model_2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gözetimsiz Öğrenme ile Müşteri Segmentasyonu\n",
    "# MODEL2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "#define the library\n",
    "\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime as dt\n",
    "import warnings\n",
    "from scipy import stats\n",
    "\n",
    "from sklearn.exceptions import ConvergenceWarning\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "\n",
    "from scipy.cluster.hierarchy import dendrogram\n",
    "from scipy.cluster.hierarchy import linkage\n",
    "\n",
    "from yellowbrick.cluster import KElbowVisualizer\n",
    "\n",
    "from sklearn.metrics import silhouette_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make some settings\n",
    "\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "warnings.simplefilter(\"ignore\", category=ConvergenceWarning)\n",
    "\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)\n",
    "pd.set_option('display.float_format', lambda x: '%.2f' % x)\n",
    "pd.set_option('display.width', 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reading the dataset\n",
    "# flo is the original dataset\n",
    "\n",
    "flo = pd.read_csv(\"C:/Users/avcil/projects/project/flo_data.csv\")  \n",
    "\n",
    "\n",
    "# flo = pd.read_csv(\"/kaggle/input/flo-data/flo_data_20k.csv\")\n",
    "# df = flo.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>master_id</th>\n",
       "      <th>order_channel</th>\n",
       "      <th>last_order_channel</th>\n",
       "      <th>first_order_date</th>\n",
       "      <th>last_order_date</th>\n",
       "      <th>last_order_date_online</th>\n",
       "      <th>last_order_date_offline</th>\n",
       "      <th>order_num_total_ever_online</th>\n",
       "      <th>order_num_total_ever_offline</th>\n",
       "      <th>customer_value_total_ever_offline</th>\n",
       "      <th>customer_value_total_ever_online</th>\n",
       "      <th>interested_in_categories_12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cc294636-19f0-11eb-8d74-000d3a38a36f</td>\n",
       "      <td>Android App</td>\n",
       "      <td>Offline</td>\n",
       "      <td>2020-10-30</td>\n",
       "      <td>2021-02-26</td>\n",
       "      <td>2021-02-21</td>\n",
       "      <td>2021-02-26</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>139.99</td>\n",
       "      <td>799.38</td>\n",
       "      <td>[KADIN]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>f431bd5a-ab7b-11e9-a2fc-000d3a38a36f</td>\n",
       "      <td>Android App</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>2017-02-08</td>\n",
       "      <td>2021-02-16</td>\n",
       "      <td>2021-02-16</td>\n",
       "      <td>2020-01-10</td>\n",
       "      <td>19.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>159.97</td>\n",
       "      <td>1853.58</td>\n",
       "      <td>[ERKEK, COCUK, KADIN, AKTIFSPOR]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69b69676-1a40-11ea-941b-000d3a38a36f</td>\n",
       "      <td>Android App</td>\n",
       "      <td>Android App</td>\n",
       "      <td>2019-11-27</td>\n",
       "      <td>2020-11-27</td>\n",
       "      <td>2020-11-27</td>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>189.97</td>\n",
       "      <td>395.35</td>\n",
       "      <td>[ERKEK, KADIN]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1854e56c-491f-11eb-806e-000d3a38a36f</td>\n",
       "      <td>Android App</td>\n",
       "      <td>Android App</td>\n",
       "      <td>2021-01-06</td>\n",
       "      <td>2021-01-17</td>\n",
       "      <td>2021-01-17</td>\n",
       "      <td>2021-01-06</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>39.99</td>\n",
       "      <td>81.98</td>\n",
       "      <td>[AKTIFCOCUK, COCUK]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>d6ea1074-f1f5-11e9-9346-000d3a38a36f</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>2019-08-03</td>\n",
       "      <td>2021-03-07</td>\n",
       "      <td>2021-03-07</td>\n",
       "      <td>2019-08-03</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>49.99</td>\n",
       "      <td>159.99</td>\n",
       "      <td>[AKTIFSPOR]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              master_id order_channel last_order_channel first_order_date last_order_date last_order_date_online last_order_date_offline  order_num_total_ever_online  order_num_total_ever_offline  customer_value_total_ever_offline  customer_value_total_ever_online       interested_in_categories_12\n",
       "0  cc294636-19f0-11eb-8d74-000d3a38a36f   Android App            Offline       2020-10-30      2021-02-26             2021-02-21              2021-02-26                         4.00                          1.00                             139.99                            799.38                           [KADIN]\n",
       "1  f431bd5a-ab7b-11e9-a2fc-000d3a38a36f   Android App             Mobile       2017-02-08      2021-02-16             2021-02-16              2020-01-10                        19.00                          2.00                             159.97                           1853.58  [ERKEK, COCUK, KADIN, AKTIFSPOR]\n",
       "2  69b69676-1a40-11ea-941b-000d3a38a36f   Android App        Android App       2019-11-27      2020-11-27             2020-11-27              2019-12-01                         3.00                          2.00                             189.97                            395.35                    [ERKEK, KADIN]\n",
       "3  1854e56c-491f-11eb-806e-000d3a38a36f   Android App        Android App       2021-01-06      2021-01-17             2021-01-17              2021-01-06                         1.00                          1.00                              39.99                             81.98               [AKTIFCOCUK, COCUK]\n",
       "4  d6ea1074-f1f5-11e9-9346-000d3a38a36f       Desktop            Desktop       2019-08-03      2021-03-07             2021-03-07              2019-08-03                         1.00                          1.00                              49.99                            159.99                       [AKTIFSPOR]"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a copy of flo dataset\n",
    "\n",
    "df = flo.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 19945 entries, 0 to 19944\n",
      "Data columns (total 12 columns):\n",
      " #   Column                             Non-Null Count  Dtype  \n",
      "---  ------                             --------------  -----  \n",
      " 0   master_id                          19945 non-null  object \n",
      " 1   order_channel                      19945 non-null  object \n",
      " 2   last_order_channel                 19945 non-null  object \n",
      " 3   first_order_date                   19945 non-null  object \n",
      " 4   last_order_date                    19945 non-null  object \n",
      " 5   last_order_date_online             19945 non-null  object \n",
      " 6   last_order_date_offline            19945 non-null  object \n",
      " 7   order_num_total_ever_online        19945 non-null  float64\n",
      " 8   order_num_total_ever_offline       19945 non-null  float64\n",
      " 9   customer_value_total_ever_offline  19945 non-null  float64\n",
      " 10  customer_value_total_ever_online   19945 non-null  float64\n",
      " 11  interested_in_categories_12        19945 non-null  object \n",
      "dtypes: float64(4), object(8)\n",
      "memory usage: 1.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting data containing date information to datetime type\n",
    "\n",
    "date_columns = df.columns[df.columns.str.contains(\"date\")]\n",
    "df[date_columns] = df[date_columns].apply(pd.to_datetime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 19945 entries, 0 to 19944\n",
      "Data columns (total 12 columns):\n",
      " #   Column                             Non-Null Count  Dtype         \n",
      "---  ------                             --------------  -----         \n",
      " 0   master_id                          19945 non-null  object        \n",
      " 1   order_channel                      19945 non-null  object        \n",
      " 2   last_order_channel                 19945 non-null  object        \n",
      " 3   first_order_date                   19945 non-null  datetime64[ns]\n",
      " 4   last_order_date                    19945 non-null  datetime64[ns]\n",
      " 5   last_order_date_online             19945 non-null  datetime64[ns]\n",
      " 6   last_order_date_offline            19945 non-null  datetime64[ns]\n",
      " 7   order_num_total_ever_online        19945 non-null  float64       \n",
      " 8   order_num_total_ever_offline       19945 non-null  float64       \n",
      " 9   customer_value_total_ever_offline  19945 non-null  float64       \n",
      " 10  customer_value_total_ever_online   19945 non-null  float64       \n",
      " 11  interested_in_categories_12        19945 non-null  object        \n",
      "dtypes: datetime64[ns](4), float64(4), object(4)\n",
      "memory usage: 1.8+ MB\n"
     ]
    }
   ],
   "source": [
    "#check chancing\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2021-05-30 00:00:00')"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"last_order_date\"].max() \n",
    "# output 2021-05-30 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "# determined the last_date\n",
    "\n",
    "last_date = dt.datetime(2021,6,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a new variable called recency\n",
    "\n",
    "df[\"recency\"] = (last_date - df[\"last_order_date\"]).astype('timedelta64[D]')\n",
    "\n",
    "# recency: It keeps information on how many days ago the person last shopped.\n",
    "# Recency shows when our customer last shopped from our brand."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a new variable called tenure\n",
    "\n",
    "df[\"tenure\"] = (df[\"last_order_date\"]-df[\"first_order_date\"]).astype('timedelta64[D]')\n",
    "\n",
    "# tenure : It keeps the difference between the first purchase and the last purchase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a new variable total order num (online + offline)\n",
    "\n",
    "df[\"order_num_total\"] = df[\"order_num_total_ever_online\"] + df[\"order_num_total_ever_offline\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a new variable total customer value (online + offline)\n",
    "\n",
    "df[\"customer_value_total\"] = df[\"customer_value_total_ever_offline\"] + df[\"customer_value_total_ever_online\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a new variable called ratio \n",
    "\n",
    "df[\"ratio\"] = df[\"customer_value_total\"] / df[\"order_num_total\"] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>master_id</th>\n",
       "      <th>order_channel</th>\n",
       "      <th>last_order_channel</th>\n",
       "      <th>first_order_date</th>\n",
       "      <th>last_order_date</th>\n",
       "      <th>last_order_date_online</th>\n",
       "      <th>last_order_date_offline</th>\n",
       "      <th>order_num_total_ever_online</th>\n",
       "      <th>order_num_total_ever_offline</th>\n",
       "      <th>customer_value_total_ever_offline</th>\n",
       "      <th>customer_value_total_ever_online</th>\n",
       "      <th>interested_in_categories_12</th>\n",
       "      <th>recency</th>\n",
       "      <th>tenure</th>\n",
       "      <th>order_num_total</th>\n",
       "      <th>customer_value_total</th>\n",
       "      <th>ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cc294636-19f0-11eb-8d74-000d3a38a36f</td>\n",
       "      <td>Android App</td>\n",
       "      <td>Offline</td>\n",
       "      <td>2020-10-30</td>\n",
       "      <td>2021-02-26</td>\n",
       "      <td>2021-02-21</td>\n",
       "      <td>2021-02-26</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>139.99</td>\n",
       "      <td>799.38</td>\n",
       "      <td>[KADIN]</td>\n",
       "      <td>95.00</td>\n",
       "      <td>119.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>939.37</td>\n",
       "      <td>187.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>f431bd5a-ab7b-11e9-a2fc-000d3a38a36f</td>\n",
       "      <td>Android App</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>2017-02-08</td>\n",
       "      <td>2021-02-16</td>\n",
       "      <td>2021-02-16</td>\n",
       "      <td>2020-01-10</td>\n",
       "      <td>19.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>159.97</td>\n",
       "      <td>1853.58</td>\n",
       "      <td>[ERKEK, COCUK, KADIN, AKTIFSPOR]</td>\n",
       "      <td>105.00</td>\n",
       "      <td>1469.00</td>\n",
       "      <td>21.00</td>\n",
       "      <td>2013.55</td>\n",
       "      <td>95.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69b69676-1a40-11ea-941b-000d3a38a36f</td>\n",
       "      <td>Android App</td>\n",
       "      <td>Android App</td>\n",
       "      <td>2019-11-27</td>\n",
       "      <td>2020-11-27</td>\n",
       "      <td>2020-11-27</td>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>189.97</td>\n",
       "      <td>395.35</td>\n",
       "      <td>[ERKEK, KADIN]</td>\n",
       "      <td>186.00</td>\n",
       "      <td>366.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>585.32</td>\n",
       "      <td>117.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1854e56c-491f-11eb-806e-000d3a38a36f</td>\n",
       "      <td>Android App</td>\n",
       "      <td>Android App</td>\n",
       "      <td>2021-01-06</td>\n",
       "      <td>2021-01-17</td>\n",
       "      <td>2021-01-17</td>\n",
       "      <td>2021-01-06</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>39.99</td>\n",
       "      <td>81.98</td>\n",
       "      <td>[AKTIFCOCUK, COCUK]</td>\n",
       "      <td>135.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>121.97</td>\n",
       "      <td>60.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>d6ea1074-f1f5-11e9-9346-000d3a38a36f</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>2019-08-03</td>\n",
       "      <td>2021-03-07</td>\n",
       "      <td>2021-03-07</td>\n",
       "      <td>2019-08-03</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>49.99</td>\n",
       "      <td>159.99</td>\n",
       "      <td>[AKTIFSPOR]</td>\n",
       "      <td>86.00</td>\n",
       "      <td>582.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>209.98</td>\n",
       "      <td>104.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              master_id order_channel last_order_channel first_order_date last_order_date last_order_date_online last_order_date_offline  order_num_total_ever_online  order_num_total_ever_offline  customer_value_total_ever_offline  customer_value_total_ever_online       interested_in_categories_12  recency  tenure  order_num_total  customer_value_total  ratio\n",
       "0  cc294636-19f0-11eb-8d74-000d3a38a36f   Android App            Offline       2020-10-30      2021-02-26             2021-02-21              2021-02-26                         4.00                          1.00                             139.99                            799.38                           [KADIN]    95.00  119.00             5.00                939.37 187.87\n",
       "1  f431bd5a-ab7b-11e9-a2fc-000d3a38a36f   Android App             Mobile       2017-02-08      2021-02-16             2021-02-16              2020-01-10                        19.00                          2.00                             159.97                           1853.58  [ERKEK, COCUK, KADIN, AKTIFSPOR]   105.00 1469.00            21.00               2013.55  95.88\n",
       "2  69b69676-1a40-11ea-941b-000d3a38a36f   Android App        Android App       2019-11-27      2020-11-27             2020-11-27              2019-12-01                         3.00                          2.00                             189.97                            395.35                    [ERKEK, KADIN]   186.00  366.00             5.00                585.32 117.06\n",
       "3  1854e56c-491f-11eb-806e-000d3a38a36f   Android App        Android App       2021-01-06      2021-01-17             2021-01-17              2021-01-06                         1.00                          1.00                              39.99                             81.98               [AKTIFCOCUK, COCUK]   135.00   11.00             2.00                121.97  60.98\n",
       "4  d6ea1074-f1f5-11e9-9346-000d3a38a36f       Desktop            Desktop       2019-08-03      2021-03-07             2021-03-07              2019-08-03                         1.00                          1.00                              49.99                            159.99                       [AKTIFSPOR]    86.00  582.00             2.00                209.98 104.99"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>master_id</th>\n",
       "      <th>order_num_total</th>\n",
       "      <th>customer_value_total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order_channel</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Android App</th>\n",
       "      <td>9495</td>\n",
       "      <td>52269.00</td>\n",
       "      <td>7819062.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Desktop</th>\n",
       "      <td>2735</td>\n",
       "      <td>10920.00</td>\n",
       "      <td>1610321.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ios App</th>\n",
       "      <td>2833</td>\n",
       "      <td>15351.00</td>\n",
       "      <td>2525999.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mobile</th>\n",
       "      <td>4882</td>\n",
       "      <td>21679.00</td>\n",
       "      <td>3028183.16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               master_id  order_num_total  customer_value_total\n",
       "order_channel                                                  \n",
       "Android App         9495         52269.00            7819062.76\n",
       "Desktop             2735         10920.00            1610321.46\n",
       "Ios App             2833         15351.00            2525999.93\n",
       "Mobile              4882         21679.00            3028183.16"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"order_channel\").agg({\"master_id\": \"count\",\n",
    "                                 \"order_num_total\": \"sum\",\n",
    "                                 \"customer_value_total\": \"sum\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>master_id</th>\n",
       "      <th>order_channel</th>\n",
       "      <th>last_order_channel</th>\n",
       "      <th>first_order_date</th>\n",
       "      <th>last_order_date</th>\n",
       "      <th>last_order_date_online</th>\n",
       "      <th>last_order_date_offline</th>\n",
       "      <th>order_num_total_ever_online</th>\n",
       "      <th>order_num_total_ever_offline</th>\n",
       "      <th>customer_value_total_ever_offline</th>\n",
       "      <th>customer_value_total_ever_online</th>\n",
       "      <th>interested_in_categories_12</th>\n",
       "      <th>recency</th>\n",
       "      <th>tenure</th>\n",
       "      <th>order_num_total</th>\n",
       "      <th>customer_value_total</th>\n",
       "      <th>ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11150</th>\n",
       "      <td>5d1c466a-9cfd-11e9-9897-000d3a38a36f</td>\n",
       "      <td>Android App</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>2013-10-11</td>\n",
       "      <td>2021-04-30</td>\n",
       "      <td>2021-04-30</td>\n",
       "      <td>2020-12-24</td>\n",
       "      <td>200.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>684.97</td>\n",
       "      <td>45220.13</td>\n",
       "      <td>[AKTIFCOCUK, ERKEK, COCUK, KADIN, AKTIFSPOR]</td>\n",
       "      <td>32.00</td>\n",
       "      <td>2758.00</td>\n",
       "      <td>202.00</td>\n",
       "      <td>45905.10</td>\n",
       "      <td>227.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7223</th>\n",
       "      <td>cba59206-9dd1-11e9-9897-000d3a38a36f</td>\n",
       "      <td>Android App</td>\n",
       "      <td>Android App</td>\n",
       "      <td>2013-02-21</td>\n",
       "      <td>2021-05-09</td>\n",
       "      <td>2021-05-09</td>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>49.99</td>\n",
       "      <td>12232.25</td>\n",
       "      <td>[AKTIFCOCUK, ERKEK, KADIN, AKTIFSPOR]</td>\n",
       "      <td>23.00</td>\n",
       "      <td>2999.00</td>\n",
       "      <td>131.00</td>\n",
       "      <td>12282.24</td>\n",
       "      <td>93.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8783</th>\n",
       "      <td>a57f4302-b1a8-11e9-89fa-000d3a38a36f</td>\n",
       "      <td>Android App</td>\n",
       "      <td>Offline</td>\n",
       "      <td>2019-08-07</td>\n",
       "      <td>2020-11-04</td>\n",
       "      <td>2020-09-07</td>\n",
       "      <td>2020-11-04</td>\n",
       "      <td>2.00</td>\n",
       "      <td>109.00</td>\n",
       "      <td>10239.46</td>\n",
       "      <td>143.98</td>\n",
       "      <td>[AKTIFCOCUK, ERKEK, COCUK, KADIN, AKTIFSPOR]</td>\n",
       "      <td>209.00</td>\n",
       "      <td>455.00</td>\n",
       "      <td>111.00</td>\n",
       "      <td>10383.44</td>\n",
       "      <td>93.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2619</th>\n",
       "      <td>fdbe8304-a7ab-11e9-a2fc-000d3a38a36f</td>\n",
       "      <td>Android App</td>\n",
       "      <td>Offline</td>\n",
       "      <td>2018-10-18</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>2018-10-18</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>1.00</td>\n",
       "      <td>87.00</td>\n",
       "      <td>8432.25</td>\n",
       "      <td>139.98</td>\n",
       "      <td>[ERKEK]</td>\n",
       "      <td>336.00</td>\n",
       "      <td>621.00</td>\n",
       "      <td>88.00</td>\n",
       "      <td>8572.23</td>\n",
       "      <td>97.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6322</th>\n",
       "      <td>329968c6-a0e2-11e9-a2fc-000d3a38a36f</td>\n",
       "      <td>Ios App</td>\n",
       "      <td>Ios App</td>\n",
       "      <td>2019-02-14</td>\n",
       "      <td>2021-04-05</td>\n",
       "      <td>2021-04-05</td>\n",
       "      <td>2020-02-17</td>\n",
       "      <td>2.00</td>\n",
       "      <td>81.00</td>\n",
       "      <td>3997.55</td>\n",
       "      <td>242.81</td>\n",
       "      <td>[ERKEK]</td>\n",
       "      <td>57.00</td>\n",
       "      <td>781.00</td>\n",
       "      <td>83.00</td>\n",
       "      <td>4240.36</td>\n",
       "      <td>51.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7613</th>\n",
       "      <td>73fd19aa-9e37-11e9-9897-000d3a38a36f</td>\n",
       "      <td>Ios App</td>\n",
       "      <td>Offline</td>\n",
       "      <td>2014-01-14</td>\n",
       "      <td>2021-05-18</td>\n",
       "      <td>2021-01-30</td>\n",
       "      <td>2021-05-18</td>\n",
       "      <td>81.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1263.76</td>\n",
       "      <td>32654.34</td>\n",
       "      <td>[ERKEK, COCUK, KADIN, AKTIFSPOR]</td>\n",
       "      <td>14.00</td>\n",
       "      <td>2681.00</td>\n",
       "      <td>82.00</td>\n",
       "      <td>33918.10</td>\n",
       "      <td>413.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9347</th>\n",
       "      <td>44d032ee-a0d4-11e9-a2fc-000d3a38a36f</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>2019-02-11</td>\n",
       "      <td>2021-02-11</td>\n",
       "      <td>2021-02-11</td>\n",
       "      <td>2020-12-24</td>\n",
       "      <td>3.00</td>\n",
       "      <td>74.00</td>\n",
       "      <td>4640.77</td>\n",
       "      <td>543.28</td>\n",
       "      <td>[KADIN, AKTIFSPOR]</td>\n",
       "      <td>110.00</td>\n",
       "      <td>731.00</td>\n",
       "      <td>77.00</td>\n",
       "      <td>5184.05</td>\n",
       "      <td>67.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10954</th>\n",
       "      <td>b27e241a-a901-11e9-a2fc-000d3a38a36f</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>2015-09-12</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2019-10-08</td>\n",
       "      <td>72.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>292.93</td>\n",
