CLV_Online_Retail.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "          InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "0 2010-12-01 08:26:00       2.55     17850.0  United Kingdom  \n",
       "1 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
       "2 2010-12-01 08:26:00       2.75     17850.0  United Kingdom  \n",
       "3 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
       "4 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "df = pd.read_excel(\"Online_Retail.xlsx\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "sns.set_palette(\"husl\")\n",
    "sns.set(rc={'image.cmap': 'coolwarm'})\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "InvoiceNo       object\n",
       "StockCode       object\n",
       "Description     object\n",
       "Quantity         int64\n",
       "InvoiceDate     object\n",
       "UnitPrice      float64\n",
       "CustomerID     float64\n",
       "Country         object\n",
       "dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import datetime as dt\n",
    "df['InvoiceDate'] = pd.to_datetime(df['InvoiceDate']).dt.date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[pd.notnull(df['CustomerID'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[(df['Quantity']>0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Sales'] = df['Quantity'] * df['UnitPrice']\n",
    "cols_of_interest = ['CustomerID', 'InvoiceDate', 'Sales']\n",
    "df = df[cols_of_interest]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   CustomerID InvoiceDate  Sales\n",
      "0     17850.0  2010-12-01  15.30\n",
      "1     17850.0  2010-12-01  20.34\n",
      "2     17850.0  2010-12-01  22.00\n",
      "3     17850.0  2010-12-01  20.34\n",
      "4     17850.0  2010-12-01  20.34\n",
      "Number of Entries: 397924\n"
     ]
    }
   ],
   "source": [
    "print(df.head())\n",
    "print('Number of Entries: %s' % len(df))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* frequency represents the number of repeat purchases the customer has made. This means that it’s one less than the total number of purchases.\n",
    "* T represents the age of the customer in whatever time units chosen (daily, in our 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.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>CustomerID</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>325.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12347.0</th>\n",
       "      <td>6.0</td>\n",
       "      <td>365.0</td>\n",
       "      <td>367.0</td>\n",
       "      <td>599.701667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12348.0</th>\n",
       "      <td>3.0</td>\n",
       "      <td>283.0</td>\n",
       "      <td>358.0</td>\n",
       "      <td>301.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12349.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.000000</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",
       "CustomerID                                           \n",
       "12346.0           0.0      0.0  325.0        0.000000\n",
       "12347.0           6.0    365.0  367.0      599.701667\n",
       "12348.0           3.0    283.0  358.0      301.480000\n",
       "12349.0           0.0      0.0   18.0        0.000000\n",
       "12350.0           0.0      0.0  310.0        0.000000"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from lifetimes.plotting import *\n",
    "from lifetimes.utils import *\n",
    "from lifetimes.estimation import *\n",
    "\n",
    "data = summary_data_from_transaction_data(df, 'CustomerID', 'InvoiceDate', monetary_value_col='Sales', observation_period_end='2011-12-9')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basic Frequency/Recency analysis using the BG/NBD model\n",
    "We’ll use the BG/NBD model first, because this is the simplest to start with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<lifetimes.BetaGeoFitter: fitted with 4339 subjects, a: 0.00, alpha: 69.03, b: 1.06, r: 0.83>\n"
     ]
    }
   ],
   "source": [
    "from lifetimes import BetaGeoFitter\n",
    "\n",
    "# similar API to scikit-learn and lifelines.\n",
    "bgf = BetaGeoFitter(penalizer_coef=0.0)\n",
    "bgf.fit(data['frequency'], data['recency'], data['T'])\n",
    "print(bgf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After fitting, we have lots of nice methods and properties attached to the fitter object.\n",
    "\n",
    "For small samples sizes, the parameters can get implausibly large, so by adding an l2 penalty the likelihood, we can control how large these parameters can be. This is implemented as setting as positive penalizer_coef in the initialization of the model. In typical applications, penalizers on the order of 0.001 to 0.1 are effective."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing our Frequency/Recency Matrix\n",
    "Consider: a customer bought from us every day for three weeks straight, and we haven’t heard from them in months. What are the chances they are still “alive”? Pretty small. On the other hand, a customer who historically buys from us once a quarter, and bought last quarter, is likely still alive. We can visualize this relationship using the Frequency/Recency matrix, which computes the expected number of transactions a artificial customer is to make in the next time period, 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": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b9e76fa080>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MeSLwPFX9F+BU4BUi8rih9GyO6DT26I9XBgKBIbPIJnO+AtxmH/8B43p/acBz\njpzgXgcCs0sexX3f5gODTuZsoqqfA1DVdZglZ9/Sz4EisiNwgqruISLbAxcB/2M3n66qXxeR44F9\ngSZwlKpeN2B/+6ZT3jeYqueuLUzqBAIzYJ6MPfbLoEL5sIjso6rfAxCRFwAP9jpIRP4VE1bk9t0e\n+KSq+rXktsekH+0IbA6cBzxnwP7WUrUgY7KW4cjq4mN+IY1AIDB95oul2C+DCuXhwJdF5EuYEKHf\nYwSwF7/BBKk7N30HQERkf4xVeRSmZtzlqpoDd4rIhIhs1qsS8bAorMgoKuKdwqROIDAk4vGazImG\n4TqKyGOAhg067/eYrYD/VNWdRORQ4EZVXSsixwIbY0of3a2qp9v9fwy83i/zXsdtdzyYb73lhjN9\nKYFAoAcr9lvNmotWDuQ737f2sr6FZ/kOe4/cTx/IohSRLYEzga2A3UTk2xgx++00T3WBqrqacBdg\nZtC/Q2udub7qxh1y5FquunB3Vh64htiGIMQT5j5JkuKxsxiTiaR28iZtGqsxbTRpNhoAZLYtS9Ni\nu6P6h7PmopWs2G91r+7OKqEP86MPo77+fOlDC2Pmeg/a289hKgc/APyZcu2c6XKZiDzXPn4+sBZT\nM25vEYlFZAtMQc67BuxvV9ySEOCnNdYvCxGCzwOBmZMT9X2bDwz6S99UVS8HUNVcVb+AKc0+Xd4C\nfFpErsBUI/qwqq7FhBtdg5nIOWLAvgYCgXnCYgsPelhEnozN9RaRXTFLRvbEuuc72cc/x6yUVt1n\nFbBqwD5OmyiKiuT1yKYyAkUx3yjPw4ROIDAI80QA+2VQoXwncDHwFBG5AdgEePnAvZoHOLc6inPv\nsXUDajQyFMoIBPonG7NZ70FzvX8qIs8BnoYpu34LpoLQgsaVXgPI7OhFiKsMBKbBYgg4F5HNMNbk\n/wM+paq/FJEYeDPGVR6rfO8qURwVxeLiOCLzsnQMwe0OBAZhvow99stMLcqvYJZ/3BRYIiIXYGa8\nlwFHD6lvc4ab6e5UAMMPPgczVhk5NzuMVQYC02a+zGb3y0yF8imq+hQRWYaZlX4rJvbxk6o6NbTe\njZAi1zuP6deCDPnfgUB/LBaL8j4AVb1fRDYBXqqq1wyvW/OLcjLHm9Sx2ukXyggEAv2RR4tjMsc3\nmf48jiLZb7B4y8RNTUWhIowoFMoIBPpmvqyF0y8zFcplIrIbJmB9Q/u4eOWq+uNhdG5UxJ6IZmT1\n4UB+TCWEscpAYBosFtf798CH7OP/9R6DMbKeN0in5huFKNZUFKrdf8z+LQOBuWZRTOao6p7D7sg4\n4I9VVoPP4zgKMZWBQJ8sFovixtn1AAAgAElEQVRy0RDHMXmW28flWGXVygzz3IFA/yyWMcoFjx/q\nU7Uefdc66pL/HdIaA4F6skUy671oqBNFSMvg8xH0KRAYdxbFGKXD1pDcFfgMpjjGPwAHq+qlQ+jb\nyKhOxrhZcOeC+7Uoi8D0rD3/OxAI1DOsMUqbOn0asB2mctlh1VUQbMr11cAzVfUREYkwE9JuMcNr\nVPW93a4zqEV5CvAB4GXAQ5hFws4HxlIo4w6xldUUx+nkf4dsnUCgnSFalAcA66nqziKyE3ASsL/b\nKCJ7Ax+ntf7EU4Cfq+p+/V5kUFmPbeHefYHzVPV3LBB3Piqqm7d/oC7g3NWqjKOyCnqoeh4I9GaI\nhXt3xRpmqnot8OzK9gx4AaaAj2MH4Eki8iMRuUREpNdFBv1VPyQi78LETV4sIm/HFMtYkMRx3NHq\nbN2vam0GAgGfIS4FsRy413ueikhhrKnq91X17soxfwQ+ZsMcPwp8uddFBv0lvxrYEJPr/VfgScBB\nA55z3lFYl5F38yxIF1fpr7nTdo4u2wKBxUZG3PetB/fRughhrKrNHsf8DLN4Iap6Fca67PrjnGk9\nyg9gxiSvV9UiK0dVj5nJ+eYzURwVkzh+W23+d2wne8J4ZCDQlXx4E55rgP2Ab9gxypv6OOZ44G7g\nRBHZDrhTVbv+aGc6nvg3wMO0+v0Lluo4ZZzH5fo5XlpjHrJ1AoG+GOJkzgXAXiJyNabexKEi8k7g\nVlW9sMMxHwe+LCL7Ak3gdb0uMtMUxkMBROQy4KyZnGOc8d3olhJsaVps73ZsmAEPLHaGJZSqmgGH\nV5pvqdlvK+/xXzET0H0z6Az1+iKyuZ3t7hsRmcQI7FaYNXY+DPw3cDYmG/Bm4AhVzUTkeMyLagJH\nqep1A/a5L3wrsqxsbp7nWd4SKmS2eWmNcRlvGaoKBQLtLKqAc2Az4Lci8n8YVzwCclXdusdxrwHu\nVtWDReQxwPXADcBxqnqFiJwB7C8idwArgR2BzTHrez9nwD5PizhqF8w8johzI4q+C16XrRMWIQsE\n2sny8YoIGVQoXzjD474JfMt73sTENq22z78H/COgwOV2oPVOEZkQkc1U9S8z7XA/dFo7x98eZdb1\njvzJnEq2jrc+eMvxIQg9sMgZN4syGvTHKiIHAX8PfAR4maqeO41jlwEXAl8APqGqT7TtzwNejxlr\nuFtVT7ftPwZeX01R8rntjgfzrbfccKYvJxAI9GDFfqtZc9HKgZTu5lv/1LfwbPPUx49cVQfN9f44\n8GSMNXgCZsZpO1V9Vx/Hbo6ZsTpNVb8qIid6m5cB99AeI+XaO3LIkWu56sLdWXngGuLEVCiJJ8x9\nkiTFY7ctmUgKCy8ptpX1Jl3WjU+eZaR2kZzM3jcbTZpTDQDSRpPLv7w9z3vFdWR2gse53FmWt7nf\ns2VZrrloJSv2W917x1kk9GH0158vffAZN4ty0IGCvYGDgUdU9T5gL2CfXgeJyOOAy4FjVNXNml8v\nInvYx/sAV2JipPYWkVhEtsAEk941YJ8Hpggw7xKEbvbrHoQeCCxW8jzq+zYfGHSM0plGziRaSn+V\nx94HbAy8X0Teb9veAZwiIkuAXwHfUtVURK7ELIkbA0cM2N+BcFZmnuUtEztuW19B6FlaCGmY2Aks\nVrIxsygHFcpvAF8HNhGRozDW5Vd7HaSq78AIY5WVNfuuAlYN1MsBaFn2oWiLcMa4E8A4rg9Cd1JY\ntMURWTXTJ8RWBhYZi2rWW1VPsGWM7gC2AI5X1YuH0rMxoQgJ6hCE7sZE06ms3GZjK4NlGVisLLYx\nSoA/ABcB3wbuE5Hdh3DOeY0Zj2wtweaXZYs9d7zcXo5dxt4+LecMY5mBRcKiGqMUkf/EFOv9X695\nQSxXG9eIVsuyEFXrMc+LEmx1QejFWGZWH1sZCCwmxs2iHHSMcjvg6aq6aPP0ijCiLCJPrMVocx2T\niaQYe8xyl9aYl+OadgQzIy7c7xCMHlgMzBdLsV8GFcqfAE/FZNAsGny32Q0vRnHcMrFTtHkTO2As\nS5fVE6QwsFgZt1H5QYXyh8AvReQPmDTEfnO9xwp/xrrjPnFEnJUTO+64lupCUFQYammrCRkKM+GB\nhcyimvXGxEM+DzPrvagoxdM8zzJ/ttvNcHePrcytaNaFDAUCC5nF5nrfBVzZqzrwQqNuUieOyw8/\nsWOVSZIU1dFje594KYzOBSeOW8YrwViWYbwysFBZbJM5vwauFZHvA1Ou0V8eYjHRHkfZeRVHc99q\nWQYCi4Vxc6AGFco77Q0Ys7+IPulUcq2tPSvdcGdZGte7nAEHYyk6i7JbyFAUx2G8MrBgWVQWpap+\nUEQ2wxTWnQCuUdU/D6Vn85hqoHmBmwF31YhaXO9phgyN219uIDANsmy8hHKgqSebvngDcCjwWuBG\nEfmnYXRsLunkIrft15Km2Dkzx41RusdJEhdrgruQIReMHkeVKkR2nzguH1evHQiMOxlR37f5wKCu\n90eAXVX1dgAR2Ro4H1hU+d4Ov7oQdMjkqQkZivI8jFcGFhXjNus9aDDTpBNJAFW9bQjnHCtizzJ0\nOMFMElMUw9xi4omYZCIpbnESm5uXO+5bli4n3C8cHCzLwEIgz/u/zQcGnsyx5dX+wz4/jEUYUwku\n4Nw8dqs1+m54nrnJnLwYryyKZEwkLeOV5iRZywRPCEgPLCTGbTJnUOvvDcDOwG3A7fbxGwft1Hyj\nrtpP1YqEcpwy9scxCwvRry7kxizL87aMV7YEp8ddM4ICgXEky/u/zQcGLoqhqq/wG0TkQMw45YKl\n46x3pS2OoiIEyLcsi5lwt6ZPlpfjmp5lWTde6VuWISA9MK6M26z3jIRSRF6BWfbhQyLygcr53scC\nF8o6qguQmbaIpMi0ceIYk1jXO3OxlXnNgmNZRozZntE9bCgIZmDcmC+z2f0yU4tyGbDC3u/ptTeB\nYwft1LhSNS6NeBqBiydKIc1zuwKki6fMsjIg3bYlSUKKrYSeRUU0e11ptkBg3Bi3//QZCaWqngmc\nKSLPV9UfunYRWW5XY1w01FmSjjgqi/i6XO88jtpKr9WttxPFcVFpKIrjnm64OzZYlYFxYNzCgwYd\no9xARE4A/g34KbCZiLxbVc8euGfznOrYZBy1t5mZ8FIgAaLcmwn3LMvMip1vWSZ2DDMlbSmgAcay\nDG54YFwZ1iSNiMTAaZgi4uuAw1T11so+mwFXA89U1UdEZH3gy8BjgfuB16rqX7pdZ1Ch/AAmJOiV\nwHWY5WRXA2d3O0hEJoGzgK0wY50fBn6PWXvnf+xup6vq10XkeGBfjFt/lKpeN2CfZ0w/2TvQGlzu\n4gqSmgAD53rnWUI+6R63VhsCk+rohw2ZHeorDlX7EAjMR4b4P34AsJ6q7iwiOwEnAfu7jTZ78OPA\n47xj3gLcpKqrROSVwHHUrwpbMHBwuKr+AiNkF6rqA8BkH4e9BrhbVXcD9gE+g1l755Oquoe9fV1E\ntscsYbsjRow/O2h/55r28KC4DAWqCR0qgtG90KJq2FDHa3UZBggE5hNpHvV968GuwKUAqnot8OzK\n9gx4AfD/6o4Bvme3d2VQi/LPInKq7dxrROQkympC3fgm8C3veRPYARAR2R9jVR6FeUGX23qXd4rI\nhIhs1stMHgVR3J4xU7dkhPGmbViQm7jJ85aAdIBksvxo6tzorJm2T/B0cMWDGx6YbwzxK7kcuNd7\nnorIhKo2AVT1+wAi0umY+4FH9brIoEL5KuAlwMmq+qCI3AYc3+sga3kiIsswgnkcxgU/U1XXisix\n9jz3AHd7h7oX1VEozz11BwBWn79iJq9nqJxxzCaj7gJXXTj61YPXXLRy1F0YeR9Gff1h9mHFfqsH\nPscQhfI+TPSNI3Yi2ecxyzA605VBhfIl9n4XEdkFI2QHAuf2OlBENgcuAE5T1a+KyKNV1XX4AuBU\n4Du0vgk9X9QhR67lqgt3Z+WBa8qAbjtBkiRJ8dhtcznXUC4KFtuKP+7Y4nGlKpA5p1ek11tb54xj\nNuEtJ/61to9pai1Ae5+mGc2GmdV292mjWTxuTDVIm2W7OSY1ViXl2KQfj5llOVd+e1d2ffGP264/\nlxbmmotWDuWHNc59GPX150sffLLhzXqvAfYDvmHHKG/q85gXYeZV9gGu7HXAoELpx1BOArsBP6aH\nUIrI44DLgbd54UWXiciRdrLm+cBazAs6UUQ+ATwZ829x14B9Hpi6Nb99WqoGVcYV8ywvXPKiwG8e\nkyQ2ptKJaBwXa+/ESRkz6dbeibMy6yfvEk7pFwAOBOYLQ/yvvgDYS0SuxhQPP1RE3gncqqoXdjjm\ndOAcEbkKszLDQb0uMmjh3kP95yKyCfD1Pg59H7Ax8H4Reb9teyfwaRGZAv4EvElV7xORK4FrMBNP\nRwzS39mmmpPti2SxyBgQtWUlZLhxy6TmG5R7KY5xl7iKunFL048QbxmYX6RD+u9W1Qw4vNJ8S81+\nW3mPHwL+eTrXGdSirPIAJuSnK6r6Duqn43ep2XcVsGrAfs06ncJx2opptDz3Z6ndN6f9I6mzCP1B\nGFd5KJ6gcMcLwfRWeKzLEy+uEYQzMIcsqoBzEfkRFNXAImBr4LuDdmqh4ItinZAWa+xkfjk2u4Rt\nFBXrg8cTCVFaLoHrzp1nrRk+Kf5a4aW4FtbsfCnFElj0jNv/8qAW5SrvcQ7cpar/PeA5R8awgrSj\nGoGM6kIcrdAlE3EhYp0+kLxPkXP54c5ajZNyjZ5ueeLBHQ/MJeP2nz1joRSRjYFfuskVEVlJl7Cd\nhUZttaA+hLZlIsgMS5Ll7ccO+g9WFNSI45ZsHrDpj5Vcg1C2LTCXjNtXbEapHCLyD8B/0xoF/4/A\nDSKy7TA6thAwWTfGmvQL/fqZOOUCZJG92cXIkpiJyaTtNrlkgsklE0xMlrfE3SaS8rEXGuUKANct\nM1H0tWa5iZAGGZgtxm0piJnmvH0CeJWqujQgVPVY4PXAJ4fRsYVKt3XCzc0Ia5J4qzV6a+8UVdKt\noLat8Oiqort40R7pj3XV2wOB2SbN+r/NB2bq4W2sqldUG1X1MltNKNCFssxa2ebGbIpJnSjH+eat\nItc5ld63At2suBNMnzzLizl2N1YZx1FXd7xomy9/8YGxZtxCe2cqlJMiEtsYpgJb8mjJ4N0aH+os\nNd9C891twBa5aD+HkzMXPJ5FUVExqHYiiMm+qhm5DCSflLSonp5745LVnPG6YPUw6RMYBuP2FZqp\n672a+pzu44Cfzbw7i4s6l7hcoIyW6kGJ52qXY5qtbdVlccGkaPpjk+WtP3fc7d/SxzB+GRiQcRuj\nnKlF+V7gEhF5LXAD8AimTNr/AS8eUt8WJH6BX19squGPURIVC43FUVzWo7R0W9TMZ8KrQuTyxaM4\nKgPTLXmWt1iX0FocuJrd4/ofrMvATFgU4UGqer+I7I7J9f4HTFrJZ1W1Z3L5YsJ3mbtZYHFcbvc9\nZadBJvDcBpwvseOWTXAfX8vyEZXrTS6ZaC0kTGtWjyOLczK3oa44cAd3PIxhBmbC9JIfRu+9zDhc\nz9aI/C97W/RMZ+a4rDJUPq8/3glcXlQ28ocMyzV33H1ajH8663Jy6WQhoOV64RFpw2bzOBH1phdd\nxk+n3HF/0ieMYQZmwmKZzAnMkJ5Vyms2m3xt99it0hhRZN84VzmLi6IZzuWOk7LNLZPrry1eWIp5\nTuwEshDWvC1YPc+ylpTITi45BOsy0Jlx+2oEoZxlugmjX7+yKpAtaZBAnLiSalbYopwocmOY2Puo\nxboEWLpeOTveOkHTOhufNqKu1qW7bu5ZjP2MYbr7IJoBn0UxRhkYjG5L2xSiFrWO/xVCk5QC52pY\npmnZVhQhtvst8YSy6YoMR1GbUJqAdVsUuGbSJ7OWbNakdgzT0cktD1ZmwGfcvgZBKIdIN+uxm8cd\nRa0CadoqYUPV9XjIyyK+RbC6L6xlBfZ8onU9nnyyLJThr9UTZ10U3FVTj0uXnyxrccmhvqxb3esI\ngrm46bfIi2GMJ3MCg9NrAsjNmtdXVI+KOpSu0nmcRCSpP4YJS5cmxeNCjJO45bG7d1lBbgmKKIoK\nMU69mfU0dUtQRC0uOXQOXHf4xYOLtiCai475kprYL0Eo55iOBX4rlqQrpGHavP38kCM3fljMdLeK\nJsDkkqRFDAEanig2EuNuJ0lcuubWBY/iiCxNWtrSZlqIbNZMC5e8bqY8cULfwcoMorl4GbfaqEEo\nZ4HpZq3UpkFG5QSPH2fpU81OzL1J6iQvLcpmM7P7R8V9c9KK2URpRU5N2aLBtm2ikdBY1zBthbXZ\nLMQzTVIya11mzbKsW3XRsyiOC9H0Fz+ruuYht3zxMG4faxDKeUZ9Xre3vetYZymuaYsL7k7qVmss\nq6PnE+UFy39587XIs9xbX7xp20p1zrOsrLLuim8008Kqddf1Z70jLy6zalWE3PLFw7h9pEEoR0Cd\nddg+edMakO5bl53O4eOszcnJqHjsalROTMQ0J4wgOWuz2cgKd73ZKC3QchldmzveSIslc5tJQtM+\ndpZlanPKwZvgmSjjNnNvxnymViYES3Pcycbs8wtCOUL69dCrolm0dT3ebJyYiIqFnFyw+sREVLjh\nE871nsiYmHTutRGuqcm4eNyYsuLYSGmsc2uep4WbnhWC2yxF07rgE5OTpRjmLswobRFNMGOaVYuy\nV7hRsW3MfniLnW5LLM9HRiKUIpIAXwAEsybWoZhf9tmYtXduBo5Q1UxEjgf2xfh+R9l1v8eCXut/\n9zqm37HObu56kvhfSj80pzpR1H4S3zP2Xe46mtY1n2CiLZc8Tspoy9iJ3UTSnlsOfYUb1a1KGVz0\n8cINDY0Lo7Io9wNQ1RUisgemKnoEHKeqV4jIGcD+InIHsBLYEdgcOA94zmi6PDr8yZzCBY/aw4fq\ndDWJo0IDE1dkIwc3pJhmzrLMaTat6920kzkTWfG4OWkOmGqkhZU5MZkUrnljXWKPTZmw+7ptS9Zf\nWrjrTvSyNC1WmSzCnLKsa7iRo1pg2B0bXPTxYdw+l5EIpap+W0Qutk+3BP6MsRpX27bvYdbgUeBy\nW4DjThGZEJHNVHVBLWJW50J3yt4pi160C2SdOz7hfcK+ZemsxQl7n2bGJYdSzJrNiIYTyqZpm2wk\nNOzseHNJUsyUTy4xF2pMNQuBTBoujXIJqZ0UKsc0E9LEzpgXcZl5S4yma6uWf5uJix5CkOYXYxYd\nRDTKL42InAO8BHgZcLaqPtG2Pw+z/s4twN2qerpt/zHwelW9tdM5b7vjwXzrLTec9b4HAouVFfut\nZs1FKwdKlzn2rHV9C89HXr905Kk5I53MUdXXisgxwE+A9b1Ny4B7gPvs42p7Rw45ci1XXbg7Kw9c\nUyyD4EJXkiQpF91KygrgLkbQzQq7lRHdsUklYNtVEzePo7LN237q0cs56pT7y2Bv5zLb6uStx0bF\njLOzEpPEL59WPnYz2H6cpZs9NlamfRzBm/8x4vPf7+/76FeTtkadXdwpb2lrNvPCumw2cxpusqe4\nT1smgM44ZhMO+/BfvNlze99MS+uyy0RQnudlvnmdi55lxX7Fa/EsyizLufLbu7LbAVfVbm9/H4Zv\nOKy5aCUr9lvde8dZZD70wWfcjPpRTeYcDDxZVT8GPIQJ8PuZiOxhFy3bB/gRcCtwooh8AngyELt1\nxBcqdeOM9eFE7fvGUasbXm3rdg0ov7xu/DLLIbMucCGek1Ehns1mOZ451XDhRoknlOZEGyxbWjt7\n3mxMFI/BBLinNrcts/dpM20TzyzN2mbRq+ObvpsOremincY4zXsTxjnngnTMchhHZVGeD3zRutKT\nwFHAr4AviMgS+/hbqpqKyJXANZgpiSNG1N+RUzuO6Y1XQqt4+pZlXVu387bMdntjmO65mwBqNsvH\nS91EUJqX4mmFcvnypUxNOSE1q0g2ptJyIqhFPEuLE0wYkWtz45dZmpUWZ1YKqy+ehaVpX0ycJG3i\n2WJ5TlM8zXsRBHSmDCs8yC5oeBqwHbAOOMwfmhORNwJvxkTNfFhVLxaRTYBfY6JrAC5Q1ZO7XWdU\nkzkPAi+v2bSyZt9VwKpZ7tKs08+KiS37d8wJ7zNkqGaCpyqYfpuPV4SoEEq3X563njtxa/x4Oerl\nBIo5eOnSpHj9ScMNMXgCX1OsoygN5wWwR/bYLC5nuJ21CWWee5TllK1lGmVbWW1vxqylgnuXvHSf\nEMs5c4YYcH4AsJ6q7iwiOwEnAfsDiMjjgbcDzwbWA64Ske9j1vf6mqoe2e9FQsD5mNLJKqzqse96\n11uZedHWD3kOWR4Vj52lucQuN55lkavIVtTJ3GijuAwzsuObU42keFzMojcyphrlY7Ot2T6+2UgL\n17ycRc881zwlySaKxwCTS5f0dNfNa8qnbXE6gnD2zxDfm12BSwFU9VoReba37bnAGlVdB6wTkVuB\nbYEdgO1FZDVmQcS3q+ofu10kCGUN07X+ZotuBX6hPdDc/512syiTOK8dyyyP7S6eRS3LvAwzKl30\nqHjsBHP5hqV4FrGaaUzDCWXDxWDmxVhnKZ6TbeLZbKRe6qWtatRoddHdBJFz15esv7Snu+62VcUT\naFsFM88yiE2/6yrhVOM6g4i2MsTqQcuBe73nqYhMqGqzZtv9wKMw0TRrVfUHIvJq4FRM5E1HglDO\nMb3Er45e1l6dQNa511GtRena8q5t9XgzzYVgluJpJoIiNlo/L7zeprUy06x87O6nGv7kUCmejWKi\nyFqjU2kppE1PPD2LszrWuf6G63WdKPID4asz6nVjnnUi2mJhxkmLGFSzinplXS10IR3iy6tGxsRW\nJOu2uaiZn2AmkQEuAD7U6yJBKBcRzs328a1HXyCLtg77t5/I3OU5xWhfHudAzJKJvHDR/fXLEy/8\nyd03bdC7C36fauRFGJUTyjiJihz1QjAnYqa8MK4iDMxanJNLJ4mdULqScFFE6vrghDKOSsvTG/N0\nwujX38zLfwSznzdhBK3jni4Vs67oR+3bucAt0CHOeq/BZPp9w45R3uRtuw74iIisBywFno6ZwDkH\nk+X3DeD5wNpeFwlCOSb0Kr8GnWfG68KD6iZ4nAgmtVZmuzse0f3Hm1v1XH9JWsyO+5anS/ctZtHT\nqM3ybKQRjSK10m1LmGo4t9266s2chp1Zn5rKSkvTit7yjTcsHtePdXZ2230X3Y/v7GV5+lZnnCQt\n7ntZECTp6oYuVAt0ektBdOUCYC8RuRrzd32oiLwTuFVVLxSRU4ArMf/5x6rqIyLyHuAsEXkr8CBw\nWK+LBKEcM6bjhnfbVrUMfVFstTKtFVfT5gtlt+suSVKwsZmFUBK1TAqBmSRyopnabWZyyAkp9j6i\nUbSZEzcaMFUEwpcCOjVl2h6z2QZeiFI5Dlp10ZuNtLB20kI8s7bg+DzPWyaPTF/zlnRM31pMlky2\njH+6zIE8y9xQZ33QfJdxUH+/6RaLHjXDEkpVzYDDK823eNu/gCnA4x9zO7DndK4ThHKB0u1302mW\nvGq0xjVjlHGUFwIZRe2C65/DbZuIveVv3boV5KWL7s+iW9fUiWiaRUy4dXvst7WZRkxWxzcTirZG\noyziMTlp+rDBBgmTS5y7bgVzSVzEepZCmZXiOVlOGLnsrNRz313WVVHxvZkWC7mlfhk5zDrrvniW\nk0ftLrx5I1s/jWphELN7/648lGI6H6zPccv1DkI5AnotKtbXOfo8RSfBrHPHq7PdUVRajUlcWpHF\n7Lm3tngpnnmb4E7GKVUi8sI193Gi6YtoZleULKxMz/J0gtpMY5qeC++szynrtj9u0wRrFDJlVrew\ns+zOhXfWaFZveTZbXfRmyyx7OUlU58IDLN1gPXLP4sw8t90Jlxs7Na+rkqJZsVCBjq58cY6aCvJ2\nA/0wm4I6RNd7TghCOcb0K5Z1kzgOJ3S+uPmudSGanhWZRKUoFkJa01aOeabtlmmL214TXpOXB2SU\nFicYt70QVE9E/cdNu/SuEdQlPPZRaYuQghHRYizUiWgzoWGFtAxfyr0UzTL2swhXapb3dUIKsHyT\nZW1CCsaVd4Lkj39WQ5n6GRMt3jtXy7Pi0icTSUtYU/H+dhKtPgV1JswHq3Y6BKFcAEynUrq5r/+S\n1p2nKrJRxYr0BdLsn7WIKhgRjaOsOKbah6LNnxzq8ZqcNVrc51HxOMvjQlyNxbmETTd8qBBPJ6jN\nrBTURuq1FeLpBDWmabeXVumkFzxfCqoTTX9CCWDTxy9vdetrxkQzbwLKWZduW2sJujKUKauIZydB\nBUis+1+8h56gVoVrWoI6A0Kud2DsqS/MUdfWLnb+vrEnilWBNLJWEVnqz9cPeRR5lmeraAKsl0yR\nWdezmZeCWYjnRCmUDVvNvZhEysq6nFNFwLwvpOXYqNvPt0IBNlq2hGYja4//bGZtLnynCaV28Zzo\nSzxjK4JuSQ5/fLR4/yrHVEOdoL4O6EwJrndgZExn4rOuQEbd+aqCFVO2xfjjmqUAOsFzVmRM5olh\nVuxf7Gczs42rXxnz7CCeUc3khrcRN1JqBHMzliX3t4lnTkRmp+NTJ57EpcWZ221ZXIhr6kS0gzVa\nCmpcPAbY8kmTTDUp3PqpYky03a3vJKhpJeSpzhpN06wMqPfce4ANlm9oJpkq45/V2XpHXdbSsAhC\nGeib+ZIqCV0CySvb28KKKoLWMinUMsGTtRyfkBVi57vlUVU8c8/yzL39KuVnWkS0pjTN+s37i8e5\nF5RauO5RKZ65m3mPnHgmRa53lpdthZDa+2aekGal1doo1hnagK0eu45GWrrwU55732ian+FUESda\nTjgVk0xpXoQ6FeOkjczLWvLc9tQJbmvc6KM3W24t01JUwebJV0S1ZZzUE8phud9hFcbASBlEe7tN\n+vh0E9W4xmX2RazOvfbvS1H02jyBBIjztFYoi2NatllhzhqdXk3LszyKywIZnohmUdLSlkUJmR3D\nS61VmuUxzcT8pNI8KW/9yEYAABMqSURBVAQUNmD5kodJ86R06z1rtHzsxDMu3fnC1Y8KIfUnmxqN\nVvGcamS1k0wAGy1f38aEloH0ULFMU88yrRRM9ieeBiVYlIGxp9/xQX9ip+74ljHMipUZk9a62XFu\nfpzu3m/zBTPO3IxyKY6+UFat0MnGw+2W5nR+9FGr5QmQx63imUdxIZ55lBQWKTyGJ8R/IIsS0tgK\nqf3pZXlMA1N6KfUsVCeoztVvZAkNZ606YW3GnmXq2hLcZHs1HGrrp2xkJ5ysqBYWqpfJ5IlsWrFM\n07R1gmoQwmROYF4wDK++LuC8E91m1P1xS39ssWpRxnnabmXmWSFwsRPKPCPKS9e8ONYJYZ4TZU5o\nTVuSriuEsRDcPAf/MRTP3XmmTfFGxF4K47Y86r7fm8rrzsX3BNU9dlZrmkySu8d2dbg0mmgT2TRP\naOStItvIJ2hWrdYsAdbnGVs0aKRxMaY65UrfpRNeeBS2jdowKSeygxIsysC8YliZbdMpelQb7lPd\nx5sJb2n3xBAq4llsS1sEEiDK0sr2rGgHiNNmKYrOGs3ztv3IszZBxRPUop9Ze1txfA2T9/yJloT9\nFss0am2LokI88YS1EFfvPkuMUDphzZLJQlDdNrMC5jb83Ya304yXFEMFTSuyjXyiFNoWC9Y+dmmi\naVwMDwxKiKMMLGimG7YDfUwUUbrUtdtrflRVQQVaLcBC7EpL0RdIaBXXFhGtWJctolgnni1rZ9T0\nC4imHql9bVCOkvpufVQu2m4bYi/uyvxs8yQphwQK8Zwgt+OkhaWaTALbsPzBP5LFk2TOMk2WmPt4\ngjQ2otmctPdMkuZ2CWJ738wnvAmqR3d8Pf0wzBn0uSAI5SJg2PUSZlBSsy8rs26/1v1L8SvaW8Yl\nS7GrE8B2oWy2iKbbP0rTlmPJ0kIMo8z6pi3iafuSpkVbnmUtwp0/cF95nH9uv63YuYeIWBGNoBDS\nyN9WsVCjJIHtX8D6t99kl/e0ouqW9PTaChGemCC3FmlhtSYTxWN4Qfc+9mCYwetzQRDKwIyZiXXp\nH9v3pFHFSqsL/2lxo1smdjxBzSti549l+uLoRDPtIZ5F7GG7oJJn5fFA/sgj5vougyYtRbs8vjy2\nbQwvq5lx9qyyXq7sesDUb29vbXRrEUVR+dgX2WIto6TYf8I93n4woQyud2BRMYhYFueYwY+mVix9\nqq5yp3O0zYRnbce27OdE1C9SUdaJK/dLW6sHkdpA7zZxzUvR9Ncor65XXrOWeYvIFt333svKtsZ9\nD9QKVG2Jtjhui/ON4tL936D9iGmRDWn2fK4Y1breCaZGnAApcChmLYuLgP+xu52uql8XkeOBfTHL\nTR6lqteNoMuzylzVEpzN+Pa6+Mnp4s9w9xRC7xiw45xdBNc/N76V6fC3OTfbH4P0xRDI02aLaAJG\nDNPSWs19i7LZNKLmhNIvAFwRyqxZjpMWFYU8izL3Cmb4qYuuL23LVtjnD/3fX2urEJm2vNKWtWyv\nsnFby/TIhrVe7RwxKotyPwBVXSEiewCfxIjkJ1X1JLeTiGyPWcJ2R2BzTPn258x5bwN9MwwLs+V8\neQ7TPGeU560ut39f3bc6ZtinlekLauuxTkjTVtfbimQhcqknlJW2rJmWoukJa50oZpUqQ1kza3ns\nb1t338Ot4locmxfZPP6xZVu5LU+H8/mG8KA+UNVvi8jF9umWwJ8xS0iKiOyPsSqPwixFebmq5sCd\nIjIhIpup6l9G0e9Af/Sa5Z7xeQcd16paq72sGn+JyTa3t30yJs+y0t3NMtpc7yz3VrAsrcO8zR3P\nWgQSXFZMVmlL28Qwa2YtAuj2A2g+MmWqEDXKcwJkqSe4VgjTZkbusnSmSgvVPR6UcRPKaJSDqiJy\nDvASzFKRTwJuVNW1InIsxrq/B7hbVU+3+/8YeL2q3trpnLfd8WC+9ZYbzn7nA4FFyo+eui173nrj\nQAM5+79F+xae75wuIy+KMNLJHFV9rYgcg1k+chdV/V+76QLMWrvfoX65yY4ccuRarrpwd1YeuKaY\nrXMr8iVJUq7OZ7clE0lR0j8p9o+J7bhhPJEUywCU+8XeOaOyzdt+6tHLOeqU+722qLgvz2fb4qiI\nNXYrE5r9TFscl4+L7THedso27/HBKyO+cmVeVCdPinjmvHgcu21RuZ/L+U7ivHzs1Zv0H7uCFnVt\nMRnP/NvHc/OtfyRxBTBssYs4yopCGX7aYjWFMfaycFzaYpyXoT5+Bk4RXO6HAmUpy3bajweu/nYx\n2+1vK2azvZnulhAgt81ZeG4M0msrt5XWoe9mb/SWj/HAace0Wo+em51XrUffRfeX0fUsSXPv5WP7\nbnSzYjE2U55+3g+4+cV7kjUz0karFZo2MjKXpjhVthXW5Tq77eGM9OHhWJTZmMVRziQkbmBE5GAR\nea99+hCm8v/5IvJc2+aWkFwD7C0isYhsgVmz96657/HCYszWoeqLfid/poUbmxzCuZ3bXdxqJlA6\nHpfldpKmesvaztO+vXWsMUvL9jzNzc22Z6l3bJqTrkvNzQpk+nBG1szJhpDGmKVp37f5wKgsyvOB\nL1pXehIzHvk74DMiMgX8CXiTqt4nIlcC12BE/YgR9Tew2JmtWdohV+OZTsZLUeA3La3HrMdkTd4I\nkzlzhqo+CLy8ZtMuNfuuAlbNcpcCs8gwQof6YVasyiqVMKGhnNIJ1pDOXYpmq3j2a8WCcY2d5ejf\nZ0EoA4EFwlzF6M3ij70Uz+G/lrwS9tPLipwNQhxlILDImIsCD3k13Gjo53duuL0fkuXY63rjQhDK\nQCBQy2yKpb+G+TgQhDIQGDPm2hqbDcGcjSGF2SQIZWBhMoof4hzHBo6zYIYya4FAINCDULg3EAgE\nejAsa1hEYuA0YDtgHXCYn+IsIm8E3oypPvZhVb1YRDYFvgqsD/wBOFRVH+p2nZFk5gQWH5G3jncg\nkOdZ37ceHACsp6o7A+8B/OpjjwfeDqwA9gY+JiJLgQ8AX1XV3YDrMULalSCUgUBgzjGVj/q79WBX\n4FIAVb0WeLa37bnAGlVdp6r3ArcC2/rHAN+jj3UtFpzrfdWFu0cAq89fMequ8Om3L+u90yzz6t0i\nvBVVRsIznvrEkV4fYKNdDhjp9Ze97cSRXh9g20tWj7oLBVddtHJYX8rlwL3e81REJlS1WbPtfkyB\ncL/dtXUlWJSBQGCcuY/WCmOxFcm6ba76mN/esyIZBKEMBALjzRrgRQAishNwk7ftOmA3EVlPRB4F\nPB242T8G2Ae4stdFRlq4NxAIBAbBm/XeFjPGdChGBG9V1QvtrPebMEbhR1X1PBF5HHAOxpq8CzjI\nFurpSBDKQCAQ6EFwvQOBQKAHQSgDgUCgBwsmPKhXhP4sXncSOAvYClgKfBj4b+BszDqrNwNHqOqs\nR1uLyGMxS2jshclEmNM+2OU9XgwswXwWq+eyD/azOAfzWaTAG5nD90FEdgROUNU9ROSpdded7XXq\nK314FmbtqRTzmzhEVf9cl60yzD4sRBaSRdkxQn+WeQ1mpcjdMDNon8GsU36cbYuA/We7E1YkPgc8\nbJvmtA92ffZdMFkQKzHrsM/1+/AiYEJVdwE+BHxkrvogIv8KnAmsZ5varltZp/6VwGdnuQ8nA0eq\n6h6Y5VeO6ZKtEujCQhLKbhH6s8k3gfd7z5uYNcpddG9fkf9D4BPAGZjcVUbQh70xoRkXABcBF4+g\nD78GJqx3sRxozGEffgMc6D2vu26xTr2q3mn7utks9uGVqnqDfTwBPELnbJVAFxaSUNZG6M/2RVX1\nAVW9X0SWAd8CjgMi1WLd4r4i/wdBRF4H/EVVL/Oa57QPwKaYP6d/Bg4HvoIJ/p3LPjyAcbtvAb4A\nnMIcvQ+qeh5GmB111+2UKTIrfVDVPwKIyC7A24BPzXYfFioLSSi7RejPKiKyOfAj4Euq+lVoqf7Q\nV+T/gLwe2EtErgCeBZwLPHaO+3A3cJmqTqmqYqwX/wc4F3042vbhaZix6nMw46Vz2QdH3XegU6bI\nrCEir8B4Gvuq6l9G0YeFwEISym4R+rOGDV69HDhGVc+yzdfbMTvoM/J/EFR1d1VdaceibgAOAb43\nl30ArgJeKCKRiDwR2BD44Rz34a+U1tL/wyyFPKefhUfdded0nXoReQ3GktxDVW+zzZ2yVQJdWDCz\n3pixsb1E5GrKCP254H3AxsD7RcSNVb4DOEVElgC/wrjkc827gC/MVR9snb/dMT9Etwb77XPZB4xr\neZZdC34J5rP52Rz3wdH2/qtqOlfr1ItIghl6uBM4X0QAVqvq8SJyCka4Y+BYVX1ktvqxUAiZOYFA\nINCDheR6BwKBwKwQhDIQCAR6EIQyEAgEehCEMhAIBHoQhDIQCAR6sJDCg0aKiCwHPobJ5W1iYvre\npao/n8G53gg8oKpfG24vO17vbOC3qrqqj32vAFap6hWV469Q1bNF5AZVfVaX43+kqntOo29PBM5U\n1Rf13Ln92FxVo0rb6zB52Hd6zX9W1b2ne/7A4iEI5RCwucWXYLJznqWqTRHZExP0/QxVvXuap1wB\nXDHkbnbjYaDrusb90k0kLXtM83x/oCzbPywuVNXXDfmcgQVMEMrhsCewBXC8K+Glqj8SkUOBxGZo\nrLKZM4UFhqno8jXg8fY8H8QI1ouB54nIHzGZNv9hz98E3qeql4rIKtv2tP/f3rmGWFVFcfyXg0Jk\nfVCsQMVHyH8ysxxNU5LMR0mIZUmWZgSJSZhIT1NJLZK0DxlGlkhqIWRhM1oZpQyiWTOa5RsWxqBB\n+EAqv5T1Ifuw1tHj5XrvnUdCsH8wMPfcs/de+5x711577bP/F+iCK+WMwpVp9uGCCOckzQEeAqqA\nr4AXgR64gMhp3EmuB45J6obv0b4K34I3KwRGKiaL4iSNApbiMmO/AY/gv6eMpEYzGyJpHC5L1w5o\nAp4MGbCjQCO+HXMq8LGZ9ZTUA1iNb8/8A5fS2y8p63snXBRkkpmdbI7dYVe+3eHAWGB22LcHl0o7\nK+kxYB6+HbABuNrMHo/yI8zsaP6eh+TaCqBz2P20mf0Yn4MzuIBGV+AVM1stqRN+z6txebRngF7A\nSDObErYuBP40syXN7Wei+aQcZdswANhbqHNoZpvN7FSJchPwKe9A4AlguJltBTYBL4fIxXKg3sz6\nAxPxnSfXRfmb8QhtOu5AlgD9gBqgv6Sx+JfwtrCxKzAlygp41MzGmNkqM9sSNnxuZoNwp3bHJexe\nJWlv9oc79kLmAzOiri1AjZnNiusyJLQz3wPuj77txCXqMr40MwH56/cOsMHM+gELgfnhhKqBYbHH\n+2dc+q4U4/P2R/Rf2G4XXM9yWETJp4DnYjB5A0+xDI3rWI61wAtmVoPfq49y73XHnfJ4XAEK4FX8\nN19uxAeK1/DBbHSIr4APPB9W0HaiDUgRZdvwDy4C0Vy+BRZL6gp8gX9BChmJf2ExsyZJjXjUCLAl\npvnHgONmdhhA0i/4tsrRce6eOP9K3JF8A5wys6MFbW3Ft7sNCHvepjjTiuQoC9kE1EqqAzaGI84z\nGNiVs2El8FLu/cYidd6JOwjMbDOe7kDSs8A0+T69objcWClKTb2zdu8C+gANsf2vA/BD1L/TzE5E\n22uAuy/VkKSO+EC1OuoB6Cipc/z/dUT+B/GIOOvn5OjngWgTSZuBByQ1AU2RlkhcBlJE2TZ8D9RI\nKlw4WBzRyjl8/3lGewAzO4JHQ+vwqGJX5DvzFL6+ggsD3N+548WUkqqAZWZ2a0RFQ/DoBC4I/J7H\nzHYCffEp+iRcV7JFmNmbeLT7E7BU0ryCU0r1q6h95CTEQnyjr6SBuChJO3wfdy0XX+vmkrVbhU/5\ns2s3GBeYKLQrL62Wv8/tc/WczerJ3Ydf4/2zADlJtqzO868lVcfn4n3cgU7G1dMTl4nkKNuGHfjU\nbEGIESDpHlyY4zCeC+wdii2dcKeIpJnAIjP7BHgKz71dgzu9zGnU41NiJPXGF3q+q9CuemCqpI6h\nzVmHT9+LImkpPh1fizuFmgrbKVZXI567W4aLVWR1ZTqhjcDtknrG8en4YlgptuPK4ODR8ko8+tpm\nZu/iwr3jcOfUWrYBEyRdGwPgCjxf2QAMktQ9nNfDuTKngZvi//sAQhz3SCj5IGlM9KMU24nIWVI1\nnk8+Z2Y7gG54tFvX6h4mKiY5yjYgooHxwA3AQUn78UWTe83spJkdwqeyh3BF9Ezq6wNAkg7EsefN\n7Hd8CjxX0kRctn9knFOHT3uPV2jXZ8AG3CkdxBeG1pYoshyYGHnHWlyuraXMBdZI2oM7+jlxfCO+\n2HQGd461kg7h0eeMMnXOBB4M+xZF+fXALXF9tuHRfa9W2A2Ame2LNurx+1YFvB6yaNNxBffdXIgc\nARYAb0nazcUaj1Pw1MB+/BGySQURZCELgD6S9uGzjam58z/Fc9Z/tbaPicpJ6kGJRCuI5zJH/NeP\nG0VU2wFfGJvdkudzEy0nRZSJxP+D64ETQENykpefFFEmEolEGVJEmUgkEmVIjjKRSCTKkBxlIpFI\nlCE5ykQikShDcpSJRCJRhuQoE4lEogz/AhK9WKfBaZH2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b9e89e51d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from lifetimes.plotting import plot_frequency_recency_matrix\n",
