cohort retention and CLV.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load in libraries\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import datetime as dt\n",
    "\n",
    "#libraries for handling data\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.metrics import r2_score, mean_squared_error\n",
    "\n",
    "#label encoders\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "enc = OneHotEncoder()\n",
    "\n",
    "\n",
    "#libraries for data visualization\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.pylab as pylab\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "#sns.set_palette('Set2')\n",
    "sns.set_color_codes(\"pastel\")\n",
    "\n",
    "#libaries for modelling\n",
    "# Regression Modelling Algorithms\n",
    "import statsmodels.api as sm\n",
    "#from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.ensemble import RandomForestRegressor , GradientBoostingRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Online Retail']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filepath = \"/Users/reejungkim/Documents/Git/Production analysis/Online Retail.xlsx\"\n",
    "pd.ExcelFile(filepath).sheet_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel(filepath, sheet_name = 'Online Retail')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 541909 entries, 0 to 541908\n",
      "Data columns (total 8 columns):\n",
      " #   Column       Non-Null Count   Dtype         \n",
      "---  ------       --------------   -----         \n",
      " 0   InvoiceNo    541909 non-null  object        \n",
      " 1   StockCode    541909 non-null  object        \n",
      " 2   Description  540455 non-null  object        \n",
      " 3   Quantity     541909 non-null  int64         \n",
      " 4   InvoiceDate  541909 non-null  datetime64[ns]\n",
      " 5   UnitPrice    541909 non-null  float64       \n",
      " 6   CustomerID   406829 non-null  float64       \n",
      " 7   Country      541909 non-null  object        \n",
      "dtypes: datetime64[ns](1), float64(2), int64(1), object(4)\n",
      "memory usage: 33.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.CustomerID = df.CustomerID.astype(str)"
   ]
  },
  {
   "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>Quantity</th>\n",
       "      <th>UnitPrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>541909.000000</td>\n",
       "      <td>541909.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.552250</td>\n",
       "      <td>4.611114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>218.081158</td>\n",
       "      <td>96.759853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-80995.000000</td>\n",
       "      <td>-11062.060000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.080000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>4.130000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80995.000000</td>\n",
       "      <td>38970.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Quantity      UnitPrice\n",
       "count  541909.000000  541909.000000\n",
       "mean        9.552250       4.611114\n",
       "std       218.081158      96.759853\n",
       "min    -80995.000000  -11062.060000\n",
       "25%         1.000000       1.250000\n",
       "50%         3.000000       2.080000\n",
       "75%        10.000000       4.130000\n",
       "max     80995.000000   38970.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.plotting import scatter_matrix\n",
    "\n",
    "scatter_matrix(df, figsize=(10, 10));\n",
    "plt.tight_layout()\n",
    "\n",
    "#df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7fda34044e80>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df,\n",
    "             diag_kind='kde',\n",
    "             hue=\"Country\", \n",
    "             palette='bright') # pastel, bright, deep, muted, colorblind, dark\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate total purchase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>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",
       "      <th>TotalPurchase</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",
       "      <td>15.30</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",
       "      <td>20.34</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",
       "\n",
       "          InvoiceDate  UnitPrice CustomerID         Country  TotalPurchase  \n",
       "0 2010-12-01 08:26:00       2.55    17850.0  United Kingdom          15.30  \n",
       "1 2010-12-01 08:26:00       3.39    17850.0  United Kingdom          20.34  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['TotalPurchase'] = df.Quantity * df.UnitPrice\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4))\n",
    "\n",
    "ax = sns.barplot(x='Country', y='TotalPurchase', color='b', data=df)\n",
    "\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha=\"right\")\n",
    "plt.title('Count of purchases per country')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4))\n",
    "\n",
    "#ax = sns.barplot(x='Country', y='TotalPurchase', color='b', data=df, estimator=sum)\n",
    "ax = df.groupby('Country')['TotalPurchase'].sum().sort_values( ascending=False).plot(kind='bar', color='b')\n",
    "\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha=\"right\")\n",
    "plt.title('Total purchase per country')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4))\n",
    "\n",
    "#ax = sns.barplot(x='Country', y='TotalPurchase', color='b', data=df, estimator=sum)\n",
    "ax = df.loc[df.Country != 'United Kingdom'].groupby('Country')['TotalPurchase'].sum().sort_values( ascending=False).plot(kind='bar', color='b')\n",
    "\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha=\"right\")\n",
    "plt.title('Total purchase per country exluding UK')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Australia', 'Austria', 'Bahrain', 'Belgium', 'Brazil', 'Canada',\n",
       "       'Channel Islands', 'Cyprus', 'Czech Republic', 'Denmark', 'EIRE',\n",
       "       'European Community', 'Finland', 'France', 'Germany', 'Greece',\n",
       "       'Hong Kong', 'Iceland', 'Israel', 'Italy', 'Japan', 'Lebanon',\n",
       "       'Lithuania', 'Malta', 'Netherlands', 'Norway', 'Poland',\n",
       "       'Portugal', 'RSA', 'Saudi Arabia', 'Singapore', 'Spain', 'Sweden',\n",
       "       'Switzerland', 'USA', 'United Arab Emirates', 'United Kingdom',\n",
       "       'Unspecified'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort( df.Country.unique() )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4))\n",
    "ax = (df.groupby('Country')['TotalPurchase'].sum()/ df.groupby('Country')['TotalPurchase'].count() ).sort_values( ascending=False).plot(kind='bar', color='b')\n",
    "\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha=\"right\")\n",
    "plt.title('Average spending per order by country')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 5 highest total purchase countries\n"
     ]
    },
    {
     "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>TotalPurchase</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>8.187806e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>2.846615e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EIRE</th>\n",
       "      <td>2.632768e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>2.216982e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>1.974039e+05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                TotalPurchase\n",
       "Country                      \n",
       "United Kingdom   8.187806e+06\n",
       "Netherlands      2.846615e+05\n",
       "EIRE             2.632768e+05\n",
       "Germany          2.216982e+05\n",
       "France           1.974039e+05"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Top 5 highest total purchase countries\")\n",
    "df.groupby('Country')[['TotalPurchase']].sum().sort_values(by ='TotalPurchase', ascending=False)[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['year-m'] = df['InvoiceDate'].dt.strftime('%Y-%m') #df['InvoiceDate'].apply(lambda x: x.strftime(\"%Y-%m\"))\n",
    "\n",
    "df.sort_values(by='InvoiceDate', ascending=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4))\n",
    "\n",
    "#ax = sns.barplot(x='year-m', y='TotalPurchase', color='b', data=df, estimator=sum)\n",
    "ax = df.groupby('year-m')['TotalPurchase'].sum().sort_values( ascending=False).plot(kind='bar', color='b')\n",
    "\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha=\"right\")\n",
    "plt.title('Total purchase')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfOrg = df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cohort Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>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",
       "      <th>TotalPurchase</th>\n",
       "      <th>year-m</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",
       "      <td>15.30</td>\n",
       "      <td>2010-12</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",
       "      <td>20.34</td>\n",
       "      <td>2010-12</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",
       "      <td>22.00</td>\n",
       "      <td>2010-12</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",
       "      <td>20.34</td>\n",
       "      <td>2010-12</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",
       "      <td>20.34</td>\n",
       "      <td>2010-12</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  TotalPurchase  \\\n",
       "0 2010-12-01 08:26:00       2.55    17850.0  United Kingdom          15.30   \n",
       "1 2010-12-01 08:26:00       3.39    17850.0  United Kingdom          20.34   \n",
       "2 2010-12-01 08:26:00       2.75    17850.0  United Kingdom          22.00   \n",
       "3 2010-12-01 08:26:00       3.39    17850.0  United Kingdom          20.34   \n",
       "4 2010-12-01 08:26:00       3.39    17850.0  United Kingdom          20.34   \n",
       "\n",
       "    year-m  \n",
       "0  2010-12  \n",
       "1  2010-12  \n",
       "2  2010-12  \n",
       "3  2010-12  \n",
       "4  2010-12  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "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>InvoiceDate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustomerID</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12346.0</th>\n",
       "      <td>2011-01-18 10:01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12347.0</th>\n",
       "      <td>2010-12-07 14:57:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12348.0</th>\n",
       "      <td>2010-12-16 19:09:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12349.0</th>\n",
       "      <td>2011-11-21 09:51:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12350.0</th>\n",
       "      <td>2011-02-02 16:01:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   InvoiceDate\n",
       "CustomerID                    \n",
       "12346.0    2011-01-18 10:01:00\n",
       "12347.0    2010-12-07 14:57:00\n",
       "12348.0    2010-12-16 19:09:00\n",
       "12349.0    2011-11-21 09:51:00\n",