       "      <td>5004.95</td>\n",
       "      <td>[AKTIFCOCUK, ERKEK, KADIN, AKTIFSPOR]</td>\n",
       "      <td>61.00</td>\n",
       "      <td>2028.00</td>\n",
       "      <td>75.00</td>\n",
       "      <td>5297.88</td>\n",
       "      <td>70.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8068</th>\n",
       "      <td>d696c654-2633-11ea-8e1c-000d3a38a36f</td>\n",
       "      <td>Ios App</td>\n",
       "      <td>Ios App</td>\n",
       "      <td>2017-05-10</td>\n",
       "      <td>2021-04-13</td>\n",
       "      <td>2021-04-13</td>\n",
       "      <td>2019-08-15</td>\n",
       "      <td>69.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>82.48</td>\n",
       "      <td>16836.09</td>\n",
       "      <td>[AKTIFCOCUK, ERKEK, COCUK, KADIN, AKTIFSPOR]</td>\n",
       "      <td>49.00</td>\n",
       "      <td>1434.00</td>\n",
       "      <td>70.00</td>\n",
       "      <td>16918.57</td>\n",
       "      <td>241.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7330</th>\n",
       "      <td>a4d534a2-5b1b-11eb-8dbd-000d3a38a36f</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>2020-02-16</td>\n",
       "      <td>2021-04-30</td>\n",
       "      <td>2021-04-30</td>\n",
       "      <td>2020-12-18</td>\n",
       "      <td>66.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>843.68</td>\n",
       "      <td>17599.89</td>\n",
       "      <td>[ERKEK, KADIN, AKTIFSPOR]</td>\n",
       "      <td>32.00</td>\n",
       "      <td>439.00</td>\n",
       "      <td>70.00</td>\n",
       "      <td>18443.57</td>\n",
       "      <td>263.48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  master_id order_channel last_order_channel first_order_date last_order_date last_order_date_online last_order_date_offline  order_num_total_ever_online  order_num_total_ever_offline  customer_value_total_ever_offline  customer_value_total_ever_online                   interested_in_categories_12  recency  tenure  order_num_total  customer_value_total  ratio\n",
       "11150  5d1c466a-9cfd-11e9-9897-000d3a38a36f   Android App            Desktop       2013-10-11      2021-04-30             2021-04-30              2020-12-24                       200.00                          2.00                             684.97                          45220.13  [AKTIFCOCUK, ERKEK, COCUK, KADIN, AKTIFSPOR]    32.00 2758.00           202.00              45905.10 227.25\n",
       "7223   cba59206-9dd1-11e9-9897-000d3a38a36f   Android App        Android App       2013-02-21      2021-05-09             2021-05-09              2020-01-25                       130.00                          1.00                              49.99                          12232.25         [AKTIFCOCUK, ERKEK, KADIN, AKTIFSPOR]    23.00 2999.00           131.00              12282.24  93.76\n",
       "8783   a57f4302-b1a8-11e9-89fa-000d3a38a36f   Android App            Offline       2019-08-07      2020-11-04             2020-09-07              2020-11-04                         2.00                        109.00                           10239.46                            143.98  [AKTIFCOCUK, ERKEK, COCUK, KADIN, AKTIFSPOR]   209.00  455.00           111.00              10383.44  93.54\n",
       "2619   fdbe8304-a7ab-11e9-a2fc-000d3a38a36f   Android App            Offline       2018-10-18      2020-06-30             2018-10-18              2020-06-30                         1.00                         87.00                            8432.25                            139.98                                       [ERKEK]   336.00  621.00            88.00               8572.23  97.41\n",
       "6322   329968c6-a0e2-11e9-a2fc-000d3a38a36f       Ios App            Ios App       2019-02-14      2021-04-05             2021-04-05              2020-02-17                         2.00                         81.00                            3997.55                            242.81                                       [ERKEK]    57.00  781.00            83.00               4240.36  51.09\n",
       "7613   73fd19aa-9e37-11e9-9897-000d3a38a36f       Ios App            Offline       2014-01-14      2021-05-18             2021-01-30              2021-05-18                        81.00                          1.00                            1263.76                          32654.34              [ERKEK, COCUK, KADIN, AKTIFSPOR]    14.00 2681.00            82.00              33918.10 413.64\n",
       "9347   44d032ee-a0d4-11e9-a2fc-000d3a38a36f        Mobile             Mobile       2019-02-11      2021-02-11             2021-02-11              2020-12-24                         3.00                         74.00                            4640.77                            543.28                            [KADIN, AKTIFSPOR]   110.00  731.00            77.00               5184.05  67.33\n",
       "10954  b27e241a-a901-11e9-a2fc-000d3a38a36f        Mobile             Mobile       2015-09-12      2021-04-01             2021-04-01              2019-10-08                        72.00                          3.00                             292.93                           5004.95         [AKTIFCOCUK, ERKEK, KADIN, AKTIFSPOR]    61.00 2028.00            75.00               5297.88  70.64\n",
       "8068   d696c654-2633-11ea-8e1c-000d3a38a36f       Ios App            Ios App       2017-05-10      2021-04-13             2021-04-13              2019-08-15                        69.00                          1.00                              82.48                          16836.09  [AKTIFCOCUK, ERKEK, COCUK, KADIN, AKTIFSPOR]    49.00 1434.00            70.00              16918.57 241.69\n",
       "7330   a4d534a2-5b1b-11eb-8dbd-000d3a38a36f       Desktop            Desktop       2020-02-16      2021-04-30             2021-04-30              2020-12-18                        66.00                          4.00                             843.68                          17599.89                     [ERKEK, KADIN, AKTIFSPOR]    32.00  439.00            70.00              18443.57 263.48"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List the top 10 customers who place the most orders\n",
    "\n",
    "df.sort_values(\"order_num_total\", ascending=False)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>master_id</th>\n",
       "      <th>order_channel</th>\n",
       "      <th>last_order_channel</th>\n",
       "      <th>first_order_date</th>\n",
       "      <th>last_order_date</th>\n",
       "      <th>last_order_date_online</th>\n",
       "      <th>last_order_date_offline</th>\n",
       "      <th>order_num_total_ever_online</th>\n",
       "      <th>order_num_total_ever_offline</th>\n",
       "      <th>customer_value_total_ever_offline</th>\n",
       "      <th>customer_value_total_ever_online</th>\n",
       "      <th>interested_in_categories_12</th>\n",
       "      <th>recency</th>\n",
       "      <th>tenure</th>\n",
       "      <th>order_num_total</th>\n",
       "      <th>customer_value_total</th>\n",
       "      <th>ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11150</th>\n",
       "      <td>5d1c466a-9cfd-11e9-9897-000d3a38a36f</td>\n",
       "      <td>Android App</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>2013-10-11</td>\n",
       "      <td>2021-04-30</td>\n",
       "      <td>2021-04-30</td>\n",
       "      <td>2020-12-24</td>\n",
       "      <td>200.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>684.97</td>\n",
       "      <td>45220.13</td>\n",
       "      <td>[AKTIFCOCUK, ERKEK, COCUK, KADIN, AKTIFSPOR]</td>\n",
       "      <td>32.00</td>\n",
       "      <td>2758.00</td>\n",
       "      <td>202.00</td>\n",
       "      <td>45905.10</td>\n",
       "      <td>227.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7223</th>\n",
       "      <td>cba59206-9dd1-11e9-9897-000d3a38a36f</td>\n",
       "      <td>Android App</td>\n",
       "      <td>Android App</td>\n",
       "      <td>2013-02-21</td>\n",
       "      <td>2021-05-09</td>\n",
       "      <td>2021-05-09</td>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>49.99</td>\n",
       "      <td>12232.25</td>\n",
       "      <td>[AKTIFCOCUK, ERKEK, KADIN, AKTIFSPOR]</td>\n",
       "      <td>23.00</td>\n",
       "      <td>2999.00</td>\n",
       "      <td>131.00</td>\n",
       "      <td>12282.24</td>\n",
       "      <td>93.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8783</th>\n",
       "      <td>a57f4302-b1a8-11e9-89fa-000d3a38a36f</td>\n",
       "      <td>Android App</td>\n",
       "      <td>Offline</td>\n",
       "      <td>2019-08-07</td>\n",
       "      <td>2020-11-04</td>\n",
       "      <td>2020-09-07</td>\n",
       "      <td>2020-11-04</td>\n",
       "      <td>2.00</td>\n",
       "      <td>109.00</td>\n",
       "      <td>10239.46</td>\n",
       "      <td>143.98</td>\n",
       "      <td>[AKTIFCOCUK, ERKEK, COCUK, KADIN, AKTIFSPOR]</td>\n",
       "      <td>209.00</td>\n",
       "      <td>455.00</td>\n",
       "      <td>111.00</td>\n",
       "      <td>10383.44</td>\n",
       "      <td>93.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2619</th>\n",
       "      <td>fdbe8304-a7ab-11e9-a2fc-000d3a38a36f</td>\n",
       "      <td>Android App</td>\n",
       "      <td>Offline</td>\n",
       "      <td>2018-10-18</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>2018-10-18</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>1.00</td>\n",
       "      <td>87.00</td>\n",
       "      <td>8432.25</td>\n",
       "      <td>139.98</td>\n",
       "      <td>[ERKEK]</td>\n",
       "      <td>336.00</td>\n",
       "      <td>621.00</td>\n",
       "      <td>88.00</td>\n",
       "      <td>8572.23</td>\n",
       "      <td>97.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6322</th>\n",
       "      <td>329968c6-a0e2-11e9-a2fc-000d3a38a36f</td>\n",
       "      <td>Ios App</td>\n",
       "      <td>Ios App</td>\n",
       "      <td>2019-02-14</td>\n",
       "      <td>2021-04-05</td>\n",
       "      <td>2021-04-05</td>\n",
       "      <td>2020-02-17</td>\n",
       "      <td>2.00</td>\n",
       "      <td>81.00</td>\n",
       "      <td>3997.55</td>\n",
       "      <td>242.81</td>\n",
       "      <td>[ERKEK]</td>\n",
       "      <td>57.00</td>\n",
       "      <td>781.00</td>\n",
       "      <td>83.00</td>\n",
       "      <td>4240.36</td>\n",
       "      <td>51.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7613</th>\n",
       "      <td>73fd19aa-9e37-11e9-9897-000d3a38a36f</td>\n",
       "      <td>Ios App</td>\n",
       "      <td>Offline</td>\n",
       "      <td>2014-01-14</td>\n",
       "      <td>2021-05-18</td>\n",
       "      <td>2021-01-30</td>\n",
       "      <td>2021-05-18</td>\n",
       "      <td>81.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1263.76</td>\n",
       "      <td>32654.34</td>\n",
       "      <td>[ERKEK, COCUK, KADIN, AKTIFSPOR]</td>\n",
       "      <td>14.00</td>\n",
       "      <td>2681.00</td>\n",
       "      <td>82.00</td>\n",
       "      <td>33918.10</td>\n",
       "      <td>413.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9347</th>\n",
       "      <td>44d032ee-a0d4-11e9-a2fc-000d3a38a36f</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>2019-02-11</td>\n",
       "      <td>2021-02-11</td>\n",
       "      <td>2021-02-11</td>\n",
       "      <td>2020-12-24</td>\n",
       "      <td>3.00</td>\n",
       "      <td>74.00</td>\n",
       "      <td>4640.77</td>\n",
       "      <td>543.28</td>\n",
       "      <td>[KADIN, AKTIFSPOR]</td>\n",
       "      <td>110.00</td>\n",
       "      <td>731.00</td>\n",
       "      <td>77.00</td>\n",
       "      <td>5184.05</td>\n",
       "      <td>67.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10954</th>\n",
       "      <td>b27e241a-a901-11e9-a2fc-000d3a38a36f</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>2015-09-12</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2019-10-08</td>\n",
       "      <td>72.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>292.93</td>\n",
       "      <td>5004.95</td>\n",
       "      <td>[AKTIFCOCUK, ERKEK, KADIN, AKTIFSPOR]</td>\n",
       "      <td>61.00</td>\n",
       "      <td>2028.00</td>\n",
       "      <td>75.00</td>\n",
       "      <td>5297.88</td>\n",
       "      <td>70.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8068</th>\n",
       "      <td>d696c654-2633-11ea-8e1c-000d3a38a36f</td>\n",
       "      <td>Ios App</td>\n",
       "      <td>Ios App</td>\n",
       "      <td>2017-05-10</td>\n",
       "      <td>2021-04-13</td>\n",
       "      <td>2021-04-13</td>\n",
       "      <td>2019-08-15</td>\n",
       "      <td>69.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>82.48</td>\n",
       "      <td>16836.09</td>\n",
       "      <td>[AKTIFCOCUK, ERKEK, COCUK, KADIN, AKTIFSPOR]</td>\n",
       "      <td>49.00</td>\n",
       "      <td>1434.00</td>\n",
       "      <td>70.00</td>\n",
       "      <td>16918.57</td>\n",
       "      <td>241.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7330</th>\n",
       "      <td>a4d534a2-5b1b-11eb-8dbd-000d3a38a36f</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>2020-02-16</td>\n",
       "      <td>2021-04-30</td>\n",
       "      <td>2021-04-30</td>\n",
       "      <td>2020-12-18</td>\n",
       "      <td>66.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>843.68</td>\n",
       "      <td>17599.89</td>\n",
       "      <td>[ERKEK, KADIN, AKTIFSPOR]</td>\n",
       "      <td>32.00</td>\n",
       "      <td>439.00</td>\n",
       "      <td>70.00</td>\n",
       "      <td>18443.57</td>\n",
       "      <td>263.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4315</th>\n",
       "      <td>d5ef8058-a5c6-11e9-a2fc-000d3a38a36f</td>\n",
       "      <td>Android App</td>\n",
       "      <td>Android App</td>\n",
       "      <td>2018-08-06</td>\n",
       "      <td>2021-02-23</td>\n",
       "      <td>2021-02-23</td>\n",
       "      <td>2020-07-06</td>\n",
       "      <td>67.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>130.49</td>\n",
       "      <td>36687.80</td>\n",
       "      <td>[AKTIFCOCUK, ERKEK, KADIN, AKTIFSPOR]</td>\n",
       "      <td>98.00</td>\n",
       "      <td>932.00</td>\n",
       "      <td>68.00</td>\n",
       "      <td>36818.29</td>\n",
       "      <td>541.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6337</th>\n",
       "      <td>f7e2335e-a7c4-11e9-a2fc-000d3a38a36f</td>\n",
       "      <td>Ios App</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>2015-11-23</td>\n",
       "      <td>2020-08-08</td>\n",
       "      <td>2020-08-08</td>\n",
       "      <td>2020-06-26</td>\n",
       "      <td>54.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>820.33</td>\n",
       "      <td>7133.14</td>\n",
       "      <td>[COCUK, KADIN]</td>\n",
       "      <td>297.00</td>\n",
       "      <td>1720.00</td>\n",
       "      <td>61.00</td>\n",
       "      <td>7953.47</td>\n",
       "      <td>130.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14402</th>\n",
       "      <td>03f502d4-a559-11e9-a2fc-000d3a38a36f</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>Offline</td>\n",
       "      <td>2019-02-21</td>\n",
       "      <td>2021-02-23</td>\n",
       "      <td>2019-03-20</td>\n",
       "      <td>2021-02-23</td>\n",
       "      <td>1.00</td>\n",
       "      <td>60.00</td>\n",
       "      <td>2658.48</td>\n",
       "      <td>99.99</td>\n",
       "      <td>[AKTIFCOCUK, ERKEK, KADIN, AKTIFSPOR]</td>\n",
       "      <td>98.00</td>\n",
       "      <td>733.00</td>\n",
       "      <td>61.00</td>\n",
       "      <td>2758.47</td>\n",
       "      <td>45.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15524</th>\n",
       "      <td>8f364236-a6ad-11e9-a2fc-000d3a38a36f</td>\n",
       "      <td>Android App</td>\n",
       "      <td>Android App</td>\n",
       "      <td>2014-09-12</td>\n",
       "      <td>2021-03-13</td>\n",
       "      <td>2021-03-13</td>\n",
       "      <td>2019-09-14</td>\n",
       "      <td>59.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>89.98</td>\n",
       "      <td>5649.78</td>\n",
       "      <td>[AKTIFCOCUK, COCUK, KADIN, AKTIFSPOR]</td>\n",
       "      <td>80.00</td>\n",
       "      <td>2374.00</td>\n",
       "      <td>60.00</td>\n",
       "      <td>5739.76</td>\n",
       "      <td>95.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16335</th>\n",
       "      <td>f24f6988-acd9-11e9-a2fc-000d3a38a36f</td>\n",
       "      <td>Android App</td>\n",
       "      <td>Android App</td>\n",
       "      <td>2017-09-07</td>\n",
       "      <td>2020-12-30</td>\n",
       "      <td>2020-12-30</td>\n",
       "      <td>2020-01-14</td>\n",
       "      <td>41.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>1644.79</td>\n",
       "      <td>4630.89</td>\n",
       "      <td>[ERKEK, COCUK, KADIN, AKTIFSPOR]</td>\n",
       "      <td>153.00</td>\n",
       "      <td>1210.00</td>\n",
       "      <td>57.00</td>\n",
       "      <td>6275.68</td>\n",
       "      <td>110.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5107</th>\n",
       "      <td>b1882316-a867-11e9-a2fc-000d3a38a36f</td>\n",
       "      <td>Android App</td>\n",
       "      <td>Android App</td>\n",
       "      <td>2013-06-06</td>\n",
       "      <td>2020-10-10</td>\n",
       "      <td>2020-10-10</td>\n",
       "      <td>2020-07-03</td>\n",
       "      <td>55.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>15.49</td>\n",
       "      <td>4680.39</td>\n",
       "      <td>[ERKEK, COCUK, KADIN, AKTIFSPOR]</td>\n",
       "      <td>234.00</td>\n",
       "      <td>2683.00</td>\n",
       "      <td>56.00</td>\n",
       "      <td>4695.88</td>\n",
       "      <td>83.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15271</th>\n",
       "      <td>00cf8494-9da2-11e9-9897-000d3a38a36f</td>\n",
       "      <td>Ios App</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>2013-02-04</td>\n",
       "      <td>2021-05-27</td>\n",
       "      <td>2021-05-27</td>\n",
       "      <td>2020-08-28</td>\n",
       "      <td>48.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>296.94</td>\n",
       "      <td>5978.39</td>\n",
       "      <td>[AKTIFCOCUK, KADIN]</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3034.00</td>\n",
       "      <td>53.00</td>\n",
       "      <td>6275.33</td>\n",
       "      <td>118.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8043</th>\n",
       "      <td>4a51668a-a155-11e9-a2fc-000d3a38a36f</td>\n",
       "      <td>Android App</td>\n",
       "      <td>Offline</td>\n",
       "      <td>2019-07-10</td>\n",
       "      <td>2021-03-11</td>\n",
       "      <td>2020-12-11</td>\n",
       "      <td>2021-03-11</td>\n",
       "      <td>5.00</td>\n",
       "      <td>46.00</td>\n",
       "      <td>3738.91</td>\n",
       "      <td>654.95</td>\n",
       "      <td>[ERKEK, KADIN, AKTIFSPOR]</td>\n",
       "      <td>82.00</td>\n",
       "      <td>610.00</td>\n",
       "      <td>51.00</td>\n",
       "      <td>4393.86</td>\n",
       "      <td>86.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16094</th>\n",
       "      <td>2c28231e-9f2b-11e9-9897-000d3a38a36f</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>2013-05-21</td>\n",
       "      <td>2020-10-07</td>\n",
       "      <td>2020-10-07</td>\n",
       "      <td>2019-06-30</td>\n",
       "      <td>49.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>129.98</td>\n",
       "      <td>4165.04</td>\n",
       "      <td>[KADIN]</td>\n",
       "      <td>237.00</td>\n",
       "      <td>2696.00</td>\n",
       "      <td>51.00</td>\n",
       "      <td>4295.02</td>\n",
       "      <td>84.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17616</th>\n",
       "      <td>1d033d3a-b090-11e9-9757-000d3a38a36f</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>2013-01-14</td>\n",
       "      <td>2021-04-18</td>\n",
       "      <td>2021-04-18</td>\n",
       "      <td>2019-12-15</td>\n",
       "      <td>49.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>329.97</td>\n",
       "      <td>4517.95</td>\n",
       "      <td>[ERKEK, COCUK, KADIN]</td>\n",
       "      <td>44.00</td>\n",
       "      <td>3016.00</td>\n",
       "      <td>51.00</td>\n",
       "      <td>4847.92</td>\n",
       "      <td>95.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18645</th>\n",
       "      <td>e70a6778-b128-11e9-9757-000d3a38a36f</td>\n",
       "      <td>Android App</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>2013-05-11</td>\n",
       "      <td>2021-05-06</td>\n",
       "      <td>2021-05-06</td>\n",
       "      <td>2020-01-06</td>\n",
       "      <td>49.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>184.97</td>\n",
       "      <td>4775.59</td>\n",
       "      <td>[AKTIFCOCUK, COCUK, KADIN, AKTIFSPOR]</td>\n",
       "      <td>26.00</td>\n",
       "      <td>2917.00</td>\n",
       "      <td>51.00</td>\n",
       "      <td>4960.56</td>\n",
       "      <td>97.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7425</th>\n",
       "      <td>7a551e1c-9ed5-11e9-9897-000d3a38a36f</td>\n",
       "      <td>Ios App</td>\n",
       "      <td>Ios App</td>\n",
       "      <td>2013-02-07</td>\n",
       "      <td>2020-12-11</td>\n",
       "      <td>2020-12-11</td>\n",
       "      <td>2019-04-07</td>\n",
       "      <td>49.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>112.98</td>\n",
       "      <td>6727.56</td>\n",
       "      <td>[KADIN]</td>\n",
       "      <td>172.00</td>\n",
       "      <td>2864.00</td>\n",
       "      <td>50.00</td>\n",
       "      <td>6840.54</td>\n",
       "      <td>136.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15548</th>\n",
       "      <td>e205f2ee-acdf-11e9-a2fc-000d3a38a36f</td>\n",
       "      <td>Ios App</td>\n",
       "      <td>Offline</td>\n",
       "      <td>2017-09-17</td>\n",
       "      <td>2021-04-09</td>\n",
       "      <td>2021-04-04</td>\n",
       "      <td>2021-04-09</td>\n",
       "      <td>13.00</td>\n",
       "      <td>36.00</td>\n",
       "      <td>3589.65</td>\n",
       "      <td>3658.32</td>\n",
       "      <td>[ERKEK, COCUK, KADIN, AKTIFSPOR]</td>\n",
       "      <td>53.00</td>\n",
       "      <td>1300.00</td>\n",
       "      <td>49.00</td>\n",
       "      <td>7247.97</td>\n",
       "      <td>147.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5746</th>\n",
       "      <td>6083756a-66a3-11ea-82da-000d3a38a36f</td>\n",
       "      <td>Android App</td>\n",
       "      <td>Android App</td>\n",
       "      <td>2018-05-27</td>\n",
       "      <td>2021-05-24</td>\n",
       "      <td>2021-05-24</td>\n",
       "      <td>2020-11-19</td>\n",
       "      <td>40.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>1043.32</td>\n",
       "      <td>5113.84</td>\n",
       "      <td>[AKTIFCOCUK, COCUK, KADIN, AKTIFSPOR]</td>\n",
       "      <td>8.00</td>\n",
       "      <td>1093.00</td>\n",
       "      <td>49.00</td>\n",
       "      <td>6157.16</td>\n",
       "      <td>125.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14181</th>\n",
       "      <td>0e8c7b30-ac18-11e9-a2fc-000d3a38a36f</td>\n",
       "      <td>Android App</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>2018-05-10</td>\n",
       "      <td>2021-01-26</td>\n",
       "      <td>2021-01-26</td>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>46.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>381.19</td>\n",
       "      <td>8236.13</td>\n",
       "      <td>[AKTIFCOCUK, COCUK, KADIN, AKTIFSPOR]</td>\n",
       "      <td>126.00</td>\n",
       "      <td>992.00</td>\n",
       "      <td>48.00</td>\n",
       "      <td>8617.32</td>\n",
       "      <td>179.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13134</th>\n",
       "      <td>a77882ea-a0d2-11e9-a2fc-000d3a38a36f</td>\n",
       "      <td>Ios App</td>\n",
       "      <td>Offline</td>\n",
       "      <td>2019-02-17</td>\n",
       "      <td>2021-03-26</td>\n",
       "      <td>2020-11-27</td>\n",