    "\n",
    "plot_frequency_recency_matrix(bgf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that if a customer has bought 120 times from us, and their latest purchase was when they were 350 days old (given the individual is 350 days old), then they are our best customer (bottom-right). Customers who have purchased a lot and purchased recently will likely be the best customers in the future.\n",
    "\n",
    "Customers who have purchased a lot but not recently (top-right corner), have probably dropped out."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There’s also that beautiful “tail” around (20,250). That represents the customer who buys infrequently, and we’ve not seen him or her very recently, so they might buy again - we’re not sure if they dropped out or just between purchases.\n",
    "\n",
    "We can predict which customers are still alive:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b9e696fa90>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Xd3pkbe2iXOnupVlVU7derwCq6Q1y9qbTN+FwTDVT6tMbRxR/Q0R+c81XssZk\nSVaYuGAHgMcxWaypK5GfEPkJYegRhh5B4BXR2zx9xaWwOByrYANHb49GBd/PgQ7q18uMMXdd05VN\nkNzMBYok5aQXk7R0AHjYmac5qzl7M40mANVatKgkDbSffSH4UmfnOhzLMqWa3jhC73Frvop1oBgS\nFA9SV/JZuFFngYavpm6ztgOAWi0kqtqEZTspzQ+Coseew+FYnmxKo7cr6pW2ycBJwLPQjsanDDUe\n2BDkScrF8zQjSxJNQen3CL2Y0IuJwowozAgjnyDQW9m0dYnKDseYTKl5u+LVRORvgEei8y5C4BwR\nedNaL2ySlIVd0kvsLSZpd0naXe24Eu+lHu+lWU1pVlPqjbBoLBqEAUEYuICGw7EaNqrQA05H++l1\njDF7gNPQzsdTT1m7K7op24BG0k+Iuz3ibo+stUCtt5daT6O4zWqfRj0gqoZEpUlpQRDsE9RwHZUd\njtFknjf2bT0Zx6eXe+xzdanKlNfeLkeer5fGadFmKm23iLq248qMBjQatRo1m74SROqb8MPACTmH\nY1w2cHLyh4APAjtE5IXA04APrOmq1oBC40sG96kNamT9GC9WARj5GqiIokFJWh699TxvkW/P4XAs\nw5QqCOPU3r5ORE5HW8UfCbzSGPPJNV/ZAZBaAZc3HkjjjOE4Uhong+qMdpuosxdgURS3VlvcY8/5\n9RyO8dmw0VsRuRPwMGPM/wPeBjxJRG6/5itbIwY+vZS40yPu9EjaHfzWXvzWXurZAvVsgZl6Qq0e\nUCv59gLr18t9e2D77rlpaQ7HvkxpIGMc8/b9wL/bxz9FRzdeCPzWWi1qLSjmZdh8vby/HthhQbEd\nEpTaxqJBSsX68sLcp1duIe+EnMOxLNmEhJmI+MA7gGOALnBueQqjiJwBvNo+/TpwnjFmyT4B4wi9\nHcaYdwMYY7roGMc/HHOxxwOvM8acKiL3A3YC/21ffqcx5oMi8mrgUWjBwwuNMV8e59wHSpakhXkb\nt7tF84Fq3kK+0qdR1+Tkqu28EkTBoPNK3zUhcDiWZXKKwWOBmjHmRBE5AXgT8BgAEZkF3gCcaoy5\nWUReChyG5hSPZByh1xaRM4wxl9qLPALsGLFlsBc/u7Tv/YA3G2PeVNrnfuhYyOOBI4CPAMeNsab9\nphzQSPKStG6P1Aq9qLMHgOZcl2a9AVDy7Q1K0/p2qhrsW5rm2k45HJPT9ICTgU8DGGOuEZEHlF57\nEPBN4E0iclfgvcaYJQUejCf0ngP8q4hciKat/C8qzFbie2hC84X2+f0BEZHHoNreC+2bucyqoj8S\nkVBEDl9p0ftDOV8PIO2nRfR6v5xGAAAgAElEQVQ27vRJbR1u0LHdlLe3makfAkCtboVedd92U57v\nQ+pK0xyOffAnFsiYQ9vb5SQiEhpjYlSreyhwLDAPXCUiV9s52yMZJ3p7LXC0iBwK9G2C8ooYYz4i\nIkeVNn0ZlcJfE5GXozb4bcAtpX32AttYRjUFeMi1OwE4MzbjLGW/uBdwr7vbJydV7YM72tuAyy86\ncc3WMC67du4zQ33LreFgX3+zreGks6444HNMMOl4DzBbeu5bgQcqP75ijLkJQESuRAXg/gs9EbkL\n8F7gKODBIvIx4BnGmB+scuEXG2Nuyx+jkeCPs/jNzKKCcFmuPPYszowNl4Sy7H7lWRlB3V90Xz20\nQvPwOgDb7rydHb9xJAD1+2gXrVvu8SCub+v5r/u++vS+f+NufvmzWwGYv3Uvn77gGB72pC+T9LUX\nX96MYD3N2107T5nIH+hGXsPBvr5bwxJMzrzdBZwFfMj69L5Zeu1rqFJ2GCo7TgDes9zJxlnVu1FH\n4Tzwc+DfgH9Z/br5jIg80D5+uF3sLuB0EfFF5EhUgt+8H+deNXnaStLXgEbc7hC3O2TtBbK2lqU1\nKz29NTyaDY9aIyKqRIt8e8P99vIUFodjq5PhjX1bgYuBjoh8EXgL8CIRebGIPNq6wv4U+AzwJeCj\nxpjrlzvZOD69w4wxl4nI66zv7T0ict4Yxw3zh8DbRaQH3AQ8yxizR0SuAq5GBfD+nHe/yUqzcLO8\nZVRPAxRB3KESqQZdyYcGhdp1BYZ8eg6HYx8mFcgwxqRobKHMd0uv/zuDtLoVGTd6e2ds7a2InIzm\nyqyINYFPsI+/jkZahvc5Hzh/rNWuknJlRh7A8Er5evlYyLgbE7f1LaW2hXzYmacxq/32iqFBdU1S\nBhY1GE2sZpeXprkUFoeDDV17+2LgEuBuInItOhXtiWu6qnUg62ckPTsOMk6IO+qXy6O4YXueJhrJ\nnakdDkCzESzK2QNtMFpoe3Y8pEZ0XfqKY2uTTi56O1HGid5+RUSOA+4JBKhaWV3+qOknjQcVGXF3\nUIcbW6EXtfZS72s97kxNTd5mo07NJixHFSv8wkHCcprPxHUpLA7Hxms4ICKHo1rer4C3GGO+ZctB\nno2aoxuy/rZoKFovbUtTUuvTS/s2Et7vESZq8lZt55VKpH49YJFvz7dfrgtgOBwDJpicPFGW0/Te\nj+bNHQZURORiNHI7C7xoHda2pmT9rGgzlSVZqeOK+vGy1gIVW53RmMt9ezPUm6rhVWyvvTAK6Q0F\nNcqlaW44uGOrMkZU9qCwnNC7mzHmbra27WrguWhu3ZuNMb1ljptKCg0vHHwR+UjIJE4XDQEHSFsL\nBLbdVHO7lqjNNrbTsMPAq9VB26nABjVim6+nqSt5ZNhFNRxbk42o6e0BMMbsFZEdwO8YY65en2Wt\nD0XHlSQdNBfNE4z7fYKemrdRpjK+EqREUR69HXRTzqO2Ln3F4RiQeRsvkFG2x36+WQReOXVlUfOB\nPJBhW8gnrTZhSzW9Wh7QqPSYteZtUY9b0vTKvr3h9BXP85yJ69hSrPfsi3FZTujNisiD0aThpn1c\nvAtjzJVrvbj1IrWVGUCRuhIvtKm01aytdbQybqbZYaauw8AbDf3oFg0Ft/dJkhQCzpm5jq3KRjRv\n/xd4rX38k9JjUC3wYWu1qEmT9rNFdbjF9nig6aV5m6lyj7151fDCtvXtzS0UnVeaTVXda41KMRQ8\nCBd3YHE4tjIbLpBhjHnoei5kvcn62aK0lTRd7NNLk4TMdlP2Y/XthfSphqqxFb69EaVpfql9fNnM\ndZFcx1ZiI2p6m57cp5clg0Tl3MxNOj2SIlHZ9tjr7WGmogJwtqHaXb0RUaktTljud3tFZ+VylYYz\ncR1biY3o09sypGlK0hsybzv9QuhVbECj2t3NTENz9tS359FohEVpWhHRDVyVhsORbsDo7aaknK+X\n+/Syflbk7JVTV9KeBjWyvPNKv0PF123VSPeLIm9R+grY0rShKg2XsOzYamw4n16O7YF3MvB2tPHA\nfYGzjTGfXuO1rSlpnBGUghvZkE8v7vQHnVfs/IywvYfGdjs4qHooEDDb9KlbUzc3c7utkP7QACEv\ny1wk17Gl2Mg+vbcCrwKeALTQAT8fxQ7q2Ayk8aAkrfDp9Urtpuat0GvNU+9pq/65SgeoMtNYnL4C\n0KmEdIPF2l9h5pZwuXuOzcy0anrjiGLfGHMZOqbxI8aYH7NJzOKsnw1udgh42k9I+wlJPymGgcet\ntnZfmd9NtaO3Zqj+vpl6SrMZ0GwG1OoVanVNYQmjkLA0HNwPtHLDL3Vadjg2M5nnj31bT8YRXi0R\n+WM0L+95IvJ8tBHBhqLcUHTk69bkHPj0BtPSyp1XgtiWpHl6Xw1TIuvTiyqDHnuDnL3B9QajIl1Q\nw7H52cia3lOAJlp7eyvwa8CT13RVB5Fc40v66UDTW2gTL7RJFxYIWrcRtG6jmWoHlrlaj9mGx2xD\nI7l5NDeIAh0Onmt5YYBn8/dyjQ8otjkcm40Uf+zberJcP71XoT68bxhjimoMY8zL1mNha03Wz4qO\nK2mpDrcIaPSTUucVW4+70CrSV+pd9e3NRB1mG5rl3Gzqx1lvVmkvqD+w17bH9mNS25fP5e45tgLZ\nOguzcVnOvP11oI02Ed2U5CkrQWmGRlpqNxV3Vej1WyrAklabbF41vErbCr1gnrm6TrGcKTUjKBKW\nq4OE5eGghk/qGhI4Ni3Tat4uV4Z2DoCIfAZ437qt6CCTFY1F01Iai/Xt9frFtDTP+vairEclyDsr\n29SVSlCUphX3YUDf5v0VeXoM/HxO43NsNjac0CtRF5EjbNR2bEQkQoXlUehMjb8Avg1cgP7erwfO\nM8akIvJqNDocAy80xnx5Ndc6ULJ+RhYOpqSBanxxN8/ZUwEXt7skC5q+Eixo55VG7zZmK4cBMNfQ\n0SHNZkC9qY9zMzfoaCQXKMxcL8sWBTVc0rJjM7GRhd7hwA9E5BeouesBmTHmrisc91TgFmPM2SJy\nKPAN4FrgFcaYy0XkXcBjROSHwCnA8cARwEeA4/bv7azMSlHcosdePx3ZeSVZsCMirZlb7exmdk63\nzdZnAJhphvskLIetcFHrKVDtLg9opDj/nmNzkWYbz6eX89v7ee4PAxeVnsfA/YEr7PNLgd8CDHCZ\nHST+IxEJReRwO7l83Sj8e+Vtw00IevGgNK1vNbh+h8imr9QiFWbVSkgU6Reel6gFgV8IuLxELfX9\nIqjhcGw2plXT88YxpUTkycC9gb8EnmCM+ZdxL2BnbHwCeA/wRmPMnez2hwHPQEdK3mKMeafdfiXw\nDGPMjUudc8/1N2RzR99z3CU4HI5VctJZV7Br5ykHJLWuv/Gmsf00R9/9DusmIcepvf0b4M6olvY6\n4BwROcYY88djHHsEcDHwDmPMB0Tk9aWXZ4Hb0FkcsyO2L8mVx57FmbHhklBWWsKSlM1bL/LwbfpK\nULca2lxAdVYnntW21QBoHtZk9k47AJi5y52Yff4b6ex8B+0j7gXAz2Z0Pd/ffTj/c5N+tD/5qXZl\nufnn8+z5ldbttvaqOdxrd4lt4nPS7xeaZdnMXemf0q6dp3DSWVcsu89ac7DXcLCv79YwmmnV9MYx\nuk8HzgY6xpg9wGnAGSsdJCK3By4DXmaMyaO/3xCRU+3jM4CrgF3A6SLii8iRaNnbzat7G5Mn62fW\nr5cOStN6cSlhWQVXtjBP1NpN1NpNM9tDM9vDXK3HXBPmmtBsRjSbUdFhOapGBJH69/wwwPM9e/Nd\nmZpjU5Fl3ti39WQcn16uduQqR7W0bTn+DNgOvFJEXmm3vQB4q4hUgO8AFxljEhG5Ch0z6QPnjbv4\n9aLoqlwaIFT49rod/L5qc1Fqh4MH/UWtp0BL1ILCvzfou5cLN8/zcDFbx2YinVJNbxyh9yHgg8AO\nEXkhqvV9YKWDjDEvQIXcMKeM2Pd84Pwx1jIxVpqb4cUZSS+vvR1EcfPBQf0FFXTpwgLhXrXGa9v0\nfnbmcLY1GgDMzWr0du/eiAU7ILyXDx/qxyTWvM3CdGDKujQWxyZgw0ZvjTGvE5HTgR8CRwKvNsZc\nsuYrW0eyflaornmPvayfFTl7ufCLu3EpfUWFXjzfIrClaZXWrQDMNHczW1U35ZxNXdk9E7Iwr0Kv\nY0vTeu2QuJTGMjxXo5zG4oSfY6MxrT69cVtE/RTYmT8RkYdsphGQZcqdlfd5LckGg4OKDix9so4K\nwMLMTbpUAxWOtYruV614RReWsDQwvJzGkvqDhqN6cpfO4ti4rLevblzGid7+O9o49CelzRtqBOT+\nkgvAIk9vRD1uf2+LyrxGZQdm7q3MzWiUd65uzdyZCntsbe7CfN5hOSLqVey5E9JscfTW972iA0VZ\n43PanmMjsJE1vWOAexljtpTakcYZXj5DI29C0EuK0rRylUY8r5HcIG9GsHArzYY+3ladA3R62syM\nanoLC7mZW6Xfzf17EantPpDZMZNZluFbw3tY+Dkc086G1fSALwF3RysnNhXlkrSi/My+FpS3WUGT\nlYaC99s2oNHqaldlILLDwf2F22jMaHOa2aZqfIc06uyeUWE3bzW9dqtCr63b4n5CYk3nvEzN8zwY\n0XjU+fccG4Fp/fc8jtD7HPAtEfkpWko2bu3thqbcb6/ca284gXjU1DSv2yGI1b9X8dQMroUJNnhL\ntWoToEO/NEltdJlalrehcjW6jg3Gho3eovl2D0Ojt1uK4XrctKTpDTqw9Iv0lf4e9e0Fe3YTbbOR\n3Ibez1VmmWuq1JvLzdzZCp1cY+zFJHlqTB4sybJCwI3S6VzHZcc0MynzVkR84B2oq60LnDtcpmr3\n+STwcWPMu5Y73zhC72bgKtsQYEuSC7+kn5D09L9Xkgc02v0iqJGbucn8XsJ5G9TIzdxt2zmkpkGN\nPU3ttLx3JqTV0hZUvV6yyL8HkCZp4d8bkLjghmNDMMFAxmOBmjHmRBE5AXgT8Jihff4C2DHOycYR\nejcA14jIZ4FevrHcQn4zUp6LW6Sx1Evt5EtmbpHGYoMbWbcHXduFpaeCsJJ2qAYq1GoVreWtVj0q\nlYGpm7ee8m3jUT/w8ZLcRNhScSTHJiCd3P/hk7EjZ40x14jIA8ovisgTUBfipeOcbByh9yN7A6Y0\nBn2AlKszcgHnlQIZZd9eGg1maICauYMqDRVw8UKLYI9qeuGsanrN5q+Yq28DYFtDhd7emQoLC3ZW\nbrtSVGokfTWD0yTdJ6KrWCFbKp12wQ3HtDFBTW8O2F16nohIaIyJReRodFDZE9D53CsyTkXGa0Tk\ncLTJZwhcbYz5+erXvXEpl6YN0lds7l43LkVyVRHu710g3Kv+vWivfleVmV8xWz0EgEOq1rxthszP\nqn+v06nQ7dg0mFygliK6hW8vzYrk5TydpTxgyAk/x7SQphMTesOdmHxjjJ3LytPQCY2fR7u090Tk\nB8aYTy91snGSk09H275fgzYEeLeI/MFmK0UbRXlwEKiml9gARmDN0iROBzl7tq18f6FDZIVeYIVe\nOPsrGo3tAMw1NHdve6NGa1a1vlY7pGPTV/r5+eJkUZdloEhghsUpAW7WhmPamGDDgV3AWcCHrE/v\nm/kLxpiX5o9F5HzgpuUEHoxn3v4lcLIx5n/sie8KfBTY9EIvZ1RpWloaIJRXbOSlaYs6LFvfnt/r\nEPY1ibmKRntrQUytouep13yqNf06ooreB4FfdGTJhZrvefvkP/m+V/gYc1xww3GwmWBy8sXAaSLy\nRdTFdo6IvBi40RjzidWebByhF+UCD8AY830bHt5UDM/OyPoZ3lAXltFVGmkpYVk1vbDVpWerNHIz\n19+zm6hp/Xs1NXPnqjPMN1S7W5ip0Grr19HtDDS+xPr0yhpfOiTMPN8fWbnhTF3HwWRSf3bGmBR4\nztDm747Y7/xxzjdWIMO2lPpH+/xctmDOXk5Rj5uXo/UH9bhBVz/OuNMnztNY9trpabtvI2zq4KB6\nXc3bucosrao1b2cCWl3V6rrdir1PCv9eESFOBi2ocqfGqMoNN0jccbDZyLW3fwC8DXg5qlp+Hnjm\nWi5q2sh9ez6M7L6Sm7qDDixJ4edLuqr9Zd0eWdd2Y+mpFliJW9TCfKhQSs12YckrNqrVgHZRsZE3\nHvUHLajsvTeqcqNk7jqNz3EwmGDKykQZq+GAMeZJ5Q0i8njUr7epKaev5JQjuaAJy4GdfJb79uJu\nPOjCYqs1wr3z+Hs0qOE3tUqjXp9jbpsGpdq1Ku0ZfdyxCdDdboVeKZKr10+KXMFciOUCEQbBDZ/U\nJTE7DioTjN5OlCWFnog8CW0N/1oRKee/hGhp2qYUeivNxc0p6nH7WSHsghG5e715K/TqLULbecW3\npWlRY5YZ22y0U6vTsv69Tk9N3m4voN/Xio04b2+VDHx6uQALo7AwdQuT1vcXmbr5a07rc6wXG7Fd\n/Cxwkr1/aGl7jJq6W440zop04LS0bTh3L+7G9Nu20qKWp7G0CfPa3BkVfkHjFqrVJgBzUYOOrdTo\nzujX0u5FdK2fsNcbdGPJE5bzaHEYhQPtLxw0QgisBpjn+rl8Psd6Mq1/WksKPWPMe4H3isjDjTGf\ny7eLyJydirZlGGXmlilaT6W5RpWV/HylNBY7DyO1reb9bocg9+/1W9Rqur0eqiBsVEPqtYF/D6BS\nDelEeQfmwYAhP7CCzZateX62rJ/P4VhrNnI/vYaIvA74c+ArwOEi8hJjzAVrurKDzFKDg4bJ+hlZ\naDU933Y8jhKSMG9IoJpeUAnp2Xm3QV1NVr9xG0FNqzOqlQZzkTYk6Fb19U4zoGtL0nq2CUEcpyTx\noDoDoFKvFBrbcGQXnJ/PcXCY1v+x4wi9V6FpKr8LfBkd0XgFcMFyB4lIhFZyHIX6Bv8C+F901sZ/\n293eaYz5oIi8GngU+lt9oTHmy6t9I+vFokgui3P3vCifmzFIY/HbViur9gr/XlC1aSz1Gn5NTdqw\n1qBRUQG4bU7vu7WInm1D1Y/1Po4rJLk5bc3caq1SCMAy8dDzFFw+n2PdmNY/p7EGAxlj/suWePyr\nMWbeCrSVeCpwizHmbBE5FPgG8FrgzcaYN+U7icj90LGQxwNHAB8Bjlvd21gfRiUs59uBQuNLo4F5\nm2tUSb/UjaWvQY602yO1Q4WCTqvoyFKNVSOsBT3qlXzAkE1nqXhUKgNTF7SCI29LlXdo8Txv0IzU\nrnPUbF3n53OsFckGNm9/LiJvAx4APFVE3sSg68pyfBi4qPQ8Bu4PiIg8BtX2Xoi2jbnM9uv7kYiE\nInK4MeaXq3kja8Fykdxc4yt3WC733fMD3ZY3G/XDfpHaEuRlZtV5/Kqar36tRmgTlRuRmreHzFWJ\nbWlaf1aPjZOIJNX/ObmmV29W96nSGKn59eNSXxYrgNNsn7pdZ/I6JsG0/gl5K/1xi8gs8Djgi8aY\nG0XkPOCfjTHz41zAHv8J4D2omXudMeZrIvJyYDtwG6oRvtPufyXwjOHOqGX2XH9DNnf0Pce5vMPh\n2A9OOusKdu085YBUtQ9dPb5X74kn+uumFo6j6T3O3j9IRB4E7AUeD/zLSgeKyBFosfA7jDEfEJFD\njDG32ZcvRis9Ps7itjGzqCBckiuPPYszY8MloYyx/ANnlKbnRR5n7PkOl87dC99qekHdL17LH0dW\nUwtrAZUZ1eAqtm18bVud+g4tTasddgjV2x2m57nd7QFIDr0jrUPuDMCt9TsC8IvuDn45rwGPX9zm\n8+zf8virD8b86hZNht6zW83l1t4OnbZu69lgSr/bI+nn7atsw9M0JU0GWl9OuYRtpX+Mu3aewkln\nXbHsPmvJwb6+W8No0g1s3pZz9CLgwcCVrCD0ROT2wGXA80opL58RkT+ygYqHA19D28a8XkTeCNwZ\n7ZV18+rextoybsLycCsqKJWoleZrLFWuVqSytNW353fbRH0NelSruq0R9qhXVHjWbHurWtWnVrfN\nCrpq+vZ7MXE+XS2/bhIUOX55FUcas09/vlEdW8D5+hyrY1r/XMZpInpO+bmI7AA+OMa5/ww1X18p\nIq+0214M/K2I9ICbgGcZY/aIyFXA1WhQ9LxVrP+gUbSbWuK1ZKgBlB94g4iuza/rLfQK4RNUQvxI\nBZZn74MwpBLq41nf+vFmQmKbu5fMzQARO7Z5hZ8v/0NbUUB19920XGoLOOHnWB3JlPa7GCt6O8Q8\nmoayLMaYFwAvGPHSg0bsez5w/n6sZSrYJ42l9Licw+f5eXBDhZ/ne0Vwww8DvLypgJ2V4UUhgRWA\n1UC3zQURad1qaXUf2MahswlpltffjhNYH+D1vbFSW8AFOhyrY8MmJ4vIfzKYQOgBd0VHrW05xk1Y\nXopiqJA1N7NqWNTtauNRa+p2VA1LO92BqdtRMzfq7qVa0dZUReVGpU/DdmZZqOr6KlW/SGnp2VK2\nfjcmjvLSNNucNE6K1JbMCmUvHbSq8klHVnI4rc+xEtP6pzGOpnd+6XEG3GyM+fbaLGfjUc7dK2t8\nRW5cnH/zg2lmZY0vf+wHHn7oL3rdCwI82zk576Bc8UNmfX2czfjA7Ti0Nl/0LssyO1F8ia/WGxoo\n7vkefSvgvDzIQUlAs7LW53CMYkNWZIjIduBbeWBBRE4BDnr+3MFkVFBjudrc/LWUQcVG0rcCpxMX\ndbH9wBv0zLP3Xhgsegwq/CpWAM54IXAvtnu3kNXya8/Y+wr51+sX/fcGdbgrkXdqztcOiwMdLrfP\nsRLT+uewXGup+wKfAs7BzpwEfgv4gIicYYy5bh3WtyEZ1Y3Fp+Tfs1pfObjhBR6er2Ztoen5Hp5n\nz2QFV8XzCOy2mt02u/BzaNoL1fJVzOB5NvHZs4LTqw7ObY8NAn8wf8Pe930Pzwrmsr8vK7TDbDCo\naMQYymL/af2rd6wL0/r1L6fpvRH4PWPM5fkGY8zLbfLwm4FHrPHaNh3DKS1pMihXS/opfjjw7+X3\n5ZI1gLTVxm9qmZrXsmMmu3upVlXDa9imBe2wStP69Do28NHt+XR71r/Xs73/4kFL+qTI1wsLP15Q\nslFSKwGzkhaYM6o9vdP+tjYbMXq7vSzwcowxn7FdV7Y06YiUlbKZu1xEdzidJads6oLV9Hx/1I7A\n4MsLd/+iUPSYs/cV8L3c9GzY+xDPtyVwhS9x0H6+7O9bjdaXr92luTjKTOuIluWEXiQivp1EVGAn\noVWWOGbLMSqiu1Jwo5zOkvl5RHfQmSVHhV5naJs/GAKUc8svCK1Glgs/bzbFr+ZCz2ptfoPA5vsF\nufALPIJgYOrm9x1bxeF5A8GbC7DE8wZaYalB6bgBDycAtwbT+jUvJ/SuAF5tb2VeAXx1zVa0hUjj\nrAhueIlXdF72o4HJW0xaqwwGige2ciO1ffnSdpuglrerUpO3WqlTC1XDawS2I3MlolOzpm5fBVOv\n79Pr2ylutk+fDhkfdGAuevXlzVJH/DWXJ7ItF/Ao9l3iPI7Nw7R+vcsJvT8FPiUiTweuBTrA/YBf\nAI9eh7VtGMaJ6C4V3Bhl6nqd4XRhiq4to4h/dWthSwSZ7bGXJMVfXdDU84XVhNBT+zf0tWdfGARE\ntuojzGuIfY+wiBYv1vpAtbrY6xePQSPO5TQXfX+D9+ZM363HhktZMcbsFZGHoLW390X/lv/eGHPV\nei1us1JOYymbugn7BgjGofur3YNWUnkdbZpStQLQT2yvvZmEoGo1R38bAFHYoBLapgi26iMKPUJb\nKRJGQdGWvm3NX7/jF3378gYGUbVSNDHIfYNlLdGZvluP1Y0nWL98z2Xz9GyPu8/bm2MFVtL4Cv+e\n1aiyfjbQhdopQa4Nlf18+xSJ7Uvn1vnBYCArSKI0JbDT0CIr9JpJTDBj29fX7Wt+QuSrGRyF6qqN\nooDICr2oEiwSgADBQkDPlsrlHVyiaqUQXnlTg8T3C5/fSukuTgBuPjZiIMMxQUYGN4YGh6dDbee9\nxCuio4XpGCeDxgX5tLNeXAwVz0vYglYbv2Lz9Gxz0qBSo2IblDZC3dYPo0KA9Wt5a3qPOLER2zgg\ntj6/3M+XJOmi9YBOZMuJe3275rQobSt02jR1vr8twrR+bU7orQGrqdEdZeqOTmlZWuPr7mkNtKdc\ns4qTIp8uzKei9ftUYmvqxlbjm+kR1XRbNVB/XyVoUolUYFYjn6oVnvlEtvkoILLdn3Ptr9as0e8t\nblmfhEFh8ubrSpKkeDws/PRzcNrfZmHD+fQcB8bYwY0Rpu5Kwi+zGlc+b7e30BtcIx1ohrlwqdj7\nqB8T9HXf0N434x7hjEaDK/VDdb/6DmqBbW4a1ahZAVfN76s+1Xl9nM/rmD2kUQQ8yqavb0vmCn9f\nPyYZ0upG+f5g+eAHOAE47Uzr1+OE3kFgOT/fUvvmfr6EFC9YPMe2nNqSm7xxp09QUSEUt2ynlkqE\nZwWSbzU5P4wKX14tsAPFaxUSq8HFmU+cWlM3seZ06pPkjxM9X60RlVJbFt8D+6S96HvBroF9tL8s\nTQfzekcIP3DNDqadUXNalmZKAhmOA2e5rsvDfr5RKS3DIqLc1CDX9Prt/iCHruRrG2XyptYEjazm\nFfR7RF3bsdlOYwtn21TrdiJbdTu1QLv5122JW6Ma0bD+v9tsQ9MdO+os2LK3BasRdtr9ItDR61h/\nYRQWWl/u+0uTtND+yoGPfbQ/P1iypb3z/00fG7EMzTFBlvLzrVS6Rj51Lc+hw98ntSVu72vyZmlW\nEoCDmt48xSSv5Y16PcJ8DGVP72vdFuGsJjtXmi2qNTV7a4GmuTSiBg0bHKlbQXe7w0PmG7YRgm1d\nvzDfp72QC0Br8nb6dDvWxLZaZ9yPBwIwyDXIZJ/Ib5ZlS/r/8tpf1/Rgelhdysr64YTeOrIarQ9U\n+A1vo53ixYuPjzvJPqZEmmaL5nPoOZJCAOYNS9N+XAjAMI/8djtEnbZ9vEA4owKwVt8BQKO6nUao\nQQ8dUN7g1w5L2GMbG6K5FJkAAB9ASURBVNSt9jdfD5i3ArDV0gTo9kKPSkcf9zoq6Lqd3iIBCKoF\nlgUg2CFGuSAcEoDl2t98XxhtAjtBuD5M68fshN6UM1avvjTFsykmeSmbF/gkuZ8vGDQqLZoKhPu2\nqS/f5wEILwiI8qal9j71A1J7fBZ5QIPZap/U+v7SLBc+PlnRxn55uvT22ZZrfzkJ6v8DG6gpvZbX\n/ubaxagUmGJf1/1lXZjWj9gJvYPAUhrfqOhuzshuLXmQo79vNUeW9sms360c8IiKiWwDkzfu5Dl+\n1uRtd4hsfW/QbhO0raZnW9aHM/NUG3sBqNe2Azu4Y/1XzFif32xNS9xm6hF7G7qGPQs23WUhYKGh\nml6rpUJtlPbX7/bpD2l/SZwMxlXGCUmpIaofBGReOa9xtB8wf82ZwWvP8AD6acEJvYPIOH6+Yt8R\n3VpykpLJmx8bVAcalr/Iz5cHNwZBjnL/PtBxlImdmRu22kQLtplBy87pmJsnnNkDQGVmD3BvDu/8\niGb1EABmGur7m6002G0F4GxD/9T2NoNFAhCg1YxotfTabZt+0+3Gg3m9dl39bn9g/vZj/FJfvzAK\nSbNsRT8gjN/23gnCAyObUCDDdnZ6B3AMOsfvXGPMjaXXXwT8rn36KWPMa5Y735oJPREJgPcAglom\n56Bx6QvQWRvXA+cZY1IReTXwKDQD94V2Lu6WYFw/3/B2GIrk5v39wjydJWWExViYt3mys7avGjQS\nGGyzgqH82HZsDjy/SDDI+/JVW7cO1mKHE5X/unwvb+kckeuqXtHRed/36JW0uFGPPd8r2u4DBFGI\nVwoXZrmpHSfFnJFyNHjYDNZjXBPUSZIkE/vsHgvUjDEnisgJwJuAxwCIyF2BpwDHo3LlKhG5eLnO\n7mup6Z0FYIw5SURORbste8ArjDGXi8i7gMeIyA+BU+yijwA+Ahy3huuaSsbV+kaavKVttK3mEntF\ne/riGklWRHdTOyg8S7JiIlvYHWh8sTUzo1aX2Gp9kc33i9ptQqv1+Qtq5kY//wHB7G0AVJp636wf\nwozV/mZL2t+8bYm1p6l5gfMzPnsW9PGCjfIuLAy0v047v+/RLczfuKgAAag168T9mCQfpJ6bwUFS\nCoRYjXeUGcxiDTB/zWmA+88EP6eTsSMrjDHXiMgDSq/9GPhtY0wCICIR2hFqSdZM6BljPiYil9in\ndwF+jmpzV9htl6IzNwxwmW1u8CMRCUXkcGPMlhtAtJLWB6NN3vLg8ZVy/MopLaCCMChMXZuQ3E2I\n6mUBmCc5d4v7aK/1783OUweSm36KP5+bvLfZ1w6h2lCh16xboVc9hIWGRn7nbIv7+UaVeSsA97St\n6dv2WWhZAdjS9bVaFdqtPOIbFxUgAM25Bv1evMj8Be0CMxCEee1wsk9eY5am++0PdIxmghkrc8Du\n0vNEREJjTGyM6QM3i4gHvAH4hjHmhuVOtqY+PWNMLCL/DDwOeAJwphVuAHuBbegbuqV0WL59SaH3\nkGt3AnBmbNZg1atjGtZw+q8O/kTO5jP/Yqz9Dl3VWYeaFRBSmny0iH/9q19b1ZnXgl07TznYS5jY\nGk4664qVd1qB1VVkLMseYLb03DfGFMXoIlID3ofKjueudLI1D2QYY54uIi8DvgTUSy/NArex7xvK\nty/JlceexZmx4ZJQJr3cVbEea1ipccEj29/l0rl7DfYvlbMN+wP90Cu25ft5kUeQt4yyLaS8wCOs\nhcW20CYgB7bSIqpHRA01UcNaxBF/fxE3vfRsoqYKpLCpX3MwO0Mwo1+tN6NaXdacI2mq9tevq8bX\nqW2nFenjhVQb3i/Edfb29Bp7O3rd+bbPgjVcFloZCwuqwZ1/dsSfvKdNpx3TaeeaYNkMXqz9xf24\n0PqKjjClIEgedSxXtZRNtUUmsf1hX/Wxk3nwY78wMkVm+Pi1YtfOUyYirCbFBN/yLtRd9iHr0/tm\n/oLV8D4OfN4YM9bsnrUMZJwN3NkY89dAC7W2vioip9qBQ2cA/wncCLxeRN4I3BmV4jev1bo2GsuZ\nvDmjEpuBIQ/V6HQXL/YG9b3VPDfPW+T7y31+QTQweftWuER1m37yy9uIFqwgbGgJWzTfIrSpLeGM\nprP4zSZBUxX7YEZN3kpzjqYVgHM13daubqMVqaBcqKkgnG/WmO/p9eY7AfPtwZ/vne5YY6GV0mqr\n4Br4AeOBACyiwMsLwrJfcFgQZmk60iQGO9hphEmsJ1g6lLlZfYTJ5OrQLgZOE5Evour/OSLyYlR2\nBGhMoCoiZ9j9/9QYc/VSJ1tLTe+jwD/ZkZER8ELgO8B7RKRiH19kjElE5CrgavQ3ed4armnDUp6+\ntpzPL6dc1lYcR0nY5RofA99fGtt63LpfCL2knxLY/n558EO3LU5zad/aot+ylR21jr1vFRphZLW/\nsFknaFhNsKkRX39mlqCpAi5q2DrfxhwzddUIe1Xd1qrPsWD9gK1GjYV+1a68zl3ukNDq+Oxt28oP\nm/fXak9eEOZCr+wbBAiiaB9BCFYz9BfnTBbbWb5xwkYWiJNKWbHDyZ4ztPm7pcejfR5LsJaBjAXg\niSNe2sfpYIw5Hzh/rdbiWJpyo4NcECbttHBE+Pj7tLhKk4wsXVxpUW56kLOoy0pJOyo6quSmY5IQ\n2AirlwveNKGat7lP8/uYsGI1zHCGyM/z9Opsr3epBBGRrSqZtxUjYeAXsz8G937RDbrbGcwCKapU\nuoOpcPlM4EF7fK8QgJ7vDd4L+ZwQb58u1pRmAhedY0qNUwef0ejqkeL1DSYAXXKyY2KMmrk7TFnz\nW8n0LX5qpTZXuaBLSAc9/0rmb9rPKzr0R99b6JFYYdFv54OIAvo24hvW7PCh+c7gcUMnt4X1KmFj\nt92m0tafaeI31KytWS2w2pilUVetb64+S7eaD/k9lLtUfkyrOkvLHr8wq1rgQi9ioavrXrC+wYVO\nSKut0eK2TfHpdBK6diBTx973Stpf36bzlCPEaZIWjwEqtaoK9bz5Q1qOFi8WhH4wot3WEqbxRtUI\np3FN4ITepmA1pu84vr+knxUVHn7oFSZxufdfYR73rKBb6BNbLSawZnDcDQgi1czCat++1isFRmwr\nq9ogMBJU7ZyOZo2gbtvcF+Zwg6CpgjBszFKt2ym//+c4Dr3ZsK02UwjCTkWFY7vapNVUf2IrtrmA\nvQqtXi4Ag+K+ZU3iTiez9wltaxr3rNDrdgaCsNeLC2EIMLOtSVIShGXTOB3KFVwqbcZawYsF4gjT\nOGdYIE5TeZ3rsuJYFybi+wsHwY2kFCQpB0GKsjebAN3vxPj+wOcHEEQpsW12EHf1Rx+EfXwbLQ6t\n5uiHAVHd1vrmbehrEWFNhVRok5nDenWRIAysMAMIfnwDYb1ZCMIZqxHGtRl6VhB2K6oxtqsztFI9\nth3ruVtxSRAWmmFU+AbbXX2f7XZKtzvwEeaPAXbcfhu9XkK/qwI+N42Tfjx4nAxyBocTqReVCY4I\nnPgjI8mLv+Nhk3ml6TxrKRSnVNFzQm8zs9rIb3FcWQssCcDyz2e44Wm56UFe95uQ4g/9KLMkxR8y\n9cLq4Mce5oKg3AYr39aPCXMtqt8n6w8qMpJbb8XvdPC6i3sDev0Ofj83sW1FSaVNJVKhWLNNEqpB\ng1pQtdtU0NUqIS2rtba6ttyuEtCxZny1GtD5/+2debRkVXWHv7q3xtfdqCBIGATHjUMQUQZbkQZB\nEBHFEDECBg2gKw4hMREFtFvjEEniSESFMOgyxhgGgeCAgRZEaBRtFUy2IAFciC2C0OCruSp/7HNv\n3bqvXr2q96qq+1HnW+utqrrDubtuvfrV3ufss0+lI3orVxWoVpvUXQn9mgv969U6YUIAgZRH6O5X\nIkUmmlLXzmRoxUnT7ocmIY6kRC7ZZwj9p9nBeD3DEY7ejhQvelPAsN4fDN8P2Cwnctf6hMGZMEMQ\nusEBN5hQn+14f1GuYLaQJchW4udgXmDk/YX5bBwSbwPM3n0vYakYe4VRSByUSmRLbonLqI+wtIKV\nJfP6mkUTv3p+BbXIE3R9iJWZGcpN8yyj0Hi2nqNcd+sAVwNmq50Bnd12m6FSbVOpdPoJwULiWj0S\nQPdYa1CLCjy4fY16s29o3EzUEkyGydARtNAJ9iBe4bCpNcMywuTkkeJFb8pID4LM5wUu1A9Iam2P\nZrnV8RiT1Z7LURut+PjFCmGQDckWnCeXC+NQ+I+AR+59oCskjvoGw2KObKpvMCgWCEr2PBLCfHGG\nGRcab+PyAhvFldRdukzNeYSV4goqbRcaN4uUmzl3Q1Yhu9Qo10PK1cg7NPsrtTyzZbsnNVfvsFxp\nUXWiWK93xDEKgzvpM82uMBnSXmL3oElhptgz0ToZJkcErdYczy4tjEvBi55nq2SQ5Sr7FTJNCuFC\nq71BKkG6a05wKn2jmYkrPkfpMWGzuwR+5CEB1GerXWWyskX3WM/FVaJDVyE6LBYI4yrR9hiUylCx\npOrQJVcHlT+QLdoIc67oRpDzKyg6j7AUzlAJo37FVTyx9CiVXJ7ZvOsHdB7qbC1gxlWeqdQ6XmLF\nDaJUqyZY1WI27iOs15IeYeQJdjzDzlrEzXgb2KLr7XabZjhIf+Hc9JpMGI4sxN1KNc+Lnqd3CsxS\nwuCu1Je0YDba8XofcRuNRIpMtTM9LhoYaVSiXLlM7Ana805oOfvgLEE27PIKwULjyCNMPkZpM5FH\nmC0VCCLvMOEZZgoWLuddiFwolmKPsFVcQbPgRpDZmd3Lt1HLr6Dmjq24gZZKq0i5ae1UXJhcbmQp\n1yJP0NUVrGZxWky15kaQq62OKDpBrNVaXaIInUGTx+/wOJr1Ztyf1ll3uNU1iAImdMmQ2ba14+OW\nivf0PMuKhfoBI+IpbEOEw830CHJizY9YEMvdIXHUbq+QGKDyUJVMmCF0gwhR3b0wF8QC2B0mzxXC\nOeJYyBNGOYWREBYLBEV7HhQL5Nxz9nkFM3dupFQs0Y5EMW+i1yzM0MjbtlrOHqvFGapuIkGl7R6b\n+S5RBKjUA8pVe152U/AqtYQouvSaSBh33X3brj7EhhtJt/zCTt9h9NhKi2OzZbUYR4DP0/MsWwb1\nBCMG9gijthLPuzzD1EpwSSGE7uIKtdk6QRB0eYXgCqIm1ggx2zseYTS6GeaCjti5fWE+2xHMRCpN\nmI+8xFzsKZaAsiphqUhQNK8uEsewUKTg+hBnCiaE7WKpSxQBGrkZ6q7vsOq2VYMS1ZbzEqPHRo5q\nMxLCSBzNzj2elqdcy+Mi+Vgca7WOxxj1K9ZrzTkDLI1Gpw9xqfjRW89jikFmhSTp5QnGbaVSZNLH\n90qVSW9vllu0s530m6iPMBNm4rSZtls8qdVs06p3BNDeTyaRXxhNR2t0KtA40bNF1OeGyQDVBzcT\nFsudQRTnHQb5/BwhzBRLZAuR9+hGlwsl8k4IiwkhjAZRaqEJZzUsxt5h1QlwpZkHVvCkx9eo1kMq\ndXsPlZq9z3ItpOrC6EGFcKn48NbzmGYYb7CXJxi302vjPKFxMixulltutbQoFO7tEdq+Tn9hJuys\nGBeXog+jmSVhV5gM5hl2wuQgFsWdgM2/ut/6FXv0IcZC6LYFhXy8LepLDIoFQieKkSAWC0Xabp0R\n8ratVeh4iY287WtkS8D+PKeo1FaUqOG8wkgcmzmqbqQ58hIr9TD2ECsudK7W7W8UeNHzTB3zeYPD\nhsZxe+kNPUaOY1GMU2Uy8TpxwTxhcrRvTv3BIJgTGqfD5eRgyiP3bU6FztFUvk4fYlTUoFfoHOaz\nBC7PLkwMtMTimLfHTCE/RxwzhSI8d3+2++WNtPNFWs5TbOVsfzNfopkzgay7x1qpRC2w/bW2iWS1\nlY/F0Wr5Lh5fcMDjcQyaK5hm0BAZFg6T5+QZpuYbg6XUpMNloEv0MrVOq41qg0yjRaOa9g47qTSx\n6OXCWPSGFcIgl5vrJTpBbG26j0y+QFCI5jJ3xLHthLDghLCVL9HIRZ6ibatni9RyUa3fpYme9/Q8\nnnkYhUeYft0vTIZusYuIRC/Zo9UJjTs5hcl9ydflh6oDeYdgi7H3C52TXmI6DaeXOAbZgJXAw7f+\ngiCXJXRJ2kGuMxKd9BQBsvk8ubyrSehEMlMo0nZCydOf0eMmDo4fyPB4hmShwZLFhsmwsChC7/Qa\nC43niiNAY3MTEt5hsp2IeDZKMI/nmOpXDMIMGVfLL8x2BmeSo852jYBdgAd/uakrTadbKDseZbQv\n9iITfY2RUJZe/uYeN2lwvKfn8YyYpCgO6y0uJIrQXWGmix4eIyTmHyf6E6G/55g8Lt1etC9IFRWw\nHMWOFxltA3h00yNdXmRUgCDIdQZl4tSdxEBM0rOMhPIJc9/5UPg8PY9nCzBsjmGafjmH0N2X2Ks8\nf5o5M1RggBksc8uBuaLSiVFoO772h8Y8HmMj9hiTghjvjzzDMBN7hEtlvkWStjRe9DxTxzA5hoNO\nx+u1vafARfRI0u6anlfuPO15fmRfyjOsba7Pu3+h0DreFoym6IAvIurxLEOGEcj5ijcMEkpDalCl\nhyfY8xz3GImb9St2GFQw+x23WHx46/FMAYsZie7FQkI5X8rOICF2RFefZR9hXSxREditjXGuexsC\n5wKC/SC9CUv8uQK43R12jqp+VUTWAq8EGsCpqnrzuOzyeLYEw07bSzKKELsXyfnN46A1qjUgR8w4\nPb1XAajqi0VkDfBxTPA+rqr/HB0kIntjy0LuB+wKXAzsM0a7PJ5lxbAh9nykxXPQsHuxTF3Kiqpe\nJiJXupe7AZuAFwAiIq/GvL1TgZcA31bVNnCPiGRFZHtVvX9ctnk808hSvM3FMHWiB6CqDRG5CDga\nOAbYGThPVW8RkTOAtcBDwAOJ0x7BwuB5Re+lG68A4MiGjsnywfE2bB02bOnrP9ZsuDIrS25jagcy\nVPXPReQ0YAOwWlXvdbsuBT4DfB1YlThlFSaE83LdXq/iyIaO5INZCt6GrcOGLX19b0NvRlWMdNSM\nJguxByJygoi8172cxWb+XCIi+7ptLwNuAW4ADhORQESeDASq+rtx2eXxeCZDq9kc+G+SjNPTuwS4\nQESuA3JY/92vgLNFpAb8BjhFVTeLyPXAjZgIv22MNnk8ngkxdX16qvoH4HU9dq3ucew6YN24bPF4\nPJNn6kTP4/FMN9OYp+fxeKYY7+l5PJ6potWY7ADFoHjR83g8Y6Htw1uPxzNN+NJSHo9nqvBFRD0e\nz1QxqoEMEQmAzwLPA6rASap6R2L/ycBbsCpNH1LVK3s25BjbjAyPxzPdtNutgf8W4DVAUVVfBLwH\nSFZp2hF4J/Bi4DDgoyJS6NeYFz2PxzMWWo3mwH8L8BLgmwCqehPwwsS+fYEbVLWqqg8DdwB79mts\nWYa3RzY04x63tCnehq3Ehi19fW/DXL53xYGjqkG/DfBw4nVTRLKq2uixL6rSNC/e0/N4PFs7m+mu\nxBQ4weu1b8EqTV70PB7P1s4NwBEAIrI/8LPEvpuBA0SkKCKPA54F3NqvsczWWujP4/F4oGv0dk8g\ng623cwRwh6pe7kZvT8GcuI+o6sX92vOi5/F4pgof3no8nqnCi57H45kqllXKykKZ2WO8bg44H9gd\nKAAfAn4OXAi0sY7Tt6nq2OfdiMgOWJn9Q7EM9Ina4JYAOArIY5/Fdydpg/ssLsI+iyZwMhO8DyKy\nH/AxVV0jIk/vdd1xr+OcsmEvbK2ZJvadeKOqbhp2lsI0sdw8vXkzs8fM8cADqnoA8ArgbGwd3zPd\ntgzw6nEb4b7wnwfKbtNEbXDrF6/Gst8PxNYpnvR9OALIqupq4IPAhydlg4i8GzgPKLpNc66bWsf5\n9cC/jNmGTwHvUNU12BINpy1mlsI0sdxEr19m9jj5GvC+xOsGtobvd93rbwCHTMCOfwI+B/zavZ60\nDYdh6QKXYgu3X7kFbPgFkHVe/zZAfYI2/BJ4beJ1r+vG6zir6j3O1u3HaMPrVXWje54FKixilsI0\nsdxEr2dm9rgvqqqPquojIrIK+E/gTCDjFiiHAbLAl4qInAjcr6rfSmyeqA3AE7Efmj8F3gp8GUsU\nnaQNj2Kh7f8C5wKfZkL3waVC1BObel136BkCS7FBVe8DEJHVwNuBT4zbhuXOchO9fpnZY0VEdgWu\nBb6kqv+GLWkZsWAW+Ah4M3CoiKwH9gK+COwwYRseAL6lqjVVVcyrSH6ZJmHDXzsbnon17V6E9S9O\n0oaIXv8DQ88QWCoiciwWAbxSVe/fEjYsJ5ab6PXLzB4bIvIk4NvAaap6vtv8Y9fHBdbPd/04bVDV\nl6rqga7vZiPwRuAbk7QB+B5wuIhkRGQnYAXw3xO24fd0vJgHseVFJ/pZJOh13Ymu4ywix2Me3hpV\nvdNtHnqWwjSxrEZvsb6kQ0Xk+3QysyfB6cATgPeJSNS391fAp0UkD/wPFvZOmncB507KBlW9UkRe\nin2pojWK/2+SNmDh2/lureQ89tn8cMI2RMy5/6ranNQ6ziISYuH9PcAlIgLwXVVdKyKfxkQ4AM5Q\n1cq47Fhu+BkZHo9nqlhu4a3H4/EsCS96Ho9nqvCi5/F4pgoveh6PZ6rwoufxeKaK5ZayskURkW2A\nj2JzKxtYzti7VPVHi2jrZOBRVf3KaK2c93oXAnep6roBjl0PrFPV9anz16vqhSKyUVX36nP+tap6\n0BC27QScp6pHDHpO4ty2qmZS207E5sXek9i8SVUPG7Z9z2MPL3oD4uZ6XoXNythLVRsichCWIPxs\nVX1gyCZfDKwfsZn9KAOzo2ion+A51gzZ3q9xSecj5HJVPXHEbXoeA3jRG5yDgCcDa6OyRap6rYi8\nCQhdZv46N2Mi9oywyhdfAXZ07XwAE5+jgINF5D5shsW/uvYbwOmq+k0RWee2PRPYHqso8jKsgsdP\nsMnmbRF5D/A6IAS+BZwG7IYVZ/gdJnhfBe4WkV2wObMrsGlU73TFGwYm8q5E5GXAWVhppd8Dfwa8\n3x2zQVX3E5EjsVJcAXAn8BZX+uguYAM2pe4E4D9UdXcR2Q24AJtiN4uVD/upiETvfVus4MKxqrpp\nGLudXcnrHgAcDpzq7LsFKw9VEZE3AmdgU7puAlap6onu/DWqelfyM3dlps4BtnN2v0NVf+z+Dx7G\nihPsDHxQVS8QkW2xz3wPrCTU3wBPAQ5W1eOcreuAsqp+bNj36Zkf36c3OM8HNqbrtKnqVar62z7n\nHY2FlS8A/gI4QFW/A1wOvN8VEPgMcI2q7gkcg804eJI7/48xz+kUTAw+BjwX2BvYU0QOx75Q+zgb\ndwaOc+cKcLyqHqqq56nq1c6GK1X1hZhAvWQeu88TkY3RHybSac4E3urauhrYW1Xf6e7Lfq723+eB\n17j3dgNWliviG6oqQPL+fRa4WFWfC6wDznSCsgew2s25vQcr99WPo5L2O688fd3tsXp8q533+lvg\nb90Pwz9i3RgvcvdxIS4C3q2qe2Of1b8n9u2KCexRWKUcgL/H1nh4Fib6H8Z+mA5xhS3AfkS+NMC1\nPUPgPb3BaWET7Ifl+8BHRGRn4L+wf/Y0B2NfPlT1ThHZgHlzAFe7UPpu4D5V/TmAiNyLTY07xB17\nizu+hInC94DfqupdqWt9B5uy9Hxnz9n05qQefXppLgcuFZHLgK87UU2yL3BzwoYvAO9N7N/Qo80D\nsS87qnoV1qWAiLwLOElsrtWLsBJL/egX3kbXPQh4BnCTm8KVB37k2r9BVX/jrn0h8PL5LiQiK7Ef\nnQtcOwArRWQ79/zbziO/FfNUo/f5Bvc+f+auiYhcBbxWRO4E7nShv2eEeE9vcH4I7C0i6U7zjzgv\noo3NB47IAajq7ZiX8mXs1/5m1z+YJP06Q+cHqZbY3quiTAh8UlX3ct7KfpjXAJ1iozGqegPwbCwM\nPhari7coVPUTmBd6B3CWiJyROqTf++ppH4mySa6wwbNF5AVYwYcAm1d7Kd33elii64ZYWB3du32x\nyftpu5LlpJKfcy7RTiVqJ/E5POj2VwASZaiiNuPXIrKH+784HxPDN2BVmT0jxove4FyPhT9r3URv\nROQwrOjBz7G+s6e6yhbbYgKHiLwd+ICqfg34S6yvahtMwCIBuAYLOxGRp2KDHDcOaNc1wAkistLV\nFrwMC5F7IiJnYSHvRdgXfO8Br9OrrQ1YX9cnsUIAUVtRncMNwP4isrvbfgo2ENSP67CKw2Be7Bcw\nr2i9qn4OKyJ6JCY0S2U9cLSI7OB+zM7B+vduAl4oIrs6IXp94pzfAc9xz18N4Ap13u4qniAih7r3\n0Y/rcB6tiOyB9b+2VfV6YBfMC71sye/QMwcvegPifqWPAp4G3CoiP8UGDI5Q1U2qehsWLt6GVVqO\nyht9ERAR+Znb9neq+hAWZp4uIsdgpb0PdsdchoWW9w1o1xXAxZjA3IoNilzU55TPAMe4frpLsRJV\ni+V04EIRuQUT7fe47V/HBloexoTuUhG5DfMK37pAm28H/sTZ9wF3/leB57n7sx7zup+yBLsBUNWf\nuGtcg31uIfAPrhTUKVhl6B/Q8egA1gKfEpEf0F2j7jgs/P4pltZ0bMqzS7MWeIaI/ASLAk5IHH8J\n1sdbXep79MzFV1nxeBbA5f2tGXcKjPM289ig0KmLyf/0LIz39DyerYcdgd8AN3nBGx/e0/N4PFOF\n9/Q8Hs9U4UXP4/FMFV70PB7PVOFFz+PxTBVe9Dwez1ThRc/j8UwV/w/b9/cgK1cC3wAAAABJRU5E\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b9e64f9278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from lifetimes.plotting import plot_probability_alive_matrix\n",
    "\n",
    "plot_probability_alive_matrix(bgf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Customers who have purchased recently are almost surely \"alive\". \n",
    "\n",
    "Customers who have purchased a lot but not recently, are likely to have dropped out. And the more they bought in the past, the more likely they have dropped out. They are represented in the upper-right.\n",
    "\n",
    "The matrix lets us estimate a behavioral propensity that we can never observe if someone is alive or not. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ranking customers from best to worst"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s return to our customers and rank them from “highest expected purchases in the next period” to lowest. Models expose a method that will predict a customer’s expected purchases in the next period using their history."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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",
       "      <th>predicted_purchases</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustomerID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14606.0</th>\n",
       "      <td>88.0</td>\n",
       "      <td>372.0</td>\n",
       "      <td>373.0</td>\n",
       "      <td>135.890114</td>\n",
       "      <td>0.200951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15311.0</th>\n",
       "      <td>89.0</td>\n",
       "      <td>373.0</td>\n",
       "      <td>373.0</td>\n",
       "      <td>677.729438</td>\n",
       "      <td>0.203214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17841.0</th>\n",
       "      <td>111.0</td>\n",
       "      <td>372.0</td>\n",
       "      <td>373.0</td>\n",
       "      <td>364.452162</td>\n",
       "      <td>0.252984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12748.0</th>\n",
       "      <td>113.0</td>\n",
       "      <td>373.0</td>\n",
       "      <td>373.0</td>\n",
       "      <td>298.360885</td>\n",
       "      <td>0.257509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14911.0</th>\n",
       "      <td>131.0</td>\n",
       "      <td>372.0</td>\n",
       "      <td>373.0</td>\n",
       "      <td>1093.661679</td>\n",
       "      <td>0.298229</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            frequency  recency      T  monetary_value  predicted_purchases\n",
       "CustomerID                                                                \n",
       "14606.0          88.0    372.0  373.0      135.890114             0.200951\n",
       "15311.0          89.0    373.0  373.0      677.729438             0.203214\n",
       "17841.0         111.0    372.0  373.0      364.452162             0.252984\n",
       "12748.0         113.0    373.0  373.0      298.360885             0.257509\n",
       "14911.0         131.0    372.0  373.0     1093.661679             0.298229"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = 1\n",
    "data['predicted_purchases'] = bgf.conditional_expected_number_of_purchases_up_to_time(t, data['frequency'], data['recency'], data['T'])\n",
    "data.sort_values(by='predicted_purchases').tail(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the customer who has made 131 purchases, and bought very recently from us, has a probability of 29.8% to buy again in the next period (tomorrow)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assessing model fit\n",
    "we can predict and we can visualize our customers’ behaviour, but is our model correct? There are a few ways to assess the model’s correctness. The first is to compare our data versus artificial data simulated with your fitted model’s parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b9e87cd940>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JQkREEilBiIhIIg21ISKN0lAhrZNqECIikkgJQkREEhW1icnM2gO3Ab2AjsAl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\nkRKEiIgkUoIQEZFEShAiIpLo/wFbMf0fq2nIbgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b9e79a6710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from lifetimes.plotting import plot_period_transactions\n",
    "plot_period_transactions(bgf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This plot tells us for all customers in our data, how many purchases they make in our observation period, and what does the model predict? Looks nice?\n",
    "\n",
    "More importantly, it tells us that our model doesn’t suck. So, we can continue on with our analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### More model fitting\n",
    "We can partition the dataset into a calibration period dataset and a holdout dataset. This is important as we want to test how our model performs on data not yet seen (think cross-validation in standard machine learning literature). Lifetimes has a function to partition our dataset like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            frequency_cal  recency_cal  T_cal  frequency_holdout  \\\n",
      "CustomerID                                                         \n",
      "12346.0               0.0          0.0  141.0                0.0   \n",
      "12347.0               2.0        121.0  183.0                4.0   \n",
      "12348.0               2.0        110.0  174.0                1.0   \n",
      "12350.0               0.0          0.0  126.0                0.0   \n",
      "12352.0               3.0         34.0  112.0                3.0   \n",
      "\n",
      "            duration_holdout  \n",
      "CustomerID                    \n",
      "12346.0                  184  \n",
      "12347.0                  184  \n",
      "12348.0                  184  \n",
      "12350.0                  184  \n",
      "12352.0                  184  \n"
     ]
    }
   ],
   "source": [
    "from lifetimes.utils import calibration_and_holdout_data\n",
    "\n",
    "summary_cal_holdout = calibration_and_holdout_data(df, 'CustomerID', 'InvoiceDate',\n",
    "                                        calibration_period_end='2011-06-08',\n",
    "                                        observation_period_end='2011-12-9' )   \n",
