4Oxmg0kpKSkmU9tra2jBo1CkdHRxITE3nrrbcIDAzEYDDkGHvDhg1ZuXIlr7/+Oo888ggtW7akfv36pKamsmTJEkaPHk358uXZuXMn33zzDUOGDMmyHwFOnz7NkSNHaNCgAfv376dhw4ZZbk5yO+c2b96Mo6MjM2bM4M6dO4wfP56GDRuyZ88eGjZsyLPPPovRaLR4E7Gc3Hu+JCUlmX6Xd+nSpTmeC1WqVMnzWPf09Mx2Xm3evJmrV68yefJkLl68yMyZM5VQSo5atWrFypUrady4MWfOnKFdu3amhDKvYy+na6C7u3uO66hevTr//ve/s1zDe/XqhZOTE0ajkcmTJ3PmzBm8vLyyneP29vbZ5v32229z3Z6LFy/y6quvMmDAgGzjoqOjef311xk8eDBz585l9+7dtGnThg8//JBBgwZRp04dli9f/rfKs6ClpaVx4MABAgICgKzbGx8fz/Lly5k5cyZly5Zl6tSp7N27l2bNmnHr1i1q1KjBf/7zH1auXMl3331H//79+eSTT3K9p4iOjmb8+PFYWVllq7Xy9/cnPDycZs2asXPnTpo3b17qkkmj0cihQ4do3749AAcPHiQ6Oprp06eTnp7OrFmzOHLkSLZ7vrS0tAf6fi1MZ8+eZe/evUydOhVra2s+/vhjdu7cSevWre87TYMGDfj000+ZPHkyHh4eJCUlARmvsdSqVYsxY8Zw8OBBFi1aZGpmHxMTw4QJEzhz5gwTJkxg9OjR/Oc//yEkJIQDBw7QpEkTADw8PJg2bRpLlizho48+YvLkyaSkpBAcHGx6oJ6XChUqMGvWLNatW8fPP//MoEGDsoz/7LPPePTRR3nyySdJS0sz3T+89tprODk5cevWLd58802aN29O79692bx5c5b9m2n37t2cP3+e2bNnk5iYyNixY00Ptk+fPs3cuXNxdnbmrbfe4vjx43h6evLHH38wd+5cDAbDfZsUl64zrojZsWMHXbp0AaBly5bs2LGDxo0bU7NmTTw9PQE4evQoTZs2NSWImQfw3S5evIiLiws1a9YEwNHRscBizkzQMmtX586daxr3IM0KC0JeTV6bN28OZHTdvHfv3hyn+fPPPzl//rzpc3JyMjdv3gQgMDAw1yQ9OTmZhQsXcunSJSDrCXy3xo0bY2tri62tLc7Ozly7dg3IuAn38fEBwNvbm0ceeQSDwYCPjw+xsbEAREREsH//fn766ScgIxmIi4sDoEGDBqZ9XrVqVeLi4nK9mTKXOc2Hc9oeNzc31q1bR1hYGABxcXFER0dTrlw5bGxsTMevr6+v6SnZoUOHTE/DrKyssh2/6enpfPPNN/z1118YDAbi4+O5du0aFSpUyDEuBwcHZs6cyV9//cXhw4eZN28evXv3xtfXl3PnzpkeiBiNRlxcXEzzZR7bmf9nfgnt2LEjW/Og3M65gwcPcvbsWdM7g8nJyURHR+Pn58eHH35IamoqzZo1o3r16nmWrSXcu48zH5RA7ueCucf6vZo2bYqVlRVVq1Y1Hfci96pWrRqxsbHs2LGDRo0aZRmX17H3d6+BO3fuZNOmTaSlpZGQkMD58+cxGAx/+3vV3d2d2rVr5zjO09PTdB3w9fUlNjaWGzducPPmTVNTztatW+f7fc2CdPfDqHr16tG+fXvi4+OzbG9kZCT+/v6mFiKPPfYYf/31F82aNcNgMJiut4899hjvvvvufe8pWrRokWsT2vbt2/Pjjz/SrFkztmzZwiuvvFIg210UZe6L2NhYfH19efTRR4GM76KIiAhTrVhKSgqXLl3Kdl486PdrYfrzzz+JjIw0taC7ffs2bm5uZk1jZ2eHv78/Hh4eADg5OQEZ99iZTdEbNmzIokWLTA+1GzVqhLW1ten+LLNs7743A0wPYn18fEhLS8PBwQEHBwesrKyyPSDPSWbzVl9fX/74449s448cOcLw4cMBsLa2Nl2Lfv75Z/bv3w9AfHw8ly9fzvPe4ujRo7Ru3RorKysqVKhAnTp1iIyMxMbGhlq1apnuhzKvw76+vhgMBj7++GMaN25832b+SigLyfXr1zl06BDnzp3DYDBgNBqBjAPY3t7eNJ05v+qS2zRWVlZZxt25c+e+y7K2ts5znszYjEYjZcuWzXcNmKVlPq20srLK9SY4PT2dadOm5Zg43r1P7rVixQr8/f1Nnddk1nLlFsO9cWQ2zQQwGAymz3cfF+np6YwcOTJL80LIqM2+e/68tu9hy2l7Dh8+zJ9//snUqVOxt7dn0qRJpmPI2tra9NTzQeL8/fffSUxMJCQkBBsbG4YOHcrt27fznMfKygp/f3/8/f3x8fFh69at+Pr6UrVqVaZNm5bjPHfv48DAQL7++muSkpI4deoUDRo0yDJtbudceno6/fr1Mz2xv9s777xDeHg4CxYsoGvXrrRt2/Z+m15ocjsXlixZYtaxfq+7j1H9UpXkJTAwkC+//JJJkyZx/fp10/C8rrO5XQPvrmXJ7ZoRExPDTz/9xIwZM3BycmLhwoXcuXPH7OP03u/Mu9fj4OCQ63z3xnz79u1ic27k9sDx7u19kG3J/K7L654ir7KsW7cuixcv5siRIxiNRlMCUBpk7ovk5GRCQkL45ZdfTK+fdOvWjY4dO2aZPvPVkkz5+X4tLOnp6bRr147nn3/+gafZs2ePWbWudx+3d9+L5Xb/du90997PZfbJkNd9deY8VlZWpnu+e90be0REBH/99Zfpe3r8+PH3vcfP65zMaftsbGwICQkhIiKCHTt2sGHDhjw7SyxavaqUIrt376Zt27YsWrSIhQsX8uGHH+Lp6Wlq3pOpbt267N+/n9u3b5OSkpLj00ovLy8SEhI4efIkADdv3iQtLQ1PT0+ioqIwGo3ExcWZxt+tTJkyplo4yHiiev78ee7cuUNycjJ//vlnjvE7Ojri6enJrl27gIwDNSoqKr/FUSjKlCmT5enRo48+yi+//GL6nNv2ODg4ZCmz5ORkU+cRW7duLZBYGzZsyPr1600XhNOnT993HhsbG4vXGicnJ1O2bFns7e25cOGCWb2rPvLII2zYsAHIeFCRnJycbZnOzs7Y2Nhw6NChLE8Gc3Lx4kWio6NNn6OiovDw8KBKlSokJiZy/PhxIOPp97lz53JchoODAzVr1mTp0qU0adIk25Px3M65gIAANmzYYCr3ixcvkpKSQmxsLM7OzgQFBdG+fXuz9l9hyu1cMOdYv/e8EnkQ7dq1o0ePHtmSgvxcZ52dnTl//jxGozHXlinJyck4ODjg6OjI1atXOXDgAJD7OX7v9d/Dw8N0Pp86dSrbDfuDcHJyokyZMqZr1I4dO/K9rMJWq1Ytjhw5QmJiIkajkR07dlC/fn0g434hsxVHZseED3JPkdM1pk2bNrz33nu0a9eu4DaqCHN0dKRfv3789NNPpKam0rBhQ7Zs2WIqp8yax3vv+R70+7UwPfroo+zatYvExEQgo2Ims6XW/aapU6dOlu3LbPJar149fvvtNyAjSXNzc8vz4UV+eHh4cP78eVJTU0lKSuLQoUMPNL+/vz8bN24E/v8eKTk5GScnJ+zs7Dh37pypdVFmh4I5PbCvV68eO3fuxGg0cvXqVY4dO5ZnR4s3b97k5s2bNGnShL59+973vkU1lIVkx44dpl6YMjVv3pyNGzdSsWJF07CaNWvSpEkTgoOD8fDwwM/PL1vTm8z3RpYuXWrqsGD8+PHUqVMHT09PRo0ahbe3NzVq1MgWR7ly5ahTpw4jR44kICCAl156iX/84x+MGjWKypUr5zhPpmHDhvHpp5+yatUqUlNTadWqVaE25bv3nbCAgAB69+6d6/RNmjRh7ty5hIWF8fLLL9OvXz8WL17MqFGjSEtLo169etnaskNG86o1a9YQHBzMv/71L5555hkWLlzI2rVrs3R49DD16NGDZcuWmd4Z8vDwMDXpyE2HDh0IDg6mRo0aD6VTHnMEBASwceNGRo0aRZUqVahVq9Z95+nbty+ffPIJmzdvxsrKioEDB2ZpJta6dWtmzpzJm2++SfXq1e/b8VNmB0Y3btzA2tqaSpUqMWjQIGxsbBg5ciRLly4lOTmZtLQ0OnfujLe3d47LadmyJXPnzmXSpEnZxuV2zrVv356YmBhTpxzly5cnODjY1KGRtbU1Dg4O+epd0pJyOxfMOdbvPa9EHoSbm5uphuVu+bnO9u7dm5kzZ+Lm5oa3t3eODzqqV69O9erVGTlyJJ6enqbmprmd4/de/1u0aMH27dsJDg7Gz88vWyuSBzV48GA+/vhj7O3t8ff3L9BXWAqSi4sLL7zwgqkmuVGjRqbOXjJ79B0zZgyOjo688cYbgPn3FPdeY+rVq8djjz3G//73P1q1amWxbSxqatSoQbVq1di5cydt2rThwoULvPXWW0DGQ9L//ve/VKpUKcs93zPPPPNA36+FycfHh+eee44pU6aQnp6OtbU1AwcOzNKMN7dpatasycCBA5k1axaQcXyOGzeOnj17smjRIkaNGoWDgwOvvvrqQ4/b09OTpk2bMnLkyPveV+ekf//+fPTRR2zcuBFra2sGDRpE48aN2bRpE8HBwdnutdq3b8+oUaPw8/PLsj0tWrTgxIkTpvvkPn364OzsnOt6k5OTeffdd0lNTcVoNNKnT5884zSkF5c2FqVYSkoKDg4O3Lp1i4kTJzJo0KBsHfOIiIhI8Zb5fQ+wevVqEhIS6NevXyFH9XAVRO+hu3fvJiwsjP/+978PdbkiYh7VUBYDH3/8sakZatu2bZVMioiIlEDh4eH88MMPGI1G3N3dGTp0aGGHVOQtWbKEP/74o8T8zqdIcaQaShEREREREckXdcojIiIiIiIi+aKEUkRERERERPJFCaWIiIiIiIjkixJKERERERERyRcllCIiIiIiIpIvSihFREREREQkX5RQioiIiIiISL4ooRQREREREZF8UUIpIiIiIiIi+aKEUkRERERERPJFCaWIiIiIiIjkixJKERERERERyRcllCIiIiIiIpIvSihFREREREQkX5RQioiIiIiISL4ooRQREREREZF8UUIpIiIiIiIi+aKEUkRERERERPJFCaWIiBQrUVFRGAwGzp8/b9H12tjYsHXrVouuU0REpKhTQikiIkXC448/ztSpUwtk2X379mXAgAEFsuwHtWzZMmrWrFlgyw8NDaVTp05UqFABZ2dn/P39mTBhAteuXTNr/urVq/PVV18VWHwiIlKyKKEUEREpYu7cuZOv+ZYtW8bTTz9Np06dOHbsGNeuXePnn3/m+vXrREREPOQoC05+t19ERCxPCaWIiBQ5ERERtG/fHhcXF3x9fZk6dSppaWlZpvnll1+oXbs2FSpU4JlnniEmJsbs5RsMBhYtWkTTpk0pV64cLVq04OjRo6bx169fp0+fPri6ulKtWjU+//zzLPNPmjSJoKCgLMPurmFNSEjgueeew83NDWdnZxo0aMBvv/3Grl27GDx4MKdOncLJyQknJye2bt3K1q1bsbGx4csvv8TX1xdXV1c+/PBDGjZsmGUdkZGR2NjYcObMmWzblJSUxPDhwxk7diyjRo2iYsWKANSoUYN58+bx2GOPAfDee+9Rt25dypUrh4+PD2PHjjWV7dNPP83Zs2cZMGAATk5OdOrUCYDU1FSmT59uKu9WrVqxf/9+07rv3LnDG2+8gaenJ5UqVWLWrFnUrFmTZcuWmab5/vvvadiwIc7OzjRs2JAffvjBNC6z1nb27NlUrVqVgIAAxowZwzPPPJNlGzdv3oyzszM3btzIY++KiIglKaEUEZEi5dq1a3Ts2JF27dpx6dIl1q5dy5IlS5g7d26W6b744gu2b9/O2bNnsbKy4sUXX3yg9Sxbtozvv/+euLg4vL29+e9//2saN3z4cE6cOMGRI0eIiIhgzZo12RLavMyePZvk5GTOnDnD1atXWbVqFVWrVuUf//gHH330Eb6+viQlJZGUlMTjjz8OQFpaGuvXr+ePP/7g8uXL9O7dm8jISMLCwkzLXbx4MUFBQVSrVi3bOnfu3Mm1a9d44YUX8oytatWqrF+/nsTERNasWcOSJUv47LPPAPjpp5/w8fHhs88+IykpiQ0bNgAwYcIE1qxZwy+//MKVK1d4+eWXeeKJJ0hISABgxowZrF+/nt27d3P69GnOnz+fJendtWsXvXv3JiQkhCtXrjB9+nR69erFnj17TNNERUVx8eJFTpw4QVhYGIMGDWL9+vVER0ebpvnss8/o1asXZcuWNXtfiIhIwVJCKSIiRcratWuxs7Pj7bffxt7ennr16jFmzBhT0pNp4sSJVKpUifLlyzN79mw2btzIxYsXzV5PcHAwPj4+2Nvb07dvX/bt2weA0Whk+fLlTJkyhUqVKuHs7MzMmTMfaBvs7Oy4cuUKx44dIz09ndq1a1OjRo37zhcSEoKzszOOjo6UL1+e559/nsWLFwMZCefnn3/OwIEDc5w3NjYWAC8vrzzX0b17d2rUqIHBYKBRo0a89NJLbNq0Kdfp09PTWbBgAbNnz8bX1xdra2v69+9P5cqVWbt2LZCR3I8ePRpfX1/KlCnDzJkzsbL6/1uMpUuX0r17d/75z39iY2NDly5d+Ne//sWSJUtM09ja2hISEkKZMmVwdHTEz8+PNm3amGqHExIS+OGHH3LdfhERKRxKKEVEpEg5d+4c1atXx2AwmIb5+flx7ty5LNNVr1492/8P0vNr5cqVTf+XLVuW69evAxmJ2a1bt7Is35xk8G7BwcF06NCBPn364OHhQZ8+fbh8+XKe81hZWeHt7Z1l2CuvvMI333xDcnIy69atIzU1la5du+Y4v4eHBwAXLlzIcz3ffPMNTZs2NTXHXbhwoSkZzUlcXBxJSUk8/fTTVKhQwfR36tQpU3lfuHAhS61pmTJlTPFAxj719fXNstx792nlypWxt7fPtv2ZCfVXX31FvXr1aNKkSZ7bJyIilqWEUkREihRvb2/OnDlDenq6adipU6eyJVtRUVHZ/q9aterfXr+Hhwd2dnZZln/69Oks0zg5OWV7j+/u2tGyZcsybdo0Dh06xOHDh7lw4QLBwcEAWWru7mYwGLIk0QBNmzbFz8+P7zUgszsAAAQwSURBVL77jsWLF9O3b19sbW1znL9ly5Y4OzvzzTff5Lpt586d48UXX+Ttt98mOjqaa9euMXTo0CxlfW987u7ulC1bltDQUK5evWr6u3HjBm+++SaQUSt6dxPXmzdvZklSvb29s5Xhvfs0p3Lp1q0b169fZ9u2bSxevFi1kyIiRZASShERKVK6dOlCSkoK06dP5/bt2xw7doyZM2fSv3//LNNNmTKFy5cvk5iYyJgxY+jQoQNVqlT52+u3srLihRdeYOLEiabljx07Nss0gYGBhIeHs3//flJTU/nggw+yJEw//fQTf/31F2lpaTg5OeHg4ICNjQ0AlSpVIiYmhsTERLPiGTRoEHPmzGHdunV5/vSJk5MT8+bNY8aMGcybN8+U0J05c4ZRo0bx22+/kZSUhNFoxMPDA1tbW3bv3s2XX36ZZTmVKlXixIkTps8Gg4HXX3+dUaNGmYYnJSXx66//197du6QWx3Ec/0QaRODxDCUGkUpCDlFETyhBBNIDiEtF0QM6hFvQA4Hg0lAILbW1NAQ2RUEEUdIi9Ac4tEU41xJBQRjl3eQ2VBeLq5f7fq2/3/nyPZzpw+97zjkvhujZ2Vltbm4ql8vp+flZ8Xhcb29vxRqRSESHh4c6Pz8vvit6dHSkaDT66b1brVZFIhEtLi7q+vr6y/dDAQB/H4ESAFAxqqqqZBiG0um0Li4u5HA4NDQ0pLm5OS0tLb3bOzMzo/7+fjU1NSmfz//ovxO3t7fldrvV2tqqtrY2hUIhVVdXF9cHBga0vLys4eFhOZ1O3d7eKhAIFNdvbm4UCoVks9nkcrlUW1urZDIpSRocHFQwGJTb7Zbdblcmk/m0l+npaeVyOQUCAXm93k/3RqNRHR8f6/T0VC0tLTIMQ6Ojo6qrq1N7e7t8Pp/W1tYUDodlt9uVTCY1NTX1rkYikVAqlZJpmhoZGZGk4jXhcFg2m01er1c7OzvF0BiPxxUMBtXT0yOXyyWn06nGxsbiCKvf79fe3p5WVlZkmqZWV1eVSqXU19f35bOYn59XNpvVxMSEDMP4cj8A4O+qKvw+5wIAQJl0dnYqFospFouVu5WKUigU5PF4tL6+/s+c0D0+Pso0TWUyGfn9/m/Venp6ksPhUDqd/nYtAMDP44QSAFB22WxWV1dX6u3tLXcrFWd/f1/5fF5jY2PlbuVD9/f3Ojs708vLix4eHrSwsKDm5mZ1d3d/q26hUNDW1pZ8Ph9hEgAqlKXcDQAA/m+Tk5O6vLzUxsaGOjo6yt1ORamvr5fFYtHu7q5qamrK3c6HXl9flUgkND4+LqvVqq6uLp2cnHz4AaE/cXd3J4/Ho4aGBh0cHPxgtwCAn8TIKwAAAACgJIy8AgAAAABKQqAEAAAAAJSEQAkAAAAAKAmBEgAAAABQEgIlAAAAAKAkBEoAAAAAQEl+Aa4+Jx82zJ3UAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_df.groupby(\"job_industry_category\")[\"past_3_years_bike_related_purchases\"].count().plot(kind='bar',figsize=[15,5], width=0.6)\n",
    "plt.title(\"Bike Related Purchases Based on industry\\n\", fontsize=16)\n",
    "plt.xlabel(\"\\nJob Industry Category\", fontsize=13,color=\"black\")\n",
    "plt.ylabel(\"Past three years bike purchased\\n\",fontsize=13, color=\"black\")\n",
    "plt.xticks(rotation=0)\n",
    "plt.yticks(np.arange(0,4500,500))\n",
    "\n",
    "plt.legend(loc='upper right',fontsize=11)\n",
    "\n",
    "label = new_df.groupby(\"job_industry_category\")[\"past_3_years_bike_related_purchases\"].count()\n",
    "for i in range(9):\n",
    "    plt.text(x = i, y = label[i], s = str(\"\\n\"+str(int(label[i]))+\"\\n\"),horizontalalignment='center',rotation = 360, color = \"black\", fontsize=11)\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "job_industry_category\n",
       "Argiculture            256388.0\n",
       "Entertainment          345259.0\n",
       "Financial Services    1898996.0\n",
       "Health                1480275.0\n",
       "IT                     318906.0\n",
       "Manufacturing         1868154.0\n",
       "Property               597923.0\n",
       "Retail                 818723.0\n",
       "Telecommunications     167483.0\n",
       "Name: profit, dtype: float64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.groupby(\"job_industry_category\")[\"profit\"].sum().round()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_df.groupby(\"job_industry_category\")[\"profit\"].sum().plot(kind='bar',figsize=[15,5], width=0.6)\n",
    "plt.title(\"Profit in terms of Job Industry\\n\", fontsize=16)\n",
    "plt.xlabel(\"\\nJob Industry Category\", fontsize=13,color=\"black\")\n",
    "plt.ylabel(\"Profit in Dolalrs\\n\",fontsize=13, color=\"black\")\n",
    "plt.xticks(rotation=0)\n",
    "plt.yticks(np.arange(150000,2500000,250000))\n",
