RFMAnalysis-OnlineRetail.ipynb

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  "metadata": {
    "colab": {
      "name": "Challenge3_Kelompok1.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c60-ieiESWWf"
      },
      "source": [
        "# **Import Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fHDkXICPQPlg"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zOiMI4CcAstd",
        "outputId": "fb691f70-0100-4b2f-915e-f1ecf645e55f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "df = pd.read_csv('/content/drive/My Drive/OnlineRetail.csv', encoding= 'unicode_escape')\n",
        "df.head()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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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>InvoiceNo</th>\n",
              "      <th>StockCode</th>\n",
              "      <th>Description</th>\n",
              "      <th>Quantity</th>\n",
              "      <th>InvoiceDate</th>\n",
              "      <th>UnitPrice</th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Country</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>536365</td>\n",
              "      <td>85123A</td>\n",
              "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
              "      <td>6</td>\n",
              "      <td>12/1/2010 8:26</td>\n",
              "      <td>2.55</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>536365</td>\n",
              "      <td>71053</td>\n",
              "      <td>WHITE METAL LANTERN</td>\n",
              "      <td>6</td>\n",
              "      <td>12/1/2010 8:26</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>536365</td>\n",
              "      <td>84406B</td>\n",
              "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
              "      <td>8</td>\n",
              "      <td>12/1/2010 8:26</td>\n",
              "      <td>2.75</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>536365</td>\n",
              "      <td>84029G</td>\n",
              "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
              "      <td>6</td>\n",
              "      <td>12/1/2010 8:26</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>536365</td>\n",
              "      <td>84029E</td>\n",
              "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
              "      <td>6</td>\n",
              "      <td>12/1/2010 8:26</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  InvoiceNo StockCode  ... CustomerID         Country\n",
              "0    536365    85123A  ...    17850.0  United Kingdom\n",
              "1    536365     71053  ...    17850.0  United Kingdom\n",
              "2    536365    84406B  ...    17850.0  United Kingdom\n",
              "3    536365    84029G  ...    17850.0  United Kingdom\n",
              "4    536365    84029E  ...    17850.0  United Kingdom\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eF_eT7V6yPNu"
      },
      "source": [
        "# **Data Understanding**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "96a8U05qxvui",
        "outputId": "f4457bde-90ac-4a4f-f248-3fe411304297",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 418
        }
      },
      "source": [
        "# 1) Data Understanding\n",
        "def summary(df, pred=None):\n",
        "  obs = df.shape[0]\n",
        "  types = df.dtypes\n",
        "  counts = df.apply(lambda x: x.count())\n",
        "  min = df.min()\n",
        "  uniques = df.apply(lambda x: x.unique().shape[0])\n",
        "  nulls = df.apply(lambda x: x.isnull().sum())\n",
        "  print('Data shape:', df.shape)\n",
        "\n",
        "  if pred is None:\n",
        "        cols = ['types', 'counts', 'uniques', 'nulls', 'min']\n",
        "        str = pd.concat([types, counts, uniques, nulls, min], axis = 1, sort=True)\n",
        "\n",
        "  str.columns = cols\n",
        "  dtypes = str.types.value_counts()\n",
        "  print('___________________________\\nData types:')\n",
        "  print(str.types.value_counts())\n",
        "  print('___________________________')\n",
        "  return str\n",
        "\n",
        "details = summary(df)\n",
        "display(details.sort_values(by='nulls', ascending=False))"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Data shape: (541909, 8)\n",
            "___________________________\n",
            "Data types:\n",
            "object     5\n",
            "float64    2\n",
            "int64      1\n",
            "Name: types, dtype: int64\n",
            "___________________________\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>types</th>\n",
              "      <th>counts</th>\n",
              "      <th>uniques</th>\n",
              "      <th>nulls</th>\n",
              "      <th>min</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>CustomerID</th>\n",
              "      <td>float64</td>\n",
              "      <td>406829</td>\n",
              "      <td>4373</td>\n",
              "      <td>135080</td>\n",
              "      <td>12346</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Description</th>\n",
              "      <td>object</td>\n",
              "      <td>540455</td>\n",
              "      <td>4224</td>\n",
              "      <td>1454</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country</th>\n",
              "      <td>object</td>\n",
              "      <td>541909</td>\n",
              "      <td>38</td>\n",
              "      <td>0</td>\n",
              "      <td>Australia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>InvoiceDate</th>\n",
              "      <td>object</td>\n",
              "      <td>541909</td>\n",
              "      <td>23260</td>\n",
              "      <td>0</td>\n",
              "      <td>1/10/2011 10:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>InvoiceNo</th>\n",
              "      <td>object</td>\n",
              "      <td>541909</td>\n",
              "      <td>25900</td>\n",
              "      <td>0</td>\n",
              "      <td>536365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Quantity</th>\n",
              "      <td>int64</td>\n",
              "      <td>541909</td>\n",
              "      <td>722</td>\n",
              "      <td>0</td>\n",
              "      <td>-80995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>StockCode</th>\n",
              "      <td>object</td>\n",
              "      <td>541909</td>\n",
              "      <td>4070</td>\n",
              "      <td>0</td>\n",
              "      <td>10002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>UnitPrice</th>\n",
              "      <td>float64</td>\n",
              "      <td>541909</td>\n",
              "      <td>1630</td>\n",
              "      <td>0</td>\n",
              "      <td>-11062.1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               types  counts  uniques   nulls              min\n",
              "CustomerID   float64  406829     4373  135080            12346\n",
              "Description   object  540455     4224    1454              NaN\n",
              "Country       object  541909       38       0        Australia\n",
              "InvoiceDate   object  541909    23260       0  1/10/2011 10:04\n",
              "InvoiceNo     object  541909    25900       0           536365\n",
              "Quantity       int64  541909      722       0           -80995\n",
              "StockCode     object  541909     4070       0            10002\n",
              "UnitPrice    float64  541909     1630       0         -11062.1"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b-m3JWs4ybmw"
      },
      "source": [
        "From the summary table above, there are several things that need to be reviewed:\n",
        "\n",
        "1.   Null Values - The **CustomerID** and **Description** fields have null values.\n",
        "2.   Negative Value - **Quantity** and **UnitPrice** should have a value >= 0, but from the summary above there are negative values for the two columns."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hKIZ48agxwBU",
        "outputId": "67554a8a-25cb-4c4a-8d0f-17aa1d0e4679",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        }
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Quantity</th>\n",
              "      <th>UnitPrice</th>\n",
              "      <th>CustomerID</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>541909.000000</td>\n",
              "      <td>541909.000000</td>\n",
              "      <td>406829.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>9.552250</td>\n",
              "      <td>4.611114</td>\n",
              "      <td>15287.690570</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>218.081158</td>\n",
              "      <td>96.759853</td>\n",
              "      <td>1713.600303</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-80995.000000</td>\n",
              "      <td>-11062.060000</td>\n",
              "      <td>12346.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.250000</td>\n",
              "      <td>13953.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.080000</td>\n",
              "      <td>15152.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>10.000000</td>\n",
              "      <td>4.130000</td>\n",
              "      <td>16791.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>80995.000000</td>\n",
              "      <td>38970.000000</td>\n",
              "      <td>18287.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Quantity      UnitPrice     CustomerID\n",
              "count  541909.000000  541909.000000  406829.000000\n",
              "mean        9.552250       4.611114   15287.690570\n",
              "std       218.081158      96.759853    1713.600303\n",
              "min    -80995.000000  -11062.060000   12346.000000\n",
              "25%         1.000000       1.250000   13953.000000\n",
              "50%         3.000000       2.080000   15152.000000\n",
              "75%        10.000000       4.130000   16791.000000\n",
              "max     80995.000000   38970.000000   18287.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SWNgUGqSxwQN",
        "outputId": "27c891fc-f8b5-4f84-f5b5-e9ee6bc0fe5f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Check how many negative values\n",
        "print('% Total Negative Value (Quantity): {:3.2%}'.format(df[(df.Quantity<0)].shape[0]/df.shape[0]))\n",
        "print('% Total Negative Value (Unit Price): {:3.2%}'.format(df[(df.UnitPrice<0)].shape[0]/df.shape[0]))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "% Total Negative Value (Quantity): 1.96%\n",
            "% Total Negative Value (Unit Price): 0.00%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dFssIwsQyfux",
        "outputId": "cdb471a0-5b66-4a6f-e3fa-265ea853ef10",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Check how many rows are zero\n",
        "print('% Total Zero Value (Quantity): {:3.2%}'.format(df[(df.Quantity==0)].shape[0]/df.shape[0]))\n",
        "print('% Total Zero Value (Unit Price): {:3.2%}'.format(df[(df.UnitPrice==0)].shape[0]/df.shape[0]))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "% Total Zero Value (Quantity): 0.00%\n",
            "% Total Zero Value (Unit Price): 0.46%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GLTglbKay25r"
      },
      "source": [
        "# **Data Cleaning**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2gjwOEwKxwb3",
        "outputId": "60e35b84-12f8-4a29-c1d6-57763ae67ed1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 418
        }
      },
      "source": [
        "# 2) Data Cleaning\n",
        "# Drop CustomerID with null value\n",
        "df = df[~(df.CustomerID.isnull())]\n",
        "\n",
        "# Drop negative value with/or return transaction (Unit Price < 0)\n",
        "df = df[~(df.Quantity<0)]\n",
        "df = df[df.UnitPrice>0]\n",
        "\n",
        "# Check summary table\n",
        "details = summary(df)\n",
        "display(details.sort_values(by='uniques', ascending=False))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Data shape: (397884, 8)\n",
            "___________________________\n",
            "Data types:\n",
            "object     5\n",
            "float64    2\n",
            "int64      1\n",
            "Name: types, dtype: int64\n",
            "___________________________\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "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>types</th>\n",
              "      <th>counts</th>\n",
              "      <th>uniques</th>\n",
              "      <th>nulls</th>\n",
              "      <th>min</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>InvoiceNo</th>\n",
              "      <td>object</td>\n",
              "      <td>397884</td>\n",
              "      <td>18532</td>\n",
              "      <td>0</td>\n",
              "      <td>536365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>InvoiceDate</th>\n",
              "      <td>object</td>\n",
              "      <td>397884</td>\n",
              "      <td>17282</td>\n",
              "      <td>0</td>\n",
              "      <td>1/10/2011 10:32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CustomerID</th>\n",
              "      <td>float64</td>\n",
              "      <td>397884</td>\n",
              "      <td>4338</td>\n",
              "      <td>0</td>\n",
              "      <td>12346</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Description</th>\n",
              "      <td>object</td>\n",
              "      <td>397884</td>\n",
              "      <td>3877</td>\n",
              "      <td>0</td>\n",
              "      <td>4 PURPLE FLOCK DINNER CANDLES</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>StockCode</th>\n",
              "      <td>object</td>\n",
              "      <td>397884</td>\n",
              "      <td>3665</td>\n",
              "      <td>0</td>\n",
              "      <td>10002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>UnitPrice</th>\n",
              "      <td>float64</td>\n",
              "      <td>397884</td>\n",
              "      <td>440</td>\n",
              "      <td>0</td>\n",
              "      <td>0.001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Quantity</th>\n",
              "      <td>int64</td>\n",
              "      <td>397884</td>\n",
              "      <td>301</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country</th>\n",
              "      <td>object</td>\n",
              "      <td>397884</td>\n",
              "      <td>37</td>\n",
              "      <td>0</td>\n",
              "      <td>Australia</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               types  counts  uniques  nulls                             min\n",
              "InvoiceNo     object  397884    18532      0                          536365\n",
              "InvoiceDate   object  397884    17282      0                 1/10/2011 10:32\n",
              "CustomerID   float64  397884     4338      0                           12346\n",