       "12350.0    2011-02-02 16:01:00"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cohort = pd.DataFrame(df.groupby('CustomerID')['InvoiceDate'].min() )\n",
    "cohort.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fillna(df['InvoiceDate']) due to invoices without customer ID\n",
    "df = df.merge(cohort, how='left', on='CustomerID', suffixes=('', '_Cohort')).fillna(df['InvoiceDate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "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>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",
       "      <th>TotalPurchase</th>\n",
       "      <th>year-m</th>\n",
       "      <th>InvoiceDate_Cohort</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [InvoiceNo, StockCode, Description, Quantity, InvoiceDate, UnitPrice, CustomerID, Country, TotalPurchase, year-m, InvoiceDate_Cohort]\n",
       "Index: []"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[pd.isnull(df['InvoiceDate_Cohort'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 541909 entries, 0 to 541908\n",
      "Data columns (total 11 columns):\n",
      " #   Column              Non-Null Count   Dtype         \n",
      "---  ------              --------------   -----         \n",
      " 0   InvoiceNo           541909 non-null  object        \n",
      " 1   StockCode           541909 non-null  object        \n",
      " 2   Description         540455 non-null  object        \n",
      " 3   Quantity            541909 non-null  int64         \n",
      " 4   InvoiceDate         541909 non-null  datetime64[ns]\n",
      " 5   UnitPrice           541909 non-null  float64       \n",
      " 6   CustomerID          541909 non-null  object        \n",
      " 7   Country             541909 non-null  object        \n",
      " 8   TotalPurchase       541909 non-null  float64       \n",
      " 9   year-m              541909 non-null  object        \n",
      " 10  InvoiceDate_Cohort  541909 non-null  datetime64[ns]\n",
      "dtypes: datetime64[ns](2), float64(2), int64(1), object(6)\n",
      "memory usage: 49.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df['year-m_Cohort'] = pd.to_datetime(df['InvoiceDate_Cohort'], errors='coerce').dt.strftime('%Y-%m')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df.loc[df['year-m']==df['year-m_Cohort'], 'new_user'] =1\n",
    "df.loc[df['year-m']!=df['year-m_Cohort'], 'new_user'] =0\n",
    "df['new_user'] = df['new_user'].astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "plt.figure(figsize=(10,4))\n",
    "#ax = df.loc[df.new_user==1].groupby('year-m_Cohort')['CustomerID'].count().plot(kind='bar', color='g') \n",
    "ax = sns.countplot(x='year-m', data=df.loc[df.new_user==1], palette=[\"C2\"]) #color='b')\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha=\"right\")\n",
    "plt.title('New User acquisition')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4))\n",
    "ax = sns.countplot(x='year-m', hue='new_user', data=df, dodge=False,palette=[\"C0\", \"C1\"])\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha=\"right\" )\n",
    "plt.title('Total user per invoiced date')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['cohort index'] = ( df['InvoiceDate'] - df['InvoiceDate_Cohort'] ) /np.timedelta64(1, 'M')  \n",
    "df['cohort index'] = df['cohort index'].astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cohort Retention count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>cohort index</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year-m_Cohort</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-12</th>\n",
       "      <td>44151.0</td>\n",
       "      <td>23376.0</td>\n",
       "      <td>18637.0</td>\n",
       "      <td>21041.0</td>\n",
       "      <td>16640.0</td>\n",
       "      <td>21365.0</td>\n",
       "      <td>20406.0</td>\n",
       "      <td>23577.0</td>\n",
       "      <td>20122.0</td>\n",
       "      <td>23798.0</td>\n",
       "      <td>25686.0</td>\n",
       "      <td>43596.0</td>\n",
       "      <td>9957.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01</th>\n",
       "      <td>12223.0</td>\n",
       "      <td>2793.0</td>\n",
       "      <td>3052.0</td>\n",
       "      <td>3233.0</td>\n",
       "      <td>3142.0</td>\n",
       "      <td>3283.0</td>\n",
       "      <td>2779.0</td>\n",
       "      <td>3655.0</td>\n",
       "      <td>4635.0</td>\n",
       "      <td>5550.0</td>\n",
       "      <td>4581.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-02</th>\n",
       "      <td>9363.0</td>\n",
       "      <td>2199.0</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>2460.0</td>\n",
       "      <td>1771.0</td>\n",
       "      <td>2344.0</td>\n",
       "      <td>2186.0</td>\n",
       "      <td>2565.0</td>\n",
       "      <td>3471.0</td>\n",
       "      <td>1770.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-03</th>\n",
       "      <td>11950.0</td>\n",
       "      <td>1713.0</td>\n",
       "      <td>2464.0</td>\n",
       "      <td>2546.0</td>\n",
       "      <td>2220.0</td>\n",
       "      <td>2283.0</td>\n",
       "      <td>3257.0</td>\n",
       "      <td>3725.0</td>\n",
       "      <td>3205.0</td>\n",
       "      <td>283.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04</th>\n",
       "      <td>7683.0</td>\n",
       "      <td>1845.0</td>\n",
       "      <td>996.0</td>\n",
       "      <td>1425.0</td>\n",
       "      <td>1296.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2443.0</td>\n",
       "      <td>1647.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05</th>\n",
       "      <td>6510.0</td>\n",
       "      <td>1057.0</td>\n",
       "      <td>952.0</td>\n",
       "      <td>1157.0</td>\n",
       "      <td>2163.0</td>\n",
       "      <td>1620.0</td>\n",
       "      <td>2099.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-06</th>\n",
       "      <td>6122.0</td>\n",
       "      <td>783.0</td>\n",
       "      <td>989.0</td>\n",
       "      <td>1637.0</td>\n",
       "      <td>1978.0</td>\n",
       "      <td>2087.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07</th>\n",
       "      <td>5367.0</td>\n",
       "      <td>831.0</td>\n",
       "      <td>1416.0</td>\n",
       "      <td>1448.0</td>\n",
       "      <td>1594.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08</th>\n",
       "      <td>5889.0</td>\n",
       "      <td>2404.0</td>\n",
       "      <td>3480.0</td>\n",
       "      <td>1196.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09</th>\n",
       "      <td>9567.0</td>\n",
       "      <td>2716.0</td>\n",
       "      <td>2063.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10</th>\n",
       "      <td>13922.0</td>\n",
       "      <td>2431.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-11</th>\n",
       "      <td>12297.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12</th>\n",
       "      <td>996.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cohort index        0        1        2        3        4        5        6   \\\n",
       "year-m_Cohort                                                                  \n",
       "2010-12        44151.0  23376.0  18637.0  21041.0  16640.0  21365.0  20406.0   \n",
       "2011-01        12223.0   2793.0   3052.0   3233.0   3142.0   3283.0   2779.0   \n",
       "2011-02         9363.0   2199.0   1990.0   2460.0   1771.0   2344.0   2186.0   \n",
       "2011-03        11950.0   1713.0   2464.0   2546.0   2220.0   2283.0   3257.0   \n",
       "2011-04         7683.0   1845.0    996.0   1425.0   1296.0   2006.0   2443.0   \n",
       "2011-05         6510.0   1057.0    952.0   1157.0   2163.0   1620.0   2099.0   \n",
       "2011-06         6122.0    783.0    989.0   1637.0   1978.0   2087.0     90.0   \n",
       "2011-07         5367.0    831.0   1416.0   1448.0   1594.0      1.0      NaN   \n",
       "2011-08         5889.0   2404.0   3480.0   1196.0     18.0      NaN      NaN   \n",
       "2011-09         9567.0   2716.0   2063.0     73.0      NaN      NaN      NaN   \n",
       "2011-10        13922.0   2431.0     75.0      NaN      NaN      NaN      NaN   \n",
       "2011-11        12297.0    104.0      NaN      NaN      NaN      NaN      NaN   \n",
       "2011-12          996.0      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "cohort index        7        8        9        10       11      12  \n",
       "year-m_Cohort                                                       \n",
       "2010-12        23577.0  20122.0  23798.0  25686.0  43596.0  9957.0  \n",
       "2011-01         3655.0   4635.0   5550.0   4581.0    121.0     NaN  \n",
       "2011-02         2565.0   3471.0   1770.0     17.0      NaN     NaN  \n",
       "2011-03         3725.0   3205.0    283.0      NaN      NaN     NaN  \n",
       "2011-04         1647.0    206.0      NaN      NaN      NaN     NaN  \n",
       "2011-05           49.0      NaN      NaN      NaN      NaN     NaN  \n",
       "2011-06            NaN      NaN      NaN      NaN      NaN     NaN  \n",
       "2011-07            NaN      NaN      NaN      NaN      NaN     NaN  \n",
       "2011-08            NaN      NaN      NaN      NaN      NaN     NaN  \n",
       "2011-09            NaN      NaN      NaN      NaN      NaN     NaN  \n",
       "2011-10            NaN      NaN      NaN      NaN      NaN     NaN  \n",
       "2011-11            NaN      NaN      NaN      NaN      NaN     NaN  \n",
       "2011-12            NaN      NaN      NaN      NaN      NaN     NaN  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cohort_counts = df.pivot_table(index='year-m_Cohort', \n",
    "               columns='cohort index', \n",
    "               values='CustomerID', \n",
    "               aggfunc='count')#.fillna(\"\")\n",
    "\n",
    "cohort_counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cohort retention %"
   ]
  },
  {
   "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>cohort index</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year-m_Cohort</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-12</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5295</td>\n",
       "      <td>0.4221</td>\n",
       "      <td>0.4766</td>\n",
       "      <td>0.3769</td>\n",
       "      <td>0.4839</td>\n",
       "      <td>0.4622</td>\n",
       "      <td>0.5340</td>\n",
       "      <td>0.4558</td>\n",
       "      <td>0.5390</td>\n",
       "      <td>0.5818</td>\n",
       "      <td>0.9874</td>\n",
       "      <td>0.2255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2285</td>\n",
       "      <td>0.2497</td>\n",
       "      <td>0.2645</td>\n",
       "      <td>0.2571</td>\n",
       "      <td>0.2686</td>\n",
       "      <td>0.2274</td>\n",
       "      <td>0.2990</td>\n",
       "      <td>0.3792</td>\n",
       "      <td>0.4541</td>\n",
       "      <td>0.3748</td>\n",
       "      <td>0.0099</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-02</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2349</td>\n",
       "      <td>0.2125</td>\n",