    "plt.legend(loc='upper right',fontsize=11)\n",
    "\n",
    "label = new_df.groupby(\"job_industry_category\")[\"profit\"].sum().round()\n",
    "for i in range(9):\n",
    "    plt.text(x = i, y = label[i], s = str(\"\\n\"+\"$\"+str(int(label[i]))+\"\\n\"),horizontalalignment='center',rotation = 360, color = \"black\", fontsize=11)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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>age_group</th>\n",
       "      <th>&lt;30</th>\n",
       "      <th>30-40</th>\n",
       "      <th>40-50</th>\n",
       "      <th>50-60</th>\n",
       "      <th>60+</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Female</td>\n",
       "      <td>1280</td>\n",
       "      <td>1248</td>\n",
       "      <td>2536</td>\n",
       "      <td>1235</td>\n",
       "      <td>836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Male</td>\n",
       "      <td>1324</td>\n",
       "      <td>1207</td>\n",
       "      <td>2354</td>\n",
       "      <td>1202</td>\n",
       "      <td>832</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "age_group   <30  30-40  40-50  50-60  60+\n",
       "gender                                   \n",
       "Female     1280   1248   2536   1235  836\n",
       "Male       1324   1207   2354   1202  832"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(data=new_df, index=\"gender\", columns=\"age_group\", values=\"past_3_years_bike_related_purchases\",aggfunc='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.pivot_table(data=new_df, index=\"gender\", columns=\"age_group\", values=\"past_3_years_bike_related_purchases\",aggfunc='count').plot(kind='bar',figsize=[9,5])\n",
    "\n",
    "plt.title(\"Bike Purchased w.r.t Gender - Subcategory: Age Group\\n\", fontsize=16)\n",
    "plt.xlabel(\"\\nGender\", fontsize=14,color=\"black\")\n",
    "plt.ylabel(\"Number of bike purchased in last 3 years\\n\",fontsize=11, color=\"black\")\n",
    "plt.legend(loc='upper right',fontsize=12)\n",
    "plt.xticks(rotation=0)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['customer_id', 'gender', 'past_3_years_bike_related_purchases',\n",
       "       'job_title', 'job_industry_category', 'wealth_segment',\n",
       "       'deceased_indicator', 'owns_car', 'tenure', 'transaction_id',\n",
       "       'product_id', 'transaction_date', 'online_order', 'order_status',\n",
       "       'brand', 'product_line', 'product_class', 'product_size',\n",
       "       'product_first_sold_date', 'address', 'postcode', 'state', 'country',\n",
       "       'property_valuation', 'age', 'profit', 'recency', 'age_group',\n",
       "       'owns_car_flag'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>past_3_years_bike_related_purchases</th>\n",
       "      <th>job_title</th>\n",
       "      <th>job_industry_category</th>\n",
       "      <th>wealth_segment</th>\n",
       "      <th>deceased_indicator</th>\n",
       "      <th>owns_car</th>\n",
       "      <th>tenure</th>\n",
       "      <th>transaction_id</th>\n",
       "      <th>...</th>\n",
       "      <th>address</th>\n",
       "      <th>postcode</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>property_valuation</th>\n",
       "      <th>age</th>\n",
       "      <th>profit</th>\n",
       "      <th>recency</th>\n",
       "      <th>age_group</th>\n",
       "      <th>owns_car_flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>81</td>\n",
       "      <td>Administrative Officer</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2261.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6 Meadow Vale Court</td>\n",
       "      <td>2153.0</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>448.68</td>\n",
       "      <td>240</td>\n",
       "      <td>40-50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>81</td>\n",
       "      <td>Administrative Officer</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6743.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6 Meadow Vale Court</td>\n",
       "      <td>2153.0</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>547.28</td>\n",
       "      <td>202</td>\n",
       "      <td>40-50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>81</td>\n",
       "      <td>Administrative Officer</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16411.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6 Meadow Vale Court</td>\n",
       "      <td>2153.0</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1230.30</td>\n",
       "      <td>128</td>\n",
       "      <td>40-50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>9</td>\n",
       "      <td>Female</td>\n",
       "      <td>97</td>\n",
       "      <td>Business Systems Development Analyst</td>\n",
       "      <td>Argiculture</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2501.0</td>\n",
       "      <td>...</td>\n",
       "      <td>97736 7th Trail</td>\n",
       "      <td>2023.0</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>12.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>327.98</td>\n",
       "      <td>234</td>\n",
       "      <td>40-50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>9</td>\n",
       "      <td>Female</td>\n",
       "      <td>97</td>\n",
       "      <td>Business Systems Development Analyst</td>\n",
       "      <td>Argiculture</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2979.0</td>\n",
       "      <td>...</td>\n",
       "      <td>97736 7th Trail</td>\n",
       "      <td>2023.0</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>12.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>547.28</td>\n",
       "      <td>299</td>\n",
       "      <td>40-50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19953</td>\n",
       "      <td>3490</td>\n",
       "      <td>Male</td>\n",
       "      <td>45</td>\n",
       "      <td>VP Product Management</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>14.0</td>\n",
       "      <td>18685.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3609 Shelley Road</td>\n",
       "      <td>2126.0</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1279.40</td>\n",
       "      <td>201</td>\n",
       "      <td>40-50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19954</td>\n",
       "      <td>3491</td>\n",
       "      <td>Female</td>\n",
       "      <td>69</td>\n",
       "      <td>Business Systems Development Analyst</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1282.0</td>\n",
       "      <td>...</td>\n",
       "      <td>82 Dahle Crossing</td>\n",
       "      <td>3195.0</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>50.66</td>\n",
       "      <td>331</td>\n",
       "      <td>40-50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19955</td>\n",
       "      <td>3491</td>\n",
       "      <td>Female</td>\n",
       "      <td>69</td>\n",
       "      <td>Business Systems Development Analyst</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6569.0</td>\n",
       "      <td>...</td>\n",
       "      <td>82 Dahle Crossing</td>\n",
       "      <td>3195.0</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>431.33</td>\n",
       "      <td>189</td>\n",
       "      <td>40-50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19956</td>\n",
       "      <td>3491</td>\n",
       "      <td>Female</td>\n",
       "      <td>69</td>\n",
       "      <td>Business Systems Development Analyst</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14219.0</td>\n",
       "      <td>...</td>\n",
       "      <td>82 Dahle Crossing</td>\n",
       "      <td>3195.0</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>812.44</td>\n",
       "      <td>245</td>\n",
       "      <td>40-50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19957</td>\n",
       "      <td>3491</td>\n",
       "      <td>Female</td>\n",
       "      <td>69</td>\n",
       "      <td>Business Systems Development Analyst</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>10.0</td>\n",
       "      <td>19444.0</td>\n",
       "      <td>...</td>\n",
       "      <td>82 Dahle Crossing</td>\n",
       "      <td>3195.0</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>135.85</td>\n",
       "      <td>231</td>\n",
       "      <td>40-50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4890 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       customer_id  gender  past_3_years_bike_related_purchases  \\\n",
       "11               2    Male                                   81   \n",
       "12               2    Male                                   81   \n",
       "13               2    Male                                   81   \n",
       "48               9  Female                                   97   \n",
       "49               9  Female                                   97   \n",
       "...            ...     ...                                  ...   \n",
       "19953         3490    Male                                   45   \n",
       "19954         3491  Female                                   69   \n",
       "19955         3491  Female                                   69   \n",
       "19956         3491  Female                                   69   \n",
       "19957         3491  Female                                   69   \n",
       "\n",
       "                                  job_title job_industry_category  \\\n",
       "11                   Administrative Officer    Financial Services   \n",
       "12                   Administrative Officer    Financial Services   \n",
       "13                   Administrative Officer    Financial Services   \n",
       "48     Business Systems Development Analyst           Argiculture   \n",
       "49     Business Systems Development Analyst           Argiculture   \n",
       "...                                     ...                   ...   \n",
       "19953                 VP Product Management    Financial Services   \n",
       "19954  Business Systems Development Analyst    Financial Services   \n",
       "19955  Business Systems Development Analyst    Financial Services   \n",
       "19956  Business Systems Development Analyst    Financial Services   \n",
       "19957  Business Systems Development Analyst    Financial Services   \n",
       "\n",
       "          wealth_segment deceased_indicator owns_car  tenure  transaction_id  \\\n",