              "Description   object  397884     3877      0   4 PURPLE FLOCK DINNER CANDLES\n",
              "StockCode     object  397884     3665      0                           10002\n",
              "UnitPrice    float64  397884      440      0                           0.001\n",
              "Quantity       int64  397884      301      0                               1\n",
              "Country       object  397884       37      0                       Australia"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oz8atD1Ey54H",
        "outputId": "4f6d02aa-f4cc-44fb-bb14-3c4220f1122f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 229
        }
      },
      "source": [
        "# There are still a unique Description > StockCode value (data inconsistency)\n",
        "cat_des_df = df.groupby([\"StockCode\",\"Description\"]).count().reset_index()\n",
        "display(cat_des_df.StockCode.value_counts()[cat_des_df.StockCode.value_counts()>1].reset_index().head())\n",
        "df[df['StockCode'] == cat_des_df.StockCode.value_counts()[cat_des_df.StockCode.value_counts()>1]\n",
        "      .reset_index()['index'][4]]['Description'].unique()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "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>index</th>\n",
              "      <th>StockCode</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>23196</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>23236</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>23413</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>23244</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>23209</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   index  StockCode\n",
              "0  23196          4\n",
              "1  23236          4\n",
              "2  23413          3\n",
              "3  23244          3\n",
              "4  23209          3"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['LUNCH BAG DOILEY PATTERN ', 'LUNCH BAG VINTAGE DOILY ',\n",
              "       'LUNCH BAG VINTAGE DOILEY '], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aYZ9Vq5Xy6Hn",
        "outputId": "f17669ae-8721-409c-b9b1-958a44ef2eb6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!pip install pandasql"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting pandasql\n",
            "  Downloading https://files.pythonhosted.org/packages/6b/c4/ee4096ffa2eeeca0c749b26f0371bd26aa5c8b611c43de99a4f86d3de0a7/pandasql-0.7.3.tar.gz\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from pandasql) (1.18.5)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from pandasql) (1.1.3)\n",
            "Requirement already satisfied: sqlalchemy in /usr/local/lib/python3.6/dist-packages (from pandasql) (1.3.20)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas->pandasql) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.6/dist-packages (from pandas->pandasql) (2.8.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.7.3->pandas->pandasql) (1.15.0)\n",
            "Building wheels for collected packages: pandasql\n",
            "  Building wheel for pandasql (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pandasql: filename=pandasql-0.7.3-cp36-none-any.whl size=26819 sha256=44f37d5467892a49a386a0fe7005258bfeaee1a5b5b6d13697a89b2d57369faa\n",
            "  Stored in directory: /root/.cache/pip/wheels/53/6c/18/b87a2e5fa8a82e9c026311de56210b8d1c01846e18a9607fc9\n",
            "Successfully built pandasql\n",
            "Installing collected packages: pandasql\n",
            "Successfully installed pandasql-0.7.3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dib6WN3jzW3w"
      },
      "source": [
        "# After checking, there are still typos for the same type of item\n",
        "# Inner Join to retrieve the same data\n",
        "\n",
        "from pandasql import sqldf\n",
        "pysqldf = lambda q: sqldf(q, globals())\n",
        "\n",
        "unique_desc = df[[\"StockCode\", \"Description\"]].groupby(by=[\"StockCode\"]).\\\n",
        "                apply(pd.DataFrame.mode).reset_index(drop=True)\n",
        "q = '''\n",
        "select df.InvoiceNo, df.StockCode, un.Description, df.Quantity, df.InvoiceDate,\n",
        "       df.UnitPrice, df.CustomerID, df.Country\n",
        "from df as df INNER JOIN \n",
        "     unique_desc as un on df.StockCode = un.StockCode\n",
        "'''\n",
        "\n",
        "df = pysqldf(q)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2mYKe-GWzXDL",
        "outputId": "bc3563f3-894e-422a-a1ad-0f430632d278",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        }
      },
      "source": [
        "# Convert format to datetime\n",
        "from datetime import datetime\n",
        "df.InvoiceDate = pd.to_datetime(df.InvoiceDate)\n",
        "df.CustomerID = df.CustomerID.astype('Int64')\n",
        "\n",
        "details = summary(df)\n",
        "display(details.sort_values(by='uniques', ascending=False))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Data shape: (397884, 8)\n",
            "___________________________\n",
            "Data types:\n",
            "object            4\n",
            "datetime64[ns]    1\n",
            "int64             1\n",
            "Int64             1\n",
            "float64           1\n",
            "Name: types, dtype: int64\n",
            "___________________________\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "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>types</th>\n",
              "      <th>counts</th>\n",
              "      <th>uniques</th>\n",
              "      <th>nulls</th>\n",
              "      <th>min</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>InvoiceNo</th>\n",
              "      <td>object</td>\n",
              "      <td>397884</td>\n",
              "      <td>18532</td>\n",
              "      <td>0</td>\n",
              "      <td>536365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>InvoiceDate</th>\n",
              "      <td>datetime64[ns]</td>\n",
              "      <td>397884</td>\n",
              "      <td>17282</td>\n",
              "      <td>0</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CustomerID</th>\n",
              "      <td>Int64</td>\n",
              "      <td>397884</td>\n",
              "      <td>4338</td>\n",
              "      <td>0</td>\n",
              "      <td>12346</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>StockCode</th>\n",
              "      <td>object</td>\n",
              "      <td>397884</td>\n",
              "      <td>3665</td>\n",
              "      <td>0</td>\n",
              "      <td>10002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Description</th>\n",
              "      <td>object</td>\n",
              "      <td>397884</td>\n",
              "      <td>3647</td>\n",
              "      <td>0</td>\n",
              "      <td>4 PURPLE FLOCK DINNER CANDLES</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>UnitPrice</th>\n",
              "      <td>float64</td>\n",
              "      <td>397884</td>\n",
              "      <td>440</td>\n",
              "      <td>0</td>\n",
              "      <td>0.001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Quantity</th>\n",
              "      <td>int64</td>\n",
              "      <td>397884</td>\n",
              "      <td>301</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country</th>\n",
              "      <td>object</td>\n",
              "      <td>397884</td>\n",
              "      <td>37</td>\n",
              "      <td>0</td>\n",
              "      <td>Australia</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                      types  counts  ...  nulls                             min\n",
              "InvoiceNo            object  397884  ...      0                          536365\n",
              "InvoiceDate  datetime64[ns]  397884  ...      0             2010-12-01 08:26:00\n",
              "CustomerID            Int64  397884  ...      0                           12346\n",
              "StockCode            object  397884  ...      0                           10002\n",
              "Description          object  397884  ...      0   4 PURPLE FLOCK DINNER CANDLES\n",
              "UnitPrice           float64  397884  ...      0                           0.001\n",
              "Quantity              int64  397884  ...      0                               1\n",
              "Country              object  397884  ...      0                       Australia\n",
              "\n",
              "[8 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1gPxeHTJzXOA",
        "outputId": "42473920-1e0d-4d2e-fd49-a824488a987e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Check how many country\n",
        "df.Country.nunique()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "37"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vt5w64k01UvU",
        "outputId": "3a590b4b-e297-424b-96c6-8a1a3072b1f7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "# Check the total Customer per Country\n",
        "customer_country = df[['Country','CustomerID']].drop_duplicates()\n",
        "cust_group = customer_country.groupby(['Country'])['CustomerID'].aggregate('count').reset_index().sort_values('CustomerID',ascending=False)\n",
        "cust_group.head()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Country</th>\n",
              "      <th>CustomerID</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>3920</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Germany</td>\n",
              "      <td>94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>France</td>\n",
              "      <td>87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Spain</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Belgium</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Country  CustomerID\n",
              "35  United Kingdom        3920\n",
              "14         Germany          94\n",
              "13          France          87\n",
              "30           Spain          30\n",
              "3          Belgium          25"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MjD7ZDhM1Utl",
        "outputId": "a55dd106-cb21-4111-e41f-86a57c462b7a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "# Insert new column (Total Price)\n",
        "df['TotalPrice'] = df['Quantity']*df['UnitPrice']\n",
        "df.head()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>InvoiceNo</th>\n",
              "      <th>StockCode</th>\n",
              "      <th>Description</th>\n",
              "      <th>Quantity</th>\n",
              "      <th>InvoiceDate</th>\n",
              "      <th>UnitPrice</th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Country</th>\n",
              "      <th>TotalPrice</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>536365</td>\n",
              "      <td>85123A</td>\n",
              "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
              "      <td>6</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>2.55</td>\n",
              "      <td>17850</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>15.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>536365</td>\n",
              "      <td>71053</td>\n",
              "      <td>WHITE METAL LANTERN</td>\n",
              "      <td>6</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>20.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>536365</td>\n",
              "      <td>84406B</td>\n",
              "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
              "      <td>8</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>2.75</td>\n",
              "      <td>17850</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>22.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>536365</td>\n",
              "      <td>84029G</td>\n",
              "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
              "      <td>6</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>20.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>536365</td>\n",
              "      <td>84029E</td>\n",
              "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
              "      <td>6</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>20.34</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  InvoiceNo StockCode  ...         Country  TotalPrice\n",
              "0    536365    85123A  ...  United Kingdom       15.30\n",
              "1    536365     71053  ...  United Kingdom       20.34\n",
              "2    536365    84406B  ...  United Kingdom       22.00\n",
              "3    536365    84029G  ...  United Kingdom       20.34\n",
              "4    536365    84029E  ...  United Kingdom       20.34\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9PxJ3VMB1Uq6",
        "outputId": "8f89bb7e-3ea9-45fb-ad70-fdcdbbe583b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        }
      },
      "source": [
        "grouped_2 = df.groupby('Country', as_index=False,).agg({'TotalPrice':'sum'})\n",
        "sorted_2 = grouped_2.sort_values(by='TotalPrice', ascending=False)\n",
        "top_10 = sorted_2[:10]\n",
        "top_10"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Country</th>\n",
              "      <th>TotalPrice</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>7.308392e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Netherlands</td>\n",
              "      <td>2.854463e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>EIRE</td>\n",
              "      <td>2.655459e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Germany</td>\n",
              "      <td>2.288671e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>France</td>\n",
              "      <td>2.090240e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Australia</td>\n",
              "      <td>1.385213e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Spain</td>\n",
              "      <td>6.157711e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Switzerland</td>\n",
              "      <td>5.644395e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Belgium</td>\n",
              "      <td>4.119634e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Sweden</td>\n",
              "      <td>3.837833e+04</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Country    TotalPrice\n",
              "35  United Kingdom  7.308392e+06\n",
              "23     Netherlands  2.854463e+05\n",
              "10            EIRE  2.655459e+05\n",
              "14         Germany  2.288671e+05\n",
              "13          France  2.090240e+05\n",
              "0        Australia  1.385213e+05\n",
              "30           Spain  6.157711e+04\n",
              "32     Switzerland  5.644395e+04\n",
              "3          Belgium  4.119634e+04\n",
              "31          Sweden  3.837833e+04"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ScNA04t51UoQ",