       "      <td>0.2627</td>\n",
       "      <td>0.1891</td>\n",
       "      <td>0.2503</td>\n",
       "      <td>0.2335</td>\n",
       "      <td>0.2740</td>\n",
       "      <td>0.3707</td>\n",
       "      <td>0.1890</td>\n",
       "      <td>0.0018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-03</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1433</td>\n",
       "      <td>0.2062</td>\n",
       "      <td>0.2131</td>\n",
       "      <td>0.1858</td>\n",
       "      <td>0.1910</td>\n",
       "      <td>0.2726</td>\n",
       "      <td>0.3117</td>\n",
       "      <td>0.2682</td>\n",
       "      <td>0.0237</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2401</td>\n",
       "      <td>0.1296</td>\n",
       "      <td>0.1855</td>\n",
       "      <td>0.1687</td>\n",
       "      <td>0.2611</td>\n",
       "      <td>0.3180</td>\n",
       "      <td>0.2144</td>\n",
       "      <td>0.0268</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1624</td>\n",
       "      <td>0.1462</td>\n",
       "      <td>0.1777</td>\n",
       "      <td>0.3323</td>\n",
       "      <td>0.2488</td>\n",
       "      <td>0.3224</td>\n",
       "      <td>0.0075</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-06</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1279</td>\n",
       "      <td>0.1615</td>\n",
       "      <td>0.2674</td>\n",
       "      <td>0.3231</td>\n",
       "      <td>0.3409</td>\n",
       "      <td>0.0147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1548</td>\n",
       "      <td>0.2638</td>\n",
       "      <td>0.2698</td>\n",
       "      <td>0.2970</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4082</td>\n",
       "      <td>0.5909</td>\n",
       "      <td>0.2031</td>\n",
       "      <td>0.0031</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2839</td>\n",
       "      <td>0.2156</td>\n",
       "      <td>0.0076</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1746</td>\n",
       "      <td>0.0054</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-11</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0085</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cohort index    0       1       2       3       4       5       6       7   \\\n",
       "year-m_Cohort                                                                \n",
       "2010-12        1.0  0.5295  0.4221  0.4766  0.3769  0.4839  0.4622  0.5340   \n",
       "2011-01        1.0  0.2285  0.2497  0.2645  0.2571  0.2686  0.2274  0.2990   \n",
       "2011-02        1.0  0.2349  0.2125  0.2627  0.1891  0.2503  0.2335  0.2740   \n",
       "2011-03        1.0  0.1433  0.2062  0.2131  0.1858  0.1910  0.2726  0.3117   \n",
       "2011-04        1.0  0.2401  0.1296  0.1855  0.1687  0.2611  0.3180  0.2144   \n",
       "2011-05        1.0  0.1624  0.1462  0.1777  0.3323  0.2488  0.3224  0.0075   \n",
       "2011-06        1.0  0.1279  0.1615  0.2674  0.3231  0.3409  0.0147     NaN   \n",
       "2011-07        1.0  0.1548  0.2638  0.2698  0.2970  0.0002     NaN     NaN   \n",
       "2011-08        1.0  0.4082  0.5909  0.2031  0.0031     NaN     NaN     NaN   \n",
       "2011-09        1.0  0.2839  0.2156  0.0076     NaN     NaN     NaN     NaN   \n",
       "2011-10        1.0  0.1746  0.0054     NaN     NaN     NaN     NaN     NaN   \n",
       "2011-11        1.0  0.0085     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "2011-12        1.0     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "\n",
       "cohort index       8       9       10      11      12  \n",
       "year-m_Cohort                                          \n",
       "2010-12        0.4558  0.5390  0.5818  0.9874  0.2255  \n",
       "2011-01        0.3792  0.4541  0.3748  0.0099     NaN  \n",
       "2011-02        0.3707  0.1890  0.0018     NaN     NaN  \n",
       "2011-03        0.2682  0.0237     NaN     NaN     NaN  \n",
       "2011-04        0.0268     NaN     NaN     NaN     NaN  \n",
       "2011-05           NaN     NaN     NaN     NaN     NaN  \n",
       "2011-06           NaN     NaN     NaN     NaN     NaN  \n",
       "2011-07           NaN     NaN     NaN     NaN     NaN  \n",
       "2011-08           NaN     NaN     NaN     NaN     NaN  \n",
       "2011-09           NaN     NaN     NaN     NaN     NaN  \n",
       "2011-10           NaN     NaN     NaN     NaN     NaN  \n",
       "2011-11           NaN     NaN     NaN     NaN     NaN  \n",
       "2011-12           NaN     NaN     NaN     NaN     NaN  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cohort_retentions = cohort_counts.divide( cohort_counts[0], axis=0).round(4)\n",
    "cohort_retentions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Australia', 'Austria', 'Bahrain', 'Belgium', 'Brazil', 'Canada',\n",
       "       'Channel Islands', 'Cyprus', 'Czech Republic', 'Denmark', 'EIRE',\n",
       "       'European Community', 'Finland', 'France', 'Germany', 'Greece',\n",
       "       'Hong Kong', 'Iceland', 'Israel', 'Italy', 'Japan', 'Lebanon',\n",
       "       'Lithuania', 'Malta', 'Netherlands', 'Norway', 'Poland',\n",
       "       'Portugal', 'RSA', 'Saudi Arabia', 'Singapore', 'Spain', 'Sweden',\n",
       "       'Switzerland', 'USA', 'United Arab Emirates', 'United Kingdom',\n",
       "       'Unspecified'], dtype=object)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort( df.Country.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Export cohort size and retention rate per country to excel file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "writer = pd.ExcelWriter('Output Cohort analysis.xlsx', engine='xlsxwriter')\n",
    "#os.chdir(folder)\n",
    "\n",
    "#export cohort sizes\n",
    "cohort_counts.to_excel(writer, sheet_name='COHORT SIZE', index=True)\n",
    "#export cohort retention rates\n",
    "cohort_retention = cohort_retentions.fillna(\"\")*100\n",
    "cohort_retention.to_excel(writer, sheet_name='COHORT RETENTION', index=True)\n",
    "\n",
    "\n",
    "for country in np.sort( df.Country.unique() ):\n",
    "    tbl_size = df.loc[df.Country== country].pivot_table(index='year-m_Cohort', \n",
    "               columns='cohort index', \n",
    "               values='CustomerID', \n",
    "               aggfunc='count')#.fillna(\"\")\n",
    "    tbl_size.to_excel(writer, sheet_name=country+' size', index=True)\n",
    "\n",
    "    #print(country)\n",
    "    tbl_retention = (tbl_size.divide( tbl_size[ tbl_size.columns.min() ], axis=0).round(4)*100).fillna(\"\")\n",
    "    tbl_retention.to_excel(writer, sheet_name=country + \" retention\", index=True)\n",
    "\n",
    "writer.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# User Count heatmap\n",
    "plt.figure(figsize=(15,15))\n",
    "plt.title('Users Active')\n",
    "ax = sns.heatmap(data=cohort_retentions, annot=True, vmin=0.0,cmap='Blues', fmt='.0%')\n",
    "ax.set_yticklabels(cohort_counts.index)\n",
    "fig=ax.get_figure()\n",
    "fig.savefig(\"img_RetentionHeatmap.png\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature engineering for CLV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getAggValuePerCustomer(dataframe):\n",
    "    data = dataframe.groupby(['CustomerID', 'Country']).agg({\n",
    "                                        'InvoiceDate': lambda date: (date.max() - date.min()).days,\n",
    "                                        'InvoiceNo': lambda num: len(num),\n",
    "                                        'Quantity': lambda quant: quant.sum(),\n",
    "                                        'TotalPurchase': lambda price: price.sum()})\n",
    "    data.columns = ['num_days','num_transactions','num_units','spent_money']\n",
    "    data['avg_order_value'] = data['spent_money']/data['num_transactions']\n",
    "    data['profit_margin'] = data['spent_money']*0.05   #assume the business has approx 5% profit on the total sale.\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = getAggValuePerCustomer(df).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['data_United_Kingdom',\n",
       " 'data_Iceland',\n",
       " 'data_Finland',\n",
       " 'data_Italy',\n",
       " 'data_Norway',\n",
       " 'data_Bahrain',\n",
       " 'data_Spain',\n",
       " 'data_Portugal',\n",
       " 'data_Switzerland',\n",
       " 'data_Austria',\n",
       " 'data_Cyprus',\n",
       " 'data_Belgium',\n",
       " 'data_Unspecified',\n",
       " 'data_Denmark',\n",
       " 'data_Australia',\n",
       " 'data_France',\n",
       " 'data_Germany',\n",
       " 'data_RSA',\n",
       " 'data_Greece',\n",
       " 'data_Sweden',\n",
       " 'data_Israel',\n",
       " 'data_USA',\n",
       " 'data_Saudi_Arabia',\n",
       " 'data_Poland',\n",
       " 'data_United_Arab_Emirates',\n",
       " 'data_Singapore',\n",
       " 'data_Japan',\n",
       " 'data_Netherlands',\n",
       " 'data_Lebanon',\n",
       " 'data_Brazil',\n",
       " 'data_Czech_Republic',\n",
       " 'data_EIRE',\n",
       " 'data_Channel_Islands',\n",
       " 'data_European_Community',\n",
       " 'data_Lithuania',\n",
       " 'data_Canada',\n",
       " 'data_Malta',\n",
       " 'data_Hong_Kong']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create data frames for each country\n",
    "\n",
    "df_name_list = list()\n",
    "\n",
    "for i in df.Country.unique():\n",
    "    df_name = \"data_\" + str(i).replace(\" \", \"_\")\n",
    "    #print(df_name)\n",
    "    df_name_list.append(df_name)\n",
    "    globals()[df_name] = df.loc[df.Country == i]\n",
    "    \n",
    "df_name_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "      <th>num_days</th>\n",
       "      <th>num_transactions</th>\n",
       "      <th>num_units</th>\n",
       "      <th>spent_money</th>\n",
       "      <th>avg_order_value</th>\n",
       "      <th>profit_margin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12346.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>12747.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>366</td>\n",
       "      <td>103</td>\n",
       "      <td>1275</td>\n",
       "      <td>4196.01</td>\n",
       "      <td>40.737961</td>\n",
       "      <td>209.8005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    CustomerID         Country  num_days  num_transactions  num_units  \\\n",
       "0      12346.0  United Kingdom         0                 2          0   \n",
       "337    12747.0  United Kingdom       366               103       1275   \n",
       "\n",
       "     spent_money  avg_order_value  profit_margin  \n",
       "0           0.00         0.000000         0.0000  \n",
       "337      4196.01        40.737961       209.8005  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_United_Kingdom.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#CLTV = ((Average Order Value x Purchase Frequency)/Churn Rate) x Profit margin.\n",
    "#Customer Value = Average Order Value * Purchase Frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getChurnRates(dataframe):\n",
    "    purchase_frequency=sum(dataframe['num_transactions'])/dataframe.shape[0]\n",
