analysis.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 sns\n",
    "from datetime import datetime\n",
    "\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is an analysis of the Online Retail dataset, provided by the UCI, which contains transactions (including cancellations) carried out by an online retailer (mainly focused on gift items) between 01/12/2009 and 09/12/2011."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory data analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset contains 1067371 entries and has 8 columns. This is a short description of the columns:\n",
    "\n",
    "* Invoice: Invoice number. Nominal. A 6-digit integral number uniquely assigned to each transaction. If this code starts with the letter 'c', it indicates a cancellation. \n",
    "* StockCode: Product (item) code. Nominal. A 5-digit integral number uniquely assigned to each distinct product. \n",
    "* Description: Product (item) name. Nominal. \n",
    "* Quantity: The quantities of each product (item) per transaction. Numeric. \n",
    "* InvoiceDate: Invice date and time. Numeric. The day and time when a transaction was generated. \n",
    "* Price: Unit price. Numeric. Product price per unit in sterling (£). \n",
    "* CustomerID: Customer number. Nominal. A 5-digit integral number uniquely assigned to each customer. \n",
    "* Country: Country name. Nominal. The name of the country where a customer resides."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat(pd.read_excel(\"data/online_retail_II.xlsx\", sheet_name=None).values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Invoice', 'StockCode', 'Description', 'Quantity', 'InvoiceDate',\n",
       "       'Price', 'Customer ID', 'Country'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1067371, 8)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I have a look at the first 10 entries in the dataset but also at 10 entries chosen randomly to see if the data is clean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Invoice</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>Price</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>489434</td>\n",
       "      <td>85048</td>\n",
       "      <td>15CM CHRISTMAS GLASS BALL 20 LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>6.95</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>489434</td>\n",
       "      <td>79323P</td>\n",
       "      <td>PINK CHERRY LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>6.75</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>489434</td>\n",
       "      <td>79323W</td>\n",
       "      <td>WHITE CHERRY LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>6.75</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>489434</td>\n",
       "      <td>22041</td>\n",
       "      <td>RECORD FRAME 7\" SINGLE SIZE</td>\n",
       "      <td>48</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>2.10</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>489434</td>\n",
       "      <td>21232</td>\n",
       "      <td>STRAWBERRY CERAMIC TRINKET BOX</td>\n",
       "      <td>24</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>489434</td>\n",
       "      <td>22064</td>\n",
       "      <td>PINK DOUGHNUT TRINKET POT</td>\n",
       "      <td>24</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>1.65</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>489434</td>\n",
       "      <td>21871</td>\n",
       "      <td>SAVE THE PLANET MUG</td>\n",
       "      <td>24</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>489434</td>\n",
       "      <td>21523</td>\n",
       "      <td>FANCY FONT HOME SWEET HOME DOORMAT</td>\n",
       "      <td>10</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>5.95</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>489435</td>\n",
       "      <td>22350</td>\n",
       "      <td>CAT BOWL</td>\n",
       "      <td>12</td>\n",
       "      <td>2009-12-01 07:46:00</td>\n",
       "      <td>2.55</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>489435</td>\n",
       "      <td>22349</td>\n",
       "      <td>DOG BOWL , CHASING BALL DESIGN</td>\n",
       "      <td>12</td>\n",
       "      <td>2009-12-01 07:46:00</td>\n",
       "      <td>3.75</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Invoice StockCode                          Description  Quantity  \\\n",
       "0  489434     85048  15CM CHRISTMAS GLASS BALL 20 LIGHTS        12   \n",
       "1  489434    79323P                   PINK CHERRY LIGHTS        12   \n",
       "2  489434    79323W                  WHITE CHERRY LIGHTS        12   \n",
       "3  489434     22041         RECORD FRAME 7\" SINGLE SIZE         48   \n",
       "4  489434     21232       STRAWBERRY CERAMIC TRINKET BOX        24   \n",
       "5  489434     22064           PINK DOUGHNUT TRINKET POT         24   \n",
       "6  489434     21871                  SAVE THE PLANET MUG        24   \n",
       "7  489434     21523   FANCY FONT HOME SWEET HOME DOORMAT        10   \n",
       "8  489435     22350                            CAT BOWL         12   \n",
       "9  489435     22349       DOG BOWL , CHASING BALL DESIGN        12   \n",
       "\n",
       "          InvoiceDate  Price  Customer ID         Country  \n",
       "0 2009-12-01 07:45:00   6.95      13085.0  United Kingdom  \n",
       "1 2009-12-01 07:45:00   6.75      13085.0  United Kingdom  \n",
       "2 2009-12-01 07:45:00   6.75      13085.0  United Kingdom  \n",
       "3 2009-12-01 07:45:00   2.10      13085.0  United Kingdom  \n",
       "4 2009-12-01 07:45:00   1.25      13085.0  United Kingdom  \n",
       "5 2009-12-01 07:45:00   1.65      13085.0  United Kingdom  \n",
       "6 2009-12-01 07:45:00   1.25      13085.0  United Kingdom  \n",
       "7 2009-12-01 07:45:00   5.95      13085.0  United Kingdom  \n",
       "8 2009-12-01 07:46:00   2.55      13085.0  United Kingdom  \n",
       "9 2009-12-01 07:46:00   3.75      13085.0  United Kingdom  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Invoice</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>Price</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>194139</th>\n",
       "      <td>507881</td>\n",
       "      <td>21529</td>\n",
       "      <td>RED RETROSPOT TOASTRACK</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-05-11 15:08:00</td>\n",
       "      <td>5.91</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>518937</th>\n",
       "      <td>580128</td>\n",
       "      <td>23300</td>\n",
       "      <td>GARDENERS KNEELING PAD CUP OF TEA</td>\n",
       "      <td>3</td>\n",
       "      <td>2011-12-01 17:56:00</td>\n",
       "      <td>1.65</td>\n",
       "      <td>17576.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250566</th>\n",
       "      <td>559038</td>\n",
       "      <td>22725</td>\n",
       "      <td>ALARM CLOCK BAKELIKE CHOCOLATE</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-07-05 15:31:00</td>\n",
       "      <td>8.29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337989</th>\n",
       "      <td>522327</td>\n",
       "      <td>84569A</td>\n",
       "      <td>PACK 3 IRON ON DOG PATCHES</td>\n",
       "      <td>12</td>\n",
       "      <td>2010-09-13 16:15:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>18069.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355279</th>\n",
       "      <td>523946</td>\n",
       "      <td>22952</td>\n",
       "      <td>72 CAKE CASES VINTAGE CHRISTMAS</td>\n",
       "      <td>48</td>\n",
       "      <td>2010-09-26 11:23:00</td>\n",
       "      <td>0.55</td>\n",
       "      <td>16833.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271337</th>\n",
       "      <td>515724</td>\n",
       "      <td>20681</td>\n",
       "      <td>PINK SPOTTY CHILDS UMBRELLA</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-07-14 12:47:00</td>\n",
       "      <td>3.25</td>\n",
       "      <td>18123.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409234</th>\n",
       "      <td>528595</td>\n",
       "      <td>22625</td>\n",
       "      <td>RED KITCHEN SCALES</td>\n",
       "      <td>2</td>\n",
       "      <td>2010-10-22 14:30:00</td>\n",
       "      <td>8.50</td>\n",
       "      <td>15067.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323738</th>\n",
       "      <td>565329</td>\n",
       "      <td>21849</td>\n",
       "      <td>SILVER DIAMANTE PEN IN GIFT BOX</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-09-02 13:54:00</td>\n",
       "      <td>4.95</td>\n",
       "      <td>14606.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414212</th>\n",
       "      <td>572380</td>\n",
       "      <td>22573</td>\n",
       "      <td>STAR WOODEN CHRISTMAS DECORATION</td>\n",
       "      <td>12</td>\n",
       "      <td>2011-10-24 11:51:00</td>\n",
       "      <td>0.85</td>\n",
       "      <td>16019.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33966</th>\n",
       "      <td>492226</td>\n",
       "      <td>21935</td>\n",
       "      <td>SUKI  SHOULDER BAG</td>\n",
       "      <td>6</td>\n",
       "      <td>2009-12-15 18:33:00</td>\n",
       "      <td>3.43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Invoice StockCode                         Description  Quantity  \\\n",
       "194139  507881     21529             RED RETROSPOT TOASTRACK         1   \n",
       "518937  580128     23300  GARDENERS KNEELING PAD CUP OF TEA          3   \n",
       "250566  559038     22725      ALARM CLOCK BAKELIKE CHOCOLATE         1   \n",
       "337989  522327    84569A          PACK 3 IRON ON DOG PATCHES        12   \n",
       "355279  523946     22952     72 CAKE CASES VINTAGE CHRISTMAS        48   \n",
       "271337  515724     20681         PINK SPOTTY CHILDS UMBRELLA         1   \n",
       "409234  528595     22625                  RED KITCHEN SCALES         2   \n",
       "323738  565329     21849     SILVER DIAMANTE PEN IN GIFT BOX         1   \n",
       "414212  572380     22573    STAR WOODEN CHRISTMAS DECORATION        12   \n",
       "33966   492226     21935                  SUKI  SHOULDER BAG         6   \n",
       "\n",
       "               InvoiceDate  Price  Customer ID         Country  \n",
       "194139 2010-05-11 15:08:00   5.91          NaN  United Kingdom  \n",
       "518937 2011-12-01 17:56:00   1.65      17576.0  United Kingdom  \n",
       "250566 2011-07-05 15:31:00   8.29          NaN  United Kingdom  \n",
       "337989 2010-09-13 16:15:00   1.25      18069.0  United Kingdom  \n",
       "355279 2010-09-26 11:23:00   0.55      16833.0  United Kingdom  \n",
       "271337 2010-07-14 12:47:00   3.25      18123.0  United Kingdom  \n",
       "409234 2010-10-22 14:30:00   8.50      15067.0  United Kingdom  \n",
       "323738 2011-09-02 13:54:00   4.95      14606.0  United Kingdom  \n",
       "414212 2011-10-24 11:51:00   0.85      16019.0  United Kingdom  \n",
       "33966  2009-12-15 18:33:00   3.43          NaN  United Kingdom  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that there are some missing values in the **Customer ID** column, calling the *info* method of the Pandas dataframe we see that also the column **Description** contains missing values. In fact ~ 20 % of the rows have a missing value for the **Customer ID** column which is huge. The datatypes for each columns seem valid except for the **Customer ID** columns which have a float type and it should be int with 5 digits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1067371 entries, 0 to 541909\n",
      "Data columns (total 8 columns):\n",
      " #   Column       Non-Null Count    Dtype         \n",
      "---  ------       --------------    -----         \n",
      " 0   Invoice      1067371 non-null  object        \n",
      " 1   StockCode    1067371 non-null  object        \n",
      " 2   Description  1062989 non-null  object        \n",
      " 3   Quantity     1067371 non-null  int64         \n",
      " 4   InvoiceDate  1067371 non-null  datetime64[ns]\n",
      " 5   Price        1067371 non-null  float64       \n",
      " 6   Customer ID  824364 non-null   float64       \n",
      " 7   Country      1067371 non-null  object        \n",
      "dtypes: datetime64[ns](1), float64(2), int64(1), object(4)\n",
      "memory usage: 73.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calling the method **describe** of the Pandas dataframe shows some summary statistics, such as mean, median, quartiles etcs..., for the numerical columns **Quantity** and **Price**. This shows that both columns have negative values which doesn't make sense, you cannot buy less than 0 items and/or for a negative price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Quantity</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.067371e+06</td>\n",
       "      <td>1.067371e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.938898e+00</td>\n",
       "      <td>4.649388e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.727058e+02</td>\n",
       "      <td>1.235531e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-8.099500e+04</td>\n",
       "      <td>-5.359436e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.250000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>2.100000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>4.150000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.099500e+04</td>\n",
       "      <td>3.897000e+04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Quantity         Price\n",
       "count  1.067371e+06  1.067371e+06\n",
       "mean   9.938898e+00  4.649388e+00\n",
       "std    1.727058e+02  1.235531e+02\n",
       "min   -8.099500e+04 -5.359436e+04\n",
       "25%    1.000000e+00  1.250000e+00\n",
       "50%    3.000000e+00  2.100000e+00\n",
       "75%    1.000000e+01  4.150000e+00\n",
       "max    8.099500e+04  3.897000e+04"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"Quantity\", \"Price\"]].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now explore the values of the columns in the dataset and in particular the unique values for the non-numerical columns. The compary has shipped orders towards 43 different countries, most of them are in Europe. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Country\"].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['United Kingdom', 'France', 'USA', 'Belgium', 'Australia', 'EIRE',\n",
       "       'Germany', 'Portugal', 'Japan', 'Denmark', 'Nigeria',\n",
       "       'Netherlands', 'Poland', 'Spain', 'Channel Islands', 'Italy',\n",
       "       'Cyprus', 'Greece', 'Norway', 'Austria', 'Sweden',\n",
       "       'United Arab Emirates', 'Finland', 'Switzerland', 'Unspecified',\n",
       "       'Malta', 'Bahrain', 'RSA', 'Bermuda', 'Hong Kong', 'Singapore',\n",
       "       'Thailand', 'Israel', 'Lithuania', 'West Indies', 'Lebanon',\n",
       "       'Korea', 'Brazil', 'Canada', 'Iceland', 'Saudi Arabia',\n",
       "       'Czech Republic', 'European Community'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Country\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "92 % of the transactions are made with customers residing in the United Kingdom, other transactions are mostly made in neighbour countries such as Republic of Ireland, Germany or France."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "United Kingdom    91.938979\n",
       "EIRE               1.673832\n",
       "Germany            1.651160\n",
       "France             1.342551\n",
       "Netherlands        0.481557\n",
       "Spain              0.357045\n",
       "Switzerland        0.298771\n",
       "Belgium            0.292588\n",
       "Portugal           0.245463\n",
       "Australia          0.179225\n",
       "Name: Country, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Country\"].value_counts(normalize=True).head(10) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transactions registered from 2009-12-01 07:45:00 to 2011-12-09 12:50:00\n"
     ]
    }
   ],
   "source": [
    "print(f\"Transactions registered from {df['InvoiceDate'].min()} to {df['InvoiceDate'].max()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of transactions registered: 53628\n"
     ]
    }
   ],
   "source": [
    "print(f\"Number of transactions registered: {df['Invoice'].nunique()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The number of transaction is more or less constant during the year but then increases from September to December, as expected since in this period there are a lot of major events such as Halloween and Christmas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEUCAYAAADXzmpaAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAABSRElEQVR4nO3deVxVdfrA8c9d2RFBEFTccBeVyn3BMsU9TdvUappWm5aZFs3MyXHanHJs2nSa31Qzo22mhWaklZWmuBDmjuKGCii77Nz1/P5ASEzg3stdWJ7369XLOPfcc74XDjznfJfnUSmKoiCEEELYQO3pBgghhGg6JGgIIYSwmQQNIYQQNpOgIYQQwmYSNIQQQthMgoYQQgibaT3dAFcrKCjFarVvVnFIiD95eSUuapEQDSPXp3AltVpF69Z+tb7e7IOG1arYHTSq3idEYyXXp/AU6Z4SQghhMwkaQgghbCZBQwghhM0kaAghhLCZBA0hhBA2k6AhRAMkJe3mjTf+TlLSbk83RQi3aPZTboVwlT17djNjxlTMZhN6vZ516zYwaNAQTzdLCJeSJw0hHLQ+4RuMJiNWqwWTyUhi4nZPN0kIl5OgIYSD2kT2Q63RolJr0On0DB8+0tNNEsLlpHtKCAcZvDsy4rYXGNKhmBtGj5auKdEiSNAQwkEx3dowPHoasQPaebopQriNdE8J4aAJQzoSEeLLpt1nPd0UIdxGgoYQDkjPKaGswsTh0/l89sMJzBarp5skhFtI95QQDvjXhsME+ukZ3LstClBQbCA0yMfTzRLC5eRJQwg7FZYYSM8ppXen1oQEegOQX1Th4VYJ4R7ypCGEnVLOFADQt0swXjoNAPlFBk82SQi3kaAhhJ2OpBXg562lY1gAJnPlWEZ+sTxpiJbBpUHjjTfeYPPmzahUKm655RZ+//vf8+yzz5KcnIyPT2X/76OPPsq4ceNISUnhueeeo7S0lIEDB7JkyRK0Wi2ZmZnMmzePvLw8unTpwrJly/Dzq70UoRCupCgKh9Py6d2pNWq1Ci+9huWPjiDQT+/ppgnhFi4b09izZw+7du1iw4YNrFu3jlWrVnHq1CkOHTrE6tWrWb9+PevXr2fcuHEAzJs3j+eff57NmzejKApr1qwBYMmSJcyePZtNmzYRHR3NihUrXNVkIWzy5O0x3DSiS/XXQf5eqFUqD7ZICPdxWdAYPHgw//vf/9BqteTl5WGxWPD29iYzM5OFCxcydepU3nzzTaxWKxkZGVRUVBATEwPAjBkz2LRpEyaTiaSkJMaPH19juxCeolKpaN/Gjw5h/tXb9qRksWH7aQ+2Sgj3cWn3lE6n48033+T9999nwoQJmM1mhg4dyuLFiwkICOChhx5i7dq1dO/endDQ0Or3hYaGkpWVRUFBAf7+/mi12hrb7RES4l//TlcRGhrg0PtE87Zx+ynCgn0Z3Ce8etuZbafYvi+D+27u77Z2yPUpPMXlA+GPP/44DzzwAHPnzmXnzp2888471a/dddddxMfHExUVheqyx3tFUVCpVNX/Xu7Kr+uTl1eC1arY9Z7Q0ABycorteo9o/ixWK/9LSGFQrzC6hP46ruarU1NcZiI982L1bCpXkutT2CJx504Sd25n9KhYu/KiqdWqOm+2XdY9dfLkSVJSUgDw8fEhLi6OhIQENm/eXL2PoihotVrCw8PJycmp3p6bm0tYWBjBwcEUFxdjsVgAyMnJISwszFVNFqJOaReKKTeY6dO5dY3twQGyVkM0LklJu7nllpt47W8vMXPmTU4tEuayoJGens6iRYswGo0YjUa2bNnCoEGDePnllyksLMRkMvHpp58ybtw42rdvj5eXF8nJyQCsX7+e2NhYdDodAwcOJCEhAYD4+HhiY2Nd1WQh6nQkrXJ9Ru9OVwSNQC9A1mqIxiMxcTsWswlFsTq91ovLuqdGjx7NgQMHmD59OhqNhri4OB599FFat27NrFmzMJvNxMXFMWXKFACWLVvGokWLKCkpoW/fvtx9990ALF68mAULFrBy5UoiIiJYvny5q5osRJ1S0vLp2NafAN+a02tbB3qjUasorTB5qGVC1DRs2Ei0Oh0mk8nptV5UiqLY1+HfxMiYhnAGq1Xh+ff30D8qhNtu6FbjNUVRUJTKvmB3kOtT1Gfb/kxefy+evPRD/G3+3QwbOtTm99Y3piErwoWwgVqt4sX7h1w1m61KpUKWaYjG5ER6Ia3b9aJ1u1706nuNU48tCQuFsINWc/Vfma92pvH5tpNubo0QV3cioxCA1gFeGEwWpx5bgoYQNvj7J7/wZWJara+nnS8m+VhOra8L4S7FZUYu5Jcxc3RX/v7ICMKDfZ16fAkaQtSjqMzI4bQC6uqBah3oRX6xgWY+RCiagJOZRQB0a9/KJceXoCFEPVIuTbXt0zm41n1CAr0xGC2UG8zuapYQVxUc4MWN13UgPNiX19fsJ/lYtlOPL0FDiHocScvHx0tL5/DaU3cEXyrGlCdrNYSHdWwbwJxxPfDz0XHwVB4ZuaVOPb4EDSHqoCgKRy5LhV6bNq28adOq8mlDCE8xW6ycuVCMxWpFq1HjrddQUu7c9UMSNISog9micF3PMIb0aVvnfl0iAnn14eF06+CafmQhbHEuu4Ql/0mqnpTh76NzetCQdRpC1EGnVXPHjd093QwhbHIivXKqbdUguCuChjxpCFGH83mlV13QdzXvfXWEdVtlrYbwnBMZhYQEelWPsXUI86eVk6tKypOGELWwWhVeXpXMwF5h/G5Cr3r3zyooJ/eiZLoVnnMio5Dul3WR3jupt9PPIU8aQtTiTFYxpRVmenYMsmn/4AAv8oslaAjPyC+qoKDY4LL1GVUkaAhRiyNp+QD07lT7+ozLhQR6U1BswCoL/IQHBPjqeOr2GK7t8WsV1J2HLrD4/T02d7HaQoKGELU4klZAh1Db+4SDA70xWxSKyyRFunA/nVZD3y7B1eMZABVGM+eyS5w6GC5BQ4irMJosHE8v/E2VvrqEh/jSvUMrpyeIE8IWP/6SwenzRTW2+V+q/VLixBsZGQgX4io0GhVP3jaAVv62zzzp2zmYvnWkGhHCVQxGC6u/SWXSsI50iQis3u7vXfkn3plPGhI0hLgKjVpNr062P2UI4UlpF4qwKspvBsGrnzSke0oI1/om6dxvHvVt8cJ/k4j/6ZQLWiRE7Y5fWtTXtV3NoBHop6dHh1Z46zVOO5cEDSGuUFxm5NMtxzl4Ks/u95ZVmDmfV+aCVglRuxMZhUSE+OLvo6uxvZWfngV3Xkd01xCnnUuChhBXOHr2Igo4ND4RHOgtazWEWymKQnpOicvXZ1SRMQ0hrnD4dD4+Xho6R9SeCr02wYFeHLlUf0MId1CpVPxt7jAqasmwvOyTX4gI9mNOXA+nnE+eNIS4wpG0fHp1bI1Gbf+vR3CANxdLDFiszltMJUR9NGo1ft66q75WWmEm+2K5084lQUOIyxSVGSmrMNdZpa8uXdoFMrh3W4wmCRrCPTYmpvH5ttonXwQ4OdOtdE8JcZlAXz1v/nGUw08KMd3aENOtjZNbJUTtdh6+QGiQT62v+/voyCpw3uQMedIQ4gpqtQqdtmFTFCX/lHCHknIT5/PK6hwEd3ZNDQkaQlxiVRReWvUzOw9fcPgYBqOFR17fxubdZ53YMiGu7lRmzaJLV9OlXSD9o9o47UZGgoYQl5zLKuFkhv0L+i7npdcACvlFBuc0Sog6nMgoRK1S0aVdYK37DOsbzkM39UWtqr3GvT0kaAhxSVUq9D4NTB8SHCBrNYR7aDVq+keF4KVz3orv+rg0aLzxxhtMmjSJyZMn88EHHwCQmJjI1KlTiYuL4/XXX6/eNyUlhRkzZjB+/Hiee+45zGYzAJmZmcyZM4cJEybw8MMPU1pa6somixbsSFo+7UP9aOXv1aDjtA70kicN4RY3jejC47f0r3OfE+mFPPL6Vo6ddc76IZcFjT179rBr1y42bNjAunXrWLVqFUePHmXhwoWsWLGChIQEDh06xNatWwGYN28ezz//PJs3b0ZRFNasWQPAkiVLmD17Nps2bSI6OpoVK1a4qsmiBTOZLaSmF9LHxoJLdQmRVeHCDWwdo9Dr1JQbLE6r8+KyoDF48GD+97//odVqycvLw2KxUFRURKdOnYiMjESr1TJ16lQ2bdpERkYGFRUVxMTEADBjxgw2bdqEyWQiKSmJ8ePH19guhLOVGSwM7BlKTLeG5+jp1zWEkf0jUGQGlXCh75PTmbcikbKKuoNBQFWm23r2s5VL12nodDrefPNN3n//fSZMmEB2djahob+WIgwLCyMrK+s320NDQ8nKyqKgoAB/f3+0Wm2N7fYICfF3qO2hofankBBNV2goLLzXOUndxrvh2pHrU6TnlqFSQafIup+OWwX5AqCo1E65bly+uO/xxx/ngQceYO7cuaSlpaG6bARfURRUKhVWq/Wq26v+vdyVX9cnL68Eq9W+O77Q0ABycorteo9o2r778ScO7dvNiBGjGDRoSIOPV24wo1arXDJAKdenADh8Kpeu7VrZdC146TRk5ZbYtK9ararzZttlQePkyZMYjUZ69+6Nj48PcXFxbNq0CY3m11+inJwcwsLCCA8PJycnp3p7bm4uYWFhBAcHU1xcjMViQaPRVO8vhDNt/WkHd86+Gaxm9Hov1q3b0KDAkVVQxrPv7uK+yb0Z0S/CiS0VolJ+UQV5RQbiBtmW2XZ0TDuHEnBejcvGNNLT01m0aBFGoxGj0ciWLVu44447OH36NGfOnMFisbBx40ZiY2Np3749Xl5eJCcnA7B+/XpiY2PR6XQMHDiQhIQEAOLj44mNjXVVk0ULFf/VN1gtZqxWKyaTkcTE7Q06XutLs6/yi2QwXLjGiYxLi/o62BY07rixO0P7hDvl3C570hg9ejQHDhxg+vTpaDQa4uLimDx5MsHBwTz22GMYDAZGjx7NhAkTAFi2bBmLFi2ipKSEvn37cvfddwOwePFiFixYwMqVK4mIiGD58uWuarJoobSte6DR6EAxo9PpGT58ZIOOp9dp8PfRkV8s026Fa4QG+XDDte2JDLNtzFZRFMwWa4PT4wColGY+xUPGNERdyg1m/vjmT3T2y8fPlMbw4SOdMqbxlw/20MrPiyduG+CEVtYk16ew1/99eYSTGYUsnTus3n09NqYhRFNw8FQeZovCzClj6NmxYSvBLxcS6O3UGgZCVDGZLWTll9OujR9qtW0Tg3y9tU5LWihpRESL1q9rCHOn9aV7hyCnHndkvwjGDYx06jGFADiVWcTz7++xq4a9v4+OMoPZKcXB5ElDtGg+XloG927r9ONe0yO0/p2EcEDVIHiUHTXB/X0qq/qVlpsJ9NM36PzypCFarBPphXy96wwVRrPTj20yW8nILaXc4Pxji5btRHohESG+1YHAFlX7Fjuhi0qChmixth/M5MvENIdqgdcn7UIRf/737uq7QiGcQVEUTmQU2vWUAdAhzJ/Jwzrh69XwziXpnhItktWq8MvxXPpHhaDTOj9oBAd4A7JWQzjXhfwySivMdRZdupr2bfyYOTrKKW2QoCFapBMZhRSXmbjWRWMPQQF6VCokRbpwquAAb/50a386trVvdbeiKJRWmNGoVfg08GlDuqdEi5R8LAetRk2/rs5JUngljVpNkL+XpEgXTuWl19A/qg1BdtZ8MZqtPP7GT3y/N73BbZCgIVqkcoOZfl2DG3zXVZdgKcYknOyHvemczbJ/YaeXToNeq3bKWg3pnhIt0r2Te9tcxMZRN43ogsbGxVdC1Ke0wsSqb1K5Obar3d1TAP6+OkqcUIhJgoZoccwWK1qNGrWdafbt5aquL9EyncwoArB7ELyKv4/OKU8a0j0lWpwX/vszH3933OXnKS4zcuhUHgajxeXnEs3fiYxC1CoVXSMCHXp/gAQNIeyXlV/GuewS2rTydvm5Us9dZPma/VzIL3P5uUTzdzKjkMi2/njpHctUOzqmPWOu69Dgdkj3lGhR9qZWFvu6pkcbl58rOPDXtRqdwqU8q3CcVVE4l13CkD6Op7wZ2Ms5BewkaIgWZW9qDp3CA2jTysfl5woOuFSMSepqiAZSq1Qsf3QEBpPjXZ3lBjN5hRW0C/Vr0Hie3d1TJpNz0usK4W4FxQZOZha5bEHflQL89GjUKlkVLpxCq1Hj5217vqkrJR66wPPv72nwuEa9QePnn39mxYoVGI1Gbr311hrlV4VoSvQ6NXfc2J3Bvd1TZ16tUtE6wEueNESDrd9+mq92pjXoGH4+lR1Lpa4OGq+99hoxMTF89913BAUF8dVXX/H+++836KRCeIKft464QZG0be3rtnPeP6UPN43o7LbzieZp+4HznMkqadAxAnwqU6IXN3CtRr1Bw2KxMHz4cBITExk7diwdOnTA6oRCHkK4U2mFiR0Hz1NW4d5U5T0ig4gI8XPrOUXzUlBsIK+owuH1GVWq0qO7vHvKarVy4MABfvzxR4YPH05qaqqMa4gmZ9/xXN77KsXt018v5JexbX+m3XXqhaiycfMPnNizlvLcEw06jrOCRr2zp+bOnctTTz3FLbfcQmRkJGPGjOG5555r0EmFcLe9qTm0DvCic4R7p76mpOWz6ptU+nUNoXWAfUnmhEhK2s2CP92FyWTisZ/X0W7dBgYNGuLQsQL99NwzsRc9IoMa1KZ6g0ZcXBxxcXHVX3/77bdoNI4tLhHCEwxGC4dP5zOqfzuXpw65UuvL1mpI0BD2SkzcjtlsAsWKyWQkMXG7w0FDp1UTO6Bdg9tUb/dUTk4ODz74IOPHjyc3N5cHH3yQ7OzsBp9YCHc5dDoPo9nKtW5Y0HclWashGmL48JF46b3QaDTodHqGDx/ZoOOlZ5eQntOwAfV6g8aSJUsYO3YsXl5etGrVil69erFo0aIGnVQIdzqVWYSft5YeHYPcfu7LV4ULYY9jZwtoFd6Tdes2sGDBItY1oGuqyv9tPMLnW0816Bj1dk9lZGRw22238dFHH6HT6Zg3bx5Tp05t0EmFcKdbb+jGhCEdXVILvD5+3lr0OrXU1RB2MZktfJBwFI1GxQv3DWlwsKji76OjuNzYoGPUGzRUKlWNKbYlJSUy5VY0OQG+eo+cV6VSsejugTKeIezy9a6zZF8s5+k7YlA7sSaLv4+Os9kNu4GxaSD86aefpri4mE8++YTPPvuMiRMnNuikQrjLmh9OUFJu4t5JvT3Whg6h/h47t2h6si+W89WuMwzuHUafzsFOPXZlISYXP2nMnTuX+Ph4rFYriYmJ3H777dx6660NOqkQ7mBVFHYevtDgRVENlXruIiczCpk4tJNH2yGaho+/TUWtVnH7mO5OP7a/t46yCjNWq+LwE4xNWW6nT5/O9OnT7T7422+/zddffw3A6NGjmT9/Ps8++yzJycn4+FRmGX300UcZN24cKSkpPPfcc5SWljJw4ECWLFmCVqslMzOTefPmkZeXR5cuXVi2bBl+frLCVtTvVGYRhSVGrnNTgsLapJwpYMP204wbFIlWIyVsRO2sikKvTq3pF+WadT1D+rQlqn0gCgrg5KBxzTXXoLrKnHZFUVCpVOzdu7fOAycmJrJ9+3a++OILVCoV999/P99++y2HDh1i9erVhIXVTBo3b948XnzxRWJiYli4cCFr1qxh9uzZLFmyhNmzZzN58mTeeecdVqxYwbx58xz6sKJl2XssB41aRf8o90+1vVxwgBcKlekgQoNcn5JdNF1qlYrxgzu67Pjt2vjRrk3DbrprDRobN25s0IFDQ0NZsGABen3lAGRUVBSZmZlkZmaycOFCsrKyGDduHI8++ijnz5+noqKCmJgYAGbMmMGbb77JrbfeSlJSEu+880719jvvvFOChqiXoijsTc2hd6fW+Hp7tmzM5dNuJWiI2mzec5ZAPz3D+oa77BylFSZSz12ka0Qgrfwde5Kp9bepffv21f9/5MgRysrKUBQFi8XC2bNnue222+o8cPfuv/bHpaWl8fXXX/Phhx+yZ88eFi9eTEBAAA899BBr166le/fuhIb+2oUQGhpKVlYWBQUF+Pv7o9Vqa2y3R0iIY4OQoaFSaa0pM5ktjLqmPd0jW3v8Zxl1abKhGZXT2uLpzyScKzOnhHVbTzEyph03Xe/8sYwqhecu8ta6gzz3+8F06+LYE3i9t2CLFi1iy5YtGAwGwsLCOHv2LNddd129QaPK8ePHeeihh5g/fz5du3atfmoAuOuuu4iPjycqKqpGV1hVF1jVv5e7WpdZXfLySuxOFhcaGkBOTrFd7xGNz+QhlY/5nv5ZqsyV1dbOZBaS44QFhnJ9Ni+KovDmmv3otCqmDevk0p+tyVA5cyrzQhE5ba9+Q61Wq+q82a53VC4xMZEtW7Ywbtw4/vWvf/HBBx/g7e1tUwOTk5O55557eOqpp7j55ps5duwYmzdvrn5dURS0Wi3h4eHk5ORUb8/NzSUsLIzg4GCKi4uxWCp/6XJycn4zFiLE1RxPv4jZ0jjWE3npNfzjsZFMHOK6vmrRdCUfy+Hw6Xymj+rqcJeRrZyR6bbeoBEaGoqvry9du3YlNTWVIUOGcOHChXoPfP78eR555BGWLVvG5MmTgcog8fLLL1NYWIjJZOLTTz9l3LhxtG/fHi8vL5KTkwFYv349sbGx6HS6GpUC4+PjiY2NdfjDipYh52I5r6zey/d7MzzdlGqBfnq7n5JF82c0Wfh4y3E6hvkz5tr29b+hgbx0GrQaNcUNCBr1dk/pdDqSkpKIiopi27ZtDBkyhLKy+msSvPfeexgMBpYuXVq97Y477uDBBx9k1qxZmM1m4uLimDJlCgDLli1j0aJFlJSU0LdvX+6++24AFi9ezIIFC1i5ciUREREsX77c0c8qqEy1nJi4neHDRzotNUFjsze18qk1prtnZ01dbtfhC1zIL2P6qK6ebopoRHRaNbPH9iAoQO+WNDcqlYoAX12DnjRUiqLU2eG/b98+Vq1axdKlS5kzZw6HDx/mwQcf5I9//KPDJ3UnGdP4VVLSbmbMnIrJaEKv1zslAVpj9PLqZAxGC0vuHezpplRbtfkYe1KyeOtPDX9Sbq7XZ0tztTFbdziZUUiAn56wWmby1TemUe+TRkxMTPVU2DVr1lBcXExAgMzcaIq27/gJg8HolNz8jVVhqZGT6YXcNLKLp5tSQ3CgF6UVZgxGC156qUfT0imKwhtrD9C3czDjBkW69dxRDcyQUOfz0GeffcaJE5UlBj/++GOmTp3Kyy+/bFP3lGh82nUdgFqjRaVSo9PpGpybvzHafyIXBTy+CvxKwQGX1moUS4p0AbtTsjhwMg+t1v0ZAk5mFLLrSP3j0rWptcXvvvsuH3/8MSqVipSUFJYuXcoDDzxAmzZteO211xw+oagpKWk3b7zxd5KSdrv8XMXq9lw/6yWeWfAc69Z9yXUDBze4XnBj4204RwfjTjLTDnu6KTUEB14qxiQp0lu8coOZT78/QefwAEY7oZKevRIPXeCjb487/P5au6cSEhL48MMP8ff35/XXX2f06NHcdNNNKIrCpEmTHD6h+FVS0m5mzJiKyWREr/dy6RiDxWrlwMk84m6Mrc74uvqbY6ScKeCZOdcS6KHU4c6UlLSbW265CZPJyH/+/WajGrNpHeiNVqOitKJ5BWlhv/XbT1NUYuTxmf2dmvbcVv4+OkorTFgVxaHyx3U+G/n7Vw6G7N27l6FDhwKVo+9VK7RFw2z7aRsGoxGr9dcxBlfRqNUsfWgoN182e2dw77bkFlbw+pr9lBvMLju3u3z8+dcYjAYsFovLv5/2Cm3lzT+fvp7Bvdt6uinCgwpLDHy/N53RMe3oEhHokTb4++hQFCircOx3vtagoSgKiqJQXl7OwYMHGTKk8o6toqICg6HpPGIfOLDP002olU+bXqg1WnDTGIOvt65G5swekUH8YXo06dklvLXuAKZLK5ebqnLvzmg0OqfVU3YmlUrl0F2daD6Sknbzn/feZko/mDE6ymPt8Pdt2AK/Wh8ZRo0axZ/+9CfMZjNRUVFERUWRlpbGm2++yQ033OBYaz1g7tz7ef31txtNN0UVs8XKyeJgbvvDcqICcly6bqKk3MRb6w4wc3QUPSKDarw2oFsb7pvcm//78gjvfZXC3GnRLmmDq6Vnl2Dw6siipe9jyk9tlOtQNiamYTBZmOnBPxjCM5KSdjNzZmXXqU7n2enu1avCy0zgQI2nWoPGE088wX//+19ycnJ4/vnngcrZVF5eXjz55JOOtdYDzGZTo5xaWmYwE9W+FSMnTKZ/VAiFpUYKig0uyaGfdDSb4+mFeNcy1XNo33AqjBbaBvs6/dzusuPQeTRqFXfNHE+Ab+OsYX8mq5jM3FIJGi1QYuJ2DAYDimIFPDvdvVv7Vrx4/xBCg2xLB3WlWoOGVqvlvvvuq7GtKaYk12i1jaqbokqgr54/TK+8qzeaLDz77k6G9Q3nrvE9nX6uXYcv0K6NH5FhtS/Yuf6aX1MYnD5fROfwgCaT9sJssbLzcBYDurXxWC1wWwQHeHPodL7HFnUJzxlwzRBUGi0qq8XjXac+Xlp8vBwfl272ZcSm/X5xo3vKOH2+iIzc0uqv9ToN13Rvw64jFzAYnTuukFtYzvH0Qob2aWvTH6pjZwt44b8/szExzantcKUKo4V+XYMZHeP+6Yv2CA70wmC0UNYMJh0IOwV0Ydgtf+UPj8/3+Kw+q1Xh26RzpJ676ND7m33QKFOHY7E2jmynUDnBYPU3qby17gDWyzK4xA5oR7nBws/Hsp16vt1HKuuPDO1j26yd7pFBDI8O54ufTvP93nSntsVV/H103De5D/26hni6KXX6tRhT05lIIpxjb2oOPftew5+fXeDxm1iVCj778QT7T+Y69P5mHzQqjGbSzjeePD1Hz17k9PkiJgzuWGM2TY/IIMKDfdm6P9Op5wsP9uXGazvQxsaKcWqVit9P6kVMtzZ8+E1qg1aOukNphYkzF4qpJ4Vao9CmlTehQd4YTE17lpqwT1mFiZQzBVzbI7RRdEuqVCr8fHSVA+EOqLVj65tvvqnzjXFxcQ6d0N3un9Knzr58d0vYmUYrPz0j+tUs6ahSqYgd0I7PfjhBXmEFIa0cG6S60nU9w7iup301SDRqNQ9P78vyT/fz3sYUukQE0rZ14xwkTzx0gY+/O85LDwwhIqRhtY9drUtEIH+bO9zTzRBupgDTR3VpVE/CAT6OZ7qtNWisWrWq1jepVKomEzR6RAbZneXWVdIuFHE4rYBbr49Cp/3tTKZRAyIY2DPUaQHjREYh4cG+1VPs7KHTanj8lv7sO5FL29a+jTal+o4D5+nUNqDRBwzRcvl565g8rLOnm1GDv7uDRlOSW1jOj79kMHFIpwbNGHCGjJxSgvz1NWYqXc7PW4eft/1/4K/GalVY8cVBukQE8tjM/g4dw8dLy7C+4ZelO2lcKdXPZhVzNruEOeN6eLopNvv3xiME+Xtxy/Uy7bYlMJos7D+ZR7+uwXjrG08mDX8fXY3JOPao91OkpaWxevVqysrKUBQFq9XKmTNn+OSTTxw6obsVFBvYmHiGqHatGNDNs0V5RvSLYEiftmg1tQ8lFZcZee+rFIZHhzco5cSxswVcLDEyxMYB8Lrs2PETRqMRpZGlVN9x8AIatcopn9Fdsi+Wk1comW5bisOn81kZf4inbo+hbxcHVtK5yN0TeqFxMO9VvQPhTz31FCaTiV9++YX27dtz4sQJevRoOnd2XSIC0WnVHE7L92g7LuRXBt26AgaAn4+OzNxStu5r2ID4ziNZeOk1TgmUI0aMQu+lR6VSg0pL3wGeL26kKAr7T+QS072NQ91vnhIS6C3p0VuQvak5+Hpp6dkxyNNNqcHfR+dwz0u9QaO0tJQlS5YwcuRIYmNj+eCDD9i3b59DJ/MErUZNj8ggjqQVeKwN+UUV/Pnfu/l699l691WrVIzqH0HKmQKyCxyrW2IyW0g+ls11PULx0jW84M+gQUP4fN2X/OHx+Yye9SJbjmkpLDU2+LgNoVKpWHLvYGbd2N2j7bBXcIAXBcWGGtOtRSV3lglwB7PFyr4TuQzo1qbem0V3O32+iDU/nHAoUWm9nyQoKAiATp06cfz4cQIDAxvFtDF79O0cTGZuKQXFnpkf/03SORQFBvWybRbTyP7tUKlg2/7zDp3veHoh5QYLQ/s6r9tm0KAhLH5uIS8+cQcFJQY22xAAXc1Lr6le+9BUBAd6Y7YoFDs43bG5qsrN9MorLzJz5k3NInCknrtIaYWZaxtZQTCA83mlbNp9liIHbv7qDRqdOnXipZde4tprr2X16tWsWrUKs7lprWjt07k1XnoN5/McG/hpiJJyE1v3ZTK4TxihNq6VaB3gxYCoNuw4eB6zxf6FiX06B7P0oaH07tTa7vfWp1uHViy88zpmjO5a/84uUlRq5C/v7+HYWc89PToqIsSXnpFBGGWtRg2JidsxGA1YrRYMRgPbftrm6SY12NGzF9Fr1UR3bTxjGVX8fSrT7RQ7MIOq3k6tv/zlL2zbto0+ffpw6623smPHDl544QX7W+lBkWH+vPXHUR55RPw+OR2DycKkIZ3set+4QZGcyizEYlW4yuzceoW5cF1Fx7aVNeILSwxsSEzjjjHdrjqF2FV2HcnibHZJkxrLqNKnczB9Oje+PyKeNmzYSNRqHVbFhEqt5djFUHIultt8o9UY3TyqC6MHtHNKF7GzBTQgPXq9f0Xfffddxo8fD8Ds2bN55513SEhIsPtEnqRSqTwSMBRFYc/RbAZEhdDBzgWGvTu1ZvKwznZfcDsOnuetdQfcUlTpeHohP+zNYGX8YYeeiBy14+B5ukQE0D608SzaFA0zePAQPvk0nj8+uYB/rPgYfeuuHD3T9J4kL6dSqZy25srZ/C5Pj26nWp803nzzTYqKikhISKCkpKR6u8lkYvv27SxatMiBpnrOqcwi/vN1Cg9O7Wv3H3BHqVQqnv/dQEodrJBlMlvZm5pD9w6tbO6733HwPAUlxlrToDvTwF5hzBnXgw+/TeX9hBTun9LH5YWGzlwo5lx2CXfGNZ0ZfFda8kES/aNCuDnWc118jUlVCpjRo0YwetQIACaVGgn0q+xCOZ9XSniwb5MaS/1qZxrZBeXcM7FXo2x3wKWgUeZA+eFab78HDBhAUFAQarWaoKCg6v/Cw8NZtmyZ4631kCB/Pek5pW6bemuxWrFYreh1GodrZBSWGPjXhsNsszEfVX5RBcfOXmSYjRltneHG6zowI7Yruw5nsfqbVJfngNpx8DxaTdNam