       "      <td>2021-03-26</td>\n",
       "      <td>2.00</td>\n",
       "      <td>46.00</td>\n",
       "      <td>4435.28</td>\n",
       "      <td>167.98</td>\n",
       "      <td>[KADIN]</td>\n",
       "      <td>67.00</td>\n",
       "      <td>768.00</td>\n",
       "      <td>48.00</td>\n",
       "      <td>4603.26</td>\n",
       "      <td>95.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3699</th>\n",
       "      <td>ec236462-9d8c-11e9-9897-000d3a38a36f</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>2013-07-18</td>\n",
       "      <td>2021-05-07</td>\n",
       "      <td>2021-05-07</td>\n",
       "      <td>2019-07-13</td>\n",
       "      <td>47.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>108.96</td>\n",
       "      <td>5981.57</td>\n",
       "      <td>[KADIN]</td>\n",
       "      <td>25.00</td>\n",
       "      <td>2850.00</td>\n",
       "      <td>48.00</td>\n",
       "      <td>6090.53</td>\n",
       "      <td>126.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4867</th>\n",
       "      <td>e35bbd56-240b-11ea-a30f-000d3a38a36f</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>Offline</td>\n",
       "      <td>2019-07-18</td>\n",
       "      <td>2020-08-01</td>\n",
       "      <td>2020-06-06</td>\n",
       "      <td>2020-08-01</td>\n",
       "      <td>1.00</td>\n",
       "      <td>46.00</td>\n",
       "      <td>3197.19</td>\n",
       "      <td>49.98</td>\n",
       "      <td>[ERKEK, AKTIFSPOR]</td>\n",
       "      <td>304.00</td>\n",
       "      <td>380.00</td>\n",
       "      <td>47.00</td>\n",
       "      <td>3247.17</td>\n",
       "      <td>69.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18067</th>\n",
       "      <td>695889b4-d6ed-11e9-93bc-000d3a38a36f</td>\n",
       "      <td>Android App</td>\n",
       "      <td>Offline</td>\n",
       "      <td>2019-06-05</td>\n",
       "      <td>2021-03-09</td>\n",
       "      <td>2021-02-13</td>\n",
       "      <td>2021-03-09</td>\n",
       "      <td>6.00</td>\n",
       "      <td>41.00</td>\n",
       "      <td>7247.60</td>\n",
       "      <td>998.93</td>\n",
       "      <td>[AKTIFCOCUK, ERKEK, KADIN, AKTIFSPOR]</td>\n",
       "      <td>84.00</td>\n",
       "      <td>643.00</td>\n",
       "      <td>47.00</td>\n",
       "      <td>8246.53</td>\n",
       "      <td>175.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17462</th>\n",
       "      <td>9d71d5e2-ada9-11e9-a2fc-000d3a38a36f</td>\n",
       "      <td>Android App</td>\n",
       "      <td>Android App</td>\n",
       "      <td>2017-12-26</td>\n",
       "      <td>2021-04-16</td>\n",
       "      <td>2021-04-16</td>\n",
       "      <td>2019-12-11</td>\n",
       "      <td>43.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>59.99</td>\n",
       "      <td>7130.93</td>\n",
       "      <td>[AKTIFCOCUK, ERKEK, COCUK, AKTIFSPOR]</td>\n",
       "      <td>46.00</td>\n",
       "      <td>1207.00</td>\n",
       "      <td>44.00</td>\n",
       "      <td>7190.92</td>\n",
       "      <td>163.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18534</th>\n",
       "      <td>8da3ff88-a934-11e9-a2fc-000d3a38a36f</td>\n",
       "      <td>Android App</td>\n",
       "      <td>Android App</td>\n",
       "      <td>2018-01-14</td>\n",
       "      <td>2021-04-03</td>\n",
       "      <td>2021-04-03</td>\n",
       "      <td>2021-02-22</td>\n",
       "      <td>40.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>719.81</td>\n",
       "      <td>6476.61</td>\n",
       "      <td>[ERKEK, KADIN, AKTIFSPOR]</td>\n",
       "      <td>59.00</td>\n",
       "      <td>1175.00</td>\n",
       "      <td>43.00</td>\n",
       "      <td>7196.42</td>\n",
       "      <td>167.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10489</th>\n",
       "      <td>7af5cd16-b100-11e9-9757-000d3a38a36f</td>\n",
       "      <td>Android App</td>\n",
       "      <td>Android App</td>\n",
       "      <td>2019-04-10</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2019-04-10</td>\n",
       "      <td>42.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>39.99</td>\n",
       "      <td>6715.85</td>\n",
       "      <td>[ERKEK, COCUK, KADIN, AKTIFSPOR]</td>\n",
       "      <td>61.00</td>\n",
       "      <td>722.00</td>\n",
       "      <td>43.00</td>\n",
       "      <td>6755.84</td>\n",
       "      <td>157.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17669</th>\n",
       "      <td>c8c62724-ab4d-11e9-a2fc-000d3a38a36f</td>\n",
       "      <td>Ios App</td>\n",
       "      <td>Ios App</td>\n",
       "      <td>2017-01-09</td>\n",
       "      <td>2021-02-07</td>\n",
       "      <td>2021-02-07</td>\n",
       "      <td>2019-08-04</td>\n",
       "      <td>42.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>79.99</td>\n",
       "      <td>4658.81</td>\n",
       "      <td>[ERKEK, KADIN, AKTIFSPOR]</td>\n",
       "      <td>114.00</td>\n",
       "      <td>1490.00</td>\n",
       "      <td>43.00</td>\n",
       "      <td>4738.80</td>\n",
       "      <td>110.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14526</th>\n",
       "      <td>859e230a-b02c-11e9-9757-000d3a38a36f</td>\n",
       "      <td>Android App</td>\n",
       "      <td>Offline</td>\n",
       "      <td>2019-02-12</td>\n",
       "      <td>2021-03-09</td>\n",
       "      <td>2019-10-25</td>\n",
       "      <td>2021-03-09</td>\n",
       "      <td>2.00</td>\n",
       "      <td>41.00</td>\n",
       "      <td>3002.46</td>\n",
       "      <td>141.48</td>\n",
       "      <td>[ERKEK, KADIN]</td>\n",
       "      <td>84.00</td>\n",
       "      <td>756.00</td>\n",
       "      <td>43.00</td>\n",
       "      <td>3143.94</td>\n",
       "      <td>73.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6599</th>\n",
       "      <td>55e898a4-9e28-11e9-9897-000d3a38a36f</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>2014-12-16</td>\n",
       "      <td>2021-05-10</td>\n",
       "      <td>2021-05-10</td>\n",
       "      <td>2021-01-27</td>\n",
       "      <td>37.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>383.93</td>\n",
       "      <td>3886.58</td>\n",
       "      <td>[ERKEK, KADIN, AKTIFSPOR]</td>\n",
       "      <td>22.00</td>\n",
       "      <td>2337.00</td>\n",
       "      <td>42.00</td>\n",
       "      <td>4270.51</td>\n",
       "      <td>101.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3725</th>\n",
       "      <td>15e5ea24-a7d1-11e9-a2fc-000d3a38a36f</td>\n",
       "      <td>Android App</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>2015-01-08</td>\n",
       "      <td>2021-05-25</td>\n",
       "      <td>2021-05-25</td>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>39.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>260.47</td>\n",
       "      <td>5141.95</td>\n",
       "      <td>[ERKEK, AKTIFSPOR]</td>\n",
       "      <td>7.00</td>\n",
       "      <td>2329.00</td>\n",
       "      <td>41.00</td>\n",
       "      <td>5402.42</td>\n",
       "      <td>131.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10536</th>\n",
       "      <td>e143b6fa-d6f8-11e9-93bc-000d3a38a36f</td>\n",
       "      <td>Android App</td>\n",
       "      <td>Android App</td>\n",
       "      <td>2019-03-30</td>\n",
       "      <td>2021-03-31</td>\n",
       "      <td>2021-03-31</td>\n",
       "      <td>2019-10-28</td>\n",
       "      <td>36.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>179.95</td>\n",
       "      <td>6868.05</td>\n",
       "      <td>[AKTIFCOCUK, ERKEK, COCUK, KADIN, AKTIFSPOR]</td>\n",
       "      <td>62.00</td>\n",
       "      <td>732.00</td>\n",
       "      <td>40.00</td>\n",
       "      <td>7048.00</td>\n",
       "      <td>176.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19626</th>\n",
       "      <td>0319003a-a75d-11e9-a2fc-000d3a38a36f</td>\n",
       "      <td>Android App</td>\n",
       "      <td>Offline</td>\n",
       "      <td>2015-02-12</td>\n",
       "      <td>2021-03-14</td>\n",
       "      <td>2018-12-29</td>\n",
       "      <td>2021-03-14</td>\n",
       "      <td>39.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>59.99</td>\n",
       "      <td>3505.91</td>\n",
       "      <td>[AKTIFSPOR]</td>\n",
       "      <td>79.00</td>\n",
       "      <td>2222.00</td>\n",
       "      <td>40.00</td>\n",
       "      <td>3565.90</td>\n",
       "      <td>89.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2233</th>\n",
       "      <td>6f64713e-9df8-11e9-9897-000d3a38a36f</td>\n",
       "      <td>Android App</td>\n",
       "      <td>Offline</td>\n",
       "      <td>2013-05-06</td>\n",
       "      <td>2021-04-05</td>\n",
       "      <td>2021-01-16</td>\n",
       "      <td>2021-04-05</td>\n",
       "      <td>38.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>85.98</td>\n",
       "      <td>3967.43</td>\n",
       "      <td>[AKTIFCOCUK, ERKEK, KADIN, AKTIFSPOR]</td>\n",
       "      <td>57.00</td>\n",
       "      <td>2891.00</td>\n",
       "      <td>40.00</td>\n",
       "      <td>4053.41</td>\n",
       "      <td>101.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19517</th>\n",
       "      <td>97ca1964-aaa3-11e9-a2fc-000d3a38a36f</td>\n",
       "      <td>Ios App</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>2016-10-10</td>\n",
       "      <td>2020-11-27</td>\n",
       "      <td>2020-11-27</td>\n",
       "      <td>2019-03-25</td>\n",
       "      <td>39.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>59.99</td>\n",
       "      <td>5404.53</td>\n",
       "      <td>[AKTIFSPOR]</td>\n",
       "      <td>186.00</td>\n",
       "      <td>1509.00</td>\n",
       "      <td>40.00</td>\n",
       "      <td>5464.52</td>\n",
       "      <td>136.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14943</th>\n",
       "      <td>275c65ee-9fa2-11e9-a2fc-000d3a38a36f</td>\n",
       "      <td>Ios App</td>\n",
       "      <td>Android App</td>\n",
       "      <td>2017-08-05</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>2021-02-28</td>\n",
       "      <td>2019-11-17</td>\n",
       "      <td>36.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>439.96</td>\n",
       "      <td>4340.80</td>\n",
       "      <td>[ERKEK, KADIN, AKTIFSPOR]</td>\n",
       "      <td>93.00</td>\n",
       "      <td>1303.00</td>\n",
       "      <td>39.00</td>\n",
       "      <td>4780.76</td>\n",
       "      <td>122.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3044</th>\n",
       "      <td>53206d4c-190f-11ea-9213-000d3a38a36f</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>2015-03-30</td>\n",
       "      <td>2020-10-13</td>\n",
       "      <td>2020-10-13</td>\n",
       "      <td>2019-11-25</td>\n",
       "      <td>37.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>149.99</td>\n",
       "      <td>5426.48</td>\n",
       "      <td>[COCUK, KADIN, AKTIFSPOR]</td>\n",
       "      <td>231.00</td>\n",
       "      <td>2024.00</td>\n",
       "      <td>38.00</td>\n",
       "      <td>5576.47</td>\n",
       "      <td>146.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15546</th>\n",
       "      <td>c94fead0-a95a-11e9-a2fc-000d3a38a36f</td>\n",
       "      <td>Ios App</td>\n",
       "      <td>Android App</td>\n",
       "      <td>2015-12-09</td>\n",
       "      <td>2021-05-17</td>\n",
       "      <td>2021-05-17</td>\n",
       "      <td>2021-03-18</td>\n",
       "      <td>37.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>61.84</td>\n",
       "      <td>3593.03</td>\n",
       "      <td>[AKTIFCOCUK, ERKEK, COCUK, KADIN, AKTIFSPOR]</td>\n",
       "      <td>15.00</td>\n",
       "      <td>1986.00</td>\n",
       "      <td>38.00</td>\n",
       "      <td>3654.87</td>\n",
       "      <td>96.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17998</th>\n",
       "      <td>5e67cc58-a8ae-11e9-a2fc-000d3a38a36f</td>\n",
       "      <td>Android App</td>\n",
       "      <td>Android App</td>\n",
       "      <td>2016-08-21</td>\n",
       "      <td>2020-12-20</td>\n",
       "      <td>2020-12-20</td>\n",
       "      <td>2020-01-17</td>\n",
       "      <td>35.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>149.96</td>\n",
       "      <td>3459.92</td>\n",
       "      <td>[AKTIFCOCUK, COCUK, KADIN, AKTIFSPOR]</td>\n",
       "      <td>163.00</td>\n",
       "      <td>1582.00</td>\n",
       "      <td>37.00</td>\n",
       "      <td>3609.88</td>\n",
       "      <td>97.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6028</th>\n",
       "      <td>9bc062fa-9de0-11e9-9897-000d3a38a36f</td>\n",
       "      <td>Android App</td>\n",
       "      <td>Android App</td>\n",
       "      <td>2017-08-30</td>\n",
       "      <td>2021-05-29</td>\n",
       "      <td>2021-05-29</td>\n",
       "      <td>2020-07-10</td>\n",
       "      <td>36.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>135.47</td>\n",
       "      <td>4393.94</td>\n",
       "      <td>[COCUK, KADIN, AKTIFSPOR]</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1368.00</td>\n",
       "      <td>37.00</td>\n",
       "      <td>4529.41</td>\n",
       "      <td>122.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>fef57ffa-aae6-11e9-a2fc-000d3a38a36f</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>2016-11-08</td>\n",
       "      <td>2021-05-12</td>\n",
       "      <td>2021-05-12</td>\n",
       "      <td>2020-07-09</td>\n",
       "      <td>36.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>180.73</td>\n",
       "      <td>12545.37</td>\n",
       "      <td>[ERKEK, AKTIFSPOR]</td>\n",
       "      <td>20.00</td>\n",
       "      <td>1646.00</td>\n",
       "      <td>37.00</td>\n",
       "      <td>12726.10</td>\n",
       "      <td>343.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12621</th>\n",
       "      <td>5249b58c-2bfb-11ea-91ab-000d3a38a36f</td>\n",
       "      <td>Ios App</td>\n",
       "      <td>Offline</td>\n",
       "      <td>2019-02-21</td>\n",
       "      <td>2020-07-12</td>\n",
       "      <td>2020-05-04</td>\n",
       "      <td>2020-07-12</td>\n",
       "      <td>4.00</td>\n",
       "      <td>33.00</td>\n",
       "      <td>3186.02</td>\n",
       "      <td>855.66</td>\n",
       "      <td>[ERKEK, KADIN]</td>\n",
       "      <td>324.00</td>\n",
       "      <td>507.00</td>\n",
       "      <td>37.00</td>\n",
       "      <td>4041.68</td>\n",
       "      <td>109.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13617</th>\n",
       "      <td>e626cea6-a7ae-11e9-a2fc-000d3a38a36f</td>\n",
       "      <td>Android App</td>\n",
       "      <td>Android App</td>\n",
       "      <td>2014-12-04</td>\n",
       "      <td>2020-09-18</td>\n",
       "      <td>2020-09-18</td>\n",
       "      <td>2019-11-07</td>\n",
       "      <td>34.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>300.70</td>\n",
       "      <td>4817.31</td>\n",
       "      <td>[ERKEK]</td>\n",
       "      <td>256.00</td>\n",
       "      <td>2115.00</td>\n",
       "      <td>36.00</td>\n",
       "      <td>5118.01</td>\n",
       "      <td>142.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1468</th>\n",
       "      <td>c233dfb8-afe0-11e9-9757-000d3a38a36f</td>\n",
       "      <td>Android App</td>\n",
       "      <td>Android App</td>\n",
       "      <td>2013-04-21</td>\n",
       "      <td>2021-03-30</td>\n",
       "      <td>2021-03-30</td>\n",
       "      <td>2020-08-02</td>\n",
       "      <td>32.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>125.46</td>\n",
       "      <td>3152.95</td>\n",
       "      <td>[AKTIFCOCUK, ERKEK, COCUK, AKTIFSPOR]</td>\n",
       "      <td>63.00</td>\n",
       "      <td>2900.00</td>\n",
       "      <td>35.00</td>\n",
       "      <td>3278.41</td>\n",
       "      <td>93.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>900</th>\n",
       "      <td>a665d39e-a0d2-11e9-a2fc-000d3a38a36f</td>\n",
       "      <td>Ios App</td>\n",
       "      <td>Offline</td>\n",
       "      <td>2017-02-20</td>\n",
       "      <td>2021-02-12</td>\n",
       "      <td>2020-06-16</td>\n",
       "      <td>2021-02-12</td>\n",
       "      <td>25.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>1203.48</td>\n",
       "      <td>4819.80</td>\n",
       "      <td>[ERKEK, AKTIFSPOR]</td>\n",
       "      <td>109.00</td>\n",
       "      <td>1453.00</td>\n",
       "      <td>35.00</td>\n",
       "      <td>6023.28</td>\n",
       "      <td>172.09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  master_id order_channel last_order_channel first_order_date last_order_date last_order_date_online last_order_date_offline  order_num_total_ever_online  order_num_total_ever_offline  customer_value_total_ever_offline  customer_value_total_ever_online                   interested_in_categories_12  recency  tenure  order_num_total  customer_value_total  ratio\n",
       "11150  5d1c466a-9cfd-11e9-9897-000d3a38a36f   Android App            Desktop       2013-10-11      2021-04-30             2021-04-30              2020-12-24                       200.00                          2.00                             684.97                          45220.13  [AKTIFCOCUK, ERKEK, COCUK, KADIN, AKTIFSPOR]    32.00 2758.00           202.00              45905.10 227.25\n",
       "7223   cba59206-9dd1-11e9-9897-000d3a38a36f   Android App        Android App       2013-02-21      2021-05-09             2021-05-09              2020-01-25                       130.00                          1.00                              49.99                          12232.25         [AKTIFCOCUK, ERKEK, KADIN, AKTIFSPOR]    23.00 2999.00           131.00              12282.24  93.76\n",
       "8783   a57f4302-b1a8-11e9-89fa-000d3a38a36f   Android App            Offline       2019-08-07      2020-11-04             2020-09-07              2020-11-04                         2.00                        109.00                           10239.46                            143.98  [AKTIFCOCUK, ERKEK, COCUK, KADIN, AKTIFSPOR]   209.00  455.00           111.00              10383.44  93.54\n",
       "2619   fdbe8304-a7ab-11e9-a2fc-000d3a38a36f   Android App            Offline       2018-10-18      2020-06-30             2018-10-18              2020-06-30                         1.00                         87.00                            8432.25                            139.98                                       [ERKEK]   336.00  621.00            88.00               8572.23  97.41\n",
       "6322   329968c6-a0e2-11e9-a2fc-000d3a38a36f       Ios App            Ios App       2019-02-14      2021-04-05             2021-04-05              2020-02-17                         2.00                         81.00                            3997.55                            242.81                                       [ERKEK]    57.00  781.00            83.00               4240.36  51.09\n",
       "7613   73fd19aa-9e37-11e9-9897-000d3a38a36f       Ios App            Offline       2014-01-14      2021-05-18             2021-01-30              2021-05-18                        81.00                          1.00                            1263.76                          32654.34              [ERKEK, COCUK, KADIN, AKTIFSPOR]    14.00 2681.00            82.00              33918.10 413.64\n",
       "9347   44d032ee-a0d4-11e9-a2fc-000d3a38a36f        Mobile             Mobile       2019-02-11      2021-02-11             2021-02-11              2020-12-24                         3.00                         74.00                            4640.77                            543.28                            [KADIN, AKTIFSPOR]   110.00  731.00            77.00               5184.05  67.33\n",
       "10954  b27e241a-a901-11e9-a2fc-000d3a38a36f        Mobile             Mobile       2015-09-12      2021-04-01             2021-04-01              2019-10-08                        72.00                          3.00                             292.93                           5004.95         [AKTIFCOCUK, ERKEK, KADIN, AKTIFSPOR]    61.00 2028.00            75.00               5297.88  70.64\n",
       "8068   d696c654-2633-11ea-8e1c-000d3a38a36f       Ios App            Ios App       2017-05-10      2021-04-13             2021-04-13              2019-08-15                        69.00                          1.00                              82.48                          16836.09  [AKTIFCOCUK, ERKEK, COCUK, KADIN, AKTIFSPOR]    49.00 1434.00            70.00              16918.57 241.69\n",
       "7330   a4d534a2-5b1b-11eb-8dbd-000d3a38a36f       Desktop            Desktop       2020-02-16      2021-04-30             2021-04-30              2020-12-18                        66.00                          4.00                             843.68                          17599.89                     [ERKEK, KADIN, AKTIFSPOR]    32.00  439.00            70.00              18443.57 263.48\n",
       "4315   d5ef8058-a5c6-11e9-a2fc-000d3a38a36f   Android App        Android App       2018-08-06      2021-02-23             2021-02-23              2020-07-06                        67.00                          1.00                             130.49                          36687.80         [AKTIFCOCUK, ERKEK, KADIN, AKTIFSPOR]    98.00  932.00            68.00              36818.29 541.45\n",
       "6337   f7e2335e-a7c4-11e9-a2fc-000d3a38a36f       Ios App             Mobile       2015-11-23      2020-08-08             2020-08-08              2020-06-26                        54.00                          7.00                             820.33                           7133.14                                [COCUK, KADIN]   297.00 1720.00            61.00               7953.47 130.38\n",
       "14402  03f502d4-a559-11e9-a2fc-000d3a38a36f        Mobile            Offline       2019-02-21      2021-02-23             2019-03-20              2021-02-23                         1.00                         60.00                            2658.48                             99.99         [AKTIFCOCUK, ERKEK, KADIN, AKTIFSPOR]    98.00  733.00            61.00               2758.47  45.22\n",
       "15524  8f364236-a6ad-11e9-a2fc-000d3a38a36f   Android App        Android App       2014-09-12      2021-03-13             2021-03-13              2019-09-14                        59.00                          1.00                              89.98                           5649.78         [AKTIFCOCUK, COCUK, KADIN, AKTIFSPOR]    80.00 2374.00            60.00               5739.76  95.66\n",
       "16335  f24f6988-acd9-11e9-a2fc-000d3a38a36f   Android App        Android App       2017-09-07      2020-12-30             2020-12-30              2020-01-14                        41.00                         16.00                            1644.79                           4630.89              [ERKEK, COCUK, KADIN, AKTIFSPOR]   153.00 1210.00            57.00               6275.68 110.10\n",
       "5107   b1882316-a867-11e9-a2fc-000d3a38a36f   Android App        Android App       2013-06-06      2020-10-10             2020-10-10              2020-07-03                        55.00                          1.00                              15.49                           4680.39              [ERKEK, COCUK, KADIN, AKTIFSPOR]   234.00 2683.00            56.00               4695.88  83.86\n",
       "15271  00cf8494-9da2-11e9-9897-000d3a38a36f       Ios App            Desktop       2013-02-04      2021-05-27             2021-05-27              2020-08-28                        48.00                          5.00                             296.94                           5978.39                           [AKTIFCOCUK, KADIN]     5.00 3034.00            53.00               6275.33 118.40\n",
       "8043   4a51668a-a155-11e9-a2fc-000d3a38a36f   Android App            Offline       2019-07-10      2021-03-11             2020-12-11              2021-03-11                         5.00                         46.00                            3738.91                            654.95                     [ERKEK, KADIN, AKTIFSPOR]    82.00  610.00            51.00               4393.86  86.15\n",