    "    repeat_rate =  dataframe[dataframe.num_transactions > 1].shape[0]/dataframe.shape[0]\n",
    "    churn_rate = churn_rate=1-repeat_rate\n",
    "    return [ purchase_frequency, repeat_rate, churn_rate]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[125.40572007086813, 0.9807643634522906, 0.019235636547709434]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "getChurnRates(data_United_Kingdom)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[97.23863636363636, 1.0, 0.0]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "getChurnRates(data_France)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getCustomerValue(dataframe, purchase_frequency, churn_rate):\n",
    "    dataframe['CLV'] = dataframe['avg_order_value'] * purchase_frequency / churn_rate\n",
    "    dataframe['cust_lifetime_value'] = dataframe['CLV'] * dataframe['profit_margin']\n",
    "    return dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "for country in df_name_list:\n",
    "    indicators = getChurnRates( globals()[country] )\n",
    "    purchase_frequency = indicators[0]\n",
    "    churn_rate = indicators[1]\n",
    "    globals()[country] = getCustomerValue( globals()[country], purchase_frequency, churn_rate )\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "      <th>num_days</th>\n",
       "      <th>num_transactions</th>\n",
       "      <th>num_units</th>\n",
       "      <th>spent_money</th>\n",
       "      <th>avg_order_value</th>\n",
       "      <th>profit_margin</th>\n",
       "      <th>CLV</th>\n",
       "      <th>cust_lifetime_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12346.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>12747.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>366</td>\n",
       "      <td>103</td>\n",
       "      <td>1275</td>\n",
       "      <td>4196.01</td>\n",
       "      <td>40.737961</td>\n",
       "      <td>209.8005</td>\n",
       "      <td>5208.971232</td>\n",
       "      <td>1.092845e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    CustomerID         Country  num_days  num_transactions  num_units  \\\n",
       "0      12346.0  United Kingdom         0                 2          0   \n",
       "337    12747.0  United Kingdom       366               103       1275   \n",
       "\n",
       "     spent_money  avg_order_value  profit_margin          CLV  \\\n",
       "0           0.00         0.000000         0.0000     0.000000   \n",
       "337      4196.01        40.737961       209.8005  5208.971232   \n",
       "\n",
       "     cust_lifetime_value  \n",
       "0           0.000000e+00  \n",
       "337         1.092845e+06  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_United_Kingdom.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "      <th>num_days</th>\n",
       "      <th>num_transactions</th>\n",
       "      <th>num_units</th>\n",
       "      <th>spent_money</th>\n",
       "      <th>avg_order_value</th>\n",
       "      <th>profit_margin</th>\n",
       "      <th>CLV</th>\n",
       "      <th>cust_lifetime_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>12413.0</td>\n",
       "      <td>France</td>\n",
       "      <td>270</td>\n",
       "      <td>40</td>\n",
       "      <td>282</td>\n",
       "      <td>694.15</td>\n",
       "      <td>17.353750</td>\n",
       "      <td>34.7075</td>\n",
       "      <td>1687.454986</td>\n",
       "      <td>58567.343919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>12437.0</td>\n",
       "      <td>France</td>\n",
       "      <td>329</td>\n",
       "      <td>201</td>\n",
       "      <td>2918</td>\n",
       "      <td>4896.66</td>\n",
       "      <td>24.361493</td>\n",
       "      <td>244.8330</td>\n",
       "      <td>2368.878314</td>\n",
       "      <td>579979.584279</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID Country  num_days  num_transactions  num_units  spent_money  \\\n",
       "55    12413.0  France       270                40        282       694.15   \n",
       "81    12437.0  France       329               201       2918      4896.66   \n",
       "\n",
       "    avg_order_value  profit_margin          CLV  cust_lifetime_value  \n",
       "55        17.353750        34.7075  1687.454986         58567.343919  \n",
       "81        24.361493       244.8330  2368.878314        579979.584279  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_France.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Life Time Value per User Country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "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>num_days</th>\n",
       "      <th>num_transactions</th>\n",
       "      <th>num_units</th>\n",
       "      <th>spent_money</th>\n",
       "      <th>avg_order_value</th>\n",
       "      <th>profit_margin</th>\n",
       "      <th>CLV</th>\n",
       "      <th>cust_lifetime_value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>2007</td>\n",
       "      <td>1259</td>\n",
       "      <td>83653</td>\n",
       "      <td>137077.270</td>\n",
       "      <td>386.699665</td>\n",
       "      <td>6853.8635</td>\n",
       "      <td>5.409499e+04</td>\n",
       "      <td>1.405368e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>806</td>\n",
       "      <td>401</td>\n",
       "      <td>4827</td>\n",
       "      <td>10154.320</td>\n",
       "      <td>386.367227</td>\n",
       "      <td>507.7160</td>\n",
       "      <td>1.408484e+04</td>\n",
       "      <td>6.867254e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bahrain</th>\n",
       "      <td>16</td>\n",
       "      <td>19</td>\n",
       "      <td>260</td>\n",
       "      <td>548.400</td>\n",
       "      <td>57.588462</td>\n",
       "      <td>27.4200</td>\n",
       "      <td>3.647269e+02</td>\n",
       "      <td>5.768001e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>3750</td>\n",
       "      <td>2069</td>\n",
       "      <td>23152</td>\n",
       "      <td>40910.960</td>\n",
       "      <td>503.969730</td>\n",
       "      <td>2045.5480</td>\n",
       "      <td>4.170853e+04</td>\n",
       "      <td>3.496423e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>356</td>\n",
       "      <td>1143.600</td>\n",
       "      <td>35.737500</td>\n",
       "      <td>57.1800</td>\n",
       "      <td>1.143600e+03</td>\n",
       "      <td>6.539105e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>25</td>\n",
       "      <td>151</td>\n",
       "      <td>2763</td>\n",
       "      <td>3666.380</td>\n",
       "      <td>580.384074</td>\n",
       "      <td>183.3190</td>\n",
       "      <td>2.921267e+04</td>\n",
       "      <td>8.857348e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Channel Islands</th>\n",
       "      <td>1264</td>\n",
       "      <td>758</td>\n",
       "      <td>9479</td>\n",
       "      <td>20086.290</td>\n",
       "      <td>711.937492</td>\n",
       "      <td>1004.3145</td>\n",
       "      <td>5.996096e+04</td>\n",
       "      <td>5.202913e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cyprus</th>\n",
       "      <td>648</td>\n",
       "      <td>622</td>\n",
       "      <td>6317</td>\n",
       "      <td>12946.290</td>\n",
       "      <td>203.221122</td>\n",
       "      <td>647.3145</td>\n",
       "      <td>1.580044e+04</td>\n",
       "      <td>1.257057e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Czech Republic</th>\n",
       "      <td>263</td>\n",
       "      <td>30</td>\n",
       "      <td>592</td>\n",
       "      <td>707.720</td>\n",
       "      <td>23.590667</td>\n",
       "      <td>35.3860</td>\n",
       "      <td>7.077200e+02</td>\n",
       "      <td>2.504338e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denmark</th>\n",
       "      <td>1091</td>\n",
       "      <td>389</td>\n",
       "      <td>8188</td>\n",
       "      <td>18768.140</td>\n",
       "      <td>579.349282</td>\n",
       "      <td>938.4070</td>\n",
       "      <td>2.504076e+04</td>\n",
       "      <td>4.691346e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EIRE</th>\n",
       "      <td>1300</td>\n",
       "      <td>8196</td>\n",
       "      <td>142637</td>\n",
       "      <td>263276.820</td>\n",
       "      <td>147.297683</td>\n",
       "      <td>13163.8410</td>\n",
       "      <td>3.018130e+05</td>\n",
       "      <td>1.268732e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>European Community</th>\n",
       "      <td>85</td>\n",
       "      <td>61</td>\n",
       "      <td>497</td>\n",
       "      <td>1291.750</td>\n",
       "      <td>21.176230</td>\n",
       "      <td>64.5875</td>\n",
       "      <td>1.291750e+03</td>\n",
       "      <td>8.343090e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finland</th>\n",
       "      <td>1193</td>\n",
       "      <td>695</td>\n",
       "      <td>10666</td>\n",
       "      <td>22326.740</td>\n",
       "      <td>433.924904</td>\n",
       "      <td>1116.3370</td>\n",
       "      <td>2.513148e+04</td>\n",
       "      <td>3.260424e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>12273</td>\n",
       "      <td>8557</td>\n",
       "      <td>110480</td>\n",
       "      <td>197403.900</td>\n",
       "      <td>1916.051614</td>\n",
       "      <td>9870.1950</td>\n",
       "      <td>1.863142e+05</td>\n",
       "      <td>3.345068e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>14057</td>\n",
       "      <td>9495</td>\n",
       "      <td>117448</td>\n",
       "      <td>221698.210</td>\n",
       "      <td>2348.679414</td>\n",
       "      <td>11084.9105</td>\n",
       "      <td>2.347443e+05</td>\n",
       "      <td>3.531067e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greece</th>\n",
       "      <td>94</td>\n",
       "      <td>146</td>\n",
       "      <td>1556</td>\n",
       "      <td>4710.520</td>\n",
       "      <td>137.202639</td>\n",
       "      <td>235.5260</td>\n",
       "      <td>5.007896e+03</td>\n",
       "      <td>4.712213e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hong Kong</th>\n",
       "      <td>293</td>\n",
       "      <td>288</td>\n",
       "      <td>4769</td>\n",
       "      <td>10117.040</td>\n",
       "      <td>35.128611</td>\n",
       "      <td>505.8520</td>\n",
       "      <td>1.011704e+04</td>\n",
       "      <td>5.117725e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iceland</th>\n",
       "      <td>365</td>\n",
       "      <td>182</td>\n",
       "      <td>2458</td>\n",
       "      <td>4310.000</td>\n",
       "      <td>23.681319</td>\n",
       "      <td>215.5000</td>\n",
       "      <td>4.310000e+03</td>\n",
       "      <td>9.288050e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Israel</th>\n",
       "      <td>389</td>\n",
       "      <td>297</td>\n",
       "      <td>4353</td>\n",
       "      <td>7907.820</td>\n",
       "      <td>-10.328961</td>\n",
       "      <td>395.3910</td>\n",
       "      <td>-6.135403e+02</td>\n",