        "outputId": "7c01fa59-6403-4f48-b13b-54294aedf376",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "grouped_1 = df.groupby('Description', as_index=False,).agg({'TotalPrice':'sum'})\n",
        "sorted_1 = grouped_1.sort_values(by='TotalPrice', ascending=False)\n",
        "sorted_1['cum_sum'] = sorted_1['TotalPrice'].cumsum()\n",
        "sorted_1['cum_perc'] = 100*sorted_1['cum_sum']/sorted_1['TotalPrice'].sum()\n",
        "sorted_1['cum_perc'] = sorted_1['cum_perc'].round(decimals=2)\n",
        "top_item = sorted_1.reset_index(drop=True)\n",
        "top_item"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Description</th>\n",
              "      <th>TotalPrice</th>\n",
              "      <th>cum_sum</th>\n",
              "      <th>cum_perc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>PAPER CRAFT , LITTLE BIRDIE</td>\n",
              "      <td>168469.600</td>\n",
              "      <td>168469.600</td>\n",
              "      <td>1.89</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>REGENCY CAKESTAND 3 TIER</td>\n",
              "      <td>142592.950</td>\n",
              "      <td>311062.550</td>\n",
              "      <td>3.49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
              "      <td>100603.500</td>\n",
              "      <td>411666.050</td>\n",
              "      <td>4.62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>JUMBO BAG RED RETROSPOT</td>\n",
              "      <td>85220.780</td>\n",
              "      <td>496886.830</td>\n",
              "      <td>5.58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>MEDIUM CERAMIC TOP STORAGE JAR</td>\n",
              "      <td>81416.730</td>\n",
              "      <td>578303.560</td>\n",
              "      <td>6.49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3642</th>\n",
              "      <td>PINK CRYSTAL GUITAR PHONE CHARM</td>\n",
              "      <td>0.850</td>\n",
              "      <td>8911405.791</td>\n",
              "      <td>100.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3643</th>\n",
              "      <td>PURPLE FRANGIPANI HAIRCLIP</td>\n",
              "      <td>0.850</td>\n",
              "      <td>8911406.641</td>\n",
              "      <td>100.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3644</th>\n",
              "      <td>VINTAGE BLUE TINSEL REEL</td>\n",
              "      <td>0.840</td>\n",
              "      <td>8911407.481</td>\n",
              "      <td>100.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3645</th>\n",
              "      <td>HEN HOUSE W CHICK IN NEST</td>\n",
              "      <td>0.420</td>\n",
              "      <td>8911407.901</td>\n",
              "      <td>100.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3646</th>\n",
              "      <td>PADS TO MATCH ALL CUSHIONS</td>\n",
              "      <td>0.003</td>\n",
              "      <td>8911407.904</td>\n",
              "      <td>100.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3647 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                             Description  TotalPrice      cum_sum  cum_perc\n",
              "0            PAPER CRAFT , LITTLE BIRDIE  168469.600   168469.600      1.89\n",
              "1               REGENCY CAKESTAND 3 TIER  142592.950   311062.550      3.49\n",
              "2     WHITE HANGING HEART T-LIGHT HOLDER  100603.500   411666.050      4.62\n",
              "3                JUMBO BAG RED RETROSPOT   85220.780   496886.830      5.58\n",
              "4         MEDIUM CERAMIC TOP STORAGE JAR   81416.730   578303.560      6.49\n",
              "...                                  ...         ...          ...       ...\n",
              "3642     PINK CRYSTAL GUITAR PHONE CHARM       0.850  8911405.791    100.00\n",
              "3643          PURPLE FRANGIPANI HAIRCLIP       0.850  8911406.641    100.00\n",
              "3644            VINTAGE BLUE TINSEL REEL       0.840  8911407.481    100.00\n",
              "3645           HEN HOUSE W CHICK IN NEST       0.420  8911407.901    100.00\n",
              "3646          PADS TO MATCH ALL CUSHIONS       0.003  8911407.904    100.00\n",
              "\n",
              "[3647 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TLblPn_C1w82",
        "outputId": "f95806ee-8700-431b-8e1f-238fcb1924fa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 77
        }
      },
      "source": [
        "idx = top_item[top_item['cum_perc'] == 80.01]\n",
        "idx"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Description</th>\n",
              "      <th>TotalPrice</th>\n",
              "      <th>cum_sum</th>\n",
              "      <th>cum_perc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>776</th>\n",
              "      <td>ENAMEL WATERING CAN CREAM</td>\n",
              "      <td>2646.6</td>\n",
              "      <td>7129884.41</td>\n",
              "      <td>80.01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   Description  TotalPrice     cum_sum  cum_perc\n",
              "776  ENAMEL WATERING CAN CREAM      2646.6  7129884.41     80.01"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kRZT3q731xNs",
        "outputId": "697ecda0-9d14-4195-b8af-7be5ae01a0d7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "777/3647*100"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "21.30518234165067"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TWP_AxIO2Bo6"
      },
      "source": [
        "80% of sales revenue comes from 21.30% of all items with the most sales (in terms of income)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TfMD3dvf1-wm"
      },
      "source": [
        "def pareto_plot(df, x=None, y=None, title=None, show_pct_y=False, pct_format='{0:.0%}'):\n",
        "    xlabel = x\n",
        "    ylabel = y\n",
        "    tmp = df.sort_values(y, ascending=False)\n",
        "    x = tmp[x].values\n",
        "    y = tmp[y].values\n",
        "    weights = y / 8911407.904\n",
        "    cumsum = weights.cumsum()\n",
        "\n",
        "    fig, ax1 = plt.subplots(figsize=(12,6))\n",
        "    ax1.bar(x, y)\n",
        "    ax1.set_xlabel(xlabel, rotation='horizontal')\n",
        "    ax1.set_ylabel(ylabel)\n",
        "\n",
        "    ax2 = ax1.twinx()\n",
        "    ax2.plot(x, cumsum, '-ro', alpha=0.5)\n",
        "    ax2.set_ylabel('', color='r')\n",
        "    ax2.tick_params('y', colors='r')\n",
        "    \n",
        "    vals = ax2.get_yticks()\n",
        "    ax2.set_yticklabels(['{:,.2%}'.format(x) for x in vals])\n",
        "\n",
        "    # hide y-labels on right side\n",
        "    if not show_pct_y:\n",
        "        ax2.set_yticks([])\n",
        "    \n",
        "    formatted_weights = [pct_format.format(x) for x in cumsum]\n",
        "    for i, txt in enumerate(formatted_weights):\n",
        "        ax2.annotate(txt, (x[i], cumsum[i]), fontweight='heavy')    \n",
        "    \n",
        "    if title:\n",
        "        plt.title(title)\n",
        "    \n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "    plt.savefig('test.png')"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u50hEypO1-8P",
        "outputId": "71719c03-42da-4b40-c8c6-ee70181269ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        }
      },
      "source": [
        "pareto_plot(top_10, x='Country', y='TotalPrice', title='Total Sales by Country')"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ReA0NKYO1_HL",
        "outputId": "8f28d1b2-e8f5-4a1f-a2ce-cbec47327038",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df.TotalPrice.sum()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8911407.904"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pg1pdXHF2ZCB"
      },
      "source": [
        "# **Exploratory Data Analysis**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TyX7n1BV2Oof",
        "outputId": "4ea4f464-59ba-4841-e64b-f7008bb97340",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# EDA\n",
        "# Sales per Customer\n",
        "fig = plt.figure(figsize=(25, 7))\n",
        "f2 = fig.add_subplot(122)\n",
        "g = df.groupby([\"CustomerID\"]).TotalPrice.sum().sort_values(ascending = False)[:10].plot(kind='bar', title='Amount Sales by Country', color='purple')\n",
        "plt.title('Top 10 Customer with Highest Spending (Monetary Value)')\n",
        "\n",
        "# Sales per StockCode\n",
        "fig = plt.figure(figsize=(25, 12))\n",
        "f3 = fig.add_subplot(221)\n",
        "g = df.groupby([\"Description\"]).Quantity.sum().sort_values(ascending = False)[:10].plot(kind='bar', title='Amount Sales by Country', color='green')\n",
        "plt.title('Top 10 Items Contribute to Overall Sales') \n",
        "\n",
        "fig = plt.figure(figsize=(25, 12))\n",
        "f4 = fig.add_subplot(222)\n",
        "g = df.groupby([\"Description\"]).Quantity.count().sort_values(ascending = False)[:10].plot(kind='bar', title='Amount Sales by Country', color='green')\n",
        "plt.title('Top 10 Items Sold') # Paling Laku"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Top 10 Items Sold')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1800x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1800x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1800x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G4SDu9Of254G"
      },
      "source": [
        "# **Data Modeling**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-8rH8XxMgzex"
      },
      "source": [
        "Clustering based on:\n",
        "* R (Recency): Number of Days since Last Purchase\n",
        "* F (Frequency): Number of Tracsactions\n",
        "* M (Monetary): Total Amount of Transactions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KWHXFBzciUwK"
      },
      "source": [
        "**RECENCY : NUMBER OF DAYS SINCE LAST PURCHASE**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vGfCYI4uW9VI",
        "outputId": "604da2ff-0c18-4256-ecfa-52da219d037c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "df['InvoiceDate'] = pd.to_datetime(df['InvoiceDate'])\n",
        "df.head()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>InvoiceNo</th>\n",
              "      <th>StockCode</th>\n",
              "      <th>Description</th>\n",
              "      <th>Quantity</th>\n",
              "      <th>InvoiceDate</th>\n",
              "      <th>UnitPrice</th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Country</th>\n",
              "      <th>TotalPrice</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>536365</td>\n",
              "      <td>85123A</td>\n",
              "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
              "      <td>6</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>2.55</td>\n",
              "      <td>17850</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>15.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>536365</td>\n",
              "      <td>71053</td>\n",
              "      <td>WHITE METAL LANTERN</td>\n",
              "      <td>6</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>20.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>536365</td>\n",
              "      <td>84406B</td>\n",
              "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
              "      <td>8</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>2.75</td>\n",
              "      <td>17850</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>22.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>536365</td>\n",
              "      <td>84029G</td>\n",
              "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
              "      <td>6</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>20.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>536365</td>\n",
              "      <td>84029E</td>\n",
              "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
              "      <td>6</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>20.34</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  InvoiceNo StockCode  ...         Country  TotalPrice\n",
              "0    536365    85123A  ...  United Kingdom       15.30\n",
              "1    536365     71053  ...  United Kingdom       20.34\n",
              "2    536365    84406B  ...  United Kingdom       22.00\n",
              "3    536365    84029G  ...  United Kingdom       20.34\n",
              "4    536365    84029E  ...  United Kingdom       20.34\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zJzGN1_KaqrZ",
        "outputId": "1d601915-860f-4fbf-f01b-52b9c755ca7c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "maxdate = max(df['InvoiceDate'])\n",
        "maxdate"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp('2011-12-09 12:50:00')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kwwonAeSg1gU",
        "outputId": "79bc084f-6995-4a6e-a186-193e1da370f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "# calculate the differences between maxdate and InvoiceDate\n",
        "df['Recency'] = (maxdate - df['InvoiceDate']).dt.days\n",
        "df.head()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>InvoiceNo</th>\n",
              "      <th>StockCode</th>\n",
              "      <th>Description</th>\n",
              "      <th>Quantity</th>\n",
              "      <th>InvoiceDate</th>\n",
              "      <th>UnitPrice</th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Country</th>\n",
              "      <th>TotalPrice</th>\n",
              "      <th>Recency</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>536365</td>\n",
              "      <td>85123A</td>\n",
              "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
              "      <td>6</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>2.55</td>\n",
              "      <td>17850</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>15.30</td>\n",
              "      <td>373</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>536365</td>\n",
              "      <td>71053</td>\n",
              "      <td>WHITE METAL LANTERN</td>\n",
              "      <td>6</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>20.34</td>\n",
              "      <td>373</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>536365</td>\n",
              "      <td>84406B</td>\n",
              "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
              "      <td>8</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>2.75</td>\n",
              "      <td>17850</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>22.00</td>\n",
              "      <td>373</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>536365</td>\n",
              "      <td>84029G</td>\n",
              "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
              "      <td>6</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>20.34</td>\n",
              "      <td>373</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>536365</td>\n",
              "      <td>84029E</td>\n",
              "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
              "      <td>6</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>20.34</td>\n",