3GlcqOZC/mOzYxrjvam5vDi/5JrTFSpChjZF8tZ8p8k/vXlkSaTs0tRFLYfvEB+UUWjDBgA3noN7z49mrjBHe1+b61PGqNHj2b06NHExsbSv39/zGYziqKg0zW9fmSonLUSHuzL4bR8xjvwjbLXrsNZbNhxmgVzrnM4aLQJ8qFPl2C2HzzPTSO61FuEfveRLBRgiBNnTdli8rBOlBvM7DuRS7nBjK+TVrVfTa9OrWnlr3faynlPkLUaNW3ec47iMiOBfr/9mYa28mbKsM58se0UmbmlPDqjX6Mf58jMKyMrv4y4gR083ZRaqVQqh8ch6+3ob9euHffffz8xMTH079+fu+++m6ysLIdO5ml9OweTeu4iJrNr+9+tikLCrjN467UE+TesKNDoAe3ILzJw6HRevfvuPJxF13buTy6oUqm45fooFt09EF9vnUufNq7tEdro8vjYKzjAS9KjX3Iyo5ATGYWMGxiJRv3bP0cqlYopwzvzx1sHkFdYwV//k8QRDy/Urc/eS+UNYro3vqm2l0vYdYZvks7Z/b56g8YLL7xATEwMiYmJJCYmMnDgQP7yl7840kaP69OlNUaTlZMZhS49z77juZzPK2PS0E4NfjyN6d6GAF9dvSvELVYr1/Zow1gP3d2oVCp8vLSYLVZWrj/MlmTn1+JIOprtsbU2ztQ60JuLJYZGVefFUzYnncPHS8vI/hF17tc/KoQ/3zOQIH8vfjnuWB0Id9mbmktUu0CXlG52pkOn8tibmmP3++oNGmlpaTz66KMEBgbSunVrHn/8cc6e9fzCLkf06tiaQb3C8HLhILGiKHy18wyhQd4M7NXwOw2tRs2IfhHkXKyoc4aSRq1m+qiuDO0TXus+7mI2W/nw21R2HnJeLY7CEgPvrj/Mdz/bf2fU2HRtF8jQPm0xmlp20Mi5WE7ysWyuj2lnU0qLtq19WXjXddw+phsAWflljW6cw2pV6BEZxKgBjbuKJFTOoHLJlFuz2YzB8OvdXXl5uc13z2+//TaTJ09m8uTJvPrqqwAkJiYydepU4uLieP3116v3TUlJYcaMGYwfP57nnnuuegFhZmYmc+bMYcKECTz88MOUljq+QM/HS8vD06PpEhHo8DHqczy9kNPni5g4pNNVH7cdcfOoLiy5d1Ct04atl/IwuXPaa220mspaHL07tea9r1JY/flmp6SG2Hk4C6uiMKJf3XekTUFMtzbEhJfwr3++0SxWPjuqlZ+eu8b35MbrbH869vHSotWoMZmt/P3TfbyyKpnci+UubKV91GoVs8Z2J7YJBI0AHx0lZU5cEW40Vh5s0qRJ3HPPPXz22WesXbuWe++9t3rdRl0SExPZvn07X3zxBfHx8Rw+fJiNGzeycOFCVqxYQUJCAocOHWLr1q0AzJs3j+eff57NmzejKApr1qwBYMmSJcyePZtNmzYRHR3NihUr7P6QV8q5WO70WtxVunVoxaMz+v2myFJD6LQaVCoVBpPlqjmLTqQX8sbaA/x81LmlYh2l02p4bGY/vAxnefrRObzyygsNSg2hKAo7Dp2na7tA2rVp+nUzmmPKDEfodRquj2nvUCoYnVbNnXE9yCms4PGXVrHwLy80iu/j2aziRlO/pz6VTxpmu8cgaw0at99+OwCPPPIIt9xyCzt27GDbtm3MmDGDRx99tN4Dh4aGsmDBAvR6PTqdjqioKNLS0ujUqRORkZFotVqmTp3Kpk2byMjIoKKigpiYGABmzJjBpk2bMJlMJCUlVQepqu0NcSqziGf+uZODp+ofWHaEWqXi2h6hTl8hffp8EU++vZ1jV1nwtPPwBfQ6NTHdPZv6/XLeei2dfLNQLGas1l9Tqjsi7UIxGTmlzeIpAy6lzDBUpsxoyPelKdtx8DxbktMbNGmif1QbbuoPP3z0HP9e+XemTZ/Cjp2JTmylfYrKjCz5TxJf7UzzWBvsEeirx8dLQ4WdN9C1diRe/sOcOXMmM2fOtOvA3bv/mn00LS2Nr7/+mjvvvJPQ0F/7+cPCwsjKyiI7O7vG9tDQULKysigoKMDf3x+tVltjuz1CQmou5Gsd7IePl5ZTWSVMHFV7IZzQ0AC7zgPw9mf7aNfGnxk3dLP7vfUJDPJFrVaz+2gOsYN+TUliMltJPpbDsOh2RLZ3fq6phpgxfRLvvPV3jEYjer2eVu2jUeu1hLSyb8rknmM5eOs1TBoV1SRTh1xp8uTxvPra3zCZKr8vkyePt/t6c+T6tNfOnTv54YcfuOGGGxg2bJjTjmu2WFm//TQRbfy5Y0LvBh3r3OkDYDWDYsViMZNyeC/TbxrPsTP5RHUIcmsmiF92n0FRYPTAjm75+TTU7El9mD2pj93vqzVoGAwGjhw5UuudQN++fW06wfHjx3nooYeYP38+Go2GtLS06tcURUGlUmG1WmuMk1Rtr/r3cvbORsrLK/nN42LPyCCSUy6Qk3P1FbmhoQHk5BTbdZ7NW7bxzjufMHXCWEZFu2adxNA+bdm6L4NTZ/II8K2cyvtLag4l5Sau6RZid5tdrVu3aNau3UBi4naiBwxmbbKVva/9wANT+9hVL3lwz1CiO42gvKSC8pKmv76hW7doVv77U5b96zPuvnUy3bpF2/Wzc+T6tFdVtUaD0YiXXs/nn3/ptMJbuw5fILewgtnjejT4cwwYMAidTg8Y0en0DBgwiJNn8ljwTiIBvjrGDYwkdkA7fL1dXzVva/I52rTyJkCvbnS/i/ZQq1W/udm+XK3fyXPnzvHYY49dNWioVCq2bNlS78mTk5N5/PHHWbhwIZMnT2bPnj3k5Pw6xSsnJ4ewsDDCw8NrbM/NzSUsLIzg4GCKi4uxWCxoNJrq/RuqT+fW7DuRS/bFcsKcsFAoKWk39/5uJiaTibSf13L9Ne1dUtlu9IB2bElOZ+ehC9UrOQ+ezifAV0ffLo3rKaPKoEFDqr8XffqVsjL+EK+v2c+koZ24ObZLvZMFLFYrGrXaLb/07jR1/PUkpnlTqGqcuYkSE7djMplAsWIwGtnyw49OuaYVRWHznnOEB/vSP8r2G4faDBo0hHXrNtSoX29VFB6dEc2m3WdZ88MJNuw4zeiYdkwY0olWfg1bN1WbcoOZI2n5jLm2Q6NdBX6l3IvlrPnhBOMHdySqfSub31frb2K3bt2Ij493uEHnz5/nkUce4fXXX69+tB0wYACnT5/mzJkzdOjQgY0bNzJz5kzat2+Pl5cXycnJXHfddaxfv57Y2Fh0Oh0DBw4kISGBqVOnEh8fT2xsrMNtqlJVdvHI6XzCaqnXbY+Eb7ZU/4KZTCaXlUPtEOZP13aBbN2fybhBkahUKu6K68GkoR2dNlPLlSJC/Fh090A++u44CbvOcDarmCduG1DnL9k7nx/Cz0fLfZPtf4xuzFQqFYN6h/H1rrMUlRkJ9HXNHzNHDR8+Er1ej8lkBJWG9IpwzBZrg7t7jp29yJmsYu6e0NNpecouvzGBynHF/lFt6B/VhrQLRWzafZbvfk5ndEx7WvnpnfI5rnTwVB5mi9Ioa2fUxqoo/HwshwHd2jgnaDTUe++9h8FgYOnSpdXb7rjjDpYuXcpjjz2GwWBg9OjRTJgwAYBly5axaNEiSkpK6Nu3L3fffTcAixcvZsGCBaxcuZKIiAiWL1/e4LaFB/vy8PRop5VgLNR0RKPRgmJBp9MzfPhIpxz3am4f0w2dVl39h1alUtHGzjECT9LrNNwzsRe9OgWhQlVnwLhYYmD/yVwmDbUvrXxTETugHT0jg/B1sOymK3Xs1o+/v72a9FP7CekQzY8n9Hy85Th3xfVs0HG1GjUDokIY3tc964k6hwcyd1o0xWXG6i7df64/jMFopoNvLudP7WfEiFENvsm7tkcoT90RQzc7/vh6WtX4oL1rNVRKLYMWL774YpPLZHs1VxvTqI+9fcanzxexI3EneemHqh+R3eGf6w/RPtSfqcM7u+V8rrJ1XwZZBeXMiO1a4w7w691n+OyHk7z84FDCg92bGqUxc8eYxpofTvBt0jneeHwkvt461m09SUigN9c74cnckxRFYdOes3z4+Td8t+pZFIsZLy8969Y5b8ymqVAUhQde/ZGJQzsyc/Svk4LqG9Oo9RmtOQSMupRWmPj253NOqebXJSKQO2eO549/fMotF96F/DL+8uYaPvrvCo4d/sXl53O1zNwyNu0+y98+2kteYeVAt6Io7Dh4gW7tWzXrgJFfVMHaH09ysaTxpEdRFIW9qTn07tS6OvnkzNFR1QHD0XUI+07kOlRe1JlUKhUTh3TiuvBCsJpRFCsGg5Hvf9zq8DGPnS3gsx9OOJRm3JNUKhX+vvavCm/8HeEuYrEofPzdcYdyr1TZtj+T979KcXvpzj17dvPPpX/g6I6PeOOFhxrFoqaGmDW2O3On9SUjp5S/fLCHfcdzSbtQTGZuqVMXSTZGBpOFhF1nSGokCzMBMnJKyS4ov2r//C+pOfzlgz12/6EpLjOyMv4QX/x0ylnNbJCRI0eh13uh1mjQaHUMGTrC4WMlHrrAD79kuLV6pbO0C/FFb2e7W2zQCPTTExnmz+HTjmXMLDeY+XzrSbILytBp3fttPHU0GcVSOTfdfGngvakb3Lsti38/iJBW3ry17gC/7E3CO/cH1KVnPN00l4oI8aNDqD9JKY0naOxNzUEFXHOVxaKBfnrO55Xxf18euWp2gtr88EsGJrOVsQMjndhSx1XNunp2wSLiv9jI9aNGYLZY7X5asFoV9p3IZUC3Nm7/O+AM82dfy6yx3evf8TJN71M6UZ/OrTmRUehQ0rOvd5+hqMzE7Td2d/sUuxEjRuHl5YVGo3H5wLs7tW3ty3N3XccN3U386eHZfPHh28yZdXOTf5Kqz5A+YZzIKKzumvO0o2cLiOrQilb+v83SGtW+FbPH9eDgqTw2bD9t0/FMZgvfJ6fTr2sI7RtRGphBg4bwxz8+xeDBlV3K/954hNc+3kepHYHjePpFistMTWrWVEO16KDRt3MwZotC6rmLdr0vv6iCzXvOMbRPW5cmP6xN1V3SggWLWLduQ7MawNNpNeSlH8JkMmKxtIw0G4N6Vy4GbSxdVE/dEcPD06Jrff36mHaMiA5nw4409p+oP035rsNZFJWZGD+4cTxl1GZ4dDjpOSX8/ZN9Nj9xJKfmoNWo6dc12MWtc40tyem88dl+u97TooNG98gg9Do15/PsK70Zv/00igIzRnuuxnPVXVJzChhVhg8fiU6nb3ZPUrUJC/KhZ2QQ5QbHask7m0atrrMWhEql4q7xPekY5k9q+sV6j5eZV0rHtv707tQ4F6BW6R/Vhkdu7se57BKWr9lv089DrVIxsGco3vrGN23aFhdLDBw6nW9XDrBap9w2F/VNuTUYLb+pr1HflMb8ogpOZRYxsFfDV6eLq0tK2l1jlW9zd7WUObVx5ZTbf204TOeIQOIG1f9UcLXfndqYzJYmM1C8NzWHlfGH6NM5mCduG+Dp5rhU1ar5t/8UW511weE0Ii2FPQWZquJrcKC3Q+mche2uXOXb3FUFjHKD2aaCRK5QWGpk95Es2to4xbnqd+fMhWL2pGRxy/VRvwl8BcUGWgd4NZmAAZUL9eZOi6638p4nf1bOEuB7aYFfhcnmVD0tunsKKtMZv/bxLzb1J+87kctrH/9CoYfnmovm6f2vUnhldbLHzv/L8RwUsHtQ98CpPL7efZbv92bU2H4uu4SnV+xoNHVe7HFdz1C6tqscr9x15MJV6++8tCqZ9xNS3N00p6peFV5m++B/iw8a/j46zmYVc6CeAT2zxcpnP5zkYokRv2aWPE80Dh3b+pOeU0pGbsMXnDpib2oOYUE+dAi1b4bT5GGdGBAVwidbjnMivbB6+zd7zqLTqunduXGPZdQlI7eU//vyCG+uO1BjluWF/DIyc0vpGFZ7N05T0DrAi87hAdgzAbTFBw21SkXvzsEcOVNQ52DQ1n2ZXMgv47Yburk1R79oOQb2CkMFJKXYVzPGGcoqzKSkFXBtj1C7p5CrVSoemNqHkEBvVsQfpLDEwMUSA7uOZDGqXzv8vJtuDZT2bfy4f3Ifjp4p4K11B6oX8lYtCm7qU207tg3g+XsG2TULVP76AX07t6ag2FDrLKqyCjPrt5+mV8cgBnRreDpnIa4myN+Lnh2D2JOS3aCKdo6oMJoZ3DvM4ckdvt46HpnRj7IKM5v2nGVLcjpWq8K4QbbX/26shkWHc+/k3qSkFfDW5wcxmS0kH8uhc3hAixzblKBB5XoNgMNpV18d/u3P5ygtN3H7GPcv5BMty+DebbmQX8a57BK3njc40JsHpvat7sd3RGSYP8/MuZaOvnm8u/IfhGqzCGvdPPKGjegXwT0Te3EkLZ8P1nzNt+vfJ9Ca6elmNZiiKLzw35/ZtPusze+RznmgTZAPQ/u2pfVVVsACjB8cSYdQPzqFN/4SjqJpu65nKFqNmlAnFAezlclsIftiBe1CfBt8U5SbnsLtt03DaDRyNPFTZo7u2mxmwY0a0I6y3BM88Pv7MBoNLP95LSP6hTfpz6dSqcgtLCe7wPa1avKkccmDU/te9dHcalXw1mu5rqesyRCuF+CrZ2T/CLdO5Tx8uoA//3s3x85ebPCxKiv+GbFaLdUFyZqTo4eSLn0+a7P5fP4+9mW6laBxmbIKc41v3qnMIp77927Sc9zbVSBatnKDmW+SzrntuktOzcbHS0u3Dg0vINTcV/M3x88XYGfQkO6pSwxGC3966ycmDunEgx2DURSFT74/TrnBTEgLHOwSnmNVFD774QQ3XteBO260LwOpvSxWK/tP5BHTLcQpswKvVrO7OWmOn8/PR0f2xXKb95egcYmXXkPHtgEcOVM5GL43NYcT6YXcPaFnk1/1KZoWP28d0V2CSTqazW1jujmtlvbVpJ4rpKTcuVlam/tq/ub2+aLat7J5NThI91QNfToHczqzmMISA5/9eJL2bfwY1T/C080SLdDgPm0pKDZwMqOw/p0bYG9qDjqtmuguMpW8pZo0tBP3Te5j8/4SNC7Tt3NrrIrC25/tI7ugnNvGdEOjlm+RcL+YS0V99ri4ONOM2K48edsAu3KwiZZN/iJeJqp9K0qyj7Pnuw+J622hX1e5+xKe4eOlZUBUCMVlrs1z5uOlpWfHppvmQzTc3tQcnnhrOzk2jmtIZ/1lftmbxM7P/ozZbOKbz/9NVLvmVeBINC1zp0WjVrtuPGPrvgwMRgtxgzu67Byi8VOrVBSWGikpN9m0PkieNC6TmLgds9nUYirGicatKmCYzPaXI7bF5j3n2H8yzyXHFk2Hf1V6dBun3UrQuExznIMtmravdqYxf+VOzBarU4+bmVvKhfwyruvZtBPuiYazNz26dE9dpmoO9v79SQwYMEi6poTHtQvxo7DUyNEzBUQ7cYytKkvrNd0laLR01UHDxicNCRpXGDRoCJMmjXVZOU0h7BHdNQQfLw27U7KcGjSSU3Po2i6w3up0ovnz9dYysFcYbYJsW8Qs3VNCNGI6rZpruoeyNzUXk9k5XVQmsxV/by2DpMa9oHIg/A/To21+6pSgIUQjN7h3W8oNZg6dds6gtU6r5qk7rmG8zJoSl7G1hosEDSEauT6dW3PbDd3oHO54rYvLlRvMTjmOaD5eX7OfZZ/ss2lflwaNkpISpkyZQnp6OgDPPvsscXFxTJs2jWnTpvHtt98CkJKSwowZMxg/fjzPPfccZnPlRZ2ZmcmcOXOYMGECDz/8MKWlnqmdLIQnaTVqJgzp6JTxh+IyI3988yd+3JfhhJaJ5kKrUVFs4+wplwWN/fv3M2vWLNLS0qq3HTp0iNWrV7N+/XrWr1/PuHHjAJg3bx7PP/88mzdvRlEU1qxZA8CSJUuYPXs2mzZtIjo6mhUrVriquUI0amaLlT0pWZxoYC6qfSdyMVsUujjpqUU0D5U1NWzLPuCyoLFmzRoWL15MWFjlYFt5eTmZmZksXLiQqVOn8uabb2K1WsnIyKCiooKYmBgAZsyYwaZNmzCZTCQlJTF+/Pga24VoiVQq+OjbVN77JIFXXnmFpKTdDh3nl9RcQgK96djW38ktFE1ZZdAw2zSu4bIpty+99FKNr3Nzcxk6dCiLFy8mICCAhx56iLVr19K9e3dCQ38dtQ8NDSUrK4uCggL8/f3RarU1ttsrJMSxX47QUCntKhqXdj65vL70MVAs6PV6tmzZwrBhw2x+f1mFicNp+Uwc1pmwMHnSEL8KD/XHbLES0Mq33lIQblunERkZyTvvvFP99V133UV8fDxRUVE16hIrioJKpar+93KO1C/OyyvBarVtVkCV0NAAWachGp2S80ewWMygWDEYjGzY8DXdukXb/P6ko9mYzFZ6R7aS61vUEBboxdiBHcjJKcbPR1fnzbbbZk8dO3aMzZs3V3+tKAparZbw8HBycnKqt+fm5hIWFkZwcDDFxcVYLJV5d3Jycqq7uoRoiaZOHIuXXo9KrUal1jJyZCxQWabYFt3at2LOuB507xDkwlaKpqhnx9bMHtvDpoJzbgsaiqLw8ssvU1hYiMlk4tNPP2XcuHG0b98eLy8vkpOTAVi/fj2xsbHodDoGDhxIQkICAPHx8cTGxrqruUI0OoMGDeHzz7/kpRdf5LO16xk8eAgms4WF/7eLf3y2nyNp+XX2SbcO8OLG6zq4NHOuaLpMZotNC0hViq0rOhw0ZswY/ve//9GhQwc+/PBDPvzwQ8xmM3FxcTz99NMAHD16lEWLFlFSUkLfvn155ZVX0Ov1ZGRksGDBAvLy8oiIiGD58uW0atXKrvNL95Robi6/PiuMZr7Zc47v96ZTVGaiQ6g/cYMiGdKnLTrtr/eEaReKyMwtZVCvMHRaKbgkarqQX8bCf+3iwal9GN4vos7uKZcHDU+ToCGam6tdnyazhV1Hsvgm6RwZOaU8M/uaGsWV/vP1UfakZPHG4yMlaIjfqFy/s51ZY7szfnDHOoOGJCwUohnQaTWM6t+Okf0iOJ5eSPcOlU/ka344Qcqhvfy0/ScGDxkuAUNclZ+3DhW2pUeXoCFEM6JSqegRGVT99cmj+/nnK3/AYjFzNPFTbh7VVVL+i99Qq1X4+egoqag/aEjuKSGaMV9jGorVAooVi9kk1ShFrfx9dPKkIURLN3z4SPR6PSaTUapRijqNHdihuiBTXWQg/CpkIFw0ZvZen0lJu0lM3M7w4SOla0rUS61WyUC4EC3ZoEFDJFiIehmMForLjYS19q1zPxnTEEIIwfodp1n4r931Ji2UoCGEEIIAHx1mixWTWYKGEEKIevhdGgQvrai7roYEDSGEEARcChplFZY695OgIYQQ4rInjbrXakjQEEIIQXiwL7Nu7E5oK+8695Mpt0IIIQj00zNuUGS9qfPlSUMIIQQAmbmlXCw21LmPBA0hhBAAvLI6mR/3ZdS5jwQNIYQQQGXSwtJ6ygdL0BBCCAGAv6+OMpk9JYQQwhYBPnpKy+VJQwghhA38fLSUGeoOGjLlVgghBACjY9pjNFvr3EeChhBCCAC6tW8l6zSEEELYprjMyPH0i3XuI0FDCCEEAKnnCvm/L4/UuY8EDSGEEAAE+NZfI1yChhBCCODXTLd1kaAhhBAC+LWmRl0kaAghhAAq12nUR4KGEEIIADRqNfdO7l3nPi4NGiUlJUyZMoX09HQAEhMTmTp1KnFxcbz++uvV+6WkpDBjxgzGjx/Pc889h9lcuSIxMzOTOXPmMGHCBB5++GFKS0td2VwhhGjxjBfP1Pm6y4LG/v37mTVrFmlpaQBUVFSwcOFCVqxYQUJCAocOHWLr1q0AzJs3j+eff57NmzejKApr1qwBYMmSJcyePZtNmzYRHR3NihUrXNVcIYRo8ZKSdjN37n117uOyoLFmzRoWL15MWFgYAAcOHKBTp05ERkai1WqZOnUqmzZtIiMjg4qKCmJiYgCYMWMGmzZtwmQykZSUxPjx42tsF0II4RqJidure3pq47I0Ii+99FKNr7OzswkNDa3+OiwsjKysrN9sDw0NJSsri4KCAvz9/dFqtTW22yskxN+h9oeGBjj0PiHcQa5P4QqTJ49n7dpP6tzHbbmnrFYrKtWvOU0URUGlUtW6verfy135tS3y8kqwWhW73hMaGkBOTrHd5xLCHeT6FK7SrVs0K1f+u8593DZ7Kjw8nJycnOqvc3JyCAsL+8323NxcwsLCCA4Opri4GIvFUmN/IYQQrtO/f0ydr7staAwYMIDTp09z5swZLBYLGzduJDY2lvbt2+Pl5UVycjIA69evJzY2Fp1Ox8CBA0lISAAgPj6e2NhYdzVXCCHEVbite8rLy4ulS5fy2GOPYTAYGD16NBMmTABg2bJlLFq0iJKSEvr27cvdd98NwOLFi1mwYAErV64kIiKC5cuXu6u5QgghrkKlKIp9Hf5NjIxpiOZGrk/hSmq1qs4JRLIiXAghhM0kaAghhLBZsy/3Wl/pQme/Twh3kOtTuEp911azH9MQQgjhPNI9JYQQwmYSNIQQQthMgoYQQgibSdAQQghhMwkaQgghbCZBQwghhM0kaAghhLCZBI0mIDU1lZ49e7J582ZPN8Umn3/+OQsWLKj19QULFvD555+7sUW/Ki0tZcmSJYwbN46bbrqJ2bNns3Pnzlr3Ly4u5pFHHnFjC5uepnR9yrXZcBI0moB169YxYcIEPv30U083pUlTFIW5c+ei0+n46quv2LBhA4sWLWLevHns3r37qu8pLCwkJSXFzS1tWuT6bLimdG22+KBR352Hp5lMJr788kv+9Kc/cfjwYc6ePQvAmDFjePXVV5k+fTrTp0/nyJEjANx11108+uijjB8/3uN/7O66667qCz49PZ0xY8Z4tD179uwhMzOTZ599Fr1eD0CfPn14+OGHWbFiBSkpKdx6661MnTqVO++8kwsXLvDiiy+SnZ3tkTu6xn5tQtO9PuXadFyLDxqN3datW2nXrh1dunRh7NixNe7mfH19iY+P5/HHH+eZZ56p3l7VVdC7d29PNLnROnjwINHR0b8pGzxo0CAOHjzI008/zR/+8Ae+/PJLJk2axH//+18WLVpEWFgY77zzjoda3bjJ9ekcTenalKBxyZ49e5g1axY333wzN954I9999x1Q2cf54osvMmvWLMaMGcO6devc2q5169YxZcoUACZNmsTnn3+O0WgE4LbbbgMq7+qysrLIz88HoH///m5tY1OhUqmqywdfzmQyYbVaycnJ4YYbbgBg9uzZNf7QeVJjvTZBrk9naUrXZrPPcmur1atX8+KLLxIVFcXOnTt5+eWXGTt2LAAXLlzgo48+IjU1lbvvvpuZM2e6pU15eXn89NNPHD58mP/9738oikJRURHffvstAFrtrz8+q9WKRqMBwNvb2y3tu9zPP/9MZGQkbdu2RVGU6rZU5cM0m81ub9OVBgwYwKpVqzCZTOh0uurt+/bto1+/fqSmplZvMxgMZGdn/+bOzxMa47UJTef6lGvTueRJ45LXXnuN48eP88477/DBBx9QWlpa/dqIESNQqVT06NGDixcvuq1N69evZ+jQoWzbto3vv/+eH374gblz5/LJJ58A8NVXXwHw7bffEhUVRatWrdzWtiutW7eu+g742LFjREZG0rp1a06cOAFQ/ZonDRw4kG7duvHyyy9jMpkAOHToECtXruTRRx+lbdu2bN++Haj83r/xxhtotVqP/1FpjNcmNJ3rU65N52qRQePnn38mKysLoPrOY/bs2Rw4cIDo6Gjmzp1bY38vLy8At0f2L774gtmzZ9fYNmfOHA4cOIDBYGDv3r1MmzaN9957j6VLl7q1bVd68MEHiY+PZ+LEiRw/fpxZs2Zx//3389FHH3HzzTdTUVHh0fZVefvtt9Hr9UyZMoVJkybx0ksv8dprrzFkyBBee+013nnnHaZNm0ZCQgLz588nJCSEdu3acdddd7mlfU3l2oSmc33KtelkSgu0YMECZfXq1YqiKMpLL72krFy5Urn22muViooKxWq1Kq+++qoSGxurKIqiPPPMM8q6deuq39ujRw+PtPlKN9xwg3Lu3DlPN0M4WXO4NhVFrs/mrEWOaTz44IPMnz+f1atXEx4ezj/+8Q8KCgqYPHkyWq2WoUOHUlFRQVlZmaebKloYuTZFYyeV+4QQQtisRY5pCCGEcIwEDSGEEDaToCGEEMJmLSZovP3220yePJnJkyfz6quvApCYmMjUqVOJi4vj9ddf/8175s+fXyPjZWZmJnPmzGHChAk8/PDDNebLC9EQzrg+AT777LNGn69KNG0tImgkJiayfft2vvjiC+Lj4zl8+DAbN25k4cKFrFixgoSEBA4dOsTWrVsByMrKYu7cub9J9bxkyRJmz57Npk2biI6OZsWKFZ74OKKZccb1aTAYWLZsGS+//LKnPoZoIVpE0AgNDWXBggXo9Xp0Oh1RUVGkpaXRqVMnIiMj0Wq1TJ06lU2bNgHw5ZdfcuONNzJx4sTqY5hMJpKSkhg/fjwAM2bMqN5fiIZwxvWZlJSE1Wpl3rx5nvoYooVoEUGje/fuxMTEAJCWlsbXX3+NSqUiNDS0ep+wsLDqlbj3338/t956a41jFBQU4O/vX51PJzQ0tHp/IRrCGdfnyJEjmT9/vkfyjomWpUUEjSrHjx/n3nvvZf78+URGRtZIvaAoSp2pGK72emNIZieaj4Zcn0K4S4sJGsnJydxzzz089dRT3HzzzYSHh5OTk1P9ek5ODmFhYbW+Pzg4mOLi4ur0xfXtL4Q9Gnp9CuEuLSJonD9/nkceeYRly5YxefJkoDIV8enTpzlz5gwWi4WNGzcSGxtb6zF0Oh0DBw4kISEBgPj4+Dr3F8JWzrg+hXCXFpF76r333sNgMNTItHnHHXewdOlSHnvsMQwGA6NHj2bChAl1Hmfx4sUsWLCAlStXEhERwfLly13ddNECOOv6FMIdJPeUEEIIm7WI7ikhhBDOIUFDCCGEzSRoCCGEsJkEDSGEEDaToCGEEMJmEjREk5aenk7Pnj258847f/PaggUL6NmzJ/n5+Q4f/+233+a7776rPt57773n8LEAPv/8c66//nruu+++37x27733Vrd1zJgxHDx40OHzPPTQQzz++OM1tpWWljJu3Dg+++wzh48rhAQN0eR5eXlx+vRpMjIyqreVlZWxd+/eBh979+7dmM3mBh+nSnx8PE888cRVg8+OHTucdp5XXnmFvXv3sn79+uptL7/8Mv379/9N3ioh7NEiFveJ5k2j0TBx4kS+/PJL5s6dC8A333zDjTfeyPvvv1+936effsqqVatQq9W0adOGP//5z3Tp0oUFCxbg7+/PsWPHuHDhAj179uRvf/sb8fHxHDp0iFdffRWNRgPAL7/8wh133EFubi7du3fn73//O76+vjXaU1xczJIlSzh69CgqlYpRo0bx5JNP8uqrr3Lw4EHS09MpKCjgnnvuqX7Ps88+C8Dvfvc7/vWvf1W3d/HixeTn5zNt2jSeeOIJAL7//ntWrlyJyWTC29ubZ555hmuuuaZGG4KDg1m6dClPPvkkQ4cO5ciRI/z888/V9TdWrlzJN998g9VqpX379ixevJi2bduyb98+XnvtNYxGIzk5OQwfPpyXX36Z9PR05syZQ1RUFBkZGaxatUrSmrRUihBN2Llz55SYmBjl4MGDyoQJE6q3/+53v1OOHTum9OjRQ8nLy1MSExOVsWPHKnl5eYqiKMq6deuUiRMnKlarVXnmmWeU22+/XTEYDIrRaFSmT5+urF27VlEURbnzzjuVr7/+WlEURXnmmWeUW265RSkrK1PMZrNy8803K1988cVv2jR//nzlhRdeUKxWq2IwGJR7771Xeffdd39zvCtVtVVRFOWGG25Q/vrXvyqKoijZ2dlKdHS0kpmZqZw+fVqZMmWKkp+fryiKoqSmpiojRoxQSktLr3rMV155RbnvvvuUG2+8UUlJSVEURVG++OIL5U9/+pNiMpkURVGUTz75RLn//vsVRVGUJ554Qtm1a5eiKIpSUlKiDBkyRDl48KBy7tw5pUePHkpSUpItPxbRjMmThmgWoqOj0Wg0HDp0iJCQEEpLS+nRo0f16z/99BOTJk0iODgYqKyH8tJLL5Geng7AqFGj0Ov1APTo0YPCwsKrnmfs2LH4+PgAlSnNrzZesm3bNj7++GNUKhV6vZ477riD//73vzz44IN2faYpU6YAlWn427RpQ15eHvv37yc7O7vGU4pKpeLs2bP06tXrN8d48sknmTJlCrfddlv16z/88AMHDx5k5syZAFitVsrLywFYunQp27Zt45///CenTp3CYDBQVlZGUFAQWq22OoW7aLkkaIhm46abbmLDhg0EBwczbdq0Gq9Zrdbf7K8oSvV4xeV1KFQqFUot2XWq6qnUtZ/Vaq2RxtxqtTo0LnK1c1mtVoYNG8Y//vGP6tfOnz9fa1eRXq+nbdu2dOzYsUZ77r//fmbPng2A0WisDpJ33nknPXv2ZNSoUUycOJH9+/dXf0a9Xl+jTaJlkoFw0WxMmzaNTZs2kZCQUH2XXmXUqFEkJCRUPxmsW7eOoKAgOnXqVOcxNRqN3X/wR44cyerVq1EUBaPRyJo1axg+fHi977PlXMOGDWPHjh2cPHkSgK1bt3LTTTdRUVFhV/vWrl1LSUkJAG+88Qbz58+nqKiIgwcP8vTTTxMXF8eFCxc4e/bsVQOuaLnktkE0G23btiUqKoqAgACCgoJqvDZixAjuuecefve732G1WgkODubdd99Fra77vmnMmDEsX74ck8lkczsWLVrEiy++yNSpUzGZTIwaNap6gL4uEyZM4K677uKtt96qdZ9u3brx17/+lSeffBJFUdBqtaxcuRI/Pz+b23frrbeSlZXFbbfdhkqlIiIigqVLlxIYGMiDDz7IzTffjK+vL23btuXaa6/lzJkzREZG2nx80bxJllshhBA2k+4pIYQQNpOgIYQQwmYSNIQQQthMgoYQQgibSdAQQghhMwkaQgghbCZBQwghhM3+H7JFq+0hyeQqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df.set_index('InvoiceDate').resample(\"M\")[\"Invoice\"].nunique().plot(marker=\".\", \n",
    "                                                                         linestyle=\"--\", \n",
    "                                                                         markerfacecolor=\"black\",\n",
    "                                                                         markeredgecolor=\"black\"\n",
    "                                                                         )\n",
    "ax.set_ylabel(\"Total Sales\")\n",
    "ax.set_xlabel(\"Month of the Year\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of item descriptions: 5698\n"
     ]
    }
   ],
   "source": [
    "print(f\"Number of item descriptions: {df['Description'].nunique()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of item stock code: 5305\n"
     ]
    }
   ],
   "source": [
    "print(f\"Number of item stock code: {df['StockCode'].nunique()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique customers: 5942\n"
     ]
    }
   ],
   "source": [
    "print(f\"Number of unique customers: {df['Customer ID'].nunique()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are some missing values in the **Description** and **Customer ID** columns. Let's have a look at the rows where the values of the column **Description** are missing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Missing values in the **Description** column: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4105414143723223"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Description\"].isnull().mean() * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "0.41 % percent of the rows in the fisrt sheet have missing values in the **Description** column, in the next cells an example of 10 of those rows is shown. It seems those rows have negative values for the **Quantity** column, a lot of 0.0 in the **Price** column and missing values in the **Customer ID** column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Invoice</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>Price</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>75240</th>\n",
       "      <td>542564</td>\n",
       "      <td>84526</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>2011-01-28 14:52:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350805</th>\n",
       "      <td>523526</td>\n",
       "      <td>21538</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>2010-09-22 13:07:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296415</th>\n",
       "      <td>518235</td>\n",
       "      <td>22753</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5000</td>\n",
       "      <td>2010-08-05 15:53:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202599</th>\n",
       "      <td>554488</td>\n",
       "      <td>84913A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-75</td>\n",
       "      <td>2011-05-24 13:35:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437275</th>\n",
       "      <td>531223</td>\n",
       "      <td>22591</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-20</td>\n",
       "      <td>2010-11-05 16:29:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74659</th>\n",
       "      <td>542515</td>\n",
       "      <td>35607A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-28 12:08:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88573</th>\n",
       "      <td>497727</td>\n",
       "      <td>20710</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-16</td>\n",
       "      <td>2010-02-11 18:13:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282935</th>\n",
       "      <td>561674</td>\n",
       "      <td>21445</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-07-28 17:20:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354894</th>\n",
       "      <td>523916</td>\n",
       "      <td>22898</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-150</td>\n",
       "      <td>2010-09-24 16:13:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418524</th>\n",
       "      <td>572731</td>\n",
       "      <td>35914</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2011-10-25 15:20:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Invoice StockCode Description  Quantity         InvoiceDate  Price  \\\n",
       "75240   542564     84526         NaN        -1 2011-01-28 14:52:00    0.0   \n",
       "350805  523526     21538         NaN        14 2010-09-22 13:07:00    0.0   \n",
       "296415  518235     22753         NaN      5000 2010-08-05 15:53:00    0.0   \n",
       "202599  554488    84913A         NaN       -75 2011-05-24 13:35:00    0.0   \n",
       "437275  531223     22591         NaN       -20 2010-11-05 16:29:00    0.0   \n",
       "74659   542515    35607A         NaN         1 2011-01-28 12:08:00    0.0   \n",
       "88573   497727     20710         NaN       -16 2010-02-11 18:13:00    0.0   \n",
       "282935  561674     21445         NaN         1 2011-07-28 17:20:00    0.0   \n",
       "354894  523916     22898         NaN      -150 2010-09-24 16:13:00    0.0   \n",
       "418524  572731     35914         NaN         2 2011-10-25 15:20:00    0.0   \n",
       "\n",
       "        Customer ID         Country  \n",
       "75240           NaN  United Kingdom  \n",
       "350805          NaN  United Kingdom  \n",
       "296415          NaN  United Kingdom  \n",
       "202599          NaN  United Kingdom  \n",
       "437275          NaN  United Kingdom  \n",
       "74659           NaN  United Kingdom  \n",
       "88573           NaN  United Kingdom  \n",
       "282935          NaN  United Kingdom  \n",
       "354894          NaN  United Kingdom  \n",
       "418524          NaN  United Kingdom  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"Description\"].isnull()].sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the rows contains missing values for the **Customer ID** column as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.all(df[df[\"Description\"].isnull()][\"Customer ID\"].isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61.364673664993155"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df[df[\"Description\"].isnull()][\"Quantity\"] <= 0).mean() * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all(df[df[\"Description\"].isnull()][\"Country\"] == \"United Kingdom\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all(df[df[\"Description\"].isnull()][\"Price\"] == 0.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "61 % of the rows have a negative value for the column **Quantity**. Also the column **Country** is filled only with *United Kingdom*, and the **Price** column is filled with zeros. Those rows maybe revealed some dirty data patterns like negative quantities or products that a price of 0 that we can find in other rows. For the latter it might be possible that some items are free, I will see with further exploration. \n",
    "\n",
    "#### Missing values in the **Customer ID** column:\n",
    "\n",
    "Let's now look at the rows filled only with missing values in the **Customer ID** column, which represent 22% of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22.766872999172733"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Customer ID\"].isnull().mean() * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Invoice</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>Price</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>138115</th>\n",
       "      <td>502619</td>\n",
       "      <td>22474</td>\n",
       "      <td>SPACEBOY TV DINNER TRAY</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-03-25 14:56:00</td>\n",
       "      <td>11.02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5108</th>\n",
       "      <td>489856</td>\n",
       "      <td>22197</td>\n",
       "      <td>POPCORN HOLDER , SMALL</td>\n",
       "      <td>8</td>\n",
       "      <td>2009-12-02 14:36:00</td>\n",
       "      <td>1.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382229</th>\n",
       "      <td>569898</td>\n",
       "      <td>22944</td>\n",
       "      <td>CHRISTMAS METAL POSTCARD WITH BELLS</td>\n",
       "      <td>6</td>\n",
       "      <td>2011-10-06 16:08:00</td>\n",
       "      <td>0.97</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336330</th>\n",
       "      <td>522138</td>\n",
       "      <td>22136</td>\n",
       "      <td>LOVE HEART SOCK HANGER</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-09-13 10:30:00</td>\n",
       "      <td>3.36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91285</th>\n",
       "      <td>544089</td>\n",
       "      <td>15056N</td>\n",
       "      <td>EDWARDIAN PARASOL NATURAL</td>\n",
       "      <td>3</td>\n",
       "      <td>2011-02-15 17:51:00</td>\n",
       "      <td>5.95</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365063</th>\n",
       "      <td>568693</td>\n",
       "      <td>23377</td>\n",
       "      <td>PACK OF 12 DOLLY GIRL TISSUES</td>\n",
       "      <td>2</td>\n",
       "      <td>2011-09-28 14:54:00</td>\n",
       "      <td>0.39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126578</th>\n",
       "      <td>501475</td>\n",
       "      <td>21833</td>\n",
       "      <td>CAMOUFLAGE LED TORCH</td>\n",
       "      <td>2</td>\n",
       "      <td>2010-03-16 16:59:00</td>\n",
       "      <td>3.36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10955</th>\n",
       "      <td>537240</td>\n",
       "      <td>22695</td>\n",
       "      <td>WICKER WREATH SMALL</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-12-06 10:08:00</td>\n",
       "      <td>3.36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>518456</th>\n",
       "      <td>537638</td>\n",
       "      <td>47591B</td>\n",
       "      <td>SCOTTIES CHILDRENS APRON</td>\n",
       "      <td>7</td>\n",
       "      <td>2010-12-07 15:28:00</td>\n",
       "      <td>1.66</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67391</th>\n",
       "      <td>541830</td>\n",
       "      <td>22427</td>\n",
       "      <td>ENAMEL FLOWER JUG CREAM</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-21 17:09:00</td>\n",
       "      <td>11.63</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Invoice StockCode                          Description  Quantity  \\\n",
       "138115  502619     22474              SPACEBOY TV DINNER TRAY         1   \n",
       "5108    489856     22197              POPCORN HOLDER , SMALL          8   \n",
       "382229  569898     22944  CHRISTMAS METAL POSTCARD WITH BELLS         6   \n",
       "336330  522138     22136               LOVE HEART SOCK HANGER         1   \n",
       "91285   544089    15056N            EDWARDIAN PARASOL NATURAL         3   \n",
       "365063  568693     23377        PACK OF 12 DOLLY GIRL TISSUES         2   \n",
       "126578  501475     21833                 CAMOUFLAGE LED TORCH         2   \n",
       "10955   537240     22695                  WICKER WREATH SMALL         1   \n",
       "518456  537638    47591B             SCOTTIES CHILDRENS APRON         7   \n",
       "67391   541830     22427              ENAMEL FLOWER JUG CREAM         1   \n",
       "\n",
       "               InvoiceDate  Price  Customer ID         Country  \n",
       "138115 2010-03-25 14:56:00  11.02          NaN  United Kingdom  \n",
       "5108   2009-12-02 14:36:00   1.70          NaN  United Kingdom  \n",
       "382229 2011-10-06 16:08:00   0.97          NaN  United Kingdom  \n",
       "336330 2010-09-13 10:30:00   3.36          NaN  United Kingdom  \n",
       "91285  2011-02-15 17:51:00   5.95          NaN  United Kingdom  \n",
       "365063 2011-09-28 14:54:00   0.39          NaN  United Kingdom  \n",
       "126578 2010-03-16 16:59:00   3.36          NaN  United Kingdom  \n",
       "10955  2010-12-06 10:08:00   3.36          NaN  United Kingdom  \n",
       "518456 2010-12-07 15:28:00   1.66          NaN  United Kingdom  \n",
       "67391  2011-01-21 17:09:00  11.63          NaN  United Kingdom  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"Customer ID\"].isnull()].sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4872"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"Customer ID\"].isnull()][\"Description\"].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.7308143386816017"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df[df[\"Customer ID\"].isnull()][\"Quantity\"] <= 0).mean() * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.5250301431646003"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df[df[\"Customer ID\"].isnull()][\"Price\"] <= 0.0).mean() * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is not a pattern or a clear reason fo why these rows don't have a **Customer ID** from the data.\n",
    "\n",
    "#### Negative prices and quantities\n",
    "\n",
    "0.6 % of the dataset has negative prices. Let's have a look at rows the values in the **Quantity** column is less or equal than zero, they represent 2.15 % of the total dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5815222635803297"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df[\"Price\"] <= 0).mean() * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.1501427338760375"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df[\"Quantity\"] <= 0).mean() * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>Invoice</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>Price</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4566</th>\n",
       "      <td>489821</td>\n",
       "      <td>85049G</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-240</td>\n",
       "      <td>2009-12-02 13:25:00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47880</th>\n",
       "      <td>C493850</td>\n",
       "      <td>22061</td>\n",
       "      <td>LARGE CAKE STAND  HANGING STRAWBERY</td>\n",
       "      <td>-1</td>\n",
       "      <td>2010-01-07 15:34:00</td>\n",
       "      <td>9.95</td>\n",
       "      <td>14267.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46236</th>\n",
       "      <td>C493673</td>\n",
       "      <td>84854</td>\n",
       "      <td>GIRLY PINK TOOL SET</td>\n",
       "      <td>-1</td>\n",
       "      <td>2010-01-05 12:26:00</td>\n",
       "      <td>4.25</td>\n",
       "      <td>16779.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123588</th>\n",
       "      <td>C546901</td>\n",
       "      <td>22968</td>\n",
       "      <td>ROSE COTTAGE KEEPSAKE BOX</td>\n",
       "      <td>-1</td>\n",
       "      <td>2011-03-17 18:29:00</td>\n",
       "      <td>9.95</td>\n",
       "      <td>14112.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288295</th>\n",
       "      <td>C562149</td>\n",