       "      <td>8.810898e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>1562</td>\n",
       "      <td>803</td>\n",
       "      <td>7999</td>\n",
       "      <td>16890.510</td>\n",
       "      <td>284.589715</td>\n",
       "      <td>844.5255</td>\n",
       "      <td>1.632325e+04</td>\n",
       "      <td>1.067227e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>935</td>\n",
       "      <td>358</td>\n",
       "      <td>25218</td>\n",
       "      <td>35340.620</td>\n",
       "      <td>1559.508872</td>\n",
       "      <td>1767.0310</td>\n",
       "      <td>6.978802e+04</td>\n",
       "      <td>2.041026e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lebanon</th>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>386</td>\n",
       "      <td>1693.880</td>\n",
       "      <td>37.641778</td>\n",
       "      <td>84.6940</td>\n",
       "      <td>1.693880e+03</td>\n",
       "      <td>1.434615e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lithuania</th>\n",
       "      <td>3</td>\n",
       "      <td>35</td>\n",
       "      <td>652</td>\n",
       "      <td>1661.060</td>\n",
       "      <td>47.458857</td>\n",
       "      <td>83.0530</td>\n",
       "      <td>1.661060e+03</td>\n",
       "      <td>1.379560e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malta</th>\n",
       "      <td>176</td>\n",
       "      <td>127</td>\n",
       "      <td>944</td>\n",
       "      <td>2505.470</td>\n",
       "      <td>39.634051</td>\n",
       "      <td>125.2735</td>\n",
       "      <td>2.516762e+03</td>\n",
       "      <td>1.569689e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>1347</td>\n",
       "      <td>2371</td>\n",
       "      <td>200128</td>\n",
       "      <td>284661.540</td>\n",
       "      <td>359.891232</td>\n",
       "      <td>14233.0770</td>\n",
       "      <td>9.481135e+04</td>\n",
       "      <td>4.949120e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <td>1150</td>\n",
       "      <td>1086</td>\n",
       "      <td>19247</td>\n",
       "      <td>35163.460</td>\n",
       "      <td>329.910124</td>\n",
       "      <td>1758.1730</td>\n",
       "      <td>3.582824e+04</td>\n",
       "      <td>7.277300e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>748</td>\n",
       "      <td>341</td>\n",
       "      <td>3653</td>\n",
       "      <td>7213.140</td>\n",
       "      <td>162.726458</td>\n",
       "      <td>360.6570</td>\n",
       "      <td>9.248287e+03</td>\n",
       "      <td>4.594378e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portugal</th>\n",
       "      <td>2276</td>\n",
       "      <td>1519</td>\n",
       "      <td>16180</td>\n",
       "      <td>29367.020</td>\n",
       "      <td>405.963285</td>\n",
       "      <td>1468.3510</td>\n",
       "      <td>3.083291e+04</td>\n",
       "      <td>2.551599e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RSA</th>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "      <td>352</td>\n",
       "      <td>1002.310</td>\n",
       "      <td>17.281207</td>\n",
       "      <td>50.1155</td>\n",
       "      <td>1.002310e+03</td>\n",
       "      <td>5.023127e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saudi Arabia</th>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>75</td>\n",
       "      <td>131.170</td>\n",
       "      <td>13.117000</td>\n",
       "      <td>6.5585</td>\n",
       "      <td>1.311700e+02</td>\n",
       "      <td>8.602784e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singapore</th>\n",
       "      <td>266</td>\n",
       "      <td>229</td>\n",
       "      <td>5234</td>\n",
       "      <td>9120.390</td>\n",
       "      <td>39.827031</td>\n",
       "      <td>456.0195</td>\n",
       "      <td>9.120390e+03</td>\n",
       "      <td>4.159076e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>4064</td>\n",
       "      <td>2533</td>\n",
       "      <td>26824</td>\n",
       "      <td>54774.580</td>\n",
       "      <td>-200.143754</td>\n",
       "      <td>2738.7290</td>\n",
       "      <td>-1.689880e+04</td>\n",
       "      <td>2.774334e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>1112</td>\n",
       "      <td>462</td>\n",
       "      <td>35637</td>\n",
       "      <td>36595.910</td>\n",
       "      <td>305.663218</td>\n",
       "      <td>1829.7955</td>\n",
       "      <td>1.765205e+04</td>\n",
       "      <td>1.364731e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>1945</td>\n",
       "      <td>2002</td>\n",
       "      <td>30325</td>\n",
       "      <td>56385.350</td>\n",
       "      <td>650.570585</td>\n",
       "      <td>2819.2675</td>\n",
       "      <td>5.920192e+04</td>\n",
       "      <td>1.062807e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USA</th>\n",
       "      <td>51</td>\n",
       "      <td>291</td>\n",
       "      <td>1034</td>\n",
       "      <td>1730.920</td>\n",
       "      <td>47.384939</td>\n",
       "      <td>86.5460</td>\n",
       "      <td>3.447254e+03</td>\n",
       "      <td>1.710324e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Arab Emirates</th>\n",
       "      <td>20</td>\n",
       "      <td>68</td>\n",
       "      <td>982</td>\n",
       "      <td>1902.280</td>\n",
       "      <td>56.300281</td>\n",
       "      <td>95.1140</td>\n",
       "      <td>1.914210e+03</td>\n",
       "      <td>9.072023e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>528899</td>\n",
       "      <td>495478</td>\n",
       "      <td>4263829</td>\n",
       "      <td>8187806.364</td>\n",
       "      <td>113780.918303</td>\n",
       "      <td>409390.3182</td>\n",
       "      <td>1.454863e+07</td>\n",
       "      <td>4.726867e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unspecified</th>\n",
       "      <td>400</td>\n",
       "      <td>446</td>\n",
       "      <td>3300</td>\n",
       "      <td>4749.790</td>\n",
       "      <td>74.034840</td>\n",
       "      <td>237.4895</td>\n",
       "      <td>6.603908e+03</td>\n",
       "      <td>2.911252e+05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      num_days  num_transactions  num_units  spent_money  \\\n",
       "Country                                                                    \n",
       "Australia                 2007              1259      83653   137077.270   \n",
       "Austria                    806               401       4827    10154.320   \n",
       "Bahrain                     16                19        260      548.400   \n",
       "Belgium                   3750              2069      23152    40910.960   \n",
       "Brazil                       0                32        356     1143.600   \n",
       "Canada                      25               151       2763     3666.380   \n",
       "Channel Islands           1264               758       9479    20086.290   \n",
       "Cyprus                     648               622       6317    12946.290   \n",
       "Czech Republic             263                30        592      707.720   \n",
       "Denmark                   1091               389       8188    18768.140   \n",
       "EIRE                      1300              8196     142637   263276.820   \n",
       "European Community          85                61        497     1291.750   \n",
       "Finland                   1193               695      10666    22326.740   \n",
       "France                   12273              8557     110480   197403.900   \n",
       "Germany                  14057              9495     117448   221698.210   \n",
       "Greece                      94               146       1556     4710.520   \n",
       "Hong Kong                  293               288       4769    10117.040   \n",
       "Iceland                    365               182       2458     4310.000   \n",
       "Israel                     389               297       4353     7907.820   \n",
       "Italy                     1562               803       7999    16890.510   \n",
       "Japan                      935               358      25218    35340.620   \n",
       "Lebanon                      0                45        386     1693.880   \n",
       "Lithuania                    3                35        652     1661.060   \n",
       "Malta                      176               127        944     2505.470   \n",
       "Netherlands               1347              2371     200128   284661.540   \n",
       "Norway                    1150              1086      19247    35163.460   \n",
       "Poland                     748               341       3653     7213.140   \n",
       "Portugal                  2276              1519      16180    29367.020   \n",
       "RSA                          0                58        352     1002.310   \n",
       "Saudi Arabia                 7                10         75      131.170   \n",
       "Singapore                  266               229       5234     9120.390   \n",
       "Spain                     4064              2533      26824    54774.580   \n",
       "Sweden                    1112               462      35637    36595.910   \n",
       "Switzerland               1945              2002      30325    56385.350   \n",
       "USA                         51               291       1034     1730.920   \n",
       "United Arab Emirates        20                68        982     1902.280   \n",
       "United Kingdom          528899            495478    4263829  8187806.364   \n",
       "Unspecified                400               446       3300     4749.790   \n",
       "\n",
       "                      avg_order_value  profit_margin           CLV  \\\n",
       "Country                                                              \n",
       "Australia                  386.699665      6853.8635  5.409499e+04   \n",
       "Austria                    386.367227       507.7160  1.408484e+04   \n",
       "Bahrain                     57.588462        27.4200  3.647269e+02   \n",
       "Belgium                    503.969730      2045.5480  4.170853e+04   \n",
       "Brazil                      35.737500        57.1800  1.143600e+03   \n",
       "Canada                     580.384074       183.3190  2.921267e+04   \n",
       "Channel Islands            711.937492      1004.3145  5.996096e+04   \n",
       "Cyprus                     203.221122       647.3145  1.580044e+04   \n",
       "Czech Republic              23.590667        35.3860  7.077200e+02   \n",
       "Denmark                    579.349282       938.4070  2.504076e+04   \n",
       "EIRE                       147.297683     13163.8410  3.018130e+05   \n",
       "European Community          21.176230        64.5875  1.291750e+03   \n",
       "Finland                    433.924904      1116.3370  2.513148e+04   \n",
       "France                    1916.051614      9870.1950  1.863142e+05   \n",
       "Germany                   2348.679414     11084.9105  2.347443e+05   \n",