       "11         Mass Customer                  N      Yes    16.0          2261.0   \n",
       "12         Mass Customer                  N      Yes    16.0          6743.0   \n",
       "13         Mass Customer                  N      Yes    16.0         16411.0   \n",
       "48     Affluent Customer                  N      Yes     8.0          2501.0   \n",
       "49     Affluent Customer                  N      Yes     8.0          2979.0   \n",
       "...                  ...                ...      ...     ...             ...   \n",
       "19953     High Net Worth                  N       No    14.0         18685.0   \n",
       "19954  Affluent Customer                  N       No    10.0          1282.0   \n",
       "19955  Affluent Customer                  N       No    10.0          6569.0   \n",
       "19956  Affluent Customer                  N       No    10.0         14219.0   \n",
       "19957  Affluent Customer                  N       No    10.0         19444.0   \n",
       "\n",
       "       ...              address postcode  state    country property_valuation  \\\n",
       "11     ...  6 Meadow Vale Court   2153.0    NSW  Australia               10.0   \n",
       "12     ...  6 Meadow Vale Court   2153.0    NSW  Australia               10.0   \n",
       "13     ...  6 Meadow Vale Court   2153.0    NSW  Australia               10.0   \n",
       "48     ...      97736 7th Trail   2023.0    NSW  Australia               12.0   \n",
       "49     ...      97736 7th Trail   2023.0    NSW  Australia               12.0   \n",
       "...    ...                  ...      ...    ...        ...                ...   \n",
       "19953  ...    3609 Shelley Road   2126.0    NSW  Australia               10.0   \n",
       "19954  ...    82 Dahle Crossing   3195.0    VIC  Australia               10.0   \n",
       "19955  ...    82 Dahle Crossing   3195.0    VIC  Australia               10.0   \n",
       "19956  ...    82 Dahle Crossing   3195.0    VIC  Australia               10.0   \n",
       "19957  ...    82 Dahle Crossing   3195.0    VIC  Australia               10.0   \n",
       "\n",
       "        age   profit recency age_group owns_car_flag  \n",
       "11     41.0   448.68     240     40-50             1  \n",
       "12     41.0   547.28     202     40-50             1  \n",
       "13     41.0  1230.30     128     40-50             1  \n",
       "48     48.0   327.98     234     40-50             1  \n",
       "49     48.0   547.28     299     40-50             1  \n",
       "...     ...      ...     ...       ...           ...  \n",
       "19953  41.0  1279.40     201     40-50             0  \n",
       "19954  45.0    50.66     331     40-50             0  \n",
       "19955  45.0   431.33     189     40-50             0  \n",
       "19956  45.0   812.44     245     40-50             0  \n",
       "19957  45.0   135.85     231     40-50             0  \n",
       "\n",
       "[4890 rows x 29 columns]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = new_df[new_df[\"age_group\"]==\"40-50\"]\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\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>customer_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>past_3_years_bike_related_purchases</th>\n",
       "      <th>job_title</th>\n",
       "      <th>job_industry_category</th>\n",
       "      <th>wealth_segment</th>\n",
       "      <th>deceased_indicator</th>\n",
       "      <th>owns_car</th>\n",
       "      <th>tenure</th>\n",
       "      <th>transaction_id</th>\n",
       "      <th>...</th>\n",
       "      <th>address</th>\n",
       "      <th>postcode</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>property_valuation</th>\n",
       "      <th>age</th>\n",
       "      <th>profit</th>\n",
       "      <th>recency</th>\n",
       "      <th>age_group</th>\n",
       "      <th>owns_car_flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>81</td>\n",
       "      <td>Administrative Officer</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2261.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6 Meadow Vale Court</td>\n",
       "      <td>2153.0</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>448.68</td>\n",
       "      <td>240</td>\n",
       "      <td>40-50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>81</td>\n",
       "      <td>Administrative Officer</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6743.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6 Meadow Vale Court</td>\n",
       "      <td>2153.0</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>547.28</td>\n",
       "      <td>202</td>\n",
       "      <td>40-50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>81</td>\n",
       "      <td>Administrative Officer</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16411.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6 Meadow Vale Court</td>\n",
       "      <td>2153.0</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1230.30</td>\n",
       "      <td>128</td>\n",
       "      <td>40-50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>110</td>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>72</td>\n",
       "      <td>Project Manager</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6657.0</td>\n",
       "      <td>...</td>\n",
       "      <td>032 Bartelt Crossing</td>\n",
       "      <td>2444.0</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>8.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>451.65</td>\n",
       "      <td>59</td>\n",
       "      <td>40-50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>111</td>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>72</td>\n",
       "      <td>Project Manager</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11330.0</td>\n",
       "      <td>...</td>\n",
       "      <td>032 Bartelt Crossing</td>\n",
       "      <td>2444.0</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>8.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>641.64</td>\n",
       "      <td>31</td>\n",
       "      <td>40-50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19867</td>\n",
       "      <td>3474</td>\n",
       "      <td>Male</td>\n",
       "      <td>93</td>\n",
       "      <td>Assistant Manager</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>14.0</td>\n",
       "      <td>9955.0</td>\n",
       "      <td>...</td>\n",
       "      <td>06 Lake View Plaza</td>\n",
       "      <td>4515.0</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>209.84</td>\n",
       "      <td>111</td>\n",
       "      <td>40-50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19868</td>\n",
       "      <td>3474</td>\n",
       "      <td>Male</td>\n",
       "      <td>93</td>\n",
       "      <td>Assistant Manager</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>14.0</td>\n",
       "      <td>16442.0</td>\n",
       "      <td>...</td>\n",
       "      <td>06 Lake View Plaza</td>\n",
       "      <td>4515.0</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>198.29</td>\n",
       "      <td>71</td>\n",
       "      <td>40-50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19869</td>\n",
       "      <td>3474</td>\n",
       "      <td>Male</td>\n",
       "      <td>93</td>\n",
       "      <td>Assistant Manager</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>14.0</td>\n",
       "      <td>16803.0</td>\n",
       "      <td>...</td>\n",
       "      <td>06 Lake View Plaza</td>\n",
       "      <td>4515.0</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>114.93</td>\n",
       "      <td>170</td>\n",
       "      <td>40-50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19941</td>\n",
       "      <td>3488</td>\n",
       "      <td>Male</td>\n",
       "      <td>63</td>\n",
       "      <td>Speech Pathologist</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>9.0</td>\n",
       "      <td>871.0</td>\n",
       "      <td>...</td>\n",
       "      <td>42293 Banding Plaza</td>\n",
       "      <td>2136.0</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>751.02</td>\n",
       "      <td>69</td>\n",
       "      <td>40-50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19942</td>\n",
       "      <td>3488</td>\n",
       "      <td>Male</td>\n",
       "      <td>63</td>\n",
       "      <td>Speech Pathologist</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16036.0</td>\n",
       "      <td>...</td>\n",
       "      <td>42293 Banding Plaza</td>\n",
       "      <td>2136.0</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>64.93</td>\n",
       "      <td>17</td>\n",
       "      <td>40-50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2487 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       customer_id gender  past_3_years_bike_related_purchases  \\\n",
       "11               2   Male                                   81   \n",
       "12               2   Male                                   81   \n",
       "13               2   Male                                   81   \n",
       "110             20   Male                                   72   \n",
       "111             20   Male                                   72   \n",
       "...            ...    ...                                  ...   \n",
       "19867         3474   Male                                   93   \n",
       "19868         3474   Male                                   93   \n",
       "19869         3474   Male                                   93   \n",
       "19941         3488   Male                                   63   \n",
       "19942         3488   Male                                   63   \n",
       "\n",
       "                    job_title job_industry_category wealth_segment  \\\n",
       "11     Administrative Officer    Financial Services  Mass Customer   \n",
       "12     Administrative Officer    Financial Services  Mass Customer   \n",
       "13     Administrative Officer    Financial Services  Mass Customer   \n",
       "110           Project Manager         Manufacturing  Mass Customer   \n",
       "111           Project Manager         Manufacturing  Mass Customer   \n",
       "...                       ...                   ...            ...   \n",
       "19867       Assistant Manager         Manufacturing  Mass Customer   \n",