       "16094  2c28231e-9f2b-11e9-9897-000d3a38a36f        Mobile            Desktop       2013-05-21      2020-10-07             2020-10-07              2019-06-30                        49.00                          2.00                             129.98                           4165.04                                       [KADIN]   237.00 2696.00            51.00               4295.02  84.22\n",
       "17616  1d033d3a-b090-11e9-9757-000d3a38a36f        Mobile            Desktop       2013-01-14      2021-04-18             2021-04-18              2019-12-15                        49.00                          2.00                             329.97                           4517.95                         [ERKEK, COCUK, KADIN]    44.00 3016.00            51.00               4847.92  95.06\n",
       "18645  e70a6778-b128-11e9-9757-000d3a38a36f   Android App            Desktop       2013-05-11      2021-05-06             2021-05-06              2020-01-06                        49.00                          2.00                             184.97                           4775.59         [AKTIFCOCUK, COCUK, KADIN, AKTIFSPOR]    26.00 2917.00            51.00               4960.56  97.27\n",
       "7425   7a551e1c-9ed5-11e9-9897-000d3a38a36f       Ios App            Ios App       2013-02-07      2020-12-11             2020-12-11              2019-04-07                        49.00                          1.00                             112.98                           6727.56                                       [KADIN]   172.00 2864.00            50.00               6840.54 136.81\n",
       "15548  e205f2ee-acdf-11e9-a2fc-000d3a38a36f       Ios App            Offline       2017-09-17      2021-04-09             2021-04-04              2021-04-09                        13.00                         36.00                            3589.65                           3658.32              [ERKEK, COCUK, KADIN, AKTIFSPOR]    53.00 1300.00            49.00               7247.97 147.92\n",
       "5746   6083756a-66a3-11ea-82da-000d3a38a36f   Android App        Android App       2018-05-27      2021-05-24             2021-05-24              2020-11-19                        40.00                          9.00                            1043.32                           5113.84         [AKTIFCOCUK, COCUK, KADIN, AKTIFSPOR]     8.00 1093.00            49.00               6157.16 125.66\n",
       "14181  0e8c7b30-ac18-11e9-a2fc-000d3a38a36f   Android App            Desktop       2018-05-10      2021-01-26             2021-01-26              2020-01-29                        46.00                          2.00                             381.19                           8236.13         [AKTIFCOCUK, COCUK, KADIN, AKTIFSPOR]   126.00  992.00            48.00               8617.32 179.53\n",
       "13134  a77882ea-a0d2-11e9-a2fc-000d3a38a36f       Ios App            Offline       2019-02-17      2021-03-26             2020-11-27              2021-03-26                         2.00                         46.00                            4435.28                            167.98                                       [KADIN]    67.00  768.00            48.00               4603.26  95.90\n",
       "3699   ec236462-9d8c-11e9-9897-000d3a38a36f        Mobile            Desktop       2013-07-18      2021-05-07             2021-05-07              2019-07-13                        47.00                          1.00                             108.96                           5981.57                                       [KADIN]    25.00 2850.00            48.00               6090.53 126.89\n",
       "4867   e35bbd56-240b-11ea-a30f-000d3a38a36f       Desktop            Offline       2019-07-18      2020-08-01             2020-06-06              2020-08-01                         1.00                         46.00                            3197.19                             49.98                            [ERKEK, AKTIFSPOR]   304.00  380.00            47.00               3247.17  69.09\n",
       "18067  695889b4-d6ed-11e9-93bc-000d3a38a36f   Android App            Offline       2019-06-05      2021-03-09             2021-02-13              2021-03-09                         6.00                         41.00                            7247.60                            998.93         [AKTIFCOCUK, ERKEK, KADIN, AKTIFSPOR]    84.00  643.00            47.00               8246.53 175.46\n",
       "17462  9d71d5e2-ada9-11e9-a2fc-000d3a38a36f   Android App        Android App       2017-12-26      2021-04-16             2021-04-16              2019-12-11                        43.00                          1.00                              59.99                           7130.93         [AKTIFCOCUK, ERKEK, COCUK, AKTIFSPOR]    46.00 1207.00            44.00               7190.92 163.43\n",
       "18534  8da3ff88-a934-11e9-a2fc-000d3a38a36f   Android App        Android App       2018-01-14      2021-04-03             2021-04-03              2021-02-22                        40.00                          3.00                             719.81                           6476.61                     [ERKEK, KADIN, AKTIFSPOR]    59.00 1175.00            43.00               7196.42 167.36\n",
       "10489  7af5cd16-b100-11e9-9757-000d3a38a36f   Android App        Android App       2019-04-10      2021-04-01             2021-04-01              2019-04-10                        42.00                          1.00                              39.99                           6715.85              [ERKEK, COCUK, KADIN, AKTIFSPOR]    61.00  722.00            43.00               6755.84 157.11\n",
       "17669  c8c62724-ab4d-11e9-a2fc-000d3a38a36f       Ios App            Ios App       2017-01-09      2021-02-07             2021-02-07              2019-08-04                        42.00                          1.00                              79.99                           4658.81                     [ERKEK, KADIN, AKTIFSPOR]   114.00 1490.00            43.00               4738.80 110.20\n",
       "14526  859e230a-b02c-11e9-9757-000d3a38a36f   Android App            Offline       2019-02-12      2021-03-09             2019-10-25              2021-03-09                         2.00                         41.00                            3002.46                            141.48                                [ERKEK, KADIN]    84.00  756.00            43.00               3143.94  73.11\n",
       "6599   55e898a4-9e28-11e9-9897-000d3a38a36f       Desktop            Desktop       2014-12-16      2021-05-10             2021-05-10              2021-01-27                        37.00                          5.00                             383.93                           3886.58                     [ERKEK, KADIN, AKTIFSPOR]    22.00 2337.00            42.00               4270.51 101.68\n",
       "3725   15e5ea24-a7d1-11e9-a2fc-000d3a38a36f   Android App            Desktop       2015-01-08      2021-05-25             2021-05-25              2020-02-01                        39.00                          2.00                             260.47                           5141.95                            [ERKEK, AKTIFSPOR]     7.00 2329.00            41.00               5402.42 131.77\n",
       "10536  e143b6fa-d6f8-11e9-93bc-000d3a38a36f   Android App        Android App       2019-03-30      2021-03-31             2021-03-31              2019-10-28                        36.00                          4.00                             179.95                           6868.05  [AKTIFCOCUK, ERKEK, COCUK, KADIN, AKTIFSPOR]    62.00  732.00            40.00               7048.00 176.20\n",
       "19626  0319003a-a75d-11e9-a2fc-000d3a38a36f   Android App            Offline       2015-02-12      2021-03-14             2018-12-29              2021-03-14                        39.00                          1.00                              59.99                           3505.91                                   [AKTIFSPOR]    79.00 2222.00            40.00               3565.90  89.15\n",
       "2233   6f64713e-9df8-11e9-9897-000d3a38a36f   Android App            Offline       2013-05-06      2021-04-05             2021-01-16              2021-04-05                        38.00                          2.00                              85.98                           3967.43         [AKTIFCOCUK, ERKEK, KADIN, AKTIFSPOR]    57.00 2891.00            40.00               4053.41 101.34\n",
       "19517  97ca1964-aaa3-11e9-a2fc-000d3a38a36f       Ios App             Mobile       2016-10-10      2020-11-27             2020-11-27              2019-03-25                        39.00                          1.00                              59.99                           5404.53                                   [AKTIFSPOR]   186.00 1509.00            40.00               5464.52 136.61\n",
       "14943  275c65ee-9fa2-11e9-a2fc-000d3a38a36f       Ios App        Android App       2017-08-05      2021-02-28             2021-02-28              2019-11-17                        36.00                          3.00                             439.96                           4340.80                     [ERKEK, KADIN, AKTIFSPOR]    93.00 1303.00            39.00               4780.76 122.58\n",
       "3044   53206d4c-190f-11ea-9213-000d3a38a36f        Mobile            Desktop       2015-03-30      2020-10-13             2020-10-13              2019-11-25                        37.00                          1.00                             149.99                           5426.48                     [COCUK, KADIN, AKTIFSPOR]   231.00 2024.00            38.00               5576.47 146.75\n",
       "15546  c94fead0-a95a-11e9-a2fc-000d3a38a36f       Ios App        Android App       2015-12-09      2021-05-17             2021-05-17              2021-03-18                        37.00                          1.00                              61.84                           3593.03  [AKTIFCOCUK, ERKEK, COCUK, KADIN, AKTIFSPOR]    15.00 1986.00            38.00               3654.87  96.18\n",
       "17998  5e67cc58-a8ae-11e9-a2fc-000d3a38a36f   Android App        Android App       2016-08-21      2020-12-20             2020-12-20              2020-01-17                        35.00                          2.00                             149.96                           3459.92         [AKTIFCOCUK, COCUK, KADIN, AKTIFSPOR]   163.00 1582.00            37.00               3609.88  97.56\n",
       "6028   9bc062fa-9de0-11e9-9897-000d3a38a36f   Android App        Android App       2017-08-30      2021-05-29             2021-05-29              2020-07-10                        36.00                          1.00                             135.47                           4393.94                     [COCUK, KADIN, AKTIFSPOR]     3.00 1368.00            37.00               4529.41 122.42\n",
       "163    fef57ffa-aae6-11e9-a2fc-000d3a38a36f        Mobile            Desktop       2016-11-08      2021-05-12             2021-05-12              2020-07-09                        36.00                          1.00                             180.73                          12545.37                            [ERKEK, AKTIFSPOR]    20.00 1646.00            37.00              12726.10 343.95\n",
       "12621  5249b58c-2bfb-11ea-91ab-000d3a38a36f       Ios App            Offline       2019-02-21      2020-07-12             2020-05-04              2020-07-12                         4.00                         33.00                            3186.02                            855.66                                [ERKEK, KADIN]   324.00  507.00            37.00               4041.68 109.23\n",
       "13617  e626cea6-a7ae-11e9-a2fc-000d3a38a36f   Android App        Android App       2014-12-04      2020-09-18             2020-09-18              2019-11-07                        34.00                          2.00                             300.70                           4817.31                                       [ERKEK]   256.00 2115.00            36.00               5118.01 142.17\n",
       "1468   c233dfb8-afe0-11e9-9757-000d3a38a36f   Android App        Android App       2013-04-21      2021-03-30             2021-03-30              2020-08-02                        32.00                          3.00                             125.46                           3152.95         [AKTIFCOCUK, ERKEK, COCUK, AKTIFSPOR]    63.00 2900.00            35.00               3278.41  93.67\n",
       "900    a665d39e-a0d2-11e9-a2fc-000d3a38a36f       Ios App            Offline       2017-02-20      2021-02-12             2020-06-16              2021-02-12                        25.00                         10.00                            1203.48                           4819.80                            [ERKEK, AKTIFSPOR]   109.00 1453.00            35.00               6023.28 172.09"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List the top 100 customers who place the most orders\n",
    "\n",
    "df.sort_values(\"order_num_total\", ascending=False)[:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "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>master_id</th>\n",
       "      <th>order_channel</th>\n",
       "      <th>last_order_channel</th>\n",
       "      <th>first_order_date</th>\n",
       "      <th>last_order_date</th>\n",
       "      <th>last_order_date_online</th>\n",
       "      <th>last_order_date_offline</th>\n",
       "      <th>order_num_total_ever_online</th>\n",
       "      <th>order_num_total_ever_offline</th>\n",
       "      <th>customer_value_total_ever_offline</th>\n",
       "      <th>customer_value_total_ever_online</th>\n",
       "      <th>interested_in_categories_12</th>\n",
       "      <th>recency</th>\n",
       "      <th>tenure</th>\n",
       "      <th>order_num_total</th>\n",
       "      <th>customer_value_total</th>\n",
       "      <th>ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9055</th>\n",
       "      <td>47a642fe-975b-11eb-8c2a-000d3a38a36f</td>\n",
       "      <td>Android App</td>\n",
       "      <td>Offline</td>\n",
       "      <td>2021-04-07</td>\n",
       "      <td>2021-04-27</td>\n",
       "      <td>2021-04-07</td>\n",
       "      <td>2021-04-27</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>18119.14</td>\n",
       "      <td>2587.20</td>\n",
       "      <td>[AKTIFSPOR]</td>\n",
       "      <td>35.00</td>\n",
       "      <td>20.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>20706.34</td>\n",
       "      <td>5176.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13880</th>\n",
       "      <td>7137a5c0-7aad-11ea-8f20-000d3a38a36f</td>\n",
       "      <td>Ios App</td>\n",
       "      <td>Offline</td>\n",
       "      <td>2021-03-01</td>\n",
       "      <td>2021-04-13</td>\n",
       "      <td>2021-03-18</td>\n",
       "      <td>2021-04-13</td>\n",
       "      <td>10.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>538.94</td>\n",
       "      <td>30688.47</td>\n",
       "      <td>[ERKEK, KADIN, AKTIFSPOR]</td>\n",
       "      <td>49.00</td>\n",
       "      <td>43.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>31227.41</td>\n",
       "      <td>2838.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17323</th>\n",
       "      <td>f59053e2-a503-11e9-a2fc-000d3a38a36f</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>2019-06-25</td>\n",
       "      <td>2020-06-21</td>\n",
       "      <td>2020-06-21</td>\n",
       "      <td>2019-06-25</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>79.99</td>\n",
       "      <td>7665.28</td>\n",
       "      <td>[AKTIFCOCUK, ERKEK, COCUK, KADIN, AKTIFSPOR]</td>\n",
       "      <td>345.00</td>\n",
       "      <td>362.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>7745.27</td>\n",
       "      <td>1106.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15516</th>\n",
       "      <td>9083981a-f59e-11e9-841e-000d3a38a36f</td>\n",
       "      <td>Ios App</td>\n",
       "      <td>Ios App</td>\n",
       "      <td>2019-10-23</td>\n",
       "      <td>2021-01-10</td>\n",
       "      <td>2021-01-10</td>\n",
       "      <td>2019-10-23</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>89.99</td>\n",
       "      <td>4271.45</td>\n",
       "      <td>[ERKEK, AKTIFSPOR]</td>\n",
       "      <td>142.00</td>\n",
       "      <td>445.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4361.44</td>\n",
       "      <td>1090.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6402</th>\n",
       "      <td>851de3b4-8f0c-11eb-8cb8-000d3a38a36f</td>\n",
       "      <td>Android App</td>\n",
       "      <td>Android App</td>\n",
       "      <td>2021-03-27</td>\n",
       "      <td>2021-05-24</td>\n",
       "      <td>2021-05-24</td>\n",
       "      <td>2021-03-27</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1074.89</td>\n",
       "      <td>650.49</td>\n",
       "      <td>[ERKEK, COCUK, KADIN]</td>\n",
       "      <td>8.00</td>\n",
       "      <td>58.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1725.38</td>\n",
       "      <td>862.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16410</th>\n",
       "      <td>6fecd6c8-261a-11ea-8e1c-000d3a38a36f</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>2019-08-07</td>\n",
       "      <td>2020-09-09</td>\n",
       "      <td>2020-09-09</td>\n",
       "      <td>2019-08-07</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>199.99</td>\n",
       "      <td>1519.17</td>\n",
       "      <td>[KADIN]</td>\n",
       "      <td>265.00</td>\n",
       "      <td>399.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1719.16</td>\n",
       "      <td>859.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7936</th>\n",
       "      <td>ae4ce104-dbd4-11ea-8757-000d3a38a36f</td>\n",
       "      <td>Ios App</td>\n",
       "      <td>Ios App</td>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>2020-09-06</td>\n",
       "      <td>2020-09-06</td>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>80.74</td>\n",
       "      <td>2452.30</td>\n",
       "      <td>[KADIN]</td>\n",
       "      <td>268.00</td>\n",
       "      <td>26.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2533.04</td>\n",
       "      <td>844.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9738</th>\n",
       "      <td>3a27b334-dff4-11ea-acaa-000d3a38a36f</td>\n",
       "      <td>Android App</td>\n",
       "      <td>Offline</td>\n",
       "      <td>2020-08-17</td>\n",
       "      <td>2021-05-24</td>\n",
       "      <td>2020-08-17</td>\n",
       "      <td>2021-05-24</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>301.34</td>\n",
       "      <td>2209.83</td>\n",
       "      <td>[ERKEK, KADIN]</td>\n",
       "      <td>8.00</td>\n",
       "      <td>280.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2511.17</td>\n",
       "      <td>837.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1853</th>\n",
       "      <td>f02473b0-43c3-11eb-806e-000d3a38a36f</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>Offline</td>\n",
       "      <td>2020-12-21</td>\n",
       "      <td>2021-04-21</td>\n",
       "      <td>2020-12-21</td>\n",
       "      <td>2021-04-21</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1406.85</td>\n",
       "      <td>264.90</td>\n",
       "      <td>[COCUK, AKTIFSPOR]</td>\n",
       "      <td>41.00</td>\n",
       "      <td>121.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1671.75</td>\n",
       "      <td>835.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2291</th>\n",
       "      <td>26ac1432-1dd3-11ea-8bf2-000d3a38a36f</td>\n",
       "      <td>Ios App</td>\n",
       "      <td>Ios App</td>\n",
       "      <td>2019-07-18</td>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>2019-07-18</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>19.99</td>\n",
       "      <td>2321.68</td>\n",
       "      <td>[ERKEK, COCUK, KADIN]</td>\n",
       "      <td>294.00</td>\n",
       "      <td>390.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2341.67</td>\n",
       "      <td>780.56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  master_id order_channel last_order_channel first_order_date last_order_date last_order_date_online last_order_date_offline  order_num_total_ever_online  order_num_total_ever_offline  customer_value_total_ever_offline  customer_value_total_ever_online                   interested_in_categories_12  recency  tenure  order_num_total  customer_value_total   ratio\n",
       "9055   47a642fe-975b-11eb-8c2a-000d3a38a36f   Android App            Offline       2021-04-07      2021-04-27             2021-04-07              2021-04-27                         1.00                          3.00                           18119.14                           2587.20                                   [AKTIFSPOR]    35.00   20.00             4.00              20706.34 5176.59\n",
       "13880  7137a5c0-7aad-11ea-8f20-000d3a38a36f       Ios App            Offline       2021-03-01      2021-04-13             2021-03-18              2021-04-13                        10.00                          1.00                             538.94                          30688.47                     [ERKEK, KADIN, AKTIFSPOR]    49.00   43.00            11.00              31227.41 2838.86\n",
       "17323  f59053e2-a503-11e9-a2fc-000d3a38a36f       Desktop            Desktop       2019-06-25      2020-06-21             2020-06-21              2019-06-25                         6.00                          1.00                              79.99                           7665.28  [AKTIFCOCUK, ERKEK, COCUK, KADIN, AKTIFSPOR]   345.00  362.00             7.00               7745.27 1106.47\n",
       "15516  9083981a-f59e-11e9-841e-000d3a38a36f       Ios App            Ios App       2019-10-23      2021-01-10             2021-01-10              2019-10-23                         3.00                          1.00                              89.99                           4271.45                            [ERKEK, AKTIFSPOR]   142.00  445.00             4.00               4361.44 1090.36\n",
       "6402   851de3b4-8f0c-11eb-8cb8-000d3a38a36f   Android App        Android App       2021-03-27      2021-05-24             2021-05-24              2021-03-27                         1.00                          1.00                            1074.89                            650.49                         [ERKEK, COCUK, KADIN]     8.00   58.00             2.00               1725.38  862.69\n",
       "16410  6fecd6c8-261a-11ea-8e1c-000d3a38a36f       Desktop            Desktop       2019-08-07      2020-09-09             2020-09-09              2019-08-07                         1.00                          1.00                             199.99                           1519.17                                       [KADIN]   265.00  399.00             2.00               1719.16  859.58\n",
       "7936   ae4ce104-dbd4-11ea-8757-000d3a38a36f       Ios App            Ios App       2020-08-11      2020-09-06             2020-09-06              2020-08-11                         2.00                          1.00                              80.74                           2452.30                                       [KADIN]   268.00   26.00             3.00               2533.04  844.35\n",
       "9738   3a27b334-dff4-11ea-acaa-000d3a38a36f   Android App            Offline       2020-08-17      2021-05-24             2020-08-17              2021-05-24                         1.00                          2.00                             301.34                           2209.83                                [ERKEK, KADIN]     8.00  280.00             3.00               2511.17  837.06\n",
       "1853   f02473b0-43c3-11eb-806e-000d3a38a36f        Mobile            Offline       2020-12-21      2021-04-21             2020-12-21              2021-04-21                         1.00                          1.00                            1406.85                            264.90                            [COCUK, AKTIFSPOR]    41.00  121.00             2.00               1671.75  835.88\n",