       "      <td>23204</td>\n",
       "      <td>CHARLOTTE BAG APPLES DESIGN</td>\n",
       "      <td>-1</td>\n",
       "      <td>2011-08-03 10:49:00</td>\n",
       "      <td>0.85</td>\n",
       "      <td>13534.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24920</th>\n",
       "      <td>C538350</td>\n",
       "      <td>20725</td>\n",
       "      <td>LUNCH BAG RED RETROSPOT</td>\n",
       "      <td>-3</td>\n",
       "      <td>2010-12-10 15:01:00</td>\n",
       "      <td>1.45</td>\n",
       "      <td>13798.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306322</th>\n",
       "      <td>C519250</td>\n",
       "      <td>85099B</td>\n",
       "      <td>JUMBO BAG RED RETROSPOT</td>\n",
       "      <td>-1</td>\n",
       "      <td>2010-08-15 15:18:00</td>\n",
       "      <td>1.95</td>\n",
       "      <td>17107.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289669</th>\n",
       "      <td>C517562</td>\n",
       "      <td>22215</td>\n",
       "      <td>CAKE STAND WHITE TWO TIER LACE</td>\n",
       "      <td>-2</td>\n",
       "      <td>2010-07-30 07:47:00</td>\n",
       "      <td>8.50</td>\n",
       "      <td>15827.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81664</th>\n",
       "      <td>496885</td>\n",
       "      <td>47554</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-4</td>\n",
       "      <td>2010-02-04 14:15:00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74415</th>\n",
       "      <td>C542424</td>\n",
       "      <td>21733</td>\n",
       "      <td>RED HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>-1</td>\n",
       "      <td>2011-01-28 09:18:00</td>\n",
       "      <td>2.95</td>\n",
       "      <td>18075.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Invoice StockCode                          Description  Quantity  \\\n",
       "4566     489821    85049G                                  NaN      -240   \n",
       "47880   C493850     22061  LARGE CAKE STAND  HANGING STRAWBERY        -1   \n",
       "46236   C493673     84854                  GIRLY PINK TOOL SET        -1   \n",
       "123588  C546901     22968           ROSE COTTAGE KEEPSAKE BOX         -1   \n",
       "288295  C562149     23204          CHARLOTTE BAG APPLES DESIGN        -1   \n",
       "24920   C538350     20725              LUNCH BAG RED RETROSPOT        -3   \n",
       "306322  C519250    85099B              JUMBO BAG RED RETROSPOT        -1   \n",
       "289669  C517562     22215       CAKE STAND WHITE TWO TIER LACE        -2   \n",
       "81664    496885     47554                                  NaN        -4   \n",
       "74415   C542424     21733     RED HANGING HEART T-LIGHT HOLDER        -1   \n",
       "\n",
       "               InvoiceDate  Price  Customer ID         Country  \n",
       "4566   2009-12-02 13:25:00   0.00          NaN  United Kingdom  \n",
       "47880  2010-01-07 15:34:00   9.95      14267.0  United Kingdom  \n",
       "46236  2010-01-05 12:26:00   4.25      16779.0  United Kingdom  \n",
       "123588 2011-03-17 18:29:00   9.95      14112.0  United Kingdom  \n",
       "288295 2011-08-03 10:49:00   0.85      13534.0  United Kingdom  \n",
       "24920  2010-12-10 15:01:00   1.45      13798.0  United Kingdom  \n",
       "306322 2010-08-15 15:18:00   1.95      17107.0  United Kingdom  \n",
       "289669 2010-07-30 07:47:00   8.50      15827.0  United Kingdom  \n",
       "81664  2010-02-04 14:15:00   0.00          NaN  United Kingdom  \n",
       "74415  2011-01-28 09:18:00   2.95      18075.0  United Kingdom  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"Quantity\"] <= 0].sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the **Invoice** column there is a *C* before the number in most of the rows (85% of them) and according to the data description it means that the order was canceled."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "84.93681917211329"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"Quantity\"] <= 0][\"Invoice\"].astype(\"str\").str.startswith(\"C\").mean() * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99.99487021647685"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df[df[\"Invoice\"].astype(\"str\").str.startswith(\"C\")][\"Quantity\"] <= 0.).mean() * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a new columns called **TotalPrice** which simply equals to **Quantity** $\\times$ **Price**. Rows with a less or equal to zero represent 2.4% of the dataset and consist of the rows with either a negative price or a negative quantity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"TotalPrice\"] = df[\"Quantity\"] * df[\"Price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.4077851093949527"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df[\"TotalPrice\"] <= 0).mean() * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.4077851093949527"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((df[\"Price\"] <= 0) | (df[\"Quantity\"] <= 0) ).mean() * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>Invoice</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>Price</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Country</th>\n",
       "      <th>TotalPrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>C489449</td>\n",
       "      <td>22087</td>\n",
       "      <td>PAPER BUNTING WHITE LACE</td>\n",
       "      <td>-12</td>\n",
       "      <td>2009-12-01 10:33:00</td>\n",
       "      <td>2.95</td>\n",
       "      <td>16321.0</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-35.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>C489449</td>\n",
       "      <td>85206A</td>\n",
       "      <td>CREAM FELT EASTER EGG BASKET</td>\n",
       "      <td>-6</td>\n",
       "      <td>2009-12-01 10:33:00</td>\n",
       "      <td>1.65</td>\n",
       "      <td>16321.0</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-9.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>C489449</td>\n",
       "      <td>21895</td>\n",
       "      <td>POTTING SHED SOW 'N' GROW SET</td>\n",
       "      <td>-4</td>\n",
       "      <td>2009-12-01 10:33:00</td>\n",
       "      <td>4.25</td>\n",
       "      <td>16321.0</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-17.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>C489449</td>\n",
       "      <td>21896</td>\n",
       "      <td>POTTING SHED TWINE</td>\n",
       "      <td>-6</td>\n",
       "      <td>2009-12-01 10:33:00</td>\n",
       "      <td>2.10</td>\n",
       "      <td>16321.0</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-12.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>C489449</td>\n",
       "      <td>22083</td>\n",
       "      <td>PAPER CHAIN KIT RETRO SPOT</td>\n",
       "      <td>-12</td>\n",
       "      <td>2009-12-01 10:33:00</td>\n",
       "      <td>2.95</td>\n",
       "      <td>16321.0</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-35.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540449</th>\n",
       "      <td>C581490</td>\n",
       "      <td>23144</td>\n",
       "      <td>ZINC T-LIGHT HOLDER STARS SMALL</td>\n",
       "      <td>-11</td>\n",
       "      <td>2011-12-09 09:57:00</td>\n",
       "      <td>0.83</td>\n",
       "      <td>14397.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>-9.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541541</th>\n",
       "      <td>C581499</td>\n",
       "      <td>M</td>\n",
       "      <td>Manual</td>\n",
       "      <td>-1</td>\n",
       "      <td>2011-12-09 10:28:00</td>\n",
       "      <td>224.69</td>\n",
       "      <td>15498.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>-224.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541715</th>\n",
       "      <td>C581568</td>\n",
       "      <td>21258</td>\n",
       "      <td>VICTORIAN SEWING BOX LARGE</td>\n",
       "      <td>-5</td>\n",
       "      <td>2011-12-09 11:57:00</td>\n",
       "      <td>10.95</td>\n",
       "      <td>15311.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>-54.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541716</th>\n",
       "      <td>C581569</td>\n",
       "      <td>84978</td>\n",
       "      <td>HANGING HEART JAR T-LIGHT HOLDER</td>\n",
       "      <td>-1</td>\n",
       "      <td>2011-12-09 11:58:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>17315.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>-1.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541717</th>\n",
       "      <td>C581569</td>\n",
       "      <td>20979</td>\n",
       "      <td>36 PENCILS TUBE RED RETROSPOT</td>\n",
       "      <td>-5</td>\n",
       "      <td>2011-12-09 11:58:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>17315.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>-6.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25700 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Invoice StockCode                       Description  Quantity  \\\n",
       "178     C489449     22087          PAPER BUNTING WHITE LACE       -12   \n",
       "179     C489449    85206A      CREAM FELT EASTER EGG BASKET        -6   \n",
       "180     C489449     21895     POTTING SHED SOW 'N' GROW SET        -4   \n",
       "181     C489449     21896                POTTING SHED TWINE        -6   \n",
       "182     C489449     22083        PAPER CHAIN KIT RETRO SPOT       -12   \n",
       "...         ...       ...                               ...       ...   \n",
       "540449  C581490     23144   ZINC T-LIGHT HOLDER STARS SMALL       -11   \n",
       "541541  C581499         M                            Manual        -1   \n",
       "541715  C581568     21258        VICTORIAN SEWING BOX LARGE        -5   \n",
       "541716  C581569     84978  HANGING HEART JAR T-LIGHT HOLDER        -1   \n",
       "541717  C581569     20979     36 PENCILS TUBE RED RETROSPOT        -5   \n",
       "\n",
       "               InvoiceDate   Price  Customer ID         Country  TotalPrice  \n",
       "178    2009-12-01 10:33:00    2.95      16321.0       Australia      -35.40  \n",
       "179    2009-12-01 10:33:00    1.65      16321.0       Australia       -9.90  \n",
       "180    2009-12-01 10:33:00    4.25      16321.0       Australia      -17.00  \n",
       "181    2009-12-01 10:33:00    2.10      16321.0       Australia      -12.60  \n",
       "182    2009-12-01 10:33:00    2.95      16321.0       Australia      -35.40  \n",
       "...                    ...     ...          ...             ...         ...  \n",
       "540449 2011-12-09 09:57:00    0.83      14397.0  United Kingdom       -9.13  \n",
       "541541 2011-12-09 10:28:00  224.69      15498.0  United Kingdom     -224.69  \n",
       "541715 2011-12-09 11:57:00   10.95      15311.0  United Kingdom      -54.75  \n",
       "541716 2011-12-09 11:58:00    1.25      17315.0  United Kingdom       -1.25  \n",
       "541717 2011-12-09 11:58:00    1.25      17315.0  United Kingdom       -6.25  \n",
       "\n",
       "[25700 rows x 9 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"TotalPrice\"] <= 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Keeping all rows with **TotalPrice** $>$ 0 removes all rows with missing values in the **Description** columns, and also removes all canceled orders but 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all(~df[df[\"TotalPrice\"] > 0][\"Description\"].isnull())"
   ]
  },
  {
   "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>Invoice</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>Price</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Country</th>\n",
       "      <th>TotalPrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>76799</th>\n",
       "      <td>C496350</td>\n",
       "      <td>M</td>\n",
       "      <td>Manual</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-01 08:24:00</td>\n",
       "      <td>373.57</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>373.57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Invoice StockCode Description  Quantity         InvoiceDate   Price  \\\n",
       "76799  C496350         M      Manual         1 2010-02-01 08:24:00  373.57   \n",
       "\n",
       "       Customer ID         Country  TotalPrice  \n",
       "76799          NaN  United Kingdom      373.57  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df[\"Invoice\"].astype(\"str\").str.startswith(\"C\")) & (df[\"TotalPrice\"] > 0)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Further exploration with cleaned data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now create a function that loads the data, and filter out rows with negative **TotalPrice** and cancelled orders. There is an argument in the function of keep rows with missing values in the **Customer ID** column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_df(sheet_name=\"Year 2009-2010\", keepna=True):\n",
    "    \n",
    "    df = pd.read_excel(\"data/online_retail_II.xlsx\", \n",
    "                       sheet_name=sheet_name, \n",
    "                       parse_dates=[\"InvoiceDate\"],\n",
    "                       dtype={\"Invoice\": \"str\", \"StockCode\": \"str\"})\n",
    "    \n",
    "    if isinstance(df, dict):\n",
    "        df = pd.concat(df.values())\n",
    "    \n",
    "    df[\"TotalPrice\"] = df[\"Quantity\"] * df[\"Price\"]\n",
    "    df = df.query(\"TotalPrice > 0\")\n",
    "    df = df[~df[\"Invoice\"].str.startswith(\"C\")]\n",
    "    df = df[~df[\"StockCode\"].str.contains(\"TEST\")]\n",
    "    \n",
    "    if not keepna:\n",
    "        df.dropna(inplace = True)\n",
    "        \n",
    "    df.drop_duplicates(inplace=True)\n",
    "        \n",
    "    assert all(df[\"InvoiceDate\"] > datetime(2009,1,1))\n",
    "    assert all(df[\"InvoiceDate\"] < datetime(2012,1,1))\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = load_df(None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1007903 entries, 0 to 541909\n",
      "Data columns (total 9 columns):\n",
      " #   Column       Non-Null Count    Dtype         \n",
      "---  ------       --------------    -----         \n",
      " 0   Invoice      1007903 non-null  object        \n",
      " 1   StockCode    1007903 non-null  object        \n",
      " 2   Description  1007903 non-null  object        \n",
      " 3   Quantity     1007903 non-null  int64         \n",
      " 4   InvoiceDate  1007903 non-null  datetime64[ns]\n",
      " 5   Price        1007903 non-null  float64       \n",
      " 6   Customer ID  779415 non-null   float64       \n",
      " 7   Country      1007903 non-null  object        \n",
      " 8   TotalPrice   1007903 non-null  float64       \n",
      "dtypes: datetime64[ns](1), float64(3), int64(1), object(4)\n",
      "memory usage: 76.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have prepared the dataset let's explore further. 22 % percent of the rows have missing values in the **Customer ID**. values which is a lot and quite unfortunate as we cannot track next orders from those customers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a total of 100k orders in the dataset. Below are listed the most sold items for customers with and without **Customer ID**, we see they don't buy the same items."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Description\n",
       "WORLD WAR 2 GLIDERS ASSTD DESIGNS     105185\n",
       "WHITE HANGING HEART T-LIGHT HOLDER     91757\n",
       "PAPER CRAFT , LITTLE BIRDIE            80995\n",
       "ASSORTED COLOUR BIRD ORNAMENT          78234\n",
       "MEDIUM CERAMIC TOP STORAGE JAR         77916\n",
       "Name: Quantity, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna().groupby(\"Description\")[\"Quantity\"].sum().sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Description\n",
       "CHARLOTTE BAG SUKI DESIGN          12139\n",
       "PAPER CHAIN KIT 50'S CHRISTMAS      6704\n",
       "POPCORN HOLDER                      5830\n",
       "SMALL POPCORN HOLDER                5606\n",
       "RED RETROSPOT CHARLOTTE BAG         5495\n",
       "Name: Quantity, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"Customer ID\"].isnull()].groupby(\"Description\")[\"Quantity\"].sum().sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However the item than brought more money to the company is not \"WORLD WAR 2 GLIDERS ASSTD DESIGNS\" but \"REGENCY CAKESTAND 3 TIER\" for a total of 278k £. In comparison \"WORLD WAR 2 GLIDERS ASSTD DESIGNS\" only grossed 25k £."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Description\n",
       "REGENCY CAKESTAND 3 TIER              277656.25\n",
       "WHITE HANGING HEART T-LIGHT HOLDER    247048.01\n",
       "PAPER CRAFT , LITTLE BIRDIE           168469.60\n",
       "Manual                                151777.67\n",
       "JUMBO BAG RED RETROSPOT               134307.44\n",
       "Name: TotalPrice, dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna().groupby(\"Description\")[\"TotalPrice\"].sum().sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24445.61"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df[\"Description\"] == \"WORLD WAR 2 GLIDERS ASSTD DESIGNS\"][\"TotalPrice\"].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check what are the most expensive items. As shown below the 5 most expensive items are all bought by unknown/unregistered customers with prices up to 25k £. Looking only at the customers with a **Customer ID** the 5 most expensive items have lower prices than for unknown customers. The plot below shows the distributions of the price per item for rows with and without missing values in the **Customer ID**, the distributions are highly skewed but we can see that unknown customers buy more expensive items the known customers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>Invoice</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>Price</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Country</th>\n",
       "      <th>TotalPrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>241827</th>\n",
       "      <td>512771</td>\n",
       "      <td>M</td>\n",
       "      <td>Manual</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-06-17 16:53:00</td>\n",
       "      <td>25111.09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>25111.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>517955</th>\n",
       "      <td>537632</td>\n",
       "      <td>AMAZONFEE</td>\n",
       "      <td>AMAZON FEE</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-12-07 15:08:00</td>\n",
       "      <td>13541.33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>13541.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299982</th>\n",
       "      <td>A563185</td>\n",
       "      <td>B</td>\n",
       "      <td>Adjust bad debt</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-08-12 14:50:00</td>\n",
       "      <td>11062.06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>11062.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135013</th>\n",
       "      <td>502263</td>\n",
       "      <td>M</td>\n",
       "      <td>Manual</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-03-23 15:22:00</td>\n",
       "      <td>10953.50</td>\n",
       "      <td>12918.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>10953.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135015</th>\n",
       "      <td>502265</td>\n",
       "      <td>M</td>\n",
       "      <td>Manual</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-03-23 15:28:00</td>\n",
       "      <td>10953.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>10953.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Invoice  StockCode      Description  Quantity         InvoiceDate  \\\n",
       "241827   512771          M           Manual         1 2010-06-17 16:53:00   \n",
       "517955   537632  AMAZONFEE       AMAZON FEE         1 2010-12-07 15:08:00   \n",
       "299982  A563185          B  Adjust bad debt         1 2011-08-12 14:50:00   \n",
       "135013   502263          M           Manual         1 2010-03-23 15:22:00   \n",
       "135015   502265          M           Manual         1 2010-03-23 15:28:00   \n",
       "\n",
       "           Price  Customer ID         Country  TotalPrice  \n",
       "241827  25111.09          NaN  United Kingdom    25111.09  \n",
       "517955  13541.33          NaN  United Kingdom    13541.33  \n",
       "299982  11062.06          NaN  United Kingdom    11062.06  \n",
       "135013  10953.50      12918.0  United Kingdom    10953.50  \n",
       "135015  10953.50          NaN  United Kingdom    10953.50  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(\"Price\", ascending = False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>Invoice</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>Price</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Country</th>\n",
       "      <th>TotalPrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>135013</th>\n",
       "      <td>502263</td>\n",
       "      <td>M</td>\n",
       "      <td>Manual</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-03-23 15:22:00</td>\n",
       "      <td>10953.50</td>\n",
       "      <td>12918.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>10953.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358639</th>\n",
       "      <td>524159</td>\n",
       "      <td>M</td>\n",
       "      <td>Manual</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-09-27 16:12:00</td>\n",
       "      <td>10468.80</td>\n",
       "      <td>14063.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>10468.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74356</th>\n",
       "      <td>496115</td>\n",
       "      <td>M</td>\n",
       "      <td>Manual</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-01-29 11:04:00</td>\n",
       "      <td>8985.60</td>\n",
       "      <td>17949.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>8985.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173382</th>\n",
       "      <td>551697</td>\n",
       "      <td>POST</td>\n",
       "      <td>POSTAGE</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-05-03 13:46:00</td>\n",
       "      <td>8142.75</td>\n",
       "      <td>16029.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>8142.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129987</th>\n",
       "      <td>501768</td>\n",
       "      <td>M</td>\n",
       "      <td>Manual</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-03-19 11:45:00</td>\n",
       "      <td>6958.17</td>\n",
       "      <td>15760.0</td>\n",
       "      <td>Norway</td>\n",
       "      <td>6958.17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Invoice StockCode Description  Quantity         InvoiceDate     Price  \\\n",
       "135013  502263         M      Manual         1 2010-03-23 15:22:00  10953.50   \n",
       "358639  524159         M      Manual         1 2010-09-27 16:12:00  10468.80   \n",
       "74356   496115         M      Manual         1 2010-01-29 11:04:00   8985.60   \n",
       "173382  551697      POST     POSTAGE         1 2011-05-03 13:46:00   8142.75   \n",
       "129987  501768         M      Manual         1 2010-03-19 11:45:00   6958.17   \n",
       "\n",
       "        Customer ID         Country  TotalPrice  \n",
       "135013      12918.0  United Kingdom    10953.50  \n",
       "358639      14063.0  United Kingdom    10468.80  \n",
       "74356       17949.0  United Kingdom     8985.60  \n",
       "173382      16029.0  United Kingdom     8142.75  \n",
       "129987      15760.0          Norway     6958.17  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna().sort_values(\"Price\", ascending = False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bounds=(0, 26000)\n",
    "bins=50\n",
    "ax = df.dropna()[\"Price\"].plot(kind=\"hist\", range=bounds, bins=bins, density=True, label=\"Known customers\")\n",
    "_ = df[df[\"Customer ID\"].isnull()][\"Price\"].plot(kind=\"hist\", range=bounds, bins=bins, alpha=0.6, density=True, \n",
    "                                                 label=\"Unknown customers\")\n",
    "ax.set_yscale(\"log\")\n",
    "ax.set_xscale(\"log\")\n",
    "ax.set_xlabel(\"Price in £\")\n",
    "_ = ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ecdf(data):\n",
    "    \"\"\" Compute ECDF \"\"\"\n",
    "    x = np.sort(data)\n",
    "    n = x.size\n",
    "    y = np.arange(1, n+1) / n\n",
    "    return(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fcc669b95e0>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "price_known = df.dropna()[\"Price\"].values\n",
    "price_unknown = df[df[\"Customer ID\"].isnull()][\"Price\"].values\n",
    "\n",
    "f, ax = plt.subplots()\n",
    "ax.scatter(*ecdf(price_known), label=\"Known customers\")\n",
    "ax.scatter(*ecdf(price_unknown), label=\"Unknown customers\")\n",
    "ax.set_xscale(\"log\")\n",
    "ax.set_ylabel(\"ECDF\")\n",
    "ax.set_xlabel(\"Price in £\")\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot below shows that customers with unknown **Customer ID** also order less items than registered customers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bounds=(0, 40000)\n",
    "bins=50\n",
    "ax = df.dropna()[\"Quantity\"].plot(kind=\"hist\", range=bounds, bins=bins, density=True, label=\"Known customers\", alpha=0.7)\n",
    "_ = df[df[\"Customer ID\"].isnull()][\"Quantity\"].plot(kind=\"hist\", range=bounds, bins=bins, density=True, \n",
    "                                                    label=\"Unknown customers\", alpha=0.8)\n",
    "ax.set_yscale(\"log\")\n",
    "ax.set_xscale(\"log\")\n",
    "ax.set_xlabel(\"Quantity\")\n",
    "_ = ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fcc43fd2c70>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "quant_known = df.dropna()[\"Quantity\"].values\n",
    "quant_unknown = df[df[\"Customer ID\"].isnull()][\"Quantity\"].values\n",
    "\n",
    "f, ax = plt.subplots()\n",
    "ax.scatter(*ecdf(quant_known), label=\"Known customers\")\n",
    "ax.scatter(*ecdf(quant_unknown), label=\"Unknown customers\")\n",
    "ax.set_xscale(\"log\")\n",
    "ax.set_ylabel(\"ECDF\")\n",
    "ax.set_xlabel(\"Quantity\")\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the total price and total quantity of items ordered per invoice. Customers without **Customer ID** pay higher invoice bills that registered customers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_inv = df.dropna().groupby(\"Invoice\").agg({\"TotalPrice\": \"sum\", \"Quantity\":\"sum\"})\n",
    "df_inv_na = df[df[\"Customer ID\"].isnull()].groupby(\"Invoice\").agg({\"TotalPrice\": \"sum\", \"Quantity\":\"sum\"})\n",
    "\n",
    "bounds=(0, 27000)\n",
    "bins=40\n",
    "ax = df_inv[\"TotalPrice\"].plot(kind=\"hist\", range=bounds, bins=bins, density=True, label=\"Known customers\")\n",
    "_ = df_inv_na[\"TotalPrice\"].plot(kind=\"hist\", range=bounds, bins=bins, alpha=0.6, density=True, label=\"Unknown customers\")\n",
    "ax.set_yscale(\"log\")\n",
    "#ax.set_xscale(\"log\")\n",
    "ax.set_xlabel(\"Price in £\")\n",
    "_ = ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fcc44e0ba30>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "totprice_known = df.dropna().groupby(\"Invoice\")[\"TotalPrice\"].sum().values\n",
    "totprice_unknown = df[df[\"Customer ID\"].isnull()].groupby(\"Invoice\")[\"TotalPrice\"].sum().values\n",
    "\n",
    "f, ax = plt.subplots()\n",
    "ax.scatter(*ecdf(totprice_known), label=\"Known customers\")\n",
    "ax.scatter(*ecdf(totprice_unknown), label=\"Unknown customers\")\n",
    "ax.set_xscale(\"log\")\n",
    "ax.set_ylabel(\"ECDF\")\n",
    "ax.set_xlabel(\"Price in £\")\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The figure below shows a scatter plot with the total quantity of items per invoice in the x axis versus the total price per invoice on the y-axis. We clearly see two different buying patterns between the two sets of customers, the customers without **Customer ID** buy less items but more expensive items than customers with a **Customer ID**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_inv.plot(kind=\"scatter\", x=\"Quantity\", y=\"TotalPrice\", alpha=0.8, label=\"known customers\")\n",
    "df_inv_na.plot(kind=\"scatter\", x=\"Quantity\", y=\"TotalPrice\", ax=ax, color=\"crimson\", alpha=0.8, label=\"Unknown customers\")\n",
    "ax.set_ylim(-100, 30000)\n",
    "ax.set_xlim(-100, 20000)\n",
    "ax.set_ylabel(\"Total price payed per transaction\")\n",
    "ax.set_xlabel(\"Total number of items ordered per transaction\")\n",
    "_ = ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Segmentation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A way to create customer segments based on their purchases patterns is to perform a RFM (Recency, Frequency, Monetary) analysis. It groups the customers on the basis of their previous purchase transactions. Below are the definitions of each term in RFM:\n",
    "\n",
    "* Recency: Time since customer's last transaction (with a reference date being the 10th of December 2011)\n",
    "\n",
    "* Frequency: Total number of transactions \n",
    "\n",
    "* Monetary: Total money spend by the customer.\n",
    "\n",
    "We will create a new dataframe with these metrics computed for each customers. We therefore need to drop the rows with missing values in the **Customer ID** column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import timedelta\n",
    "snapshot_date = max(df.InvoiceDate) +timedelta(days=1)\n",
    "#snapshot_date = datetime(2012, 1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>frequency</th>\n",