    "print(summary_cal_holdout.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b9e91aaeb8>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2jElRFKXTqrM5eGlJCsXltVx/6SBio4M7OqRmuXKtUSyEOPNXscellM3OMqUo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\nyFmiN8aYkLNEb4wxIff/QHA4pf+JrakAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b9e91aa518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from lifetimes.plotting import plot_calibration_purchases_vs_holdout_purchases\n",
    "\n",
    "bgf.fit(summary_cal_holdout['frequency_cal'], summary_cal_holdout['recency_cal'], summary_cal_holdout['T_cal'])\n",
    "plot_calibration_purchases_vs_holdout_purchases(bgf, summary_cal_holdout)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Customer Predictions\n",
    "Based on customer history, we can predict what an individuals future purchases might look like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.088760211599876931"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = 10 #predict purchases in 10 periods\n",
    "individual = data.iloc[20]\n",
    "# The below function is an alias to `bfg.conditional_expected_number_of_purchases_up_to_time`\n",
    "bgf.predict(t, individual['frequency'], individual['recency'], individual['T'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "frequency                3.000000\n",
       "recency                309.000000\n",
       "T                      360.000000\n",
       "monetary_value         651.623333\n",
       "predicted_purchases      0.008919\n",
       "Name: 12370.0, dtype: float64"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Customer ID 12370's predicted purchase is 0.008919 in 10 periods."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Customer Probability Histories\n",
    "Given a customer transaction history, we can calculate their historical probability of being alive, according to our trained model. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b9e8ebc0f0>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NjTUAhMIVQ+yxXm/q7gcg4POGvRZpbclS1yoRUPE+s0G+tjWMqwIgXF1p+/eI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\nAb8RI4iGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b9e98b3c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from lifetimes.plotting import plot_history_alive\n",
    "\n",
    "id = 12347\n",
    "days_since_birth = 365\n",
    "sp_trans = df.loc[df['CustomerID'] == id]\n",
    "plot_history_alive(bgf, days_since_birth, sp_trans, 'InvoiceDate')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Estimating customer lifetime value using the Gamma-Gamma model\n",
    "For this whole time we didn’t take into account the economic value of each transaction and we focused mainly on transactions’ occurrences. To estimate this we can use the Gamma-Gamma submodel. But first we need to create summary data from transactional data also containing economic values for each transaction (i.e. profits or revenues)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            frequency  recency      T  monetary_value  predicted_purchases\n",
      "CustomerID                                                                \n",
      "12347.0           6.0    365.0  367.0      599.701667             0.015657\n",
      "12348.0           3.0    283.0  358.0      301.480000             0.008961\n",
      "12352.0           6.0    260.0  296.0      368.256667             0.018702\n",
      "12356.0           2.0    303.0  325.0      269.905000             0.007174\n",
      "12358.0           1.0    149.0  150.0      683.200000             0.008340\n"
     ]
    }
   ],
   "source": [
    "returning_customers_summary = data[data['frequency']>0]\n",
    "\n",
    "print(returning_customers_summary.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Gamma-Gamma model and the independence assumption\n",
    "The model we are going to use to estimate the CLV for our userbase is called the Gamma-Gamma submodel, which relies upon an important assumption. The Gamma-Gamma submodel, in fact, assumes that there is no relationship between the monetary value and the purchase frequency. In practice we need to check whether the Pearson correlation between the two vectors is close to 0 in order to use this model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>monetary_value</th>\n",
       "      <th>frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>monetary_value</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.015882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frequency</th>\n",
       "      <td>0.015882</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                monetary_value  frequency\n",
       "monetary_value        1.000000   0.015882\n",
       "frequency             0.015882   1.000000"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returning_customers_summary[['monetary_value', 'frequency']].corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this point we can train our Gamma-Gamma submodel and predict the conditional, expected average lifetime value of our customers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<lifetimes.GammaGammaFitter: fitted with 2790 subjects, p: 2.10, q: 3.45, v: 485.57>\n"
     ]
    }
   ],
   "source": [
    "from lifetimes import GammaGammaFitter\n",
    "\n",
    "ggf = GammaGammaFitter(penalizer_coef = 0)\n",
    "ggf.fit(returning_customers_summary['frequency'],\n",
    "        returning_customers_summary['monetary_value'])\n",
    "print(ggf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now estimate the average transaction value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CustomerID\n",
      "12346.0    416.917661\n",
      "12347.0    569.988799\n",
      "12348.0    333.762677\n",
      "12349.0    416.917661\n",
      "12350.0    416.917661\n",
      "12352.0    376.166865\n",
      "12353.0    416.917661\n",
      "12354.0    416.917661\n",
      "12355.0    416.917661\n",
      "12356.0    324.008947\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(ggf.conditional_expected_average_profit(\n",
    "        data['frequency'],\n",
    "        data['monetary_value']\n",
    "    ).head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Expected conditional average profit: 441.1283979957275, Average profit: 477.3586892535673\n"
     ]
    }
   ],
   "source": [
    "print(\"Expected conditional average profit: %s, Average profit: %s\" % (\n",
    "    ggf.conditional_expected_average_profit(\n",
    "        data['frequency'],\n",
    "        data['monetary_value']\n",
    "    ).mean(),\n",
    "    data[data['frequency']>0]['monetary_value'].mean()\n",
    "))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While for computing the total CLV using the [DCF method](https://en.wikipedia.org/wiki/Discounted_cash_flow) adjusting for cost of capital:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CustomerID\n",
      "12346.0     295.248437\n",
      "12347.0    3010.933981\n",
      "12348.0    1008.692681\n",
      "12349.0    1337.106359\n",
      "12350.0     306.932918\n",
      "12352.0    2372.739730\n",
      "12353.0     426.098827\n",
      "12354.0     386.464241\n",
      "12355.0     411.043305\n",
      "12356.0     784.161525\n",
      "Name: clv, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# refit the BG model to the summary_with_money_value dataset\n",
    "bgf.fit(data['frequency'], data['recency'], data['T'])\n",
    "\n",
    "print(ggf.customer_lifetime_value(\n",
    "    bgf, #the model to use to predict the number of future transactions\n",
    "    data['frequency'],\n",
    "    data['recency'],\n",
    "    data['T'],\n",
    "    data['monetary_value'],\n",
    "    time=12, # months\n",
    "    discount_rate=0.01 # monthly discount rate ~ 12.7% annually\n",
    ").head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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