       "2291   26ac1432-1dd3-11ea-8bf2-000d3a38a36f       Ios App            Ios App       2019-07-18      2020-08-11             2020-08-11              2019-07-18                         2.00                          1.00                              19.99                           2321.68                         [ERKEK, COCUK, KADIN]   294.00  390.00             3.00               2341.67  780.56"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List the top 10 customers with the highest ratio \n",
    "\n",
    "df.sort_values(\"ratio\", ascending=False)[:10]\n",
    "\n",
    "# A high ratio represents the money spent per shopping.    \n",
    "# Rationun yuksek olmasi alisveris basina harcanan parayi temsil ediyor\n",
    "# Yuksek fiyatli alisverisler yapildigini gosteriyor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "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>order_num_total</th>\n",
       "      <th>customer_value_total</th>\n",
       "      <th>recency</th>\n",
       "      <th>ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.00</td>\n",
       "      <td>939.37</td>\n",
       "      <td>95.00</td>\n",
       "      <td>187.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21.00</td>\n",
       "      <td>2013.55</td>\n",
       "      <td>105.00</td>\n",
       "      <td>95.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.00</td>\n",
       "      <td>585.32</td>\n",
       "      <td>186.00</td>\n",
       "      <td>117.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.00</td>\n",
       "      <td>121.97</td>\n",
       "      <td>135.00</td>\n",
       "      <td>60.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.00</td>\n",
       "      <td>209.98</td>\n",
       "      <td>86.00</td>\n",
       "      <td>104.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_num_total  customer_value_total  recency  ratio\n",
       "0             5.00                939.37    95.00 187.87\n",
       "1            21.00               2013.55   105.00  95.88\n",
       "2             5.00                585.32   186.00 117.06\n",
       "3             2.00                121.97   135.00  60.98\n",
       "4             2.00                209.98    86.00 104.99"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# selected some variables and assigned them to model_df\n",
    "\n",
    "model_df = df[[\"order_num_total\", \n",
    "               \"customer_value_total\",\n",
    "               \"recency\",\n",
    "               \"ratio\"]]\n",
    "\n",
    "model_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 19945 entries, 0 to 19944\n",
      "Data columns (total 4 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   order_num_total       19945 non-null  float64\n",
      " 1   customer_value_total  19945 non-null  float64\n",
      " 2   recency               19945 non-null  float64\n",
      " 3   ratio                 19945 non-null  float64\n",
      "dtypes: float64(4)\n",
      "memory usage: 623.4 KB\n"
     ]
    }
   ],
   "source": [
    "model_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_num_total         0\n",
       "customer_value_total    0\n",
       "recency                 0\n",
       "ratio                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>order_num_total</th>\n",
       "      <td>19945.00</td>\n",
       "      <td>5.02</td>\n",
       "      <td>4.74</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>202.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_value_total</th>\n",
       "      <td>19945.00</td>\n",
       "      <td>751.24</td>\n",
       "      <td>895.40</td>\n",
       "      <td>44.98</td>\n",
       "      <td>339.98</td>\n",
       "      <td>545.27</td>\n",
       "      <td>897.78</td>\n",
       "      <td>45905.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recency</th>\n",
       "      <td>19945.00</td>\n",
       "      <td>134.46</td>\n",
       "      <td>103.28</td>\n",
       "      <td>2.00</td>\n",
       "      <td>43.00</td>\n",
       "      <td>111.00</td>\n",
       "      <td>202.00</td>\n",
       "      <td>367.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ratio</th>\n",
       "      <td>19945.00</td>\n",
       "      <td>152.40</td>\n",
       "      <td>83.50</td>\n",
       "      <td>22.49</td>\n",
       "      <td>103.49</td>\n",
       "      <td>136.73</td>\n",
       "      <td>182.44</td>\n",
       "      <td>5176.59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        count   mean    std   min    25%    50%    75%      max\n",
       "order_num_total      19945.00   5.02   4.74  2.00   3.00   4.00   6.00   202.00\n",
       "customer_value_total 19945.00 751.24 895.40 44.98 339.98 545.27 897.78 45905.10\n",
       "recency              19945.00 134.46 103.28  2.00  43.00 111.00 202.00   367.00\n",
       "ratio                19945.00 152.40  83.50 22.49 103.49 136.73 182.44  5176.59"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "# check the skewness\n",
    "\n",
    "def check_skew(df_skew, column):\n",
    "    skew = stats.skew(df_skew[column])\n",
    "    skewtest = stats.skewtest(df_skew[column])\n",
    "    plt.title('Distribution of ' + column)\n",
    "    sns.distplot(df_skew[column],color = \"g\")\n",
    "    print(\"{}'s: Skew: {}, : {}\".format(column, skew, skewtest))\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "order_num_total's: Skew: 9.142209056779548, : SkewtestResult(statistic=146.5525046688009, pvalue=0.0)\n",
      "customer_value_total's: Skew: 17.40528126665466, : SkewtestResult(statistic=176.82377384378034, pvalue=0.0)\n",
      "recency's: Skew: 0.6172488156761073, : SkewtestResult(statistic=32.86977170491703, pvalue=5.944419293752164e-237)\n",
      "ratio's: Skew: 14.026592674273598, : SkewtestResult(statistic=166.66841266578655, pvalue=0.0)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x1080 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# draw all variables to check&see skewness\n",
    "plt.figure(figsize=(15, 15))\n",
    "\n",
    "plt.subplot(6, 1, 1)\n",
    "check_skew(model_df,'order_num_total')\n",
    "\n",
    "plt.subplot(6, 1, 2)\n",
    "check_skew(model_df,'customer_value_total')\n",
    "\n",
    "plt.subplot(6, 1, 3)\n",
    "check_skew(model_df,'recency')\n",
    "\n",
    "plt.subplot(6, 1, 4)\n",
    "check_skew(model_df,'ratio')\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.savefig('before_transform.png', format='png', dpi=1000)   #if you want to save the output image\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['order_num_total', 'customer_value_total', 'recency', 'ratio'], dtype='object')"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\avcil\\AppData\\Local\\Temp\\ipykernel_16724\\3428881644.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  model_df['order_num_total'] = np.log1p(model_df['order_num_total'])\n",
      "C:\\Users\\avcil\\AppData\\Local\\Temp\\ipykernel_16724\\3428881644.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  model_df['customer_value_total'] = np.log1p(model_df['customer_value_total'])\n",
      "C:\\Users\\avcil\\AppData\\Local\\Temp\\ipykernel_16724\\3428881644.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  model_df['recency'] = np.log1p(model_df['recency'])\n",
      "C:\\Users\\avcil\\AppData\\Local\\Temp\\ipykernel_16724\\3428881644.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  model_df['ratio'] = np.log1p(model_df['ratio'])\n"
     ]
    }
   ],
   "source": [
    "# applying Log transformation to ensure normal distribution\n",
    "\n",
    "model_df['order_num_total'] = np.log1p(model_df['order_num_total'])\n",
    "model_df['customer_value_total'] = np.log1p(model_df['customer_value_total'])\n",
    "model_df['recency'] = np.log1p(model_df['recency'])\n",
    "model_df['ratio'] = np.log1p(model_df['ratio'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "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>order_num_total</th>\n",
       "      <th>customer_value_total</th>\n",
       "      <th>recency</th>\n",
       "      <th>ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.79</td>\n",
       "      <td>6.85</td>\n",
       "      <td>4.56</td>\n",
       "      <td>5.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.09</td>\n",
       "      <td>7.61</td>\n",
       "      <td>4.66</td>\n",
       "      <td>4.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.79</td>\n",
       "      <td>6.37</td>\n",
       "      <td>5.23</td>\n",
       "      <td>4.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.10</td>\n",
       "      <td>4.81</td>\n",
       "      <td>4.91</td>\n",
       "      <td>4.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.10</td>\n",
       "      <td>5.35</td>\n",
       "      <td>4.47</td>\n",
       "      <td>4.66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_num_total  customer_value_total  recency  ratio\n",
       "0             1.79                  6.85     4.56   5.24\n",
       "1             3.09                  7.61     4.66   4.57\n",
       "2             1.79                  6.37     5.23   4.77\n",
       "3             1.10                  4.81     4.91   4.13\n",
       "4             1.10                  5.35     4.47   4.66"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "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>order_num_total</th>\n",
       "      <th>customer_value_total</th>\n",
       "      <th>recency</th>\n",
       "      <th>ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.16</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.47</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.16</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_num_total  customer_value_total  recency  ratio\n",
       "0             0.16                  0.44     0.72   0.39\n",
       "1             0.47                  0.55     0.74   0.26\n",
       "2             0.16                  0.37     0.86   0.30\n",
       "3             0.00                  0.14     0.79   0.18\n",
       "4             0.00                  0.22     0.70   0.28"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Scaling     \n",
    "\n",
    "sc = MinMaxScaler((0, 1))\n",
    "model_scaling = sc.fit_transform(model_df)\n",
    "model_df=pd.DataFrame(model_scaling,columns=model_df.columns)\n",
    "model_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# KMEANS MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "# I am copying my dataset to use it in the Kmeans model, so that the original version is not corrupted.\n",
    "\n",
    "k_means_model_df = model_df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Distortion Score Elbow for KMeans Clustering'}, xlabel='k', ylabel='distortion score'>"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine the optimal number of segments\n",
    "# Let's see the distribution and decide the optimal number of segments with KElbowVisualizer\n",
    "  \n",
    "kmeans = KMeans()\n",
    "elbows = KElbowVisualizer(kmeans, k=(2, 20))\n",
    "\n",
    "elbows.fit(k_means_model_df)\n",
    "elbows.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3, 1, ..., 5, 3, 6])"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create model \n",
    "\n",
    "k_means = KMeans(n_clusters = 7, random_state= 42)\n",
    "k_means.fit(k_means_model_df)\n",
    "\n",
    "segments = k_means.labels_\n",
    "segments\n",
    "\n",
    "# created a total of 7 segments/groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "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>order_num_total</th>\n",
       "      <th>customer_value_total</th>\n",
       "      <th>recency</th>\n",
       "      <th>ratio</th>\n",
       "      <th>segment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.16</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.39</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.47</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.26</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.16</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.18</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.28</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.07</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.20</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.12</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.26</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.23</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.16</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.41</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.24</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_num_total  customer_value_total  recency  ratio  segment\n",
       "0             0.16                  0.44     0.72   0.39        3\n",
       "1             0.47                  0.55     0.74   0.26        4\n",
       "2             0.16                  0.37     0.86   0.30        2\n",
       "3             0.00                  0.14     0.79   0.18        7\n",
       "4             0.00                  0.22     0.70   0.28        3\n",
       "5             0.07                  0.21     0.69   0.20        3\n",
       "6             0.12                  0.30     0.90   0.26        7\n",
       "7             0.00                  0.18     0.95   0.23        7\n",
       "8             0.16                  0.45     0.70   0.41        3\n",
       "9             0.00                  0.19     0.91   0.24        7"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# appending labels to data points\n",
    "\n",
    "k_means_model_df[\"segment\"] = segments + 1\n",
    "k_means_model_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "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>KM_size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>segment</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3261</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         KM_size\n",
       "segment         \n",
       "1           2424\n",
       "2           4208\n",
       "3           3991\n",
       "4           2532\n",
       "5           1892\n",
       "6           1637\n",
       "7           3261"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Size of the segments\n",
    "\n",
    "size = k_means_model_df.groupby('segment').size().to_frame()\n",
    "size.columns = [\"KM_size\"]\n",
    "size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# VISUALIZATION "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualization of total number of orders and total value\n",
    "\n",
    "fig1, ax = plt.subplots(figsize=(15, 10))\n",
    "\n",
    "scat_1 = sns.scatterplot(x='order_num_total' , y= 'customer_value_total', \n",
    "                         data= k_means_model_df, hue='segment', \n",
    "                         ax=ax, palette='Set1', legend='full')\n",
    "\n",
    "ax.scatter(k_means.cluster_centers_[:, 1], k_means.cluster_centers_[:, 2], marker='s', s=40, c=\"blue\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualization of ratio and total value\n",
    "\n",
    "fig1, ax = plt.subplots(figsize=(15, 10))\n",
    "\n",
    "scat_1 = sns.scatterplot(x='ratio' , y= 'customer_value_total', \n",
    "                         data= k_means_model_df, hue='segment', \n",
    "                         ax=ax, palette='Set1', legend='full')\n",
    "\n",
    "ax.scatter(k_means.cluster_centers_[:, 1], k_means.cluster_centers_[:, 2], marker='s', s=40, c=\"blue\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cluster membership chart\n",
    "\n",
    "sns.countplot(x='segment', data=k_means_model_df)\n",
    "plt.title('Graph of Segment')\n",
    "plt.xlabel('Segments')\n",
    "plt.ylabel('Number of Data')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['order_num_total', 'customer_value_total', 'recency', 'ratio', 'segment'], dtype='object')"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k_means_model_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\avcil\\AppData\\Local\\Temp\\ipykernel_16724\\3918667130.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  final[\"KM_segment\"] = k_means_model_df[\"segment\"]\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>master_id</th>\n",
       "      <th>order_num_total_ever_online</th>\n",
       "      <th>order_num_total_ever_offline</th>\n",
       "      <th>customer_value_total_ever_offline</th>\n",
       "      <th>customer_value_total_ever_online</th>\n",
       "      <th>recency</th>\n",
       "      <th>tenure</th>\n",
       "      <th>order_num_total</th>\n",
       "      <th>customer_value_total</th>\n",
       "      <th>ratio</th>\n",
       "      <th>KM_segment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cc294636-19f0-11eb-8d74-000d3a38a36f</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>139.99</td>\n",
       "      <td>799.38</td>\n",
       "      <td>95.00</td>\n",
       "      <td>119.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>939.37</td>\n",
       "      <td>187.87</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>f431bd5a-ab7b-11e9-a2fc-000d3a38a36f</td>\n",
       "      <td>19.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>159.97</td>\n",
       "      <td>1853.58</td>\n",
       "      <td>105.00</td>\n",
       "      <td>1469.00</td>\n",
       "      <td>21.00</td>\n",
       "      <td>2013.55</td>\n",
       "      <td>95.88</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69b69676-1a40-11ea-941b-000d3a38a36f</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>189.97</td>\n",
       "      <td>395.35</td>\n",
       "      <td>186.00</td>\n",
       "      <td>366.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>585.32</td>\n",
       "      <td>117.06</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1854e56c-491f-11eb-806e-000d3a38a36f</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>39.99</td>\n",
       "      <td>81.98</td>\n",
       "      <td>135.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>121.97</td>\n",
       "      <td>60.98</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>d6ea1074-f1f5-11e9-9346-000d3a38a36f</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>49.99</td>\n",
       "      <td>159.99</td>\n",
       "      <td>86.00</td>\n",
       "      <td>582.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>209.98</td>\n",
       "      <td>104.99</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              master_id  order_num_total_ever_online  order_num_total_ever_offline  customer_value_total_ever_offline  customer_value_total_ever_online  recency  tenure  order_num_total  customer_value_total  ratio  KM_segment\n",
       "0  cc294636-19f0-11eb-8d74-000d3a38a36f                         4.00                          1.00                             139.99                            799.38    95.00  119.00             5.00                939.37 187.87           3\n",
       "1  f431bd5a-ab7b-11e9-a2fc-000d3a38a36f                        19.00                          2.00                             159.97                           1853.58   105.00 1469.00            21.00               2013.55  95.88           4\n",
       "2  69b69676-1a40-11ea-941b-000d3a38a36f                         3.00                          2.00                             189.97                            395.35   186.00  366.00             5.00                585.32 117.06           2\n",
       "3  1854e56c-491f-11eb-806e-000d3a38a36f                         1.00                          1.00                              39.99                             81.98   135.00   11.00             2.00                121.97  60.98           7\n",
       "4  d6ea1074-f1f5-11e9-9346-000d3a38a36f                         1.00                          1.00                              49.99                            159.99    86.00  582.00             2.00                209.98 104.99           3"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Segments combined with original data\n",
    "\n",
    "final =  df[[\"master_id\",\"order_num_total_ever_online\",\"order_num_total_ever_offline\",\n",
    "               \"customer_value_total_ever_offline\",\n",
    "               \"customer_value_total_ever_online\",\n",
    "               \"recency\",\n",
    "               \"tenure\",\n",
    "               \"order_num_total\",\n",
    "               \"customer_value_total\",\n",
    "               \"ratio\"]]\n",
    "\n",
    "final[\"KM_segment\"] = k_means_model_df[\"segment\"]  \n",
    "\n",
    "final.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    .dataframe thead tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">order_num_total_ever_online</th>\n",
       "      <th colspan=\"3\" halign=\"left\">order_num_total_ever_offline</th>\n",
       "      <th colspan=\"3\" halign=\"left\">customer_value_total_ever_offline</th>\n",
       "      <th colspan=\"3\" halign=\"left\">customer_value_total_ever_online</th>\n",
       "      <th colspan=\"3\" halign=\"left\">recency</th>\n",
       "      <th colspan=\"3\" halign=\"left\">tenure</th>\n",
       "      <th colspan=\"3\" halign=\"left\">order_num_total</th>\n",
       "      <th colspan=\"3\" halign=\"left\">customer_value_total</th>\n",
       "      <th colspan=\"4\" halign=\"left\">ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KM_segment</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",
       "      <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",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.69</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.47</td>\n",
       "      <td>6.00</td>\n",
       "      <td>12.99</td>\n",
       "      <td>178.56</td>\n",
       "      <td>789.94</td>\n",
       "      <td>19.99</td>\n",
       "      <td>253.85</td>\n",
       "      <td>1135.26</td>\n",
       "      <td>8.00</td>\n",
       "      <td>30.19</td>\n",
       "      <td>61.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>566.83</td>\n",
       "      <td>2928.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.16</td>\n",
       "      <td>7.00</td>\n",
       "      <td>44.98</td>\n",
       "      <td>432.41</td>\n",
       "      <td>1341.33</td>\n",
       "      <td>22.49</td>\n",
       "      <td>142.93</td>\n",
       "      <td>596.10</td>\n",
       "      <td>2424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.97</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.69</td>\n",
       "      <td>6.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>260.60</td>\n",
       "      <td>1719.96</td>\n",
       "      <td>12.99</td>\n",
       "      <td>366.11</td>\n",
       "      <td>3535.68</td>\n",
       "      <td>114.00</td>\n",
       "      <td>246.87</td>\n",
       "      <td>367.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>544.76</td>\n",
       "      <td>2803.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.66</td>\n",
       "      <td>8.00</td>\n",
       "      <td>295.47</td>\n",
       "      <td>626.71</td>\n",
       "      <td>3615.67</td>\n",
       "      <td>63.27</td>\n",
       "      <td>182.94</td>\n",
       "      <td>859.58</td>\n",
       "      <td>4208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.86</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.75</td>\n",
       "      <td>7.00</td>\n",
       "      <td>11.98</td>\n",
       "      <td>240.68</td>\n",
       "      <td>1416.85</td>\n",
       "      <td>14.00</td>\n",
       "      <td>295.38</td>\n",
       "      <td>1499.97</td>\n",
       "      <td>41.00</td>\n",
       "      <td>90.21</td>\n",
       "      <td>153.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>619.18</td>\n",
       "      <td>2964.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.60</td>\n",
       "      <td>8.00</td>\n",
       "      <td>139.98</td>\n",
       "      <td>536.06</td>\n",
       "      <td>1965.90</td>\n",
       "      <td>42.74</td>\n",
       "      <td>156.98</td>\n",
       "      <td>835.88</td>\n",
       "      <td>3991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.00</td>\n",
       "      <td>7.17</td>\n",
       "      <td>67.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>109.00</td>\n",
       "      <td>10.49</td>\n",
       "      <td>387.94</td>\n",
       "      <td>10239.46</td>\n",
       "      <td>19.99</td>\n",
       "      <td>1083.03</td>\n",