       "      <th>recency</th>\n",
       "      <th>monetary</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",
       "      <th>12346.0</th>\n",
       "      <td>3</td>\n",
       "      <td>326</td>\n",
       "      <td>77352.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12347.0</th>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>4921.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12348.0</th>\n",
       "      <td>5</td>\n",
       "      <td>75</td>\n",
       "      <td>2019.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12349.0</th>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>4428.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12350.0</th>\n",
       "      <td>1</td>\n",
       "      <td>310</td>\n",
       "      <td>334.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             frequency  recency  monetary\n",
       "Customer ID                              \n",
       "12346.0              3      326  77352.96\n",
       "12347.0              8        2   4921.53\n",
       "12348.0              5       75   2019.40\n",
       "12349.0              4       19   4428.69\n",
       "12350.0              1      310    334.40"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm = df.dropna().groupby(\"Customer ID\").agg({\"Invoice\": lambda x: x.nunique(),\n",
    "                                              \"InvoiceDate\": lambda date: (snapshot_date - date.max()).days,\n",
    "                                              \"TotalPrice\": \"sum\"})\n",
    "\n",
    "rfm.rename(columns= {'InvoiceDate': 'recency',\n",
    "                     'Invoice': 'frequency',\n",
    "                      'TotalPrice': 'monetary'}, inplace= True)\n",
    "\n",
    "rfm.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>frequency</th>\n",
       "      <th>recency</th>\n",
       "      <th>monetary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5878.000000</td>\n",
       "      <td>5878.000000</td>\n",
       "      <td>5878.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.287683</td>\n",
       "      <td>201.331916</td>\n",
       "      <td>2955.865646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.009300</td>\n",
       "      <td>209.338707</td>\n",
       "      <td>14440.674831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>342.280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>867.740000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>380.000000</td>\n",
       "      <td>2248.305000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>398.000000</td>\n",
       "      <td>739.000000</td>\n",
       "      <td>580987.040000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         frequency      recency       monetary\n",
       "count  5878.000000  5878.000000    5878.000000\n",
       "mean      6.287683   201.331916    2955.865646\n",
       "std      13.009300   209.338707   14440.674831\n",
       "min       1.000000     1.000000       2.950000\n",
       "25%       1.000000    26.000000     342.280000\n",
       "50%       3.000000    96.000000     867.740000\n",
       "75%       7.000000   380.000000    2248.305000\n",
       "max     398.000000   739.000000  580987.040000"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the distributions of the three quantities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(1, 3, figsize=(20, 6))\n",
    "for a in ax:\n",
    "    a.set_ylabel(\"# of occurences\")\n",
    "\n",
    "sns.distplot(rfm.query(\"frequency < 100\")[\"frequency\"], kde=False, ax=ax[0])\n",
    "\n",
    "sns.distplot(rfm[\"recency\"], kde=False, ax=ax[1])\n",
    "\n",
    "sns.distplot(rfm.query(\"monetary < 20000\")[\"monetary\"], kde=False, ax=ax[2]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next task is two find clusters in the RFM values, this will be done with the KMeans algorithm of scikit-learn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score, calinski_harabasz_score, davies_bouldin_score\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "KMeans algorithm assumptions:\n",
    "* Symmetric distribution of variables (not skewed)\n",
    "* Variables with same average values\n",
    "* Variables with same variance\n",
    "\n",
    "So we need to unskew the variables (with a log transformation) and standardize them afterwards."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_dist_transformed(transformation=lambda x: x):\n",
    "\n",
    "    f, ax = plt.subplots(1, 3, figsize=(20, 6))\n",
    "    for i, a in enumerate(ax):\n",
    "        a.set_ylabel(\"# of occurences\")\n",
    "        \n",
    "        col = rfm.columns[i]\n",
    "        sns.distplot(transformation(rfm[col]), kde=False, ax=a)\n",
    "        a.set_xlabel(col)\n",
    "        \n",
    "plot_dist_transformed(lambda x: stats.boxcox(x)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfm_unskewed = np.array([stats.boxcox(rfm[[\"frequency\", \"recency\", \"monetary\"]].values[:,i])[0] for i in range(3)]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "scaler.fit(rfm_unskewed)\n",
    "rfm_scaled = pd.DataFrame(scaler.transform(rfm_unskewed), columns=[\"frequency\", \"recency\", \"monetary\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_dist_scaled(rfms=rfm_scaled):\n",
    "\n",
    "    f, ax = plt.subplots(1, 3, figsize=(20, 6))\n",
    "    for i, a in enumerate(ax):\n",
    "        a.set_ylabel(\"# of occurences\")\n",
    "        \n",
    "        col = rfms.columns[i]\n",
    "        sns.distplot(rfms[col], kde=False, ax=a)\n",
    "        a.set_xlabel(col)\n",
    "        \n",
    "plot_dist_scaled()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we need to find the optimum number of clusters, to do so we will compute different metrics:\n",
    "\n",
    "* the Silhouette score (higher is best)\n",
    "* the Calinski and Harabasz score (higher is best)\n",
    "* the Davies-Bouldin score (lower is best)\n",
    "* Elbow method on the inertia metric (best number of clusters at the elbow/kink)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "inertias = []\n",
    "silhouettes = []\n",
    "calinski = []\n",
    "davies = []\n",
    "n_clusters = np.arange(2, 10, 1)\n",
    "\n",
    "for n in n_clusters:\n",
    "    kmeans = KMeans(n_clusters=n)\n",
    "    kmeans.fit(rfm_scaled)\n",
    "    inertias.append(kmeans.inertia_)\n",
    "    silhouettes.append(silhouette_score(rfm_scaled, kmeans.predict(rfm_scaled)))\n",
    "    calinski.append(calinski_harabasz_score(rfm_scaled, kmeans.predict(rfm_scaled)))\n",
    "    davies.append(davies_bouldin_score(rfm_scaled, kmeans.predict(rfm_scaled)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Inertia score')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(2, 2, figsize=(20, 10))\n",
    "for a in ax.flatten():\n",
    "    a.set_xlabel(\"Number of clusters\")\n",
    "\n",
    "ax[0, 0].plot(n_clusters, silhouettes, \".--\")\n",
    "ax[0, 0].set_ylabel(\"Silhouette score\")\n",
    "\n",
    "ax[0, 1].plot(n_clusters, calinski, \".--\")\n",
    "ax[0, 1].set_ylabel(\"Calinski and Harabasz score\")\n",
    "\n",
    "ax[1, 0].plot(n_clusters, davies, \".--\")\n",
    "ax[1, 0].set_ylabel(\"Davies-Bouldin score\")\n",
    "\n",
    "ax[1, 1].plot(n_clusters, inertias, \".--\")\n",
    "ax[1, 1].set_ylabel(\"Inertia score\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot suggest that the best number of clusters is 4, Kmeans will compute and assign the clusters for each customer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "nclusters=4\n",
    "kmeans = KMeans(n_clusters=nclusters, random_state=13)\n",
    "kmeans.fit(rfm_scaled)\n",
    "\n",
    "rfm[\"cluster\"] = kmeans.predict(rfm_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>frequency</th>\n",
       "      <th>recency</th>\n",
       "      <th>monetary</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customer ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12346.0</th>\n",
       "      <td>3</td>\n",
       "      <td>326</td>\n",
       "      <td>77352.96</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12347.0</th>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>4921.53</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12348.0</th>\n",
       "      <td>5</td>\n",
       "      <td>75</td>\n",
       "      <td>2019.40</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12349.0</th>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>4428.69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12350.0</th>\n",
       "      <td>1</td>\n",
       "      <td>310</td>\n",
       "      <td>334.40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             frequency  recency  monetary  cluster\n",
       "Customer ID                                       \n",
       "12346.0              3      326  77352.96        2\n",
       "12347.0              8        2   4921.53        0\n",
       "12348.0              5       75   2019.40        2\n",
       "12349.0              4       19   4428.69        0\n",
       "12350.0              1      310    334.40        1"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below is the number of customers in each cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1671\n",
       "1    1558\n",
       "2    1389\n",
       "3    1260\n",
       "Name: cluster, dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm[\"cluster\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>frequency</th>\n",
       "      <th>recency</th>\n",
       "      <th>monetary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15.624776</td>\n",
       "      <td>33.388390</td>\n",
       "      <td>8318.087115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.185494</td>\n",
       "      <td>428.143132</td>\n",
       "      <td>276.641291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.250540</td>\n",
       "      <td>295.868970</td>\n",
       "      <td>1571.544726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.459524</td>\n",
       "      <td>39.387302</td>\n",
       "      <td>683.469795</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         frequency     recency     monetary\n",
       "cluster                                    \n",
       "0        15.624776   33.388390  8318.087115\n",
       "1         1.185494  428.143132   276.641291\n",
       "2         4.250540  295.868970  1571.544726\n",
       "3         2.459524   39.387302   683.469795"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm.groupby(\"cluster\").mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We computed the mean value of the RFM for each cluster of customers:\n",
    "\n",
    "Cluster #0 seems to be best cluster, which contains most people, is the group that spends more money and made the most transactions, and in average had their last transactions 34 days ago. Those are the **regular customers** and some marketing decisions can be taken for those users, free shipping is an example.\n",
    "\n",
    "The cluster #2 is the next best cluster, because the spend a lot of money. However the customers in that group haven't come for a while. It might be a good idea to remind them of the company and proposes new products/deals etc... in oder to move those customers to another group.\n",
    "\n",
    "The cluster #3 is the best after the #2. Those spent less money, ordered less and had their last transactions few months ago. Those might categorized as **occasionnal customers**, who comes from time to time.\n",
    "\n",
    "The cluster #1 didn't spend much and didn't come for a very long time in avergae they have probably stop using the service, churned by now.\n",
    "\n",
    "Let's plot the normalized distributions of the RFM quantities for each cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABJoAAAF2CAYAAAAm1/LYAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAADbUUlEQVR4nOzdeXxV9Z3/8dc5d0tCAiEhCRB3RbAqosZdwXasoQKCjLZWKuNStPwUlKm0KBSr1aEquC9Vq85I1ZHaFuoU0Y6OVYutgAtQqcomECAkJED23HvO+f1xF7KT5a7J+/l4/B6Te87NvZ+bnz1fzud+Pp+v4TiOg4iIiIiIiIiISA+ZiQ5ARERERERERER6ByWaREREREREREQkKpRoEhERERERERGRqFCiSUREREREREREokKJJhERERERERERiQolmkREREREREREJCqUaBIRERERkah5/fXXueSSS7j44ot56aWXWp3fsGEDkydPpri4mLlz5xIIBADYuXMnU6ZMYezYsUyfPp2amhoANm7cyJVXXsmll17K1VdfTUlJCQCNjY3Mnj2b73znO1x22WVs2rQpfh9SRETaZTiO4yQ6iO6qrKzBtuMffm5uJnv3Vsf9fbtDscaGYo0NxdpzpmkwcGC/RIeRNBK1TkRDsv43Fkv6zH2DPnNixXqdKC0t5fvf/z6///3v8Xq9XHnllTz44IMcd9xxkeeMHz+ee+65h1GjRnHHHXdw0kkncdVVV3HjjTdy6aWXMm7cOJ544glqa2uZPXs2V199NdOmTWP06NG88sorrF69mkWLFvHcc8/x9ddfc/fdd7Nq1SoeeOABlixZ0qV4U2GdSKb/froiVeMGxZ4oij3+2oo7GuuEu0e/nWC27SRsYUj2BakpxRobijU2FKtEUyLXiWhI5di7S5+5b9Bn7r1WrlzJ2WefTXZ2NgDFxcWsWLGCm2++GYCSkhLq6+sZNWoUAJMnT+bRRx/liiuuYNWqVTzxxBOR4z/4wQ+YPXs2L7zwAm63G9u22blzJ/379wfg3Xff5ZZbbgHgjDPOoKKigp07dzJ06NBOx5sq60QqxNiWVI0bFHuiKPb4i0XcKZ1oEhERERGR5LFnzx7y8vIij/Pz81m7dm275/Py8igtLaWyspLMzEzcbnez4wBut5sDBw5wySWXUF9fz+LFi9t9rd27d3cp0ZSbm9m9DxpneXlZiQ6hW1I1blDsiaLY4y8WcSvRJCIiIiIiUWHbNoZhRB47jtPscXvnWz4PaPa4f//+fPDBB7z33ntMnz6dt99+u9XvOI6DaXZtBO3evdVJX4WQl5dFWVlVosPoslSNGxR7oij2+GsrbtM0epyE1zBwERERERGJisGDB1NWVhZ5XFZWRn5+frvny8vLyc/PJycnh6qqKizLavV7y5cvJzxWdvTo0dTX17N//34KCgrYs2dPq9cSEZHEUkWTiMSVZQWorCwjEGhsdW7PHhPbthMQVdclQ6xut5eBA/NwuXQpF5Heo6N1IlUlas1IxDpx7rnn8thjj1FRUUF6ejpvvfUWv/jFLyLnCwsL8fl8rFmzhtNPP51ly5YxevRoPB4PRUVFLF++nAkTJrB06VJGjx4NwPPPP4/b7ebiiy/mb3/7GwMHDiQnJ4cxY8awbNkyioqKWL16NT6fr0ttcyKSmpJxnUiGe4OuMk0XjpMLpLWqKO0p3Z2ISFxVVpaRlpZBv36DW13Q3G6TQCA1LtCJjtVxHGpqDlBZWcagQUMSFoeISLR1tE6kqkSsGYlaJwoKCpg1axZTp07F7/dz+eWXM3LkSKZNm8bMmTM5+eSTWbhwIfPmzaO6upoTTzyRqVOnAnDnnXcyZ84cnnrqKYYMGcKDDz4IwC9/+Ut+9rOf8cQTT5CVlcWjjz4KwNVXX838+fMZN24cXq+X+++/P26fU0QSJxnXiUTfG3SV4zhYVoCqqv0EAlXk5ES3GlSJJhGJq0CgMakWhVRlGAb9+vWnunpfokMREYkqrRPRkch1YsKECUyYMKHZsWeffTby84gRI3jttdda/V5hYWFk0HdTxx13HK+88kqr4z6fj/vuuy8KEYtIKtE60XOGYeB2exg4cBAlJdui/vqa0SQicadFITr0dxSR3krXt+jQ31FEeitd36LDMEwg+hsiKNEkIiIiIiIiIiJRoUSTiPRpNTXVLFp0H1df/V2uueYqZsy4kS+++CcAH3+8mptvvqHLr1ldXc3tt9/Wo7jeemsFP/jBFVx55WX87ndLevRaIiLSfVonRESkIzU11TzwwAKtE01oRpOI9Fm2bXPbbbdw2mlFvPDCy7jdbj7+eDW33TaT3/ym+xfjqqoDfPXVF93+/bKyPTz77JM899xiPB4vP/rRdZx2WhFHH31Mt19TRES6TuuEiIh0JLxOFBWdoXWiCSWaRCSh/rpuFx+s3QWAYYATxRbh80cO4byT299p5+OPV1Naupvrr78R0wwWeJ52WhF33DG/1fakN998A9dddwOnnVbErl07mTHjRl577XXeemsFL7/8IqZpMnToUH72s1/w8MMPUF5exu2338aCBQt5443/4be/fQXbdhg+fAT//u8/xefzMX78RQwf/g327i3n179+Ebc7eElevfojTjutiP79BwDwzW/+C++++7ZuIESkz2m6RkTbodYIiN468corL2IYWidERKItWdaJadN+RHhZ0P2EWudiwrH8iQ5BRDrhyy+/YNiw4yM3D2HnnHM+AwfmdOo1nn32KR566HGef/43DBlSyLZtW7n11tkMGpTHggUL2bx5E6+/vpSnnnqe//zPlxk4MIdXXgnuqLNv3z6mTJnKf/7ny5FFAaC8vIzc3EGRx7m5g9izZ08UPrGkIn/AwolmBlZEOi1a68QjjzypdUJ6zLEtHNtKdBgi0oTuJ9qmiqYoa/zsDRr+/iqZUx/HSMtMdDgiSe+8kw9+U+B2mwQC9iF+I3pM08Dr9fXoNc477wKmT7+e0aMvZMyYbzFs2HB27doZOf/JJ6vZsWM7N954LQCBgJ/jjx8ROX/iiSe1ek3btpvtpOE4DqapnTX6oka/xeynVnLFhcdx/siOv1ET6Y2arhGJEK114oYbrtU6IT1WvXgmZvoA+n33PxIdikjS6C3rRG+7n1CiKcoaPv4jAI6/XokmkSQ3YsQ3+MMfXsNxnGYX4qeffoIzzjir2XObng8EApGfb731NjZunMiHH37AL37xM6677gZGjhwVOW9ZNt/61kXceutsAGpra7Gsg99G+nxpreLKzy/gs88+iTyuqNjLoEF53f+gkrK27ammqtbPnn11iQ5FpE+K1jqxZcskPvjgfa0T0jMNNdgNNYmOQkSaaLpONNXX7yfUOhdtft0MiKSKU045lYEDc3j++WciF+u///1Dli//I0cddXSz5w4YkM2WLZsAeP/9d4HgAnHllZeRnZ3N1Vdfy9ix4/jyyy9wuVyR1zv11NN57713qayswHEcFi1awJIlL3cYV1HRmaxZs4rKykrq6+t59913OOusc6L62SU1fL27CghWNolI/EVvnRiodUKiRu1zIskjvE78+tdP636iCVU0xYoTv/YfEekewzD45S8f5LHHFjF16vdwu90MGJDNAw88Qk5OLlu3bok8d8qUqdx778/505/+yAUXXAiA2+3m+utv5NZbb8Ln8zFw4EDmzv05WVn9KSgYzIwZN/LYY09z7bXTmDnzRziOw3HHHc8PfnBNh3Hl5eUzbdr/Y+bMG/H7A0yYMJFvfKN1Saz0flt3HQCgQYkmkYSI1joxc+Z0vF6tExIdTu1+jMzOzX4RkdgKrxOPP/6g7ieaMJwUnjC6d281th3/8PPysigrq2rzXNUz1wDQ73u/xBwwOI5Rta2jWJONYo2NZIt19+6vGTz4yDbPxXtGU08kS6wt/56maZCbq7bdsEStE9GQl5fFjxb8LyXlNZx9YgE3TDgx0SHFXLJdr+JBn7m1jtaJVJXINUPrRMdSYZ0I/28m2e4zDiWVr2+KPTE6G3syrhPJcm/QVW63yY4dW6K+Tqh1LkYcVTSJiEgP1DcE2Lk3OIujoVEVTSIifZnTZJt0J6AdrkUkuSnRFCtJ/s2IiIgkt8079xOuOVbrnIhIHxdoOPiz1Zi4OEREOkGJpphRRZOIiHTfxh37ACgc1E+JJhGRPs5pkmhyAko0iUhyU6Ip6kJbFtpKNImISPftq2rANAzyB6bT0Kg1RUSkT2uaXFLrnIgkOSWaoi2UZyJ1Z6yLiEgSsCwHl8vA53HRqIomEZE+zfE3qWhS65yIJDklmqIu9CfVMHAREemBgG3jMg28Hpda50RE+jqrSRWTWudEJMkp0RRtkYomJZpEUkFNTTWLFt3H1Vd/l2uuuYoZM27kiy/+CcDHH6/m5ptv6PJrVldXc/vtt0Ultquv/i67du3s8WtJ6rEsB5dpkOZ1Ua9Ek0jCaJ2QZODYB9cBx1LrnEgyqamp5oEHFmidaMId13frC4xgpslRokkk6dm2zW233cJppxXxwgsv43a7+fjj1dx220x+85sl3X7dqqoDfPXVFz2K7R//WM/999/D9u3bevQ6kroClo3bZeL1uGhstHAcB8MwDv2LIhI1WickadiBgz9rRpNI0givE0VFZ2idaEIVTVGnYeAiqeLjj1dTWrqb66+/Ebc7mHc/7bQi7rhjPnaL/w3ffPMNfPzxagB27drJpEnjAHjrrRVcc81VXHfdD5g37yc0NDTw8MMPUF5eFvkW4o03/ofrrpvCNddcxYIFd9PQEJyzMH78Rfz4xzO55pqrCAQCzd7v9df/wL//+08ZNCgvpn8DSV4HZzSZOIA/oHVFJN56sk5cfvkEILhOXH31lVonpGea/vemGU0iSSO8Tkyb9qMerRO97X5CFU1RF+mdS2gUIqnC/+Vf8X/xHgCGYeBEcZC+Z/hoPMef1+75L7/8gmHDjsc0m+fczznnfAC2bNl8yPd49tmneOaZFxg4MIcnnniEbdu2cuuts5kx40YWLFjI5s2beP31pTz11PP4fD5+9avHeeWVxVxzzQ/Zt28fU6ZM5bTTilq97pw5P+vip5XeJjyjyedxAdDgt/CGfhbpK5quEdF2qDUCordOPPfcf9G/f7bWCem+pq1zqmgSiUimdaJpYqmv308o0RRt4TyTKppEkp5pGni9vh69xnnnXcD06dczevSFjBnzLYYNG96sB/qTT1azY8d2brzxWgACAT/HHz8icv7EE0/q0ftL7xWc0WQ2SzRlJTgmkb4mWuvEDTdcq3VCesZpMqtPM5pEkobuJ9qmRFO0Gdp1TqQrPMefF/mmwO02CcSxPWjEiG/whz+81mr2zdNPP8EZZ5zV7LlNzzctS7311tvYuHEiH374Ab/4xc+47robGDlyVOS8Zdl861sXceutswGora3Fsg7+Y9HnS4v2x5JeImDZwdY5bzjRpHVF+p6ma0QiRGud2LJlEh988L7WCem2ZsPA7UAHzxTpW5JpnWiqr99PaEZT1IX+41GiSSTpnXLKqQwcmMPzzz8TuVj//e8fsnz5HznqqKObPXfAgGy2bNkEwPvvvwsEF4grr7yM7Oxsrr76WsaOHceXX36By+WKvN6pp57Oe++9S2VlBY7jsGjRApYseTleH1FSWHjXuXBFU6N2nhOJu+itEwO1TkjPNJvRpESTSLIIrxO//vXTup9oQhVN0RYZ0aREk0iyMwyDX/7yQR57bBFTp34Pt9vNgAHZPPDAI+Tk5LJ165bIc6dMmcq99/6cP/3pj1xwwYUAuN1urr/+Rm699SZ8Ph8DBw5k7tyfk5XVn4KCwcyYcSOPPfY01147jZkzf4TjOBx33PH84AfXJOYDS0oJzmg62DpX36hEk0i8RWudmDlzOl6v1gnpgaZVTEo0iSSN8Drx+OMP6n6iCcOJ5uTdONu7txrbjn/4eXlZlJVVtXmu6r9ugoYa0i6egeeo0+McWWsdxZpsFGtsJFusu3d/zeDBR7Z5Lt6tcz2RLLG2/HuapkFubmZCYnn99dd56qmnCAQC/Nu//RtTpkxpdn7Dhg3MnTuXmpoaioqKuOuuu3C73axZs4YFCxbg9/vJzs7mP/7jPygsLOTAgQPcdtttbN++nZycHB5++GHy8rq2a0ai1oloeOR3a6mp8/P9fxnGL/5rNTMvH8mo4wYlOqyYSrbrVTzoM7fW0TqRqhK5ZiTTOpGMUmGdyMvLYucHy6n/y3MAuI8/j/QLpyU4qkNL5eubYk+MzsaejOtEstwbdJXbbbJjx5aorxMxbZ175JFHuOSSSxg3bhwvvPBCq/MbNmxg8uTJFBcXM3fu3Fbb8aW01M3fiYh0WWlpKQ899BAvv/wyS5cu5dVXX2Xjxo3NnjN79mzmz5/Pm2++ieM4LFmyJHL8nnvuYdmyZUyYMIF77rkHgIcffpiioiLeeOMNrrjiCu699964f65EClgObtOI7DSn1jkRkb7LCXdLuDxgaT0QkeQWs0TTRx99xN/+9jf++Mc/8rvf/Y7FixezeXPzrf3au+lIZYZmNIlIH7Ry5UrOPvtssrOzycjIoLi4mBUrVkTOl5SUUF9fz6hRowCYPHkyK1asoLGxkVtuuYURI4I7ZwwfPpxdu3YB8O677zJhwgQAxo8fz3vvvYff33d22glYdmhGU3CpblDrnIhI3xUaBm64fdp1TkSSXsxmNJ155pm8+OKLuN1uSktLsSyLjIyMyPm2bjoeffRRrrrqqliFFB/hSfK2Ek0i0nfs2bOnWVtbfn4+a9eubfd8Xl4epaWleL1eJk6cCIBt2zz++ONcdNFFrX7H7XaTmZlJRUUFBQUFnY4rldtDLMsmKyuNoYMHAODxecjLy0pwVLHXFz5jS/rMze3ZY+J29779ahL1mUzT7JP/jfU64V3nPD7tOiciSS+mw8A9Hg+PPvoozz//PGPHjm12c9DeTUdXJPIGor0Fu8Y0cID+WWlkJsminkr/uFCssZFMsR7qBiKVbi6SIdZkuYGwbbvZlq0ttwI/1PnGxkbmzJlDIBDgxhtvbPM9HMfBNLv2N0+F2RvtCVgOVsCipqoOgL2VNSk7c6GzUnmuRHfpM7dm23ZKzrnoSCJnd9i23ezvrRlNKSpc0eTxaRi4iCS9mO86N3PmTKZNm8aPfvQjlixZwve+9z3g0DcdnZGMw8CdUDwHDtRSlwT/cEylf8Aq1thItlg7uoFIpSF6yRJrstxADB48mNWrV0cel5WVkZ+f3+x8WVlZ5HF5eXnkfE1NDdOnTyc7O5unnnoKj8cDBKuiysvLGTx4MIFAgJqaGrKzs+PzgZKAZQdb59wuE8OABs1oEhHps5xwt4Tb13wHOhGRJBSzr+M3bdrEhg0bAEhPT+fiiy/miy++iJzv6KZDRERSy7nnnsuHH35IRUUFdXV1vPXWW4wePTpyvrCwEJ/Px5o1awBYtmxZ5Pzs2bM58sgjefjhh/F6vZHfGTNmDEuXLgVg+fLlFBUVRZJQfUHAcnC5TAzDwOdx0dCY+MSmiIgkSCi5ZLh9OKpoEpEkF7NE044dO5g3bx6NjY00Njby9ttvc/rpp0fOd3TT0Sto1zkR6UMKCgqYNWsWU6dOZdKkSYwfP56RI0cybdo01q1bB8DChQtZsGABY8eOpba2lqlTp/L555/z9ttv8/HHH3PZZZcxceJEpk0Lbtl8yy238OmnnzJu3Dhefvll5s+fn8iPGHdWaBg4EEw0qaJJRKTvajKjSRVNIpLsYtY6N2bMGNauXcukSZNwuVxcfPHFjBs3jmnTpjFz5kxOPvlkFi5cyLx586iurubEE09k6tSpsQonAZRoEkkFNTXV/OpXT/Dpp2twudxkZWVx882zGD58BB9/vJrnn3+Gxx9/pkuvWV1dzb33/pwFCxZ2O67nn3+Gd975XwDOPfc8/t//u6XbrxUvEyZMiOwSF/bss89Gfh4xYgSvvfZas/Pf+MY3mlW7NpWdnc2vfvWr6AeaIgKW0yzR1KhEk0hCaJ2QpODYYJgYplsVTSJJpqammmeeeYKPP9Y6ERbTGU0zZsxgxowZzY4d6qaj11BFk0jSs22b2267hdNOK+KFF17G7Xbz8cerue22mfzmN0u6/bpVVQf46qu2kyedsWrV31m16m+88MJLGIbBj388g7/85f8YM+ab3X5NST2WbeNyBQuPfV5VNIkkgtYJSRq2BaYJLo92nRNJIuF1oqjoDK0TTcR8GHifpUSTSKf8fdcaPty1CgDDiO7/dM4ZcgZnDTm93fMff7ya0tLdXH/9jZHdzE47rYg77piPbTefh3PzzTdw3XU3cNppRezatZMZM27ktdde5623VvDyyy9imiZDhw7lZz/7BQ8//ADl5WXcfvttLFiwkDfe+B9++9tXsG2H4cNH8O///lN8Ph/jx1/E8OHfYO/ecn796xdxu4OX5NzcQdx006zIPKIjjzyK0tLd0fvDSEpoWdGkRJP0RU3XiGg71BoB0VsnXnnlRQxD64R0n2NbYLrB5dKucyJNJMs6MW3ajwgvC7qfiOGMpr7OUeucSNL78ssvGDbs+MjNQ9g555zPwIE5nXqNZ599ioceepznn/8NQ4YUsm3bVm69dTaDBuWxYMFCNm/exOuvL+Wpp57nP//zZQYOzOGVVxYDsG/fPqZMmcp//ufLkUUB4JhjjuWkk04GYPv2bbzzzv9yzjnnRelTS6poPqPJVKJJJAGitU488siTWiekZ2wr1DrnAcuf6GhEJET3E21TRVOsqKJJpFPOGnJ65JsCt9skEIjfzlqmaeD1+nr0GueddwHTp1/P6NEXMmbMtxg2bDi7du2MnP/kk9Xs2LGdG2+8FoBAwM/xx4+InD/xxJPafe3Nmzfxk5/cyk033cLhhx/Rozgl9QR3nQsmmrweF/trGhMckUj8NV0jEiFa68QNN1yrdUJ6xrExTBe4XMHqJhEBes860dvuJ5RoEpE+a8SIb/CHP7yG4zgYhhE5/vTTT3DGGWc1e27T84HAwZL1W2+9jY0bJ/Lhhx/wi1/8jOuuu4GRI0dFzluWzbe+dRG33jobgNraWizr4D8Qfb60NmNbu/ZT5s37KTNn/jsXXVTco88pqcmybdymZjSJJFK01oktWybxwQfva52Q7rMDYLrApYomkWTSdJ1oqq/fT6h1LlZU0SSS9E455VQGDszh+eefiVys//73D1m+/I8cddTRzZ47YEA2W7ZsAuD9998FggvElVdeRnZ2NldffS1jx47jyy+/wOVyRV7v1FNP57333qWysgLHcVi0aAFLlrzcYVylpbu5447buPPOe3Tz0EfZtoPjEKloCs5oil+1n4gERW+dGKh1QnrEsZ3IrnNY+uJBJFmE14lf//pp3U80oYqmmFGiSSTZGYbBL3/5II89toipU7+H2+1mwIBsHnjgEXJyctm6dUvkuVOmTOXee3/On/70Ry644EIA3G43119/I7feehM+n4+BAwcyd+7PycrqT0HBYGbMuJHHHnuaa6+dxsyZP8JxHI477nh+8INrOozrlVd+Q0NDI4899lDk2KRJk5k06fJY/BkkCVmh4ZEaBi6SWNFaJ2bOnI7Xq3VCesCxg7umuNzgWDiOjWGoZkAk0cLrxOOPP6j7iSYMp2WNVwrZu7ca245/+Hl5WZSVVbV5rvrFGTj1VfjOuxrvif8S58ha6yjWZKNYYyPZYt29+2sGDz6yzXPxntHUE8kSa8u/p2ka5OZmJjCi5JKodaKn6hoC3PTQe3z3m8cx9qwj+P17m/nTh1v59U++2azsurdJtutVPOgzt9bROpGqErlmaJ3oWCqsE3l5WWx7dSFW6UY8J4yh8aPXyLzuGQy3N9GhdSiVr2+KPTE6G3syrhPJcm/QVW63yY4dW6K+TigNHjPJvWCJiEjyskI3PZFh4G4Txzl4XERE+hjHBjPUOgfBmU0iIklKiaZYSd1CMRERSbBwQsltHtx1DqBR7XMiIn1TuFXOFUw0OZYSTSKSvJRoijYlmEREpIcsKzSjyRVcpr2e4P/VQHARkT7KtsEwIVzRpESTiCQxJZpiRQknERHppkC4dc482DoH4A+ooklEkt/rr7/OJZdcwsUXX8xLL73U6vyGDRuYPHkyxcXFzJ07N7LN986dO5kyZQpjx45l+vTp1NTUALBp0yamTJnCxIkT+d73vseGDRsAKCkp4dRTT2XixIlMnDiR66+/Pn4fMt6cYKLJcHmCj9U6JyJJTImmWFGiSUREuilS0RRJNIVb51TRJCLJrbS0lIceeoiXX36ZpUuX8uqrr7Jx48Zmz5k9ezbz58/nzTffxHEclixZAsBdd93FVVddxYoVKzjppJN48sknAZg3bx7Tpk1j2bJl3Hrrrfz0pz8FYP369UyYMIFly5axbNkynnvuufh+2DhynHBFU3A9UOuciCQzJZpiRokmERHpnoPDwMOtc8EbiwZVNIlIklu5ciVnn3022dnZZGRkUFxczIoVKyLnS0pKqK+vZ9SoUQBMnjyZFStW4Pf7WbVqFcXFxc2OA1xxxRVccMEFAAwfPpxdu3YBsG7dOr788ksmTpzI1KlT+eKLL+L4SePMccAwIFzRZPkTG4+ISAfciQ6g11JFk0hKqKmp5le/eoJPP12Dy+UmKyuLm2+exfDhI/j449U8//wzPP74M116zerqau699+csWLCw23H9+te/4t133wYMxo+/lCuv/EG3X0tSj2W13TqniiaR+NM60TV79uwhLy8v8jg/P5+1a9e2ez4vL4/S0lIqKyvJzMzE7XY3Ow7BpFPYo48+ykUXXQSAz+fj0ksv5corr+T999/npptuYvny5Xi93k7H29MtvOPF6zGxLQ/ZOVnsBrL7+0jLy0p0WIeUlwIxtkexJ0ZnYt+zx8TtTp6amZqaah566DE++eRjXC4XWVn9mTlzFiNGnMCaNav59a+f5qmnnu3Sa1ZXV/GLX/yc++5b1O24nnnmKd55538xDIMJEyZx1VVtrxOmaUb9vxklmmJGiSaRZGfbNrfddgunnVbECy+8jNvt5uOPV3PbbTP5zW+WdPt1q6oO8NVX3f9W9ZNP1rBmzSr+8z9fwbIC/OAH3+Xcc8/niCOO6vZrSmqxWs5oClU0aUaTSHxpneg627YxDCPy2HGcZo/bO9/yeUCr591///189tlnvPjiiwDMmDEjcn7MmDEsWrSIzZs3M2LEiE7Hu3dvNbad3P9uz8vLorHBj2M57K8KVjJV7t2P21uV4Mg6lpeXRVlZcsfYHsWeGJ2N3bZtAoHk+PLNtm1uvXUGRUVn8PzzL0XWiVmzZvCb3yzBsmwcx+lyvJWV+/nii392+3N+8skaVq36qNk6cfbZ57ZaJ9xuE9u2m/3dTdPocRI+edKAvYwKmkSS38cfr6a0dDfXX39j5BvU004r4o475mPbzS/qN998Ax9/vBqAXbt2MmnSOADeemsF11xzFddd9wPmzfsJDQ0NPPzwA5SXl3H77bcB8MYb/8N1103hmmuuYsGCu2loaABg/PiL+PGPZ3LNNVdFBqECnHrq6Tz22NO43W4qKyuxLIu0tPSY/z0keVh2eNe5cKJJFU0iidCTdeLyyycAwXXi6quv7DPrxODBgykrK4s8LisrIz8/v93z5eXl5Ofnk5OTQ1VVFZZltfq9QCDAbbfdxrp163jxxRfJygp+87548WIqKysjr+U4TuT/n3odx8YwTHCFPp+tLx5EkkF4nZg27Uc9Wid62/1EL70SJwPdDIh0xoGVf2X/B+8BRL7RjJYB54+m/7nntXv+yy+/YNiw4zHN5jn3c845H4AtWzYf8j2effYpnnnmBQYOzOGJJx5h27at3HrrbGbMuJEFCxayefMmXn99KU899Tw+n49f/epxXnllMddc80P27dvHlClTOe20olav63a7ee65p3nllcV885sXkZeX38a7S291sHUuNKMpVB7e4NeNhfQtTdeIaDvUGgHRWyeee+6/6N8/u0+sE+eeey6PPfYYFRUVpKen89Zbb/GLX/wicr6wsBCfz8eaNWs4/fTTWbZsGaNHj8bj8VBUVMTy5cuZMGECS5cuZfTo0QDcd999VFdX8/zzzzdri1u1ahX19fVMmzaNjz76CNu2OeaYY+L+mePCscEwMMKJJs1oEgGSa51omljq6/cTqmiKFVU0iSQ90zTwen09eo3zzruA6dOv58knH2HMmG8xbNjwZuc/+WQ1O3Zs58Ybr+Waa67igw/+wrZtX0fOn3jiSe2+9vXX38j//M//smdPKX/84x96FKeklvZb5/Qlhkg8RWuduOGGa/vMOlFQUMCsWbOYOnUqkyZNYvz48YwcOZJp06axbt06ABYuXMiCBQsYO3YstbW1TJ06FYA777yTJUuWcMkll7B69WpuvfVWKioqeOmll9iyZQtXXHEFEydOZOLEiQDMnTuXlStXMn78eO677z4WLVrUKinYa9jhXeeCiSbH1q5zIslA9xNtU0VTzCjTJNIZ/c89L/JNgdttxrXfesSIb/CHP7zWai7E008/wRlnnNXsuU3PNy1LvfXW29i4cSIffvgBv/jFz7juuhsYOXJU5Lxl2XzrWxdx662zAaitrY20BQD4fGmt4vr66600NjYwbNhw0tLSGD36m2za9FWPP6+kjlatc+5goqlRFU3SxzRdIxIhWuvEli2T+OCD9/vMOjFhwgQmTJjQ7Nizzx4chDtixAhee+21Vr9XWFjI4sWLWx3//PPP23yfgoICXnjhhR5Gmxqclq1zlhJNIpBc60RTff1+opem/JOAhjSJJL1TTjmVgQNzeP75ZyIX67///UOWL/8jRx11dLPnDhiQzZYtmwB4//13geACceWVl5Gdnc3VV1/L2LHj+PLLL3C5XJHXO/XU03nvvXeprKzAcRwWLVrAkiUvdxjXzp07uO++e2lsbMTv9/PBB39ptthI7xdunXOHW+dCM5oaVNEkElfRWycGap2QnnFsME0Mlyf4WIkmkaQQXid+/eundT/RhCqaosyJVDIp0SSS7AzD4Je/fJDHHlvE1Knfw+12M2BANg888Ag5Obls3bol8twpU6Zy770/509/+iMXXHAhEOx7vv76G7n11pvw+XwMHDiQuXN/TlZWfwoKBjNjxo089tjTXHvtNGbO/BGO43Dcccfzgx9c02Fc55xzPp9//g+uu24KpmkyZsy3uOii4hj+JSTZtGydc7tMTMPQrnMicRatdWLmzOl4vVonpAccJ9Q6F6xwVeucSHIIrxOPP/6g7ieaMJxoTt6Ns0RtR9rRtotV/3UTNNTgPf0yfKdPjHNkraXS9paKNTaSLdbdu79m8OAj2zwX79a5nkiWWFv+PaOxHWlvkgrbVrflb5/v5pk/fs69085iSG4/AP7fg39h9ClDufJfhiU4uthJtutVPOgzt9bROpGqErlmaJ3oWCqsE3l5WWz91SyMfjmkjbmOmsUz8Z13Nd4T/yXRoXUola9vij0xOht7Mq4TyXJv0FVut8mOHVuivk6odS5WnNT7j0xERJLDwV3nDvbyez0uzWgSEemrHBvDNJvsOqeKJhFJXko0iYiIJJlw65zbdXCZ9rpNGvz6EkNEpE9yWu46509wQCIi7VOiKdoczWgSEZGeaTmjCYIVTZrRJCLSR9nNE01YWg9EJHkp0RR1oQRT6o6+Eom5FB4Nl1T0d+y9LCtYueRqUdHUmIK9/yLdoetbdOjv2Hs4oWHghmkGE06WKpqkb9P1LTocxwaMQz6vq5RoijZHiSaRjrjdXmpqDmhx6CHHcaipOYDb7U10KBIDbVY0uU3NaJI+QetEdGid6GUcG4zQmuBya9c56dO0TvSc4zgEAn4qKsrxetOi/vruqL+ihOg/epG2DByYR2VlGdXV+1qdM00T206Nio1kiNXt9jJwYF5CY5DYCIQrmlq0ztU26MZCer+O1olUlag1Q+tEL+LYYIZqBEy3hoFLn5aM60Qy3Bt0lWm6yMvLBZRoSn7h3eaUXRVpk8vlZtCgIW2eS6XtWFMpVkk9kYomV/NE077qhkSFJBI3Ha0TqUprhvSY42AYwUST4XKDKpqkD0vGdSJVr/Oxilutc9Gm/JKIiPSQZQUXE9No2TqXWt+UiYhIlDRtnTPdOKpoEpEkpkRT1AVvAhxHNwMiItI9lu3gdhkYTRNNHpNG7TonItI3OaFd5wBcHlU0iUhSU6Ip2lTRJCIiPWTZdrMd5wC8bpcqmkRE+ir7YKLJcGlGk4gkNyWaok27zomISA9ZloPbbL7VrEcVTSIifZbTtKJJrXMikuSUaIo6JZpERKRnLNtpVdHkc7sIWA62rfVFRKTPadY6p2HgIpLclGiKOqfF/xUREekay7Zxu5pXNHk9LgBVNYmI9EWODWbT1jl/ggMSEWmfEk3RpkomERHpIctqXdHkcQcfa06TiEgf5NgYTVvnbH3pICLJS4mmKHKaJpm065yIiHSTZTu4zRbDwD3hRJNuLkRE+hzbad46p4omEUliSjRFldPmjyIiIl0RsB1cLVrnfJHWOX2RISLS99hgBNcFw3SDpS8dRCR5KdEUTU67D0RERDrNsmzc7bXOaUaTiEif4jhOcDxHk4omx1ZFk4gkL3csX/zxxx/njTfeAGDMmDH85Cc/aXX+d7/7Hf379wfgu9/9LlOmTIllSDHW5FtmzWoSEZFustqoaIoMA9eMJhGRviU8kqNZ65x2nROR5BWzRNPKlSv54IMP+MMf/oBhGPzwhz/kz3/+M9/+9rcjz1m/fj0PPvggp556aqzCiC9VNImISBS0NaPJ5w4nmlTRJCLSp0QSTeHWOY8STSKS1GKWaMrLy2POnDl4vV4Ajj32WHbu3NnsOevXr+fpp5+mpKSEM844g5/+9Kf4fL5YhRQHTWc0KdEkIiLdY1l2+7vOaUaTiEif4tih6374CwiXC8dWoklEklfMEk3Dhg2L/Lx161beeOMNXnnllcixmpoaTjjhBGbPns2RRx7JnDlzePLJJ5k1a1an3yM3NzOqMXdFXl5Wq2O2v4Hq0M9paZ42n5MIyRJHZyjW2FCssZFKsUpqsWwHr1e7zomICJGKJiPSOqeKJhFJbjGd0QTw1VdfceONN/KTn/yEo446KnK8X79+PPvss5HH1113HXfccUeXEk1791Zj2/GvHMrLy6KsrKrVcSfQEPm5vq6xzefEW3uxJiPFGhuKNTaSNVbTNBKahJfosGwHl6ld50REBLCbz2gyTDeooklEklhMd51bs2YN11xzDT/+8Y+57LLLmp3buXMnr732WuSx4zi43THPe8VWk3Y5R61zIiLSTQHLxu1uWdEUTDQ1qKJJRKRPcdoaBm5bB4+LiCSZmCWadu3axU033cTChQsZN25cq/NpaWk88MADbN++HcdxeOmll5oNCk9JzZJLSjSJiEj3WLaDu8WMJp9a50RE+qbwPUY40WSGvpxX+5yIJKmYlRA999xzNDQ08Mtf/jJy7Morr+Sdd95h5syZnHzyydx9991Mnz4dv9/PaaedxrXXXhurcOJEiSYREek5y2rdOud2mRiGKppERPqayDDw8K5zrtAtnK31QESSU8wSTfPmzWPevHmtjn//+9+P/FxcXExxcXGsQog/R7vOiYhIz1m23aqiyTAM0rwu6ht1YyEi0qe0bJ0LVTQ5lh+D9AQFJSLSvpjOaOpzlGgSEZEosGwHl8toddzrcal1TkSkrwknmswmM5pAFU0ikrSUaBIREUkyltV6RhNAmkcVTSIifU24dc4I7zrn8gRPWP5EhSQi0iElmqLIQRVNIiLSc5bdekYTgM/jotGvXYZERPqUVq1zrtBhDQMXkeSkRFM0adc5ERGJgrZmNAH4vC7qG3VjISLSl7QcBk6koknrgYgkJyWaokkzmkREJAosy8HVItHk2DZ5dXtpUOuciEjf0qKiyXAFK5qUaBKRZKVEU1SpoklERHrOsh3cLYaBV/zpdUb//WWO2rE2QVGJiEhCtBwGbnpCh5VoEpHkpERTNKmiSUREosCyHcwWM5r2f/AeAEeWfZmIkEREJEEirXOEW+dCu86poklEkpQSTdGkRJOIiPSQHVo/XObBJdqqriawdy8AebVl2I2NCYlNREQSoEVFk6FEk4gkOSWaokrJJRER6RnbDq4lTfJMNGzfBsDeY0fhcmz8ZXsSEZqIiCRCqKLJiOw6F0w0ObY/URGJiHRIiaZo0q5zIiLSQ+FEU9OKpsY9pQDUHTEcgIbdu+MfmIiIJITTYhj4wdY5bQ4hIslJiaaoUuuciIj0jBWuaDIOzmjyl5eDy4V1+NEA1O0qTUhsIiKSAK12nQsOA8dSRZOIJCclmqKpWZ5JiSYREem68PrharLrXGBvOZ6cXDxZWdSbXhrUOici0mdEhoFHWudcoeOa0SQiyUmJpqhS65yIiPRMmxVNe/fizs3F53VR7UoncOBAosITEZF4azEMnEhFkxJNIpKclGiKJu06JyIiPRTKMzWraLIOHMDdfwA+j4sadxrWgf0Jik5EROKuRUWTEapoQhVNIpKklGiKJiWXRKQPe/3117nkkku4+OKLeemll1qd37BhA5MnT6a4uJi5c+cSCDT/B/LDDz/MY489Fnn80UcfcdZZZzFx4kQmTpzI7bffHvPPkAzsNiqarOoqXJmZ+LwualzpOFVViQpPRETiLDKSI7wuhCqaHFU0iUiSUqIpqtQ6JyJ9U2lpKQ899BAvv/wyS5cu5dVXX2Xjxo3NnjN79mzmz5/Pm2++ieM4LFmyBICqqiruuOMOXnjhhWbPX79+Pddddx3Lli1j2bJlLFiwIG6fJ5EO7joXvKFwAgHsujpcWVnBiiZXOk61WudERPqMUOucEZnRFNp1ThVNIpKklGiKIketcyLSR61cuZKzzz6b7OxsMjIyKC4uZsWKFZHzJSUl1NfXM2rUKAAmT54cOf/2229z1FFHce211zZ7zXXr1vHBBx8wYcIEfvSjH7Fr1664fZ5EskLrhxlKNFk1NQC4MjNJ87iocaVhNDZgNzQkLEYRkY50t8J1586dTJkyhbFjxzJ9+nRqQte/TZs2MWXKFCZOnMj3vvc9NmzYAEBjYyOzZ8/mO9/5DpdddhmbNm2K34eMp1atcya43DiN9QkMSkSkfe5EB9C7KNEkIn3Tnj17yMvLizzOz89n7dq17Z7Py8ujtLQUgEmTJgE0a5sDyMrK4jvf+Q4XX3wxr7zyCrNmzeK///u/uxRXbm5mVz9KwvkJJphcpkFeXha1dZUADByaR9bg/tS40wEY4LFIy8tKWJyxktcLP9Oh6DP3DX3lM4crXH//+9/j9Xq58sorOeusszjuuOMiz5k9ezb33HMPo0aN4o477mDJkiVcddVV3HXXXVx11VWMGzeOJ554gieffJLZs2czb948brzxRi688EI+/PBDfvrTn/LHP/6RxYsXk56ezhtvvMGqVau4/fbbI9WyvYnjtNh1DjAyc3Gq9yYoIhGRjinRFE1KLolIH2XbNkaTmUKO4zR7fKjzbbn77rsjP3//+99n0aJFVFVVkZXV+Zu1vXurI61oqaKsPPgNvmkalJVVUbstmJCrsd0EquqpcaUBsGfLTtLNjITFGQt5eVmUlfWt+VP6zH1DMn1m0zRimoRvWuEKRCpcb775ZqDtCtdHH32UK664glWrVvHEE09Ejv/gBz9g9uzZXHHFFVxwwQUADB8+PFLh+u6773LLLbcAcMYZZ1BRUcHOnTsZOnRozD5fQkQqmg6um2bmIKzSrzq1noqIxJsSTVHVtKLJTlwYIiJxNnjwYFavXh15XFZWRn5+frPzZWVlkcfl5eXNzrdk2zZPP/00N9xwAy6XK3K86c+9le2EZzQFv7m2qoM3p67MLFyhGU2Adp4TkaTU3QrXyspKMjMzcbvdzY5DMOkU9uijj3LRRRe1+1q7d+/uUqIpFSpfq8uD9xU5uVl4Q5VxZYMKqCr5B5lVm8g49tREhtehVK7kU+yJodjjLxZxK9EUTan1pbmISNSce+65PPbYY1RUVJCens5bb73FL37xi8j5wsJCfD4fa9as4fTTT2fZsmWMHj263