              "      <td>373</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  InvoiceNo StockCode  ... TotalPrice  Recency\n",
              "0    536365    85123A  ...      15.30      373\n",
              "1    536365     71053  ...      20.34      373\n",
              "2    536365    84406B  ...      22.00      373\n",
              "3    536365    84029G  ...      20.34      373\n",
              "4    536365    84029E  ...      20.34      373\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "56MZkVG5iI2s",
        "outputId": "ee98c49d-f2d9-40a7-c776-2acde95ef574",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "rfm_r = df.groupby('CustomerID')['Recency'].min()\n",
        "rfm_r = rfm_r.reset_index()\n",
        "rfm_r.head()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Recency</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12346</td>\n",
              "      <td>325</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12347</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12348</td>\n",
              "      <td>74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12349</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12350</td>\n",
              "      <td>309</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   CustomerID  Recency\n",
              "0       12346      325\n",
              "1       12347        1\n",
              "2       12348       74\n",
              "3       12349       18\n",
              "4       12350      309"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q7QIrtTx0u90",
        "outputId": "d86d3fec-b4a6-4280-ef25-cf311765879b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        }
      },
      "source": [
        "import seaborn as sns\n",
        "sns.distplot(rfm_r['Recency'], kde=False, bins=50)\n",
        "plt.title('Recency Value Distribution', fontsize = 15)\n",
        "plt.xlabel('Recency')\n",
        "plt.ylabel('Count');"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YQEiSfjaow0k"
      },
      "source": [
        "**CLUSTERING WITH K-MEANS**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rxxzpupb_NYj"
      },
      "source": [
        "from sklearn.cluster import KMeans\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import LabelEncoder, MinMaxScaler"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GqVnq5pEKOJI"
      },
      "source": [
        "distortions = []\n",
        "K = range(1,10)\n",
        "for k in K:\n",
        "    kmeanModel = KMeans(n_clusters=k)\n",
        "    kmeanModel.fit(rfm_r)\n",
        "    distortions.append(kmeanModel.inertia_)"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L17PKokUK-bu",
        "outputId": "88480741-fbe9-4649-bb8e-6c70098c199b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        }
      },
      "source": [
        "plt.figure(figsize=(10,8))\n",
        "plt.plot(K, distortions, 'bx-')\n",
        "plt.xlabel('k')\n",
        "plt.ylabel('Distortion')\n",
        "plt.title('The Elbow Method : Optimal K')\n",
        "plt.show()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hX603GyZlyPR"
      },
      "source": [
        "preprocessor = Pipeline([\n",
        "  (\"scaler\", MinMaxScaler())\n",
        "])"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JmyAharc_Q_E"
      },
      "source": [
        "clusterer = Pipeline(\n",
        "   [\n",
        "       (\n",
        "          \"kmeans\",\n",
        "           KMeans(\n",
        "               n_clusters= 3,\n",
        "               init=\"k-means++\",\n",
        "               random_state=42,\n",
        "           ),\n",
        "       ),\n",
        "   ]\n",
        ")"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GKvi-nTF_Y0-"
      },
      "source": [
        "pipe = Pipeline(\n",
        "    [\n",
        "        (\"preprocessor\", preprocessor),\n",
        "        (\"clusterer\", clusterer)\n",
        "    ]\n",
        ")"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G7fsd4GwouyJ",
        "outputId": "a8ca7fcc-0bd4-41ed-c509-d419f5ff18e4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "pipe.fit(rfm_r[['Recency']])"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(memory=None,\n",
              "         steps=[('preprocessor',\n",
              "                 Pipeline(memory=None,\n",
              "                          steps=[('scaler',\n",
              "                                  MinMaxScaler(copy=True,\n",
              "                                               feature_range=(0, 1)))],\n",
              "                          verbose=False)),\n",
              "                ('clusterer',\n",
              "                 Pipeline(memory=None,\n",
              "                          steps=[('kmeans',\n",
              "                                  KMeans(algorithm='auto', copy_x=True,\n",
              "                                         init='k-means++', max_iter=300,\n",
              "                                         n_clusters=3, n_init=10, n_jobs=None,\n",
              "                                         precompute_distances='auto',\n",
              "                                         random_state=42, tol=0.0001,\n",
              "                                         verbose=0))],\n",
              "                          verbose=False))],\n",
              "         verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZhhC7wXr_gtN",
        "outputId": "acce2a88-d52d-4be2-8601-4ca1d18a73cb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "preprocessed_data = pipe[\"preprocessor\"].transform(rfm_r[['Recency']])\n",
        "labels_r = pipe[\"clusterer\"][\"kmeans\"].labels_\n",
        "rfm_r[\"RecencyCluster\"] = labels_r\n",
        "rfm_r.head()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Recency</th>\n",
              "      <th>RecencyCluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12346</td>\n",
              "      <td>325</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12347</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12348</td>\n",
              "      <td>74</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12349</td>\n",
              "      <td>18</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12350</td>\n",
              "      <td>309</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   CustomerID  Recency  RecencyCluster\n",
              "0       12346      325               1\n",
              "1       12347        1               0\n",
              "2       12348       74               0\n",
              "3       12349       18               0\n",
              "4       12350      309               1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aMycq9SG4aTe",
        "outputId": "d1f90928-1c17-41f2-9f9e-9e74a69e5be8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from sklearn.metrics import silhouette_score, adjusted_rand_score\n",
        "silhouette_score(preprocessed_data, labels_r)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6747352836926265"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W_3Axl4AArnz",
        "outputId": "ff43e6e0-9fec-435e-89d9-8add6de287a8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        }
      },
      "source": [
        "rfm_r.groupby('RecencyCluster')['Recency'].describe()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "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",
              "    <tr>\n",
              "      <th>RecencyCluster</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>0</th>\n",
              "      <td>2906.0</td>\n",
              "      <td>31.163799</td>\n",
              "      <td>25.089417</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>50.00</td>\n",
              "      <td>91.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>626.0</td>\n",
              "      <td>293.431310</td>\n",
              "      <td>45.109849</td>\n",
              "      <td>224.0</td>\n",
              "      <td>256.0</td>\n",
              "      <td>286.5</td>\n",
              "      <td>325.75</td>\n",
              "      <td>373.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>806.0</td>\n",
              "      <td>152.400744</td>\n",
              "      <td>38.405673</td>\n",
              "      <td>92.0</td>\n",
              "      <td>118.0</td>\n",
              "      <td>151.5</td>\n",
              "      <td>185.00</td>\n",
              "      <td>222.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 count        mean        std  ...    50%     75%    max\n",
              "RecencyCluster                                 ...                      \n",
              "0               2906.0   31.163799  25.089417  ...   24.0   50.00   91.0\n",
              "1                626.0  293.431310  45.109849  ...  286.5  325.75  373.0\n",
              "2                806.0  152.400744  38.405673  ...  151.5  185.00  222.0\n",
              "\n",
              "[3 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t5wdN6ENCIM3"
      },
      "source": [
        "def cluster_order(cluster, target, df, ascending):\n",
        "    new_cluster = 'new_' + cluster\n",
        "    df_new = df.groupby(cluster)[target].mean().reset_index()\n",
        "    df_new = df_new.sort_values(by = target, ascending = ascending).reset_index(drop=True)\n",
        "    df_new['index'] = df_new.index\n",
        "    df_fin = pd.merge(df, df_new[[cluster,'index']], on=cluster)\n",
        "    df_fin = df_fin.drop([cluster],axis=1)\n",
        "    df_fin = df_fin.rename(columns={\"index\":cluster})\n",
        "    return df_fin"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AE1No-_1BIUq",
        "outputId": "2944d4f2-b4f5-45d3-9aa7-13ac0ae3dcdc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "rfm_r = cluster_order('RecencyCluster', 'Recency',rfm_r,False)\n",
        "rfm_r.head()"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "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",
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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>CustomerID</th>\n",
              "      <th>Recency</th>\n",
              "      <th>RecencyCluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12346</td>\n",
              "      <td>325</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12350</td>\n",
              "      <td>309</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12354</td>\n",
              "      <td>231</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12361</td>\n",
              "      <td>286</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12365</td>\n",
              "      <td>290</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   CustomerID  Recency  RecencyCluster\n",
              "0       12346      325               0\n",
              "1       12350      309               0\n",
              "2       12354      231               0\n",
              "3       12361      286               0\n",
              "4       12365      290               0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kqMx2OiLnfN_",
        "outputId": "6b001f49-05e0-46c2-b800-0ee1d7d4efe9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        }
      },
      "source": [
        "rfm_r.groupby('RecencyCluster')['Recency'].describe()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "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",
              "    <tr>\n",
              "      <th>RecencyCluster</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>0</th>\n",
              "      <td>626.0</td>\n",
              "      <td>293.431310</td>\n",
              "      <td>45.109849</td>\n",
              "      <td>224.0</td>\n",
              "      <td>256.0</td>\n",
              "      <td>286.5</td>\n",
              "      <td>325.75</td>\n",
              "      <td>373.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>806.0</td>\n",
              "      <td>152.400744</td>\n",
              "      <td>38.405673</td>\n",
              "      <td>92.0</td>\n",
              "      <td>118.0</td>\n",
              "      <td>151.5</td>\n",
              "      <td>185.00</td>\n",
              "      <td>222.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2906.0</td>\n",
              "      <td>31.163799</td>\n",
              "      <td>25.089417</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>50.00</td>\n",
              "      <td>91.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 count        mean        std  ...    50%     75%    max\n",
              "RecencyCluster                                 ...                      \n",
              "0                626.0  293.431310  45.109849  ...  286.5  325.75  373.0\n",
              "1                806.0  152.400744  38.405673  ...  151.5  185.00  222.0\n",
              "2               2906.0   31.163799  25.089417  ...   24.0   50.00   91.0\n",
              "\n",
              "[3 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cQFfufJLidM_"
      },
      "source": [
        "**FREQUENCY : NUMBER OF TRANSACTIONS**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2iQo9tsWiPOC",
        "outputId": "ab16182e-0589-43ce-9dad-400c9a7e1601",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "rfm_f = df.groupby('CustomerID')['InvoiceNo'].count()\n",
        "rfm_f = rfm_f.reset_index()\n",
        "rfm_f.columns = ['CustomerID', 'Frequency']\n",
        "rfm_f.head()"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Frequency</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12346</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12347</td>\n",
              "      <td>182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12348</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12349</td>\n",
              "      <td>73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12350</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   CustomerID  Frequency\n",
              "0       12346          1\n",
              "1       12347        182\n",
              "2       12348         31\n",
              "3       12349         73\n",
              "4       12350         17"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dkFJasOj5HbX",
        "outputId": "ae7a6e4a-3753-459f-c84f-253d04217832",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        }
      },
      "source": [
        "sns.distplot(rfm_f['Frequency'], kde=False, bins=50)\n",
        "plt.title('Frequency Value Distribution', fontsize = 15)\n",
        "plt.xlabel('Frequency')\n",
        "plt.ylabel('Count');"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gxJvNYe95UQI"
      },
      "source": [
        "**CLUSTERING WITH K-MEANS**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fwpU2Nht5iJ6",
        "outputId": "7058bdc2-7bdc-43da-f398-2c190b7500b4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "pipe.fit(rfm_f[['Frequency']])"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(memory=None,\n",