       "19868       Assistant Manager         Manufacturing  Mass Customer   \n",
       "19869       Assistant Manager         Manufacturing  Mass Customer   \n",
       "19941      Speech Pathologist         Manufacturing  Mass Customer   \n",
       "19942      Speech Pathologist         Manufacturing  Mass Customer   \n",
       "\n",
       "      deceased_indicator owns_car  tenure  transaction_id  ...  \\\n",
       "11                     N      Yes    16.0          2261.0  ...   \n",
       "12                     N      Yes    16.0          6743.0  ...   \n",
       "13                     N      Yes    16.0         16411.0  ...   \n",
       "110                    N       No    11.0          6657.0  ...   \n",
       "111                    N       No    11.0         11330.0  ...   \n",
       "...                  ...      ...     ...             ...  ...   \n",
       "19867                  N       No    14.0          9955.0  ...   \n",
       "19868                  N       No    14.0         16442.0  ...   \n",
       "19869                  N       No    14.0         16803.0  ...   \n",
       "19941                  N      Yes     9.0           871.0  ...   \n",
       "19942                  N      Yes     9.0         16036.0  ...   \n",
       "\n",
       "                    address postcode  state    country property_valuation  \\\n",
       "11      6 Meadow Vale Court   2153.0    NSW  Australia               10.0   \n",
       "12      6 Meadow Vale Court   2153.0    NSW  Australia               10.0   \n",
       "13      6 Meadow Vale Court   2153.0    NSW  Australia               10.0   \n",
       "110    032 Bartelt Crossing   2444.0    NSW  Australia                8.0   \n",
       "111    032 Bartelt Crossing   2444.0    NSW  Australia                8.0   \n",
       "...                     ...      ...    ...        ...                ...   \n",
       "19867    06 Lake View Plaza   4515.0    QLD  Australia                1.0   \n",
       "19868    06 Lake View Plaza   4515.0    QLD  Australia                1.0   \n",
       "19869    06 Lake View Plaza   4515.0    QLD  Australia                1.0   \n",
       "19941   42293 Banding Plaza   2136.0    NSW  Australia               10.0   \n",
       "19942   42293 Banding Plaza   2136.0    NSW  Australia               10.0   \n",
       "\n",
       "        age   profit recency age_group owns_car_flag  \n",
       "11     41.0   448.68     240     40-50             1  \n",
       "12     41.0   547.28     202     40-50             1  \n",
       "13     41.0  1230.30     128     40-50             1  \n",
       "110    41.0   451.65      59     40-50             0  \n",
       "111    41.0   641.64      31     40-50             0  \n",
       "...     ...      ...     ...       ...           ...  \n",
       "19867  42.0   209.84     111     40-50             0  \n",
       "19868  42.0   198.29      71     40-50             0  \n",
       "19869  42.0   114.93     170     40-50             0  \n",
       "19941  46.0   751.02      69     40-50             1  \n",
       "19942  46.0    64.93      17     40-50             1  \n",
       "\n",
       "[2487 rows x 29 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df1[df1[\"wealth_segment\"]==\"Mass Customer\"]\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>past_3_years_bike_related_purchases</th>\n",
       "      <th>job_title</th>\n",
       "      <th>job_industry_category</th>\n",
       "      <th>wealth_segment</th>\n",
       "      <th>deceased_indicator</th>\n",
       "      <th>owns_car</th>\n",
       "      <th>tenure</th>\n",
       "      <th>transaction_id</th>\n",
       "      <th>...</th>\n",
       "      <th>address</th>\n",
       "      <th>postcode</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>property_valuation</th>\n",
       "      <th>age</th>\n",
       "      <th>profit</th>\n",
       "      <th>recency</th>\n",
       "      <th>age_group</th>\n",
       "      <th>owns_car_flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>81</td>\n",
       "      <td>Administrative Officer</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2261.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6 Meadow Vale Court</td>\n",
       "      <td>2153.0</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>448.68</td>\n",
       "      <td>240</td>\n",
       "      <td>40-50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>81</td>\n",
       "      <td>Administrative Officer</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6743.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6 Meadow Vale Court</td>\n",
       "      <td>2153.0</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>547.28</td>\n",
       "      <td>202</td>\n",
       "      <td>40-50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>81</td>\n",
       "      <td>Administrative Officer</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16411.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6 Meadow Vale Court</td>\n",
       "      <td>2153.0</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1230.30</td>\n",
       "      <td>128</td>\n",
       "      <td>40-50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>142</td>\n",
       "      <td>25</td>\n",
       "      <td>Male</td>\n",
       "      <td>72</td>\n",
       "      <td>Accounting Assistant III</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2354.0</td>\n",
       "      <td>...</td>\n",
       "      <td>28 5th Center</td>\n",
       "      <td>4413.0</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>3.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>693.76</td>\n",
       "      <td>301</td>\n",
       "      <td>40-50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>143</td>\n",
       "      <td>25</td>\n",
       "      <td>Male</td>\n",
       "      <td>72</td>\n",
       "      <td>Accounting Assistant III</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>21.0</td>\n",
       "      <td>4309.0</td>\n",
       "      <td>...</td>\n",
       "      <td>28 5th Center</td>\n",
       "      <td>4413.0</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>3.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>1230.27</td>\n",
       "      <td>221</td>\n",
       "      <td>40-50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19570</td>\n",
       "      <td>3420</td>\n",
       "      <td>Male</td>\n",
       "      <td>96</td>\n",
       "      <td>Financial Advisor</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>19.0</td>\n",
       "      <td>13137.0</td>\n",
       "      <td>...</td>\n",
       "      <td>00283 Loftsgordon Center</td>\n",
       "      <td>4118.0</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>75.48</td>\n",
       "      <td>159</td>\n",
       "      <td>40-50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19571</td>\n",
       "      <td>3420</td>\n",
       "      <td>Male</td>\n",
       "      <td>96</td>\n",
       "      <td>Financial Advisor</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>19.0</td>\n",
       "      <td>15146.0</td>\n",
       "      <td>...</td>\n",
       "      <td>00283 Loftsgordon Center</td>\n",
       "      <td>4118.0</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1055.82</td>\n",
       "      <td>296</td>\n",
       "      <td>40-50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19572</td>\n",
       "      <td>3420</td>\n",
       "      <td>Male</td>\n",
       "      <td>96</td>\n",
       "      <td>Financial Advisor</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>19.0</td>\n",
       "      <td>17188.0</td>\n",
       "      <td>...</td>\n",
       "      <td>00283 Loftsgordon Center</td>\n",
       "      <td>4118.0</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>690.49</td>\n",
       "      <td>252</td>\n",
       "      <td>40-50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19573</td>\n",
       "      <td>3420</td>\n",
       "      <td>Male</td>\n",
       "      <td>96</td>\n",
       "      <td>Financial Advisor</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>19.0</td>\n",
       "      <td>18658.0</td>\n",
       "      <td>...</td>\n",
       "      <td>00283 Loftsgordon Center</td>\n",
       "      <td>4118.0</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>802.26</td>\n",
       "      <td>259</td>\n",
       "      <td>40-50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19574</td>\n",
       "      <td>3420</td>\n",
       "      <td>Male</td>\n",
       "      <td>96</td>\n",
       "      <td>Financial Advisor</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19874.0</td>\n",
       "      <td>...</td>\n",
       "      <td>00283 Loftsgordon Center</td>\n",
       "      <td>4118.0</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>17.87</td>\n",
       "      <td>216</td>\n",
       "      <td>40-50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>498 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       customer_id gender  past_3_years_bike_related_purchases  \\\n",
       "11               2   Male                                   81   \n",
       "12               2   Male                                   81   \n",
       "13               2   Male                                   81   \n",
       "142             25   Male                                   72   \n",
       "143             25   Male                                   72   \n",
       "...            ...    ...                                  ...   \n",
       "19570         3420   Male                                   96   \n",
       "19571         3420   Male                                   96   \n",
       "19572         3420   Male                                   96   \n",
       "19573         3420   Male                                   96   \n",
       "19574         3420   Male                                   96   \n",
       "\n",
       "                      job_title job_industry_category wealth_segment  \\\n",
       "11       Administrative Officer    Financial Services  Mass Customer   \n",
       "12       Administrative Officer    Financial Services  Mass Customer   \n",
       "13       Administrative Officer    Financial Services  Mass Customer   \n",
       "142    Accounting Assistant III    Financial Services  Mass Customer   \n",