dczTZM///nPHHnkkVxyySUsXbqUU045hYyM3lXB05bIrnOhDgmruhoAV1YmpsekNtQ6FziggeAikny6W+HaVmVOy+fdf//9fPbZZ7z44ottvrbjOJhm10bQpkLla3roC+yKfXW4jOCXD/aJ4+Czd6j4ci01/Y/r6NcTJpkq+bpKsSeGYo+/tuKORuWrhoFHU9MqJrXRiUgfUlBQwKxZs5g6dSqTJk1i/PjxjBw5kmnTprFu3ToAFi5cyIIFCxg7diy1tbVMnTq1w9e87777ePHFFxk3bhy/+93vuOeee+LxURKuVUVTVXDxN/tlYhoGgfR+weNKNIlIEmpZwdrZCtecnByqqqqwLKvV7wUCAW677TbWrVvHiy++GGmhLigoYM+ePa1eq9exW+w6B5iZuZjZQ7H3bk9UVCIi7VJFU8wo0SQifcuECROYMGFCs2PPPvts5OcRI0bw2muvtfv7M2bMaPZ42LBhXR7+3RtYdotd56qrMdPSMD0eADxeDwG3D6umOmExioi0p7sVrh6Ph6KiIpYvX86ECRNYunRppPL1vvvuo7q6mueffx6v1xt5rTFjxrBs2TKKiopYvXo1Pp+v981nou1h4ABGen+chpoERCQi0jElmqLJ0a5zIiLSM+H7CdMIJ5qqcGUe7J1P87jwe9OwanRzISLJp2mFq9/v5/LLL49UuM6cOZOTTz6ZhQsXMm/ePKqrqznxxBMjFa533nknc+bM4amnnmLIkCE8+OCDVFRU8NJLL3HYYYdxxRVXRN5n2bJlXH311cyfP59x48bh9Xq5//77E/WxY8tpPQwcAE8aTk1F/OMRETkEJZqiSokmERHpGSvUIuFqWtGUebBP3utx0ej2YSvRJCJJqrsVroWFhSxevLjV8c8//7zN9/H5fNx33309jDb5OXY7FU3eNOx99QmISESkY5rRFE1NkkuOWudERKQbwjNpw61zdl0drvSDQ9DTvC4aXT5VNImI9BXttc550sCvRJOIJB8lmqJKySUREekZu8WMJru+HiPNFznv85jUuzSjSUSkzwhXNLXcUc+ThqNEk4gkISWaoqnZjCa7/eeJiIi04+Cuc6FEU0M9Zlpa5LzP66bO5cWuqU1IfCIiEl/tDgP3pEGg8WBrnYhIklCiKZoiiSZDxU0iItItbVU0NUs0eUxqDS9WbQ2O5gGKiPR+oWu90VaiCSDQEO+IREQ6pERTFEX+uW+YKNMkIiLd0TLR5NTXY/qaVzTVGF6wLJwGtUyIiPR6Hew6B6h9TkSSjhJN0XRwT2rtOiciIt3StHXOCQRwAoFWFU3VjgdAA8FFRPqAdnedcwXXAix/nCMSEemYEk0xoYomERHpHqtJRZNdH/yWunmiyUUNSjSJiPQZ7cxoIpRocpRoEpEko0RTNDlNdoRQnklERLqhaUWT3dBGosnros7lDT5XiSYRkd6vvdY5lyv4f61AfOMRETkEJZpiwTAA7f4gIiJdZx+ioinN46Le9AGqaBIR6QvabZ0zQ61zthJNIpJclGiKpvBcJkMVTSIi0j3h+wmXaR5MNPlaVjQp0SQi0me02zrnDp5WRZOIJBklmqKp2dajyjSJiEjXhVvnTKP9GU31Zrh1rjr+AYqISFwdrGhq0TpnhlrnbCu+AYmIHIISTVHVtKJJiSYREem6cOucy9VOosnrImC6weXGqq1NSIwiIhJHjgOGgdEi0aRd50QkWSnRFE2R1jlDiSYREemWyK5zTSqajBYVTQBOegaWKppERHo/x6bN27ZQRZOjGU0ikmSUaIqqJhVNap0TEZFuiOw652qy65yvdaLJTkvXrnMiIn2A49hgGq1PRCqalGgSkeQS00TT448/zrhx4xg3bhz3339/q/MbNmxg8uTJFBcXM3fuXAKBFL9IhquYTLXOiYhI99hNKpqcdlrnAGxfuoaBi4j0BbbdehA4YISGgSvRJCLJJmaJppUrV/LBBx/whz/8gaVLl/KPf/yDP//5z82eM3v2bObPn8+bb76J4zgsWbIkVuHEycGKJqWZRESkOyIVTWaodc40MTyeyPm0UEVTwKtEk4hIn+C0nWjCDO06p9Y5EUkyMUs05eXlMWfOHLxeLx6Ph2OPPZadO3dGzpeUlFBfX8+oUaMAmDx5MitWrIhVOPHhaBi4iIj0TKSiKZRoMtPSmg2A9YYSTX6PD7tWiSYRkd7OcezWO84BqKJJRJKUO1YvPGzYsMjPW7du5Y033uCVV16JHNuzZw95eXmRx3l5eZSWlnbpPXJzM3seaDfl5WW1OlZTmU4d4Ha7cGyrzeckQrLE0RmKNTYUa2ykUqySOiK7zoUTTU3mM8HBGU1+dxpWtYaBi4j0eu21zoWGgSvRJCLJJmaJprCvvvqKG2+8kZ/85CccddRRkeO2bTf7htZxnFZbdh7K3r3VkX+Qx1NeXhZlZVWtjvv3B79ZtmxwAlabz4m39mJNRoo1NhRrbCRrrKZpJDQJLz1nNa1oaqhvNp8pfNzrNmlw+3AaG7H9fswmrXUiItK7OI6N0VbrXGgYuFrnRCTZxHQY+Jo1a7jmmmv48Y9/zGWXXdbs3ODBgykrK4s8Li8vJz8/P5bhxF4456Vd50REpJtsJ9ghYRgGTmMjhtfb6jk+r4t6dzABpZ3nRER6Ocdpu3XOVOuciCSnmCWadu3axU033cTChQsZN25cq/OFhYX4fD7WrFkDwLJlyxg9enSswokPxw7+X81oEhGRbrJtBzN0Q2H7/ZhtJZo8LurN4HENBBcR6eXabZ0zg8dV0SQiSSZmrXPPPfccDQ0N/PKXv4wcu/LKK3nnnXeYOXMmJ598MgsXLmTevHlUV1dz4oknMnXq1FiFE1+mEk0iItI9tuPgMoOJJqexsVXrHAQrmmotHwBWjeY0iYj0Zk57u84BmG4cVTSJSJKJWaJp3rx5zJs3r9Xx73//+5GfR4wYwWuvvRarEBIg0juX0ChERCR12baDEU40+Rsx+vdv9Zw0j4taKzibQ61zIiK9nOMEv8hui8utiiYRSToxndHU54SrmEzNaBIRke5p1jrX2Pagb6/HRbWh1jkRkT7BsWnvi2zD5daMJhFJOko0RVMo0WRoRpOIiHST1bR1zt+I4Wk9oynN66LKDiag1DonItK7ObbVfkWTWudEJAkp0RRVoeSSYSjRJCIi3eLYDqE8E06jv+1d5zwuamwTXC61zomI9HaOHfwiuy1qnRORJKREUzSFk0uGWudERKR7LNvBNMO7zjW22Trn87qoD9i4MvqpoklEpLdznHaHgRumGyx/nAMSEemYEk3R1DTRpIomERHpBtsJJpocx8FpbGy3oqmh0cLVr59mNImI9HKObQc7JtricoNtxTcgEZFDUKIpFtrroRYRETkE2wbTMHACAXAcjLYqmjwuGv0WZr9+ap0TEentHLvdiqbgjCZVNIlIclFGJJocO/h/DRMn/LOIiEgXhCua7MZGAMy2Kpq8LhzASFdFk4hIbxesaGqndU4VTSKShDqVaJoxYwYrV66MdSy9SDulrSIiKUDX/MSy7eCuc+FEU1u7zvk8LgCc9Aysas1oEpHY0HqQJDqY0YRLFU0iknw6lWj69re/zZNPPklxcTHPPfcc+/bti3FYKSo0l8kwzYPVTSIiKUbX/MSybQfTaJJo8rbdOgehRJMqmkQkRrQeJAmngxlNpiqaRCT5dCrRdOmll/Kb3/yGJ598kr1793L55Zcze/Zs1q5dG+v4UopD02HgiY1FRKS7dM1PrEjrXEOoda6NiqY0bzDRZHvTcRrqg/OcRESiTOtBcnAcG6Oj1jlLa4CIJJdOz2iybZuvv/6arVu3YlkWubm5/PznP+fRRx+NZXyppemuc8o0iUgK0zU/caxWFU1tz2gCCPjSgr+jqiYRiRGtB0nAttvfbMh0g1rnRCTJuDvzpIceeojf//73HH744Vx11VU88sgjeDweamtr+eY3v8nMmTNjHWdqaJpocpRoEpHUpGt+YrUcBt7ernMAfm86boKJJveAAfEMU0T6AK0HSaKjXedcbhy1zolIkulUoqmiooJnn32WESNGNDuekZHBokWLYhJYagonmgxU0SQiqUrX/MSybQfTpONd58KJJrcPN2CroklEYkDrQXJwHBsMV5vnDFU0iUgS6lTrnGVZrRaY8DcY559/fvSjSlWOZjSJSOrTNT+xbNvBZRhYDR3sOhdqnWtwh1vntPOciESf1oMkYXdc0aQZTSKSbDqsaLrzzjspLS1lzZo1VFRURI4HAgG2b98e8+BST9MZTdp1TkRSi675ySFY0dS5XeciiabqqvgFKCK9ntaDJNNR65yp1jkRST4dJpouv/xyvvrqK7744guKi4sjx10uF6NGjYp1bKknVNFkmIYqmkQk5eianxxsB9ymgd3YALS961w40VQXTjQdOBC/AEWk19N6kFwcxwmN5mjNcLnBVuuciCSXDhNNJ598MieffDLnnXceBQUF8Yop9WkYuIikIF3zk0PrXefaqGjyBr/ZrndcGD4fgSpVNIlI9Gg9SDK2jdFuRZMLLAvHcTDaSUaJiMRbh4mmW265hUceeYQf/vCHbZ5//fXXYxJUymo6o0klTSKSYnTNTw6td51rXdHkMk3cLpN6v4U7q78qmkQkqrQeJBenw13nPIATaq9re2C4iEi8dZhomjZtGgA/+9nP4hJMynO065yIpC5d85ODHa5oamh/1zmANK+LBr+Fq3+WZjSJSFRpPUgyjt1u6xxm6HbODgSrm0REkkCHu86ddNJJAJx55pkMGTKEM888k9raWlatWsUJJ5wQlwBTS9Nd55RoEpHUomt+crAdB1e4oskwwNX2jYPPY9LQaOFSRZOIRJnWgyTTQUWTEV4jtPOciCSRDhNNYfPnz+fZZ59l06ZNzJs3jx07dnDHHXfEOrbU06x1LjS4T0Qkxeian1i27WCEEk2G19vuzA2f1x2saMrqT6BKiSYRiT6tB8nBsQ/VOgeOEk0ikkQ6lWhav349P//5z/nzn//MZZddxoIFCygpKYl1bCmoeaJJ7XMikop0zU8s2z5Y0dTWjnNh4Yomd//+WFVV+nJDRKJO60GS6LB1LlTRZCvRJCLJo1OJJsdxME2Tv/71r5x99tkA1NfXxzSwVBT+R75hhv6s+je/iKQgXfMTK7jrHNiN/jZ3nAvzeUIzmrKywLKwa2vjGKWI9AXdXQ9ef/11LrnkEi6++GJeeumlVuc3bNjA5MmTKS4uZu7cuQQCwSTJzp07mTJlCmPHjmX69OnU1NQ0+73f/va3zJkzJ/K4pKSEU089lYkTJzJx4kSuv/76nnzc5OU4YLbTOheZ0WTFMSARkY51KtF0xBFHMG3aNHbs2MGZZ57Jj3/8Y4YPHx7r2FKQKppEJPXpmp9YjhMaBu5vxPC0n2hK87qDM5r69wfAUvuciERZd9aD0tJSHnroIV5++WWWLl3Kq6++ysaNG5s9Z/bs2cyfP58333wTx3FYsmQJAHfddRdXXXUVK1as4KSTTuLJJ58EoKGhgYULF/If//EfzV5n/fr1TJgwgWXLlrFs2TKee+65KH765OHYNu3etrmCiSbH8scvIBGRQ+hUomnBggWMHz+exYsX4/F4KCoqanWhF1rNaNJAcBFJRbrmJ5ZlO5imgeP3Y7jbTzR5PWZkRhNAQAPBRSTKurMerFy5krPPPpvs7GwyMjIoLi5mxYoVkfMlJSXU19czatQoACZPnsyKFSvw+/2sWrWK4uLiZscBVq1ahW3bzJ49u9l7rVu3ji+//JKJEycydepUvvjiiyh++iTi2BhmO61zoUSThoGLSDJxd+ZJGRkZFBUVsX//fv7xj38wcuRINm/ezIknnhjr+FJLJNEUXgiUaBKR1KNrfmLZDpimge0PYLjbX6bTvC7q/cEZTQDW/v3xClFE+ojurAd79uwhLy8v8jg/P5+1a9e2ez4vL4/S0lIqKyvJzMzEHbruhY8DnH/++Zx//vn8/ve/b/ZePp+PSy+9lCuvvJL333+fm266ieXLl+P1tj/frqXc3MxOPzdRvnZs0tLTyMvLanWudn8Wu4Hs/j7S2jifaG3FnCoUe2Io9viLRdydSjQ98sgjPP/88+Tm5kaOGYbB22+/HfWAegVVNIlICtM1P7HsUEWT7fd3mGjyelw0+i3c2QMBCFRWxitEEekjurMe2LbdbLdMx3GaPW7vfMvnhd+rIzNmzIj8PGbMGBYtWsTmzZsZMWLEoT9cyN691dh2cv+b3XEc6hsClJVVtToXqAq2zFXu3Y/b2/p8IuXlZbUZcypQ7Imh2OOvrbhN0+hxEr5TiaZly5bx1ltvUVBQ0KM36/Vats6poklEUpCu+Yll28EZTc4hEk1pXhf1jRZGRgaGx0NgvxJNIhJd3VkPBg8ezOrVqyOPy8rKyM/Pb3a+rKws8ri8vJz8/HxycnKoqqrCsixcLler32vL4sWLGT9+PAMHBhPujuNEKqJ6FbuDXedcGgYuIsmnUzOahgwZohuOTmk5oylxkYiIdJeu+YllOw6uTrTO+TwuHAcCloM7O5tA5b74BSkifUJ31oNzzz2XDz/8kIqKCurq6njrrbcYPXp05HxhYSE+n481a9YAwWTW6NGjIzOgli9fDsDSpUub/V5bVq1axWuvvQbARx99hG3bHHPMMV2KNxU4jt3ki+zmDM1oEpEk1KmU/znnnMP999/Pv/zLv5CWlhY5rnkdLYQqmiJlvo6dwGBERLpH1/zEilQ0BfwY/dovW/Z5XAA0hNrnAvtU0SQi0dWd9aCgoIBZs2YxdepU/H4/l19+OSNHjmTatGnMnDmTk08+mYULFzJv3jyqq6s58cQTmTp1KgB33nknc+bM4amnnmLIkCE8+OCDHcY3d+5c5syZw7Jly/D5fCxatAjT7NT36KnFbj/RhBnadc5WoklEkkenEk3hwXtNd4zQvI62OIABHbeTi4gkNV3zE8t2Ds5och2iogmgodHCPXAg9Vu2xCtEEekjurseTJgwgQkTJjQ79uyzz0Z+HjFiRKQSqanCwkIWL17c7utOnjyZyZMnRx4XFBTwwgsvdPwheoMOKprCiSZVNIlIMulUoumdd96JdRy9g+OEkkya0SQiqUvX/MSybAfTBNvv73DWiM97sKLJmz2QwL6P2xymKyLSXVoPkoPj2O1e2w1XcC1AFU0ikkQ6VVtaU1PD3Xffzb/927+xb98+5s+fT01NTaxjSz2OA5gHK5q065yIpCBd8xPHcRwch9Aw8ACG29Puc8MVTfV+C3d2No7fj11bG69QRaQP0HqQJDpqnXOF1glVNIlIEulUoumee+4hKyuLvXv34vP5qK6uZv78+bGOLQWFKpoiw8CVaBKR1KNrfuLYoXUj3DpneDredQ6gsTE4ownQnCYRiSqtB0nCcaC92VNmcC3QjCYRSSadSjRt2LCBWbNm4Xa7SU9PZ+HChWzYsCHWsaUeJzSjKfxQrXMikoJ0zU8cO7SHhCucaOqgosnbtKIpJwcA/969MY9RRPoOrQeJ5zgOkTmwbTA0o0lEklCnEk0td2+wLKt37ugQDYZBZCFQRZOIpCBd8xPHtkMVTYaBEwhgdDCjKa3JjCbPoEEABPaWxz5IEekztB4kgfAu1u393V2hdUIVTSKSRDo1DPyMM87ggQceoL6+nvfff5/f/OY3nHnmmbGOLeU4jk1w1zkNYhWR1KVrfuJEWucM55CJpqa7zrn6D8Bwu/GXl8UlThHpG7QeJIFwoqndGU3BdcJRRZOIJJFOfSVx2223kZGRQVZWFg8//DAjRoxgzpw5sY4tNRlNEk3hhUFEJIXomp84VriiKdRDZ3o6GAYeqWiyMUwTd+4g/OWqaBKR6NF6kATCHRLtJZqM8K5zVnziERHphA4rmq6++upmW2mmpaUB8Mknn/DDH/6QF198MbbRpRrHad46JyKSQnTNT7xwRZMr9EVF5yqagt9iewYp0SQi0aH1IImE14N2OiYMwwDTDZY/nlGJiHSow0TTD37wAwD+/Oc/U11dzb/+67/icrlYtmwZ/fv3P+SLV1dXc+WVV/KrX/2Kww47rNm5xx9/nN/97neR1/nud7/LlClTuvs5kkN4GLihGU0iknp6es1//fXXeeqppwgEAvzbv/1bq2v6hg0bmDt3LjU1NRQVFXHXXXfhbpJIefjhh3G5XMyYMQOAAwcOcNttt7F9+3ZycnJ4+OGHycvLi+InTj7hGU0uJ/jNdEeJJrfLxGUaNPiDNyGeQYOo37ol9kGKSK/X0/VAouhQrXMALjeOKppEJIl0mGgqLi4G4LnnnuO///u/I8P/LrzwQr73ve91+MKfffYZ8+bNY+vWrW2eX79+PQ8++CCnnnpqN8JOVk7zYiYlmkQkhfTkml9aWspDDz3E73//e7xeL1deeSVnnXUWxx13XOQ5s2fP5p577mHUqFHccccdLFmyhKuuuoqqqioWLFjAn/70J374wx9Gnv/www9TVFTEM888w9KlS7n33nt5+OGHo//Bk0jrRFP7rXMQHAje0Bh8rmdQHnZNDVZdHa709NgGKiK9Wk/WA4ky+9CJJsN0a9c5EUkqnZrRVFlZSUNDQ+RxTU0N+/fv7/B3lixZwp133kl+fn6b59evX8/TTz/NhAkTuPvuu5u9fspynOAiECltVaJJRFJPd675K1eu5OyzzyY7O5uMjAyKi4tZsWJF5HxJSQn19fWMGjUKgMmTJ0fOv/322xx11FFce+21zV7z3XffZcKECQCMHz+e9957D7+/d7cGRHadC30zbXg63rPD63FR7w+3zgWrvfxle2IYoYj0Jd1ZDyS6HA4xowmCA8G165yIJJFO7To3fvx4vvvd7/Ltb38bx3FYsWIF3/3udzv8nXvvvbfdczU1NZxwwgnMnj2bI488kjlz5vDkk08ya9asLgWfm5vZpedHU15eVqtj5WluLNMgKyudBiAnJwPPgNbPi7e2Yk1WijU2FGtspFKsXdGda/6ePXuatbXl5+ezdu3ads/n5eVRWloKwKRJkwB47LHH2n1Nt9tNZmYmFRUVFBQUdPqzJHKd6I5A6Eaif0awkmlAThaDOvjvrF+6B0yTvLws0kccwy4gvW5/h7+T7Hrr/646os/cN6TiZ+7OeiBRFqlo6mAGrOnSrnMiklQ6lWi65ZZbOPHEE/nb3/4GwJw5cxgzZky337Rfv348++yzkcfXXXcdd9xxR5cTTXv3Vke+/Y2nvLwsysqqWh2vr2vEsaG6OvjNT8XeaszGxLYvtBdrMlKssaFYYyNZYzVNo8fJle5c823bbjao1HGcZo8Pdb4zHMeJtG90VqLWie4q21sDQF1VDV6gqjaA08F/Zy7T4EBVA2VlVdje4E1s+ZdbcIaPjEe4UZes/7uKJX3mviGZPnNX1olo3wNIN3RyRpMqmkQkmXQq0QRw0UUXcdFFF0XlTXfu3MnKlSu5/PLLgeDNg7uDgacpxdAwcBFJfV295g8ePJjVq1dHHpeVlTVrnR48eDBlZWWRx+Xl5e22Vofl5+dTXl7O4MGDCQQC1NTUkJ2d3fkPkYLCObHOts6leVw0+IPPNX0+3Lm5NO7eFdMYRaRvieY9gHRDONHUwRcthunRjCYRSSpd+2o4StLS0njggQfYvn07juPw0ksv8e1vfzsRoUSXY4eSTF37ll5EJNWde+65fPjhh1RUVFBXV8dbb73F6NGjI+cLCwvx+XysWbMGgGXLljU735YxY8awdOlSAJYvX05RUREeT8fDsVNdqxlNhxgG7msyDBzAO3gIjbuUaBIR6TVCX1wbHVY0uXBU0SQiSSSuiaZp06axbt06cnJyuPvuu5k+fTpjx47FcZxWQ2BTkgOgiiYR6XsKCgqYNWsWU6dOZdKkSYwfP56RI0dGrvsACxcuZMGCBYwdO5ba2lqmTp3a4WvecsstfPrpp4wbN46XX36Z+fPnx+OjJFRk17lIoqnjiiZfk4omCCWaSnfjaP0REekdnM7MaNKucyKSXGLer/bOO+9Efm46l6m4uDiydWrvYTdfBPQPfRHpQyZMmBDZJS6s6XV/xIgRvPbaa+3+/owZM5o9zs7O5le/+lV0g0xydvib6y5UNNU3Hry58A4egtPQQKCyEk9OTuwCFRGR+OjEjCZDM5pEJMkkpHWu13JoPqMJJZpERKTzwhVNRidnNKV73dQ3a50bDKA5TSIivYTTmWHgphvHtto/LyISZ0o0RVGwVeHgjCZHiSYREekCq9WMpkMkmnwu6hutSCWUd8gQAPxKNImI9A7hDomOWudcap0TkeSiRFNUOdp1TkREui08W8kM3TAcOtEUPF/fEExMuQZkY6al0aCB4CIivUNnWuc0o0lEkowSTdHU8hsH5ZlERKQLrJatc51NNIXmNBmGgXfoUBp37YxhlCIiEjd2Z1rntOuciCQXJZqiKlTRRLi01U5kMCIikmIiw8AjFU0dDwNP87oAqGtoMhB86GE0lpTEKEIREYmv0LrQUaLJ5dEwcBFJKko0RVNkRlP4ccIiERGRFBT54rqLFU11TQaC+4YWYlUdIHDgQGyCFBGR+IksDO3PaDJcLrXOiUhSUaIpqsIzmsyDj0VERDopsutcuKLJ03FFU7o3PKOpSUVTYSEAjTtV1SQikvI6ueucEk0ikkyUaIomh1CiKfxYiSYREem8g61zFpgmhtnxMp3mC7XONa1oKjwMgAYlmkREUl54kwg6Wg9Mt2Y0iUhSUaIpmhwbMDBQRZOIiHRd04om8xDVTHCwoqnpjCbXgAGYGf1oLNkRmyBFRCR+OrPrnMutGU0iklSUaIoyQxVNIiLSTU0rmjqVaApVNDVtnTMMA19hIY07tfOciEjKcw49owmXGyzrYPWTiEiCKdEUTaGKpkimSRd7ERHpAitU0YQVwPB0PAgcIM3behg4gHdoIQ0lO3TTISKS6sLX8UPNaMIBx2r/OSIicaREU7QZRsffOIiIiLTDbpJo6kxFk2ka+DyuZq1zAL7CQuzaWgL79sUgShERiZvODgMHsJRoEpHkoERTNDkOzSua7ERGIyIiKabpjCbDfehEEwQHgtc3Nk80eUMDwbXznIhIigvdTxgdfJFtuEKJJs1pEpEkoURTVDmhPJMqmkREpOvCM5qwLMxOtM5BcCB4XUPzb7F9QwsBNBBcRCTVdaaiKZRociwlmkQkOSjRFE2O06x1TrMxRESkKyKtc4HOtc5BcCB4XYuKJldWFq7+/WlQRZOISGqzOzOjyRV6rhJNIpIclGiKIifSOhc5kqhQREQkBYXvJ4LDwDvZOud1U9/Qei6Hd2ghjSVKNImIpDKnE7vOGZEZTf44RCQicmhKNEVVuKIp9GdVRZOIiHSB1a2KJneriiYAX+FhNOwswbE1L1BEJGWFE01mR61zwfXC0TBwEUkSSjRFU8uKJiWaRESkC8Ktc04g0IUZTS7qG1onmryFhTgNDQQq9kY1RhERiaPI/cShZzRhq6JJRJKDEk3R1mRGk1rnRESkKw4OA+/KrnOth4HDwYHgDWqfExFJXZGKpk7sOqdh4CKSJJRoiibHJrTtXKIjERGRFHSwdc7f5WHgLTeg8IZ3ntNAcBGR1BVKNBkdDgPXrnMiklyUaIq2phVNap0TEZEusKzgDYUT6Pww8HSvG8eBRn/zWUyujAzcA3NoKNkR9ThFRCROnE7sOhea0aRd50QkWSjRFE2Oo0STiIh0m2U7mIbRpRlNab7g89oaCO4t1M5zIiIprTO7zrm065yIJBclmqIqnFjSjCYREek6y3ZwuQwcfxd2nfO6AKhrYyC4r7CQxl07tfOciEiKciKJpkMPA1frnIgkCyWaoslxMAzz4IQmVTSJiEgX2LaDaRo4AX+nW+fCFU31ja0HgnuHFuIEAvj37IlqnCIiEiedaJ0zTFU0iUhyUaIpmiILgSqaRESk6yzLwW2GWufcnWud67ii6TAAGjQQXEQkNXWidS5c0YTd+gsHEZFEUKIpqjSjSUREus+ybdyGA7bd+WHg4RlNDW1UNA0ZCkCjBoKLiKQmu+PWueq1n/L1gvup3weOKppEJEl07utS6RzHCS0CqmgSEZGuC9gO3tC9RGcrmjJCiaaa+tY3GKbPhycvj0ZVNImIpKZQRZPRRqIpsH8/O594DGyb/ZWQVVsb7+hERNqkiqZoU0WTiIh0k2U5eAjdVHSyoimrnxeAqtrGNs97Cw+jQTvPiYikpvD9hNn6tq1m/TqwLPK+931sP1T/c3OcgxMRaZsSTVHkOHYoyRRONCU0HBERSTG24+Axgommzu465/O48HldVNW23TLhLRiMf0+pdp4Tkbh5/fXXueSSS7j44ot56aWXWp3fsGEDkydPpri4mLlz5xIIBGfM7dy5kylTpjB27FimT59OTU1Ns9/77W9/y5w5cyKPGxsbmT17Nt/5zne47LLL2LRpU2w/WCKEZzTRekZTzbq1uAYMYMCFF+LyQe0mtUmLSHJQoinqDlY0Oco0iYhIF1iWHaloMj2d727vn+HhQDsVTZ78ApxAgMC+yqjEKCLSkdLSUh566CFefvllli5dyquvvsrGjRubPWf27NnMnz+fN998E8dxWLJkCQB33XUXV111FStWrOCkk07iySefBKChoYGFCxfyH//xH81eZ/HixaSnp/PGG29wxx13cPvtt8fnQ8aRE040tahochyH2s//Qb8TT8ZweUjLgfqSMqwWyTkRkURQoimaIhVN4cdKNImISOdZtoMn9CVFZ1vnAPpneDlQ007rXH4+AP49e3oeoIjIIaxcuZKzzz6b7OxsMjIyKC4uZsWKFZHzJSUl1NfXM2rUKAAmT57MihUr8Pv9rFq1iuLi4mbHAVatWoVt28yePbvZe7377rtceumlAJxxxhlUVFSwc+fOOHzKOHLaHgYeqKzErq0h7eijMQyDtBwX2A61//w8AUGKiDSnYeDRFM4rRRYCJZpERKTzLLt561xnV5GsDC/l++vbPOfJLwCgcU8pGSNOiEaYIiLt2rNnD3l5eZHH+fn5rF27tt3zeXl5lJaWUllZSWZmJu7QRgjh4wDnn38+559/Pr///e87fK+8vDx2797N0KFDOx1vbm5m1z5gnFVmeGkE8vIHYJiuyPF9JcF5THkjjiU7L4vqbC+GpxFn60byxn4rQdG2lpeXlegQuk2xJ4Zij79YxK1EU1SFdp2LbDqnRJOIiHSeZTukhYeBuzufaOrfz8OWXQfaPOceOBDD7VZFk4jEhW3bGE0q/B3Hafa4vfMtnwe0etxSy99xHAezjaHZHdm7txrbTt5/szdU1wFQVl7dbOe5yg3BeVR16dn4y6rA5caXn87ejz+jf1lVQmJtKS8vi7IkiaWrFHtiKPb4aytu0zR6nIRX61w0RRJLkUxToiIREZEUZFk27u7MaOrnparWj93GFxyGaeLJy8e/pzRqcYqItGfw4MGUlZVFHpeVlZEfauFt63x5eTn5+fnk5ORQVVWFZVlt/l5bCgoK2NMkiR5+rV4l1DpntGida9y1EzM9HdeAAcEDLg9pBQPwl+7GX1ER7yhFRJpRoimqnOCMJkO7zomISNcFZzQFb7I6u+scBFvnbMehtj7Q5nlPfj6NqmgSkTg499xz+fDDD6moqKCuro633nqL0aNHR84XFhbi8/lYs2YNAMuWLWP06NF4PB6KiopYvnw5AEuXLm32e20ZM2YMy5YtA2D16tX4fL4utc2lBNtuNZ8JoHH3LrxDhh6s6HK58RUEKxDq/rkhnhGKiLSiRFM0OQ7BaqZwoklbSYuISOdZtoO7m8PAgXYHgnvyC/DvKcVRS7eIxFhBQQGzZs1i6tSpTJo0ifHjxzNy5EimTZvGunXrAFi4cCELFixg7Nix1NbWMnXqVADuvPNOlixZwiWXXMLq1au59dZbO3yvq6++msbGRsaNG8e9997L/fffH+uPlwBOqx3nABpLd+MdPDjy2HC58WR5cWVmUbtBA8FFJLE0oymqQn3iHbeTi4iItMmynCatc11JNAWfe6CmkaGD+rU6783Px2lsxNq/H3d2dlRiFRFpz4QJE5gwYUKzY88++2zk5xEjRvDaa6+1+r3CwkIWL17c7utOnjyZyZMnRx77fD7uu+++KEScvBzbbtU25wQCwet57qCDB0032BbpI06g9p+ftznzSkQkXlTRFE1OsHXOQLvOiYhI11m2g9sJt851/rugrH6hiqba9iuaILjznIiIpBCndetcYP8+cBzcAwcePOhygx2g3zdOJFBZSWPJjvjGKSLShBJN0eQAGNp1TkREusWybdzhwa9dqWgKJZqqav1tnveEhuNq5zkRkRTj2K1a5wIVlQB4cnIixwyXByw//UaeAkDN2s/iF6OISAsxTTRVV1czfvx4duxonVHfsGEDkydPpri4mLlz5xIItD3ANLXYoWHgoT+rEk0iItIFwRlNodY5d+cTTZlpHgyjgxlNObngcmnnORGRVNNGC1ygMrirnHvgwUQTphvHCuDOzsZ31NFUf/ZpHIMUEWkuZommzz77jO9///ts3bq1zfOzZ89m/vz5vPnmmziOw5IlS2IVSvy0yisp0SQiIp1nWQ6uUOtcVyqaTNMgK91DVTutc4bLhWfQIO08JyKSatponfNHEk2tW+cAMkedSv3mTfgrKuIWpohIUzFLNC1ZsoQ777yT/FC5flMlJSXU19czatQoIDjYb8WKFbEKJY6c0EIQ3nVOiSYREem8YOtc12c0QXBO0/52KpoAPHkFqmgSEUk1jo3RsnWusgLDl4aZnhE5ZphusIKJpqwzzgTHoXr1qriGKiISFrNd5+699952z+3Zs4e8vLzI47y8PEpLu/6P39zczG7FFg15eVmtjtWbBt40DzmDMqkBsrLSyGrjefHWVqzJSrHGhmKNjVSKVVKDbTu4nKa7zjV0+nf7Z3jbndEEwZ3nDmz6SjsRiYikEsdpPQy8ogLPwIHNr+UuD04o0eQtGIzvyKM48NHfGHhxcTyjFREBYpho6oht280ujN39R+/evdXYdvyrhvLysigrq2p1PGBZOA0WFRW1ABw4UEt9G8+Lp/ZiTUaKNTYUa2wka6ymaSQ0CS89Y9kOLjvUOud205VEU3aml39u29fueU9+AXZdHVZ1Fe6s/j2MVERE4sIJzYBtIlBZ2bxtDoKtc9bBLxuyzjyL8t++SuOePXjb6DAREYmlhOw6N3jwYMrKyiKPy8vL22yxSzmOE+qa0zfFIiLSdYFwRZPL1apV4lCGDupHZVUDtfVtb66hnedERFKP49gYLSuaqg7gGjCg2THDPDijCSCr6EwAqlb9PfZBioi0kJBEU2FhIT6fjzVr1gCwbNkyRo8enYhQostxAOPgtw6a0SQiIl0QHgZudGHHubDCvGAl287ymjbPe/MLADSnSUQkldh2sFqpCevAgdaVqS53pHUOwJObS9pxw6j6SIkmEYm/uCaapk2bxrp16wBYuHAhCxYsYOzYsdTW1jJ16tR4hhIjLXqolWgSEZEusGwb07EwujgIHOCwQf0AKCmvbvO8Z9AgMAztPCcikkpsq1lFk93QgNPYiKt/60QTVvOK1qwzz6KxZAcNJTviEamISETMZzS98847kZ+fffbZyM8jRozgtddei/Xbx5fjBKuZIn3USjSJiEjn2I6D44BpW6H5TF2TMyANn8dFSVnbFU2G240ndxD+bmy+ISIiCeLY0KSV2jpwAABXi4omw+UBu/mGEFmnn0HZKy9R9dHf8V12WOxjFREJSUjrXK8VGWqu1jkREema8OYWpm1hdqN1zjQMhg7qR0k7rXMA7kGD8O8t73aMIiISZ46NYboiDwNVoURT/xY735pucByc0IYSAO4BA0gfdjw1az+NR6QiIhFKNEVTeFcIVTSJiEgXWVZwzXB1s6IJoDCvHyVlbbfOAXhycglU7O3Wa4uISPw5tgVNEk3hiqa2ZjQBzQaCA2SceBIN27cTCP2eiEg8KNEUTY4DmBoGLiIiXWbZNhBqnevGjCYIzmk6UOvnQG1jm+fdubkE9u3DCbS9M52IiCSZFrvOWZGKppatc6F1o8WcpowTTgSg9p+fxzBIEZHmlGiKquCMJiPcOiciItJJVqh1zrC7t+scwNC80EDwPe0MBM/NBcchUFnZvSBFRCS+bLt5RVNVFQCuzBatc67guuFYzec0pR11FGZ6OrUblGgSkfhRoimaWg4DV0WTiIh0khWZ0RToduvcMUP64zIN1m5uuz3OkzsIQHOaRERShWNjNBkGHqiqwvClYfp8zZ5mmG1XNBmmSdqxx1G/eXPMQxURCVOiKYqc8IymgwcSF4yIiKSU8Iwmw+p+RVNGmocTj85h9T/LcNpYg9w5OQAEKiq6H6iIiMRPGzOa3C0HgUO7M5oA0o89jsadJVi1tbGKUkSkGSWaosmxwTA1DFxERLosPKPJsAPdntEEUDQ8n70H6tmyq6rVuXCiSRVNIiKpwWlR0WRVV7Vum4NIosmxWiea0o45FhyH+q1bYhaniEhTSjRFk+MEE00o0SQiIl0TmdFkdX/XOYBTjx+EyzRY9c/SVudMjxdX//74tfOciEhqsG0wmlQ01dRg9uvX6mmGGaqEbSvRdPQxYBjUb94UszBFRJpSoimaNKNJRES66WCiKdDt1jmAfmkeTjhqIJ981XbVkjsnl8BeJZpERFKCYzWraLJranC1kWiKtM61GAYO4MrIwJNfoIomEYkbJZqiyXGAg4km5ZlERKSzms9o6n5FE8BJR+eyp7KO8v11rc55cnNV0SQikipa7jpXW4OZ0X6iyWljRhNA2pFH0fD11zEJUUSkJSWaosrGMAwOts7ZiQxGRERSSLOKph7MaAL4xlEDAdiwtbLVOU9OLoGKijaHhYuISJJpUtHk2DZ2bS2ufhmtntbernNhviOPJFBZQeDAgZiFKiISpkRTNEVmNIUfJy4UERFJLeFh4PSwdQ6gcFA/+vfzsuHr1okmd+4gnMZGrKrWw8JFRCTJNKlosuvrwHFwZWS2fp6r/RlNAGlHHQ1Aw9dbYxGliEgzSjRFU2RGU/jPqkyTiIh0Trh1jii0zhmGwTeOHMjnX1e2qlxyD8wGILCvdRJKRESSS3DXuVCiqaYWALONiqaDrXOtZzQB+I44EoB6JZpEJA6UaIqmSKKpyWMREZFOsBwHHAcj0PPWOYATjhrIgZpGSsprmh13Zwfb6qz9+3v8HiIiEmO2BaHWOasmeD13tTGj6VCtc670dDwFg5VoEpG4UKIpSoLfGIeGgaOKJhER6RrLcjAJriU9bZ0DGHZYNgBbdzVvkXMPGACooklEJCU4NkaoW8KqDSaazA53nWs70QThgeBbox2hiEgrSjRFS7h6yTBV0SQifdLrr7/OJZdcwsUXX8xLL73U6vyGDRuYPHkyxcXFzJ07l0Ag+I/hnTt3MmXKFMaOHcv06dOpCX1j+9FHH3HWWWcxceJEJk6cyO233x7XzxNvlm3jciyAHrfOAeRnp+Nxm5SUVzc77hqQDUBAFU0iIsmv6YymcEVTB4mmlrvOfX1gO8s2vUF5XUVwIHiFBoKLSOwp0RQ1oSGuTXedU6JJRPqI0tJSHnroIV5++WWWLl3Kq6++ysaNG5s9Z/bs2cyfP58333wTx3FYsmQJAHfddRdXXXUVK1as4KSTTuLJJ58EYP369Vx33XUsW7aMZcuWsWDBgrh/rniyLKdJoqnnFU2maTA0tx8lZc1b50yPB7NfPwL79vX4PUREJMaazGiKVDS11ToXGQZ+cEZTVWM1T6/9T976+v+462/381djG6CB4CISe0o0RUuTiibDMDp+rohIL7Ny5UrOPvtssrOzycjIoLi4mBUrVkTOl5SUUF9fz6hRowCYPHkyK1aswO/3s2rVKoqLi5sdB1i3bh0ffPABEyZM4Ec/+hG7du2K++eKJ8t2cDvBLy2iMaMJoDCvHzvKqlsdd2cPJLB/X1TeQ0REYsdpMqOpw4qm0Iwmxwp+YWE7Ni9ueJWaQB3/75TrOXfombxjfwVoILiIxF50/iUrTRJN4SSTAaEbBhGR3m7Pnj3k5eVFHufn57N27dp2z+fl5VFaWkplZSWZmZm4Q61i4eMAWVlZfOc73+Hiiy/mlVdeYdasWfz3f/93l+LKzW1jC+gk1S9zX6SiqX92MO68vKwevebwo3JYuX43af18ZGV4I8f35OUSqK7q8evHQjLGFGv6zH1DX/zMEgWhGU0OwWHghtuN6fW2fl54RlNo17m/7VrN53u/4LvHT+LE3OGckDOMndW72d9/H2lbNpEbv08gIn2QEk3REko0RaqZVNUkIn2IbdvNqjkdx2n2uL3zLZ8HB6+jd999d+TY97//fRYtWkRVVRVZWZ2/Wdu7txrbTo025sp9tbhCX1BU1wcoAMrKqjr+pUPIzgi2Uqz9ZynHH54dOW6nZ1K/bXuPXz/a8vKyki6mWNNn7huS6TObppFSSfg+r8mMJqu2pu1B4BBsrzOMyDDwNaWfMTgjn9GF5wBgGiZXDr+MjweuJW3zF/GJXUT6LLXORYvTdEYTBCuaUuPmRkSkpwYPHkxZWVnkcVlZGfn5+e2eLy8vJz8/n5ycHKqqqrBCpf7h37Ntm6eeeipyPMzlcsX4kyROsHUuesPAAQoHBW9ISlq0z7myswns349jq/JWRCSpOVZkRpNdW9t221yY6cGx/DRYjWzct5kTc0c0+zKnMHMI/Y8Zhq+qnl2lm2MduYj0YUo0RU04qRT6kxpNj4mI9G7nnnsuH374IRUVFdTV1fHWW28xevToyPnCwkJ8Ph9r1qwBYNmyZYwePRqPx0NRURHLly8HYOnSpYwePRrTNPnzn//Mm2++GTl+yimnkJGREf8PFyfBYeChGU1RGAYOMDDLR7rPzY4WA8HdA7LBsrBqWs9vEhGRJGLbkRlNVk1Nm4PAI1wusAJ8WbmRgGPxjdzhrZ7yjVO+CcDqNStanRMRiRYlmqKl5YwmQxVNItJ3FBQUMGvWLKZOncqkSZMYP348I0eOZNq0aaxbtw6AhQsXsmDBAsaOHUttbS1Tp04F4M4772TJkiVccsklrF69mltvvRWA++67jxdffJFx48bxu9/9jnvuuSdRHy8ubLvprnPRqWgyDIPCvH6tKprc2dkAWPv2R+V9REQk+hzHBhwMI1TRVFODq4MvXAyXB+wAn+/9Eq/p4djso1s9J+eYEQBUbvqc/Q0HYhK3iIhmNEVLG8PAHSWaRKQPmTBhAhMmTGh27Nlnn438PGLECF577bVWv1dYWMjixYtbHR82bFiXh3+nMqtposkTnYomgMPyMlm1obTZPCz3gGwAAvsr8R1+eNTeS0REoig8miNc0VRbg++wDq7Zphs74Ofzvf/k+IHH4TFb3+q5MjIw8wYxaG8V/7f9AyYdd0ksIheRPk4VTVHitDWjqZ3WucbP3sC/ZXVc4hIRkdRg2TbuSOtc9L4HKhzUj5r6APuqGyPHXNkDAAiooklEJHmF5uhFZjTV1GD266CF3OWh3K6nvL6CE9tomwvrd/SxHLbf4P2Sv1EfqI9qyCIioERT9EQqmsIzmtrfda7h769S/+fH4xCUiIikiuCMpui2zgEcltd6IPjBiqZ9UXsfERGJMju0IYbpwrEs7Pp6XP3a3zHQcLn4wg7ubtjWfKYw35FHkVZVj1FTy992r4lqyCIioERT9IQrmuh4RpPTWBu/mEREJGUEbKdJRVP0WucK84I3JU0HgpteL2ZGBoF9+6L2PiIiEmXhNcE0sWuD9xBmR5timB620cAAb38Gpee2+7S0o48B4JTabP6y46/YjnYgFZHoUqIpWsJJJTP8J20v0VQXv5hERCRl2LaDm9BNhSd6FU2Z6R4GZHopKW89ENxSRZOISNI6OJrDxKoJflng6nDXOTe7jEYKs4Z0+LppRx+N4XYzqqo/e2rL2VDxZbRCFhEBlGiKomBSyYhUNB081owViFtEIiKSOizbxmNEv6IJ4LBB/ZpVNEGwfU4VTSIiSSzUOmeYLqza4DXc7Nd+oslyudljWByWObTDlzU9XtKOPob+JZUM8Gbx7va/Ri9mERGUaIqelsPADbNJO12TpynRJCIibbAsB0+oosmM4owmCLbP7SqvwbYPfgHiys7WjCYRkWRmh3edc2GHEk2uDhJNe7wuLAMKMzuuaAJIH3Y8Ddu3MTrvDD6v+ILdNXuiErKICCjRFD0thoEb7bTOYSvRJCIirVn2wURTNFvnILjzXGPApmz/wfZt94BsrP37cdpaq0REJPHCG0SYTVvn2p/RtCu4OR2HdSbRdPzxYFkUNeThNlz8ZcfKnscrIhKiRFO0RBJNoYoms+2KJix//GISEZGUYdn2wURTtFvn8kMDwfccbJ9zZ2fjBALYNTXt/ZqIiCRSkxmw4Wu12cGucztNC7fjkJc+6JAvnXbssOB9y5ZtnF4wir/tXk1dQLNkRSQ6lGiKlpaJpvZa58LblAKOrR0eREQkyLIdPI4FLhdGlFvnhuYGWy2aDgR3Z2cDENhXGdX3EhGRKGk2oym461yHFU1GI4P9Ni7TdciXdqWn4zv8COq++pILDz+PRquRD3euik7cItLnKdEUJQ7hpNHBRJNjtzUMvElFk63qJhERCQommgKYnuhWMwH4vC7ystMoaTIQ3D1gIIAGgotI1L3++utccsklXHzxxbz00kutzm/YsIHJkydTXFzM3LlzCQSCoyV27tzJlClTGDt2LNOnT6cmVMVz4MABbrjhBr7zne8wZcoUysrKACgpKeHUU09l4sSJTJw4keuvvz5+HzIOnCYzmqyaGgyfr90vIhzHYafTwOD6zt9fpB9/PPWbN3F4+mCOHXAU7+5Yid1WR4aISBcp0RQtLWY0YRjttM4F2v5ZRET6NMsKVjQZXm9MXr9wUCYl5c1b50CJJhGJrtLSUh566CFefvllli5dyquvvsrGjRubPWf27NnMnz+fN998E8dxWLJkCQB33XUXV111FStWrOCkk07iySefBODhhx+mqKiIN954gyuuuIJ7770XgPXr1zNhwgSWLVvGsmXLeO655+L7YWMtPKPJCLbOuTLaHwS+v/EANQQY0uBv1kHRkfRhx+M0NlL/9VbGHHYee+sr+HzvF1EJXUT6NiWaoqXTrXMHk0uO5jWJiEiIZTu4Y5hoOiy/H7v31uIPBNcmV/YAQK1zIhJdK1eu5OyzzyY7O5uMjAyKi4tZsWJF5HxJSQn19fWMGjUKgMmTJ7NixQr8fj+rVq2iuLi42XGAd999lwkTJgAwfvx43nvvPfx+P+vWrePLL79k4sSJTJ06lS++6GVJEqdJRVNtDWYHbXMl1bsAGNIQgEBDp14+/bjjAaj76ktG5Z3EAG+WhoKLSFREdwhEXxZeCA45DFwVTSIi0ppl2XgcCzOGFU2247C7opbD8zMxPV7MjH4E9u+PyfuJSN+0Z88e8vLyIo/z8/NZu3Ztu+fz8vIoLS2lsrKSzMxM3KHWsPDxlr/jdrvJzMykoqICn8/HpZdeypVXXsn777/PTTfdxPLly/F24Tqam9v+cO1Eq29Mo5bgrnMufwOu7P7k5WW1+dx95RUADGkMkNPfizur7ec1k5fFzqFDsb7exOCCbIqPH8OS9f9DIK2OIVn5UfkM7cWbChR7Yij2+ItF3Eo0RUuL1jmjnYombCWaRESkNctxcDuB2LXO5YUGgpdVc3hoFzp3djaWWudEJIps28YIf/FKcHZQ08ftnW/5PKDV46a/Y5omM2bMiBwbM2YMixYtYvPmzYwYMaLT8e7dW43d1lzVJGBVVAV/MF3U7zuAJy+PsrKqNp+7ac92BphppNsOe0v3YtZ37jbPe8xx7P94DXtK9zNqwKn8zniDpev+zOXDLu1x/Hl5We3Gm+wUe2Io9vhrK27TNHqchFfrXLSEEk1G09a5NnaVc5okl5q20YmISN9mWQ5uO3YVTYNzMnCZRqs5TYH9ap0TkegZPHhwZFg3QFlZGfn5+e2eLy8vJz8/n5ycHKqqqrAsq9Xv5efnU15eDkAgEKCmpobs7GwWL15MZeXBa5jjOJGKqN7ACX1pbRgmdm1thzOaSmvLyPf2D/5eJ1vnADKOH45dW0PjzhIG+LI4Nf9k/rZrNfVdeA0RkZZimmg61I4Tjz/+ON/85jcjO0W09ZyU0bJ1rr2KpqZzmVTRJCIiIZbt4LYDGDHYdQ7A7TIZnJvB9j3VB49lZ2sYuIhE1bnnnsuHH35IRUUFdXV1vPXWW4wePTpyvrCwEJ/Px5o1awBYtmwZo0ePxuPxUFRUxPLlywFYunRp5PfGjBnD0qVLAVi+fDlFRUV4PB5WrVrFa6+9BsBHH32Ebdscc8wxcfy0MdZyRlO/thNNjuNQWruHfF928IC/80mi9GEH5zQBjDnsXOoC9awq/aTbYYuIxCzlH95x4ve//z1er5crr7ySs846i+OOOy7ynPXr1/Pggw9y6qmnxiqMOAqX3IZ3nTMj30I006x1TsPARUQkyLJtXHbsWucADs/P5Itt+yKP3dkDCezfj2PbGKaKnEWk5woKCpg1axZTp07F7/dz+eWXM3LkSKZNm8bMmTM5+eSTWbhwIfPmzaO6upoTTzyRqVOnAnDnnXcyZ84cnnrqKYYMGcKDDz4IwC233MKcOXMYN24cWVlZLFy4EIC5c+cyZ84cli1bhs/nY9GiRZi96VoW3j3OAaehAVc7iaYqfzV1gXoK0nKCT+9CNZJ70CDcAwdS9+UXZH/zXzi6/5EcnjmU93as5PyhZ7Xbvigi0pGYJZqa7jgBRHacuPnmmyPPWb9+PU8//TQlJSWcccYZ/PSnP8Xn88UqpNhqueucaR481vRpap0TEZE22LaDyw7ErHUO4Ij8LP72j1KqahvJyvDiys4Gy8Kqrsbdv3/M3ldE+pYJEyZEdokLe/bZZyM/jxgxIlKJ1FRhYSGLFy9udTw7O5tf/epXrY4XFBTwwgsvRCHiJBUawxGoDyaOXO3sOldasweAgoxBQNcSTYZhkD7seGq//CIyJ2v0Yefx0j9/y8Z9mxk28NiefAIR6aNilmg61I4TNTU1nHDCCcyePZsjjzySOXPm8OSTTzJr1qxOv0cid4loOZm9vj6dWmBAdj8y8rJo9LgxPUar51WkuWgM/Twg00NGHCbTp9L0e8UaG4o1NlIpVkl+lhVMNBmeGFY0FQTXze17qvnGUTm4B2QDENhXqUSTiEiycYIVTXZtMHHUXutcaW1w5tXgjILggS60zgGkDxtO1Ud/x19ehjcvn6KCUSzd+Cf+smOlEk0i0i0xSzQdaseJfv36Nftm47rrruOOO+7oUqIpUbtEtDWZPVAZnHmx/0A9NWVV+C0Ho8Hf6nkNVQeHsO6rOEBN/9hOpk+l6feKNTYUa2wka6zR2CVCEsOyHVxWAMMbmxlNQGS3uW2loURTqOo4sG8fHHFkzN5XRES6LjyG42BFU/uJJo/pITtjEHWAE2hs83ntST8+NKfpyy/x5uXjdXk4Z+gZvLP9fSrr9zEwLbvbn0FE+qaYNTEfaseJnTt3NiuZTfldIlq0zhmd2HVOw8BFRCQsEIfWuf4ZXgZm+di+J5gkPZho0s5zIiJJJ3QvYdWFKpraa52rLSM/YxAuT2gESRd3jPMOGYqZ0S8yEBzggsJzcByHD3b+vRuBi0hfF7NE06F2nEhLS+OBBx5g+/btOI7DSy+9xLe//e1YhRMH4cqqQ+06p2HgIiLSmhWwghVNMWydAzgiP5NtoZ3n3AOywTAIVCrRJCKSdELDwK26OoB2h4GX1uyhICMPQokmp4utc4Zpkj5sGHVffRE5Nig9h5MGjeCvJX/Hr7myItJFMUs0Nd1xYtKkSYwfPz6y48S6devIycnh7rvvZvr06YwdOxbHcbj22mtjFU7shauXmgwDb3vXuYPJJQ0DFxGRMCO0JsSyogng8IIsdpXX4g9YGG437uxsAnvLY/qeIiLSDU64oqkeALON1jm/5WdvfSUFGflguoNfdnexdQ4gfdjx+EtLCezfFzk2pvA8qvzVfLJnbfu/KCLShpj2qh1qx4ni4mKKi4tjGUIcBSuaDCOUu2unosmxLHB7gwuAWudERCTMH1wTjBgnmo7Iz8R2HHaU1XD0kP64c3Lx790b0/cUEZFuCM9oqg0mmtrada6sbi8ODgUZecF5uN50nMbaLr9V+vHDAaj76iuyis4AYHjOceRnDOIvO1ZyRsGpzebtioh0JGYVTX2O06KiyTAPzm1qyvJjeNKCP6uiSUREQsxQO3WsW+eOGRrcXW7zzgMAeHIHEVCiSUQk6ThNWucMnw+jjXm24R3nCvoFd/s207Jw6g50+b3SjjgSw+ul7suD7XOmYXLhYeez9cA2Nu7b0p2PICJ9lBJN0RIZBh6uaDLaHAaObYEnPfgrqmgSEZEQIxBMNJkx3HUOYGCWjwGZXjbv3A+AOzcXf2UFTltrloiIJE54GHhNXQc7zu0BID89mGgy0vvj1Hd9V1zD7Sb9+OHU/GN9s+PnDDmDLE8mb379TpdfU0T6LiWaoqWtXefabJ3zY4R3hFBFk4iIhAXiU9FkGAbHDOnPpkhFUy5YFoF9+2L6viIi0kVOsKIpUFvb4Y5z2b4BpLmD9xdGWla3Ek0AmSNPwV+6m8bduyPHvC4P3zz8fDZUfMm2qh3del0R6XuUaIqWFommdnedswPBGU2GqRlNIiICQMCyMQLxmdEEcGzhAPZU1lFd58eTOygYg9rnRESSS6iiKVBT28GOc2UMzsiPPDbSs3Dqupdo6jfyFABq1n7W7Pjow84hzZXGW1v/r1uvKyJ9T0yHgfclDuGk0sFd59qsaAr4qd1ejWmZeJRoEhERoMFv4Xbis+scwDFDDs5pGp6bC4C/opx0hvXodQN2gJ01u9l+oITy+gr2NxygNlCL44DLMMnyZjLA15+h/QZzWNZQctNyNFxWRKQ9oe4Hq7YOc2Buq9OO41BaW8aZg0+LHDPSsnAaqnFsG8PsWk2BZ1Ae3qGFVK/9lIEXH9ywKd2dzoWHncuKr99hR9VODssa2s0PJCJ9hRJN0dJqRpOJ00aiqW7XASo/LQUg7eSu7wghIiK9T0OjhSc09NXwxHZGE8BRQ7IwDNi8cz8nnVkIdL+iqcZfyyd71rJ+7wa+qNhIox1sAXQZLvp7s8jwpGNiYDk2m/ZvpdpfE/ndAd7+nJBzPCcPOoFv5I7o+QdLQY5jYx/Yg129F8N0g9uLmT0YI9QGIyJ9V3iea6CmhrTCI1qdP9BYRb1VHxkEDsEZTTgOTkN18Ocu6jfyFCr//CZWXR2u9PTI8X85YjR/KfmQ1zevYPop13Xj04hIX6JEU7S01TrXxmDV2u0Hd4Go3byLft+KR3AiIpLM6hubVjTFPsGQ5nVzeH4mG76uZNIFx+DKzMK/t7zTv+84Dpv2b+UvO/7K2rJ/EHAsctIGcvaQIo7LPprDsw5jUHoOptH62/RGy8+umt1sq9rBF5WbWFf+OX/bvRqfy8vZh5/GmYOKOKp/6xuq3sQJNBDYsgb/VyvZWroRx1/f/AmGC3PQEbgPOwnPiNGYWXltv5CI9G6hLyAC1TWYbbTORXacy2iSaErLAgjOaepGoinzlFFUrlhO7T/WkVV0ZuR4hieDi4+4kGWb32Djvi0cl310l19bRPoOJZqipRMzmhzbpmFvPf2OGUT9zgoadlXEOUgREUlGwda5UEVTjHedCzttWB7LPtjCvuqG4M5znahochyHf+z9Jyu2vs2WA9vIcKdzXuHZnD3kdA7PLOxUG5zX5eHI/odzZP/DuaDwHCzb4qt9m1lT+il/3/EJf9n6N47IOozRhedwesEovK74/D1izXZsvq7cytdfvsOeXetpdAKku9PJG3ESR2QeydCBx2A6No6/Hrv8a6zdX9L46f/Q+Mn/4Dr8ZHynT8KVf0yiP4aIxJMdwMHErq/H1cYw8PCOc81nNAWTS05dFQzs+lumHXMsZr9+1Hz2WbNEE8CFh5/Huzs+YNmmN/j306ar9VlE2qVEU7SEk0rNEk1Os6c07tqJE3DwFWRjVVfRUHYAERGRYOtcaBh4jHedCysakc/SD7aw5osyTsjNpXHnzg6f//WB7fxh45/4at9mctNy+O7xkzh7SBE+V8/idZkuRuQMY0TOMG445/u8sf49/lLyIb/552/5w8Y/ccFh5zDmsHPp783q0fskyt66Cv5v+wd8vGsN+606AFz9vfhcmdTZfpy6bVC3jaz9n3DukDMZfdg5ZB9zBgB29V78/3wP/+fvULv0btxHF+E767uY/fM7eksR6SUc28J23EBjuxVNXtPDAN/ByiUjPVzR1L37DMPlot/IU6he+ymOZWG4XJFzXpeXcUdfzMtf/I41pZ9SNPjUbr2HiPR+SjRFSzjRZIPd0IBhGq0qmhp2BLcE9eT0x7vXR/2eAwT278c9YEC8oxURkSRS77fwhFvnfPGZzTN0UD+GDurHqn/uYWR+ATVrP2tzeGytv44/bl7BByV/I9PTj+8eP4nzhp6J24z+PyEyPOmMPuxcLig8h6/2bebd7R/w5tZ3+N9tf+GswafzL0eMbtYiksz2Neznj5tWsGr3Jxg4jKiu4zv0Y/jIyxh0ZBGmYeI4DkY/Px9uWsvasn/w1tf/x5+3vcsFhWcz7uiL6ZeZi6/oMrwjx9K4dgWNa1cQ2L4W3xmX4znxoi4P+hWRFGMFcOxgoqfNiqaaMgoy8pq1KRsZ2QA4Nfu6/baZp5xK1Ycrqdu0kYzjhzc7d87QM3h/59/4/cY/cdKgb5CmeXIi0gYlmqIllFTas+R3VH3yKXkXfgNPq0TTdjDAOzCLwIDgRbmhZIcSTSIifVxDo4XXjm+iCaBoeB6v/3Ur/qJBOIEA/vJyvPkHq2X+sfcLXtqwhAON1Vx42HmMO+Zi0t1pMY/LMAyOH3gsxw88ltLaMt7e9h5/372GlTs/YuSgb3DRkWM4ZsBRMY+jOxzHYeXOj/jDpj8RsPycW2tzQele8k4qxlt0GUaTVkDDMMjrl8s5Q4o4Z0gR5XUV/Hnbu7y340NWl37K5OPGc9bg0zG86fiKLsMzYjT17/8XDR++jH/zR6SNuQ5XtnZ/Eum17ACOHeyWMDPaqmja02qmneHLBJcHu6b7Izr6nXQShttNzaeftEo0mYbJd4+fxKI1T7Bi69tMOu6Sbr+PiPRe+iosShzbIlAPVavXgGWx79OtrYaBN5bswJ1uYHi9eAYEd3Fo3LE9AdGKiEgyafBbeG0/uN0Y7vh9B3TOiYNxgM+rgu/ZuHsXAH7Lz6tfLOXJz54jw5PBT86YweXHXxqXJFNLBRl5XDXiX/nFubdTfNS3+GrfZhateZJFa57ks7J/YLexw2ui7Kkt59FPnuHlL35HoWcAt+zYz4SKOgZ/5zZ8Z323WZKpLYPSc/j+8MncfuatDM7IZ/GGJfx6/WKqG4M79ZmZuaSPnUXahdOw9+2i9nfzafj0f3BCA4NFpHdxLAvbCt6uuVq0zjVafirq91HQr3krrWEYGP1ycKq7n2gy09JJH3EC1Z9+gtNiFAjAMQOO5OzBRby9/T22V5V0+31EpPdSoilabJuGfcEfs791Ef69VfjrAs2e0rBjB+50wHTjzkjD9Lki7XRN+cvLqPzfP2PV1rQ6JyIivU99Y7B1zkyLbyKnICeDE44cyLvb/QD4d+9mb10lD378JO+VrOSbh5/PT4tmckTWYXGNqy39vVlMOKaYe86byxXDJrK/YT/PrPsv7vn7Iv5a8nf8lj9hsVm2xf9u+wv/8dFDbKvawRUZx3PduvXkZwwi47I7cQ89oUuvV5g5hFtP+xGXHTeO9eUbWLDqYb4+EPxiyjAMPMefR78r7sV9+Ck0fvQatcvuxarseMaWiKQgO4BtBSuaWiaayurKcXDabCc2+w3Eqans0VtnnnIq/j2lNO7a1eb5ycPGk+Xpx4ufv4rfDrT5HBHpu5RoihbHorEK3INyGTDmmwA0Vh686Fq1tQQq9uJJdzBcbjBdePr7gu10TV/Gcdj55OOU/fdLlP7XC3H9CCIikhgNjQF8th/TF/+KoQtPLWRnDThp6ZRt+4L7Vj/Cntq93HDyv3H5sEvxJNmubz6XlwsPP487z/4J1554FV6Xl5e/+B0/+3ABK7a+TY2/Nq7xlFTvYuGaJ/jDxj8xYuCxzLYGc/raD/AecwYZl87FzBrUrdc1DZOLjhjDbUU3YxomD378FB/uXHXwfEY26RfPIO1fpmMfKKX29/NpXPsGjp08FV4i0kO2hRMIJZoym2+IUFpbBkBBRuvNAYzMnB61zgH0G3kKALX/WNf2eU8GV424nJ01u/nT5rd69F4i0vtoRlO02Db+OkgbcTjeoUMx07w0HjiYaGosCVYuuTMAlxtcbjyZHmq2lzTb0aFxx3Yatn2NmZFB9cdr8FdW4hnYjb1JRUQkZdT7LdITUNEEcOqwQfTv56UiPY26LevJPOl4fjTy38hP8qHbLtNFUcEoTs8/hS8rN/G/2/7C65vf5M2v/49zh5zBtw6/gNz0nJi9v98OsGLr27z19f+R4U7n2mMvZcSat3DKt+M941/xjhofla2/D88q5KdFM3n+Hy/xm3/+lm1VO/jXYRMiw9g9x56Fa8hwGt7/Lxr+9iqBLR+TduH1mAMG9/i9RSTB7AB2ADBNzBbDwHfXlAKQn9E6mW32yyFQsw/HsTGM7tUVeHJz8eQXULvhcwZ+u7jN55w06ATOG3oWf972Lkf0P4zT8kd2671EpPdRoilKnIAfqwG8BYMxDANfwUAad5dGztdv3waAJwMwPRimG0+WKzh8dU8p3iHBYZ4169cD8OUlp3Dcax9S+4/1DDj/grh/HhERiZ+GRouBTgAzrfWuQrHmdpkM/UYpu2rqOL7E5senTaeft/XQ2WRlGAbDc45jeM5xlFTv4u1t7/FeyYe8V/Ihp+WP5KIjxnB4VmHU3s9xHP6x95/8fuP/UFpbxlmDT2di1vG4/u95HCtAevFM3EdGd8vvTG8/bjrlev64eQX/u+0v7KjexQ9PupoBvmCFg5mRTdrFMwls/JD6v/6Gmtfm4T1lHN5R4zDc3qjGIiLx41gBLL+Dp3//VrtMllTvJi89F5+r9f/GjX4DwbFw6g5EdqHrjowTTqDq739r9qV4S1cMu5Sd1bt58fNXGZSWwxH9E99qLSKJp9a5KPHvqwIHvEOGAJA2JAerAQL79wHQsH0bZr9+mB6CF2qXG3dG8ILddE5T2ad/pzzbxXLPJmrSTCrWfRz3zyIiIvHV0GjhS0BFk+M4vL5pBV+bH1HqzcFbH8Bb3RDXGKKpMHMIU7/xPe4+Zw7fPOx81pdv4JerHmHRmif4v+0fsK9hf7df23EcvqrczOOf/pqn1r6A4zj8v5HXcaU1EHPFYxhpmfS7bH7Uk0xhLtPFZceN49oTr2J7VQn3r36UbVUH//1gGAaeYecGZzcddTqNHy+j5rd34P9qpdrpRFKVbWE32ngG9G91qqR6J4WZbe86aWYGqzl7MhAcIOOEb2DX11O/dUu7z/G4PNwwciqZnn48+dnz7KhqPi+uwW+xZ18dX++u4ottlZH/t620isqqBgKWrk8ivZEqmqLEX7EPAO/Q4AXfNzgXgLqNX5F1+hk0bN+Or3AohvEVuLzBgeBpDpgmDTu2k3XGmdgNDdhbtrH7GwO469zb+eiD23Ft/GeiPpKIiMRJw/9v777jpKruxo9/bpk+s73vsssuvYMgSFHEAtLUqLETY9c8iZg8iVHjT1M0xfjYojGaGI0JiR2IvWMBREB6Z1lg2d53Z6ffe35/zO7AStuFbeB5v5jXlFvmnMvsOfd+7ylhA6sId2ugSQjB6zvf5OPiz5mUeSqlq1SglODePViSuq7LWXdItCdw0YA5zMw/m6WlX/FV+de8uuO/vLbjDfol9