              "         steps=[('preprocessor',\n",
              "                 Pipeline(memory=None,\n",
              "                          steps=[('scaler',\n",
              "                                  MinMaxScaler(copy=True,\n",
              "                                               feature_range=(0, 1)))],\n",
              "                          verbose=False)),\n",
              "                ('clusterer',\n",
              "                 Pipeline(memory=None,\n",
              "                          steps=[('kmeans',\n",
              "                                  KMeans(algorithm='auto', copy_x=True,\n",
              "                                         init='k-means++', max_iter=300,\n",
              "                                         n_clusters=3, n_init=10, n_jobs=None,\n",
              "                                         precompute_distances='auto',\n",
              "                                         random_state=42, tol=0.0001,\n",
              "                                         verbose=0))],\n",
              "                          verbose=False))],\n",
              "         verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zSBTtYFnIacY",
        "outputId": "6e49d552-a2a2-4ca9-c274-f3e386e759fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "preprocessed_data = pipe[\"preprocessor\"].transform(rfm_f[['Frequency']])\n",
        "labels_f = pipe[\"clusterer\"][\"kmeans\"].labels_\n",
        "rfm_f[\"FrequencyCluster\"] = labels_f\n",
        "rfm_f.head()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Frequency</th>\n",
              "      <th>FrequencyCluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12346</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12347</td>\n",
              "      <td>182</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12348</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12349</td>\n",
              "      <td>73</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12350</td>\n",
              "      <td>17</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   CustomerID  Frequency  FrequencyCluster\n",
              "0       12346          1                 0\n",
              "1       12347        182                 0\n",
              "2       12348         31                 0\n",
              "3       12349         73                 0\n",
              "4       12350         17                 0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hCaYVfSt5DDO",
        "outputId": "eee5c9d7-a602-47e4-bc19-8cbe99974cf2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "silhouette_score(preprocessed_data, labels_f)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8172792903095373"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6pIVoVL2DEpo",
        "outputId": "20177626-301e-4db6-c30c-6433efa43b71",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        }
      },
      "source": [
        "rfm_f.groupby('FrequencyCluster')['Frequency'].describe()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "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",
              "    <tr>\n",
              "      <th>FrequencyCluster</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>0</th>\n",
              "      <td>4072.0</td>\n",
              "      <td>59.639244</td>\n",
              "      <td>60.203016</td>\n",
              "      <td>1.0</td>\n",
              "      <td>16.00</td>\n",
              "      <td>37.0</td>\n",
              "      <td>85.00</td>\n",
              "      <td>281.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.0</td>\n",
              "      <td>5807.000000</td>\n",
              "      <td>1429.730044</td>\n",
              "      <td>4595.0</td>\n",
              "      <td>4982.00</td>\n",
              "      <td>5393.0</td>\n",
              "      <td>6218.00</td>\n",
              "      <td>7847.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>262.0</td>\n",
              "      <td>503.072519</td>\n",
              "      <td>321.281281</td>\n",
              "      <td>282.0</td>\n",
              "      <td>327.25</td>\n",
              "      <td>405.5</td>\n",
              "      <td>540.25</td>\n",
              "      <td>2700.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   count         mean          std  ...     50%      75%     max\n",
              "FrequencyCluster                                    ...                         \n",
              "0                 4072.0    59.639244    60.203016  ...    37.0    85.00   281.0\n",
              "1                    4.0  5807.000000  1429.730044  ...  5393.0  6218.00  7847.0\n",
              "2                  262.0   503.072519   321.281281  ...   405.5   540.25  2700.0\n",
              "\n",
              "[3 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6UB68f6VDEwI",
        "outputId": "8fb016c0-b860-484b-d7e7-b712ceec1fb9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "rfm_f = cluster_order('FrequencyCluster', 'Frequency',rfm_f,True)\n",
        "rfm_f.head()"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Frequency</th>\n",
              "      <th>FrequencyCluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
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              "      <td>12348</td>\n",
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              "      <td>12349</td>\n",
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              "      <td>12350</td>\n",
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              "</div>"
            ],
            "text/plain": [
              "   CustomerID  Frequency  FrequencyCluster\n",
              "0       12346          1                 0\n",
              "1       12347        182                 0\n",
              "2       12348         31                 0\n",
              "3       12349         73                 0\n",
              "4       12350         17                 0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ebntPr3Mnu9d",
        "outputId": "7ad17b10-8188-4ec9-b96c-dbfc5653327a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        }
      },
      "source": [
        "rfm_f.groupby('FrequencyCluster')['Frequency'].describe()"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <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",
              "    <tr>\n",
              "      <th>FrequencyCluster</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <td>4072.0</td>\n",
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              "      <td>60.203016</td>\n",
              "      <td>1.0</td>\n",
              "      <td>16.00</td>\n",
              "      <td>37.0</td>\n",
              "      <td>85.00</td>\n",
              "      <td>281.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>262.0</td>\n",
              "      <td>503.072519</td>\n",
              "      <td>321.281281</td>\n",
              "      <td>282.0</td>\n",
              "      <td>327.25</td>\n",
              "      <td>405.5</td>\n",
              "      <td>540.25</td>\n",
              "      <td>2700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.0</td>\n",
              "      <td>5807.000000</td>\n",
              "      <td>1429.730044</td>\n",
              "      <td>4595.0</td>\n",
              "      <td>4982.00</td>\n",
              "      <td>5393.0</td>\n",
              "      <td>6218.00</td>\n",
              "      <td>7847.0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   count         mean          std  ...     50%      75%     max\n",
              "FrequencyCluster                                    ...                         \n",
              "0                 4072.0    59.639244    60.203016  ...    37.0    85.00   281.0\n",
              "1                  262.0   503.072519   321.281281  ...   405.5   540.25  2700.0\n",
              "2                    4.0  5807.000000  1429.730044  ...  5393.0  6218.00  7847.0\n",
              "\n",
              "[3 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R-ou0cKji7jx"
      },
      "source": [
        "**MONETARY : TOTAL AMOUNT OF TRANSACTIONS**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x1pwoU55it9B",
        "outputId": "dbdbd0f6-411f-4be0-ec3e-bfce0e81a76b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "df['Monetary'] = df['Quantity']*df['UnitPrice']\n",
        "rfm_m = df.groupby('CustomerID')['Monetary'].sum()\n",
        "rfm_m = rfm_m.reset_index()\n",
        "rfm_m.head()"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Monetary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12346</td>\n",
              "      <td>77183.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12347</td>\n",
              "      <td>4310.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12348</td>\n",
              "      <td>1797.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12349</td>\n",
              "      <td>1757.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12350</td>\n",
              "      <td>334.40</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   CustomerID  Monetary\n",
              "0       12346  77183.60\n",
              "1       12347   4310.00\n",
              "2       12348   1797.24\n",
              "3       12349   1757.55\n",
              "4       12350    334.40"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8UWgcwiXjcPS",
        "outputId": "3493232b-c08a-44ec-dc88-61218fc4c1af",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        }
      },
      "source": [
        "sns.distplot(rfm_m['Monetary'], kde=False, bins=50)\n",
        "plt.title('Monetary Value Distribution', fontsize = 15)\n",
        "plt.xlabel('Monetary')\n",
        "plt.ylabel('Count');"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hyvxG3QGJpDl",
        "outputId": "2582c2f1-f8b0-4e49-f0d6-4af0e87f0d70",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "pipe.fit(rfm_m[['Monetary']])"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(memory=None,\n",
              "         steps=[('preprocessor',\n",
              "                 Pipeline(memory=None,\n",
              "                          steps=[('scaler',\n",
              "                                  MinMaxScaler(copy=True,\n",
              "                                               feature_range=(0, 1)))],\n",
              "                          verbose=False)),\n",
              "                ('clusterer',\n",
              "                 Pipeline(memory=None,\n",
              "                          steps=[('kmeans',\n",
              "                                  KMeans(algorithm='auto', copy_x=True,\n",
              "                                         init='k-means++', max_iter=300,\n",
              "                                         n_clusters=3, n_init=10, n_jobs=None,\n",
              "                                         precompute_distances='auto',\n",
              "                                         random_state=42, tol=0.0001,\n",
              "                                         verbose=0))],\n",
              "                          verbose=False))],\n",
              "         verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SJvu6IesJpNg",
        "outputId": "646d5db0-4527-4619-8c43-a75713a707b7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "preprocessed_data = pipe[\"preprocessor\"].transform(rfm_m[['Monetary']])\n",
        "labels_m = pipe[\"clusterer\"][\"kmeans\"].labels_\n",
        "rfm_m[\"MonetaryCluster\"] = labels_m\n",
        "rfm_m.head()"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Monetary</th>\n",
              "      <th>MonetaryCluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12346</td>\n",
              "      <td>77183.60</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12347</td>\n",
              "      <td>4310.00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12348</td>\n",
              "      <td>1797.24</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12349</td>\n",
              "      <td>1757.55</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12350</td>\n",
              "      <td>334.40</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   CustomerID  Monetary  MonetaryCluster\n",
              "0       12346  77183.60                2\n",
              "1       12347   4310.00                0\n",
              "2       12348   1797.24                0\n",
              "3       12349   1757.55                0\n",
              "4       12350    334.40                0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xVqMmgXR5aV1",
        "outputId": "2b15a9b1-0de0-4f29-c613-ffe59f7fb63e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "silhouette_score(preprocessed_data, labels_m)"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9615878134391025"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1RaQbkgRJpXQ",
        "outputId": "7730d3d8-61d8-4479-8f63-6389712be5ba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        }
      },
      "source": [
        "rfm_m.groupby('MonetaryCluster')['Monetary'].describe()"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "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",
              "    <tr>\n",
              "      <th>MonetaryCluster</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>0</th>\n",
              "      <td>4307.0</td>\n",
              "      <td>1480.796985</td>\n",
              "      <td>2463.849989</td>\n",
              "      <td>3.75</td>\n",
              "      <td>306.5050</td>\n",
              "      <td>665.86</td>\n",
              "      <td>1624.6300</td>\n",
              "      <td>28882.44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5.0</td>\n",
              "      <td>209342.334000</td>\n",
              "      <td>58598.003581</td>\n",
              "      <td>143825.06</td>\n",
              "      <td>168472.5000</td>\n",
              "      <td>194550.79</td>\n",
              "      <td>259657.3000</td>\n",
              "      <td>280206.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>26.0</td>\n",
              "      <td>57188.600769</td>\n",
              "      <td>24729.928330</td>\n",
              "      <td>31833.68</td>\n",
              "      <td>37844.5125</td>\n",
              "      <td>53030.72</td>\n",
              "      <td>65133.4975</td>\n",
              "      <td>124914.53</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  count           mean  ...          75%        max\n",
              "MonetaryCluster                         ...                        \n",
              "0                4307.0    1480.796985  ...    1624.6300   28882.44\n",
              "1                   5.0  209342.334000  ...  259657.3000  280206.02\n",
              "2                  26.0   57188.600769  ...   65133.4975  124914.53\n",
              "\n",