       "143    Accounting Assistant III    Financial Services  Mass Customer   \n",
       "...                         ...                   ...            ...   \n",
       "19570         Financial Advisor    Financial Services  Mass Customer   \n",
       "19571         Financial Advisor    Financial Services  Mass Customer   \n",
       "19572         Financial Advisor    Financial Services  Mass Customer   \n",
       "19573         Financial Advisor    Financial Services  Mass Customer   \n",
       "19574         Financial Advisor    Financial Services  Mass Customer   \n",
       "\n",
       "      deceased_indicator owns_car  tenure  transaction_id  ...  \\\n",
       "11                     N      Yes    16.0          2261.0  ...   \n",
       "12                     N      Yes    16.0          6743.0  ...   \n",
       "13                     N      Yes    16.0         16411.0  ...   \n",
       "142                    N      Yes    21.0          2354.0  ...   \n",
       "143                    N      Yes    21.0          4309.0  ...   \n",
       "...                  ...      ...     ...             ...  ...   \n",
       "19570                  N      Yes    19.0         13137.0  ...   \n",
       "19571                  N      Yes    19.0         15146.0  ...   \n",
       "19572                  N      Yes    19.0         17188.0  ...   \n",
       "19573                  N      Yes    19.0         18658.0  ...   \n",
       "19574                  N      Yes    19.0         19874.0  ...   \n",
       "\n",
       "                        address postcode  state    country property_valuation  \\\n",
       "11          6 Meadow Vale Court   2153.0    NSW  Australia               10.0   \n",
       "12          6 Meadow Vale Court   2153.0    NSW  Australia               10.0   \n",
       "13          6 Meadow Vale Court   2153.0    NSW  Australia               10.0   \n",
       "142               28 5th Center   4413.0    QLD  Australia                3.0   \n",
       "143               28 5th Center   4413.0    QLD  Australia                3.0   \n",
       "...                         ...      ...    ...        ...                ...   \n",
       "19570  00283 Loftsgordon Center   4118.0    QLD  Australia                5.0   \n",
       "19571  00283 Loftsgordon Center   4118.0    QLD  Australia                5.0   \n",
       "19572  00283 Loftsgordon Center   4118.0    QLD  Australia                5.0   \n",
       "19573  00283 Loftsgordon Center   4118.0    QLD  Australia                5.0   \n",
       "19574  00283 Loftsgordon Center   4118.0    QLD  Australia                5.0   \n",
       "\n",
       "        age   profit recency age_group owns_car_flag  \n",
       "11     41.0   448.68     240     40-50             1  \n",
       "12     41.0   547.28     202     40-50             1  \n",
       "13     41.0  1230.30     128     40-50             1  \n",
       "142    45.0   693.76     301     40-50             1  \n",
       "143    45.0  1230.27     221     40-50             1  \n",
       "...     ...      ...     ...       ...           ...  \n",
       "19570  41.0    75.48     159     40-50             1  \n",
       "19571  41.0  1055.82     296     40-50             1  \n",
       "19572  41.0   690.49     252     40-50             1  \n",
       "19573  41.0   802.26     259     40-50             1  \n",
       "19574  41.0    17.87     216     40-50             1  \n",
       "\n",
       "[498 rows x 29 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FS = df2[df2[\"job_industry_category\"] == \"Financial Services\"]\n",
    "FS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>past_3_years_bike_related_purchases</th>\n",
       "      <th>job_title</th>\n",
       "      <th>job_industry_category</th>\n",
       "      <th>wealth_segment</th>\n",
       "      <th>deceased_indicator</th>\n",
       "      <th>owns_car</th>\n",
       "      <th>tenure</th>\n",
       "      <th>transaction_id</th>\n",
       "      <th>...</th>\n",
       "      <th>address</th>\n",
       "      <th>postcode</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>property_valuation</th>\n",
       "      <th>age</th>\n",
       "      <th>profit</th>\n",
       "      <th>recency</th>\n",
       "      <th>age_group</th>\n",
       "      <th>owns_car_flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>110</td>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>72</td>\n",
       "      <td>Project Manager</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6657.0</td>\n",
       "      <td>...</td>\n",
       "      <td>032 Bartelt Crossing</td>\n",
       "      <td>2444.0</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>8.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>451.65</td>\n",
       "      <td>59</td>\n",
       "      <td>40-50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>111</td>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>72</td>\n",
       "      <td>Project Manager</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11330.0</td>\n",
       "      <td>...</td>\n",
       "      <td>032 Bartelt Crossing</td>\n",
       "      <td>2444.0</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>8.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>641.64</td>\n",
       "      <td>31</td>\n",
       "      <td>40-50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>112</td>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>72</td>\n",
       "      <td>Project Manager</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>11.0</td>\n",
       "      <td>14766.0</td>\n",
       "      <td>...</td>\n",
       "      <td>032 Bartelt Crossing</td>\n",
       "      <td>2444.0</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>8.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>812.44</td>\n",
       "      <td>40</td>\n",
       "      <td>40-50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>113</td>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>72</td>\n",
       "      <td>Project Manager</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>11.0</td>\n",
       "      <td>18030.0</td>\n",
       "      <td>...</td>\n",
       "      <td>032 Bartelt Crossing</td>\n",
       "      <td>2444.0</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>8.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1702.55</td>\n",
       "      <td>202</td>\n",
       "      <td>40-50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>255</td>\n",
       "      <td>42</td>\n",
       "      <td>Female</td>\n",
       "      <td>24</td>\n",
       "      <td>Accountant III</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>18.0</td>\n",
       "      <td>4762.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1 Claremont Park</td>\n",
       "      <td>3931.0</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>64.51</td>\n",
       "      <td>332</td>\n",
       "      <td>40-50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19867</td>\n",
       "      <td>3474</td>\n",
       "      <td>Male</td>\n",
       "      <td>93</td>\n",
       "      <td>Assistant Manager</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>14.0</td>\n",
       "      <td>9955.0</td>\n",
       "      <td>...</td>\n",
       "      <td>06 Lake View Plaza</td>\n",
       "      <td>4515.0</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>209.84</td>\n",
       "      <td>111</td>\n",
       "      <td>40-50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19868</td>\n",
       "      <td>3474</td>\n",
       "      <td>Male</td>\n",
       "      <td>93</td>\n",
       "      <td>Assistant Manager</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>14.0</td>\n",
       "      <td>16442.0</td>\n",
       "      <td>...</td>\n",
       "      <td>06 Lake View Plaza</td>\n",
       "      <td>4515.0</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>198.29</td>\n",
       "      <td>71</td>\n",
       "      <td>40-50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19869</td>\n",
       "      <td>3474</td>\n",
       "      <td>Male</td>\n",
       "      <td>93</td>\n",
       "      <td>Assistant Manager</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>14.0</td>\n",
       "      <td>16803.0</td>\n",
       "      <td>...</td>\n",
       "      <td>06 Lake View Plaza</td>\n",
       "      <td>4515.0</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>114.93</td>\n",
       "      <td>170</td>\n",
       "      <td>40-50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19941</td>\n",
       "      <td>3488</td>\n",
       "      <td>Male</td>\n",
       "      <td>63</td>\n",
       "      <td>Speech Pathologist</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>9.0</td>\n",
       "      <td>871.0</td>\n",
       "      <td>...</td>\n",
       "      <td>42293 Banding Plaza</td>\n",
       "      <td>2136.0</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>751.02</td>\n",
       "      <td>69</td>\n",
       "      <td>40-50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19942</td>\n",
       "      <td>3488</td>\n",
       "      <td>Male</td>\n",
       "      <td>63</td>\n",
       "      <td>Speech Pathologist</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16036.0</td>\n",
       "      <td>...</td>\n",
       "      <td>42293 Banding Plaza</td>\n",
       "      <td>2136.0</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>64.93</td>\n",
       "      <td>17</td>\n",
       "      <td>40-50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>616 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       customer_id  gender  past_3_years_bike_related_purchases  \\\n",
       "110             20    Male                                   72   \n",
       "111             20    Male                                   72   \n",
       "112             20    Male                                   72   \n",
       "113             20    Male                                   72   \n",
       "255             42  Female                                   24   \n",
       "...            ...     ...                                  ...   \n",
       "19867         3474    Male                                   93   \n",
       "19868         3474    Male                                   93   \n",
       "19869         3474    Male                                   93   \n",