       "Greece                     137.202639       235.5260  5.007896e+03   \n",
       "Hong Kong                   35.128611       505.8520  1.011704e+04   \n",
       "Iceland                     23.681319       215.5000  4.310000e+03   \n",
       "Israel                     -10.328961       395.3910 -6.135403e+02   \n",
       "Italy                      284.589715       844.5255  1.632325e+04   \n",
       "Japan                     1559.508872      1767.0310  6.978802e+04   \n",
       "Lebanon                     37.641778        84.6940  1.693880e+03   \n",
       "Lithuania                   47.458857        83.0530  1.661060e+03   \n",
       "Malta                       39.634051       125.2735  2.516762e+03   \n",
       "Netherlands                359.891232     14233.0770  9.481135e+04   \n",
       "Norway                     329.910124      1758.1730  3.582824e+04   \n",
       "Poland                     162.726458       360.6570  9.248287e+03   \n",
       "Portugal                   405.963285      1468.3510  3.083291e+04   \n",
       "RSA                         17.281207        50.1155  1.002310e+03   \n",
       "Saudi Arabia                13.117000         6.5585  1.311700e+02   \n",
       "Singapore                   39.827031       456.0195  9.120390e+03   \n",
       "Spain                     -200.143754      2738.7290 -1.689880e+04   \n",
       "Sweden                     305.663218      1829.7955  1.765205e+04   \n",
       "Switzerland                650.570585      2819.2675  5.920192e+04   \n",
       "USA                         47.384939        86.5460  3.447254e+03   \n",
       "United Arab Emirates        56.300281        95.1140  1.914210e+03   \n",
       "United Kingdom          113780.918303    409390.3182  1.454863e+07   \n",
       "Unspecified                 74.034840       237.4895  6.603908e+03   \n",
       "\n",
       "                      cust_lifetime_value  \n",
       "Country                                    \n",
       "Australia                    1.405368e+08  \n",
       "Austria                      6.867254e+05  \n",
       "Bahrain                      5.768001e+03  \n",
       "Belgium                      3.496423e+06  \n",
       "Brazil                       6.539105e+04  \n",
       "Canada                       8.857348e+05  \n",
       "Channel Islands              5.202913e+06  \n",
       "Cyprus                       1.257057e+06  \n",
       "Czech Republic               2.504338e+04  \n",
       "Denmark                      4.691346e+06  \n",
       "EIRE                         1.268732e+09  \n",
       "European Community           8.343090e+04  \n",
       "Finland                      3.260424e+06  \n",
       "France                       3.345068e+07  \n",
       "Germany                      3.531067e+07  \n",
       "Greece                       4.712213e+05  \n",
       "Hong Kong                    5.117725e+06  \n",
       "Iceland                      9.288050e+05  \n",
       "Israel                       8.810898e+05  \n",
       "Italy                        1.067227e+06  \n",
       "Japan                        2.041026e+07  \n",
       "Lebanon                      1.434615e+05  \n",
       "Lithuania                    1.379560e+05  \n",
       "Malta                        1.569689e+05  \n",
       "Netherlands                  4.949120e+08  \n",
       "Norway                       7.277300e+06  \n",
       "Poland                       4.594378e+05  \n",
       "Portugal                     2.551599e+06  \n",
       "RSA                          5.023127e+04  \n",
       "Saudi Arabia                 8.602784e+02  \n",
       "Singapore                    4.159076e+06  \n",
       "Spain                        2.774334e+07  \n",
       "Sweden                       1.364731e+07  \n",
       "Switzerland                  1.062807e+07  \n",
       "USA                          1.710324e+05  \n",
       "United Arab Emirates         9.072023e+04  \n",
       "United Kingdom               4.726867e+09  \n",
       "Unspecified                  2.911252e+05  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CLV_per_Country = pd.DataFrame()\n",
    "\n",
    "for country in df_name_list:\n",
    "    temp = globals()[country].groupby('Country').agg(sum)\n",
    "    CLV_per_Country = CLV_per_Country.append(temp)\n",
    "    \n",
    "CLV_per_Country.sort_values(by='Country', ascending=True, inplace=True)\n",
    "\n",
    "CLV_per_Country"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Total life time value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6815855087.69822"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "totValue  = CLV_per_Country['cust_lifetime_value'].sum()\n",
    "totValue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'en_GB.UTF-8'"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import locale\n",
    "locale.setlocale( locale.LC_ALL, '' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total customer lifetime value  £6,815,855,087.70\n"
     ]
    }
   ],
   "source": [
    "totValue = locale.currency(totValue , grouping=True) \n",
    "print(\"Total customer lifetime value \",  totValue )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature engineer to get time series revenue per country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <th>TotalPurchase</th>\n",
       "      <th>year-m</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",
       "      <td>15.30</td>\n",
       "      <td>2010-12</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",
       "      <td>20.34</td>\n",
       "      <td>2010-12</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",
       "\n",
       "          InvoiceDate  UnitPrice CustomerID         Country  TotalPurchase  \\\n",
       "0 2010-12-01 08:26:00       2.55    17850.0  United Kingdom          15.30   \n",
       "1 2010-12-01 08:26:00       3.39    17850.0  United Kingdom          20.34   \n",
       "\n",
       "    year-m  \n",
       "0  2010-12  \n",
       "1  2010-12  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfOrg.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>year-m</th>\n",
       "      <th>2010-12</th>\n",
       "      <th>2011-01</th>\n",
       "      <th>2011-02</th>\n",
       "      <th>2011-03</th>\n",
       "      <th>2011-04</th>\n",
       "      <th>2011-05</th>\n",
       "      <th>2011-06</th>\n",
       "      <th>2011-07</th>\n",
       "      <th>2011-08</th>\n",
       "      <th>2011-09</th>\n",
       "      <th>2011-10</th>\n",
       "      <th>2011-11</th>\n",
       "      <th>2011-12</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>1005.10</td>\n",
       "      <td>9017.71</td>\n",
       "      <td>14627.47</td>\n",
       "      <td>17055.29</td>\n",
       "      <td>333.400</td>\n",
       "      <td>13628.51</td>\n",
       "      <td>25164.77</td>\n",
       "      <td>4767.570</td>\n",
       "      <td>22489.20</td>\n",
       "      <td>5031.730</td>\n",
       "      <td>17150.53</td>\n",
       "      <td>6805.99</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>257.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>518.36</td>\n",
       "      <td>1708.12</td>\n",
       "      <td>680.780</td>\n",
       "      <td>1249.43</td>\n",
       "      <td>-24.20</td>\n",
       "      <td>1191.950</td>\n",
       "      <td>1516.08</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1043.78</td>\n",
       "      <td>1329.78</td>\n",
       "      <td>683.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bahrain</th>\n",
       "      <td>205.74</td>\n",
       "      <td>-205.74</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>548.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>1809.91</td>\n",
       "      <td>1154.05</td>\n",
       "      <td>2161.32</td>\n",
       "      <td>3333.58</td>\n",
       "      <td>1954.150</td>\n",
       "      <td>2727.00</td>\n",
       "      <td>4273.17</td>\n",
       "      <td>2473.620</td>\n",
       "      <td>3536.12</td>\n",
       "      <td>4197.820</td>\n",
       "      <td>5651.38</td>\n",
       "      <td>6229.41</td>\n",
       "      <td>1409.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1143.600</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>140.54</td>\n",
       "      <td>0.000</td>\n",
       "      <td>534.24</td>\n",
       "      <td>1171.46</td>\n",
       "      <td>1768.580</td>\n",
       "      <td>51.56</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Channel Islands</th>\n",
       "      <td>363.53</td>\n",
       "      <td>645.08</td>\n",
       "      <td>1784.71</td>\n",
       "      <td>3509.33</td>\n",
       "      <td>293.000</td>\n",
       "      <td>903.79</td>\n",
       "      <td>2060.03</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4892.53</td>\n",
       "      <td>1321.650</td>\n",
       "      <td>2623.32</td>\n",
       "      <td>1495.17</td>\n",
       "      <td>194.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cyprus</th>\n",
       "      <td>1590.82</td>\n",
       "      <td>547.50</td>\n",
       "      <td>4013.55</td>\n",
       "      <td>938.39</td>\n",
       "      <td>-35.800</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1109.32</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>196.350</td>\n",
       "      <td>4216.52</td>\n",
       "      <td>460.89</td>\n",
       "      <td>-91.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Czech Republic</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>549.26</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-57.510</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>277.48</td>\n",
       "      <td>-61.51</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denmark</th>\n",
       "      <td>1281.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>399.22</td>\n",
       "      <td>3978.99</td>\n",
       "      <td>0.000</td>\n",
       "      <td>515.70</td>\n",
       "      <td>3261.15</td>\n",
       "      <td>376.240</td>\n",
       "      <td>78.60</td>\n",
       "      <td>4570.160</td>\n",
       "      <td>1438.11</td>\n",
       "      <td>2699.57</td>\n",
       "      <td>168.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EIRE</th>\n",
       "      <td>9029.95</td>\n",
       "      <td>21656.52</td>\n",
       "      <td>9674.14</td>\n",
       "      <td>18783.23</td>\n",
       "      <td>7570.500</td>\n",
       "      <td>17921.35</td>\n",
       "      <td>20060.25</td>\n",
       "      <td>42740.650</td>\n",
       "      <td>12157.47</td>\n",
       "      <td>42639.050</td>\n",
       "      <td>23307.62</td>\n",
       "      <td>30413.05</td>\n",
       "      <td>7323.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>European Community</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>191.400</td>\n",
       "      <td>0.00</td>\n",
       "      <td>423.55</td>\n",
       "      <td>676.800</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finland</th>\n",
       "      <td>892.80</td>\n",
       "      <td>889.02</td>\n",