              "[3 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wWsdU6QYKQl-",
        "outputId": "ea0e4982-7f02-4656-eada-0f8aea24cbba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "rfm_m = cluster_order('MonetaryCluster', 'Monetary',rfm_m,True)\n",
        "rfm_m.head()"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Monetary</th>\n",
              "      <th>MonetaryCluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12346</td>\n",
              "      <td>77183.60</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12415</td>\n",
              "      <td>124914.53</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12748</td>\n",
              "      <td>33719.73</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12931</td>\n",
              "      <td>42055.96</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>13089</td>\n",
              "      <td>58825.83</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   CustomerID   Monetary  MonetaryCluster\n",
              "0       12346   77183.60                1\n",
              "1       12415  124914.53                1\n",
              "2       12748   33719.73                1\n",
              "3       12931   42055.96                1\n",
              "4       13089   58825.83                1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hlyvosybnzoe",
        "outputId": "35b08f68-c209-4d21-c9ac-349f0e5b28e9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        }
      },
      "source": [
        "rfm_m.groupby('MonetaryCluster')['Monetary'].describe()"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<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",
              "    <tr>\n",
              "      <th>MonetaryCluster</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>0</th>\n",
              "      <td>4307.0</td>\n",
              "      <td>1480.796985</td>\n",
              "      <td>2463.849989</td>\n",
              "      <td>3.75</td>\n",
              "      <td>306.5050</td>\n",
              "      <td>665.86</td>\n",
              "      <td>1624.6300</td>\n",
              "      <td>28882.44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>26.0</td>\n",
              "      <td>57188.600769</td>\n",
              "      <td>24729.928330</td>\n",
              "      <td>31833.68</td>\n",
              "      <td>37844.5125</td>\n",
              "      <td>53030.72</td>\n",
              "      <td>65133.4975</td>\n",
              "      <td>124914.53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5.0</td>\n",
              "      <td>209342.334000</td>\n",
              "      <td>58598.003581</td>\n",
              "      <td>143825.06</td>\n",
              "      <td>168472.5000</td>\n",
              "      <td>194550.79</td>\n",
              "      <td>259657.3000</td>\n",
              "      <td>280206.02</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  count           mean  ...          75%        max\n",
              "MonetaryCluster                         ...                        \n",
              "0                4307.0    1480.796985  ...    1624.6300   28882.44\n",
              "1                  26.0   57188.600769  ...   65133.4975  124914.53\n",
              "2                   5.0  209342.334000  ...  259657.3000  280206.02\n",
              "\n",
              "[3 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BGDhcErVjWGs"
      },
      "source": [
        "**MERGE RFM FACTORS**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ohHlSO2jIFL",
        "outputId": "b80535f2-dca3-4200-c3b8-1a5835063c3c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "rfm = pd.merge(pd.merge(rfm_r, rfm_f, on='CustomerID'), rfm_m, on='CustomerID')\n",
        "rfm.columns = ['CustomerID', 'Recency', 'RecencyCluster', 'Frequency', 'FrequencyCluster', 'Monetary', 'MonetaryCluster']\n",
        "rfm.head()"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Recency</th>\n",
              "      <th>RecencyCluster</th>\n",
              "      <th>Frequency</th>\n",
              "      <th>FrequencyCluster</th>\n",
              "      <th>Monetary</th>\n",
              "      <th>MonetaryCluster</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12350</td>\n",
              "      <td>309</td>\n",
              "      <td>0</td>\n",
              "      <td>17</td>\n",
              "      <td>0</td>\n",
              "      <td>334.40</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12354</td>\n",
              "      <td>231</td>\n",
              "      <td>0</td>\n",
              "      <td>58</td>\n",
              "      <td>0</td>\n",
              "      <td>1079.40</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12361</td>\n",
              "      <td>286</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>189.90</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12365</td>\n",
              "      <td>290</td>\n",
              "      <td>0</td>\n",
              "      <td>22</td>\n",
              "      <td>0</td>\n",
              "      <td>641.38</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   CustomerID  Recency  ...  Monetary  MonetaryCluster\n",
              "0       12346      325  ...  77183.60                1\n",
              "1       12350      309  ...    334.40                0\n",
              "2       12354      231  ...   1079.40                0\n",
              "3       12361      286  ...    189.90                0\n",
              "4       12365      290  ...    641.38                0\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "84Mpxte6qkUW"
      },
      "source": [
        "**CALCULATE RFM SCORE**\n",
        "* Recency : the **higher** the Recency value, the **higher** the score.\n",
        "* Frequency : the **higher** the Frequency value, the **higher** the score.\n",
        "* Monetary : the **higher** the Monetary value, the **higher** the score."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GkNU8Xs0qRio",
        "outputId": "cf53d708-1c56-488a-eab1-3d60d5ed4cda",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "rfm['RFMScore'] = rfm['RecencyCluster'] + rfm['FrequencyCluster'] + rfm['MonetaryCluster']\n",
        "rfm.head()"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Recency</th>\n",
              "      <th>RecencyCluster</th>\n",
              "      <th>Frequency</th>\n",
              "      <th>FrequencyCluster</th>\n",
              "      <th>Monetary</th>\n",
              "      <th>MonetaryCluster</th>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12350</td>\n",
              "      <td>309</td>\n",
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              "      <td>17</td>\n",
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              "      <th>2</th>\n",
              "      <td>12354</td>\n",
              "      <td>231</td>\n",
              "      <td>0</td>\n",
              "      <td>58</td>\n",
              "      <td>0</td>\n",
              "      <td>1079.40</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12361</td>\n",
              "      <td>286</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>189.90</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
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              "      <td>12365</td>\n",
              "      <td>290</td>\n",
              "      <td>0</td>\n",
              "      <td>22</td>\n",
              "      <td>0</td>\n",
              "      <td>641.38</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   CustomerID  Recency  RecencyCluster  ...  Monetary  MonetaryCluster  RFMScore\n",
              "0       12346      325               0  ...  77183.60                1         1\n",
              "1       12350      309               0  ...    334.40                0         0\n",
              "2       12354      231               0  ...   1079.40                0         0\n",
              "3       12361      286               0  ...    189.90                0         0\n",
              "4       12365      290               0  ...    641.38                0         0\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2-lZDkV0rrC5",
        "outputId": "6e1fbc94-706a-4e93-a4c7-fa7e4b6a7a02",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "rfm['RFMScore'].unique()"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 2, 4, 3, 5, 6])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jQBx7NNkrs7D",
        "outputId": "2dd09938-41e5-4b74-9cb7-2b94cdc435eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "rfm['Membership'] = 'Bronze' \n",
        "rfm.loc[rfm['MonetaryCluster'] > 0, 'Membership'] = 'Silver' \n",
        "rfm.loc[rfm['MonetaryCluster'] > 1, 'Membership'] = 'Gold'\n",
        "rfm.head()"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Recency</th>\n",
              "      <th>RecencyCluster</th>\n",
              "      <th>Frequency</th>\n",
              "      <th>FrequencyCluster</th>\n",
              "      <th>Monetary</th>\n",
              "      <th>MonetaryCluster</th>\n",
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              "      <td>1</td>\n",
              "      <td>Silver</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12350</td>\n",
              "      <td>309</td>\n",
              "      <td>0</td>\n",
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              "      <td>334.40</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12354</td>\n",
              "      <td>231</td>\n",
              "      <td>0</td>\n",
              "      <td>58</td>\n",
              "      <td>0</td>\n",
              "      <td>1079.40</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Bronze</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12361</td>\n",
              "      <td>286</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>189.90</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Bronze</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12365</td>\n",
              "      <td>290</td>\n",
              "      <td>0</td>\n",
              "      <td>22</td>\n",
              "      <td>0</td>\n",
              "      <td>641.38</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Bronze</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   CustomerID  Recency  RecencyCluster  ...  MonetaryCluster  RFMScore  Membership\n",
              "0       12346      325               0  ...                1         1      Silver\n",
              "1       12350      309               0  ...                0         0      Bronze\n",
              "2       12354      231               0  ...                0         0      Bronze\n",
              "3       12361      286               0  ...                0         0      Bronze\n",
              "4       12365      290               0  ...                0         0      Bronze\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IQC8rZphMn3u",
        "outputId": "a35714fa-249d-4993-fdbd-f285952265ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        }
      },
      "source": [
        "barplot = dict(rfm['Membership'].value_counts())\n",
        "bar_names = list(barplot.keys())\n",
        "bar_values = list(barplot.values())\n",
        "plt.bar(bar_names,bar_values)\n",
        "print(pd.DataFrame(barplot, index=[' ']))"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   Bronze  Silver  Gold\n",
            "     4307      26     5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NpoRDooD53IM"
      },
      "source": [
        "# **Conclusion**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d3cMG2FZ5-cB"
      },
      "source": [
        "**1. Core - Your Best Customers**\n",
        "* RFM Score : 222\n",
        "* Who They Are : Highly engaged customers who have bought the most recent, the most often, and generated the most revenue."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kseMfPgt11tT",
        "outputId": "d73ed5a6-6ed0-45a8-ce12-571c1da8c618",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 77
        }
      },
      "source": [
        "core_cust = rfm[rfm['RFMScore'] == 6].sort_values('CustomerID', ascending=True)\n",
        "core_cust.head()"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Recency</th>\n",
              "      <th>RecencyCluster</th>\n",
              "      <th>Frequency</th>\n",
              "      <th>FrequencyCluster</th>\n",
              "      <th>Monetary</th>\n",
              "      <th>MonetaryCluster</th>\n",
              "      <th>RFMScore</th>\n",
              "      <th>Membership</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1894</th>\n",
              "      <td>14911</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>5675</td>\n",
              "      <td>2</td>\n",
              "      <td>143825.06</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>Gold</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      CustomerID  Recency  RecencyCluster  ...  MonetaryCluster  RFMScore  Membership\n",
              "1894       14911        0               2  ...                2         6        Gold\n",
              "\n",
              "[1 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wcPUw1cvD7S8",
        "outputId": "c9b6e381-640f-439f-a485-352104605ffd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "core_cust['CustomerID'].count()"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NC2V6DhsEX-W"
      },
      "source": [
        "**2. Loyal - Your Most Loyal Customers**\n",
        "* RFM Score : X2X\n",
        "* Who They Are : Customers who buy the most often from your store."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jzktki3WEN0n",
        "outputId": "d71a0220-c0f3-4bfe-9555-77e566fd02a0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        }
      },
      "source": [
        "loyal_cust = rfm[rfm['FrequencyCluster'] == 2].sort_values('Frequency', ascending=False)\n",
        "loyal_cust.head()"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Recency</th>\n",
              "      <th>RecencyCluster</th>\n",
              "      <th>Frequency</th>\n",
              "      <th>FrequencyCluster</th>\n",
              "      <th>Monetary</th>\n",
              "      <th>MonetaryCluster</th>\n",
              "      <th>RFMScore</th>\n",
              "      <th>Membership</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3333</th>\n",
              "      <td>17841</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>7847</td>\n",
              "      <td>2</td>\n",
              "      <td>40991.57</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>Silver</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1894</th>\n",
              "      <td>14911</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>5675</td>\n",
              "      <td>2</td>\n",
              "      <td>143825.06</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>Gold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1491</th>\n",