       "19941         3488    Male                                   63   \n",
       "19942         3488    Male                                   63   \n",
       "\n",
       "                job_title job_industry_category wealth_segment  \\\n",
       "110       Project Manager         Manufacturing  Mass Customer   \n",
       "111       Project Manager         Manufacturing  Mass Customer   \n",
       "112       Project Manager         Manufacturing  Mass Customer   \n",
       "113       Project Manager         Manufacturing  Mass Customer   \n",
       "255        Accountant III         Manufacturing  Mass Customer   \n",
       "...                   ...                   ...            ...   \n",
       "19867   Assistant Manager         Manufacturing  Mass Customer   \n",
       "19868   Assistant Manager         Manufacturing  Mass Customer   \n",
       "19869   Assistant Manager         Manufacturing  Mass Customer   \n",
       "19941  Speech Pathologist         Manufacturing  Mass Customer   \n",
       "19942  Speech Pathologist         Manufacturing  Mass Customer   \n",
       "\n",
       "      deceased_indicator owns_car  tenure  transaction_id  ...  \\\n",
       "110                    N       No    11.0          6657.0  ...   \n",
       "111                    N       No    11.0         11330.0  ...   \n",
       "112                    N       No    11.0         14766.0  ...   \n",
       "113                    N       No    11.0         18030.0  ...   \n",
       "255                    N       No    18.0          4762.0  ...   \n",
       "...                  ...      ...     ...             ...  ...   \n",
       "19867                  N       No    14.0          9955.0  ...   \n",
       "19868                  N       No    14.0         16442.0  ...   \n",
       "19869                  N       No    14.0         16803.0  ...   \n",
       "19941                  N      Yes     9.0           871.0  ...   \n",
       "19942                  N      Yes     9.0         16036.0  ...   \n",
       "\n",
       "                    address postcode  state    country property_valuation  \\\n",
       "110    032 Bartelt Crossing   2444.0    NSW  Australia                8.0   \n",
       "111    032 Bartelt Crossing   2444.0    NSW  Australia                8.0   \n",
       "112    032 Bartelt Crossing   2444.0    NSW  Australia                8.0   \n",
       "113    032 Bartelt Crossing   2444.0    NSW  Australia                8.0   \n",
       "255        1 Claremont Park   3931.0    VIC  Australia               10.0   \n",
       "...                     ...      ...    ...        ...                ...   \n",
       "19867    06 Lake View Plaza   4515.0    QLD  Australia                1.0   \n",
       "19868    06 Lake View Plaza   4515.0    QLD  Australia                1.0   \n",
       "19869    06 Lake View Plaza   4515.0    QLD  Australia                1.0   \n",
       "19941   42293 Banding Plaza   2136.0    NSW  Australia               10.0   \n",
       "19942   42293 Banding Plaza   2136.0    NSW  Australia               10.0   \n",
       "\n",
       "        age   profit recency age_group owns_car_flag  \n",
       "110    41.0   451.65      59     40-50             0  \n",
       "111    41.0   641.64      31     40-50             0  \n",
       "112    41.0   812.44      40     40-50             0  \n",
       "113    41.0  1702.55     202     40-50             0  \n",
       "255    44.0    64.51     332     40-50             0  \n",
       "...     ...      ...     ...       ...           ...  \n",
       "19867  42.0   209.84     111     40-50             0  \n",
       "19868  42.0   198.29      71     40-50             0  \n",
       "19869  42.0   114.93     170     40-50             0  \n",
       "19941  46.0   751.02      69     40-50             1  \n",
       "19942  46.0    64.93      17     40-50             1  \n",
       "\n",
       "[616 rows x 29 columns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M = df2[df2[\"job_industry_category\"] == \"Manufacturing\"]\n",
    "M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "       customer_id  gender  past_3_years_bike_related_purchases  \\\n",
       "157             27    Male                                   37   \n",
       "158             27    Male                                   37   \n",
       "159             27    Male                                   37   \n",
       "160             27    Male                                   37   \n",
       "161             27    Male                                   37   \n",
       "...            ...     ...                                  ...   \n",
       "19656         3434  Female                                   20   \n",
       "19657         3434  Female                                   20   \n",
       "19658         3434  Female                                   20   \n",
       "19659         3434  Female                                   20   \n",
       "19660         3434  Female                                   20   \n",
       "\n",
       "                 job_title job_industry_category wealth_segment  \\\n",
       "157         Research Nurse                Health  Mass Customer   \n",
       "158         Research Nurse                Health  Mass Customer   \n",
       "159         Research Nurse                Health  Mass Customer   \n",
       "160         Research Nurse                Health  Mass Customer   \n",
       "161         Research Nurse                Health  Mass Customer   \n",
       "...                    ...                   ...            ...   \n",
       "19656  Account Coordinator                Health  Mass Customer   \n",
       "19657  Account Coordinator                Health  Mass Customer   \n",
       "19658  Account Coordinator                Health  Mass Customer   \n",
       "19659  Account Coordinator                Health  Mass Customer   \n",
       "19660  Account Coordinator                Health  Mass Customer   \n",
       "\n",
       "      deceased_indicator owns_car  tenure  transaction_id  ...  \\\n",
       "157                    N      Yes    12.0           849.0  ...   \n",
       "158                    N      Yes    12.0          1275.0  ...   \n",
       "159                    N      Yes    12.0          5053.0  ...   \n",
       "160                    N      Yes    12.0          5662.0  ...   \n",
       "161                    N      Yes    12.0          5970.0  ...   \n",
       "...                  ...      ...     ...             ...  ...   \n",
       "19656                  N       No    16.0          9021.0  ...   \n",
       "19657                  N       No    16.0         11692.0  ...   \n",
       "19658                  N       No    16.0         13813.0  ...   \n",
       "19659                  N       No    16.0         16272.0  ...   \n",
       "19660                  N       No    16.0         16858.0  ...   \n",
       "\n",
       "                  address postcode  state    country property_valuation   age  \\\n",
       "157        96 Texas Plaza   3218.0    VIC  Australia                7.0  43.0   \n",
       "158        96 Texas Plaza   3218.0    VIC  Australia                7.0  43.0   \n",
       "159        96 Texas Plaza   3218.0    VIC  Australia                7.0  43.0   \n",
       "160        96 Texas Plaza   3218.0    VIC  Australia                7.0  43.0   \n",
       "161        96 Texas Plaza   3218.0    VIC  Australia                7.0  43.0   \n",
       "...                   ...      ...    ...        ...                ...   ...   \n",
       "19656  989 Waubesa Circle   3149.0    VIC  Australia               11.0  48.0   \n",
       "19657  989 Waubesa Circle   3149.0    VIC  Australia               11.0  48.0   \n",
       "19658  989 Waubesa Circle   3149.0    VIC  Australia               11.0  48.0   \n",
       "19659  989 Waubesa Circle   3149.0    VIC  Australia               11.0  48.0   \n",
       "19660  989 Waubesa Circle   3149.0    VIC  Australia               11.0  48.0   \n",
       "\n",
       "        profit recency age_group owns_car_flag  \n",
       "157    1630.25     165     40-50             1  \n",
       "158      50.66     266     40-50             1  \n",
       "159     356.50     158     40-50             1  \n",
       "160     155.65     216     40-50             1  \n",
       "161     209.84     195     40-50             1  \n",
       "...        ...     ...       ...           ...  \n",
       "19656   187.39      64     40-50             0  \n",
       "19657    75.48     244     40-50             0  \n",
       "19658   189.28     177     40-50             0  \n",
       "19659  1069.55     238     40-50             0  \n",
       "19660   309.81     150     40-50             0  \n",
       "\n",
       "[587 rows x 29 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H = df2[df2[\"job_industry_category\"] == \"Health\"]\n",
    "H"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
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       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>3.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>693.76</td>\n",
       "      <td>301</td>\n",
       "      <td>40-50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>Male</td>\n",
       "      <td>72</td>\n",
       "      <td>Accounting Assistant III</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>21.0</td>\n",
       "      <td>4309.0</td>\n",
       "      <td>...</td>\n",
       "      <td>28 5th Center</td>\n",
       "      <td>4413.0</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>3.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>1230.27</td>\n",
       "      <td>221</td>\n",
       "      <td>40-50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1109</td>\n",
       "      <td>3474</td>\n",
       "      <td>Male</td>\n",
       "      <td>93</td>\n",
       "      <td>Assistant Manager</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>14.0</td>\n",
       "      <td>9955.0</td>\n",
       "      <td>...</td>\n",
       "      <td>06 Lake View Plaza</td>\n",
       "      <td>4515.0</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>209.84</td>\n",
       "      <td>111</td>\n",
       "      <td>40-50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1110</td>\n",
       "      <td>3474</td>\n",