GVM6kgGJvYj05V+1O5t9cEG1lVtYkXZavY0FeO2uLhkwPlMSRtNZNm/Ce5Yhp43Bvu0m1Csji7P27j00aQ7U3l6/T94ePVTzBvyXcamj44tV12JOM6+lciQMwku+zeBT55BXfsW1jFz0PNPjY4TKUnSicGMYIYMbGnxbT4ORAJU+WuYkDH2kJsprmgZbjbXceh2SO3jHBSdyMC3ZTOOfv0Pu16c1cMPR9/An9b8lf9b9RTD1fOoLXVSWe+npiFw1O9x2HTiXVYykpxkpjjJSnaRleIiK9mFzXo8OZAkqafIs41OEq5tBMCSEQ00WVMTQQH/jh24R59CqGQfcZNOA/8OFN1CRFVRFBNrRgbBlvGbdq7+BM0UZIyZRLIjEXf/gdg/WU9peSFZGf16LG+SJElS1wqEDaxmBNVm65bvE0KwuPAdPi7+nKk5k/jugAt4vXgzYvsbNBXtxj26a1rldDeH7uCc3KmckzuV8uZKVleu4+uKdbyyYzEQHcw2y5VBhisdj8WFQ7djCJNAJEB1oJbiplIqfVUIBBnONC4f9B0mZIxFrSwi8PqvEE3VWMd+B+spc495HJRj0ceTzR2n/oi/bniBv2/6N6XecmYXTEc9IA161hC0i39FZNcqQqsXEvj4aRTHf7AMOgO93wTUpJxOGUPqcIQQEAkhgl4wIggjAmYEjDCBkAujMRTt1qdZQLei2JwoqjwtlaQDCSOCGTy4RVNpczkQbcV5KIorOr6rOM4BwTWPB1ufXHxbNpM85/xDrhMxTNbuqGbFlgpqik9B6fcVq23/xaOPYeSAU0hwWEj02HHZdWxWDUVREELgC0Ro8odp8oVo8oWp9wYpr/GxYVcNhimi+VAgJ9VNv6w4CrLiKciKIyPZidqFZZckSZ1D1uidJFzXiGoB3Rkd10LRdazuaIumUEUFIhzGmpGOWQSLGrbwubmTy2wmBdk5+It2AbBn1adkawqnTJgDwNBTzqbmk/VsWP0BWbNloEmSJOlkFQwZWCPBgwZ77SpvFX3AB3uXcHr2RL474AIURWHcyFzKFnoIbtlB5ne6JRndKsOVxuz8c5nV9xyq/bXsqN/FrobdlDdXsKpiLf6IP7augkKCLZ4cTxbjM8YwKnU4ma50TF8DoWX/JrjlUxRPCo65d6JnDuqR/MRZPdw25mZe3raQd/d8zN6mEr439DI8Vvf+fCgqln7j0QvGYezbSGjjh4TWvUVo7ZsocenoWYPR0vujJueixqUdtUWWMCMIfxPC14DwNyB8DZj+xujrQBMi4I0uD0QfGOFD7udw8wIqdg+KMx7FmYDiTECNz0BNzEJLyEKJS0VRZcsG6VvGNDBCESzxbVs07WsqAyDHc+iuc4rdA5qOeZxd5yDafa7+4w8xg8E2N0OaA2E+WrWPJWtLqPeGiHdZmTy4gKEFI1ne9C5b1K+x5lmY0Xcu1kOMI3U4EcOkqt5PabWP4somCksbWbGlkiVro13yPE4LQ/ISo4++SaQldH1LUkmSOk4GmjpJuN6L7jgguq4oWNzQvHcvzRvWAWDPzuC1JjfLm3YQh86LiRZuMW1YVlazu3IHcUWVhPOzsLcMBpvYbwhVqkL9js2EjDDWXjbFtCRJktQ5QsEwFiOE6uj6QNOS4qW8s/tDJmWeyqUDL4i1aslNd7MpLgNncRFCiC5t7dKTFEUh1ZlMqjOZSVmnxj43W1oyqYqKTbO1yb/payC4aiGhDe9BJIxl+LnYTr0IxdL9swQeyKLqXDn4Evp4cnhtx3/5/crHuHbYlfRPyG+znqKo6H1GovcZielvJLL7ayJ71hDetZLw1k/3r2hzodjcKFY7CAFmBGEY0ZZI4WC0ddIhE2KPBonsHhRXAmpyHxS7G8Ueh2J3oWgWUKMz7iqqTny8nfraxmggKhJGRELR4FRr8MrXgFm7j8j2L/Z/h6qjJmaipfVHS48+lLi0k/Z3KkkQnWxIhM2DAk0l3lIcuoNEW8Iht1MUBcWVhGiuO+40OIcMpe79d/Hv3IFr2HD8wQjvryzm/ZV78QcNhucn8b3zchhZkIyqRv8ex4hreW/3x7xV9AE7q/dw44h57Z7JVNdUMpNdZCa7GDsouo0pBOU1PgpLGti6t54te2r5akslACnxdobnJzGiIJnBeYk4bPLyVpJ6A/mX2AmEEITrvTiSDjjZUVVs8dBcZlD9yktYUlMx4h2sjHMwIa4f54c9/KVuFe+HNzMbWPfeiwxpMkg4b9L+XVitqNmZpFRWsaZyPRMyxyKE4L+73uXTfUtJtCdy+cDvMCCxoPszLUmSJHUaIxAdw0Lr4hZNqyvW8uqO/zIqdThXDL64TVcrRVFwDhqMfdkOagp3k9I//wh7OvmoiorTsv/4i5CfSMkmIrtWESlaCaaB3ncstvHfRU3I6MGUtqUoCmfkTCQ/PpdnN/6Lx9Y8zez86UzPO7PN/28r1RGHdciZWIeciRAmZl0ZZn0pZmMFwluLCPkQIX+0K6AanbwEVUfRrSiOOBRHPIozDtURH3ut6B3r8ulM9dBc1XTU9UTIj1nfkr66UozqPYR3fkl4yyfRvNs9aFmD0fuMROszAvU4ZteSpN7I8EUnZ/hm17kSbxk57swjBlpVV+Jxd50DcAwYCJqGb8tm1pPCy5/spMEbYsyAFC48vYA+ae6DtlEVlZn55zCyzyAeW/Ysf1z1BD8YdT358bnHlAZVUaJjNqW4OH1UFkIIymt9bN5dx+bdtSzfXMGStaVoqsKAnHiGFyQzPD+JPmluGYyWpB4iA02dwGhsQIQi6M79LY4URcXqAWtmFqGyUhKmnc3mpt1EVIUJCYNw1NUwr7yBp/v1JaTXMuTTQoSikHjqxDb7jh8wlMhnH/Hvne8wILGAz/Yt54O9SxiePITS5nIWbH2FX0z4XyxyXANJkqQTlz/amUht6X7dFbbW7uAfm1+iX0Jfrh16xSGDEP0mj8O/7A12fLH6hAg0CWEimuuiXblCfkTIByE/IuwHAahqdJCPlqCJouqgW1BUC+iW6OemER1DKOjF9Ddi1pdj1u3DrNkbXWZ1YBl6FtahZ6EmHHo8lN6gjyebn586n/9sfY03dr3LjrpCrhpyCUn2xMNuoygqWlI2WlJ2N6a0/RSrAy2tAC1t/w01YZqY9aUYFTsxyndglGwismslAGpybrTlVv441JQ8eYEpnfDMQGugKZ7WudlMYVLSXM6kzFMPvyHRAcGNih3HnQbVbkfL7cuupav4684U8jM9/PCiEfTLij/qtqMzh3LHqT/iT2v+yuNrn+HmEdcwOGnAcadJUZRYq6ezx+YQMUwKSxrYsKuWDbtqeHVJIa8uKSTebWVEfjLDC5IYlp+Eyy57h0hSd5HRiU4QKov2k9adB4wdoGjRAex+8mMCu4txjRrNmpVP4okYFHhyMBsaiIsY3DTmevbs+AepX24jbuLkg2b6cfTrj/7xh9irG/nl8gcxhMHkrPFcPugittbu4Ml1z7Kk+AvOzTuzG3MsSZIkdSYlGB0fqKtaNO1pLOaZDf8gw5XGzSO+j+UwXbFzBuay1uahccMGQuHvYLX0jjFxhBHBrCvBrN6DUVuM2ViJaKzEbKoCI9Kp36U44lCTcrCOmIHWZyRaRv8TZpBqh27n2mFXMiixP6/ufIMHVjzMhf1nMTlrwiEDiyciRVXRknLQknJgyJkIITBri4kUr8co3kBo3duxMagsBad2y8DnktRVIv4QEA00BVs+q/LXEDJCZLsPPT5TK9WdRGRXHUKYxzxZgSkEH67aR1mjm9OaCrn28jwmjy/o0GDcKY5kfjL2Bzyx9m/8Zf1z/M+o6xmQ2Lljz+qayqDcRAblJnLJmf2oawqyqaiWjUU1rNlRxRcbylAU6JcVz/CCaDe7vAyPHFRckrrQiXHm1Mu1BposrgNOyLXoa9Vuwz3mFAKRIFt8pYzzBlF1K2bL9MLZzhSyr/s5gWm7sPc9+O6xo1+0IL7YMoa34+o4J3cqI1OGIiIRBsflMzx5CO/t+ZjTs0/DrvfsWBGSJElSxwkhUIPRrnNdMRh4pa+KP6/7O26Lix+Mug6n5fADpyqKgnP0GPqs+IIPv9jOrGlDOj09RyOEQHirMcq2Y1TswKjeg1lTHB0nCEC3osaloyZkoeWOir52JYDViWJ1RB8WR7QlkxAIYYIw97dcMqIzn2GEoxdgqhZt7WRzozg8PT7u0vFSFIXJ2RMYnDSABVtf5cVtC/mybDWXDbyQ3Licnk5ep1MUBS05Fy05F0bPQQS8hHevJlL4VWzgczUxC8ugqVgGTkaxH9zNR5J6K9MfLfdsKcmxQFOJt3Ug8CO3sFRciWAaCH8jyjF0K21oDvG3NzezqaiWMwsGotSsY7Sl/piCM/G2OOaPuZlH1vyFp9Y/x/wxN5MX16fD+2mvRI+NKSMzmTIyE9MU7CprZOOuGjbsqmXx50Us+rwIt8MSG9tpWH4Sca72D1guSdLRyUBTJwiVl6HoGqrjgDvErXeLW+60bq/bSVgYDG8OomiW/XdHzQiKbsXRr/8h960np6DFxeGpaOJ/5/wAAP+uQkoefRiEyfSbv8fDkS0sL1vFtD5TuiyPkiRJUtcIRUxsZvSudWe3aGoINvLE2r8B8D+jbyDBdvSuDnnTz2Lvis/Y89FnlI3MJTO567rzQbT7m1lbglG+jYq6Inx7Nu8fwNbiQEvti2X4OWgpfdFS+qLEp3Xo7vy39X51siOJH42+kZUVa3h955s8uOpPjE0fxaz8c0lv56C8JyLF7sY6eCrWwVOjA58XrSK8fSnBL/9D8KtX0PPHYRkyFS1zsGzlJPVqQgiMgAEKWJOSoDbaxXpv4z40RSPTmX7E7RVXtJeEaK6DDgaatuyu5Zk3NuMLRvjejEGcMTyNwnWL8W3ZjOeUsceUH7fVxY9G38DDq5/iybXPcvspt5Dl7vrx7lRVoX92PP2z47nw9AKafCE2FdWyYVctm4pq+HJzBQB5GR5GFCQxPD+ZftlxR9mrJElHIwNNnSBUXoYeZ4/OqtLiwEASwPa6Qiyo5PnD0SBUS4snYUSOeBKsKAr2fv0J7CqMrm+aVP7zHyiqCqjor79Lwaw8Pin+gqk5k06apvGSJEnfFsGQgcOI3qtWXZ0X1PGF/Ty57lmaws3cPubmdgcXbLl5aFk5jK3exJ9eWsNd3x+Px9l5d3pFJIRRVRQdX6c82mqJULTrYMSdhJY+AC1jIFrmQNTEnJb6TjoWiqIwPuMURqQM4b3dn/DpvqWsrljHhIyxzMw/hxRH0tF3cgJTHXFYW8bXMmqLCW/5lPCOZUQKv0RNyMI68jz0ARPbnL9JUq9hhDFCoLkcKNr+XhPb6wrpG5d72C7QrVR39O/b9NaipbZvzD3TFPx3aRFvLN1NRrKTn1w2OjbYt2PAQPxbNh9jZqISbPHcNuZGHl79FH9a+1d+fMqtpDlTjmufHeVxWjltWAanDcvAFILiCi/rd9WwcVcNby/fy5vL9uCw6QzNT6JPqot+2fEUZMbJ2ewkqYPkX0wnCJWVYfXY2p6otHSNE60tmuoLybcmolMeDTS1BqKM8FH37yjoR/OarzGamvCuX0eweC8ZN96CCAWp+MdznGucxtOBT/iiZAVn5Ew86v4kSZKk3iMQNnAZLbPOeTrnLmrQCPHU+ucob67k1lHXdqiLgqIoZFx6KcajD9N/72r+70Ur/3v56GMONpm++mhQqWInRsUOzOo90W5sgJqQhaVgfDSwlDGQtIJ8qqu9x/Q90uE5dAcX9p/FWbmn88GeJXxWspyvKr7m1PQxnJEzkb5xxzYT1IlES+qDNvlqbBMuJbJrJaEN7xL47O8oq17HMvwcrEOmodi6tvWeJHVIJIQRAt2z/3fpC/vZ27SP8/qefdTN1bg0AMz6snZ9XV1TkGf+u4ltxfVMHp7BVdMHYrfuv1R0DRtB1cv/IVRZiTUtrYOZ2S/FkcyPxtzII19Hg00/OeVWEu0Jx7y/46EqCnkZHvIyPMyd1BdfIMzm3XVsLKpld0UTX2+tRBBtGZud6qZ/dhz9suPpmxlHZpITVZWtIiXpcGSg6TiZwSCR2hqcmRn7u8tBm0BSU8hLibeMWc7orCmKbkGxRKcCFpHgN3d5EHtLt7qm1SupfesN7PkFeMZPQASDVP5nAek7qxgybCCv7XyDZEcSCrC0dAUhM8zo7CGcljQBTe0dA7pKkiRJbQVDBk4jgLDZUS3H37IibIR5Zv0/KGrYw/XDr2ZI0sAO78M1fCTuceOZvGoltdZ4/vgfwU8vH3PUMSxaB+02KgtjwSXRVBVdqFnQUvOjg2xnDEBN749q97TZXnZl6lpxVg8XD5jL2bln8P6eJXxZtpIV5avJ9eRwRvZExqaPxnqSt+5RdCuWgZPRB0zCKNlEaN07hL56ldCaN6Otn0bNkuM4Sb2CMMIYQbBl7v897qzfhUAwqB2DaStWJ4o7OTqD5lGsL6zhb29uJhQxuH72ECaPOHj8J/cpp1D18n/wrllN0oyZHcvMN2S60vnhqBt4bM0zsZZNHmvP/9057RbGDU5j3OA0UlM97CmuZVdpIztLGigsbWTFlgqWrC0FwGbRyE13k5fhoW+Gh7wMGXySpAPJQNNxCpW3zDjnssRaMQEora9Ngx31uwDor7WcUGuW/YONho8eaHL0H4A1M4vKf70AikLmLf+DoigodjuuESPxrl7F9y6+n9+vepw/r3sWALfFRZzVw7/XL2JPVhlXDLpInsBLkiT1Qq2BJlzHf5JtmAZ/3/RvttbtYN6QSxmTNuKY95Vx7fVE6mqZW/gpSyI+/rDA5GdXnkKCu+VGSTiIWVuMUb0bs3ovRs0ezNqSWJdxxRGPljEAbdjZ0cBSct7+ulHqUQm2eC4deAFzC2awsvxrPi1Zzr+2vsJrO99gVMpwTkkfxeDE/if1TSpFUdBzhqPnDMeo2Uto7VvRoNPmj7GOmIF1xHTZwknqUSIcxAiBJX5/S9ft9YVYVJ2+8Xnt2oeW1g+jbGt0qI5DlL8Rw2ThZ7t4Z8VeclJd3Hrh8MOOy2dJScWWm4d39crjDjQB5MblcOuoa3li7d94fM0z/GDUdT3WsulwnHYLwwuSGV6QDERn4SurbmZ3eRO7y5vYU97EZ2tL+TBiAjL4JEkHkmd8xylUFo1qW1xa2wJcbe06F2Zb405smpU+2DBQostaAk0iHDjqdyiqSubNt1L7ztu4R41uM3C459TxeFevQt9dws9PvY2ihj1oqsbgxAFYNAsflH3Eoi3vkepI5ty8Mzsv45IkSVKnCIQjuIwAistz9JWPwBQmL2x5ifXVm7h04IWcljnuuPan2mzk/ORnlP31L0xb+xXV3h18/diHjOxvxy5qEU3lIAQAis2NmpKHdcR01ORctLR+KJ4UeYOjl3Pods7ImcTp2RPZUb+LL8tWsbZqI1+Wr8JlcTI6dQQjUoYwMLE/Nu3knZFJS87FcfatGGPmElq9iNDXiwlt/ADryPOiAacTfCZC6cQUaawDAXrC/kkcttcVUhDfF4vavks4y8DJRHZ9hf+DP+GYflt0ls0W1Q1+nl68icLSRs4cncXlZw/AajlycNkz4TSqX3mJ4L5ibDnHP2tc/4R8bhn5ff664QUeWv0kPxh1HdnuI8+m15NURSE71U12qjvW6sswTcpqfOxpCT7tLm88KPiUneoiJ9VNnzQ3OakuslPduB0nd+tRSZKBpuMU3LsXxWJBcyhtu84dMEbTpuqtDEzsj+ozMDRLtDVS60lL6OiBJgBbTh8yb7z5oM9dI0ahWK00rV5F+uAhjP7G3esrRlzA7upS/rvrXQYkFnwrxmGQJEk6kQRDBq6IHzXuyDMIHYkpTP6z9TVWVazlgoKZTM2ZdOz7CjRhVu/BqNqNWb2buKS9aHmgltZhFtVRWwSKruIZ1o+kmTPQMwpQXEkyqHQCUxSFgYn9GJjYj7ARZnPtdr6uXMfKijUsLV2Brmj0TyhgSPJABiUOINudcVJOPqIl5eA494cY1XsIrlpIaNXrhDd9iHXM+ViGnClb5EndKlIV7XasJyYCxIbimFtwXrv3oeeOwnba5QS/fJHwts+xDjkTIQTLNpbz7w+3IwTccsEwxg9pX/0TP/l0aha9Tv3HH5H+ve93OE+HMjhpALefcitPrXuWh1Y9wSUDz2dS5vgTpk7RVJWcVDc5Rwg+lVR5Wb2tks/Wlca2S/TYyEl1k5HkJD3JQXqSk/REB0lxdtQTJO+SdCSyxjxOgT27seX0QRGR/eMysb/rXJm/irpgPTPzz4Zd20GPBqNaA00i0r5A0+GoNlu0+9zXq0i74qqDZudRFIWrBl/Mnq+K+duGf3HzyGvo48mmormSLXU7cOlOBiQWtJnyOmKYhMImoYhBxDBRFQVFUVBVBU1VsOgqVl09YSoASZKk3iwQjBAfaUZPPLYZwCJmhBc2v8TqynXM7Hs20/tOa/e2wghjVO3GKN+OWbkLo3o3wlsTW67EpaGn9SNx6FkkJ/ahuCLCG2+uZXCojPz1Own7PiD79v+V9cFJxKJZGJU6jFGpwwibEQrri9hcs43NtdtYuPMtAOyajb5xuRQk9KUgPo8cd1avGF+ls2gpeTjPux2jYifBr14huOxfhDa8h23cd9D7nSZnQpS6RbCkBABrVhZAbCiO9ozPdCDLiBmEt39BeMsSgnmTeOHdbVh2L+P2uCKSJl1EUr4Ls7kO1ZV41H1pbjdxkybT8MXnJJx9Lrbs7A7m6tD6eLK449TbeGHzS/x762usr9rEhf1nk+k69hswPelQwSchBPXeEPuqvOyr9Eafq5rZVlxHKGzGtrXoKmkJDtJagk5JHhsJHlvsOdFtO2rLM0nqDWSg6TgI0yS4dw+eCRPB2Nr2TpcebWK+sSk6AN/w5CGIbetQrI7o8tZAUztbNB2JZ+ypeFevwr9zB86Bgw5a7rQ4uWnk93h6/T/446onsKpW/IY/tlw1LTgqxuKrTCEYNjBMcdTv1FQFu1XDYdOxW3WcNg27Tcdl1/E4rXicFtwOS+x167PTpssLEkmSpAOEmrxYRQRrSsenePZHAvx90wI212zjwn6zjtpFWpgRjPKdGCWbMMq3Y1Tuis1+qsSlo6X3Rxt2DmpKHlpK3kFj1BTkwVkpeTz2ynrOzujP2E3vUvHC82TccJMs209CFlVncNIABicN4CLmUBeoZ0f9LnY17GFXw27eKfoQQfScId4aR7Ynkxx3FunOVNKcKaQ6UnBbXCfsb0NL749jzp0Y+zYS/OpVAp88g7rubWynXoKWO+qEzZd0YgiVlqKoYEmPBppWVazFbXGR68np0H4URcEyeCrBZQv453OvkmpUcp57HYop4Is/412qAQL7uT/E0veUo+4v5cKLaVq1krJnniLnp3egd9JsqQm2eH44+gaWFH/BW0Uf8sCKhxmXPpop2afRL77vCf/3pigKiR4biR4bI1rGfIL9AaiKWh/ldT4qa/2U1/qoqPOzdW8d/qBx0L5cdp04l5V4l5U4l5U4Z8tzyyPPH8EMRYhzWbHoMjAu9QwZaDoO4aoqTL8fe24eonhtLHgEoOjR15t8JeR6coi3xeEP+VEs0UDT/sHAjz/Q5Bo1GtXhoOHTJW0CTUIISqu8fLZ6H4UlDVB9OkH7BoKKiRlwIerTcHtA5GygOfNL+qZNpMA6EquuYrVo2CwamqYgBJimwBQCwxSEIyb+YKTlYRAIRV83eEOUVjfT5AsTDB9cKEI0QOV2WHA7LXi+EYjKTPOAYbT5zO3Q0eSdQ0mSTmL1+8rJBtwZHZsuutpfw1/WP0+Fr4orB1/M5KwJh1zP9NVjFG8gUryeyL6NEPKDoqKm5GEZehZaxsDoYN2O9l0sjChI5vszB/P3twXJgybTd8UXuIYNJ27S5A6lXzrxJNoTGJ9xCuMzohej/oifPY37KPGWsc9bSom3jK21OzDF/rvzDt1OqiMlFnhKc6YwQOmDJeTEZXH2+otHRVHQ+4xAyxlGZNdKgitfx//eo2jpA7COvwQ98+AbfJLUGUIVVegOUB0eqn21rK/axDm5Uzs8SH+9N8jCnUmcLzTmWd4HC2gZA7GffSvBL18CVcOs2kXwixfQs4bsvyl+GJrHQ+ZNt1L6xGPsvucu4k6bhHPYMJwDB6Paj288M1VROSv3DMZnjOW9PR+zrPQrVlasIdmexLDkwQxLHsTAxH5YT6Ix4w4MQA3OO7hVmT8Yod4bpK6p7aOxOUSDL8Tu8iYam0MEQoe+9nLYWoJSTkubYFT0s7bvbbKllNSJZKDpOAT37AbAlpdHeFeg7WCRFhv1usqeUB0zs6MDsopw4IAWTbbYZ8dLtdmIm3IG9R99gH3adApDdvZ9+TWRndsoUuLZ4u5LYpyd3LR4xqbPJDctOhhdSoIdTVUJGzP4+6Z/s756OflZGmf3PQebZqPCV4U37CXBlkCqI7lDJ4OhsEGTL4zXH6bJF6LJ1/Lc5n2YvZVevL4QzYHIYffV2krqUMGpOJeFRLeNBHe0OaksICVJOtE0lETHbLCmprZ7m/VVm/jX1lcQQvDDUTcwKGn/JBFCmJhVRUT2ridSvB6zqggAxZmAJf9UtNxR6NlDj3oxcSRTRmZS2xjgpc9NbssopvLf/8QxcCCWlPbnQTrxOXRHrMVTq4gZoSZQR5Wvmkp/dfTZV01Rwx5WV6yLtoDa3Lq9nVRHMqmOFFIdyaQ4U2Lv46zuXhWEUhQVS78J6PljCW/9nNDXi/G/8Tu0PiOxjb0QLa2gp5MonUSEEISqarF5QLE5+bDwCwBOzz6t3fsIRww+/rqE/y4tIhwxKRhxBeNTmrH1n4ASn46iKDjOvgUAo7IQ36LfENr4AbZTzj/qvl3DhpN79/+jevFCGj5bQv1HH6DoOp6Jk0i56JLjbuXktrq4eMBc5hTM4OvK9ayr2sCXZSv5rGQZuqqTH5dL37hc+sb1IS+uDwm2+F5VXnQmh03HYdMPOxtgq1DYiAWfFE2juKyBxuYQjc1hGnwhGptD7KtqpnF3Hb7goa+7bFatTfAp3mUlwW2NddlLaAmIyR4qUnvIQNNx8G3fhmKzYc3KJhz2tzlpVyx2PktwoqBwWkZLoCnkQ3FEx0JSFBV023EHmiKGya7SRrYkjqCv9jnFD/4OVVEZagQQKIxEcElWgL4334yiH/q/26JZuGH41by8YzFLipfySfEXB63TL74vF/afTUE7p1O1WjSS4zWS49t3ZyNimNidNnYX1x0QkAq3ee31hais81NY0kCTP9w62VEbDpsevSvgtsaCTwluW+xOQYLbRrzLKqcZlSSpVzBME6WiDKEoWDOzjrq+PxJg4c63WFq6gj7uLK4bfhVpzlREsJlIS6slo3gDItAEioKa1g/rqRej9xmJmpzbqSeGcyf3pbSmmefXj+UW8y3Kn/0rOT+7U45f8y2nqzrpzlTSnQcHHcNmhFp/LSGrj53l+6jyV1Plq2Fv0z7WVG1o0xLKrtnIdGWQ5U4n05VBtjuDTFdGj48Fpag61qHTsAycRGjjR4TWvolv0a/RMgZiGTkDPXeM/BuQjlukthYzEMKSoRJRVD7atZRhyYNJdhx9LL+IYbJ8Uzn//aKImsYgwwuSuOqcgaQnOQ+7jZbWDz1vDKH172Idfg6K9fDrtrL1ySX7h/MxwyH8O3bgXb2KxqWf07xuLenfvw73yNEdyfKhv0OzMjFzHBMzxxE2wuxsiI4Zt7O+iI+LP8cQ0VY8Tt1Bkj2RZHsiSfZEkuwJJNkTSXWmkO5MRW/nLH0nMqtFIyXBQUqCg9RUDwXphy8rwxGTJl+IhuZQSzAqRKMvGpRqbAlKVdT62F5cj9cfPvi7dDV6ndUShPI4rDjteuzhskeHS3HadWwWDV1T0XUVXVPQNRWLpsprsW+Bk/+vrosIIfBt2oBz0GAUxYxO8XxAi6YmI8jKOAdj9WSSHdFmkMLfhJq0v1+1YnVEuzB08Hsr6vxsKqplU1EtW/bWEQwZKAqMHnUBp1WvI95lI2PqFNwjRhL64mP2LvgP5c/rZFx/+DE0NFXjikEXcXrWaayv3gRAiiOZOKuHUm8ZH+z9lIdX/5mpOZM4r+/ZnX6ip2sqiXF2Imnt268pBL5AhIbmEPXeIPVNwZbnEHXe6OvSPXU0eEOY34hIqYpCvNvaEpDaH51PjAWmrLgdFlx2iywEJUnqUqXVPpIDtZiJKajWw3cFMIXJV+Vfs6jwbbyhZs7JncqsuMGwbQW+4vUYFTtACBSbG63PCPTcUeg5w1HsXXdRrigK184awkONAd4OjGP2jqXUvPs2KbPmdNl3Sic2i6qT7kojNdVDH0vfNssM06A2UB8NPvlrKG+upKy5nLWVG1ka+Sq2nsfiJtOdQbYrg0x3OlmuDDJd6dj14+uy01GKbsM2ehbWodMIb/uM0Ib3Cbz/J5S4dKwjzsXSf+JBY5xJUns1r18LgC3VyaqKtTQEGrlq0CVH3MYXCPPF+jLeW1lMXVOQvhkerp01hKF92zfRhHXshURev4/Qhvexjb2w3WlVLVZcQ4fhGjqMhDPPouzZZyh9/FESp59H8s3Xtns/R2PRLAxJGsiQpIEAhI0w+7xl7GksptxXSW2gjkp/NVvrdhA0QrHtNEUj3ZlKtjuTbHcmWe4Mku2J2DQbdt2GTbOdlLNoHolFV6MDjccdvdwMRwzqvSHqYtdaQepauvLVe0PsLmvC6w/jD0Y4+ii/+ylK9PpPVRW0b0w6BdHhVlo/a/OsKOi6is2iYbNGh3qJPVreWy1qyxjCLcEvm46j5VnXvl3/1z1JBpqOUaCoiHBVFYkzZwMKWOyocdHxNUxh8vrON4kocBbRFkxCCIS/EdWxf3Y3xRGH6W844vcIIais97O9uJ4dxQ1s2VNHTWO0FVRqgp2JwzIY1jeJIXkJOO0WYEab7ftcegnNzUFqFr2OnpBI6iWXHvH7cjxZ5Hja3lUfnDSASVkTWFz4Dkv2LeXzki/p48nGqlkJRAJYNQsp9mRGpQ5jaPKgbrlroCotYz05LGSnHP5EzjQFTb7Q/gLxgMKxrilIaU0zm/fUHnKgPQCnTcfl0GOBJ5fDgs2ikRjvwIwYsQLNbj24gLPoWmyGPquuYtFVdE3O1idJ0n67y+rJ8VdiHzTqkMsN02BVxVo+2LuEsuYK8izxXGukkb30HULBVwBQU/Kwjp6DnjsKNbWgW1tT2CwaP7l0NI+/orDVW8yAha/z/KYwFa40nHYL/bLjGZybQJ80N3arjqKAqiq4ZSBf+gZN1Uh1JpPqTG7zuRCCxpCX0uYyyrzllDZXUOotZ2npCkLm/jvtibYEMlxpZLjSyHSmk+FKJ8OVhsty9JYZx0OxOrCOmIFl2DlEilYTWv8uwaX/IvjlS+h5Y9D7n4aeMwzwdGk6pJOLd+0adLeV2rQEXtmxmAHJ+bEAy4Eihsm2vfUs21jOqm2VhCMmg/okcM15gxhR0LFhL7SUPPS+YwmteRMteyiK3Y1ZU4yeNwZFb9+YSLY+fcj9xb1Uvfwf6t5/lw1FhSRddhX2vn3bnY72smgW8uNzyY/PbfO5EILmiC8aeGquoqS5nBJvGTvri1hZseaQ+7KqFmyaDatmxaZZcVmcZMan4sBFgi2eRHs8ibYEEmzxJ8TYcp3JomukJjhITThyd3tTCALBCM2BCL5ABF8gTHMgQjhiEjZMIoZJxBAtz9GHYUTH/zVNgSGizxaLTrMvFBsb2GxdbgpM08QwBcFQtJtgMGwQDJsEwwahkNGuQJdVV2NBp2gQyoLTHu2eeGBgymnXcTnaDtsig1Qd06URgTfeeIOnnnqKSCTCNddcw1VXXdVm+ZYtW/jFL35Bc3Mz48aN41e/+hX6Ybp39Tb1H32AYrXiGTceRbfivvox0K34wj5e2/kmKyvWMD2gkxKOtlgSgSYwIyjOAwJNrkREc23sfeusA3Xrl+DZ/jZfOU7nw+oMGrzRqLzLrjMoN5FZE/MY1jeRtMToyZMwDZQjDAyYNHsukbo66t59GxEKkTRrDnpCQofya9dtXDboQqbmTGJp6QrKmisIGkHcFhchM8SG6s18Wb6KOKuH0zLHMTJlGJmutG6/w/hNqqoQ77YR77bRN+Pw6wVCkVggqqE5hNcfLRy9/jDN/jDeQPS5st4fLczCJoEORu5btQaf9FgAKhqQ2h+U0g5Yph6wrO16rYErTVXQVBVNU1peK2hatHmqpqoETGho8LVZT29Zp3X9b1OFKXWdYy3zS0tL+dnPfkZNTQ35+fk89NBDuFwuGhsb+elPf0pxcTFJSUk8+uijpHZgHKMTQdXm7aSbQRJH7w80RYwIRVVbWbhhFV9Wb8crImSETS6vaWKktxLNlYSWNxo9eyha9lBUZ0LPZYBol+X/vWIM60ckE/r7o8wqfIeNE7/LPkcqyzeVs2RNyUHbaKpCepKTATnx9M+OJy/Dc9STWOnbSVEU4m0e4m2eNhfapjCpDdRR4i2nvLmCsuZKyn0VfFGygvABASin7iDZkUSKPYlkRxLJ9iRSHEkk2ROJt3mwa/ZOqQMVVcPSbzx6wamYVUWEty8lXPglkV1fgWbFLBiJkTIALXMwalI2yknelae76oNQKMQvfvELNm7ciN1u56GHHqJfv349lOvOEa6uwrd1C7YcK8/FmVhVOz+ZdCNms4oQgqqGANv21LFlTx3rC2vwBSM4bBpTRmRyxqgs8jKOPahpP+Namhffj/+/v4OWs1ytz0gcM25r85sVRhgiIbA6osOBHEC1WEi/6ns4Bw6m+sV/sff+X2LPL8A5bBj2vL5Y0jKwpKWiWrpmQG9FUXBbXLEZ+sYdsMwX9lHiLach2EDQCBEwggSMIEEjSNAIEYyECBpBvOFmNlftoNZf36ZLL0S7B8db40iwxRFviyPBFh979ljcOCx2nLoDh+7Artk6PHj7iUpVFJx2S0vDh2OXmuqhqqqpw9sJIQhF9gedAmEDf7Al6HXAsz8QwRcM4wtEJ7Py+kNU1vli6xxp9nWnTW8zm3rrc+t4VrkNQcxwdLY/l12OY6UIcaiRbo5fRUUFV1xxBa+//jpWq5XLL7+chx9+mP799w9YOmfOHO6//35Gjx7N3XffzfDhw7nyyivb/R01NV7MI/wYuoq6czNbf/8giefNIuXi71IfbGBr7Q621u1gY/UWAkaQGXlncfaOzdBUi+u79xMp3YL/zT8gzrqN5sRB1DUFsa1/jeSKFbyeeTtVDSFKqpuxBmu5M/4NbEqEsNB4J/U6svL7MjAnnswUF+o3frAi2Ezzy3dhP+Na9LwxB6W19Y9VmCZVL/6b+o8/bMmEiqLr0YfVij03D8egwTgGDMKel4eidaxQNEyDLbXb+axkOZtrtsWmO9ZVHYtqwarqxFk9JDuSSXUkk+xIIsEWh8L+/MTHO2ls9GMKk6ARotpfGxu/oTZQhyEM7LqtZeyHNDKcaaS7os9OS/deoKSmeqisbIwVaMFQyyMcLdjCYZNQxIhG8SMmoYhJ+DDvQy2fRV/vX+eb60WMrvut7w9QfSMY1SaAdeggVduAVbSCV5UDX4OiKqgosdYMrcsUhZZ1o+u53Xb8vuD+ZaqCwgH7+OZ+D9hHm2Vq9JcVfb1//dbv2v+9LdP+6tH+4q2BPP2A94cLxB1rRdjVVFUhObn7xzA5njL/5ptv5vzzz2f27Nk8+eST+Hw+fvazn/HrX/+ajIwMbrrpJhYtWsSSJUt49NFHO5SunqonjkQIExHwYvrq+erBP5NYX0XzlRPYZ9RQHG6kSIsQVBV0UzCkOcg44WZofAF6ekF0RqD4jF578hKurWXfQ38gUltD0twL8Ew7h9LGMCVVzbE7kxHDpKE5xN4KLztLGvAfMChposdGcrydtJa7p2kJDtKSHOSkuLFZ218vmaagtilATUOAmsYANY1BQmEDl91CXoaHgqy4gyaPaO1m/c16tqNMISiu8LKrrJHG5hBJcTb6ZcWTkew85L57a1lyPExTxMrXQ+nKPJvCpC5QT1lzBeW+Sqr9tdT4a6kO1FDrryMi2rZgtrScn8RZ44izeYizenBZnLELRadux2mJvm59b9ft7epqI8wIRuk2Iru/RpRtIlJXHl2g6qiJmaiJOWjJfVATMlGciSjOeBRH3BFvHnaGrq4nurM+ePbZZ9mzZw+//vWvWblyJX/84x95+eWXO5Te3lRPmOEwJU8+RvO2rbw6w025x8oZnouxGxns2FPLngpvbMwcj9PCyIJkThmYyrD8JKydNCGO6W8ktO6daNdPYRBatRB94BTsZ1yHaKwg8MULGGVbo92149NxTLv5oMHwRTiA6a0hOTObXYvexrvmawK7CokNsKoo6IlJWNPTsaSlY0lJwZKcghYXh2p3oDrsqHY7qt2BYrV2eZ0nhDjoO1JTPVRUNtAYaqIu0EBdsJ6GYCP1wQbqgw0HvG5sE9z+Jrtmayk/7LFnm2bFolrQNR1LSwAvYhoYZoSIMNAUDbtuw67ZsOt2XLozVj7FWT3YdTu6okWvmcwQISP6CLY8HG6Nytp6QEFXNTRF239NplmwqhasmhWLZsGqWrGovSco0pN1YmuwqrVFlrd1zOBvTm7lazu51TeHaYHotVWcy0pcbHB1yyFn+otzWXu8S9+hjnln1BNdFmhauHAhK1eu5Le//S0ATz75ZHR2nB/+EICSkhKuueYaPvwwGvhYtWoVjz/+OC+88EK7v6OurrnDFUNpdTMbi2qIbdbyLERL97aWz0QsVNJy8imgWikiZc82Rq4vpC7eyYeTsvBZmjGJtjjShA1nOANXIB816GFYcA2nGOv42hxMiqghW63m/xpn4xfRCP5gSwmXuVaw1cwjaIknzgZZkb1YDB/alOsQS59DdcSh9RkBrV0hDANhRBDBZkTYD+EAZlUR9rN/cMgZT5KT3dTUeGPvQ+Vl+HfuwPQ2I8wIImxgBgME9u4hUl0FgGK1oSckoGhaNBClaSi6BcWio1gsKLoVxWZBUTUMrxd7fj5xp02KfYcv4mdXwx7qAnX4IwHCZoSwEaYp7KUuUE9doD42eN/ReKxuEu0JJNoS0FUdfyRAjb+Gan9tm32oioamqGiqGnutKioqKigKpjAwhIkhDIQw0VQdixItbC2qBV3VsKpWdE1HPSD4RUuh27boVbBZNYIt04geqlxW2mxx8D6U2GetgQ8lml5FRVNUFEWNBk+i4Rkg+ptsbUYqTIFhRn+bQgiEaHltCszW368QCBN0i0YwGIn+5oXAhJb1ROy3rQidVNEfIbRo81TDbGmmCpEDmqq2LosIMA2BYZrRNBlif1rM6J+VIHqxIYTY/90H/J21ro9oWa9dv4jupyhEA0/q/oEMLbqG3aaDKdr0G9c1NdrPXDvgs5Ygnaq1BL9QaPnX8qzs/w0p+588TiunDU1D7WA3KFVVSEzs/nFBjrXMf/bZZ5kwYQJfffUVuq5TVlbG1VdfzUcffcRZZ53FggULyMzMJBKJMH78eFasWIHF0v67ZcdSTxgVOzDKtgPR32vUAfsQh3gtTDAjBIwwq0Q9PmFgxModE8OMvg5h4lcEflUhb3eQcZt9rB7iZP0ABwqQJDRyNQ8FrgxG54/BtGW0ndH0BGA2N1P1+qv4Nm1A0S3Y++ajJSaACZb0NBLOOHP/ukJQWeunvNZHbWMAb9CgtKqJmsYgjd5g7KgrCqQmOEhPdOKw6disWmwcBwSEDTN6wtccHTOi3hs86K6kqiixk0FNVchKceO064QiBvVNQRp9IUxD4LBHZzNN8tiIc1vRVRVFjf5ttfb2aym6Yt8vBATC0Zaxeyu8+AIHX3TYrTq5aS5SE53oanRcCkUFl9OG3x88aJ/7v0fs/57Y8/4yM7bOAT/V2PKWj4UQNPnC1La03A1HDFRVOeAEOPqwHHiiq7R5ipVT4oB0mC1lfsQ0YwPK1jeHaGoOoagKCS4r8S2Tc7gcFkwhOG1oBkP7p7Y5N+kupjDxhpupC9RTH2zAG26mKdyMN+TFG2qOvg95CUQCHK1War1os2gWdEXD0nI+YVH02MWjqkS7y6uoOGxWQn4/ir8JQn6UoA8l2IwaCUaP8QE/dkWzoGg6KDpoGqgaQ1UPaYoDRVNB1UHRUONSsQyYdIRUHlpX1xPdWR9cd911zJ8/n3Hjou1WzjnnHF544QWyso4+uUKrY6knNu2upbiy5Tcs9v8dipYTILPlP9Q84A+79fwn0nLz0G/4qdMLCRMirqGBfiXVZNQ2EecPs3y4i/psnUDJQMqb+mCxaKTFO8hKcZKT6qYgO560hM5pjXc0oY3vE97wPmp8Oqa3FjQLlgGTUCw2wtuXIQKN6AWnolidCNNE+OowSraAEUJ1elDSh2DtPwE86YSrqghXVxGpriZUXU2kpppwdTWm33fY71c0HdXpRLXbowVRa5BXOfD8SYk9FEVBtdmiASqrFdVqQ7Vao+e+wSBmMIDp92E0+zD9PkxfMyISiW5jiwa3dI8be2I8Ed0WbbElBAITEQxjhkMQDmGGw4hQGEXXEBYLEYuCoWtEdJWIruBP8dDQN5WgEcQfCURbT4UDBIwAITNMxIwQNiNEzAgKClpLQEhTVEwEwUiQkBE6alnUGRQUdFVHV/WWdKjRtKg6uqJGl8XKNku0/DugrDvwd6gcdN1z+FcCE5PWawgTU5hYbTrNvgBhEcEQBhEzjGGahM0whjBbjlf02i92s5rotZPWWua2fKZ+45oqViYr0eur6PlZNLhniGigzzDNA747gikEKrRcp2ktz0rselNRFDAVDBM0VScQiBAxwIgIIgaEI0QbIIQF4YjADOkYNdkg2v7tapqCzaJj01VsNg2brmGzqGgt41ipqtpyTUHsfeu1hqooh72uyEl1Mzz/yOO0fTNeAJ1TT3RZu93Kyso2XRzS0tJYv379YZenpqZSUVHRoe84lswnJ7sZMSi9w9tFjWjz7tyjrh+dgvTAnsNPHLTOteQe9Fnr1z3WznQdWZtoZPIAGDbg8Ct3xvfhpk/6ydW9RZKkIzvWMr+urg632x3rNn1gXXDgNrqu43a7qa2tJT29/WX4MVWSyWNg6MEtRNuro5Ocjzv6KieWZDept/9Pu1dPTfEwrAuTI/VOPdHyEiCVOPLJ7JHv/rbozvrgUPsqLy/vUKDpWOqJMzrt93voQGGvqhemXhR9HMqUuR3bV1YKMOS4kyRJUufqijq5y9pomabZJrr5zSaJR1suSZIknTiOtcw/VNl/uLpACNHhFl6SJElS9+rO+uCb28h6QpIkqXfospI4IyODqqqq2PuqqirS0tIOu7y6urrNckmSJOnEcaxlflJSEk1NTRiGcdB2aWlpVFdXAxCJRGhubiahgxMZSJIkSd2rO+uD9PR0KisrD9qXJEmS1LO6LNA0adIkli9fTm1tLX6/n/fff58zzjgjtjw7Oxubzcbq1asBWLx4cZvlkiRJ0onjWMt8i8XCuHHjePvttwFYtGhRbLupU6eyaNEiAN5++23GjRvXofGZJEmSpO7XnfXB1KlTWbx4MRAd68lms3Wo25wkSZLUNbpsMHCITm369NNPEw6HueSSS7jxxhu58cYbue222xgxYgRbt27lnnvuwev1MmzYMH73u99htXbNVJeSJElS1zrWMr+kpIQ777yTmpoaMjMzefjhh4mPj6e+vp4777yT4uJiPB4PDz30EDk5OT2dTUmSJOkouqs+CAaD3HvvvWzcuBGr1cr999/PsGFy1DdJkqSe1qWBJkmSJEmSJEmSJEmSJOnbQ46WJ0mSJEmSJEmSJEmSJHUKGWiSJEmSJEmSJEmSJEmSOoUMNEmSJEmSJEmSJEmSJEmdQgaaJEmSJEmSJEmSJEmSpE4hA02SJEmSJEmSJEmSJElSp5CBpg544403mDVrFtOnT2fBggU9nZwjmjdvHrNnz+aCCy7gggsuYN26dT2dpIN4vV7mzJnDvn37AFi2bBlz585l+vTpPPLIIz2cura+mda77rqL6dOnx47vBx980MMpjHriiSeYPXs2s2fP5sEHHwR673E9VFp763EFeOyxx5g1axazZ8/mueeeA3rvsZVObCdSXdNRHSmjtmzZwkUXXcSMGTP4xS9+QSQS6alkd4o//OEP3HnnncDJn+ePP/6Yiy66iJkzZ3L//fcDJ3+eARYvXhz7ff/hD38Avh35lrpOb6wPOqMcLy0t5aqrruK8887j1ltvpbm5uVvzcDzlcU+lvTPK1Z5Ke2eUjd2Z9vZeo3Y0rY2Njdx0003MnDmTq666iqqqqi5P+0svvcScOXOYO3cud911F6FQqPvSLqR2KS8vF9OmTRN1dXWiublZzJ07V+zYsaOnk3VIpmmKKVOmiHA43NNJOay1a9eKOXPmiGHDhoni4mLh9/vF1KlTxd69e0U4HBbXXXedWLJkSU8nUwhxcFqFEGLOnDmioqKih1PW1tKlS8Vll10mgsGgCIVC4nvf+5544403euVxPVRa33///V55XIUQYsWKFeLyyy8X4XBY+P1+MW3aNLFly5ZeeWylE9uJVNd0VEfLqNmzZ4s1a9YIIYS46667xIIFC3ow9cdn2bJlYsKECeLnP//5Eeu7kyHPe/fuFVOmTBFlZWUiFAqJK664QixZsuSkzrMQQvh8PnHqqaeKmpoaEQ6HxSWXXCI++uijkz7fUtfpjfVBZ5XjN910k3jzzTeFEEI88cQT4sEHH+y2PBxvedwTae+scrUn0t5ZZWN3pb0j16gdTeuvfvUr8fTTTwshhFi4cKGYP39+l6Z9165d4txzzxVNTU3CNE1xxx13iOeee67b0i5bNLXTsmXLOO2000hISMDpdDJjxgzefffdnk7WIe3atQuA6667jvPPP59//etfPZyig7388svcd999pKWlAbB+/Xry8vLo06cPuq4zd+7cXnN8v5lWv99PaWkpd999N3PnzuXxxx/HNM0eTiWkpqZy5513YrVasVgs9OvXj927d/fK43qotJaWlvbK4wowfvx4XnjhBXRdp6amBsMwaGxs7JXHVjqxnUh1TUd1pIwqKSkhEAgwevRoAC666KIT9jjU19fzyCOPcMsttwCHr+9Oljx/8MEHzJo1i4yMDCwWC4888ggOh+OkzjOAYRiYponf7ycSiRCJRHC73Sd9vqWu0xvrg84ox8PhMCtXrmTGjBltPu8Ox1se91TaO6Nc7am0d0bZ2J1pb+816rGkdcmSJcydOxeAOXPm8NlnnxEOh7ss7Varlfvuuw+3242iKAwcOJDS0tJuS7sMNLVTZWUlqampsfdpaWlUVFT0YIoOr7GxkYkTJ/Lkk0/y/PPP8+KLL7J06dKeTlYbDzzwAOPGjYu9783H95tpra6u5rTTTuO3v/0tL7/8MqtWreLVV1/twRRGDRgwIFZg7N69m3feeQdFUXrlcT1UWk8//fReeVxbWSwWHn/8cWbPns3EiRN79W9WOnGdzL+rjpRR3zwOqampJ+xxuPfee/nxj39MXFwccPj/45Mlz3v27MEwDG655RYuuOAC/v3vf5/0eQZwu93Mnz+fmTNnMnXqVLKzs78V+Za6Tm+sDzqjHK+rq8PtdqPrepvPu8Pxlsc9lfbOKFd7Ku2dUTZ2Z9rbe416LGk9cBtd13G73dTW1nZZ2rOzs5k8eTIAtbW1LFiwgLPPPrvb0i4DTe1kmiaKosTeCyHavO9NxowZw4MPPojH4yEpKYlLLrmETz/9tKeTdUQn0vHt06cPTz75JGlpaTgcDubNm9erju+OHTu47rrruOOOO+jTp0+vPq4HprWgoKBXH1eA2267jeXLl1NWVsbu3bt79bGVTkwnUll4rNpTRp0sx+GVV14hMzOTiRMnxj47XN5OljwbhsHy5cv57W9/y0svvcT69espLi4+qfMMsHXrVl577TU++eQTPv/8c1RVPWw9cTLlW+o6vfl3cjzl+KHy0R356ozyuKfS3hnlak+lvTPKxp5KO3Ttb0QIgap2fTimoqKCa665hosvvpgJEyZ0W9r140/6t0NGRgarVq2Kva+qqoo1S+ttVq1aRTgcjhWkQohYZLK3ysjIaDOoWG8+vtu2bWP37t2xZoW96fiuXr2a2267jbvvvpvZs2fz1Vdf9drj+s209ubjWlhYSCgUYsiQITgcDqZPn867776LpmmxdXrTsZVOXCdSXXMs2ltGfbNOqK6uPiGPw9tvv01VVRUXXHABDQ0N+Hw+SkpKDll2nCx5TklJYeLEiSQlJQFwzjnnHLa8PFnyDPDFF18wceJEkpOTgWiXg2efffakz7fUdXprfXC85XhSUhJNTU0YhoGmad2Wr84oj3sq7Z1RrvZU2jujbOyptMPhr1GPJa1paWlUV1eTkZFBJBKhubmZhISELk1/YWEhN9xwA/PmzeO66647ZJ66Ku2yRVM7TZo0ieXLl1NbW4vf7+f999/njDPO6OlkHVJTUxMPPvggwWAQr9fLwoULOffcc3s6WUc0atQoioqKYk1D33zzzV57fIUQ/Pa3v6WhoYFwOMxLL73UK45vWVkZ//M//8NDDz3E7Nmzgd57XA+V1t56XAH27dvHPffcQygUIhQK8dFHH3H55Zf3ymMrndhOpLqmozpSRmVnZ2Oz2Vi9ejUQnbHmRDwOzz33HG+++SaLFy/mtttu46yzzuJvf/vbSZ3nadOm8cUXX9DY2IhhGHz++eecd955J3WeAQYPHsyyZcvw+XwIIfj4449P+t+31LV6Y33QGeW4xWJh3LhxvP322wAsWrSoW/LVGeVxT6W9M8rVnkp7Z5SNPZV26Nzf99SpU1m0aBEQDXyOGzcOi8XSZWn3er1cf/31zJ8/PxZkArot7YoQQnRBvk5Kb7zxBk8//TThcJhLLrmEG2+8saeTdFiPPvoo7733HqZpcuWVV3LNNdf0dJIO6ayzzuKFF14gJyeH5cuX87vf/Y5gMMjUqVO56667ek0TYWib1gULFrBgwQIikQjTp0/npz/9aU8nj/vvv5/XXnuN3Nzc2GeXX345ffv27XXH9XBpNU2z1x3XVn/6059455130DSN6dOn86Mf/ajX/2alE9OJVNd0REfLqK1bt3LPPffg9XoZNmwYv/vd77BarT2Yg+Pz+uuv89VXX/H73//+sGXHyZLnV199leeff55wOMzkyZO55557WLFixUmdZ4BnnnmG119/HYvFwogRI7jvvvv4+uuvT/p8S12nt9UHnVWOl5SUcOedd1JTU0NmZiYPP/ww8fHx3ZaP4ymPeyrtnVGu9lTaO6Ns7O60t+cataNpra+v584776S4uBiPx8NDDz1ETk5Ol6X9ww8/5KGHHqJfv35tls2fP79b0i4DTZIkSZIkSZIkSZIkSVKnkF3nJEmSJEmSJEmSJEmSpE4hA02SJEmSJEmSJEmSJElSp5CBJkmSJEmSJEmSJEmSJKlTyECTJEmSJEmSJEmSJEmS1ClkoEmSJEmSJEmSJEmSJEnqFDLQJJ307rvvPs466yweeeSRnk6KJEmSJEmSJElSu1133XXU1tb2dDIkqUP0nk6AJHW1l156iSVLlpCRkdHTSZEkSZIkSZIkSWq3pUuX9nQSJKnDZKBJOqldeeWVCCG48cYb2blzJzNmzGDbtm385Cc/YeTIkfz617+mrKyMcDjM7NmzueWWWwBYsGAB//jHP/B4PEyZMoU33niDjz/+mD/96U/U1dVx7733ArR539TUxAMPPMD27dsJh8NMnDiRO+64A13XGTFiBDfddBNLly6lsrKSG264gSuvvBKAp59+moULF6LrOnl5efz+97/ntttuY+bMmVx66aUA/PnPf6a+vp677767Zw6kJEnSt8yKFSt44IEHcDqdNDc3M3/+fJ5++mnC4TB2u52f//znjBkzhkgkwh//+EeWLFmCpmmMGTOG++67D6vVylNPPcX777+PaZpkZ2dz3333kZ6ezrx58xg9ejRff/01ZWVlTJw4kd/85jeoqsonn3zCo48+immaOJ1OfvWrX/HJJ5+wc+dO/u///g+AVatWcf/997No0aKePUiSJEnfIitWrODhhx8mMzOToqIiHA4HN910E//85z8pKipi+vTp3H333bz00kv885//RFVVUlJS+H//7/+Rn5/PnXfeidvtZtu2bZSXlzNo0CD+8Ic/4HK5KCws5IEHHqC+vh7DMJg3bx6XXHIJd911FwDXXHMNzzzzDFu3buXpp58mFApRW1vLhRdeyO23335QnTV8+HDS0tL48Y9/DMDixYt5//33efLJJ3vyEErfJkKSTnIDBw4UNTU1Ytq0aeKJJ56IfT5v3jzx0UcfCSGECAQCYt68eeKtt94SGzZsEBMnThSVlZXCNE1xzz33iGnTpgkhhHj88cfFr371q9g+Dnx/5513ihdeeEEIIUQkEhE//elPxTPPPBNLwz//+U8hhBAbNmwQw4cPF4FAQHz44Ydi+vTpor6+XgghxG9/+1vx5z//WXzwwQfi4osvFkIIYRiGmDZtmigsLOzKwyRJkiQd4MsvvxSDBw8W+/btE0VFRWLOnDmitrZWCCHE9u3bxeTJk0Vzc7P4xz/+Ia666irh9/uFYRhi/vz5YuHChWLhwoXi9ttvF+FwWAghxIsvvihuuOEGIYQQV199tbjtttuEYRiiqalJTJkyRSxfvlxUVVWJsWPHik2bNgkhhHjvvffE9ddfL6qrq8Upp5wi6urqhBBC/OxnPxP/+c9/uv+gSJIkfYt9+eWXYsiQIbEy+vrrrxeXXXaZCAaDoqamRgwbNkz897//Feecc46oqakRQgjx2muviZkzZwrTNMXPf/7z2PqhUEhceOGF4tVXXxXhcFjMmjVLbNy4UQghRGNjo5g5c6ZYs2aNEGL/tYxpmuLqq68WRUVFQgghysvLxZAhQ0RNTU2bOksIITZv3iwmT54cq4OuvPJK8dlnn3Xj0ZK+7WSLJulbZdy4cQD4fD5WrlxJQ0MDjz32WOyzrVu3UlZWxuTJk0lNTQWiraLa02R1yZIlbNiwgVdffRWAQCDQZvnZZ58NwLBhwwiFQvh8PpYvX855551HfHw8QOyuhWEYPPDAA2zdupWKigpycnIoKCjohCMgSZIktVdmZibZ2dksWLCAyspKvv/978eWKYrC3r17WbZsGRdccAF2ux2ARx99FID58+ezYcMGLr74YgBM08Tv98e2nzZtGqqq4na7ycvLo6Ghga+//poBAwYwdOhQAKZPn8706dMBOPPMM1m8eDEXXnghX3zxBffdd183HAFJkiTpQDk5ObEyOjc3F4/Hg9VqJSkpCZfLxXvvvcesWbNISkoC4KKLLuKBBx5g3759AJx++ulYrVYABg4cSENDA7t372bv3r1tei4EAgE2b97M6NGjY58pisJf/vIXlixZwptvvklhYSFCiFjd0lpnAQwZMoScnByWLFlCfn4+lZWVTJkypcuPjyS1koEm6VvF6XQC0RN+IQQvvvgiDocDgNraWmw2G4sWLUIIEdvGYrHEXiuK0mZZOByOvTZNk8cee4x+/foB0NjYiKIoseU2my22DwAhBJqmtVmnsbGRxsZGcnJyuOyyy3j11VeprKzk8ssv77RjIEmSJLXPgXXGxIkTY0EkgLKyMtLS0tD1tqdS1dXVmKaJaZptukmHQiEaGhpi67UGpmB/3aLreps6QQjBtm3bGDx4MFdddRW//OUv0XWd6dOn43K5uiLLkiRJ0hG0BolafbMOOLAMbyWEIBKJAIcu+w3DwOPxsHjx4tiy6upqPB5Pm/34fD6+853vcM455zBu3DguvvhiPvzww9i1SWud1eqqq67itddeo2/fvlx66aWHTJskdRU565z0reR2uxk9ejTPPfccEA3wXHHFFXz00UdMmjSJ5cuXU1paCsDrr78e2y4xMZFNmzYhhMDr9fLJJ5/Elk2ZMoXnn38eIQShUIhbb72Vf/3rX0dMx6RJk/jggw/wer1AdMyn559/HoDvfve7fPjhh2zatIlzzz23M7MvSZIkdcDEiRNZunQphYWFAHz66aecf/75BAIBJk6cyJtvvkkoFMI0TX75y1/y1ltvMWXKFF599dVY+f7YY49xxx13HPF7Ro0aRWFhITt27ADgo48+4mc/+xkAp5xyCqqq8uyzz8qbD5IkSb3U+PHjefvtt2OzxL322mskJCSQl5d32G3y8/Ox2+2xQFNZWRlz5sxh48aNAGiaRiQSYc+ePXi9Xm6//XbOOussVqxYEat7DmXGjBls2bKF9957L9a6VpK6i2zRJH1rPfTQQ/zmN79h7ty5hEIh5syZw/nnnw/Avffey80334zFYiE/Pz+2zfnnn8/nn3/O9OnTSU9PZ/z48bG7CL/4xS944IEHmDt3LuFwmEmTJnHDDTccMQ1Tp05l586dXHHFFQD079+f3/zmNwAkJyczfPhw+vXr16ZVlSRJktS9+vfvz69//Wt+8pOfxFoePfXUU7hcLi6//HJKSkq46KKLEEIwfvx45s2bh6qqVFRUxO4iZ2Zm8vvf//6I35OSksJDDz3Ez3/+cwzDwO1288gjj8SWX3TRRbz99tsMHjy4q7MsSZIkHYMJEyagqirXXHMNpmmSlJTE008/jaoevn2H1Wrlz3/+Mw888AB/+9vfiEQizJ8/n7FjxwJw3nnnMW/ePB577DHOPPNMZs6cidVqZeDAgfTv3589e/Yc1NKqdb8zZsyguro61pVPkrqLIg7sByRJ0kE2bNjA/Pnz+fjjj7v1e2tra7nkkktYsGABmZmZ3frdkiRJUu8SiUT44Q9/yPnnn8+sWbN6OjmSJElSL+fz+bj66qu5995724z1JEndQXadk6Re6OWXX2bWrFlcf/31MsgkSZL0Lbdz504mTpxIYmIi5513Xk8nR5IkSerlPv/8c84880xOP/10GWSSeoRs0SRJkiRJkiRJkiRJkiR1CtmiSZIkSZIkSZIkSZIkSeoUMtAkSZIkSZIkSZIkSZIkdQoZaJIkSZIkSZIkSZIkSZI6hQw0SZIkSZIkSZIkSZIkSZ1CBpokSZIkSZIkSZIkSZKkTvH/AUihdooAl8+yAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(1, 3, figsize=(20, 6))\n",
    "for a in ax:\n",
    "    a.set_ylabel(\"density\")\n",
    "    \n",
    "for c in range(nclusters):\n",
    "    rfm_c =  rfm.query(f\"cluster == {c}\")\n",
    "    label = f\"Cluster {c}\"\n",
    "\n",
    "    sns.distplot(rfm_c.query(\"frequency < 30\")[\"frequency\"], kde=True, ax=ax[0], \n",
    "                 label=label, hist=False, norm_hist=True)\n",
    "\n",
    "    sns.distplot(rfm_c[\"recency\"], kde=True, ax=ax[1], label=label, hist=False, \n",
    "                 norm_hist=True)\n",
    "\n",
    "    sns.distplot(rfm_c.query(\"monetary < 10000\")[\"monetary\"], kde=True, ax=ax[2], \n",
    "                 label=label, hist=False, norm_hist=True)\n",
    "    \n",
    "for a in ax:\n",
    "    a.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>frequency</th>\n",
       "      <th>recency</th>\n",
       "      <th>monetary</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customer ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12346.0</th>\n",
       "      <td>3</td>\n",
       "      <td>326</td>\n",
       "      <td>77352.96</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12347.0</th>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>4921.53</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12348.0</th>\n",
       "      <td>5</td>\n",
       "      <td>75</td>\n",
       "      <td>2019.40</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12349.0</th>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>4428.69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12350.0</th>\n",
       "      <td>1</td>\n",
       "      <td>310</td>\n",
       "      <td>334.40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18283.0</th>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>2664.90</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18284.0</th>\n",
       "      <td>1</td>\n",
       "      <td>432</td>\n",
       "      <td>461.68</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18285.0</th>\n",
       "      <td>1</td>\n",
       "      <td>661</td>\n",
       "      <td>427.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18286.0</th>\n",
       "      <td>2</td>\n",
       "      <td>477</td>\n",
       "      <td>1296.43</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18287.0</th>\n",
       "      <td>7</td>\n",
       "      <td>43</td>\n",
       "      <td>4182.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5878 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             frequency  recency  monetary  cluster\n",
       "Customer ID                                       \n",
       "12346.0              3      326  77352.96        2\n",
       "12347.0              8        2   4921.53        0\n",
       "12348.0              5       75   2019.40        2\n",