              "      <td>14096</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>5111</td>\n",
              "      <td>2</td>\n",
              "      <td>65164.79</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>Silver</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>858</th>\n",
              "      <td>12748</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>4595</td>\n",
              "      <td>2</td>\n",
              "      <td>33719.73</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>Silver</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      CustomerID  Recency  RecencyCluster  ...  MonetaryCluster  RFMScore  Membership\n",
              "3333       17841        1               2  ...                1         5      Silver\n",
              "1894       14911        0               2  ...                2         6        Gold\n",
              "1491       14096        3               2  ...                1         5      Silver\n",
              "858        12748        0               2  ...                1         5      Silver\n",
              "\n",
              "[4 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SbfMWk4SE6_N",
        "outputId": "b341ab28-19c6-468b-9da8-ab7ba878870c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "loyal_cust['CustomerID'].count()"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DNU56yea-mga",
        "outputId": "f38d12cd-3e5f-4c59-bb61-bb564bc5cf32",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        }
      },
      "source": [
        "loyal_cust.plot.bar(x='CustomerID', y='Frequency', rot=0)\n",
        "plt.title('Most Loyal Customer', size=15)"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Most Loyal Customer')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lRhAftCivjDK",
        "outputId": "09ff4ced-4909-43b9-a612-2bdcb77633f2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        }
      },
      "source": [
        "loyal_cust.plot.bar(x='Membership', y='Frequency', rot=0)\n",
        "plt.title('Most Loyal Customer', size=15)"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Most Loyal Customer')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xcDuf5ykF7Ca"
      },
      "source": [
        "**3. Whales - Your Highest Paying Customers**\n",
        "* RFM Score : XX2\n",
        "* Who They Are : Customers who have generated the most revenue for your store."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xcD3h2wkE-NE",
        "outputId": "bda9197c-27ab-4c8a-c08f-741bb519b028",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "whales_cust = rfm[rfm['MonetaryCluster'] == 2].sort_values('Monetary', ascending=False)\n",
        "whales_cust.head()"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Recency</th>\n",
              "      <th>RecencyCluster</th>\n",
              "      <th>Frequency</th>\n",
              "      <th>FrequencyCluster</th>\n",
              "      <th>Monetary</th>\n",
              "      <th>MonetaryCluster</th>\n",
              "      <th>RFMScore</th>\n",
              "      <th>Membership</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1755</th>\n",
              "      <td>14646</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2076</td>\n",
              "      <td>1</td>\n",
              "      <td>280206.02</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>Gold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3441</th>\n",
              "      <td>18102</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>431</td>\n",
              "      <td>1</td>\n",
              "      <td>259657.30</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>Gold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3137</th>\n",
              "      <td>17450</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>337</td>\n",
              "      <td>1</td>\n",
              "      <td>194550.79</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>Gold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2646</th>\n",
              "      <td>16446</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>168472.50</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>Gold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1894</th>\n",
              "      <td>14911</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>5675</td>\n",
              "      <td>2</td>\n",
              "      <td>143825.06</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>Gold</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      CustomerID  Recency  RecencyCluster  ...  MonetaryCluster  RFMScore  Membership\n",
              "1755       14646        1               2  ...                2         5        Gold\n",
              "3441       18102        0               2  ...                2         5        Gold\n",
              "3137       17450        7               2  ...                2         5        Gold\n",
              "2646       16446        0               2  ...                2         4        Gold\n",
              "1894       14911        0               2  ...                2         6        Gold\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CloLfCrdGGwW",
        "outputId": "20b229b4-5e63-481e-c3e3-e7f4ba401c48",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "whales_cust['CustomerID'].count()"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q3PnLaRkAdds",
        "outputId": "b38ff37e-59ea-4d03-a452-4d4965dfce58",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        }
      },
      "source": [
        "whales_cust.plot.bar(x='CustomerID', y='Monetary', rot=0)\n",
        "plt.title('Highest Paying Customer', size=15)"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Highest Paying Customer')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TnKx8d-VwJHj",
        "outputId": "31591e7b-29be-4bc5-f864-d5f0f7d6e0b4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        }
      },
      "source": [
        "whales_cust.plot.bar(x='Membership', y='Monetary', rot=0)\n",
        "plt.title('Highest Paying Customer', size=15)"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Highest Paying Customer')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ZmWX5SMIa5c+MmHVtPpIwM7Msh4SZmWU5JMzMLKvJkJC0p6R7JD0h6XFJX0n18yUtl7Q43Y4uLfNNScskPSXpyFJ9XKotk3Ruqb63pAdS/SZJ7031nun5sjR9cHu+eDMza1xzjiTWA2dHxFBgNHCWpKFp2o8jYni6zQVI0yYC+wHjgJ9L6iapG3A5cBQwFDi5tJ7vpXV9BHgVqP9WqjOAV1P9x2k+MzPrIE2GRES8GBF/So/fAJ4EGvvl7/HA7Ih4JyKeBZYBh6bbsoh4JiLWArOB8ZIEHA7ckpafCUworav+u3ZvAY5I85uZWQdo0TmJNNwzAngglaZJWiKpRlLfVBsIvFBarC7VcvV+wGsRsb6ivsm60vTVaf7Kdk2VVCup1p/GNDNrP80OCUk7ArcCX42I14ErgA8Dw4EXgfb5XtpWiIirImJkRIwcMGBAZzXDzGy706yQkNSDIiBujIhfA0TEyxGxISLeBX5BMZwEsBzYs7T4oFTL1VcCO0vqXlHfZF1p+vvT/GZm1gGac3WTgGuAJyPiR6X67qXZjgMeS4/nABPTlUl7A0OAB4GHgCHpSqb3UpzcnhPFVzreA5yQlp8E/La0rknp8QnA3eGvgDQz6zDN+VqOjwOnAo9KWpxq51FcnTQcCOA54AsAEfG4pJuBJyiujDorIjYASJoGzAO6ATUR8Xha3zeA2ZIuAh6mCCXS/S8lLQNWUQSLmZl1kCZDIiLuA6pdUTS3kWUuBi6uUp9bbbmIeIaG4apyfQ1wYlNtNDOzLcOfuDYzsyyHhJmZZTkkzMwsyyFhZmZZDgkzM8tySJiZWZZDwszMshwSZmaW5ZAwM7Msh4SZmWU5JMzMLMshYWZmWQ4JMzPLckiYmVmWQ8LMzLIcEmZmluWQMDOzLIeEmZllOSTMzCzLIWFmZlkOCTMzy3JImJlZlkPCzMyyHBJmZpblkDAzsyyHhJmZZTkkzMwsyyFhZmZZTYaEpD0l3SPpCUmPS/pKqu8iab6kp9N931SXpBmSlklaIumg0rompfmfljSpVD9Y0qNpmRmS1Ng2zMysYzTnSGI9cHZEDAVGA2dJGgqcC9wVEUOAu9JzgKOAIek2FbgCih0+MB0YBRwKTC/t9K8APl9ablyq57ZhZmYdoMmQiIgXI+JP6fEbwJPAQGA8MDPNNhOYkB6PB66PwkJgZ0m7A0cC8yNiVUS8CswHxqVpO0XEwogI4PqKdVXbhpmZdYAWnZOQNBgYATwA7BYRL6ZJLwG7pccDgRdKi9WlWmP1uip1GtlGZbumSqqVVLtixYqWvCQzM2tEs0NC0o7ArcBXI+L18rR0BBDt3LZNNLaNiLgqIkZGxMgBAwZsyWaYmXUpzQoJST0oAuLGiPh1Kr+chopI96+k+nJgz9Lig1KtsfqgKvXGtmFmZh2gOVc3CbgGeDIiflSaNAeov0JpEvDbUv20dJXTaGB1GjKaB4yV1DedsB4LzEvTXpc0Om3rtIp1VduGmZl1gO7NmOfjwKnAo5IWp9p5wKXAzZLOAJ4HTkrT5gJHA8uAt4DJABGxStKFwENpvu9ExKr0+EzgOmAH4M50o5FtmHW4wefe0dlN4LlLj+nsJlgX02RIRMR9gDKTj6gyfwBnZdZVA9RUqdcC+1epr6y2DTMz6xj+xLWZmWU5JMzMLMshYWZmWQ4JMzPLckiYmVmWQ8LMzLIcEmZmluWQMDOzLIeEmZllOSTMzCzLIWFmZlkOCTMzy3JImJlZlkPCzMyyHBJmZpblkDAzsyyHhJmZZTXn50vNzDbhn3LtOnwkYWZmWQ4JMzPLckiYmVmWQ8LMzLIcEmZmluWQMDOzLIeEmZllOSTMzCzLIWFmZlkOCTMzy2oyJCTVSHpF0mOl2vmSlktanG5Hl6Z9U9IySU9JOrJUH5dqyySdW6rvLemBVL9J0ntTvWd6vixNH9xeL9rMzJqnOUcS1wHjqtR/HBHD020ugKShwERgv7TMzyV1k9QNuBw4ChgKnJzmBfheWtdHgFeBM1L9DODVVP9xms/MzDpQkyEREfcCq5q5vvHA7Ih4JyKeBZYBh6bbsoh4JiLWArOB8ZIEHA7ckpafCUworWtmenwLcESa38zMOkhbzklMk7QkDUf1TbWBwAuleepSLVfvB7wWEesr6pusK01fnebfjKSpkmol1a5YsaINL8nMzMpaGxJXAB8GhgMvApe1W4taISKuioiRETFywIABndkUM7PtSqtCIiJejogNEfEu8AuK4SSA5cCepVkHpVquvhLYWVL3ivom60rT35/mNzOzDtKqkJC0e+npcUD9lU9zgInpyqS9gSHAg8BDwJB0JdN7KU5uz4mIAO4BTkjLTwJ+W1rXpPT4BODuNL+ZmXWQJn+ZTtIsYAzQX1IdMB0YI2k4EMBzwBcAIuJxSTcDTwDrgbMiYkNazzRgHtANqImIx9MmvgHMlnQR8DBwTapfA/xS0jKKE+cT2/xqzcysRZoMiYg4uUr5miq1+vkvBi6uUp8LzK1Sf4aG4apyfQ1wYlPtMzPrTNv7T7n6E9dmZpblkDAzsyyHhJmZZTkkzMwsyyFhZmZZDgkzM8tySJiZWZZDwszMshwSZmaW5ZAwM7Msh4SZmWU5JMzMLMshYWZmWQ4JMzPLckiYmVmWQ8LMzLIcEmZmluWQMDOzLIeEmZllOSTMzCzLIWFmZlkOCTMzy3JImJlZlkPCzMyyHBJmZpblkDAzsyyHhJmZZTkkzMwsq8mQkFQj6RVJj5Vqu0iaL+npdN831SVphqRlkpZIOqi0zKQ0/9OSJpXqB0t6NC0zQ5Ia24aZmXWc5hxJXAeMq6idC9wVEUOAu9JzgKOAIek2FbgCih0+MB0YBRwKTC/t9K8APl9ablwT2zAzsw7SZEhExL3AqoryeGBmejwTmFCqXx+FhcDOknYHjgTmR8SqiHgVmA+MS9N2ioiFERHA9RXrqrYNMzPrIK09J7FbRLyYHr8E7JYeDwReKM1Xl2qN1euq1BvbxmYkTZVUK6l2xYoVrXg5ZmZWTZtPXKcjgGiHtrR6GxFxVUSMjIiRAwYM2JJNMTPrUlobEi+noSLS/SupvhzYszTfoFRrrD6oSr2xbZiZWQdpbUjMAeqvUJoE/LZUPy1d5TQaWJ2GjOYBYyX1TSesxwLz0rTXJY1OVzWdVrGuatswM7MO0r2pGSTNAsYA/SXVUVyldClws6QzgOeBk9Lsc4GjgWXAW8BkgIhYJelC4KE033ciov5k+JkUV1DtANyZbjSyDTMz6yBNhkREnJyZdESVeQM4K7OeGqCmSr0W2L9KfWW1bZiZWcfxJ67NzCzLIWFmZlkOCTMzy3JImJlZlkPCzMyyHBJmZpblkDAzsyyHhJmZZTkkzMwsyyFhZmZZDgkzM8tySJiZWZZDwszMshwSZmaW5ZAwM7Msh4SZmWU5JMzMLMshYWZmWQ4JMzPLckiYmVmWQ8LMzLIcEmZmluWQMDOzLIeEmZllOSTMzCzLIWFmZlkOCTMzy3JImJlZVptCQtJzkh6VtFhSbartImm+pKfTfd9Ul6QZkpZJWiLpoNJ6JqX5n5Y0qVQ/OK1/WVpWbWmvmZm1THscSXwqIoZHxMj0/FzgrogYAtyVngMcBQxJt6nAFVCECjAdGAUcCkyvD5Y0z+dLy41rh/aamVkzbYnhpvHAzPR4JjChVL8+CguBnSXtDhwJzI+IVRHxKjAfGJem7RQRCyMigOtL6zIzsw7Q1pAI4PeSFkmammq7RcSL6fFLwG7p8UDghdKydanWWL2uSn0zkqZKqpVUu2LFira8HjMzK+nexuU/ERHLJe0KzJe0tDwxIkJStHEbTYqIq4CrAEaOHLnFt2dm1lW06UgiIpan+1eA2yjOKbychopI96+k2ZcDe5YWH5RqjdUHVambmVkHaXVISOoj6X31j4GxwGPAHKD+CqVJwG/T4znAaekqp9HA6jQsNQ8YK6lvOmE9FpiXpr0uaXS6qum00rrMzKwDtGW4aTfgtnRVanfgVxHxO0kPATdLOgN4HjgpzT8XOBpYBrwFTAaIiFWSLgQeSvN9JyJWpcdnAtcBOwB3ppuZmXWQVodERDwDHFilvhI4oko9gLMy66oBaqrUa4H9W9tGMzNrG3/i2szMshwSZmaW5ZAwM7Msh4SZmWU5JMzMLMshYWZmWQ4JMzPLckiYmVmWQ8LMzLIcEmZmluWQMDOzLIeEmZllOSTMzCzLIWFmZlkOCTMzy3JImJlZlkPCzMyyHBJmZpblkDAzsyyHhJmZZTkkzMwsyyFhZmZZDgkzM8tySJiZWZZDwszMshwSZmaW5ZAwM7Msh4SZmWVt9SEhaZykpyQtk3RuZ7fHzKwr2apDQlI34HLgKGAocLKkoZ3bKjOzrmOrDgngUGBZRDwTEWuB2cD4Tm6TmVmXoYjo7DZkSToBGBcRn0vPTwVGRcS0ivmmAlPT048CT3VoQzfXH/hrJ7dha+G+aOC+aOC+aLC19MVeETGgsti9M1rS3iLiKuCqzm5HPUm1ETGys9uxNXBfNHBfNHBfNNja+2JrH25aDuxZej4o1czMrANs7SHxEDBE0t6S3gtMBOZ0cpvMzLqMrXq4KSLWS5oGzAO6ATUR8XgnN6s5tpqhr62A+6KB+6KB+6LBVt0XW/WJazMz61xb+3CTmZl1IoeEmZllOSRaQNJukn4l6RlJiyTdL+m4RuYfI+n2zLTnJPXfcq3dstwXDdwXDdwXDbaXvnBINJMkAb8B7o2ID0XEwRRXWw3q3JZ1PPdFA/dFA/dFg+2pLxwSzXc4sDYirqwvRMTzEfFTSb0kXSvpUUkPS/pU5cKS+kn6vaTHJV0NqCMb387cFw3cFw3cFw22m75wSDTffsCfMtPOAiIiDgBOBmZK6lUxz3TgvojYD7gN+OAWa+mW575o4L5o4L5osN30xVb9OYmtmaTLgU8Aa4E64KcAEbFU0vPAPhWL/B3wj2meOyS92oHN3aLcFw3cFw3cFw225b7wkUTzPQ4cVP8kIs4CjgA2+0KsLsB90cB90cB90WC76QuHRPPdDfSS9KVSrXe6/w/gFABJ+1AcGlZ+E+29wD+leY4C+m7R1m5Z7osG7osG7osG201fOCSaKYqPpk8APinpWUkPAjOBbwA/B94j6VHgJuD0iHinYhUXAH8n6XGKw8g/d1zr25f7ooH7ooH7osH21Bf+Wg4zM8vykYSZmWU5JMzMLMshYWZmWQ4JMzPLckiYmVmWQ8K6JEkh6YbS8+6SVuS+hbMV618gqd1/3F7S3zL1L0o6rb23Z+av5bCu6k1gf0k7RMTbwD8Ayzu5TUARWBGxviXLlL9Izqw9+UjCurK5wDHp8cnArPoJkvpIqpH0YPqmzvGpfrqk30iar+I7/qdJ+lqaZ6GkXUrrP1XSYkmPSTq0GeudI+lu4C5Ju0u6t7T8YaW2XSzpkbS93VLtfEnnpMcLJP2kcttmreGQsK5sNjAxfQPnMOCB0rRvAXdHxKHAp4AfSOqTpu1P8SnYQ4CLgbciYgRwP1Ae8ukdEcOBM4GaZqz3IOCEiPgkxVcyzEvLHwgsTvP0ARZGxIEUX93w+cxrq7ZtsxbzcJN1WRGxRNJgiqOIuRWTxwLH1r87B3rR8HXN90TEG8AbklYD/57qj1KETb1ZaTv3StpJ0s5NrHd+RKxKjx8CaiT1AH4TEfUhsRaoP2+yiGKYrJrNth0RrzXSHWZV+UjCuro5wA8pDTUlAo6PiOHp9sGIeDJNK3/Pzrul5++y6Ruvyu+8iSbW++bGGSPupfi66OXAdaWT0uui4bt0NpB/o1dt22Yt5pCwrq4GuCAiHq2ozwP+WZIAJI1oxbo/m5b9BLA6IlY3d72S9gJejohfAFdT+trpNmzbrMU83GRdWkTUATOqTLoQ+N/AEknvAZ4FPt3C1a+R9DDQA5jSwvWOAb4uaR3wNzY919HabZu1mL8F1mw7I2kBcE5E1HZ2W2zb5+EmMzPL8pGEmZll+UjCzMyyHBJmZpblkDAzsyyHhJmZZTkkzMws678A1seJ06NswWAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9TNr4AgsG3sJ"
      },
      "source": [
        "**4. Rookies - Your Newest Customers**\n",
        "* RFM Score : 20X\n",
        "* Who They Are : First time buyers on your site."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "emo8CXvxGJIo",
        "outputId": "389fcd67-c230-48f8-87e8-5c0a68de79f6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "rookies_cust = rfm[(rfm['RecencyCluster'] == 2) & (rfm['FrequencyCluster'] == 0)].sort_values('Monetary', ascending=False)\n",
        "rookies_cust.head()"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Recency</th>\n",
              "      <th>RecencyCluster</th>\n",
              "      <th>Frequency</th>\n",
              "      <th>FrequencyCluster</th>\n",
              "      <th>Monetary</th>\n",
              "      <th>MonetaryCluster</th>\n",
              "      <th>RFMScore</th>\n",
              "      <th>Membership</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2646</th>\n",
              "      <td>16446</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>168472.50</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>Gold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2440</th>\n",
              "      <td>16029</td>\n",
              "      <td>38</td>\n",
              "      <td>2</td>\n",
              "      <td>242</td>\n",
              "      <td>0</td>\n",
              "      <td>81024.84</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Silver</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2760</th>\n",
              "      <td>16684</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>277</td>\n",
              "      <td>0</td>\n",
              "      <td>66653.56</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Silver</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3379</th>\n",
              "      <td>17949</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>70</td>\n",
              "      <td>0</td>\n",
              "      <td>58510.48</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Silver</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2309</th>\n",
              "      <td>15769</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>130</td>\n",
              "      <td>0</td>\n",
              "      <td>56252.72</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Silver</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      CustomerID  Recency  RecencyCluster  ...  MonetaryCluster  RFMScore  Membership\n",
              "2646       16446        0               2  ...                2         4        Gold\n",
              "2440       16029       38               2  ...                1         3      Silver\n",
              "2760       16684        3               2  ...                1         3      Silver\n",