       "      <td>205.16</td>\n",
       "      <td>5924.62</td>\n",
       "      <td>1626.810</td>\n",
       "      <td>0.00</td>\n",
       "      <td>329.56</td>\n",
       "      <td>2996.730</td>\n",
       "      <td>1371.55</td>\n",
       "      <td>1105.450</td>\n",
       "      <td>2804.12</td>\n",
       "      <td>2950.24</td>\n",
       "      <td>1230.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>9575.36</td>\n",
       "      <td>17503.07</td>\n",
       "      <td>8438.91</td>\n",
       "      <td>14516.90</td>\n",
       "      <td>4195.210</td>\n",
       "      <td>17527.08</td>\n",
       "      <td>15991.87</td>\n",
       "      <td>9888.990</td>\n",
       "      <td>13789.26</td>\n",
       "      <td>23198.870</td>\n",
       "      <td>25032.04</td>\n",
       "      <td>30697.51</td>\n",
       "      <td>7048.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>14562.84</td>\n",
       "      <td>16451.43</td>\n",
       "      <td>8969.24</td>\n",
       "      <td>14170.02</td>\n",
       "      <td>11963.370</td>\n",
       "      <td>25571.35</td>\n",
       "      <td>13081.02</td>\n",
       "      <td>15721.660</td>\n",
       "      <td>19023.65</td>\n",
       "      <td>17720.310</td>\n",
       "      <td>30614.27</td>\n",
       "      <td>26044.35</td>\n",
       "      <td>7804.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greece</th>\n",
       "      <td>0.00</td>\n",
       "      <td>2661.24</td>\n",
       "      <td>0.00</td>\n",
       "      <td>387.31</td>\n",
       "      <td>609.740</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>371.240</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>680.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hong Kong</th>\n",
       "      <td>0.00</td>\n",
       "      <td>2439.76</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1539.640</td>\n",
       "      <td>1966.16</td>\n",
       "      <td>633.96</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2813.95</td>\n",
       "      <td>0.000</td>\n",
       "      <td>723.57</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iceland</th>\n",
       "      <td>711.79</td>\n",
       "      <td>475.39</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>636.250</td>\n",
       "      <td>0.00</td>\n",
       "      <td>382.52</td>\n",
       "      <td>0.000</td>\n",
       "      <td>584.91</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1294.32</td>\n",
       "      <td>0.00</td>\n",
       "      <td>224.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Israel</th>\n",
       "      <td>-227.44</td>\n",
       "      <td>379.84</td>\n",
       "      <td>779.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>110.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1430.750</td>\n",
       "      <td>4873.81</td>\n",
       "      <td>423.730</td>\n",
       "      <td>138.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>794.50</td>\n",
       "      <td>2361.36</td>\n",
       "      <td>251.50</td>\n",
       "      <td>1778.91</td>\n",
       "      <td>737.830</td>\n",
       "      <td>587.45</td>\n",
       "      <td>162.27</td>\n",
       "      <td>147.100</td>\n",
       "      <td>1934.80</td>\n",
       "      <td>279.800</td>\n",
       "      <td>3410.79</td>\n",
       "      <td>4243.75</td>\n",
       "      <td>200.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>7705.07</td>\n",
       "      <td>-109.80</td>\n",
       "      <td>5780.81</td>\n",
       "      <td>-73.66</td>\n",
       "      <td>4871.830</td>\n",
       "      <td>1351.45</td>\n",
       "      <td>1607.04</td>\n",
       "      <td>3854.400</td>\n",
       "      <td>-49.79</td>\n",
       "      <td>112.080</td>\n",
       "      <td>1948.04</td>\n",
       "      <td>8460.08</td>\n",
       "      <td>-116.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lebanon</th>\n",
       "      <td>0.00</td>\n",
       "      <td>1693.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lithuania</th>\n",
       "      <td>1661.06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malta</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>850.41</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1103.25</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>582.62</td>\n",
       "      <td>-30.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>8784.48</td>\n",
       "      <td>26611.16</td>\n",
       "      <td>22932.11</td>\n",
       "      <td>22416.49</td>\n",
       "      <td>2976.560</td>\n",
       "      <td>29185.88</td>\n",
       "      <td>26843.09</td>\n",
       "      <td>26.020</td>\n",
       "      <td>39655.81</td>\n",
       "      <td>26937.260</td>\n",
       "      <td>40708.65</td>\n",
       "      <td>25856.01</td>\n",
       "      <td>11728.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <td>3787.12</td>\n",
       "      <td>0.00</td>\n",
       "      <td>630.90</td>\n",
       "      <td>304.68</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5886.86</td>\n",
       "      <td>890.070</td>\n",
       "      <td>1984.46</td>\n",
       "      <td>8678.940</td>\n",
       "      <td>3687.23</td>\n",
       "      <td>6527.50</td>\n",
       "      <td>2785.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>248.16</td>\n",
       "      <td>613.22</td>\n",
       "      <td>604.05</td>\n",
       "      <td>303.39</td>\n",
       "      <td>705.840</td>\n",
       "      <td>1522.37</td>\n",
       "      <td>264.74</td>\n",
       "      <td>678.450</td>\n",
       "      <td>401.80</td>\n",
       "      <td>529.250</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1341.87</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portugal</th>\n",
       "      <td>2380.12</td>\n",
       "      <td>3998.11</td>\n",
       "      <td>1213.90</td>\n",
       "      <td>2660.85</td>\n",
       "      <td>1687.750</td>\n",
       "      <td>587.83</td>\n",
       "      <td>879.51</td>\n",
       "      <td>2287.850</td>\n",
       "      <td>1206.40</td>\n",
       "      <td>1433.220</td>\n",
       "      <td>5584.63</td>\n",
       "      <td>2644.90</td>\n",
       "      <td>2801.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RSA</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1002.31</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saudi Arabia</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>145.92</td>\n",
       "      <td>-14.75</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singapore</th>\n",
       "      <td>0.00</td>\n",
       "      <td>2053.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2118.740</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3949.320</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>999.26</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>1843.73</td>\n",
       "      <td>7178.54</td>\n",
       "      <td>2093.70</td>\n",
       "      <td>5326.65</td>\n",
       "      <td>1785.650</td>\n",
       "      <td>3257.60</td>\n",
       "      <td>3332.44</td>\n",
       "      <td>7578.170</td>\n",
       "      <td>3330.41</td>\n",
       "      <td>5163.740</td>\n",
       "      <td>5078.65</td>\n",
       "      <td>8533.87</td>\n",
       "      <td>271.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>2646.30</td>\n",
       "      <td>3446.32</td>\n",
       "      <td>509.50</td>\n",
       "      <td>4540.58</td>\n",
       "      <td>809.100</td>\n",
       "      <td>2664.98</td>\n",
       "      <td>832.00</td>\n",
       "      <td>6802.090</td>\n",
       "      <td>1390.96</td>\n",
       "      <td>4575.210</td>\n",
       "      <td>5766.16</td>\n",
       "      <td>2612.71</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>1304.92</td>\n",
       "      <td>4231.23</td>\n",
       "      <td>2642.67</td>\n",
       "      <td>1870.23</td>\n",
       "      <td>1822.190</td>\n",
       "      <td>3567.21</td>\n",
       "      <td>7882.20</td>\n",
       "      <td>3776.610</td>\n",
       "      <td>4882.19</td>\n",
       "      <td>8254.960</td>\n",
       "      <td>8033.98</td>\n",
       "      <td>8116.96</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USA</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>383.950</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>731.69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>615.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Arab Emirates</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>889.24</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1013.040</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>676742.62</td>\n",
       "      <td>434308.30</td>\n",
       "      <td>408247.91</td>\n",
       "      <td>559707.39</td>\n",
       "      <td>442254.041</td>\n",
       "      <td>596459.86</td>\n",
       "      <td>554478.35</td>\n",
       "      <td>565479.841</td>\n",
       "      <td>539130.50</td>\n",
       "      <td>862018.152</td>\n",
       "      <td>877438.19</td>\n",
       "      <td>1282805.78</td>\n",
       "      <td>388735.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unspecified</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>299.100</td>\n",
       "      <td>1055.87</td>\n",
       "      <td>185.78</td>\n",
       "      <td>1425.410</td>\n",
       "      <td>531.03</td>\n",
       "      <td>286.850</td>\n",
       "      <td>0.00</td>\n",
       "      <td>965.75</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "year-m                  2010-12    2011-01    2011-02    2011-03     2011-04  \\\n",
       "Country                                                                        \n",
       "Australia               1005.10    9017.71   14627.47   17055.29     333.400   \n",
       "Austria                  257.04       0.00     518.36    1708.12     680.780   \n",
       "Bahrain                  205.74    -205.74       0.00       0.00       0.000   \n",
       "Belgium                 1809.91    1154.05    2161.32    3333.58    1954.150   \n",
       "Brazil                     0.00       0.00       0.00       0.00    1143.600   \n",
       "Canada                     0.00       0.00       0.00     140.54       0.000   \n",
       "Channel Islands          363.53     645.08    1784.71    3509.33     293.000   \n",
       "Cyprus                  1590.82     547.50    4013.55     938.39     -35.800   \n",
       "Czech Republic             0.00       0.00     549.26       0.00     -57.510   \n",
       "Denmark                 1281.50       0.00     399.22    3978.99       0.000   \n",
       "EIRE                    9029.95   21656.52    9674.14   18783.23    7570.500   \n",
       "European Community         0.00       0.00       0.00       0.00     191.400   \n",
       "Finland                  892.80     889.02     205.16    5924.62    1626.810   \n",
       "France                  9575.36   17503.07    8438.91   14516.90    4195.210   \n",
       "Germany                14562.84   16451.43    8969.24   14170.02   11963.370   \n",