       "      <td>Male</td>\n",
       "      <td>93</td>\n",
       "      <td>Assistant Manager</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>14.0</td>\n",
       "      <td>16442.0</td>\n",
       "      <td>...</td>\n",
       "      <td>06 Lake View Plaza</td>\n",
       "      <td>4515.0</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>198.29</td>\n",
       "      <td>71</td>\n",
       "      <td>40-50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1111</td>\n",
       "      <td>3474</td>\n",
       "      <td>Male</td>\n",
       "      <td>93</td>\n",
       "      <td>Assistant Manager</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>14.0</td>\n",
       "      <td>16803.0</td>\n",
       "      <td>...</td>\n",
       "      <td>06 Lake View Plaza</td>\n",
       "      <td>4515.0</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>114.93</td>\n",
       "      <td>170</td>\n",
       "      <td>40-50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1112</td>\n",
       "      <td>3488</td>\n",
       "      <td>Male</td>\n",
       "      <td>63</td>\n",
       "      <td>Speech Pathologist</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>9.0</td>\n",
       "      <td>871.0</td>\n",
       "      <td>...</td>\n",
       "      <td>42293 Banding Plaza</td>\n",
       "      <td>2136.0</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>751.02</td>\n",
       "      <td>69</td>\n",
       "      <td>40-50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1113</td>\n",
       "      <td>3488</td>\n",
       "      <td>Male</td>\n",
       "      <td>63</td>\n",
       "      <td>Speech Pathologist</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16036.0</td>\n",
       "      <td>...</td>\n",
       "      <td>42293 Banding Plaza</td>\n",
       "      <td>2136.0</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>64.93</td>\n",
       "      <td>17</td>\n",
       "      <td>40-50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1114 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      customer_id gender  past_3_years_bike_related_purchases  \\\n",
       "0               2   Male                                   81   \n",
       "1               2   Male                                   81   \n",
       "2               2   Male                                   81   \n",
       "3              25   Male                                   72   \n",
       "4              25   Male                                   72   \n",
       "...           ...    ...                                  ...   \n",
       "1109         3474   Male                                   93   \n",
       "1110         3474   Male                                   93   \n",
       "1111         3474   Male                                   93   \n",
       "1112         3488   Male                                   63   \n",
       "1113         3488   Male                                   63   \n",
       "\n",
       "                     job_title job_industry_category wealth_segment  \\\n",
       "0       Administrative Officer    Financial Services  Mass Customer   \n",
       "1       Administrative Officer    Financial Services  Mass Customer   \n",
       "2       Administrative Officer    Financial Services  Mass Customer   \n",
       "3     Accounting Assistant III    Financial Services  Mass Customer   \n",
       "4     Accounting Assistant III    Financial Services  Mass Customer   \n",
       "...                        ...                   ...            ...   \n",
       "1109         Assistant Manager         Manufacturing  Mass Customer   \n",
       "1110         Assistant Manager         Manufacturing  Mass Customer   \n",
       "1111         Assistant Manager         Manufacturing  Mass Customer   \n",
       "1112        Speech Pathologist         Manufacturing  Mass Customer   \n",
       "1113        Speech Pathologist         Manufacturing  Mass Customer   \n",
       "\n",
       "     deceased_indicator owns_car  tenure  transaction_id  ...  \\\n",
       "0                     N      Yes    16.0          2261.0  ...   \n",
       "1                     N      Yes    16.0          6743.0  ...   \n",
       "2                     N      Yes    16.0         16411.0  ...   \n",
       "3                     N      Yes    21.0          2354.0  ...   \n",
       "4                     N      Yes    21.0          4309.0  ...   \n",
       "...                 ...      ...     ...             ...  ...   \n",
       "1109                  N       No    14.0          9955.0  ...   \n",
       "1110                  N       No    14.0         16442.0  ...   \n",
       "1111                  N       No    14.0         16803.0  ...   \n",
       "1112                  N      Yes     9.0           871.0  ...   \n",
       "1113                  N      Yes     9.0         16036.0  ...   \n",
       "\n",
       "                  address postcode  state    country property_valuation   age  \\\n",
       "0     6 Meadow Vale Court   2153.0    NSW  Australia               10.0  41.0   \n",
       "1     6 Meadow Vale Court   2153.0    NSW  Australia               10.0  41.0   \n",
       "2     6 Meadow Vale Court   2153.0    NSW  Australia               10.0  41.0   \n",
       "3           28 5th Center   4413.0    QLD  Australia                3.0  45.0   \n",
       "4           28 5th Center   4413.0    QLD  Australia                3.0  45.0   \n",
       "...                   ...      ...    ...        ...                ...   ...   \n",
       "1109   06 Lake View Plaza   4515.0    QLD  Australia                1.0  42.0   \n",
       "1110   06 Lake View Plaza   4515.0    QLD  Australia                1.0  42.0   \n",
       "1111   06 Lake View Plaza   4515.0    QLD  Australia                1.0  42.0   \n",
       "1112  42293 Banding Plaza   2136.0    NSW  Australia               10.0  46.0   \n",
       "1113  42293 Banding Plaza   2136.0    NSW  Australia               10.0  46.0   \n",
       "\n",
       "       profit recency age_group owns_car_flag  \n",
       "0      448.68     240     40-50             1  \n",
       "1      547.28     202     40-50             1  \n",
       "2     1230.30     128     40-50             1  \n",
       "3      693.76     301     40-50             1  \n",
       "4     1230.27     221     40-50             1  \n",
       "...       ...     ...       ...           ...  \n",
       "1109   209.84     111     40-50             0  \n",
       "1110   198.29      71     40-50             0  \n",
       "1111   114.93     170     40-50             0  \n",
       "1112   751.02      69     40-50             1  \n",
       "1113    64.93      17     40-50             1  \n",
       "\n",
       "[1114 rows x 29 columns]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FS = pd.concat([FS, M], ignore_index=True, sort=False)\n",
    "FS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>past_3_years_bike_related_purchases</th>\n",
       "      <th>job_title</th>\n",
       "      <th>job_industry_category</th>\n",
       "      <th>wealth_segment</th>\n",
       "      <th>deceased_indicator</th>\n",
       "      <th>owns_car</th>\n",
       "      <th>tenure</th>\n",
       "      <th>transaction_id</th>\n",
       "      <th>...</th>\n",
       "      <th>address</th>\n",
       "      <th>postcode</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>property_valuation</th>\n",
       "      <th>age</th>\n",
       "      <th>profit</th>\n",
       "      <th>recency</th>\n",
       "      <th>age_group</th>\n",
       "      <th>owns_car_flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>81</td>\n",
       "      <td>Administrative Officer</td>\n",
       "      <td>Financial Services</td>\n",
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       "      <td>Yes</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2261.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6 Meadow Vale Court</td>\n",
       "      <td>2153.0</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>448.68</td>\n",
       "      <td>240</td>\n",
       "      <td>40-50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>81</td>\n",
       "      <td>Administrative Officer</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6743.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6 Meadow Vale Court</td>\n",
       "      <td>2153.0</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>547.28</td>\n",
       "      <td>202</td>\n",
       "      <td>40-50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>81</td>\n",
       "      <td>Administrative Officer</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16411.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6 Meadow Vale Court</td>\n",
       "      <td>2153.0</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1230.30</td>\n",
       "      <td>128</td>\n",
       "      <td>40-50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>Male</td>\n",
       "      <td>72</td>\n",
       "      <td>Accounting Assistant III</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2354.0</td>\n",
       "      <td>...</td>\n",
       "      <td>28 5th Center</td>\n",
       "      <td>4413.0</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>3.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>693.76</td>\n",
       "      <td>301</td>\n",
       "      <td>40-50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>Male</td>\n",
       "      <td>72</td>\n",
       "      <td>Accounting Assistant III</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>21.0</td>\n",
       "      <td>4309.0</td>\n",
       "      <td>...</td>\n",
       "      <td>28 5th Center</td>\n",
       "      <td>4413.0</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>3.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>1230.27</td>\n",
       "      <td>221</td>\n",
       "      <td>40-50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1696</td>\n",
       "      <td>3434</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>Account Coordinator</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>16.0</td>\n",
       "      <td>9021.0</td>\n",
       "      <td>...</td>\n",
       "      <td>989 Waubesa Circle</td>\n",
       "      <td>3149.0</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Australia</td>\n",