       "      <td>36687.80</td>\n",
       "      <td>68.00</td>\n",
       "      <td>168.81</td>\n",
       "      <td>367.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>993.51</td>\n",
       "      <td>2959.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>10.17</td>\n",
       "      <td>111.00</td>\n",
       "      <td>377.80</td>\n",
       "      <td>1470.97</td>\n",
       "      <td>36818.29</td>\n",
       "      <td>32.87</td>\n",
       "      <td>151.51</td>\n",
       "      <td>1106.47</td>\n",
       "      <td>2532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.00</td>\n",
       "      <td>7.69</td>\n",
       "      <td>200.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.82</td>\n",
       "      <td>81.00</td>\n",
       "      <td>12.99</td>\n",
       "      <td>409.28</td>\n",
       "      <td>18119.14</td>\n",
       "      <td>39.99</td>\n",
       "      <td>1339.22</td>\n",
       "      <td>45220.13</td>\n",
       "      <td>5.00</td>\n",
       "      <td>36.52</td>\n",
       "      <td>79.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>913.51</td>\n",
       "      <td>3034.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>10.51</td>\n",
       "      <td>202.00</td>\n",
       "      <td>351.88</td>\n",
       "      <td>1748.50</td>\n",
       "      <td>45905.10</td>\n",
       "      <td>29.32</td>\n",
       "      <td>177.16</td>\n",
       "      <td>5176.59</td>\n",
       "      <td>1892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.00</td>\n",
       "      <td>3.16</td>\n",
       "      <td>36.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>12.99</td>\n",
       "      <td>281.20</td>\n",
       "      <td>2245.33</td>\n",
       "      <td>29.74</td>\n",
       "      <td>543.80</td>\n",
       "      <td>9723.33</td>\n",
       "      <td>2.00</td>\n",
       "      <td>8.28</td>\n",
       "      <td>18.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>721.84</td>\n",
       "      <td>3031.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>5.15</td>\n",
       "      <td>37.00</td>\n",
       "      <td>65.91</td>\n",
       "      <td>825.01</td>\n",
       "      <td>9953.31</td>\n",
       "      <td>32.95</td>\n",
       "      <td>168.50</td>\n",
       "      <td>862.69</td>\n",
       "      <td>1637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.34</td>\n",
       "      <td>7.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.33</td>\n",
       "      <td>6.00</td>\n",
       "      <td>10.49</td>\n",
       "      <td>109.65</td>\n",
       "      <td>392.94</td>\n",
       "      <td>14.00</td>\n",
       "      <td>128.19</td>\n",
       "      <td>410.93</td>\n",
       "      <td>57.00</td>\n",
       "      <td>214.54</td>\n",
       "      <td>367.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>532.70</td>\n",
       "      <td>2828.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.67</td>\n",
       "      <td>8.00</td>\n",
       "      <td>44.98</td>\n",
       "      <td>237.83</td>\n",
       "      <td>464.93</td>\n",
       "      <td>22.49</td>\n",
       "      <td>92.67</td>\n",
       "      <td>169.37</td>\n",
       "      <td>3261</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           order_num_total_ever_online             order_num_total_ever_offline             customer_value_total_ever_offline                 customer_value_total_ever_online                  recency               tenure                order_num_total              customer_value_total                  ratio                     \n",
       "                                   min mean    max                          min mean    max                               min   mean      max                              min    mean      max     min   mean    max    min   mean     max             min  mean    max                  min    mean      max   min   mean     max count\n",
       "KM_segment                                                                                                                                                                                                                                                                                                                               \n",
       "1                                 1.00 1.69   6.00                         1.00 1.47   6.00                             12.99 178.56   789.94                            19.99  253.85  1135.26    8.00  30.19  61.00   0.00 566.83 2928.00            2.00  3.16   7.00                44.98  432.41  1341.33 22.49 142.93  596.10  2424\n",
       "2                                 1.00 1.97   6.00                         1.00 1.69   6.00                             10.00 260.60  1719.96                            12.99  366.11  3535.68  114.00 246.87 367.00   0.00 544.76 2803.00            2.00  3.66   8.00               295.47  626.71  3615.67 63.27 182.94  859.58  4208\n",
       "3                                 1.00 1.86   6.00                         1.00 1.75   7.00                             11.98 240.68  1416.85                            14.00  295.38  1499.97   41.00  90.21 153.00   0.00 619.18 2964.00            2.00  3.60   8.00               139.98  536.06  1965.90 42.74 156.98  835.88  3991\n",
       "4                                 1.00 7.17  67.00                         1.00 3.00 109.00                             10.49 387.94 10239.46                            19.99 1083.03 36687.80   68.00 168.81 367.00   7.00 993.51 2959.00            4.00 10.17 111.00               377.80 1470.97 36818.29 32.87 151.51 1106.47  2532\n",
       "5                                 1.00 7.69 200.00                         1.00 2.82  81.00                             12.99 409.28 18119.14                            39.99 1339.22 45220.13    5.00  36.52  79.00   3.00 913.51 3034.00            3.00 10.51 202.00               351.88 1748.50 45905.10 29.32 177.16 5176.59  1892\n",
       "6                                 1.00 3.16  36.00                         1.00 2.00  14.00                             12.99 281.20  2245.33                            29.74  543.80  9723.33    2.00   8.28  18.00   0.00 721.84 3031.00            2.00  5.15  37.00                65.91  825.01  9953.31 32.95 168.50  862.69  1637\n",
       "7                                 1.00 1.34   7.00                         1.00 1.33   6.00                             10.49 109.65   392.94                            14.00  128.19   410.93   57.00 214.54 367.00   0.00 532.70 2828.00            2.00  2.67   8.00                44.98  237.83   464.93 22.49  92.67  169.37  3261"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's examine each segment statistically\n",
    "\n",
    "final.groupby(\"KM_segment\").agg({ \"order_num_total_ever_online\":[\"min\",\"mean\",\"max\"],\n",
    "                                  \"order_num_total_ever_offline\":[\"min\",\"mean\",\"max\"],\n",
    "                                  \"customer_value_total_ever_offline\":[\"min\",\"mean\",\"max\"],\n",
    "                                  \"customer_value_total_ever_online\":[\"min\",\"mean\",\"max\"],\n",
    "                                  \"recency\":[\"min\",\"mean\",\"max\"],\n",
    "                                  \"tenure\":[\"min\",\"mean\",\"max\"],\n",
    "                                  \"order_num_total\":[\"min\",\"mean\",\"max\"],\n",
    "                                  \"customer_value_total\": [\"min\",\"mean\",\"max\"],\n",
    "                                  \"ratio\" :[\"min\",\"mean\",\"max\", \"count\"] })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">recency</th>\n",
       "      <th colspan=\"3\" halign=\"left\">order_num_total</th>\n",
       "      <th colspan=\"3\" halign=\"left\">customer_value_total</th>\n",
       "      <th colspan=\"4\" halign=\"left\">ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KM_segment</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>1</th>\n",
       "      <td>8.00</td>\n",
       "      <td>30.19</td>\n",
       "      <td>61.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.16</td>\n",
       "      <td>7.00</td>\n",
       "      <td>44.98</td>\n",
       "      <td>432.41</td>\n",
       "      <td>1341.33</td>\n",
       "      <td>22.49</td>\n",
       "      <td>142.93</td>\n",
       "      <td>596.10</td>\n",
       "      <td>2424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>114.00</td>\n",
       "      <td>246.87</td>\n",
       "      <td>367.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.66</td>\n",
       "      <td>8.00</td>\n",
       "      <td>295.47</td>\n",
       "      <td>626.71</td>\n",
       "      <td>3615.67</td>\n",
       "      <td>63.27</td>\n",
       "      <td>182.94</td>\n",
       "      <td>859.58</td>\n",
       "      <td>4208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>41.00</td>\n",
       "      <td>90.21</td>\n",
       "      <td>153.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.60</td>\n",
       "      <td>8.00</td>\n",
       "      <td>139.98</td>\n",
       "      <td>536.06</td>\n",
       "      <td>1965.90</td>\n",
       "      <td>42.74</td>\n",
       "      <td>156.98</td>\n",
       "      <td>835.88</td>\n",
       "      <td>3991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>68.00</td>\n",
       "      <td>168.81</td>\n",
       "      <td>367.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>10.17</td>\n",
       "      <td>111.00</td>\n",
       "      <td>377.80</td>\n",
       "      <td>1470.97</td>\n",
       "      <td>36818.29</td>\n",
       "      <td>32.87</td>\n",
       "      <td>151.51</td>\n",
       "      <td>1106.47</td>\n",
       "      <td>2532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.00</td>\n",
       "      <td>36.52</td>\n",
       "      <td>79.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>10.51</td>\n",
       "      <td>202.00</td>\n",
       "      <td>351.88</td>\n",
       "      <td>1748.50</td>\n",
       "      <td>45905.10</td>\n",
       "      <td>29.32</td>\n",
       "      <td>177.16</td>\n",
       "      <td>5176.59</td>\n",
       "      <td>1892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.00</td>\n",
       "      <td>8.28</td>\n",
       "      <td>18.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>5.15</td>\n",
       "      <td>37.00</td>\n",
       "      <td>65.91</td>\n",
       "      <td>825.01</td>\n",
       "      <td>9953.31</td>\n",
       "      <td>32.95</td>\n",
       "      <td>168.50</td>\n",
       "      <td>862.69</td>\n",
       "      <td>1637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>57.00</td>\n",
       "      <td>214.54</td>\n",
       "      <td>367.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.67</td>\n",
       "      <td>8.00</td>\n",
       "      <td>44.98</td>\n",
       "      <td>237.83</td>\n",
       "      <td>464.93</td>\n",
       "      <td>22.49</td>\n",
       "      <td>92.67</td>\n",
       "      <td>169.37</td>\n",
       "      <td>3261</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           recency               order_num_total              customer_value_total                  ratio                     \n",
       "               min   mean    max             min  mean    max                  min    mean      max   min   mean     max count\n",
       "KM_segment                                                                                                                    \n",
       "1             8.00  30.19  61.00            2.00  3.16   7.00                44.98  432.41  1341.33 22.49 142.93  596.10  2424\n",
       "2           114.00 246.87 367.00            2.00  3.66   8.00               295.47  626.71  3615.67 63.27 182.94  859.58  4208\n",
       "3            41.00  90.21 153.00            2.00  3.60   8.00               139.98  536.06  1965.90 42.74 156.98  835.88  3991\n",
       "4            68.00 168.81 367.00            4.00 10.17 111.00               377.80 1470.97 36818.29 32.87 151.51 1106.47  2532\n",
       "5             5.00  36.52  79.00            3.00 10.51 202.00               351.88 1748.50 45905.10 29.32 177.16 5176.59  1892\n",
       "6             2.00   8.28  18.00            2.00  5.15  37.00                65.91  825.01  9953.31 32.95 168.50  862.69  1637\n",
       "7            57.00 214.54 367.00            2.00  2.67   8.00                44.98  237.83   464.93 22.49  92.67  169.37  3261"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's examine each segment statistically\n",
    "\n",
    "final.groupby(\"KM_segment\").agg({ \n",
    "                                  \"recency\":[\"min\",\"mean\",\"max\"],\n",
    "                                  \"order_num_total\":[\"min\",\"mean\",\"max\"],\n",
    "                                  \"customer_value_total\": [\"min\",\"mean\",\"max\"],\n",
    "                                  \"ratio\" :[\"min\",\"mean\",\"max\", \"count\"] })"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hierarchical Clustering Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "# I am copying my dataset to use it in the Hierarchical Clustering model, \n",
    "# so that the original version is not damaged.\n",
    "\n",
    "hc_model_df = model_df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We determine the optimum number of clusters for hierarchical clustering \n",
    "# The best way to determine the number of clusters in hierarchical clustering is to draw a dendrogram\n",
    "\n",
    "hc_complete = linkage(hc_model_df, 'complete')\n",
    "\n",
    "plt.figure(figsize=(15, 10))\n",
    "plt.title(\"Dendrograms\")\n",
    "dend = dendrogram(hc_complete,\n",
    "           truncate_mode=\"lastp\",\n",
    "           p=10,\n",
    "           show_contracted=True,\n",
    "           leaf_font_size=10)\n",
    "plt.axhline(y=1.2, color='r', linestyle='--')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create our model\n",
    "# Segment our customer\n",
    "\n",
    "hc = AgglomerativeClustering(n_clusters=4)\n",
    "\n",
    "segments = hc.fit_predict(hc_model_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\avcil\\AppData\\Local\\Temp\\ipykernel_16724\\1672601488.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  final_hc_df[\"hc_segment\"] = segments + 1\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>master_id</th>\n",
       "      <th>order_num_total_ever_online</th>\n",
       "      <th>order_num_total_ever_offline</th>\n",
       "      <th>customer_value_total_ever_offline</th>\n",
       "      <th>customer_value_total_ever_online</th>\n",
       "      <th>recency</th>\n",
       "      <th>tenure</th>\n",
       "      <th>order_num_total</th>\n",
       "      <th>customer_value_total</th>\n",
       "      <th>ratio</th>\n",
       "      <th>hc_segment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cc294636-19f0-11eb-8d74-000d3a38a36f</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>139.99</td>\n",
       "      <td>799.38</td>\n",
       "      <td>95.00</td>\n",
       "      <td>119.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>939.37</td>\n",
       "      <td>187.87</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>f431bd5a-ab7b-11e9-a2fc-000d3a38a36f</td>\n",
       "      <td>19.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>159.97</td>\n",
       "      <td>1853.58</td>\n",
       "      <td>105.00</td>\n",
       "      <td>1469.00</td>\n",
       "      <td>21.00</td>\n",
       "      <td>2013.55</td>\n",
       "      <td>95.88</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69b69676-1a40-11ea-941b-000d3a38a36f</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>189.97</td>\n",
       "      <td>395.35</td>\n",
       "      <td>186.00</td>\n",
       "      <td>366.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>585.32</td>\n",
       "      <td>117.06</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1854e56c-491f-11eb-806e-000d3a38a36f</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>39.99</td>\n",
       "      <td>81.98</td>\n",
       "      <td>135.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>121.97</td>\n",
       "      <td>60.98</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>d6ea1074-f1f5-11e9-9346-000d3a38a36f</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>49.99</td>\n",
       "      <td>159.99</td>\n",
       "      <td>86.00</td>\n",
       "      <td>582.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>209.98</td>\n",
       "      <td>104.99</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              master_id  order_num_total_ever_online  order_num_total_ever_offline  customer_value_total_ever_offline  customer_value_total_ever_online  recency  tenure  order_num_total  customer_value_total  ratio  hc_segment\n",
       "0  cc294636-19f0-11eb-8d74-000d3a38a36f                         4.00                          1.00                             139.99                            799.38    95.00  119.00             5.00                939.37 187.87           1\n",
       "1  f431bd5a-ab7b-11e9-a2fc-000d3a38a36f                        19.00                          2.00                             159.97                           1853.58   105.00 1469.00            21.00               2013.55  95.88           3\n",
       "2  69b69676-1a40-11ea-941b-000d3a38a36f                         3.00                          2.00                             189.97                            395.35   186.00  366.00             5.00                585.32 117.06           3\n",
       "3  1854e56c-491f-11eb-806e-000d3a38a36f                         1.00                          1.00                              39.99                             81.98   135.00   11.00             2.00                121.97  60.98           1\n",
       "4  d6ea1074-f1f5-11e9-9346-000d3a38a36f                         1.00                          1.00                              49.99                            159.99    86.00  582.00             2.00                209.98 104.99           1"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_hc_df = df[[\"master_id\",\"order_num_total_ever_online\",\n",
    "                  \"order_num_total_ever_offline\",\n",
    "                  \"customer_value_total_ever_offline\",\n",
    "                  \"customer_value_total_ever_online\",\n",
    "                  \"recency\",\n",
    "                  \"tenure\",\n",
    "                  \"order_num_total\", \n",
    "                  \"customer_value_total\",\n",
    "                  \"ratio\"]]\n",
    "\n",
    "segments\n",
    "final_hc_df[\"hc_segment\"] = segments + 1\n",
    "final_hc_df.head()\n",
    "\n",
    "# this is the final version of the model made with Hierarchical Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HC_size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hc_segment</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2155</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            HC_size\n",
       "hc_segment         \n",
       "1              9585\n",
       "2              3677\n",
       "3              4528\n",
       "4              2155"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Size of the clusters\n",
    "\n",
    "hc_size = final_hc_df.groupby('hc_segment').size().to_frame()\n",
    "hc_size.columns = [\"HC_size\"]\n",
    "hc_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cluster membership chart\n",
    "\n",
    "sns.countplot(x='hc_segment', data=final_hc_df)\n",
    "plt.title('Graph of Segment')\n",
    "plt.xlabel('Segments')\n",
    "plt.ylabel('Number of Data')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">recency</th>\n",
       "      <th colspan=\"3\" halign=\"left\">order_num_total</th>\n",
       "      <th colspan=\"3\" halign=\"left\">customer_value_total</th>\n",
       "      <th colspan=\"4\" halign=\"left\">ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hc_segment</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>1</th>\n",
       "      <td>31.00</td>\n",
       "      <td>179.83</td>\n",
       "      <td>367.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.06</td>\n",
       "      <td>10.00</td>\n",
       "      <td>44.98</td>\n",
       "      <td>450.77</td>\n",
       "      <td>7745.27</td>\n",
       "      <td>22.49</td>\n",
       "      <td>149.37</td>\n",
       "      <td>1106.47</td>\n",
       "      <td>9585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.00</td>\n",
       "      <td>33.96</td>\n",
       "      <td>74.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>5.04</td>\n",
       "      <td>21.00</td>\n",
       "      <td>69.98</td>\n",
       "      <td>675.53</td>\n",
       "      <td>2941.55</td>\n",
       "      <td>29.32</td>\n",
       "      <td>134.17</td>\n",
       "      <td>597.14</td>\n",
       "      <td>3677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12.00</td>\n",
       "      <td>178.03</td>\n",
       "      <td>367.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>9.07</td>\n",
       "      <td>202.00</td>\n",
       "      <td>301.20</td>\n",
       "      <td>1355.36</td>\n",
       "      <td>45905.10</td>\n",
       "      <td>32.87</td>\n",
       "      <td>153.51</td>\n",
       "      <td>5176.59</td>\n",
       "      <td>4528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.00</td>\n",
       "      <td>12.57</td>\n",
       "      <td>53.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>5.23</td>\n",
       "      <td>53.00</td>\n",
       "      <td>65.91</td>\n",
       "      <td>947.54</td>\n",
       "      <td>12726.10</td>\n",
       "      <td>32.95</td>\n",
       "      <td>194.64</td>\n",
       "      <td>862.69</td>\n",
       "      <td>2155</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           recency               order_num_total             customer_value_total                  ratio                     \n",
       "               min   mean    max             min mean    max                  min    mean      max   min   mean     max count\n",
       "hc_segment                                                                                                                   \n",
       "1            31.00 179.83 367.00            2.00 3.06  10.00                44.98  450.77  7745.27 22.49 149.37 1106.47  9585\n",
       "2             8.00  33.96  74.00            2.00 5.04  21.00                69.98  675.53  2941.55 29.32 134.17  597.14  3677\n",
       "3            12.00 178.03 367.00            4.00 9.07 202.00               301.20 1355.36 45905.10 32.87 153.51 5176.59  4528\n",
       "4             2.00  12.57  53.00            2.00 5.23  53.00                65.91  947.54 12726.10 32.95 194.64  862.69  2155"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's examine each segment statistically\n",
    "\n",
    "final_hc_df.groupby(\"hc_segment\").agg({ \"recency\":[\"min\",\"mean\",\"max\"],\n",
    "                                        \"order_num_total\":[\"min\",\"mean\",\"max\"],\n",
    "                                        \"customer_value_total\": [\"min\",\"mean\",\"max\"],\n",
    "                                        \"ratio\" :[\"min\",\"mean\",\"max\", \"count\"] })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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",
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       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">order_num_total_ever_online</th>\n",
       "      <th colspan=\"3\" halign=\"left\">order_num_total_ever_offline</th>\n",
       "      <th colspan=\"3\" halign=\"left\">customer_value_total_ever_offline</th>\n",
       "      <th colspan=\"3\" halign=\"left\">customer_value_total_ever_online</th>\n",
       "      <th colspan=\"3\" halign=\"left\">recency</th>\n",
       "      <th colspan=\"3\" halign=\"left\">tenure</th>\n",
       "      <th colspan=\"3\" halign=\"left\">order_num_total</th>\n",
       "      <th colspan=\"3\" halign=\"left\">customer_value_total</th>\n",
       "      <th colspan=\"4\" halign=\"left\">ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hc_segment</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",