              "3379       17949        0               2  ...                1         3      Silver\n",
              "2309       15769        6               2  ...                1         3      Silver\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hBzaE5YPHRSV",
        "outputId": "420aba3e-352b-4e3c-ed05-03ff5b232bff",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "rookies_cust['CustomerID'].count()"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2645"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ezhB80K9BJM2",
        "outputId": "ef957be2-0bfe-4476-bc05-1bd6573ff383",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        }
      },
      "source": [
        "rookies_cust.head(5).plot.bar(x='CustomerID', y='Monetary', rot=0)\n",
        "plt.title('Top 5 Newest Customer', size=15)"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Top 5 Newest Customer')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wDKBZphBwhJ-",
        "outputId": "b538d679-daea-4f4c-e38f-a3b599b02e08",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        }
      },
      "source": [
        "rookies_cust.head(5).plot.bar(x='Membership', y='Monetary', rot=0)\n",
        "plt.title('Top 5 Newest Customer', size=15)"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Top 5 Newest Customer')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IYePeBwiM45B"
      },
      "source": [
        "**5. Slipping - Once Loyal, Now Gone**\n",
        "* RFM Score : 00X\n",
        "* Who They Are : Great past customers who haven't bought in awhile."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zGn6JtTiM8Vp",
        "outputId": "b98aec8b-a951-4e80-e41f-f9dbcefa9f3c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "slip_cust = rfm[(rfm['RecencyCluster'] == 0) & (rfm['FrequencyCluster'] == 0)].sort_values('Monetary', ascending=False)\n",
        "slip_cust.head()"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Recency</th>\n",
              "      <th>RecencyCluster</th>\n",
              "      <th>Frequency</th>\n",
              "      <th>FrequencyCluster</th>\n",
              "      <th>Monetary</th>\n",
              "      <th>MonetaryCluster</th>\n",
              "      <th>RFMScore</th>\n",
              "      <th>Membership</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12346</td>\n",
              "      <td>325</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>77183.60</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Silver</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>363</th>\n",
              "      <td>15749</td>\n",
              "      <td>234</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>44534.30</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Silver</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>13093</td>\n",
              "      <td>275</td>\n",
              "      <td>0</td>\n",
              "      <td>159</td>\n",
              "      <td>0</td>\n",
              "      <td>7832.47</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Bronze</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>272</th>\n",
              "      <td>15032</td>\n",
              "      <td>255</td>\n",
              "      <td>0</td>\n",
              "      <td>52</td>\n",
              "      <td>0</td>\n",
              "      <td>4959.10</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Bronze</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>12755</td>\n",
              "      <td>280</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>3811.95</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Bronze</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     CustomerID  Recency  RecencyCluster  ...  MonetaryCluster  RFMScore  Membership\n",
              "0         12346      325               0  ...                1         1      Silver\n",
              "363       15749      234               0  ...                1         1      Silver\n",
              "86        13093      275               0  ...                0         0      Bronze\n",
              "272       15032      255               0  ...                0         0      Bronze\n",
              "47        12755      280               0  ...                0         0      Bronze\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iaQKFinhNCAm",
        "outputId": "8e6d0f4d-0fb8-434d-8b54-a672fda4816f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "slip_cust['CustomerID'].count()"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "625"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wv00dG48CHbC",
        "outputId": "d8411d2f-c9cf-4964-9654-b6da08a11f70",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        }
      },
      "source": [
        "slip_cust.head(5).plot.bar(x='CustomerID', y='Monetary', rot=0)\n",
        "plt.title('Top 5 Slipping Customer', size=15)"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Top 5 Slipping Customer')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nW7fi1lyzclb",
        "outputId": "0ca2faeb-4730-4541-9320-7876c522a062",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        }
      },
      "source": [
        "slip_cust.head(5).plot.bar(x='Membership', y='Monetary', rot=0)\n",
        "plt.title('Top 5 Slipping Customer', size=15)"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Top 5 Slipping Customer')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gHgCw4dyHehJ"
      },
      "source": [
        "**6. Regular - The customers having common behaviour across these metrics.**\n",
        "* RFM Score: Remaining Scores\n",
        "* Who They Are: Customer who have average metrics across each RFM scores."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qsaDH3_nHaQr",
        "outputId": "5762f723-80b0-42e8-ad05-a1d38ea925b1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "rfm['Status'] = 'Regular' \n",
        "rfm.loc[rfm['RFMScore'] == 6, 'Status'] = 'Core'\n",
        "rfm.loc[rfm['FrequencyCluster'] == 2, 'Status'] = 'Loyal'\n",
        "rfm.loc[rfm['MonetaryCluster'] == 2, 'Status'] = 'Whale'\n",
        "rfm.loc[((rfm['RecencyCluster'] == 2) & (rfm['FrequencyCluster'] == 0)), 'Status'] = 'Rookies'\n",
        "rfm.head()"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Recency</th>\n",
              "      <th>RecencyCluster</th>\n",
              "      <th>Frequency</th>\n",
              "      <th>FrequencyCluster</th>\n",
              "      <th>Monetary</th>\n",
              "      <th>MonetaryCluster</th>\n",
              "      <th>RFMScore</th>\n",
              "      <th>Membership</th>\n",
              "      <th>Status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12346</td>\n",
              "      <td>325</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>77183.60</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Silver</td>\n",
              "      <td>Regular</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12350</td>\n",
              "      <td>309</td>\n",
              "      <td>0</td>\n",
              "      <td>17</td>\n",
              "      <td>0</td>\n",
              "      <td>334.40</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Bronze</td>\n",
              "      <td>Regular</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12354</td>\n",
              "      <td>231</td>\n",
              "      <td>0</td>\n",
              "      <td>58</td>\n",
              "      <td>0</td>\n",
              "      <td>1079.40</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Bronze</td>\n",
              "      <td>Regular</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12361</td>\n",
              "      <td>286</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>189.90</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Bronze</td>\n",
              "      <td>Regular</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12365</td>\n",
              "      <td>290</td>\n",
              "      <td>0</td>\n",
              "      <td>22</td>\n",
              "      <td>0</td>\n",
              "      <td>641.38</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Bronze</td>\n",
              "      <td>Regular</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   CustomerID  Recency  RecencyCluster  ...  RFMScore  Membership   Status\n",
              "0       12346      325               0  ...         1      Silver  Regular\n",
              "1       12350      309               0  ...         0      Bronze  Regular\n",
              "2       12354      231               0  ...         0      Bronze  Regular\n",
              "3       12361      286               0  ...         0      Bronze  Regular\n",
              "4       12365      290               0  ...         0      Bronze  Regular\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f_xsBfwbKn4J",
        "outputId": "decdb613-a42e-4efa-d848-563743342b45",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "reg_cust = rfm[rfm['Status'].str.contains('Regular', regex=False)].sort_values('Monetary', ascending=False)\n",
        "reg_cust.head()"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Recency</th>\n",
              "      <th>RecencyCluster</th>\n",
              "      <th>Frequency</th>\n",
              "      <th>FrequencyCluster</th>\n",
              "      <th>Monetary</th>\n",
              "      <th>MonetaryCluster</th>\n",
              "      <th>RFMScore</th>\n",
              "      <th>Membership</th>\n",
              "      <th>Status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>662</th>\n",
              "      <td>12415</td>\n",
              "      <td>23</td>\n",
              "      <td>2</td>\n",
              "      <td>714</td>\n",
              "      <td>1</td>\n",
              "      <td>124914.53</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>Silver</td>\n",
              "      <td>Regular</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1521</th>\n",
              "      <td>14156</td>\n",
              "      <td>9</td>\n",
              "      <td>2</td>\n",
              "      <td>1400</td>\n",
              "      <td>1</td>\n",
              "      <td>117379.63</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>Silver</td>\n",
              "      <td>Regular</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3164</th>\n",
              "      <td>17511</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>963</td>\n",
              "      <td>1</td>\n",
              "      <td>91062.38</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>Silver</td>\n",
              "      <td>Regular</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12346</td>\n",
              "      <td>325</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>77183.60</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Silver</td>\n",
              "      <td>Regular</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1301</th>\n",
              "      <td>13694</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>568</td>\n",
              "      <td>1</td>\n",
              "      <td>65039.62</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>Silver</td>\n",
              "      <td>Regular</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      CustomerID  Recency  RecencyCluster  ...  RFMScore  Membership   Status\n",
              "662        12415       23               2  ...         4      Silver  Regular\n",
              "1521       14156        9               2  ...         4      Silver  Regular\n",
              "3164       17511        2               2  ...         4      Silver  Regular\n",
              "0          12346      325               0  ...         1      Silver  Regular\n",
              "1301       13694        3               2  ...         4      Silver  Regular\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dw88iucZK5rz",
        "outputId": "59bf65d3-c784-4e2e-b7e2-a099869e1ea7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "reg_cust['CustomerID'].count()"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1686"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-O4v1G2RcfdZ",
        "outputId": "7fdaca65-aa7d-460c-fb4f-51e2d0a2cbbf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        }
      },
      "source": [
        "reg_cust.head(5).plot.bar(x='CustomerID', y='Monetary', rot=0)\n",
        "plt.title('Top 5 Regular Customer', size=15)"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Top 5 Regular Customer')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7EMphIUp0X3O",
        "outputId": "ce92664a-c9e6-4db1-de6c-eee7814a4eca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        }
      },
      "source": [
        "reg_cust.head(5).plot.bar(x='Membership', y='Monetary', rot=0)\n",
        "plt.title('Top 5 Regular Customer', size=15)"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Top 5 Regular Customer')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k0HkX2XGK_Yv",
        "outputId": "0b67066b-ecb6-4023-f035-c646f2ddd231",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "pip install squarify"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting squarify\n",
            "  Downloading https://files.pythonhosted.org/packages/0b/2b/2e77c35326efec19819cd1d729540d4d235e6c2a3f37658288a363a67da5/squarify-0.4.3-py3-none-any.whl\n",
            "Installing collected packages: squarify\n",
            "Successfully installed squarify-0.4.3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gdzRgo8cGrRP"
      },
      "source": [
        "color_list = ['#0f7216', '#b2790c', '#ffe9a3',\n",
        "              '#f9d4d4', '#d35158', '#ea3033']"
      ],
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MeXCsbUlIoti",
        "outputId": "301b4e5a-574a-4476-ab28-95b1e124825d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 491
        }
      },
      "source": [
        "#Create our RFM Segment plot and resize it.\n",
        "import squarify\n",
        "fig = plt.gcf()\n",
        "ax = fig.add_subplot()\n",
        "fig.set_size_inches(10, 8)\n",
        "squarify.plot(sizes=[core_cust['CustomerID'].count(),\n",
        "                     reg_cust['CustomerID'].count(),\n",
        "                     loyal_cust['CustomerID'].count(),\n",
        "                     rookies_cust['CustomerID'].count(),\n",
        "                     slip_cust['CustomerID'].count(),\n",
        "                     whales_cust['CustomerID'].count()], \n",
        "              label=['Core',\n",
        "                     'Regular',\n",
        "                     'Loyal',\n",
        "                     'Rookies',\n",
        "                     'Slipping', \n",
        "                     'Whales'], alpha=0.8, color=color_list)\n",
        "plt.title(\"RFM Segments\",fontsize=18,fontweight=\"bold\")\n",
        "plt.axis('off')\n",
        "plt.show()"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RtisXfZaKS70"
      },
      "source": [
        ""
      ],
      "execution_count": 87,
      "outputs": []
    }
  ]
}