       "Greece                     0.00    2661.24       0.00     387.31     609.740   \n",
       "Hong Kong                  0.00    2439.76       0.00       0.00    1539.640   \n",
       "Iceland                  711.79     475.39       0.00       0.00     636.250   \n",
       "Israel                  -227.44     379.84     779.10       0.00     110.000   \n",
       "Italy                    794.50    2361.36     251.50    1778.91     737.830   \n",
       "Japan                   7705.07    -109.80    5780.81     -73.66    4871.830   \n",
       "Lebanon                    0.00    1693.88       0.00       0.00       0.000   \n",
       "Lithuania               1661.06       0.00       0.00       0.00       0.000   \n",
       "Malta                      0.00       0.00       0.00       0.00       0.000   \n",
       "Netherlands             8784.48   26611.16   22932.11   22416.49    2976.560   \n",
       "Norway                  3787.12       0.00     630.90     304.68       0.000   \n",
       "Poland                   248.16     613.22     604.05     303.39     705.840   \n",
       "Portugal                2380.12    3998.11    1213.90    2660.85    1687.750   \n",
       "RSA                        0.00       0.00       0.00       0.00       0.000   \n",
       "Saudi Arabia               0.00       0.00     145.92     -14.75       0.000   \n",
       "Singapore                  0.00    2053.07       0.00       0.00    2118.740   \n",
       "Spain                   1843.73    7178.54    2093.70    5326.65    1785.650   \n",
       "Sweden                  2646.30    3446.32     509.50    4540.58     809.100   \n",
       "Switzerland             1304.92    4231.23    2642.67    1870.23    1822.190   \n",
       "USA                        0.00       0.00       0.00       0.00     383.950   \n",
       "United Arab Emirates       0.00       0.00     889.24       0.00       0.000   \n",
       "United Kingdom        676742.62  434308.30  408247.91  559707.39  442254.041   \n",
       "Unspecified                0.00       0.00       0.00       0.00     299.100   \n",
       "\n",
       "year-m                  2011-05    2011-06     2011-07    2011-08     2011-09  \\\n",
       "Country                                                                         \n",
       "Australia              13628.51   25164.77    4767.570   22489.20    5031.730   \n",
       "Austria                 1249.43     -24.20    1191.950    1516.08       0.000   \n",
       "Bahrain                  548.40       0.00       0.000       0.00       0.000   \n",
       "Belgium                 2727.00    4273.17    2473.620    3536.12    4197.820   \n",
       "Brazil                     0.00       0.00       0.000       0.00       0.000   \n",
       "Canada                   534.24    1171.46    1768.580      51.56       0.000   \n",
       "Channel Islands          903.79    2060.03       0.000    4892.53    1321.650   \n",
       "Cyprus                     0.00    1109.32       0.000       0.00     196.350   \n",
       "Czech Republic             0.00       0.00       0.000       0.00       0.000   \n",
       "Denmark                  515.70    3261.15     376.240      78.60    4570.160   \n",
       "EIRE                   17921.35   20060.25   42740.650   12157.47   42639.050   \n",
       "European Community         0.00     423.55     676.800       0.00       0.000   \n",
       "Finland                    0.00     329.56    2996.730    1371.55    1105.450   \n",
       "France                 17527.08   15991.87    9888.990   13789.26   23198.870   \n",
       "Germany                25571.35   13081.02   15721.660   19023.65   17720.310   \n",
       "Greece                     0.00       0.00     371.240       0.00       0.000   \n",
       "Hong Kong               1966.16     633.96       0.000    2813.95       0.000   \n",
       "Iceland                    0.00     382.52       0.000     584.91       0.000   \n",
       "Israel                     0.00       0.00    1430.750    4873.81     423.730   \n",
       "Italy                    587.45     162.27     147.100    1934.80     279.800   \n",
       "Japan                   1351.45    1607.04    3854.400     -49.79     112.080   \n",
       "Lebanon                    0.00       0.00       0.000       0.00       0.000   \n",
       "Lithuania                  0.00       0.00       0.000       0.00       0.000   \n",
       "Malta                      0.00     850.41       0.000    1103.25       0.000   \n",
       "Netherlands            29185.88   26843.09      26.020   39655.81   26937.260   \n",
       "Norway                     0.00    5886.86     890.070    1984.46    8678.940   \n",
       "Poland                  1522.37     264.74     678.450     401.80     529.250   \n",
       "Portugal                 587.83     879.51    2287.850    1206.40    1433.220   \n",
       "RSA                        0.00       0.00       0.000       0.00       0.000   \n",
       "Saudi Arabia               0.00       0.00       0.000       0.00       0.000   \n",
       "Singapore                  0.00       0.00    3949.320       0.00       0.000   \n",
       "Spain                   3257.60    3332.44    7578.170    3330.41    5163.740   \n",
       "Sweden                  2664.98     832.00    6802.090    1390.96    4575.210   \n",
       "Switzerland             3567.21    7882.20    3776.610    4882.19    8254.960   \n",
       "USA                        0.00       0.00       0.000       0.00       0.000   \n",
       "United Arab Emirates       0.00       0.00       0.000       0.00    1013.040   \n",
       "United Kingdom        596459.86  554478.35  565479.841  539130.50  862018.152   \n",
       "Unspecified             1055.87     185.78    1425.410     531.03     286.850   \n",
       "\n",
       "year-m                  2011-10     2011-11    2011-12  \n",
       "Country                                                 \n",
       "Australia              17150.53     6805.99       0.00  \n",
       "Austria                 1043.78     1329.78     683.20  \n",
       "Bahrain                    0.00        0.00       0.00  \n",
       "Belgium                 5651.38     6229.41    1409.43  \n",
       "Brazil                     0.00        0.00       0.00  \n",
       "Canada                     0.00        0.00       0.00  \n",
       "Channel Islands         2623.32     1495.17     194.15  \n",
       "Cyprus                  4216.52      460.89     -91.25  \n",
       "Czech Republic           277.48      -61.51       0.00  \n",
       "Denmark                 1438.11     2699.57     168.90  \n",
       "EIRE                   23307.62    30413.05    7323.04  \n",
       "European Community         0.00        0.00       0.00  \n",
       "Finland                 2804.12     2950.24    1230.68  \n",
       "France                 25032.04    30697.51    7048.83  \n",
       "Germany                30614.27    26044.35    7804.70  \n",
       "Greece                     0.00        0.00     680.99  \n",
       "Hong Kong                723.57        0.00       0.00  \n",
       "Iceland                 1294.32        0.00     224.82  \n",
       "Israel                   138.03        0.00       0.00  \n",
       "Italy                   3410.79     4243.75     200.45  \n",
       "Japan                   1948.04     8460.08    -116.93  \n",
       "Lebanon                    0.00        0.00       0.00  \n",
       "Lithuania                  0.00        0.00       0.00  \n",
       "Malta                      0.00      582.62     -30.81  \n",
       "Netherlands            40708.65    25856.01   11728.02  \n",
       "Norway                  3687.23     6527.50    2785.70  \n",
       "Poland                     0.00     1341.87       0.00  \n",
       "Portugal                5584.63     2644.90    2801.95  \n",
       "RSA                     1002.31        0.00       0.00  \n",
       "Saudi Arabia               0.00        0.00       0.00  \n",
       "Singapore                999.26        0.00       0.00  \n",
       "Spain                   5078.65     8533.87     271.43  \n",
       "Sweden                  5766.16     2612.71       0.00  \n",
       "Switzerland             8033.98     8116.96       0.00  \n",
       "USA                      731.69        0.00     615.28  \n",
       "United Arab Emirates       0.00        0.00       0.00  \n",
       "United Kingdom        877438.19  1282805.78  388735.43  \n",
       "Unspecified                0.00      965.75       0.00  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfTimeSeries = dfOrg.pivot_table(index=['Country'],columns=['year-m'],\n",
    "                           values='TotalPurchase',aggfunc='sum',fill_value=0)\n",
    "\n",
    "dfTimeSeries.sort_values(by='Country', ascending=True, inplace = True)\n",
    "\n",
    "dfTimeSeries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time series customer lifetime value forecasting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dfTimeSeries\n",
    "y = CLV_per_Country.cust_lifetime_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "#split training set and test set\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import model\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# instantiate\n",
    "linreg = LinearRegression()\n",
    "\n",
    "# fit the model to the training data (learn the coefficients)\n",
    "linreg.fit(X_train, y_train)\n",
    "\n",
    "# make predictions on the testing set\n",
    "y_pred = linreg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-7372921.737272322"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linreg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-2037.98477414,  8837.30312939, 16977.86049227,  3522.21727175,\n",
       "       11004.91424857, -4475.66299341, -4667.99373609, -4469.50154092,\n",
       "        5816.16607057, 12535.78640963, -9640.4396992 , -9810.89742811,\n",
       "        8729.04337653])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linreg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-Square: 0.29266975293523323\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "# compute the R Square for model\n",
    "print(\"R-Square:\",metrics.r2_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 112750419.34903781\n",
      "MSE 1.0188923774241322e+17\n",
      "RMSE: 319200936.31193066\n"
     ]
    }
   ],
   "source": [
    "#calculate MAE using scikit-learn\n",
    "print(\"MAE:\",metrics.mean_absolute_error(y_test,y_pred))\n",
    "\n",
    "#calculate mean squared error\n",
    "print(\"MSE\",metrics.mean_squared_error(y_test, y_pred))\n",
    "# compute the RMSE of our predictions\n",
    "print(\"RMSE:\",np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.07581367e+07, -4.94746504e+06,  2.87632598e+07,  2.85047227e+07,\n",
       "       -1.02686685e+07, -5.00939881e+06,  1.35713229e+06,  2.60518487e+08,\n",
       "        1.03041036e+07, -1.20648571e+07])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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