       "12349.0              4       19   4428.69        0\n",
       "12350.0              1      310    334.40        1\n",
       "...                ...      ...       ...      ...\n",
       "18283.0             22        4   2664.90        0\n",
       "18284.0              1      432    461.68        1\n",
       "18285.0              1      661    427.00        1\n",
       "18286.0              2      477   1296.43        2\n",
       "18287.0              7       43   4182.99        0\n",
       "\n",
       "[5878 rows x 4 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>frequency</th>\n",
       "      <th>recency</th>\n",
       "      <th>monetary</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customer ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12346.0</th>\n",
       "      <td>3</td>\n",
       "      <td>326</td>\n",
       "      <td>77352.96</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12347.0</th>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>4921.53</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12348.0</th>\n",
       "      <td>5</td>\n",
       "      <td>75</td>\n",
       "      <td>2019.40</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12349.0</th>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>4428.69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12350.0</th>\n",
       "      <td>1</td>\n",
       "      <td>310</td>\n",
       "      <td>334.40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18283.0</th>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>2664.90</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18284.0</th>\n",
       "      <td>1</td>\n",
       "      <td>432</td>\n",
       "      <td>461.68</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18285.0</th>\n",
       "      <td>1</td>\n",
       "      <td>661</td>\n",
       "      <td>427.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18286.0</th>\n",
       "      <td>2</td>\n",
       "      <td>477</td>\n",
       "      <td>1296.43</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18287.0</th>\n",
       "      <td>7</td>\n",
       "      <td>43</td>\n",
       "      <td>4182.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5878 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             frequency  recency  monetary  cluster\n",
       "Customer ID                                       \n",
       "12346.0              3      326  77352.96        2\n",
       "12347.0              8        2   4921.53        0\n",
       "12348.0              5       75   2019.40        2\n",
       "12349.0              4       19   4428.69        0\n",
       "12350.0              1      310    334.40        1\n",
       "...                ...      ...       ...      ...\n",
       "18283.0             22        4   2664.90        0\n",
       "18284.0              1      432    461.68        1\n",
       "18285.0              1      661    427.00        1\n",
       "18286.0              2      477   1296.43        2\n",
       "18287.0              7       43   4182.99        0\n",
       "\n",
       "[5878 rows x 4 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_dist_transformed_cluster(transformation=lambda x: x, columns=[\"frequency\", \"recency\", \"monetary\"]):\n",
    "\n",
    "    f, ax = plt.subplots(1, 3, figsize=(20, 6))\n",
    "    \n",
    "    bins = 30\n",
    "    \n",
    "    ranges = {}\n",
    "    \n",
    "    rfm_ = pd.DataFrame(scaler.transform(transformation(rfm[columns])), columns=columns)\n",
    "    rfm_[\"cluster\"] = rfm[\"cluster\"].values\n",
    "    \n",
    "    \n",
    "    for i, a in enumerate(ax):\n",
    "        a.set_ylabel(\"# of occurences\")\n",
    "        col = rfm_.columns[i]\n",
    "        x_min, y_min = min(rfm_[col]), max(rfm_[col])\n",
    "        ranges[col] = (x_min, y_min)\n",
    "        a.set_xlim(x_min, y_min)\n",
    "        a.set_xlabel(col)\n",
    "        \n",
    "    for c in range(nclusters):\n",
    "        rfm_c =  rfm_.query(f\"cluster == {c}\")\n",
    "        label = f\"Cluster {c}\"\n",
    "    \n",
    "        for i, a in enumerate(ax):\n",
    "            col = rfm_c.columns[i]\n",
    "            toplot = rfm_c[col]\n",
    "            sns.distplot(toplot, kde=True, ax=a, label=label, hist=False)#, bins=bins, hist_kws=dict(range=ranges[col]))\n",
    "            a.legend()\n",
    "        \n",
    "plot_dist_transformed_cluster(lambda x: np.array([stats.boxcox(x.values[:,i])[0] for i in range(3)]).T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Churn prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to predict wether a customer is active or inactive (alive or dead) in a non-contractual business we can use the RFM. The BG/NBD models, described [here](http://brucehardie.com/papers/018/fader_et_al_mksc_05.pdf), can compute a \"customer lifetime value\". This is the first I work with this so I don't know the details yet, but the model and other utilities are implemented in the nice [lifetimes package](https://lifetimes.readthedocs.io/en/latest/index.html).\n",
    "\n",
    "We will build a slightly different RFM dataset using utilities from the lifetimes packages where:\n",
    "\n",
    "* frequency represents the number of repeat purchases the customer has made. This means that it’s one less than the total number of purchases. This is actually slightly wrong. It’s the count of time periods the customer had a purchase in. So if using days as units, then it’s the count of days the customer had a purchase on.\n",
    "* T represents the age of the customer in whatever time units chosen (weekly, in the above dataset). This is equal to the duration between a customer’s first purchase and the end of the period under study.\n",
    "* recency represents the age of the customer when they made their most recent purchases. This is equal to the duration between a customer’s first purchase and their latest purchase. (Thus if they have made only 1 purchase, the recency is 0.)\n",
    "* monetary_value represents the average value of a given customer’s purchases. This is equal to the sum of all a customer’s purchases divided by the total number of purchases. Note that the denominator here is different than the frequency described above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lifetimes.utils import summary_data_from_transaction_data\n",
    "rfml = summary_data_from_transaction_data(df.dropna(), \"Customer ID\", \"InvoiceDate\", \"TotalPrice\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>frequency</th>\n",
       "      <th>recency</th>\n",
       "      <th>T</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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12346.0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>647.0</td>\n",
       "      <td>38662.955000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12347.0</th>\n",
       "      <td>7.0</td>\n",
       "      <td>402.0</td>\n",
       "      <td>404.0</td>\n",
       "      <td>615.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12348.0</th>\n",
       "      <td>4.0</td>\n",
       "      <td>363.0</td>\n",
       "      <td>438.0</td>\n",
       "      <td>449.310000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12349.0</th>\n",
       "      <td>3.0</td>\n",
       "      <td>571.0</td>\n",
       "      <td>589.0</td>\n",
       "      <td>1120.056667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12350.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>310.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             frequency  recency      T  monetary_value\n",
       "Customer ID                                           \n",
       "12346.0            2.0    322.0  647.0    38662.955000\n",
       "12347.0            7.0    402.0  404.0      615.714286\n",
       "12348.0            4.0    363.0  438.0      449.310000\n",
       "12349.0            3.0    571.0  589.0     1120.056667\n",
       "12350.0            0.0      0.0  310.0        0.000000"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfml.head()"
   ]
  },
  {
   "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></th>\n",
       "      <th>frequency</th>\n",
       "      <th>recency</th>\n",
       "      <th>T</th>\n",
       "      <th>monetary_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5878.000000</td>\n",
       "      <td>5878.000000</td>\n",
       "      <td>5878.000000</td>\n",
       "      <td>5878.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.631337</td>\n",
       "      <td>273.380401</td>\n",
       "      <td>474.247193</td>\n",
       "      <td>317.689877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.999831</td>\n",
       "      <td>258.959670</td>\n",
       "      <td>223.078296</td>\n",
       "      <td>2306.280368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>313.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>221.000000</td>\n",
       "      <td>530.000000</td>\n",
       "      <td>207.117500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>512.000000</td>\n",
       "      <td>668.000000</td>\n",
       "      <td>368.045909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>254.000000</td>\n",
       "      <td>738.000000</td>\n",
       "      <td>738.000000</td>\n",
       "      <td>168469.600000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         frequency      recency            T  monetary_value\n",
       "count  5878.000000  5878.000000  5878.000000     5878.000000\n",
       "mean      4.631337   273.380401   474.247193      317.689877\n",
       "std       9.999831   258.959670   223.078296     2306.280368\n",
       "min       0.000000     0.000000     0.000000        0.000000\n",
       "25%       0.000000     0.000000   313.000000        0.000000\n",
       "50%       2.000000   221.000000   530.000000      207.117500\n",
       "75%       5.000000   512.000000   668.000000      368.045909\n",
       "max     254.000000   738.000000   738.000000   168469.600000"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfml.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will fit the model, we get a summary on the fitted parameters of the model and their uncertainty."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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>coef</th>\n",
       "      <th>se(coef)</th>\n",
       "      <th>lower 95% bound</th>\n",
       "      <th>upper 95% bound</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>r</th>\n",
       "      <td>0.663390</td>\n",
       "      <td>0.015844</td>\n",
       "      <td>0.632336</td>\n",
       "      <td>0.694443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha</th>\n",
       "      <td>63.235424</td>\n",
       "      <td>1.936315</td>\n",
       "      <td>59.440248</td>\n",
       "      <td>67.030601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.108172</td>\n",
       "      <td>0.015347</td>\n",
       "      <td>0.078091</td>\n",
       "      <td>0.138252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.401727</td>\n",
       "      <td>0.520377</td>\n",
       "      <td>1.381789</td>\n",
       "      <td>3.421666</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            coef  se(coef)  lower 95% bound  upper 95% bound\n",
       "r       0.663390  0.015844         0.632336         0.694443\n",
       "alpha  63.235424  1.936315        59.440248        67.030601\n",
       "a       0.108172  0.015347         0.078091         0.138252\n",
       "b       2.401727  0.520377         1.381789         3.421666"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from lifetimes import BetaGeoFitter\n",
    "\n",
    "bgf = BetaGeoFitter(penalizer_coef=0.0)\n",
    "bgf.fit(rfml[\"frequency\"], rfml[\"recency\"], rfml[\"T\"])\n",
    "\n",
    "bgf.summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the fitted model we can do many nice things for instance the expected number of transactions an artificial customer is to make in the next time period (1 day), given his or her recency (age at last purchase) and frequency (the number of repeat transactions he or she has made)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZAAAAFZCAYAAAC2WHmwAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAABra0lEQVR4nO2deVxUVf/HP3eGTUJSDJAHzSfTtNxwedxScQUURhS1ENwyTc0lKVEUFAFXxD3xSespU8rQREX94Zq2oOVSLj0u+aSmqIipCMo69/z+GOZy7+yDDgzD9+1rXs4999xzz5k73M98v99zvpdjjDEQBEEQhJnIqroDBEEQRPWEBIQgCIKoECQgBEEQRIUgASEIgiAqBAkIQRAEUSFIQAiCIIgKYRUC0qxZMygUCgQHB0tet27dqrQ+bNu2DSkpKWYfFxQUhJ9//lmrPCoqCgEBAXj69KmkvG3bts9tXDt27MCECROeS1umnq9nz5549913tfbpuobR0dFG2xw7diwePHhgie4CAHr37g1/f38EBwdj0KBBCAwMxKJFi8Dz/HNpv7KvgTEMXSNzuXPnDrp37673+ugb+4QJE7Bjxw6DbZ8/fx7Tpk0DAJw7dw7z5s0zu3+xsbHo3bs3Vq5cKZRdvXpV+P717NkT7du3F7a/+OILBAcH4/Hjx2afi9CNXVV3QM2mTZvg5uZWZec/ffo0mjZt+lzbzMrKwsKFC7Fw4cLn2m5VsXPnTkRERCA4OFjn/opcw59++ul5dM0gSUlJaNWqFQCguLgYI0eOxFdffYURI0ZY/NyVjbFrZE47a9aswb17955Tz6S0atUKa9asAaC66WdnZ5vdxjfffIOjR4+ifv36QlmTJk2wa9cuACqB279/Pz755BNh/5gxY56t44QEqxEQfaSlpWHdunXYtWsXOI7DkCFDMGHCBHh5eSEpKQn/+Mc/8Oeff8LJyQlLlizBq6++iuLiYiQlJeHkyZNQKpV44403EBMTAxcXF1y7dg3z5s3DgwcPIJPJMGnSJNjb2+PIkSP46aef4OTkhPDwcKxfvx4HDhwAz/Pw9vZGbGwsPD09cfXqVcyZMwcFBQVo3LixloUhZtSoUdi1axf2798Pf39/yb5bt25BoVDg119/1dresWOHcO7bt2/D09MTb731FrZs2YLr16/jnXfewdixYwEAOTk5ePfdd3Hv3j14e3sjISEB7u7uyMvLw8KFC3HlyhWUlJSgS5cumDlzJuzs7NCyZUv06dMHly5dktxcASAvLw9xcXG4dOkSOI5D9+7d8eGHHyIxMRHnz5/HrVu38PDhQ7P+EJs1a4bjx48L4qLeXrZsGQBg9OjR2LBhA8LDw7F69WqhP71798bq1atRt25dhIeH49VXX0VWVhY2b96MW7duISkpCQUFBZDJZJgyZQp69epltC8ODg5o3749/vzzT6PXYPv27SgoKICLiws2b96MTz75BGlpabCzs0OjRo2wZMkS4Rq89957uHPnDuRyOZYvX45XX30Vv/32G5YtW4bi4mLk5OSga9euWLRoEUpLS5GQkIAzZ87A3t4eDRo0wOLFi/HCCy/gzJkzOseVk5ODWbNm4eHDhwAAX19fTJ8+XTK2RYsWSa7RkCFDdF5LY9+B7OxsHDp0CJ999hkCAgJMvs666N27NwYPHozjx4/jzp07CA4OxvTp0/Hzzz8jISEBGzduxJo1a5CXl4fZs2dj8eLFkuP/+OMPxMfH49GjR+A4DmPHjsWgQYMQFhYGxhjGjx+P2NhYdOjQwaT+qL97R48eNflvbNu2bfj666/B8zzq1KmDuXPn4tVXX32mz8VmYFbAa6+9xoKCgtjAgQOF1/vvvy/s//DDD1lsbCybPXs2i4mJYYwxduLECda8eXN28uRJxhhjX331FRs8eDBjjLG1a9eyJUuWMJ7nGWOMLV++nMXGxjLGGBs0aBDbsmULY4yx27dvsz59+rC8vDw2a9Ys9umnnzLGGEtLS2PTp09nJSUljDHGtm7dysaNG8cYYyw4OJilpqYyxhg7deoUa9asGTtx4oTWmNTt/fDDD6xjx47s9u3bjDHGfHx82M2bN9nNmzeZj4+PUF+8/e2337L27duz27dvM6VSyQYMGMCmTp3KlEolu3jxImvVqhVTKpXs22+/ZT4+Puz69evCOD/44APGGGNRUVHsyy+/ZIwxVlpaymbMmME2bNggfN5paWk6r8XMmTNZQkIC43meFRUVsbFjx7JPPvmEMcbYiBEj2P/93/+ZfA3v378v7Pv7778lddXb4ve9evVi586dE+qpt2/evMlee+014Vo/evSI+fn5sZs3bzLGGLt79y7r0aMHy8rK0uqXZpt3795lAQEBLCMjw+g1+Ne//sXy8vIYY4wdOnSI+fn5sUePHjHGGFu0aBFLTk5m3377LevQoYNwDRISEtjs2bMZY4xFREQI3438/HzWqVMndv78eXby5EkWEBAgfD8TExPZ6dOnDY7r448/ZnPnzmWMMfbkyRM2ffp09vjxY63xiq+RoWtp6DsgRvPaifn222/Ze++9p1X+3nvvsW+//ZYxpvr8lyxZIoynVatW7K+//mInTpxggYGBBtspKSlhffr0Yfv37xeO7969Oztz5ozRvulrV32MqX9jP//8MwsLC2NPnz5ljDH2ww8/sICAAIOfWU3CaiwQQ+6PuLg4BAcHw8nJSeJbbd68ufDLY8iQIYiPj8fDhw9x9OhR5OXlITMzEwBQUlKCevXq4dGjR7h06RKGDRsGAPDy8sKhQ4e0zvfdd9/h/PnzGDJkCACA53kUFBTg4cOHuHz5MgYNGgQAaN++vVG3V7du3TB48GBERkbiyy+/NPnzaNWqFby8vAAADRo0QLdu3SCTydCwYUMUFRWhoKAAANC1a1c0atQIADB06FAMHToUAHD06FGcP38e27dvBwAUFhZK2tf3i+3777/H119/DY7j4ODggNDQUGzatAnvvfee0T5b0g1pZ2cHHx8fAMBvv/2GnJwcTJ48WdjPcRwuX76Mf/zjH1rHzpgxA05OTuB5Hvb29hg2bBj8/f2NxqKaNWsGFxcXAMDx48cREBCAF198EQAwe/ZsACo3SevWrYVr8Prrr+PgwYMAgCVLluD777/Hv//9b/z5558oKirC06dP0bx5c8jlcgwbNgzdunWDv78/WrdujWPHjukdV/fu3QUrp2vXrvjoo49Qu3Ztg/03di1N/dWuD5lMdwiV53nJvj59+gAAPD09Ua9ePeTm5prU/vXr11FUVAQ/Pz/heD8/P/zwww9o27btM/UdMO1v7OjRo7hx4wZCQ0OF4x4/foxHjx6hTp06z9yH6o7VCIgh/v77bxQVFaG4uBj37t1Dw4YNAQByuVyrrlwuB8/zmDNnDnx9fQEAT548QVFREezsVMPlOE6o/+eff2rddHiex7hx4xAWFgZA5TcXf+mZKH2Yuk1DfPjhh3j77bfx73//WyjjOE7STklJieQYBwcHyba+84g/A57nhXo8z2P16tWCqf348WPJuJ2dnXW2x/O8pB7P8ygtLTU4PnMoLi42uF/8mYjrOjg4CGNTKpV49dVXsW3bNmF/dna2XvHSdNGoMXYNxJ+RXC6XfC6PHz8WgrHiayNuc8SIEWjWrBm6d++O/v374+zZs2CMwdXVFbt27cKZM2dw4sQJTJ8+He+++y68vLz0jsve3h6HDx/G8ePHceLECQwbNgwbN25Ey5YtdY4ZMH4t9X0HTKVu3bp49OiRVvnff/+NunXrCtuOjo7Ce83P3BBKpVLSf0D1/Xhe30dT/sZ4nkdwcDAiIyOF7Xv37gk/JGo6VjELyxAlJSX48MMP8cEHH2DKlCmIiIgQ/tAvXbqES5cuAVAF1Nq2bQtXV1d069YNKSkpKC4uBs/zmDt3LlasWAEXFxe0aNECO3fuBKCaZTJ8+HDk5eVBLpcLX8xu3bph+/btyM/PBwCsXr0aM2fORN26ddGiRQvhD/z333/HlStXjI7BwcEBy5cvx3/+8x/BEnB1dUVJSQmuXr0KANi7d2+FPp+ff/4Zt2/fBgBs3boVPXr0EMbwxRdfgDGG4uJiTJo0CVu2bDHaXrdu3bBlyxbhuNTUVHTt2rVCfVPj5uaG8+fPAwD27Nkj2Sf+3N3c3HDhwgVhXDk5OTrb8/HxwY0bN3Dy5EkAwMWLF+Hv7292INaca9C1a1ccPHhQ+E6sXbsWX3zxhd76jx8/xvnz5zFjxgz4+fnh7t27+Ouvv8DzPL777juMGTMGbdu2xdSpUzFo0CBcuHDB4LiSkpKQnJyMvn37Ijo6Gk2aNMEff/xhcHyWuJZi2rZtixs3buDUqVNC2c8//4ysrCyzLATxd0BM48aNYWdnhwMHDgBQien+/fuf6xiM0a1bN+zdu1eYTPD1119j9OjRlXZ+a8dqLJDRo0drmcQffvghTpw4gZdeeklwOx06dAgrV66Er68vXnrpJaxatQpZWVlwc3NDYmIiAOD999/H0qVLMXjwYCiVSrz++uuIiooCACxfvhxxcXHYvHkzOI7DwoUL4e7ujh49eghB0fHjxyM7OxtvvfUWOI6Dl5eXsG/FihWYPXs2tm7dipdffhmNGzc2aXyNGzfGrFmzEBMTAwCoXbs2IiMjMX78eLi5uVU4WPnaa69hzpw5uH//Pho3boz4+HgAQHR0NBYuXAiFQoGSkhJ07doV48aNM9peTEwMFixYIBzXvXt3TJw4sUJ9E7cZHx8PV1dXdO3aFe7u7sK+gIAAjBw5EmvXrsWMGTMwf/58fPPNN2jRogVatGihsz03NzesWbMGiYmJKCoqAmMMiYmJaNCggVn9Muca+Pr64urVqxg+fDgA1WyfhIQE4eamiaurK9577z0MHjwYzs7O8PT0RLt27XDjxg0MGzYM33//PYKCguDs7IwXX3wRCQkJBsc1evRoREVFISgoCA4ODmjWrBkCAwMNjs8S11JzjB9//DGWL1+OJ0+eQKlUws3NDZ988glcXV1NbsfHxwfr1q3DlClT8PHHHwvl9vb2SE5OxoIFC7B27VoolUpMnjwZnTt3fm5jMEa3bt0wfvx4jB07FhzHwcXFBR9//DE4jsP58+cRExMjzPqqiXDMVHvSylDP4tD8RUsQBFFZTJ06FWvXrq3qblQZVu/CIgiCsEays7OFiTY1lWprgRAEQRBVC1kgBEEQRIWwmiC6tfI8Yi3NmjXDa6+9Jpkk0LJlS5tJcWIJ2rZti/T0dK3AeFRUFH766Sdhyi7P83j69ClCQ0Mxfvz4qujqc2fHjh1Ys2YNXn31VXz22WcVakOpVOLLL79Eeno6lEolSkpK0KtXL3zwwQda01dNZdu2bSguLkZ4eHiFjidsDxKQSqKqc33ZEmPGjJEkC7x9+zYGDBiA3r1720SKieeRz2r+/PnIzc3Fpk2bULt2bTx9+hQzZsxAdHS0kELGXCyRL46o3pCAmMDTp08xbdo03LhxA66uroiPj4enpyd8fX2RmpqKV155BYDqxjZixAj07dvX5LY1cxI5Oztj4cKFePToEZRKJUaOHCmsLl+9ejXS09NRt25ddOjQARcuXMDmzZsRFRWFpk2bCjdV8XZ2djbi4+Nx584dlJSUIDAwEBMnTsStW7cwZswY+Pr64uzZs3j8+DEiIyPRr18/lJaWYtmyZTh69Cjkcjnatm2L2NhYKBQKzJs3D2+++SYA1VTh1157TWte/L///W8cPnwYhYWFKCgowKxZs9CvXz+sXbsWWVlZyMnJQVZWFjw9PbFs2TJ4eHjg1KlTSEhIAMdxaNWqlVnZcu/evQvGmLBqXF8+KQA681nVrl1bb76jqKgouLi44PLly7h79y6aNWuGpUuX4oUXXsDZs2exYMECFBQUwN7eHjNnzkROTg6++uorbN26FYBK3N566y0cOXJE8su/IjnHeJ7HokWLcPbsWTx58gSMMSxYsADt27eXfB63bt1Ceno6fvzxR+EzcXZ2RlxcHM6cOaP1HdHcVvff3t4ejo6OiI+Px7Vr1yT54t566y0sWbIEx48fh1wuR+vWrTF79my4uLigd+/eCAoKwokTJ5Cbm4tx48bhzJkz+P3332FnZ4f169fD09PT4HdTM/eZh4eHyd8HohKp5NQp1Q51zq3Tp08zxlR5sYYOHcoYY2zBggVs6dKljDHGbty4wXx9fVlpaalWG8byRKlzEpWUlLABAwawCxcuMMYYe/z4Mevfvz/79ddf2f79+9mAAQNYXl4eKy4uZuPGjWMjRoxgjDFJHi/N7ZEjR7LDhw8zxhgrLCxkI0eOZHv37hXySx05coQxxlhGRgbr2bMnY4yxTZs2sfDwcFZQUMCUSiX74IMPWFpaGvv888/ZtGnTGGOM5eXlsc6dO7Pc3FzJWG/dusVGjhzJCgoKGGOM7dmzhwUFBTHGGFuzZo2Qe4wxxiZMmMBWr17NioqKWNeuXVlmZiZjjLH09HT22muvCTmhxMyaNYt169aNDRw4kPXu3Zt17NiRTZo0iR0/fpwxZjhPlr58VobyHc2aNYu9/fbbrKioiBUXF7NBgwax7du3s+LiYvbmm2+y7777jjHG2Pnz51lQUBArKipiXbp0YVeuXGGMMbZq1SqWlJSkNY6K5Bw7c+aMkK+JMcY++eQTNmHCBK16GRkZbMiQIVrlmp+jru9MaWkpa9GiBcvOzmaMqfLCbd26VeuY1atXsylTprDi4mKmVCpZVFSUkKurV69ebNGiRYwxxvbu3cuaN2/OLl68yBhj7P3332fr169njBn/bqpznxHWC1kgJtCsWTO0a9cOADB48GDMnz8feXl5CAsLw4gRIxAREYFvvvkGQ4cO1ZleBTDswlLnJLp+/Tr++usvzJkzR9hXWFiI//73v7h69Sr69esn/KJ8++23sWnTJoP9fvr0KU6ePInc3FysXr1aKLt06RJat24Ne3t7Id3LG2+8IaSlyMzMFHKPAcCqVasAqFZXr1u3Dg8ePEBGRgZ69uyptWDM29sbiYmJSE9Px40bN4Rfy2o6duwojOGNN95Abm4urly5Ajs7O3Tp0gWA6hkrhp4PoXZhPX36FBEREXBwcECnTp0AGM6TpS+fVWJiot58RwDQvXt3wXp47bXXhD7LZDL07NkTgMqSTE9PBwAMGzYM27Ztw6xZs5CWlobNmzdrjaEiOcfatm2LF198EVu3bsXNmzfx888/44UXXtCqJ5PJKvy8E7lcjoCAAISGhqJnz57o1q2b8B3R7H9ERATs7e0BACNHjpR85ur8VQ0bNsRLL72E5s2bAwBefvll5ObmGv1uinOfEdYLCYgJaK6Q5zgOdnZ2eOWVV9CsWTMcPnwYe/bsQWpqaoXaV+ckUiqVqF27tmRl6/3791G7dm2sWrVKkkNI/Yer7g/TkdOJ53kwxrB161bUqlULAPDgwQM4Ojri4cOHsLe3F8YmzjmkmRPo/v374HkeHh4eCAgIwO7du5Geno7Y2Fitsfz+++94//33MWbMGLz55pv417/+hbi4OGG/WpQ0+800ZpObkmPM2dkZiYmJGDBgAL744gu88847BvNknThxQmc+K2P5jnT1WTM3FgBcuXIFjRs3RmhoKIYOHYqOHTuiadOmQu42MRXJOXb06FEsXLgQ77zzDvr06YPGjRtj9+7dWvVat26NP//8E/n5+YJYqz+HuXPnYs2aNQbzgCUlJeHKlSvIzMzEhg0bsGvXLuEmb6j/4jbE7jrxd1Vc39B3U5z7jLBeaBqvCVy+fBkXL14EoMq51b59e+FLHxYWhsTERLRu3Rqenp7PdJ5XXnkFTk5OgoDcuXMHQUFBuHDhAnr27ImMjAzk5uaC53khnxegSmqnziGVnZ2NX375BQDg4uICHx8ffP755wBUN8zhw4fj8OHDBvvRpUsX7NmzR8glNn/+fCFPVHh4OL788kswxtC6dWutY0+ePImWLVvinXfeQceOHXH48GEolUqD52vWrBkYYzh27BgA4PDhwyZnbH3xxRcxa9YsrFmzBtnZ2QbzSenLZ1WRfEeNGzcGx3HCA7F+//13jB49GjzPw8vLCz4+Pli0aJGQ+kSTiuSp+umnn9CrVy+EhYWhZcuWOHTokM7P1tPTEwqFAnPmzBHGmp+fj/nz56NOnTpwcnLS+5158OABfH19UadOHYwZMwbTp08X8piJc1Z1794dX3/9NUpKSsDzPFJSUoTYmClU9LtJWBck8SbQuHFjfPzxx7h58ybq1asn5MUCgF69eiEmJkbi/qgoDg4OSE5OxsKFC/Hpp5+itLQUH3zwgRAkHTVqFMLCwuDo6Ahvb2/huJEjR2LGjBnw9/dHgwYNJLmCkpKSkJCQAIVCgeLiYgQFBWHgwIEGU5mHhoYiKysLISEhYIyhY8eOGDlyJABVCv0XX3xR73iDgoJw4MAB9O/fHzzPo1evXsjNzRVuZLqwt7fHunXrMH/+fKxYsQKvv/466tWrZ/LnNnDgQGzbtg1Lly7FihUr9OaTatCggc58Vi4uLnrzHenDwcEBa9euxaJFi5CYmAh7e3usXbtW+OUdEhKChIQEne4foGJ5qkJDQ/HRRx9BoVCgtLQUb775pvBQJE0rOTY2FsnJyQgNDYVcLkdxcTH69u2LqVOnAtD/nXFzc8OkSZMwZswYODk5QS6XY8GCBQAgyRc3adIkLF26FIMGDUJpaSlat26NuXPnGrtUEiry3SSsC1qJ/oz8+uuviImJwZ49ewzecJ43GRkZSElJ0elftyR//fUXRo4ciYyMDMEKI6TwPI/4+Hj84x//MOk5KgRRXSEL5BmYNWsWfvnlF6xcubJSxaOqWL16NVJTUxEXF0fioYf8/Hz06tUL7dq1EzJAE4StYlUWSHp6OtavX4/S0lKMHj2aVrwSBEFYMVZjgWRnZ2PlypXYsWOHMK2xU6dOaNKkSVV3jSAIgtCB1czCyszMROfOnVGnTh04OzvD398fGRkZVd0tgiAIQg9WIyD37t2TPKnOw8PD7EeUEgRBWDuMf1TVXXhuWI0LS3NhEmOsRgSmCYKoWXCyOuD/Hg7wd007QFYfsnpfW7ZTFcRqBKR+/fo4deqUsJ2Tk2N2ArV+smHPu1tVykF+G42pmmCL46oJY/Js5I4t15IrvR88fxtQZpla23pcRRpYTb+6du2K48eP48GDBygoKMCBAwfQo0ePqu4WQRDEc0fJeLNe1orVWCCenp6IiIjAqFGjUFJSgqFDh+pMlUEQBFHdYWX/TIEzsV5VYDUCAgAKhQIKhaKqu0EQBGFReDAwmGZZkIAQBEEQAkrGtDJQ64OznrXeWpCAEARBVDIq+8M0YZCRBUIQBEGoUZohIKbGSqoCEhCCIIhKhjdDQGDFAmI103gJgiCI6gVZIARBEJUMzxiUpgbHrTiIThYIQRBEJcOb+TKH9PR0DBgwAH5+fkhJSdHaf/DgQSgUCgQGBiIqKgrFxcUAgNu3byM8PBwBAQGYNGkSnjx5YvRcJCAEQRCVjBLMrJepqB+L8dVXX2Hnzp345ptvcPXqVWH/06dPER8fj88//xx79+5FUVER0tLSAABxcXEICwtDRkYGWrZsieRk4yleSEAIgiAqGZ4BShNfvBkeLGOPxXB2dsaRI0fw0ksvoaCgAH///TdcXV1RUlKCkydPwt/fHwAQEhJi0uM0KAZCEARRyZjjmlLXu3PnDpRKpWSfq6srXF1dhW1dj8U4d+6c5Bh7e3scO3YMM2fOhIeHB7p164aHDx/CxcUFdnYqSXB3dzfpcRpkgRAEQVQySnBmvQAgPDwcffr0kbw2bdokadfUx2L4+vri559/Rq9evTB//nyd9Ux5nAZZIARBEJUMb4ZrSl0vJSVFpwUixthjMR49eoQLFy6gW7duAFT5ByMiIuDm5oa8vDwolUrI5XKTH6dBFghBEEQlw5thffBlFoiXlxcaNGggeWkKiLHHYjDGEBkZidu3bwMAMjIy0K5dO9jb26NDhw7Yt28fAGDnzp0mPU6DLBCCIIhKRuyaMobMxHqA/sdijB8/HtOmTUOrVq2QkJCACRMmgOM4NGnSBHFxcQCA2NhYREVFYf369fDy8sKKFSuMno8EhCAIopLhGQeemSYMptZTo+uxGBs3bhTe9+3bF3379tU6ztvbG5s3bzbrXCQgBEEQlQxvhgUiN8MCqWxIQAiCICoZJWRQmhiCNrVeVUACQhAEUcmoZmGZ6sKycGeeARIQgiCISsYcFxZPLiyCIAhCjRIyKBm5sAiCIAgz4SEDb6IwmFqvKiABIQiCqGRsxYVlvdJGEARBWDVkgRAEQVQySsaZHgMxcyFhZUICQhAEUcnwohxXptS1VkhACIIgKhnejIWEFEQnCIIgBJTMjGm8JtarCkhACIIgKhlbmcZr0Z7l5+cjKCgIt27dAqB6Xq9CoYCfnx9Wrlwp1Lt48SJCQkLg7++P6OholJaWWrJbBEEQVYrqmeicSS9rTmViMQE5e/Yshg8fjuvXrwMACgsLMWfOHCQnJ2Pfvn24cOECjh07BgCIjIzEvHnzsH//fjDGkJqaaqluEQRBVDnqZIqmvqwVi/UsNTUVsbGxwmMRz507h0aNGqFhw4aws7ODQqFARkYGsrKyUFhYCB8fHwBASEgIMjIyLNUtgiCIKodnMrNe1orFYiALFy6UbN+7dw/u7u7CtoeHB7Kzs7XK3d3dkZ2dXaFzHuS3VayzVgyNqfpgi+OiMVkGmoVlJjzPg+PK5zMzxsBxnN7yitBPNuyZ+2lNHOS30ZiqCbY4rpowJs9G7thyLbnS+6GOb5ha11qpNAGpX78+cnJyhO2cnBx4eHhold+/f19wexEEQdgiqoWEplog1isglWYbtWnTBteuXcONGzegVCqxZ88e9OjRA97e3nB0dMTp06cBALt27UKPHj0qq1sEQRCVDl+2DsSUV42MgWji6OiIJUuWYOrUqSgqKoKvry8CAgIAAElJSYiJiUF+fj5atGiBUaNGVVa3CIIgKh1KZWIiR44cEd536dIFu3fv1qrTvHlzbN++3dJdIQiCsApoJbo1wnEAs+JVNwRBEFA/D6T6x0BsS0AIgiCqATw48CbOrrJmAbFe24ggCIKwasgCIQiCqGTMSVFizalMSEAIgiAqGWbG9FxGQXSCIAhCjRIclCbGNkytVxWQgBAEQVQyPONMtkBMDbZXBSQgBEEQlQxvhgVizbOwSEAIgiAqGXPStFMqE4IgCEKAVqITBEEQFYKZkQuLkQuLIAiCUKN6HoipFoj1Coj12kYEQRA2imoWlukvc0hPT8eAAQPg5+eHlJQUrf2HDh1CcHAwBg4ciPfffx+5ubkAgLS0NHTr1g3BwcEIDg7GypUrjZ6LLBCCIIhKxlKPtM3OzsbKlSuxY8cOODg4IDQ0FJ06dUKTJk0AAPn5+Zg/fz6+/fZbeHp6YvXq1Vi7di1iYmJw4cIFREVFISgoyOTzkQVCEARRyVTEArlz5w5u3boleT1+/FjSbmZmJjp37ow6derA2dkZ/v7+yMjIEPaXlJQgNjYWnp6eAIBmzZrhzp07AIDz588jLS0NCoUCM2bMECwTQ5CAEARBVDI8ZGa9ACA8PBx9+vSRvDZt2iRp9969e3B3dxe2PTw8kJ2dLWzXrVsX/fr1AwAUFhZiw4YN6Nu3LwDA3d0d77//Pnbv3g0vLy/Ex8cbHYftubDomSAEQVg5SnAmB8fVCw5TUlKgVCol+1xdXSXbPM+D48rbZYxJttXk5eVh8uTJaN68OQYPHgwAWLdunbB/3LhxgtAYgiwQgiCISoYx091Y6t/DXl5eaNCggeSlKSD169dHTk6OsJ2TkwMPDw9JnXv37iEsLAzNmjXDwoULAagE5YsvvhD1j0EulxsdBwkIQRBEJaNeiW7qy1S6du2K48eP48GDBygoKMCBAwfQo0cPYb9SqcTEiRPRv39/REdHC9aJs7MzPv30U5w9exYAsGXLFpMsENtzYREEQdRQPD09ERERgVGjRqGkpARDhw5F69atMX78eEybNg13797Ff//7XyiVSuzfvx8A0LJlSyxcuBCrVq3C/PnzUVhYiH/+859ITEw0ej4SEIIgiErGkuncFQoFFAqFpGzjxo0AgFatWuHSpUs6j+vQoQPS0tLMOhcJCEEQRCXDzFggyKx4JToJCEEQRCVD2XgJgiCICsGbkUyRngdCEARBCCiZ6UkSlVa8rI0EhCAIopJhZriwGLmwCIIgCDXmZNmlZ6ITBEEQArYSA7GobfTxxx8jMDAQgYGBwqKUzMxMKBQK+Pn5SfLNX7x4ESEhIfD390d0dDRKS0st2TWCIIgqg4cZ2XhrooBkZmbixx9/RFpaGnbu3Inff/8de/bswZw5c5CcnIx9+/bhwoULOHbsGAAgMjIS8+bNw/79+8EYQ2pqqqW6RhAEUaUwM9KYWHMMxGI9c3d3R1RUFBwcHGBvb49XX30V169fR6NGjdCwYUPY2dlBoVAgIyMDWVlZKCwshI+PDwAgJCREksPebHRknyQIgrAWeDOSKfI1cRZW06ZNhffXr1/H//3f/2HEiBE6c9Vr5rB3d3eX5LA3lYNK27NaDvLbqroLzx1bHBNgm+OiMVkGW4mBWDyI/scff2DChAmYOXMm5HI5rl+/LuxT56o3NYe9MfrJ3yrfsIFnghzkt6GfbFhVd+O5YotjAmxzXDVhTJ6N3LHlWnKl98NWZmFZ1Ll2+vRpjBkzBh999BEGDx6sN1e9Zvn9+/e1ctgTBEHYCsxk9xVn1bmwLCYgd+7cweTJk5GUlITAwEAAQJs2bXDt2jXcuHEDSqUSe/bsQY8ePeDt7Q1HR0ecPn0aALBr1y5JDnuCIAhboiLPRLdGLObC+uyzz1BUVIQlS5YIZaGhoViyZAmmTp2KoqIi+Pr6IiAgAACQlJSEmJgY5Ofno0WLFhg1apSlukYQBFGl2IoLy2ICEhMTg5iYGJ37du/erVXWvHlzbN++3VLdIQiCIJ4ztBKdIAiikqFZWARBEESFsJUHShkNom/evBn5+fmV0ReCIIgaAQ8zAulV3VkDGBWQy5cvC/mpzp8/Xxl9IgiCsGlqzCysBQsWID8/H+np6YiLiwNjDMOHD4dCoYCjo2Nl9JEgCMKmsJVZWCatA3FxcUFAQACCgoLw6NEjfPXVVwgICMCRI0cs3T+CIAjbo2yBoCkvWLGAGLVAjh8/jm+++QbHjx+Hv78/1q1bh+bNm+Ovv/5CWFgYevfuXRn9NB+Os4l0JgRB2B41ZhZWXFwcwsLCkJCQgNq1awvlL7/8Mt566y0DRxIEQRC6qDEurN27d6NOnTqoXbs2cnJy8MUXX4DnVfMCpk2bZvEOEgRB2Bqmuq8EN5aVYlRAEhIScPToUVVlmQynT5/GokWLLN2visNZ78NXCIIggBo0C+vXX3/Fnj17AAD16tXD6tWrERwcbPGOEQRB2CzMjAWCVhzKNSogJSUlKC4uhoODAwDQs8oJgiCeEfUz0U2ta60YFZCePXvi3XffRXBwMDiOw549e+Dr61sZfSMIgrBJGDN9kqg1TyY1KiAzZ85ESkoKDh8+DDs7O/Tr1w+hoaGV0TeCIAibpMZM45XL5Rg1ahQ9n4MgCOI5Yc7sKmuehWVUQA4dOoRFixYhNzcXTGRLnTlzxqIdIwiCsFVsJRuvUQFZtmwZoqKi8MYbb4DjrHcgEjgZwKw5hyVBEET1x6iAuLq6ws/PrzL6QhAEUSOwlSC60VV3bdq0wbFjxyqjL8+O5iLC6mIxEQRRo7DkSvT09HQMGDAAfn5+SElJ0dp/6NAhBAcHY+DAgXj//feRm5sLALh9+zbCw8MREBCASZMm4cmTJ0bPZVRAjh07hgkTJqBVq1Zo164d2rZti3bt2pk1IIIgCKIcSwlIdnY2Vq5cia+++go7d+7EN998g6tXrwr78/PzMX/+fGzYsAG7d+9Gs2bNsHbtWgDleQ8zMjLQsmVLJCcnGz2fURfWF198YXLnCYIgCONUJJninTt3oFQqJftcXV3h6uoqbGdmZqJz586oU6cOAMDf3x8ZGRmYMmUKANXC8NjYWHh6egIAmjVrhvT0dJSUlODkyZNYt24dACAkJAQjRoxAZGSkwb4ZtUC8vb1x/vx5pKamws3NDb/++iu8vb1NGjhBEAShDUN5HMToq+yY8PBw9OnTR/LatGmTpN179+7B3d1d2Pbw8EB2drawXbduXfTr1w8AUFhYiA0bNqBv3754+PAhXFxcYGensinc3d0lx+nDqAWyYcMG/PTTT7h79y7GjBmDjz/+GDdu3MDkyZONNk4QBEFow8zIhaUOoqekpOi0QMTwPC+ZLcsY0zl7Ni8vD5MnT0bz5s0xePBgZGdna9UzZdatUQtk79692LhxI2rVqoW6desiNTVVSK5IEARBVIAKPJHQy8sLDRo0kLw0BaR+/frIyckRtnNycuDh4SGpc+/ePYSFhaFZs2ZYuHAhAMDNzQ15eXmCQOk6ThdGBcTOzk5IpAioFE9t5hAEQRDmw8x8mUrXrl1x/PhxPHjwAAUFBThw4AB69Ogh7FcqlZg4cSL69++P6Ohowcqwt7dHhw4dsG/fPgDAzp07Jcfpw6gSeHl54ejRo+A4DsXFxfjss88oBkIQBPEMWCqViaenJyIiIjBq1CiUlJRg6NChaN26NcaPH49p06bh7t27+O9//wulUon9+/cDAFq2bImFCxciNjYWUVFRWL9+Pby8vLBixQqj5zMqIHPnzsXMmTNx+fJl+Pj4oE2bNkhKSjJ5QFUGrUYnCMJaMce0MHMhoUKhgEKhkJRt3LgRANCqVStcunRJ53He3t7YvHmzWecyKiCenp5ITk6GTCaDUqlEUVER6tWrZ1Ljq1evxv79+8FxHIYOHYp33nkHmZmZWLx4MYqKitC/f39EREQAAC5evIjo6Gg8efIEHTp0QFxc3PNxlakDQda8nJMgiBqFrSRTNBoD2bdvHwYPHoxatWohJycHQUFBOHLkiNGGf/nlF5w4cQK7d+/Gt99+i82bN+PSpUuYM2cOkpOTsW/fPly4cEFY5R4ZGYl58+Zh//79YIwhNTX12UdHEARhhZg8hdeMlCdVgVEB+fe//40vv/wSAPDKK69gx44dwspFQ3Ts2BFffvkl7Ozs8Pfff0OpVOLx48do1KgRGjZsCDs7OygUCmRkZCArKwuFhYXw8fEBoFrEkpGR8WwjIwiCsFIsmcqkMjEqIDzPo379+sK2l5cXeN602IK9vT3WrFmDwMBAdOnSRe8iF81yUxexEARBVE/Kpuea8qrOD5Ryc3PD1q1bMXToUHAch7S0NLz00ksmn2DatGkYP348Jk6ciOvXr+tc5GLq4hdjHCzdavYx1s5BfltVd+G5Y4tjAmxzXDQmy2Ar2XiNCkh8fDw+/PBDxMfHg+M4tGjRAsuXLzfa8P/+9z8UFxfj9ddfR61ateDn54eMjAzI5XKhjnqxiubil/v375u0iEWTfnYaj9oVz8Ky5qugh4P8NvSTDavqbjxXbHFMgG2OqyaMybORO7ZcM5408LljwVlYlYlRF9Y///lP7NixA8ePH8fPP/+M1NRUNGzY0GjDt27dQkxMDIqLi1FcXIzDhw8jNDQU165dw40bN6BUKrFnzx706NED3t7ecHR0xOnTpwEAu3btMmkRC0EQRHXEVmIgRi2QJ0+eICkpCX/++SdWr16NefPmYdasWXjhhRcMHufr64tz585h0KBBkMvl8PPzQ2BgINzc3DB16lQUFRXB19cXAQEBAICkpCTExMQgPz8fLVq0eD7PYKe1IARBEBbDqIAsWLAAHh4e+Pvvv+Ho6Ij8/HzMmzfPJDfW1KlTMXXqVElZly5dsHv3bq26zZs3x/bt283oOkEQRDWlpriwLl68iIiICNjZ2aFWrVpISkrCxYsXK6Nvzxd6OiFBENZCBZIpWiNGLRCZTKoxSqVSq4wgCIIwAxuxQIwKyL/+9S8sW7YMhYWF+OGHH5CSkoKOHTtWRt8IgiBsGOu1LEzFqCkxY8YMODs7o3bt2li5ciWaNWuGWbNmVUbfCIIgbBNL5XOvZIxaIPb29pg8ebLkCYRXr15FkyZNLNoxgiAIm8aKhcFU9Fogt27dwkcffYSEhAQUFBQAUE3pXbx4MQYNGlRZ/asQnKz6m4YEQdgwpqYxsfIgul4BmTNnDurWrYucnBx88sknOHfuHAIDA/HTTz/h008/rcw+EgRB2BS2ko1Xrwvr7t27+PLLL1FYWIiQkBCkpqZizJgxGDt2bLV4pC0n48B4plpMCNCCQoIgrAdbn4Xl7OwMAHByckJubi4SExPRrVu3SuuYReA465ZzgiBqBua4pqzYhWWSKeHm5lb9xYMgCMJK4JjqZWpda0WvgIjTqVcHlxVBEES1wdZdWJcvX0a7du0AAIWFhcJ79bM6zpw5Uzk9JAiCsDVs3YV18ODByuyHRRAC6QRBENaGDdya9AqIt7d3ZfaDIAii5mDrLiybg54NQhCEtWAjAlLz0upSWneCIIjngtkWSElJCezt7S3RF4IgiJqBjQTRjVogp06dQnJyMoqLizFs2DB06NAB+/btq4y+EQRB2CasfC2IsVe1dmEtW7YMPj4+OHToEOrUqYO9e/fiP//5T2X07bnAyThKrkgQhHVhI+ncjQqIUqlE165dkZmZib59+6JBgwbgeQpGEwRB1HSMCgjP8zh37hyOHj2Krl274sqVKygpKamMvhEEQdgkprqvzEl5UhUYDaJPnDgRH330EYYOHYqGDRuid+/eiI6Oroy+VQiD7ir1VF5KqkgQRFViI0F0owLi5+cHPz8/YfvgwYOQy+UW7RRBEITNYwO/YfUKSO/evfHCCy+gU6dOiImJEcpJPAiCIJ4RG1lIqFdApk6dCicnJ7i7u1dmfwiCIGweS6ZzT09Px/r161FaWorRo0cjPDxcZ72ZM2eic+fOCAkJAQCkpaVh+fLlqFevHgCgZ8+eiIiIMHguvQIyePBgAEBAQAAyMjLMG4EVQokVCYKwGixkgWRnZ2PlypXYsWMHHBwcEBoaik6dOqFJkyaSOrGxsTh+/Dg6d+4slF+4cAFRUVEICgoy+XxGZ2F5e3vjzJkzNHWXIAjieWGhdSCZmZno3Lkz6tSpA2dnZ/j7+2sZAOnp6ejTpw/69+8vKT9//jzS0tKgUCgwY8YM5ObmGj2f0SD6//73P4SFhcHOzg4ODg7V63kguhIo0kwsgiCqmIq4sO7cuQOlUinZ5+rqCldXV2H73r17krCDh4cHzp07Jzlm3LhxAIDTp09Lyt3d3TF27Fi0a9cOK1asQHx8PJYvX26wb0YFJCUlxVgVgyxduhQPHz7EkiVLkJmZicWLF6OoqAj9+/cX/GsXL15EdHQ0njx5gg4dOiAuLu7Zn4LI1bw8kQRBVBMqMI03PDwcWVlZkl1TpkzB1KlThW2e5yVPk1X/4DeFdevWCe/HjRuHfv36GT3GJBfW+fPnkZqaCjc3N/z6668mPyvk+PHjSEtLA6B6quGcOXOQnJyMffv24cKFCzh27BgAIDIyEvPmzcP+/fvBGENqaqpJ7RMEQVRLKuDCSklJweHDhyWv0aNHS5qtX78+cnJyhO2cnBx4eHgY7U5eXh6++OKL8u4xZtKMW6MCsmHDBnz99dfIyMhAYWEhPv74Y4lS6