       "      <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",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.57</td>\n",
       "      <td>8.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.49</td>\n",
       "      <td>8.00</td>\n",
       "      <td>10.49</td>\n",
       "      <td>198.62</td>\n",
       "      <td>2963.24</td>\n",
       "      <td>12.99</td>\n",
       "      <td>252.14</td>\n",
       "      <td>7665.28</td>\n",
       "      <td>31.00</td>\n",
       "      <td>179.83</td>\n",
       "      <td>367.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>542.93</td>\n",
       "      <td>2964.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.06</td>\n",
       "      <td>10.00</td>\n",
       "      <td>44.98</td>\n",
       "      <td>450.77</td>\n",
       "      <td>7745.27</td>\n",
       "      <td>22.49</td>\n",
       "      <td>149.37</td>\n",
       "      <td>1106.47</td>\n",
       "      <td>9585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.00</td>\n",
       "      <td>3.06</td>\n",
       "      <td>20.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.98</td>\n",
       "      <td>18.00</td>\n",
       "      <td>12.99</td>\n",
       "      <td>235.56</td>\n",
       "      <td>2125.50</td>\n",
       "      <td>19.99</td>\n",
       "      <td>439.97</td>\n",
       "      <td>2627.69</td>\n",
       "      <td>8.00</td>\n",
       "      <td>33.96</td>\n",
       "      <td>74.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>693.80</td>\n",
       "      <td>3028.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>5.04</td>\n",
       "      <td>21.00</td>\n",
       "      <td>69.98</td>\n",
       "      <td>675.53</td>\n",
       "      <td>2941.55</td>\n",
       "      <td>29.32</td>\n",
       "      <td>134.17</td>\n",
       "      <td>597.14</td>\n",
       "      <td>3677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.00</td>\n",
       "      <td>6.28</td>\n",
       "      <td>200.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.78</td>\n",
       "      <td>109.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>368.41</td>\n",
       "      <td>18119.14</td>\n",
       "      <td>19.99</td>\n",
       "      <td>986.95</td>\n",
       "      <td>45220.13</td>\n",
       "      <td>12.00</td>\n",
       "      <td>178.03</td>\n",
       "      <td>367.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>904.00</td>\n",
       "      <td>3016.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>9.07</td>\n",
       "      <td>202.00</td>\n",
       "      <td>301.20</td>\n",
       "      <td>1355.36</td>\n",
       "      <td>45905.10</td>\n",
       "      <td>32.87</td>\n",
       "      <td>153.51</td>\n",
       "      <td>5176.59</td>\n",
       "      <td>4528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>48.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.84</td>\n",
       "      <td>24.00</td>\n",
       "      <td>12.99</td>\n",
       "      <td>290.66</td>\n",
       "      <td>3425.14</td>\n",
       "      <td>29.74</td>\n",
       "      <td>656.88</td>\n",
       "      <td>12545.37</td>\n",
       "      <td>2.00</td>\n",
       "      <td>12.57</td>\n",
       "      <td>53.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>673.72</td>\n",
       "      <td>3034.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>5.23</td>\n",
       "      <td>53.00</td>\n",
       "      <td>65.91</td>\n",
       "      <td>947.54</td>\n",
       "      <td>12726.10</td>\n",
       "      <td>32.95</td>\n",
       "      <td>194.64</td>\n",
       "      <td>862.69</td>\n",
       "      <td>2155</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           order_num_total_ever_online             order_num_total_ever_offline             customer_value_total_ever_offline                 customer_value_total_ever_online                 recency               tenure                order_num_total             customer_value_total                  ratio                     \n",
       "                                   min mean    max                          min mean    max                               min   mean      max                              min   mean      max     min   mean    max    min   mean     max             min mean    max                  min    mean      max   min   mean     max count\n",
       "hc_segment                                                                                                                                                                                                                                                                                                                             \n",
       "1                                 1.00 1.57   8.00                         1.00 1.49   8.00                             10.49 198.62  2963.24                            12.99 252.14  7665.28   31.00 179.83 367.00   0.00 542.93 2964.00            2.00 3.06  10.00                44.98  450.77  7745.27 22.49 149.37 1106.47  9585\n",
       "2                                 1.00 3.06  20.00                         1.00 1.98  18.00                             12.99 235.56  2125.50                            19.99 439.97  2627.69    8.00  33.96  74.00   0.00 693.80 3028.00            2.00 5.04  21.00                69.98  675.53  2941.55 29.32 134.17  597.14  3677\n",
       "3                                 1.00 6.28 200.00                         1.00 2.78 109.00                             10.00 368.41 18119.14                            19.99 986.95 45220.13   12.00 178.03 367.00   0.00 904.00 3016.00            4.00 9.07 202.00               301.20 1355.36 45905.10 32.87 153.51 5176.59  4528\n",
       "4                                 1.00 3.39  48.00                         1.00 1.84  24.00                             12.99 290.66  3425.14                            29.74 656.88 12545.37    2.00  12.57  53.00   0.00 673.72 3034.00            2.00 5.23  53.00                65.91  947.54 12726.10 32.95 194.64  862.69  2155"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's examine each segment statistically\n",
    "\n",
    "final_hc_df.groupby(\"hc_segment\").agg({ \"order_num_total_ever_online\":[\"min\",\"mean\",\"max\"],\n",
    "                                  \"order_num_total_ever_offline\":[\"min\",\"mean\",\"max\"],\n",
    "                                  \"customer_value_total_ever_offline\":[\"min\",\"mean\",\"max\"],\n",
    "                                  \"customer_value_total_ever_online\":[\"min\",\"mean\",\"max\"],\n",
    "                                  \"recency\":[\"min\",\"mean\",\"max\"],\n",
    "                                  \"tenure\":[\"min\",\"mean\",\"max\"],\n",
    "                                  \"order_num_total\":[\"min\",\"mean\",\"max\"],\n",
    "                                  \"customer_value_total\": [\"min\",\"mean\",\"max\"],\n",
    "                                  \"ratio\" :[\"min\",\"mean\",\"max\", \"count\"] })"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's set up the same model as n_cluster=10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We determine the optimum number of clusters for hierarchical clustering \n",
    "# The best way to determine the number of clusters in hierarchical clustering is to draw a dendrogram\n",
    "\n",
    "hc_complete = linkage(hc_model_df, 'complete')\n",
    "\n",
    "plt.figure(figsize=(15, 10))\n",
    "plt.title(\"Dendrograms\")\n",
    "dend = dendrogram(hc_complete,\n",
    "           truncate_mode=\"lastp\",\n",
    "           p=10,\n",
    "           show_contracted=True,\n",
    "           leaf_font_size=10)\n",
    "plt.axhline(y=0.8, color='r', linestyle='--')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create our model\n",
    "# Segment our customer\n",
    "\n",
    "hc_2 = AgglomerativeClustering(n_clusters=10)\n",
    "\n",
    "hc_2_segments = hc_2.fit_predict(hc_model_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\avcil\\AppData\\Local\\Temp\\ipykernel_16724\\688210362.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  hc_2_df[\"hc_2_segment\"] = hc_2_segments + 1\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>master_id</th>\n",
       "      <th>order_num_total_ever_online</th>\n",
       "      <th>order_num_total_ever_offline</th>\n",
       "      <th>customer_value_total_ever_offline</th>\n",
       "      <th>customer_value_total_ever_online</th>\n",
       "      <th>recency</th>\n",
       "      <th>tenure</th>\n",
       "      <th>order_num_total</th>\n",
       "      <th>customer_value_total</th>\n",
       "      <th>ratio</th>\n",
       "      <th>hc_2_segment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cc294636-19f0-11eb-8d74-000d3a38a36f</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>139.99</td>\n",
       "      <td>799.38</td>\n",
       "      <td>95.00</td>\n",
       "      <td>119.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>939.37</td>\n",
       "      <td>187.87</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>f431bd5a-ab7b-11e9-a2fc-000d3a38a36f</td>\n",
       "      <td>19.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>159.97</td>\n",
       "      <td>1853.58</td>\n",
       "      <td>105.00</td>\n",
       "      <td>1469.00</td>\n",
       "      <td>21.00</td>\n",
       "      <td>2013.55</td>\n",
       "      <td>95.88</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69b69676-1a40-11ea-941b-000d3a38a36f</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>189.97</td>\n",
       "      <td>395.35</td>\n",
       "      <td>186.00</td>\n",
       "      <td>366.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>585.32</td>\n",
       "      <td>117.06</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1854e56c-491f-11eb-806e-000d3a38a36f</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>39.99</td>\n",
       "      <td>81.98</td>\n",
       "      <td>135.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>121.97</td>\n",
       "      <td>60.98</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>d6ea1074-f1f5-11e9-9346-000d3a38a36f</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>49.99</td>\n",
       "      <td>159.99</td>\n",
       "      <td>86.00</td>\n",
       "      <td>582.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>209.98</td>\n",
       "      <td>104.99</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              master_id  order_num_total_ever_online  order_num_total_ever_offline  customer_value_total_ever_offline  customer_value_total_ever_online  recency  tenure  order_num_total  customer_value_total  ratio  hc_2_segment\n",
       "0  cc294636-19f0-11eb-8d74-000d3a38a36f                         4.00                          1.00                             139.99                            799.38    95.00  119.00             5.00                939.37 187.87             2\n",
       "1  f431bd5a-ab7b-11e9-a2fc-000d3a38a36f                        19.00                          2.00                             159.97                           1853.58   105.00 1469.00            21.00               2013.55  95.88             5\n",
       "2  69b69676-1a40-11ea-941b-000d3a38a36f                         3.00                          2.00                             189.97                            395.35   186.00  366.00             5.00                585.32 117.06             6\n",
       "3  1854e56c-491f-11eb-806e-000d3a38a36f                         1.00                          1.00                              39.99                             81.98   135.00   11.00             2.00                121.97  60.98             7\n",
       "4  d6ea1074-f1f5-11e9-9346-000d3a38a36f                         1.00                          1.00                              49.99                            159.99    86.00  582.00             2.00                209.98 104.99             7"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hc_2_df = df[[\"master_id\",\"order_num_total_ever_online\",\n",
    "                  \"order_num_total_ever_offline\",\n",
    "                  \"customer_value_total_ever_offline\",\n",
    "                  \"customer_value_total_ever_online\",\n",
    "                  \"recency\",\n",
    "                  \"tenure\",\n",
    "                  \"order_num_total\", \n",
    "                  \"customer_value_total\",\n",
    "                  \"ratio\"]]\n",
    "\n",
    "segments\n",
    "hc_2_df[\"hc_2_segment\"] = hc_2_segments + 1\n",
    "hc_2_df.head()\n",
    "\n",
    "# this is the final version of the model made with Hierarchical Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        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>HC_size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hc_2_segment</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1328</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              HC_size\n",
       "hc_2_segment         \n",
       "1                4272\n",
       "2                3440\n",
       "3                1463\n",
       "4                1742\n",
       "5                 439\n",
       "6                2761\n",
       "7                1873\n",
       "8                1935\n",
       "9                 692\n",
       "10               1328"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Size of the segments\n",
    "\n",
    "hc_2_size = hc_2_df.groupby('hc_2_segment').size().to_frame()\n",
    "hc_2_size.columns = [\"HC_size\"]\n",
    "hc_2_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAJgQBQ8AgAlR8AAAmBAFDwCACVHwAACYEAUPAIAJUfAAAJgQBQ8AgAlR8AAAmBAFDwCACVHwAACYEAUPAIAJUfAAAJgQBQ8AgAlR8AAAmBAFDwCACVHwAACYEAUPAIAJUfAAAJgQBQ8AgAlR8AAAmFCwr564oaFBaWlpqqysVFBQkDIyMmQYhubNmyeLxaKoqCgtWrRIbdq0UX5+vvLy8hQcHKwZM2Zo6NChqq2t1dy5c3XkyBFZrVZlZmaqU6dOvooLAICp+GwFv3nzZklSXl6eHn74YWVkZCgjI0PJycnKzc2VYRgqKChQVVWVcnJylJeXp5deeklZWVmqq6vT2rVrZbfblZubq7Fjxyo7O9tXUQEAMB2freCHDx+uIUOGSJIOHDigzp0764MPPlD//v0lSYMGDdLWrVvVpk0b9enTR6GhoQoNDVVkZKTKy8tVWlqqqVOnuudS8AAAeM9nBS9JwcHBSklJ0aZNm7R8+XJt3rxZFotFkmS1WlVdXS2HwyGbzeZ+jNVqlcPhaDJ+eq43ysrK3Lcjm3FbmkNpaanHOVY/5Dgf3mQGALQ8Pi14ScrMzNSjjz6q+Ph4uVwu97jT6VRERITCw8PldDqbjNtstibjp+d6Izo6WmFhYZKkqo/2NOOWXLx+/fp5nFO+zQ9BzoM3mQEA/udyuZosar/LZ7+Df/PNN7V69WpJUrt27WSxWBQdHa2SkhJJUlFRkWJjYxUTE6PS0lK5XC5VV1eroqJCdrtdffv2VWFhoXsuRQMAgPd8toK/4447NH/+fE2aNEmnTp1Samqqrr/+ei1YsEBZWVnq3r27RowYoaCgICUlJSkxMVGGYWj27NkKCwtTQkKCUlJSlJCQoJCQEC1dutRXUQEAMB2LYRhGoEM0h9O7Kprsol+1JsCpmrpixr0e55SvvNsPSbzXc+ZbgY4AADiDM/Xet3GiGwAATIiCBwDAhCh4AABMiIIHAMCEKHgAAEyIggcAwIQoeAAATIiCBwDAhDyeya6urk6FhYXu88I3NDRo3759euSRR3weDgAAXBiPBT9nzhwdP35cX3zxhWJjY1VSUqK+ffv6IxsAALhAHnfR7927V3/4wx90++23a+rUqVq7dq3279/vj2wAAOACeVzBX3755bJYLOrWrZv27t2rsWPHqr6+3h/ZALRSo15/NdARmnhnwqRARwD8zmPBR0VFKT09XQkJCXr00Uf11VdfySTXpwEAwLQ87qJ//PHHddddd6lHjx6aNWuWvvrqK2VlZfkjGwAAuEAeC37x4sWKjY2VJP3kJz9RWlqafvvb3/o8GAAAuHBn3UX/2GOP6csvv1RZWZn++c9/uscbGhp04sQJv4QDAAAX5qwFP2PGDO3fv19PP/20HnroIfd4UFCQrr/+er+EAwAAF+asBd+1a1d17dpVGzZs0Ndff62TJ0/KMAw1NDRoz549uvXWW/2ZEwAAnAePR9GvWLFCv/vd73Tq1Cl16NBBX331laKjo/Xaa6/5Ix8AALgAHg+ye+ONN1RYWKiRI0cqJydHq1atUseOHf2RDQAAXCCPBd+lSxeFh4crKipK5eXlGjJkiA4ePOiPbAAA4AJ53EUfHh6uN998U7169dKaNWvUpUsX1dbW+iMbAAC4QB5X8E8//bSOHj2qm2++WT/84Q+1cOFCJScn+yEaAAC4UB5X8FdeeaXGjh2rmpoazZs3zx+ZAADARTprwTc2NmrFihVau3atjh8/Lkm66qqrNGnSJE2dOtVvAQEAwPk7a8FnZ2drx44deuGFF2S322WxWFReXq7ly5fL5XJp5syZZ33S+vp6paamav/+/aqrq9OMGTN01VVXafr06bruuuskSQkJCRo5cqTy8/OVl5en4OBgzZgxQ0OHDlVtba3mzp2rI0eOyGq1KjMzU506dWr2jQcAwKzOWvB/+tOftH79erVt29Y99qMf/UjLli3TpEmTzlnwGzZsUIcOHfTss8/q2LFjiouL08yZM/XAAw9o8uTJ7nlVVVXKycnRunXr5HK5lJiYqAEDBmjt2rWy2+2aNWuWNm7cqOzsbKWlpTXTJgMAYH5nPcguJCSkSbmfZrPZFBQUdM4nvfPOO/XII4+47wcFBamsrEwffPCBJk2apNTUVDkcDu3atUt9+vRRaGiobDabIiMjVV5ertLSUg0cOFCSNGjQIBUXF1/o9gEAcEk66wq+TRuPB9ifldVqlSQ5HA49/PDDSk5OVl1dnSZOnKjo6GitWrVKK1euVM+ePWWz2Zo8zuFwyOFwuMetVquqq6u9fu2ysjL37cgL3gLfKC0t9TjH6occ58ObzEBLx+cYl6KzFvyBAwc0f/78s/7Mk4MHD2rmzJlKTEzU6NGjdeLECUVEREiSbr/9dqWnpys2NlZOp9P9GKfTKZvNpvDwcPe40+l0P84b0dHRCgsLkyRVfbTH68f5Q79+/TzOKd/mhyDnwZvMwPdUlgc6QRN8jmFGLperyaL2u85a8Of6k7j+/fuf80UPHz6syZMna+HChe6L0kyZMkULFixQTEyMiouL1atXL8XExGjZsmVyuVyqq6tTRUWF7Ha7+vbtq8LCQsXExKioqIh/nAAAnKezFnxcXNwFP+nzzz+vEydOKDs7W9nZ2ZK++cKwePFihYSEqHPnzkpPT1d4eLiSkpKUmJgowzA0e/ZshYWFKSEhQSkpKUpISFBISIiWLl16wVkAALgUeTzRzYVIS0s741HveXl53xuLj49XfHx8k7F27dpp+fLlvogGAMAl4axH0tXU1PgzBwAAaEZnLfhJkyZJkh5//HF/ZQEAAM3krLvoT548qUcffVR//etf5XK5vvfzjIwMnwYDAAAX7qwF/8orr6ikpESlpaUej5oHAAAty1kL/gc/+IHGjh2rnj176vrrr1dlZaUaGhoUFRWl4GCfHJsHAACaicemrq+v14gRI9ShQwc1Njbq8OHDWrlypX70ox/5Ix8AALgAHgv+6aef1q9+9St3oe/YsUPp6el6/fXXfR4OAABcGI8nnK+pqWmyWu/du/cZD7oDAAAth8eCv+yyy/Tee++577/33nvq0KGDLzMBAICL5HEXfXp6uubOnavHHntMknTNNdfo2Wef9XkwAABw4TwW/HXXXafXXntNNTU1amxsVHh4uD9yAQCAi+D137u1b9/elzkAAEAz8vg7eAAA0Pp4LPgzXQEOAAC0bB4Lfs2aNf7IAQAAmpHH38FfddVVuu+++/SjH/1IYWFh7vGHHnrIp8EAAMCF81jwvXv39kMMAADQnDwW/EMPPaSamhp98cUXstvtqq2t5Yh6AABaOI+/gy8uLtbdd9+tBx98UEeOHNHQoUO1ZcsWf2QDAAAXyGPBZ2VlKTc3VxEREbriiiv06quvasmSJf7IBgAALpDHgm9sbNQVV1zhvt+jRw+fBgIAABfPq6PoN2/eLIvFohMnTujVV1/V1Vdf7Y9sAADgAnlcwT/55JN6++23dfDgQQ0fPlx79uzRk08+6Y9sAADgAnlcwV9++eXKysqSw+FQUFCQ2rVr549cAADgIngs+L1792revHk6cOCAJKl79+7KzMxUZGSkz8MBAIAL47HgFy1apOTkZA0ePFiStGnTJqWmpp7zFLb19fVKTU3V/v37VVdXpxkzZqhHjx6aN2+eLBaLoqKitGjRIrVp00b5+fnKy8tTcHCwZsyYoaFDh6q2tlZz587VkSNHZLValZmZqU6dOjXfVgMAYHIefwfvcrnc5S5Jt99+uxwOxzkfs2HDBnXo0EG5ubl68cUXlZ6eroyMDCUnJys3N1eGYaigoEBVVVXKyclRXl6eXnrpJWVlZamurk5r166V3W5Xbm6uxo4dq+zs7IvfUgAALiFnLfgDBw7owIED6tmzp1544QUdPXpUx48f15o1axQbG3vOJ73zzjv1yCOPuO8HBQVp9+7d6t+/vyRp0KBB+vDDD7Vr1y716dNHoaGhstlsioyMVHl5uUpLSzVw4ED33OLi4ubYVgAALhln3UV/7733ymKxyDAMlZSUNLlsrMViUVpa2lmf1Gq1SpIcDocefvhhJScnKzMzUxaLxf3z6upqORwO2Wy2Jo9zOBxNxk/P9VZZWZn7dks7SqC0tNTjHKsfcpwPbzIDLR2fY1yKzlrw77///kU98cGDBzVz5kwlJiZq9OjRevbZZ90/czqdioiIUHh4uJxOZ5Nxm83WZPz0XG9FR0e7r3pX9dGei9qG5tavXz+Pc8q3+SHIefAmM/A9leWBTtAEn2OYkcvlarKo/S6PB9l99tlnys/P1/Hjx5uMZ2RknPUxhw8f1uTJk7Vw4ULdeuutkqSbbrpJJSUluvnmm1VUVKRbbrlFMTExWrZsmVwul+rq6lRRUSG73a6+ffuqsLBQMTExKioq4h8nAADnyauryY0cOVI33HCD10/6/PPP68SJE8rOznYfIPfYY4/pqaeeUlZWlrp3764RI0YoKChISUlJSkxMlGEYmj17tsLCwpSQkKCUlBQlJCQoJCRES5cuvfAtBADgEuSx4CMiIvTQQw+d15OmpaWd8Xf0Z/rTuvj4eMXHxzcZa9eunZYvX35erwkAAP7LY8HHxcXpV7/6lW655RYFB/93+v/7f//Pp8EAAOe28o1DgY7gNjPuykBHwHd4LPhPPvlEH3/8sT7++GP3mMVi0R/+8AefBgMAABfOY8Hv3r1bf/nLX/yRBQAANBOPZ7KLiopSeXnL+pMXAABwbl79mVxcXJyuuOIKhYSEyDAMWSwWFRQU+CMfAPhF3LotgY7QxBvjbwt0BLRyHgt+5cqV/sgBAACakceC3759+xnHf/jDHzZ7GAAA0Dw8FnxJSYn7dn19vUpLSxUbG6uxY8f6MhcAALgIHgv+u6ek/frrrzV79myfBQIAABfP41H039W+fXvt37/fF1kAAEAz8biCT0pKcl/m1TAM7du3T4MHD/Z5MAAAcOE8FvysWbPcty0Wizp27KgePXr4NBQAALg4Zy34AwcOSJK6du16xp9dffXVvksFAAAuylkL/t5775XFYpFhGO4xi8Wiqqoq1dfXa8+ePX4JCDS3x/NHBDpCE4/H/znQEQCY0FkL/v33329y3+l0KjMzU1u2bFF6errPgwEAgAvn1VH0xcXFGjNmjCRpw4YNGjBggE9DAQCAi3POg+xqamr0zDPPuFftFDsAAK3DWVfwxcXFGj16tCTp7bffptwBAGhFzrqCf+CBBxQcHKwtW7Zo69at7nGuJgcAQMt31oKnwAEAaL3OWvBcLQ4AgNbrvM9FDwAAWj4KHgAAE6LgAQAwIQoeAAATouABADAhnxb8zp07lZSUJEnavXu3Bg4cqKSkJCUlJelPf/qTJCk/P1/jxo1TfHy8Nm/eLEmqra3VrFmzlJiYqGnTpuno0aO+jAkAgOl4vB78hXrxxRe1YcMGtWvXTpL0j3/8Qw888IAmT57snlNVVaWcnBytW7dOLpdLiYmJGjBggNauXSu73a5Zs2Zp48aNys7OVlpamq+iAgBgOj5bwUdGRmrFihXu+2VlZfrggw80adIkpaamyuFwaNeuXerTp49CQ0Nls9kUGRmp8vJylZaWauDAgZKkQYMGqbi42FcxAQAwJZ+t4EeMGKF9+/a578fExGjixImKjo7WqlWrtHLlSvXs2VM2m809x2q1yuFwyOFwuMetVquqq6u9ft2ysjL37chm2I7mVFpa6nGO1Q85zoc3mXFxeI99rzW+x95l7urzHN5qje+x2fms4L/r9ttvV0REhPt2enq6YmNj5XQ63XOcTqdsNpvCw8Pd406n0/04b0RHRyssLEySVPXRnmbcgovXr18/j3PKt/khyHnwJnNr83ZFoBM0Zcb3WJXlgU7QhFfv8b+3+D7IefAm87YvDvkhiXdM+Tlu4VwuV5NF7Xf5reCnTJmiBQsWKCYmRsXFxerVq5diYmK0bNkyuVwu1dXVqaKiQna7XX379lVhYaFiYmJUVFTEB6cFe/2VOwMdoYkJD7wb6AgA0CL4reAff/xxpaenKyQkRJ07d1Z6errCw8OVlJSkxMREGYah2bNnKywsTAkJCUpJSVFCQoJCQkK0dOlSf8UEAMAUfFrwXbt2VX5+viSpV69eysvL+96c+Ph4xcfHNxlr166dli9f7stoAACYGie6AQDAhCh4AABMiIIHAMCEKHgAAEyIggcAwIQoeAAATMhvfwcPAEBrc2hZyzoF75XJ3p/4jRU8AAAmRMEDAGBCFDwAACZEwQMAYEIUPAAAJkTBAwBgQhQ8AAAmRMEDAGBCFDwAACZEwQMAYEIUPAAAJkTBAwBgQhQ8AAAmRMEDAGBCFDwAACZEwQMAYEIUPAAAJkTBAwBgQj4t+J07dyopKUmS9PnnnyshIUGJiYlatGiRGhsbJUn5+fkaN26c4uPjtXnzZklSbW2tZs2apcTERE2bNk1Hjx71ZUwAAEzHZwX/4osvKi0tTS6XS5KUkZGh5ORk5ebmyjAMFRQUqKqqSjk5OcrLy9NLL72krKws1dXVae3atbLb7crNzdXYsWOVnZ3tq5gAAJiSzwo+MjJSK1ascN/fvXu3+vfvL0kaNGiQPvzwQ+3atUt9+vRRaGiobDabIiMjVV5ertLSUg0cONA9t7i42FcxAQAwpWBfPfGIESO0b98+933DMGSxWCRJVqtV1dXVcjgcstls7jlWq1UOh6PJ+Om53iorK3PfjrzYjWhmpaWlHudY/ZDjfHiTuSVpbXml1pm5tWmN77F3mbv6PIe3WuN77I2W8w5/43zeZ58V/He1afPfnQVOp1MREREKDw+X0+lsMm6z2ZqMn57rrejoaIWFhUmSqj7a00zpm0e/fv08zinf5ocg58FT5spdfgriJW/e47cr/BDkPHiTudWpLA90gia8eo//vcX3Qc6DN5m3fXHID0m8Y8rPsaRDf21ZX1y+/T67XK4mi9rv8ttR9DfddJNKSkokSUVFRYqNjVVMTIxKS0vlcrlUXV2tiooK2e129e3bV4WFhe65Zv3gAADgK35bwaekpGjBggXKyspS9+7dNWLECAUFBSkpKUmJiYkyDEOzZ89WWFiYEhISlJKSooSEBIWEhGjp0qX+igkAgCn4tOC7du2q/Px8SVK3bt20Zs2a782Jj49XfHx8k7F27dpp+fLlvowGAICpcaIbAABMiIIHAMCEKHgAAEyIggcAwIQoeAAATIiCBwDAhCh4AABMiIIHAMCEKHgAAEyIggcAwIQoeAAATIiCBwDAhCh4AABMiIIHAMCEKHgAAEyIggcAwISCAx0AAHBp+OS3XwU6QhN9pnYJdASfYgUPAIAJUfAAAJgQBQ8AgAlR8AAAmBAFDwCACVHwAACYEAUPAIAJUfAAAJiQ3090M3bsWNlsNklS165dNX36dM2bN08Wi0VRUVFatGiR2rRpo/z8fOXl5Sk4OFgzZszQ0KFD/R0VAIBWy68F73K5JEk5OTnusenTpys5OVk333yzFi5cqIKCAvXu3Vs5OTlat26dXC6XEhMTNWDAAIWGhvozLgAArZZfC768vFwnT57U5MmTderUKc2ZM0e7d+9W//79JUmDBg3S1q1b1aZNG/Xp00ehoaEKDQ1VZGSkysvLFRMT48+4AAC0Wn4t+LZt22rKlCmaOHGi/v3vf2vatGkyDEMWi0WSZLVaVV1dLYfD4d6Nf3rc4XB49RplZWXu25HNG/+ilZaWepxj9UOO8+FN5pakteWVWmfm1qY1vsfeZe7q8xze8iZvG13jhyTe8yZzy3mHv3E+n2W/Fny3bt107bXXymKxqFu3burQoYN2797t/rnT6VRERITCw8PldDqbjH+78M8lOjpaYWFhkqSqj/Y07wZcpH79+nmcU77ND0HOg6fMlbv8FMRL3rzHb1f4Ich58CZzq1NZHugETXj1Hv97i++DnAdvMm/74pAfknjHm7yffNKyLjbjTeZDf21ZXw6/ndnlcjVZ1H6XX4+if/311/XMM89Ikg4dOiSHw6EBAwaopKREklRUVKTY2FjFxMSotLRULpdL1dXVqqiokN1u92dUAABaNb+u4CdMmKD58+crISFBFotFixcvVseOHbVgwQJlZWWpe/fuGjFihIKCgpSUlKTExEQZhqHZs2e7V+UAAMAzvxZ8aGioli5d+r3xNWvWfG8sPj5e8fHx/ogFtHgj33gq0BHc/hSXFugIALzAiW4AADAhCh4AABOi4AEAMCEKHgAAE6LgAQAwIQoeAAATouABADAhCh4AABOi4AEAMCEKHgAAE6LgAQAwIQoeAAATouABADAhCh4AABOi4AEAMCEKHgAAE6LgAQAwIQoeAAATouABADAhCh4AABOi4AEAMCEKHgAAE6LgAQAwIQoeAAATouABADAhCh4AABMKDnSAs2lsbNTjjz+uvXv3KjQ0VE899ZSuvfbaQMcCAKBVaLEr+Pfee091dXX64x//qF/84hd65plnAh0JAIBWo8Wu4EtLSzVw4EBJUu/evVVWVnbO+YZhSJLq6urcY6dCQ3wX8AK4XC6PcxrDLvNDEu95yhwc0sE/QbzkzXvcLriD74OcB28ydwhq54ck3vEqb3Dr+7d3WbDFD0m8503msKBTfkjiHW/yGqEtJ6/kXeZTYX4Ich6+nfl0353uv++yGGf7SYA99thjuuOOOzR48GBJ0pAhQ/Tee+8pOPjM30mqq6v16aef+jMiAAABZ7fbZbPZvjfeYlfw4eHhcjqd7vuNjY1nLXdJslqtstvtCgkJkcXSsr6JAwDQ3AzDUH19vaxW6xl/3mILvm/fvtq8ebNGjhypHTt2yG63n3N+mzZtzvgNBgAAs2rbtu1Zf9Zid9GfPor+008/lWEYWrx4sa6//vpAxwIAoFVosQUPAAAuXIv9MzkAAHDhKHgAAEyoxR5k11Ls3LlTzz33nHJycgIdxaP6+nqlpqZq//79qqur04wZM/STn/wk0LHOqaGhQWlpaaqsrFRQUJAyMjIUGRkZ6FgeHTlyROPGjdPLL7/cKo4NGTt2rPsg1K5duyojIyPAiTxbvXq13n//fdXX1yshIUETJ04MdKRzWr9+vd544w1J3/yt8p49e7R161ZFREQEONmZ1dfXa968edq/f7/atGmj9PT0Fv9Zrqur0/z58/Xll18qPDxcCxcu1HXXXRfoWGf07e74/PPPNW/ePFksFkVFRWnRokVq08b362sK/hxefPFFbdiwQe3atZyTjJzLhg0b1KFDBz377LM6duyY4uLiWnzBb968WZKUl5enkpISZWRkaNWqVQFOdW719fVauHDhOY9ebUlOnxijNXxJPa2kpESffPKJ1q5dq5MnT+rll18OdCSPxo0bp3HjxkmSnnjiCY0fP77FlrskFRYW6tSpU8rLy9PWrVu1bNkyrVixItCxzik/P1/t27dXfn6+PvvsM6Wnp+ull14KdKzv+W53ZGRkKDk5WTfffLMWLlyogoIC3X777T7PwS76c4iMjGzxH/hvu/POO/XII4+47wcFBQUwjXeGDx+u9PR0SdKBAwfUuXPnACfyLDMzU/fcc4+6dOkS6CheKS8v18mTJzV58mTdd9992rFjR6AjebRlyxbZ7XbNnDlT06dP15AhQwIdyWt///vf9a9//Uv/8z//E+go59StWzc1NDSosbFRDofjnOcZaSn+9a9/adCgQZKk7t27q6KiIsCJzuy73bF79271799fkjRo0CB9+OGHfsnR8v+LBtCIESO0b9++QMfw2umTHTgcDj388MNKTk4ObCAvBQcHKyUlRZs2bdLy5csDHeec1q9fr06dOmngwIF64YUXAh3HK23bttWUKVM0ceJE/fvf/9a0adP07rvvtuj/oR87dkwHDhzQ888/r3379mnGjBl69913W8VJrFavXq2ZM2cGOoZH7du31/79+3XXXXfp2LFjev755wMdyaMbb7xRmzdv1vDhw7Vz504dOnRIDQ0NLW4x893uMAzD/dm1Wq2qrq72Sw5W8CZz8OBB3Xfffbr77rs1evToQMfxWmZmpv785z9rwYIFqqmpCXScs1q3bp0+/PBDJSUlac+ePUpJSVFVVVWgY51Tt27dNGbMGFksFnXr1k0dOnRo8Zk7dOig2267TaGhoerevbvCwsJ09OjRQMfy6MSJE/rss890yy23BDqKR7/73e9022236c9//rPeeustzZs3z6tzswfS+PHjFR4ervvuu0+bN29Wr169Wly5n8m3f9/udDr99qsbCt5EDh8+rMmTJ2vu3LmaMGFCoON45c0339Tq1aslSe3atZPFYmnR/2BfffVVrVmzRjk5ObrxxhuVmZmpK664ItCxzun11193X43x0KFDcjgcLT5zv3799Ne//lWGYejQoUM6efKkOnToEOhYHm3fvl0//vGPAx3DKxEREe4DLy+77DKdOnVKDQ0NAU51bn//+9/Vr18/5eTkaPjw4brmmmsCHckrN910k0pKSiRJRUVFio2N9cvrttx9dDhvzz//vE6cOKHs7GxlZ2dL+uZgj5Z8MNgdd9yh+fPna9KkSTp16pRSU1MVFtbCLt/Uyk2YMEHz589XQkKCLBaLFi9e3KJ3z0vS0KFDtX37dk2YMEGGYWjhwoUt+ovfaZWVleratWugY3jl/vvvV2pqqhITE1VfX6/Zs2erffv2gY51Ttdee61+/etf6+WXX5bNZtPTTz8d6EheSUlJ0YIFC5SVlaXu3btrxIgRfnldzmQHAIAJsYseAAATouABADAhCh4AABOi4AEAMCEKHgAAE6LggUvEu+++q3HjxmnMmDEaPXq0fvvb3wY6UhNJSUmBjgCYSsv+Y1gAzeLQoUPKzMzU+vXr1bFjRzmdTiUlJalbt24t5oJEH330UaAjAKZCwQOXgGPHjqm+vl61tbWSvjkf9jPPPKOwsDDt2rVLGRkZqq2tVceOHfXEE0/ommuu0aeffqp58+apoaFBsbGxKioq0qZNmzRv3jy1a9dO//jHP3TixAnNmTNHb731lsrLyzV8+HD3Y5YsWaKPPvpIDQ0NGjdunO6//36VlJRo9erVatu2rSoqKnTDDTfoueee05IlSyRJEydOVG5urlJTU/XPf/5TkpSYmKj4+PiAvXdAq2UAuCQsXLjQuOmmm4zx48cbS5YsMfbs2WO4XC5j9OjRxv79+w3DMIyioiLjf//3fw3DMIy4uDjjgw8+MAzDMF555RVj6NChhmEYRkpKivHggw8ahmEY69evN/r162ccPnzYqK6uNvr06WOcOHHCyM3NNRYvXmwYhmG4XC7j3nvvNbZv325s27bN6N27t3Hw4EGjoaHBGD9+vFFQUGAYhmHY7XbDMAyjpKTEmDZtmmEYhvGf//zHmDt3rn/eIMBkWMEDl4gnnnhCDz74oLZs2aItW7YoPj5eP/vZz/Tll19qxowZ7nkOh0Nff/219u/fr8GDB0v65iIff/jDH9xzTl+y8+qrr1ZUVJQuv/xySd9cJOb48eMqLi7Wnj17tG3bNklSTU2N9u7dqx49eigqKkpXXXWVJOn666/X8ePHm+SMiopSZWWlpkyZokGDBumXv/yl794UwMQoeOAS8MEHH6impkYjR47U+PHjNX78eOXn5+vtt99W165d9dZbb0mSGhoadPjwYQUFBck4x1msQ0JC3LfPdF77hoYGzZ07V3fccYck6ejRo7JardqxY0eTaw1YLJbvvU7Hjh21ceNGbd26VYWFhYqLi9PGjRv9dgUuwCw4ih64BLRt21ZLly51X6PaMAzt2bNHvXv31vHjx/W3v/1N0jeXw3300Udls9l0zTXXqLCwUJL09ttvn9fr3XLLLcrPz1d9fb2cTqcSExO1Y8eOcz4mKChIp06dUkFBgebOnashQ4YoLS1N7du318GDB89/o4FLHCt44BJwyy236KGHHtL06dNVX18vSRo4cKBmzZqlYcOG6emnn5bL5VJ4eLgyMzMlSUuWLFFqaqqWLVumG2644byuSnjPPffo888/V1xcnE6dOqVx48bp5ptvdl8y80x+8pOf6O6779Yf//hH/eUvf9FPf/pThYWFacyYMbrhhhsu7g0ALkFcTQ7AGf3mN79RfHy8unTpor/85S96++23tWLFikDHAuAlVvAAzujqq6/W5MmTFRwcrIiIiFZz7W0A32AFDwCACXGQHQAAJkTBAwBgQhQ8AAAmRMEDAGBCFDwAACZEwQMAYEL/H+zA/dzbtCEHAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cluster membership chart\n",