       "      <td>11.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>187.39</td>\n",
       "      <td>64</td>\n",
       "      <td>40-50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1697</td>\n",
       "      <td>3434</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>Account Coordinator</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>16.0</td>\n",
       "      <td>11692.0</td>\n",
       "      <td>...</td>\n",
       "      <td>989 Waubesa Circle</td>\n",
       "      <td>3149.0</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Australia</td>\n",
       "      <td>11.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>75.48</td>\n",
       "      <td>244</td>\n",
       "      <td>40-50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1698</td>\n",
       "      <td>3434</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>Account Coordinator</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>16.0</td>\n",
       "      <td>13813.0</td>\n",
       "      <td>...</td>\n",
       "      <td>989 Waubesa Circle</td>\n",
       "      <td>3149.0</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Australia</td>\n",
       "      <td>11.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>189.28</td>\n",
       "      <td>177</td>\n",
       "      <td>40-50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1699</td>\n",
       "      <td>3434</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>Account Coordinator</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16272.0</td>\n",
       "      <td>...</td>\n",
       "      <td>989 Waubesa Circle</td>\n",
       "      <td>3149.0</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Australia</td>\n",
       "      <td>11.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1069.55</td>\n",
       "      <td>238</td>\n",
       "      <td>40-50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1700</td>\n",
       "      <td>3434</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>Account Coordinator</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16858.0</td>\n",
       "      <td>...</td>\n",
       "      <td>989 Waubesa Circle</td>\n",
       "      <td>3149.0</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Australia</td>\n",
       "      <td>11.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>309.81</td>\n",
       "      <td>150</td>\n",
       "      <td>40-50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1701 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      customer_id  gender  past_3_years_bike_related_purchases  \\\n",
       "0               2    Male                                   81   \n",
       "1               2    Male                                   81   \n",
       "2               2    Male                                   81   \n",
       "3              25    Male                                   72   \n",
       "4              25    Male                                   72   \n",
       "...           ...     ...                                  ...   \n",
       "1696         3434  Female                                   20   \n",
       "1697         3434  Female                                   20   \n",
       "1698         3434  Female                                   20   \n",
       "1699         3434  Female                                   20   \n",
       "1700         3434  Female                                   20   \n",
       "\n",
       "                     job_title job_industry_category wealth_segment  \\\n",
       "0       Administrative Officer    Financial Services  Mass Customer   \n",
       "1       Administrative Officer    Financial Services  Mass Customer   \n",
       "2       Administrative Officer    Financial Services  Mass Customer   \n",
       "3     Accounting Assistant III    Financial Services  Mass Customer   \n",
       "4     Accounting Assistant III    Financial Services  Mass Customer   \n",
       "...                        ...                   ...            ...   \n",
       "1696       Account Coordinator                Health  Mass Customer   \n",
       "1697       Account Coordinator                Health  Mass Customer   \n",
       "1698       Account Coordinator                Health  Mass Customer   \n",
       "1699       Account Coordinator                Health  Mass Customer   \n",
       "1700       Account Coordinator                Health  Mass Customer   \n",
       "\n",
       "     deceased_indicator owns_car  tenure  transaction_id  ...  \\\n",
       "0                     N      Yes    16.0          2261.0  ...   \n",
       "1                     N      Yes    16.0          6743.0  ...   \n",
       "2                     N      Yes    16.0         16411.0  ...   \n",
       "3                     N      Yes    21.0          2354.0  ...   \n",
       "4                     N      Yes    21.0          4309.0  ...   \n",
       "...                 ...      ...     ...             ...  ...   \n",
       "1696                  N       No    16.0          9021.0  ...   \n",
       "1697                  N       No    16.0         11692.0  ...   \n",
       "1698                  N       No    16.0         13813.0  ...   \n",
       "1699                  N       No    16.0         16272.0  ...   \n",
       "1700                  N       No    16.0         16858.0  ...   \n",
       "\n",
       "                  address postcode  state    country property_valuation   age  \\\n",
       "0     6 Meadow Vale Court   2153.0    NSW  Australia               10.0  41.0   \n",
       "1     6 Meadow Vale Court   2153.0    NSW  Australia               10.0  41.0   \n",
       "2     6 Meadow Vale Court   2153.0    NSW  Australia               10.0  41.0   \n",
       "3           28 5th Center   4413.0    QLD  Australia                3.0  45.0   \n",
       "4           28 5th Center   4413.0    QLD  Australia                3.0  45.0   \n",
       "...                   ...      ...    ...        ...                ...   ...   \n",
       "1696   989 Waubesa Circle   3149.0    VIC  Australia               11.0  48.0   \n",
       "1697   989 Waubesa Circle   3149.0    VIC  Australia               11.0  48.0   \n",
       "1698   989 Waubesa Circle   3149.0    VIC  Australia               11.0  48.0   \n",
       "1699   989 Waubesa Circle   3149.0    VIC  Australia               11.0  48.0   \n",
       "1700   989 Waubesa Circle   3149.0    VIC  Australia               11.0  48.0   \n",
       "\n",
       "       profit recency age_group owns_car_flag  \n",
       "0      448.68     240     40-50             1  \n",
       "1      547.28     202     40-50             1  \n",
       "2     1230.30     128     40-50             1  \n",
       "3      693.76     301     40-50             1  \n",
       "4     1230.27     221     40-50             1  \n",
       "...       ...     ...       ...           ...  \n",
       "1696   187.39      64     40-50             0  \n",
       "1697    75.48     244     40-50             0  \n",
       "1698   189.28     177     40-50             0  \n",
       "1699  1069.55     238     40-50             0  \n",
       "1700   309.81     150     40-50             0  \n",
       "\n",
       "[1701 rows x 29 columns]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FS = pd.concat([FS, H], ignore_index=True, sort=False)\n",
    "FMH = FS\n",
    "df_highvalue_customers = FMH\n",
    "\n",
    "df_highvalue_customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['customer_id', 'gender', 'past_3_years_bike_related_purchases',\n",
       "       'job_title', 'job_industry_category', 'wealth_segment',\n",
       "       'deceased_indicator', 'owns_car', 'tenure', 'transaction_id',\n",
       "       'product_id', 'transaction_date', 'online_order', 'order_status',\n",
       "       'brand', 'product_line', 'product_class', 'product_size',\n",
       "       'product_first_sold_date', 'address', 'postcode', 'state', 'country',\n",
       "       'property_valuation', 'age', 'profit', 'recency', 'age_group',\n",
       "       'owns_car_flag'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df.to_csv(r\"C:\\Users\\My\\Desktop\\KPMG 2\\Exsisting_Customers_Transactions.csv\", \n",
    "                                    header=['customer_id', 'gender', 'past_3_years_bike_related_purchases',\n",
    "       'job_title', 'job_industry_category', 'wealth_segment',\n",
    "       'deceased_indicator', 'owns_car', 'tenure', 'transaction_id',\n",
    "       'product_id', 'transaction_date', 'online_order', 'order_status',\n",
    "       'brand', 'product_line', 'product_class', 'product_size',\n",
    "       'product_first_sold_date', 'address', 'postcode', 'state', 'country',\n",
    "       'property_valuation', 'age', 'profit', 'recency', 'age_group',\n",
    "       'owns_car_flag'], index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['customer_id', 'gender', 'past_3_years_bike_related_purchases',\n",
       "       'job_title', 'job_industry_category', 'wealth_segment',\n",
       "       'deceased_indicator', 'owns_car', 'tenure', 'transaction_id',\n",
       "       'product_id', 'transaction_date', 'online_order', 'order_status',\n",
       "       'brand', 'product_line', 'product_class', 'product_size',\n",
       "       'product_first_sold_date', 'address', 'postcode', 'state', 'country',\n",
       "       'property_valuation', 'age', 'profit', 'recency', 'age_group',\n",
       "       'owns_car_flag'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_highvalue_customers.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_highvalue_customers.to_csv(r\"C:\\Users\\My\\Desktop\\KPMG 2\\High_Value_Customers.csv\", \n",
    "                                      header=['customer_id', 'gender', 'past_3_years_bike_related_purchases',\n",
    "       'job_title', 'job_industry_category', 'wealth_segment',\n",
    "       'deceased_indicator', 'owns_car', 'tenure', 'transaction_id',\n",
    "       'product_id', 'transaction_date', 'online_order', 'order_status',\n",
    "       'brand', 'product_line', 'product_class', 'product_size',\n",
    "       'product_first_sold_date', 'address', 'postcode', 'state', 'country',\n",
    "       'property_valuation', 'age', 'profit', 'recency', 'age_group',\n",
    "       'owns_car_flag'], index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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