ePRo0dYuXIlJk6cCAA4d+4cGjVqhIYNG8LOzg4KhQIZGRnIyspCYWEhfHx8AAAhISHPN2jPyQRrhHJiEQRhDXAwYyV62TFeXl5o0KCB5CV2XwFA165dcfz4cTx48AAFBQU4cOAAevToYbQ/zs7O+PTTT3H27FkAwJYtW56PBbJ3715s3LgRtWrVQt26dZGamoo9e/YYbXjevHmIiIgQBqjLN5edna1V7u7ujuzsbKPtEwRBVFssFET39PREREQERo0ahUGDBiEoKAitW7fG+PHjcf78eb3HyeVyrFq1CvPnz0f//v3x+++/IzIy0uj5jAYa1MFzNa6urkbjE9u2bYOXlxe6dOmCHTt2ANDvm3sWn50mB0u3Vug4a+Ygv62qu/DcscUxAbY5LhpT9UOhUEChUEjKNm7cqFVvyZIlku0OHToIIQdTMSogXl5eOHr0KDiOQ3FxMT777DOjMZB9+/YhJycHwcHByM3NxdOnT5GVlSXxqal9c5o+u/v375vks9NFP7vQcjeVZhC9bDaWsBZEPTvLimdiHeS3oZ9sWFV347lii2MCbHNcNWFMno3cseVacqX3w5ILCSsTowIyd+5czJw5E5cvX4aPjw/atGmDpKQkg8d8/vnnwvsdO3bgl19+QVxcHPz8/HDjxg00aNAAe/bswZAhQ+Dt7Q1HR0ecPn0a7du3x65du0zy2ZkNPROdIAhrwdZTmajx9PTEpk2bUFBQAKVSCRcXlwqdyNHREUuWLMHUqVNRVFQEX19fBAQEAACSkpIQExOD/Px8tGjRAqNGjarQOUyBVqQTBFHl1BQBycnJQVpaGh49eiQpnzlzpkknCAkJEXKtdOnSBbt379aq07x5c2zfvt2k9giCIKo7tuLCMjoLa9KkSTh37hwYY5JXtUcdI6lgwJ4gCKKmY9QCKSkpwccff1wZfbE8FAchCMIasBEXllELpEWLFrhy5Upl9IUgCKJGUGMeaduuXTsMGjQI7u7ukvUfhw8ftmjHLAkF0gmCqHJs4BZkVEA+++wzJCUl4eWXX66M/lQNlJmXIIjKxEZcWEYFxNXVFQMGDKiMvlgG9cJCtcUhjoNQTIQgiKrAHNdUdRaQzp07Y+nSpfDz85OkNGnRooVFO/bckXHlIkIQBFGV1BQLJD09HQCwf/9+oYzjuGodAwEoDkIQRNVhK+tAjArIkSNHKqMflsGc9O0UByEIorKoKRbI06dPkZiYiO+//x6lpaV48803ER0dXeGUJlWKLjcWxUEIgqhsbERAjK4DWbx4MYqLi7Fu3TokJyeD4zgkJCRURt8qjrHH2dLjbgmCqEJqzDqQs2fPSvJXLViwAIGBgRbtFEEQhM1jxcJgKkZ/iiuVSvB8uYuH53mTnpVrtYjiIlqPuKW8WARBECZj0jTe6dOnY/jw4QCAr7/+Gh07drR4xywOrQchCKKqsJEYiFEBmT17NpKTk7FixQoolUp0794dkydProy+EQRB2CQ1Zhpvamoqpk2bhmnTpgllGzZswHvvvWfRjlkUQ4sKaTovQRCWxtYtkK+//hqFhYX44osvUFRUJJSXlJRg69at1VtARNCCQoIgKhubt0Ds7Oxw5coVFBYWStK5y+VyREVFVUrnnidcWYBc8jAsioMQBFEV2LoFMmzYMAwbNgyHDh1C3759K7NPBEEQto2NCIjRabyvvPIKtm3bBsYYpk+fjr59++LEiROV0TeLoLZEaDovQRBVBWfmy1oxKiCxsbFwdHTE0aNHkZ2djYULF2LlypWV0bfKQbwqnVaoEwRRGTAzX1aK0TtmUVERBg4ciB9//BH9+/dHp06dUFJSUhl9ey5wHFdudYjKTDjQQj0iCKKmw8GMVCZV3VkDGBWQ4uJi3L9/H0ePHkXXrl1x//59yaysao0hNxZBEISlqCkWyNtvv41evXqhffv2aNKkCYYOHYrRo0dXRt+qBnJjEQRhaWxEQIwuJAwLC0NoaChkMtWNNS0tDXXr1rV4xywNx3HlU3r1TeGlRYUEQVgAm18Houbzzz/XWf7OO+88985UCfSoW4IgKhsbmcZrVEDEiwiLi4tx8uRJdOnSxaKdsigyGVCWXVhihUC0Kp0WFRIEYUnMec5HdRaQxYsXS7azs7MRHR1tsQ49N3QFxcvccGIRETDkxgLIlUUQxPOjplggmnh6eiIrK8ukuiNHjsSDBw9gZ6c6TXx8PJ48eYLFixejqKgI/fv3R0REBADg4sWLiI6OxpMnT9ChQwfExcUJx1kckRuLcmMRBGFpamQMhDGGCxcuoF69ekYbZozh+vXr+O677wQhKCwsREBAADZv3gwvLy9MmDABx44dg6+vLyIjI7FgwQL4+Phgzpw5SE1NRVhY2DMMzQhlVoimG0uA3FgEQRAGMTpn9cqVK8Lrjz/+gJeXF5KSkow2/OeffwIAxo4di4EDB2LLli04d+4cGjVqhIYNG8LOzg4KhQIZGRnIyspCYWEhfHx8AAAhISHIyMh4tpGZi4wzPIWXFhYSBPE8sdAU3vT0dAwYMAB+fn5ISUnRW2/mzJnYsWOHsH379m2Eh4cjICAAkyZNwpMnT4yey6wYSHFxMRwcHIw2CgCPHz9Gly5dMHfuXJSUlGDUqFEYN24c3N3dhToeHh7Izs7GvXv3JOXu7u7Izs426TwmI9MhDnqsEAqmEwRhUSwUA8nOzsbKlSuxY8cOODg4IDQ0FJ06dUKTJk0kdWJjY3H8+HF07txZKI+Li0NYWBgCAwOxbt06JCcnIzIy0uD59ApIcXEx5s6di759+6Jfv34AgKlTp8LNzQ0JCQlG4xNt27ZF27Zthe2hQ4dizZo1aN++vVDGGAPHceDLbuKa5eZysHSr2cdYOwf5bVXdheeOLY4JsM1x0Zgsg6ViIJmZmejcuTPq1KkDAPD390dGRgamTJki1ElPT0efPn2EOoDqOU8nT57EunXrAKi8QCNGjKi4gKxZswb5+flo166dUBYfH4+4uDisXbtWCH7r49SpUygpKRGm/DLG4O3tjZycHKFOTk4OPDw8UL9+fUn5/fv34eHhYbB9XfSzC1WlJOFkwiys8uy7BtxTPF9ugfBMsDqY6L1AJc7GOshvQz/ZsEo7X2Vgi2MCbHNcNWFMno3cseVacuV3pAIWyJ07d6BUKiW7XF1d4erqKmxrenM8PDxw7tw5yTHjxo0DAJw+fVooe/jwIVxcXATDwFQvkN676tGjR7F8+XJJwNzT0xOJiYk4dOiQ0Ybz8vKQmJiIoqIi5OfnIy0tDR9++CGuXbuGGzduQKlUYs+ePejRowe8vb3h6OgoDGjXrl3o0aOH0XNUGI7TimnotXg4mUbGXoqFEATxbHCMmfUCgPDwcPTp00fy2rRpk6TdinpzdNUz5Ti9Foi9vT2cnJy0yl1cXEyKg/Tq1Qtnz57FoEGDwPM8wsLC0LZtWyxZsgRTp05FUVERfH19ERAQAABISkpCTEwM8vPz0aJFC4waNcroOSqE+ENRpyoRrwuRcQBPsQ+CICxIBSyQlJQUnRaImPr16+PUqVPCttrLYww3Nzfk5eVBqVRCLpebfJxeAZHJZMjPz4eLi4ukPD8/H6WlpUYbBoDp06dj+vTpkrIuXbpg9+7dWnWbN2+O7du3m9SupdAbTFdtiB5/SzmyCIKoOBWJgXh5eRmt27VrV6xduxYPHjxArVq1cODAASQkJBg9zt7eHh06dMC+ffugUCiwc+dOk7xAel1YQUFBiImJwdOnT4Wyp0+fIiYmBn5+fkYbrjboi5FQVl6CICyFhbLxenp6IiIiAqNGjcKgQYMQFBSE1q1bY/z48Th//rzBY2NjY5GamooBAwbg1KlTWj/+daHXAhk9ejRiY2Px5ptvomnTpuB5Hv/73/+gUCgwefJk00dUHRBZFBzHgckgWZkOQHtaL1khBEFUEPUDpUytaw4KhQIKhUJStnHjRq16S5YskWx7e3tj8+bNZp3LoAsrISEBEydOxO+//w6ZTIbWrVtXaHaU1WAsKKSZI4vWgRAEYQlqSi4sb29veHt7V0ZfnjsGZxGoXVa8HotCI827zsWFZIUQBFEBbCUXVs109IvjHZqxD5lMKjya03gJgiCeFRt5IiHdGYFyEREJB8dxWinhheem07oQgiCeAbUFYurLWqmkfOnVAMlaEDOeFwKQK4sgCPNgzPR7hhXfW0hANNE1IwvQfl4IBdgJgqgoNvJEwhrtwuI4ThrvELuydOXO0oyFkCuLIIiKYCMxkJpngegQBrWIMK20JjJwZe8114bofGohubIIgjABjgGciQ4Ma46B1FgLxGiiMEPJFaEnoE4QBFGDoLufCJ2p32Wy8qm9pogGubIIgjAGubCqCYaeA6IDIaGi2pUlTq6oI82JzoA6ubIIgjCArSwktH0BMYZ4rYdaGMRZecVWiaE0JzQriyAIU6FpvDaIWkx4VmZtaK8H0WuFaEJWCEEQerAVC6RGxkDKYx164hUyjam9Mpn21F71o3NhIKBO8RCCIHRhIzGQGikgJiHTsUakTEQkAmSKiJCQEAQhglKZ2BriG78oliFZI6KmbH1IeZzEhHgIubQIghAwIwZixSYIWSCA/hXmIlcWp3ZhGUi4yIldX7Q+hCAIPdiKBUJ3OX03erGIlAmDRESMxUN0tU3uLIIgAIqBVDs0b9y6AugyqUUhEQDN+prxEFF9Tl9wniAIArZjgVAMRI1k5lX5dF5BRBivCqzzkE7vVcdDZKL6jJcuMlQfr4biIQRRs+GZ5ImnRutaKTXHAqkIaotE5KISZmeJ4yF6Up0YdWcRBFEzsREXFlkggIbbSvRe6xnpahHhpZZIWeoTfZYIAP0pTzTPQxCEzUMLCW0BYzOl1EFvXQsPdVkigE5LRPWWsvcSBKGGlaczMfayYhOE7mZiDLmVdCwe1BKRsplZWiJCiw0JghBjTgDdevWjhrmwDGXm1TGbSgLjRSIC/e6ssv81c2ZRcJ0gCAFzhMGKbwlkgZiK5uNrNabqGlwjYii4rnUeskQIgqgeWFRAjhw5gpCQEPTv3x8LFiwAAGRmZkKhUMDPzw8rV64U6l68eBEhISHw9/dHdHQ0SktLLdm1crTWdxiyUsrdUYKIcDJALtfpzlI/c93gDC1xm8J5SEQIwpZRuaeYia+q7q1+LCYgN2/eRGxsLJKTk7F7927897//xbFjxzBnzhwkJydj3759uHDhAo4dOwYAiIyMxLx587B//34wxpCammqprpmGeAqv5kt90+c0ynTFRAw80ZCm+RJEDYU382WlWExADh48iAEDBqB+/fqwt7fHypUrUatWLTRq1AgNGzaEnZ0dFAoFMjIykJWVhcLCQvj4+AAAQkJCkJGRYZF+GXwWuo4cWDqRWA+c1BrRFBFhv0hENFKfGLVGSEwIwqYw3fpQvawViwXRb9y4AXt7e0ycOBF37txBz5490bRpU7i7uwt1PDw8kJ2djXv37knK3d3dkZ2dbfY5D5ZufS59tyYOKqvYErMAB/ltVd0Fi2CL46IxWQgbCaJbTECUSiVOnTqFzZs3w9nZGZMmTYKTk5PEAmBM9eQ/vmzWkma5ufSzC1X9mte0BADt3FWiesIvfPU2jFgqon4CKJ9pxcoWFfJM9V6UgoAxVr7osCwNClPXVR8LSJ5ueLB0K/rJ39J1YlM+DqvkIL8N/WTDqrobzx1bHFdNGJNnI3dsuZZcBT2xjXTuFhOQl156CV26dIGbmxsAoG/fvsjIyIBcLhfq5OTkwMPDA/Xr10dOTo5Qfv/+fXh4eFiqa+ZhQEjUe5ja68TLAPCAnAMYp4qUlQmD1lRfoHzluvpYzZXrgP7pvkC1FhKCqMnQSnQj9OrVCz/++CMeP34MpVKJH374AQEBAbh27Rpu3LgBpVKJPXv2oEePHvD29oajoyNOnz4NANi1axd69Ohhqa7ppiIrxMviE5KZVrpmaemKjQC6LSJ9U3119Y9iIwRRPTF1FTozx1KpfCxmgbRp0wbjxo1DWFgYSkpK8Oabb2L48OFo3Lgxpk6diqKiIvj6+iIgIAAAkJSUhJiYGOTn56NFixYYNWqUpbpmNEgu3NS1UsDrEZkylxQHtTUiL3NN8eUSzcuERYeQy8tdWmU5tKA+ViNnllYuLdWGuLNlZdb7JSMIQgrHq16m1rVWLLoSfejQoRg6dKikrEuXLti9e7dW3ebNm2P79u2W7M6zIRYPzYSL6n3qWA5jGkKCMh9WuVtL4tKSywHGtF1aIoQV7KqNsnOTkBBEtcQcy8LMv+n09HSsX78epaWlGD16NMLDwyX7L168iOjoaDx58gQdOnRAXFwc7OzskJaWhuXLl6NevXoAgJ49eyIiIsLguWpWKhNj6MrKq084dJXpFRLojI9wZelRJNZI2fHCcZKEjBqBdhISgqieWGgWVnZ2NlauXIkdO3bAwcEBoaGh6NSpE5o0aSLUiYyMxIIFC+Dj44M5c+YgNTUVYWFhuHDhAqKiohAUFGTy+WpuKpNnyYorXt8hXqchWvOhjnVI4iNyuXQBor51I5qxER0LEI0+f53WjxCE1VKRdSB37tzBrVu3JK/Hjx9L2s3MzETnzp1Rp04dODs7w9/fX7KmztCau/PnzyMtLQ0KhQIzZsxAbm6u0XHUXAHRA6fLolDtkP4v1FGLgXTRoPC/XA7I5eDECwk5GSCTA3LRfrkMnFyuM7Ov1gJEUxchqvtLYkIQVob56dzDw8PRp08fyWvTpk2SVjXX1KnX2unbL15z5+7ujvfffx+7d++Gl5cX4uPjjY6CXFjmoOu5IGo0y/iy+mpXkijOAY6Vua1kqsi7KD4icWsB5UICSF1bfLn7ymigXbP/5N4iiKrFnBQlZfVSUlKgVColu1xdXaVVjaypM7R/3bp1Qvm4cePQr18/o12r2QKibzaWJLW7kV/u+vYLN3rN8vI1IBIhUc/YEs/WAlTCA+iesQWQkBBENcScFCXqel5eXkbr1q9fH6dOnRK21WvtxPt1rbnLy8vDt99+izFjxgBQCYt4zZ4+yIVlKprWh67Yhy43llymesnK3gvlIteW2pWl4dZSNV0WR1GXlbm5OLncPNeWoTxb5N4iiMqFwYx1IKY327VrVxw/fhwPHjxAQUEBDhw4IFlTp2/NnbOzMz799FOcPXsWALBlyxayQJ4JUx4+ZcxSEbuw1O/lojKeCeXlFglXVl5mOdjbCXW1Zm3J5apfJ7xqdpfK7VU+dVg9a0uNQctEc2oyQRCWw0LTeD09PREREYFRo0ahpKQEQ4cORevWrTF+/HhMmzYNrVq10rnmTi6XY9WqVZg/fz4KCwvxz3/+E4mJiUbPRwJizq9vXdN8Nd4zLbeYaptTiwUgEhVWnjtLJlMJiZAzS50ZuMyMNBQnUYsQoDtOou6JeBqw2BohMSEIm0GhUEChUEjKNm7cKLzXt+auQ4cOSEtLM+tcJCCGMLbuQ7StJRwaYiHeL1lZynEiISkrE1k/nFxW1ozIMhELCc/KFySWHasVJwEkK9xNyrela7wkJgTxfKhAEN0aIQEBDK8JMfj8EA3x0CUummWMgcml4sLxZQIiQ7lpq765q2MhulxcaiEBoM7+y1i5pSNYM4BRF5eWVaJuU99nQWJCEBWGgxlB9JqYjdem0eWyMicuAqhuwGoBUqfbZEwlJgAgK/vfTj0LS9vFJaSDZ8bFBIDheIkcEsEwKig6hJEgCBOxYCqTyoQERI2pTyFUo0so9C021Nk+J8qTxQmCwtTuLHWyeOF5JiLLxFDgXT0tWA4hVgJAEnjXHS+RBti13FwAuboI4nlBAlID0RAGpiuorm+xoS5Rkeu56fLlXy6mtkDU1olM9MUrExRO/WVUKqUPuVI/4ApqN5nIMinrk2BGc6KHXGlaJoCGdWKGq0vX+AiipkMCYnuY9BREPUF0yXtdMRFN8dHYlvhDxU+QEVxYDEwmEg5NQWGqmVyCmIjdXGXHqwVFEBO5THfcBNAQFMCwu0tHDMmQqIinNxNETYSC6DbKs4qIroWGZQiioet+C3FdTvjSMHUQXUM8wDMwuWhbEAYmqSMIgiAq6tXvOgQF0C8ogGErxVj8BNI6ZKUQNRozVqJb898FCUgF0TttV/O9ur6meBgTKrXrSB12UAsMX+Z20jFji8k0xETT3aXUEzuRodzdJVpnojpMbcFox0/Kg/GQrDnRmt1VvqP8f4qjEDUZcmHZMIZWoYvRlZlXY59e4RDP5DIgJkyUj0YtHAzQiINAamkwprpxM6mAcHKphSKxWAxZJ0JnRDEUuVz13BN9MRRAZQGJ0+mInrKo0+0lqmNQYK34D4ogTIKhfBKNKXWtFBIQfZi6Qt3A2g/GcVJ3leY+QHu/JqJAu8TNJfzyh+p+rSEgaheYWFjKxUGHiPC8NCAvFhQ1ZcLCiUWmrFxdj+PLA/eaz+JkTC2eMp3BedWmEWHR9zmJPxOCsHbIAqkBVFREoMOq0CceOlxfTKwTeqcXl5XLy2b4CgdILRPJl08kLmqhMCgqjIFT8hrHlguE2FoRRAWA1mwvQGWtqHtubycscGTiwYotFi0Xl1zqDoOOesaulxX/IRI1DBKQGoKx9SGG6umKI+sSD03hELcl0xAdHTCIg93q9SWcKtQgVBKJh4yVu8E0BEVwf5UJBad252kE54UgoOaiRkB3cL5sxpfqvVw7SK8ZUxE/T149MkPJIaU7dH9QtPiRsBZIQGoYps7O0jdDSzJTS1quKRxisdC51kS9T2ypgCuf/SssHRFbH2VNsDLx4JmqvsSakAqJxIJRlyl53ZaKpiWj6QITiQHnYK9lraj7oM7jBcZU4+DFYiCTjIkTT3eWiIYea0WzLlksRFXBM60fSAbrWikkIOZgoktLn7WgKyYiFg/hOEMuLE0hErev3WHpJq/t6hKLCBgEIVCXS+rzohiHWlw0rBJNcREsDGW564tzctIZtOeE80jjK+VdMCQwBsQF0BIYVZGeP0xTBUbdD4IwF8brt5R11bVSSEDMxdS4iGZ8w1AbEitDz3EaQXeJSJnYJYnglCmXxP3Fl1stgrCILBfVjV0mcnVpWyxasRX1Dd5ONKPL3k4QCs2gPSCNrYhv0JrrVcrr8uV9LK9cvh+AeGaYUEVj/QpgwC1WXkG6rW/6NgkLYRAzXFhWPA2LBKQC6A9s60AzcC4qZxykbitOXF96PGTQslB0Wizi05jSz7J4idAt9XdVLBya22XWh1hkNC0XiaAAEncWc3YybrWI95cdr8stxolFRpflohHIl5TxPNSqKginHNDpMhCEQy4q0qgnzhVm6g8NzT4SNQMeZriwLNqTZ4IEpKLoincYQ49VIoiHsA3otDg4Xe/VbesRHmjUE/qv9aasokY18bbYOgHKBaPsOE4iJigXGfWxam+Wi6O20PC8njiMSAjUQiNyh2kJkYbICFaPqUKjKTJqRLEYfWLDyeVl7Wo/S9pgTKYidwgSHcIKIAF5VjjO9JlaomO0bujicl2LDDnN92UVZJxENMrfa/wvbkuz3MR1kwAnXdoh9hiJBEIlIur3ItEpu4mWujjoFxtdFg1Qtk6lbBKApoVTtoZFlzWjU2iE7XKx4QBtt5mORZTiMQn9Un8GwuOHNd1nKJ+erCkkFRIcgESnmiP+bppS10ohAXlemGuNANLAudY+fefRIR5it5eGcGi5vWTS/ZI6GuW6YNr3ujLh0JiWq3Z58ZxkGwCUTuXzi9WioRYY1baGNQMASgMCIxIXrfgML7I09AiMdA2L1JIRhqgr2C+0WxaDsbMrrwuUB/zLGzE6s0wrC7JwrInrYgwdY+p31IpvWDYDCQihBcdpz7Qy6Tj1sXr2odxtpUs8JFaHpqUik7alHUfRPE/5WMTo7Ju4j0y8oaOK6G+g5AWZjliL+H2ZSPDaZYJ1Y0h0lKx8v0jQOMaEfZpBf5VAifdpWzBaySoBiVXDveAs9FWyLgbQbdmohUc8IQCQCowBV5r2Ps0bjRz6ZvAYFB/xJazIDyMrvuFZDSQghtm2bRu2bNkibN+6dQvBwcHo27cvFi9ejKKiIvTv3x8REREAgIsXLyI6OhpPnjxBhw4dEBcXBzu7aqxvZTESQwsA9R4nDpjrwph4iK0NHZaI2FLRsmDEdQAtPdBVrrOvBrpf6qStsFLLRW3NqLehtS0REQ0REgtQeQxEvU90HM9UdTTcaeVCIeqXAWtHyJxc11VbfMSxGEOxHb7cstFKwS980FIB4nTNSANMEiBO2G1YhDg7+7KKZgiRrmtf0amoVnzzfCZ4XsMSNVLXSrHYHXrYsGEYNmwYAOCPP/7A5MmTMX78eAwfPhybN2+Gl5cXJkyYgGPHjsHX1xeRkZFYsGABfHx8MGfOHKSmpiIsLMxS3at8DE3rNYDOuIcmmpYHysVD0z3FOGgIBKdXOCTn0xHAV21L90vKNMrV8Pb6BiFC474hDearyzhtC0VUbkhwdImN9rY4BqN6rxICHWIDQOnqpFtoAO24jS5Lp8zK4UTCo2XpiEVC3DYvepyx5nHC56YtOMKnrmfyACdXi710tlr5fmiJjuRcAgZmr+nCnLU4etuwZvExwwKp6dN458+fj4iICNy8eRONGjVCw4YNAQAKhQIZGRlo0qQJCgsL4ePjAwAICQnBmjVrbEtA1GhM3zUbXdaHpH0N8dC8sQsiokM4tAREt/gIbUH7GPH/+oRE6aBdpqpfsc9EdZPWjrVoig7HRHW0rBZxPfX/THe9MiHRPLagvhM4pfRYYdKBhstNU4jKXWTqMg0XnJaLzYAQQdyehqUDCHU5fWKkrgOAc3mh/HhAOqtNjaYFpG5TjGifZmxIywoStamFAUtGe1q1nooykWvvWQTqWSAXlmlkZmaisLAQ/fv3x549e+Du7i7s8/DwQHZ2Nu7duycpd3d3R3Z2ttnnOli69bn02Zr47lBUVXfhufNb8odV3QWL8OOOyKruwnMn496/q7oLzx2ruE/QOhDT2Lp1K9555x0AAM/zksfGMsbAcZzecnPpZxcKTsYBnAyQcao21MkAy95z6mm3ZXVUsQpRffUxwnuu/Hh1XEPGSbYh2haC6JoxEEkZymdgia0BcfyDA44cno1efZeUnR+i48THqPdxOq0PbStDFO/QtDz0HKervt5yiCwfHW6wc6si0OrDldI+6XivVabZFxgp12OdaWJwcgA0rBjJgdL3lxIi0HzuSq3JAVrtaFg7UotHT5mkLtPez+uykLQtqHI3HcosFA2LSuzG44Hv986Eb/+l5f3XEzMyaD0JFo2mxaSxDeiPHUGjLV0xJEDLDadrdltGdjICPCYKdTy93bDpt8Val9fiMB6MUpkYpri4GCdPnsSSJaqbYP369ZGTkyPsz8nJgYeHh1b5/fv34eHhYcmuVR/EN0eZHreVmegUDz3n1uXm0t8Op33z1xAag/t0iZJGf3S1ZbBMOLb8ZqJzgoDGuYzOOtNB6Qv6f1HqiuFoTxDgpHWZnjqawiLs0xQhrnw/L90vFg69ggTgSX17jfaZpC1JrEhDuPRNYuB4sciI6mvOnlPvA/RnOdDchriOSFzU5QA419rl27Vf0HvNLAolUzTO5cuX8c9//hPOzqrpjW3atMG1a9dw48YNNGjQAHv27MGQIUPg7e0NR0dHnD59Gu3bt8euXbvQo0cPS3atemPE+qhYmxrWh4kCoCUeuo5DeV8BVT8Ntgf9x+vfZrrbhEZ98bZmPfVdU9eYIX2vdQyA0tq8zroGyyqC5v1EpzhpiJF6n4bgqOtKt8u7+qiZRn2hLqchVhBEQ/fUbEgtJU1BE1k/kn28tC2J1WSqmEF0DgBPmrsLxxS619b7MVsUioEY5+bNm6hfv76w7ejoiCVLlmDq1KkoKiqCr68vAgICAABJSUmIiYlBfn4+WrRogVGjRlmyazaNphCoyiARngpbMiaKhyF3lFT8dPSHk9bReQwgFQ2N8QpioU8odNXnIBURDQFhHJPUF9pS73fky46RClF5e2W/gPUIliSDsFAGKaI6OjVKr8/NPNQP+mL/LFD9Dw3dEl0w4f4mGABc+bZmPc2JDCIrCTr2a0180CVevLiMKxdAkSUlHkB2R3vhOLsX1TM6KhlmxjTemurCGjBgAAYMGCAp69KlC3bv3q1Vt3nz5ti+fbslu0NookMEnllgNNrX62LSvIHr6JNei0OftSG2MnQKE9Pdvg7RkIiFep+ojfL6om071c9aToeIcKJt8X5Oso+JHhMjFZvybSPlZV2RaZQLwyjb1n5KQHk9meh9vTr5euuZAtPzReI1ysX1ysMbnNZ+9XFMVC6IHdO9rX6v/jKUvvZUdTzjUORcVQJCFghh6+i9gZtgfZghQDrjLAYsBJ11RD9k9YmDTuHQFA3R8ar3muLCpO8hupnb8aobrIZICIdz5QLDaYgFJykvFwOxEIhv/hzHIJOUMUl99T6ZhjDIUN6euK7wHgwyUcKzl10fSo4vr6M+VlVXLtonqStqSy7uC7T3i/sq1zH1SLzfUJkxQl7/TXhf195df0ULwngGZqIFYtK6mSqCBISwHJwOcRC/11Om34XFjIuTLoGAieKhKRz6tsXCIToHJyu3PsSCoa6rKRJaAoLyG7/qBg+pQKhFo6xL6hu/ulwtEJriINQtEweZqD5QLggyjodcqKfaV9fhKQCIynlhnxzlbalv+DKOQc7xkEH1vxr1tozjIReJh1wkWOr21O9V7UgFqPw85e2I68nF7UuOLS8PfPE34RyOdt6oEsgCIYgqRJf1oUMg9LnATBYPHRaIWDjELh1d4iEVCd3Whj7h0GdhmCIaYsGQlIkEQlMwpNuq41zkRcL4xGKhKRSaAiG+wauO5cuPE4mDXGhPVV8tOrKyOmIxkEvaVotO+bnkUPev3NJSWz2qMhWN7fKFdfEOdoXGv2uWgGZhEQQBQCI8ulxVACosHoILC8bFQ5+1AWiKinHx0GXBmCMcYsEAyl1ShoRDIhKaAlLWnj7BANRWDhPEQs6V5zWVA5CXqfkLnAwyjoMcHOSczrw6lQBvRnC8hgbRCYKoXHS5qqTlpomH2CUFoMLi8SzCYaqloUs01IJRLiAcZGUC4sjZle2TQaaVN58wB3MTjxNExdGIR5gS/2CcjviHOe6rZ4l9aNTV6b7iUO6mgrb1UZ6woGLWh/rUplgf+uIcFREPSSziGcRDzhl2VxkTj3LBqJh4yMEJ4iEvu8iCeICDnKuaW6AqiG76yxzS09MxYMAA+Pn5ISUlRWv/xYsXERISAn9/f0RHR6O0tBQAcPv2bYSHhyMgIACTJk3CkydPjJ6LBIR4NjRu6IaEgCCIMhhTubBMepkuINnZ2Vi5ciW++uor7Ny5E9988w2uXr0qqRMZGYl58+Zh//79YIwhNTUVABAXF4ewsDBkZGSgZcuWSE5ONno+m3JheTZyBycDwKlyWWnnwhLlu5LkwtJ4CbmwIPp5Kd4veg/ozoUF0TZQlgsLKF/MpycXFieaGgvA0/PFsvOr2mAQ14ekvr41HQDK2weg64YvHKPer2+1OLTPK2lHsl/z/Kr3/6jjqrM9LbGRvNdhgajraJ1TI4AuOYeBqbvQ3M8gHj/UH6FQR2qBeL/wIjjon6qrrstB2wKBpgVSdoz69GILRLA6oGpfYomU/VpXWzTimVYcxwRLgIN6v3ibl1grQPk0V45jUkulzILguHILQSa2REQWher8PGQcBAtEbMnIRBaITNKeOIheZnFANE6I+gSVBcKh3AJR/clwZZ8fB67sPSdvUP4FkJUvdK5M6v2jrsmWRb1/1AUA3LlzB0qlUrLP1dUVrq6uwnZmZiY6d+6MOnXqAAD8/f2RkZGBKVOmAACysrJ0Zj4fNmwYTp48iXXr1gnlI0aMQGSk4QShNiUgW/63tqq78NzZumVSVXfhuXNo1rtV3QWL8L1iSlV34bkz6w3by8br5PFjVXcBq76PN6t+YWEhgoODkZubKymfMmUKpk6dKmxrZjb38PDAuXPn9O5XZz5/+PAhXFxchIf4mZoR3aYEhCAIwhYpLi7Gjh07tMrF1gegP+O5sf26MqCbkhGdBIQgCMLK0XRV6aN+/fo4deqUsK3OeC7eryvzuZubG/Ly8qBUKiGXy7WO0wcF0QmCIGyErl274vjx43jw4AEKCgpw4MABSWZzceZzAELmc3t7e3To0AH79u0DAOzcudOkjOgc0/k8SYIgCKI6kp6ejk8++QQlJSUYOnQoxo8fj/Hjx2PatGlo1aoVLl26JMl8vnjxYjg4OCArKwtRUVH4+++/4eXlhRUrVuDFF180eC4SEIIgCKJCkAuLIAiCqBAkIARBEESFIAEhCIIgKgQJCEEQBFEhqr2AGEscVl0YOXIkAgMDERwcjODgYJw9exaZmZlQKBTw8/PDypUrq7qLJpOfn4+goCDcunULAPSOQ19SN2tFc1yzZ8+Gn5+fcM0OHjwIoPqM6+OPP0ZgYCACAwORmJgIoPpfK11jqu7Xyaph1Zi7d++yXr16sYcPH7InT54whULB/vjjj6rultnwPM+6devGSkpKhLKCggLm6+vL/vrrL1ZSUsLGjh3Ljh49WoW9NI3ffvuNBQUFsRYtWrCbN28aHEdgYCD79ddfGWOMzZ49m6WkpFRhzw2jOS7GGAsKCmLZ2dladavDuH766Sf29ttvs6KiIlZcXMxGjRrF0tPTq/W10jWmAwcOVOvrZO1UawtEnDjM2dlZSBxW3fjzzz8BAGPHjsXAgQOxZcsWnDt3Do0aNULDhg1hZ2cHhUJRLcaWmpqK2NhYYRWrvnHoSupmzePTHFdBQQFu376NOXPmQKFQYM2aNeB5vtqMy93dHVFRUXBwcIC9vT1effVVXL9+vVpfK11jun37drW+TtZOtU5lYixxWHXh8ePH6NKlC+bOnYuSkhKMGjUK48aN0xqbKcnNqpqFCxdKtnVdo+zsbL1J3awVzXHdv38fnTt3RmxsLGrXro0JEyZg+/btaNq0abUYV9OmTYX3169fx//93/9hxIgR1fpa6RpTSkoKfvnll2p7naydam2BGEscVl1o27YtEhMTUbt2bbi5uWHo0KFYs2aNTYxN3zWq7teuYcOGWLduHTw8PFCrVi2MHDkSx44dq3bj+uOPPzB27FjMnDkTDRs2tIlrJR5T48aNbeI6WSvVWkA0E4OZmgDM2jh16hSOHz8ubDPG4O3tbRNj03eN9CV1qy5cvnwZ+/fvF7YZY7Czs6tW4zp9+jTGjBmDjz76CIMHD7aJa6U5Jlu4TtZMtRYQY4nDqgt5eXlITExEUVER8vPzkZaWhg8//BDXrl3DjRs3oFQqsWfPnmo5tjZt2ugch76kbtUFxhgWLVqE3NxclJSU4JtvvkG/fv2qzbju3LmDyZMnIykpCYGBgQCq/7XSNabqfp2snWodA/H09ERERARGjRolJA5r3bp1VXfLbHr16oWzZ89i0KBB4HkeYWFhaNu2LZYsWYKpU6eiqKgIvr6+CAgIqOqumo2jo6PecSQlJUmSuo0aNaqKe2s6zZs3x3vvvYfhw4ejtLQUfn5+CAoKAlA9xvXZZ5+hqKgIS5YsEcpCQ0Or9bXSN6bqfJ2sHUqmSBAEQVSIau3CIgiCIKoOEhCCIAiiQpCAEARBEBWCBIQgCIKoECQgBEEQRIUgAbEilEolPv/8c4SEhCA4OBgDBgzAsmXLUFxcXOE2t23bViVZinfs2IHevXubXP/WrVto27atVvnatWsRHx8PAIiOjkZmZqbBdmJiYnDhwgWz+pqdnY3Q0FCzjhHTu3dvnD9/Xqs8KioK3bt3F7LAql+UMoOwFar1OhBbY/78+cjNzcWmTZtQu3ZtPH36FDNmzEB0dDSWLVtWoTZPnz4tyRFUWTg5OaFWrVrPtU3NfFS6yMzMxNtvv21Wu56enti6dWtFu2WQMWPG4N1337VI2wRR1ZCAWAm3bt1Ceno6fvzxR7i4uAAAnJ2dERcXhzNnzgBQ/aJt2rSpcEMSb3/11VfYunUr7O3t4ejoiPj4eFy7dg1HjhzBTz/9BCcnJ7z11ltYsmQJjh8/DrlcjtatW2P27NlwcXFB7969ERQUhBMnTiA3Nxfjxo3DmTNn8Pvvv8POzg7r16+Hp6cnsrOzER8fjzt37qCkpASBgYGYOHEibt26hfDwcLz66qvIyspCQkICOnXqBAA4cOAA1q9fD47jIJfLMXPmTPzrX/8y+zMaOXIkwsPD0bdvXyQkJODMmTOwt7dHgwYNsHjxYmzYsAH37t3DjBkzkJiYCE9PT8yfPx9ZWVlgjGHQoEEYN26cVl+XLFmCsWPH4tdff0VpaSmWLVuGo0ePQi6Xo23btoiNjcXjx48xb948/P3338jJyYG3tzdWrVqFevXqVeh6R0VF4dGjR7h58yZ69uyJDz74AElJSTh58iSUSiXeeOMNxMTEwMXFBSdPnsSCBQvAcRzatGmDH374AV9++aXwOe/ZswcA8PPPP0u2169fjwMHDoDneXh7eyM2Nhaenp4YOXIkfHx8cObMGdy5cwddunRBQkICZDIZvvvuO6xatQo8zwvfv++++w5Xr17F8uXLAahS7yxYsAA7d+6s0NgJ24EExEr4/fff0aRJE0E81Li7u8Pf39/gsUqlEosWLcKRI0fg4eGBnTt34vTp03j77bdx+PBhNG3aFOHh4VizZg3u3buHXbt2QS6XIzo6GomJiYKLqKioCKmpqdi3bx8++ugjpKWloXnz5pg8eTLS0tIwceJEREZGYsyYMejduzeKioowfvx4vPzyy2jdujXu3r2L5cuXo0OHDgCAjh07AgASExORlJQEHx8f/Pjjj/j55591CkhhYSGCg4MlZffv39ca/2+//YZffvkF+/btA8dxWLZsGS5fvoyIiAikp6cjKSkJrVq1wogRI9CnTx+88847yMvLQ3h4OLy8vNCmTRtJX9UPiAKAr776Cr///jt27doFBwcHfPjhh9i3bx9yc3Ph4+OD9957D4wxvPfee9i1axfGjh1r8Np88cUX2L17t7A9YsQIDBs2TBjv3r17AagehCSXy7Fjxw5wHIcVK1YgKSkJc+bMwbRp07B8+XJ07doVu3fvNsla2rlzJ65cuYJt27bBzs4O33zzDWJiYrBx40YAwF9//YXNmzfj6dOn6N+/P3755Rc0adIEkZGR+PLLL/HGG2/gwIEDSEpKwtKlS+Hn54dHjx6hTp06SE1NfSaXH2E7kIBYCTKZDDzPV+hYuVyOgIAAhIaGomfPnujWrRt8fX216n3//feIiIiAvb09ANUv+smTJwv7/fz8AKgyzb700kto3rw5AODll19Gbm4unj59ipMnTyI3NxerV68GADx9+hSXLl1C69atYWdnJzxfQUxgYCCmTJkCX19fvPnmmxg/frzOcTg5OWHXrl2SsrVr1+Lhw4eSstdeew1yuRzDhg1Dt27d4O/vr5XC5unTpzhz5gz+85//AABq166NkJAQfP/992jTpo3evmZmZiI4OBhOTk4AgFWrVgn7Tp06hc8//xzXr1/HH3/8gTZt2ugchxhDLqz27dsL748ePYq8vDwhxlNSUoJ69erh8uXLcHBwQNeuXQEAAwcOREJCgtHzfvfddzh//jyGDBkCQJUVuaCgQNjfq1cvyGQyuLi4oFGjRsjNzcWZM2fQtGlTvPHGGwBU3wf1d6Jnz57YtWsXBg0ahB9//BGxsbFG+0DYPiQgVkLr1q3x559/Ij8/X2KFZGdnY+7cuUJ6d3HmmZKSEuF9UlISrly5gszMTGzYsAG7du0SbvJqNFNY8zwvacPBwUF4rxYZzeMZY9i6dasQ33jw4AEcHR3x8OFDODg4wM5O+ysVERGBIUOG4KeffsKOHTvwn//8B9u3bzfn45Hg6uqKXbt24cyZMzhx4gSmT5+Od999F+Hh4Vp91ey/+rGl+vqqWXb//n3wPI9Nmzbh3LlzGDJkCDp16oTS0lKt9s3F2dlZ0rc5c+YIwv/kyRMUFRXh77//1jqPuo+Gvg88z2PcuHEICwsDABQXFyM3N1fYrxZIcTt2dnZaKc4vX76M5s2bIzw8HPPnz4ednR38/PzwwgsvPNPYCduAZmFZCZ6enlAoFJgzZw7y8/MBqJ7BPX/+fNSpUwdOTk6oW7euMMMoOzsbv/zyCwDVTdzX1xd16tTBmDFjMH36dGFWkFwuF26a3bt3x9dff42SkhLwPI+UlBS8+eabJvfRxcUFPj4++PzzzwGoHoQ1fPhwHD58WO8xpaWl6N27NwoKCjB8+HDExsbi8uXLzzSz7LvvvsOYMWPQtm1bTJ06FYMGDRI+F/V4XVxc0KZNG2EGWl5eHnbu3Cn8ktdHly5dsGfPHhQXF4PnecyfPx979+7Fjz/+iNGjR2PQoEGoV68eMjMzoVQqKzwGTbp164aUlBThvHPnzsWKFSvwyiuvwMnJCUeOHAEAHDt2DA8ePAAAuLm54fbt24LIqN1h6va2b98ufJdWr16NmTNnGuxDmzZt8L///Q9//PEHAODw4cOIjIwEALRr1w4ymQyfffYZua8IAbJArIjY2FgkJycjNDQUcrkcxcXF6Nu3L6ZOnQpA5XKaMWMG/P390aBBA3Tu3BmA6kYyadIkjBkzBk5OTpDL5ViwYAEAoEePHkJ20kmTJmHp0qUYNGgQSktL0bp1a8ydO9esPiYlJSEhIQEKhQLFxcUICgrCwIEDJXEEMXZ2dpgzZw5mzJgh/MJdtGiRxNoxlx49euD7779HUFAQnJ2d8eKLLwpunX79+iEyMhLz589HUlIS4uPjsWPHDhQXF0OhUCAkJARZWVl62w4NDUVWVhZCQkLAGEPHjh0xcuRIeHt7IzExEatXr4a9vT3atWuHv/76q8Jj0OT999/H0qVLMXjwYCiVSrz++uuIioqCnZ0d1q5di7i4OKxZswavvfYaHB0dAQBNmjRBaGgohgwZAnd3d/Ts2VP44TBs2DBkZ2fjrbfeAsdx8PLykmSp1cVLL72EpKQkzJo1C0qlEi4uLli5cqWwPyQkBPv27RNcmwRB2XgJoprRtm1bpKeno0GDBpV2ztLSUkyZMgUDBw7EgAEDKu28hHVDLiyCIAxy9epVdOnSBXXr1q2Wz6QhLAdZIARBEESFIAuEIAiCqBAkIARBEESFIAEhCIIgKgQJCEEQBFEhSEAIgiCICkECQhAEQVSI/weAGO7Lx33uzgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from lifetimes.plotting import plot_frequency_recency_matrix\n",
    "f = plt.figure(figsize=(10, 5))\n",
    "plot_frequency_recency_matrix(bgf, cmap=\"viridis\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that if a customer has bought ~250 times from the retail service, and their latest purchase was when they were a bit older than 700 weeks (given the individual is ~700 weeks old), then they are the best customers (bottom-right). The coldest customers are those that are in the top-right corner: they bought a lot quickly, and we haven’t seen them in weeks.\n",
    "\n",
    "We can also plot the probability of still being alive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from lifetimes.plotting import plot_probability_alive_matrix\n",
    "f = plt.figure(figsize=(10, 5))\n",
    "plot_probability_alive_matrix(bgf, cmap=\"viridis\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Have any users churned during the second half of 2011?\n",
    "\n",
    "To determine this I will use the probabilities histories of still being alive as a function of the time computed from the BG/NBD model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5878/5878 [03:21<00:00, 29.12it/s] \n"
     ]
    }
   ],
   "source": [
    "from lifetimes.utils import calculate_alive_path\n",
    "from tqdm import tqdm\n",
    "\n",
    "customer_ids = df.dropna()[\"Customer ID\"].unique()\n",
    "\n",
    "alive_paths = {}\n",
    "for i, cid in enumerate(tqdm(customer_ids)):\n",
    "    days_since_birth = int(rfml.loc[cid][\"T\"])\n",
    "    sp_trans = df[df[\"Customer ID\"] == cid]\n",
    "    alive_paths[cid] = calculate_alive_path(bgf, sp_trans, \"InvoiceDate\", days_since_birth)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I computed the \"alive paths\" for each customers (probability of still being alive), an example of those paths is plotted below. The vertical red lines represent the date of each purchase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lifetimes.plotting import plot_history_alive\n",
    "\n",
    "def plot_path(cid, ax=None, july_line=False):\n",
    "    days_since_birth = int(rfml.loc[cid][\"T\"])\n",
    "    sp_trans = df[df[\"Customer ID\"] == cid]\n",
    "    plot_history_alive(bgf, days_since_birth, sp_trans, \"InvoiceDate\", ax=ax)\n",
    "    if july_line:\n",
    "        plt.vlines(datetime(2011, 7, 1), 0, 1, color=\"forestgreen\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_path(13085)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will collect the **Customer ID** of the customers that have probably churned in the second half of 2011, to do so I need to discard the customers that churned before that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ndays = (df[\"InvoiceDate\"].max() - datetime(2011, 7, 1)).days\n",
    "\n",
    "inactive_after_july = []\n",
    "for k, v in alive_paths.items():\n",
    "    \n",
    "    # if customer made his first order before july 2011\n",
    "    if len(v) >= ndays:\n",
    "        # if minimum probability of still being alive is less\n",
    "        # than 0.5, the customer is considered to have churned\n",
    "        # before july 2011\n",
    "        if min(v[:-ndays]) < 0.5:\n",
    "            continue\n",
    "            \n",
    "        v = list(v)[-ndays:]\n",
    "        \n",
    "    # if one there is one transaction after septembre 2011 we consider\n",
    "    # the user will not churn.\n",
    "    if df.loc[df[\"Customer ID\"] == k, \"InvoiceDate\"].max() > datetime(2011, 9, 1):\n",
    "        continue\n",
    "\n",
    "    # if probability of still being alive in the second half of\n",
    "    # 2011 is less than 0.3 I consider that the customer have churned.\n",
    "    if min(v) < 0.3:\n",
    "        inactive_after_july.append(k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f\"Number of users that probably churned in the second half of 2011: {len(inactive_after_july)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot some \"alive paths\" of those users to make sure they have probably churned during this period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = plt.figure(figsize=(10, 5))\n",
    "plot_path(inactive_after_july[0], july_line=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = plt.figure(figsize=(10, 5))\n",
    "plot_path(inactive_after_july[5], july_line=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = plt.figure(figsize=(10, 5))\n",
    "plot_path(inactive_after_july[10], july_line=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Are there any users in high risk of churning by the end of 2011?\n",
    "\n",
    "To determine this we need to gather customers that survided the end of the 2011 and have a low number of expected number of transactions in the future. Here the future is 22 days since the last date in the dataset is the 9th of December 2011."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "alive_after_july = []\n",
    "for k, v in alive_paths.items():\n",
    "      \n",
    "    # if customer made is first order before july 2011\n",
    "    if len(v) >= ndays:\n",
    "        v_ = list(v)[-ndays:]\n",
    "    else:\n",
    "        v_ = v\n",
    "    \n",
    "    # remove users that have less than 2 consecutives transactions\n",
    "    if rfml.loc[k, \"frequency\"] < 1:\n",
    "        continue\n",
    "        \n",
    "    # users that are still alive in the second half of 2011\n",
    "    if min(v_) > 0.5:\n",
    "        alive_after_july.append(k)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We compute the expected number of purchases in the next 22 days and rank the customers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = 22\n",
    "rfml[\"predicted_purchases\"] = bgf.conditional_expected_number_of_purchases_up_to_time(t, rfml[\"frequency\"], rfml[\"recency\"], rfml[\"T\"])\n",
    "rfml.sort_values(by=\"predicted_purchases\", ascending=False).head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below is the distribution the predicted number of transactions in the next 22 days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ax = rfml.loc[alive_after_july, \"predicted_purchases\"].plot(kind=\"hist\", bins=50)\n",
    "ax.set_xlabel(\"predicted number of transactions (in 22 days)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the normalized distributions the predicted number of transactions in the next 22 days, for customers probably alived in the second half of 2011, for each cluster group defined above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfml.loc[customer_ids, \"cluster\"] = rfm.loc[customer_ids, \"cluster\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = plt.figure(figsize=(10, 5))\n",
    "\n",
    "for c in range(5):\n",
    "    rfm_c =  rfml.loc[alive_after_july].query(f\"cluster == {c}\")\n",
    "    label = f\"Cluster {c}\"\n",
    "\n",
    "    sns.distplot(rfm_c[\"predicted_purchases\"], kde=True, \n",
    "                 label=label, hist=False, norm_hist=True)\n",
    "plt.xlim(0, 0.7)\n",
    "plt.xlabel(\"predicted number of transactions (in 22 days)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we see member of the cluster 3 have higher risks of churning in the next 22 days. We could also select a fraction of the customers with the lowest predicted number of transactions and act in order to keep them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfml.loc[alive_after_july][\"cluster\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfml.loc[alive_after_july].groupby(\"cluster\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfm.groupby(\"cluster\").mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Customer Lifetime Value\n",
    "\n",
    "Let's compute for each customer its CLV and compare for each cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfml[['monetary_value', 'frequency']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lifetimes import GammaGammaFitter\n",
    "\n",
    "rfml_m = rfml[rfml.monetary_value > 0]\n",
    "\n",
    "\n",
    "ggf = GammaGammaFitter(penalizer_coef = 0)\n",
    "ggf.fit(rfml_m['frequency'],\n",
    "        rfml_m['monetary_value'])\n",
    "print(ggf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(ggf.conditional_expected_average_profit(\n",
    "        rfml_m['frequency'],\n",
    "        rfml_m['monetary_value']\n",
    "    ).head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Expected conditional average profit: %s, Average profit: %s\" % (\n",
    "    ggf.conditional_expected_average_profit(\n",
    "        rfml_m['frequency'],\n",
    "        rfml_m['monetary_value']\n",
    "    ).mean(),\n",
    "    rfml_m[rfml_m['frequency']>0]['monetary_value'].mean()\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bgf_clv = BetaGeoFitter(penalizer_coef=0.0)\n",
    "bgf_clv.fit(rfml_m['frequency'], rfml_m['recency'], rfml_m['T'])\n",
    "\n",
    "print(ggf.customer_lifetime_value(\n",
    "    bgf_clv, #the model to use to predict the number of future transactions\n",
    "    rfml_m['frequency'],\n",
    "    rfml_m['recency'],\n",
    "    rfml_m['T'],\n",
    "    rfml_m['monetary_value'],\n",
    "    time=1, # months\n",
    "    discount_rate=0.01 # monthly discount rate ~ 12.7% annually\n",
    ").head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_sum  = ggf.customer_lifetime_value(\n",
    "    bgf_clv, #the model to use to predict the number of future transactions\n",
    "    rfml_m['frequency'],\n",
    "    rfml_m['recency'],\n",
    "    rfml_m['T'],\n",
    "    rfml_m['monetary_value'],\n",
    "    time=1, # months\n",
    "    discount_rate=0.01 # monthly discount rate ~ 12.7% annually\n",
    ")\n",
    "\n",
    "predicted_sum = pd.DataFrame(predicted_sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ids = predicted_sum.index\n",
    "predicted_sum.loc[ids, \"cluster\"] = rfm.loc[ids, \"cluster\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f, ax = plt.subplots(figsize=(10, 5))\n",
    "\n",
    "for c in range(5):\n",
    "    p_c =  predicted_sum.query(f\"cluster == {c}\")\n",
    "    label = f\"Cluster {c}\"\n",
    "\n",
    "    sns.distplot(p_c[\"clv\"], kde=True, \n",
    "                 label=label, hist=False, ax=ax)\n",
    "ax.set_xlim(10, 10000)\n",
    "ax.set_xscale(\"log\")\n",
    "ax.set_xlabel(\"predicted CLV (in 1 month)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_sum.groupby(\"cluster\").agg({\"clv\": [\"mean\", \"std\", \"median\", \"sum\"]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_sum[\"cluster\"].value_counts()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}