    "\n",
    "sns.countplot(x='hc_2_segment', data=hc_2_df)\n",
    "plt.title('Graph of Segment')\n",
    "plt.xlabel('Segments')\n",
    "plt.ylabel('Number of Data')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">recency</th>\n",
       "      <th colspan=\"3\" halign=\"left\">order_num_total</th>\n",
       "      <th colspan=\"3\" halign=\"left\">customer_value_total</th>\n",
       "      <th colspan=\"4\" halign=\"left\">ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hc_2_segment</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>1</th>\n",
       "      <td>122.00</td>\n",
       "      <td>244.27</td>\n",
       "      <td>367.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.99</td>\n",
       "      <td>10.00</td>\n",
       "      <td>182.56</td>\n",
       "      <td>441.79</td>\n",
       "      <td>1168.36</td>\n",
       "      <td>34.30</td>\n",
       "      <td>155.29</td>\n",
       "      <td>539.99</td>\n",
       "      <td>4272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31.00</td>\n",
       "      <td>105.23</td>\n",
       "      <td>360.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.53</td>\n",
       "      <td>10.00</td>\n",
       "      <td>227.77</td>\n",
       "      <td>606.64</td>\n",
       "      <td>7745.27</td>\n",
       "      <td>39.13</td>\n",
       "      <td>180.09</td>\n",
       "      <td>1106.47</td>\n",
       "      <td>3440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.00</td>\n",
       "      <td>14.79</td>\n",
       "      <td>53.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.08</td>\n",
       "      <td>7.00</td>\n",
       "      <td>65.91</td>\n",
       "      <td>609.57</td>\n",
       "      <td>3884.07</td>\n",
       "      <td>32.95</td>\n",
       "      <td>203.06</td>\n",
       "      <td>862.69</td>\n",
       "      <td>1463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.00</td>\n",
       "      <td>31.60</td>\n",
       "      <td>69.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>7.03</td>\n",
       "      <td>21.00</td>\n",
       "      <td>310.14</td>\n",
       "      <td>1019.01</td>\n",
       "      <td>2941.55</td>\n",
       "      <td>29.32</td>\n",
       "      <td>151.56</td>\n",
       "      <td>597.14</td>\n",
       "      <td>1742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12.00</td>\n",
       "      <td>116.53</td>\n",
       "      <td>364.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>23.78</td>\n",
       "      <td>202.00</td>\n",
       "      <td>1389.85</td>\n",
       "      <td>3711.51</td>\n",
       "      <td>45905.10</td>\n",
       "      <td>45.22</td>\n",
       "      <td>176.21</td>\n",
       "      <td>5176.59</td>\n",
       "      <td>439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>92.00</td>\n",
       "      <td>232.35</td>\n",
       "      <td>367.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>6.68</td>\n",
       "      <td>23.00</td>\n",
       "      <td>301.20</td>\n",
       "      <td>999.73</td>\n",
       "      <td>12103.15</td>\n",
       "      <td>32.87</td>\n",
       "      <td>152.37</td>\n",
       "      <td>605.16</td>\n",
       "      <td>2761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>36.00</td>\n",
       "      <td>169.86</td>\n",
       "      <td>367.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.39</td>\n",
       "      <td>5.00</td>\n",
       "      <td>44.98</td>\n",
       "      <td>184.97</td>\n",
       "      <td>399.93</td>\n",
       "      <td>22.49</td>\n",
       "      <td>79.46</td>\n",
       "      <td>139.99</td>\n",
       "      <td>1873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.00</td>\n",
       "      <td>36.10</td>\n",
       "      <td>74.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.24</td>\n",
       "      <td>9.00</td>\n",
       "      <td>69.98</td>\n",
       "      <td>366.31</td>\n",
       "      <td>769.15</td>\n",
       "      <td>31.63</td>\n",
       "      <td>118.51</td>\n",
       "      <td>264.49</td>\n",
       "      <td>1935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.00</td>\n",
       "      <td>7.86</td>\n",
       "      <td>22.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>9.79</td>\n",
       "      <td>53.00</td>\n",
       "      <td>314.40</td>\n",
       "      <td>1662.08</td>\n",
       "      <td>12726.10</td>\n",
       "      <td>59.73</td>\n",
       "      <td>176.83</td>\n",
       "      <td>727.09</td>\n",
       "      <td>692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>40.00</td>\n",
       "      <td>85.41</td>\n",
       "      <td>175.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>9.17</td>\n",
       "      <td>22.00</td>\n",
       "      <td>511.90</td>\n",
       "      <td>1315.87</td>\n",
       "      <td>3892.92</td>\n",
       "      <td>50.04</td>\n",
       "      <td>148.38</td>\n",
       "      <td>357.06</td>\n",
       "      <td>1328</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             recency               order_num_total              customer_value_total                  ratio                     \n",
       "                 min   mean    max             min  mean    max                  min    mean      max   min   mean     max count\n",
       "hc_2_segment                                                                                                                    \n",
       "1             122.00 244.27 367.00            2.00  2.99  10.00               182.56  441.79  1168.36 34.30 155.29  539.99  4272\n",
       "2              31.00 105.23 360.00            2.00  3.53  10.00               227.77  606.64  7745.27 39.13 180.09 1106.47  3440\n",
       "3               2.00  14.79  53.00            2.00  3.08   7.00                65.91  609.57  3884.07 32.95 203.06  862.69  1463\n",
       "4               8.00  31.60  69.00            3.00  7.03  21.00               310.14 1019.01  2941.55 29.32 151.56  597.14  1742\n",
       "5              12.00 116.53 364.00            4.00 23.78 202.00              1389.85 3711.51 45905.10 45.22 176.21 5176.59   439\n",
       "6              92.00 232.35 367.00            4.00  6.68  23.00               301.20  999.73 12103.15 32.87 152.37  605.16  2761\n",
       "7              36.00 169.86 367.00            2.00  2.39   5.00                44.98  184.97   399.93 22.49  79.46  139.99  1873\n",
       "8               8.00  36.10  74.00            2.00  3.24   9.00                69.98  366.31   769.15 31.63 118.51  264.49  1935\n",
       "9               2.00   7.86  22.00            3.00  9.79  53.00               314.40 1662.08 12726.10 59.73 176.83  727.09   692\n",
       "10             40.00  85.41 175.00            5.00  9.17  22.00               511.90 1315.87  3892.92 50.04 148.38  357.06  1328"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's examine each segment statistically\n",
    "\n",
    "hc_2_df.groupby(\"hc_2_segment\").agg({ \"recency\":[\"min\",\"mean\",\"max\"],\n",
    "                                        \"order_num_total\":[\"min\",\"mean\",\"max\"],\n",
    "                                        \"customer_value_total\": [\"min\",\"mean\",\"max\"],\n",
    "                                        \"ratio\" :[\"min\",\"mean\",\"max\", \"count\"] })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">order_num_total_ever_online</th>\n",
       "      <th colspan=\"3\" halign=\"left\">order_num_total_ever_offline</th>\n",
       "      <th colspan=\"3\" halign=\"left\">customer_value_total_ever_offline</th>\n",
       "      <th colspan=\"3\" halign=\"left\">customer_value_total_ever_online</th>\n",
       "      <th colspan=\"3\" halign=\"left\">recency</th>\n",
       "      <th colspan=\"3\" halign=\"left\">tenure</th>\n",
       "      <th colspan=\"3\" halign=\"left\">order_num_total</th>\n",
       "      <th colspan=\"3\" halign=\"left\">customer_value_total</th>\n",
       "      <th colspan=\"4\" halign=\"left\">ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hc_2_segment</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",
       "      <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",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.53</td>\n",
       "      <td>8.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.46</td>\n",
       "      <td>8.00</td>\n",
       "      <td>12.99</td>\n",
       "      <td>195.23</td>\n",
       "      <td>959.98</td>\n",
       "      <td>12.99</td>\n",
       "      <td>246.57</td>\n",
       "      <td>1068.18</td>\n",
       "      <td>122.00</td>\n",
       "      <td>244.27</td>\n",
       "      <td>367.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>506.46</td>\n",
       "      <td>2773.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.99</td>\n",
       "      <td>10.00</td>\n",
       "      <td>182.56</td>\n",
       "      <td>441.79</td>\n",
       "      <td>1168.36</td>\n",
       "      <td>34.30</td>\n",
       "      <td>155.29</td>\n",
       "      <td>539.99</td>\n",
       "      <td>4272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.83</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.70</td>\n",
       "      <td>6.00</td>\n",
       "      <td>12.99</td>\n",
       "      <td>263.42</td>\n",
       "      <td>2963.24</td>\n",
       "      <td>14.00</td>\n",
       "      <td>343.22</td>\n",
       "      <td>7665.28</td>\n",
       "      <td>31.00</td>\n",
       "      <td>105.23</td>\n",
       "      <td>360.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>588.75</td>\n",
       "      <td>2964.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.53</td>\n",
       "      <td>10.00</td>\n",
       "      <td>227.77</td>\n",
       "      <td>606.64</td>\n",
       "      <td>7745.27</td>\n",
       "      <td>39.13</td>\n",
       "      <td>180.09</td>\n",
       "      <td>1106.47</td>\n",
       "      <td>3440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.67</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.41</td>\n",
       "      <td>5.00</td>\n",
       "      <td>12.99</td>\n",
       "      <td>242.09</td>\n",
       "      <td>1821.90</td>\n",
       "      <td>29.74</td>\n",
       "      <td>367.48</td>\n",
       "      <td>3444.08</td>\n",
       "      <td>2.00</td>\n",
       "      <td>14.79</td>\n",
       "      <td>53.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>555.68</td>\n",
       "      <td>2857.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.08</td>\n",
       "      <td>7.00</td>\n",
       "      <td>65.91</td>\n",
       "      <td>609.57</td>\n",
       "      <td>3884.07</td>\n",
       "      <td>32.95</td>\n",
       "      <td>203.06</td>\n",
       "      <td>862.69</td>\n",
       "      <td>1463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.00</td>\n",
       "      <td>4.56</td>\n",
       "      <td>20.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.47</td>\n",
       "      <td>18.00</td>\n",
       "      <td>12.99</td>\n",
       "      <td>319.45</td>\n",
       "      <td>2125.50</td>\n",
       "      <td>31.99</td>\n",
       "      <td>699.56</td>\n",
       "      <td>2627.69</td>\n",
       "      <td>8.00</td>\n",
       "      <td>31.60</td>\n",
       "      <td>69.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>809.59</td>\n",
       "      <td>3028.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>7.03</td>\n",
       "      <td>21.00</td>\n",
       "      <td>310.14</td>\n",
       "      <td>1019.01</td>\n",
       "      <td>2941.55</td>\n",
       "      <td>29.32</td>\n",
       "      <td>151.56</td>\n",
       "      <td>597.14</td>\n",
       "      <td>1742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.00</td>\n",
       "      <td>19.31</td>\n",
       "      <td>200.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4.48</td>\n",
       "      <td>109.00</td>\n",
       "      <td>12.99</td>\n",
       "      <td>565.53</td>\n",
       "      <td>18119.14</td>\n",
       "      <td>49.98</td>\n",
       "      <td>3145.98</td>\n",
       "      <td>45220.13</td>\n",
       "      <td>12.00</td>\n",
       "      <td>116.53</td>\n",
       "      <td>364.00</td>\n",
       "      <td>20.00</td>\n",
       "      <td>1397.18</td>\n",
       "      <td>3016.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>23.78</td>\n",
       "      <td>202.00</td>\n",
       "      <td>1389.85</td>\n",
       "      <td>3711.51</td>\n",
       "      <td>45905.10</td>\n",
       "      <td>45.22</td>\n",
       "      <td>176.21</td>\n",
       "      <td>5176.59</td>\n",
       "      <td>439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.00</td>\n",
       "      <td>4.24</td>\n",
       "      <td>22.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.43</td>\n",
       "      <td>13.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>326.57</td>\n",
       "      <td>3215.78</td>\n",
       "      <td>19.99</td>\n",
       "      <td>673.16</td>\n",
       "      <td>12038.18</td>\n",
       "      <td>92.00</td>\n",
       "      <td>232.35</td>\n",
       "      <td>367.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>797.73</td>\n",
       "      <td>2865.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>6.68</td>\n",
       "      <td>23.00</td>\n",
       "      <td>301.20</td>\n",
       "      <td>999.73</td>\n",
       "      <td>12103.15</td>\n",
       "      <td>32.87</td>\n",
       "      <td>152.37</td>\n",
       "      <td>605.16</td>\n",
       "      <td>2761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.18</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.21</td>\n",
       "      <td>4.00</td>\n",
       "      <td>10.49</td>\n",
       "      <td>87.37</td>\n",
       "      <td>319.97</td>\n",
       "      <td>14.00</td>\n",
       "      <td>97.60</td>\n",
       "      <td>323.26</td>\n",
       "      <td>36.00</td>\n",
       "      <td>169.86</td>\n",
       "      <td>367.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>541.94</td>\n",
       "      <td>2828.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.39</td>\n",
       "      <td>5.00</td>\n",
       "      <td>44.98</td>\n",
       "      <td>184.97</td>\n",
       "      <td>399.93</td>\n",
       "      <td>22.49</td>\n",
       "      <td>79.46</td>\n",
       "      <td>139.99</td>\n",
       "      <td>1873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.70</td>\n",
       "      <td>7.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.54</td>\n",
       "      <td>6.00</td>\n",
       "      <td>12.99</td>\n",
       "      <td>160.03</td>\n",
       "      <td>522.95</td>\n",
       "      <td>19.99</td>\n",
       "      <td>206.28</td>\n",
       "      <td>603.89</td>\n",
       "      <td>8.00</td>\n",
       "      <td>36.10</td>\n",
       "      <td>74.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>589.57</td>\n",
       "      <td>2922.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.24</td>\n",
       "      <td>9.00</td>\n",
       "      <td>69.98</td>\n",
       "      <td>366.31</td>\n",
       "      <td>769.15</td>\n",
       "      <td>31.63</td>\n",
       "      <td>118.51</td>\n",
       "      <td>264.49</td>\n",
       "      <td>1935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.00</td>\n",
       "      <td>7.04</td>\n",
       "      <td>48.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.75</td>\n",
       "      <td>24.00</td>\n",
       "      <td>19.99</td>\n",
       "      <td>393.35</td>\n",
       "      <td>3425.14</td>\n",
       "      <td>59.49</td>\n",
       "      <td>1268.73</td>\n",
       "      <td>12545.37</td>\n",
       "      <td>2.00</td>\n",
       "      <td>7.86</td>\n",
       "      <td>22.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>923.27</td>\n",
       "      <td>3034.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>9.79</td>\n",
       "      <td>53.00</td>\n",
       "      <td>314.40</td>\n",
       "      <td>1662.08</td>\n",
       "      <td>12726.10</td>\n",
       "      <td>59.73</td>\n",
       "      <td>176.83</td>\n",
       "      <td>727.09</td>\n",
       "      <td>692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.00</td>\n",
       "      <td>6.22</td>\n",
       "      <td>20.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.95</td>\n",
       "      <td>17.00</td>\n",
       "      <td>14.99</td>\n",
       "      <td>390.23</td>\n",
       "      <td>2697.62</td>\n",
       "      <td>19.99</td>\n",
       "      <td>925.64</td>\n",
       "      <td>3786.33</td>\n",
       "      <td>40.00</td>\n",
       "      <td>85.41</td>\n",
       "      <td>175.00</td>\n",
       "      <td>22.00</td>\n",
       "      <td>961.89</td>\n",
       "      <td>2972.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>9.17</td>\n",
       "      <td>22.00</td>\n",
       "      <td>511.90</td>\n",
       "      <td>1315.87</td>\n",
       "      <td>3892.92</td>\n",
       "      <td>50.04</td>\n",
       "      <td>148.38</td>\n",
       "      <td>357.06</td>\n",
       "      <td>1328</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             order_num_total_ever_online              order_num_total_ever_offline             customer_value_total_ever_offline                 customer_value_total_ever_online                  recency               tenure                 order_num_total              customer_value_total                  ratio                     \n",
       "                                     min  mean    max                          min mean    max                               min   mean      max                              min    mean      max     min   mean    max    min    mean     max             min  mean    max                  min    mean      max   min   mean     max count\n",
       "hc_2_segment                                                                                                                                                                                                                                                                                                                                 \n",
       "1                                   1.00  1.53   8.00                         1.00 1.46   8.00                             12.99 195.23   959.98                            12.99  246.57  1068.18  122.00 244.27 367.00   0.00  506.46 2773.00            2.00  2.99  10.00               182.56  441.79  1168.36 34.30 155.29  539.99  4272\n",
       "2                                   1.00  1.83   6.00                         1.00 1.70   6.00                             12.99 263.42  2963.24                            14.00  343.22  7665.28   31.00 105.23 360.00   0.00  588.75 2964.00            2.00  3.53  10.00               227.77  606.64  7745.27 39.13 180.09 1106.47  3440\n",
       "3                                   1.00  1.67   5.00                         1.00 1.41   5.00                             12.99 242.09  1821.90                            29.74  367.48  3444.08    2.00  14.79  53.00   0.00  555.68 2857.00            2.00  3.08   7.00                65.91  609.57  3884.07 32.95 203.06  862.69  1463\n",
       "4                                   1.00  4.56  20.00                         1.00 2.47  18.00                             12.99 319.45  2125.50                            31.99  699.56  2627.69    8.00  31.60  69.00   2.00  809.59 3028.00            3.00  7.03  21.00               310.14 1019.01  2941.55 29.32 151.56  597.14  1742\n",
       "5                                   1.00 19.31 200.00                         1.00 4.48 109.00                             12.99 565.53 18119.14                            49.98 3145.98 45220.13   12.00 116.53 364.00  20.00 1397.18 3016.00            4.00 23.78 202.00              1389.85 3711.51 45905.10 45.22 176.21 5176.59   439\n",
       "6                                   1.00  4.24  22.00                         1.00 2.43  13.00                             10.00 326.57  3215.78                            19.99  673.16 12038.18   92.00 232.35 367.00   0.00  797.73 2865.00            4.00  6.68  23.00               301.20  999.73 12103.15 32.87 152.37  605.16  2761\n",
       "7                                   1.00  1.18   4.00                         1.00 1.21   4.00                             10.49  87.37   319.97                            14.00   97.60   323.26   36.00 169.86 367.00   0.00  541.94 2828.00            2.00  2.39   5.00                44.98  184.97   399.93 22.49  79.46  139.99  1873\n",
       "8                                   1.00  1.70   7.00                         1.00 1.54   6.00                             12.99 160.03   522.95                            19.99  206.28   603.89    8.00  36.10  74.00   0.00  589.57 2922.00            2.00  3.24   9.00                69.98  366.31   769.15 31.63 118.51  264.49  1935\n",
       "9                                   1.00  7.04  48.00                         1.00 2.75  24.00                             19.99 393.35  3425.14                            59.49 1268.73 12545.37    2.00   7.86  22.00   9.00  923.27 3034.00            3.00  9.79  53.00               314.40 1662.08 12726.10 59.73 176.83  727.09   692\n",
       "10                                  1.00  6.22  20.00                         1.00 2.95  17.00                             14.99 390.23  2697.62                            19.99  925.64  3786.33   40.00  85.41 175.00  22.00  961.89 2972.00            5.00  9.17  22.00               511.90 1315.87  3892.92 50.04 148.38  357.06  1328"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's examine each segment statistically\n",
    "\n",
    "hc_2_df.groupby(\"hc_2_segment\").agg({ \"order_num_total_ever_online\":[\"min\",\"mean\",\"max\"],\n",
    "                                  \"order_num_total_ever_offline\":[\"min\",\"mean\",\"max\"],\n",
    "                                  \"customer_value_total_ever_offline\":[\"min\",\"mean\",\"max\"],\n",
    "                                  \"customer_value_total_ever_online\":[\"min\",\"mean\",\"max\"],\n",
    "                                  \"recency\":[\"min\",\"mean\",\"max\"],\n",
    "                                  \"tenure\":[\"min\",\"mean\",\"max\"],\n",
    "                                  \"order_num_total\":[\"min\",\"mean\",\"max\"],\n",
    "                                  \"customer_value_total\": [\"min\",\"mean\",\"max\"],\n",
    "                                  \"ratio\" :[\"min\",\"mean\",\"max\", \"count\"] })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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