GMM_anomaly_Detection.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNHQjHqpJnfIsoKeNd5iMdT",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sfansaria/GaussianMixtureModel-Anomaly-Detection/blob/main/GMM_anomaly_Detection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HvsIwBfhS8rm",
        "outputId": "ad413fdb-1688-43aa-c2d9-04166be3e09f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "try:\n",
        "  import os\n",
        "  import datetime\n",
        "  import pandas as pd\n",
        "  pd.set_option('display.width',1000)\n",
        "  pd.set_option('display.max_rows', 500)\n",
        "  pd.set_option('display.max_columns',500)\n",
        "\n",
        "  from IPython.core.display import display, HTML\n",
        "  display(HTML(\"<style>.container{width:90% !important ; } </style>\"))\n",
        "except Exception as e:\n",
        "  print(\"Library not found: \",e)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "Xmp9mrhmTq4b",
        "outputId": "96401643-f95d-4f5e-afd6-9d3a4c19a889"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<style>.container{width:90% !important ; } </style>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_excel('/content/drive/My Drive/ml_datasets/Online Retail.xlsx')\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "Go1C412fYZjC",
        "outputId": "48016beb-6f21-423e-92ed-25435cfb7cd7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  InvoiceNo StockCode                          Description  Quantity         InvoiceDate  UnitPrice  CustomerID         Country\n",
              "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6 2010-12-01 08:26:00       2.55     17850.0  United Kingdom\n",
              "1    536365     71053                  WHITE METAL LANTERN         6 2010-12-01 08:26:00       3.39     17850.0  United Kingdom\n",
              "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8 2010-12-01 08:26:00       2.75     17850.0  United Kingdom\n",
              "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6 2010-12-01 08:26:00       3.39     17850.0  United Kingdom\n",
              "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6 2010-12-01 08:26:00       3.39     17850.0  United Kingdom"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e4562ac0-8d13-4bd3-985e-303c9c900110\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>InvoiceNo</th>\n",
              "      <th>StockCode</th>\n",
              "      <th>Description</th>\n",
              "      <th>Quantity</th>\n",
              "      <th>InvoiceDate</th>\n",
              "      <th>UnitPrice</th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Country</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>536365</td>\n",
              "      <td>85123A</td>\n",
              "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
              "      <td>6</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>2.55</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>536365</td>\n",
              "      <td>71053</td>\n",
              "      <td>WHITE METAL LANTERN</td>\n",
              "      <td>6</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>536365</td>\n",
              "      <td>84406B</td>\n",
              "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
              "      <td>8</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>2.75</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>536365</td>\n",
              "      <td>84029G</td>\n",
              "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
              "      <td>6</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>536365</td>\n",
              "      <td>84029E</td>\n",
              "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
              "      <td>6</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e4562ac0-8d13-4bd3-985e-303c9c900110')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e4562ac0-8d13-4bd3-985e-303c9c900110 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e4562ac0-8d13-4bd3-985e-303c9c900110');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4ZmTr2tFj7Zt",
        "outputId": "19dd896f-086c-471c-9694-9265f69178d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(541909, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1zTd8SzekBxY",
        "outputId": "018d04c7-c9fd-4a82-cb00-2ebb4ba95fdb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 541909 entries, 0 to 541908\n",
            "Data columns (total 8 columns):\n",
            " #   Column       Non-Null Count   Dtype         \n",
            "---  ------       --------------   -----         \n",
            " 0   InvoiceNo    541909 non-null  object        \n",
            " 1   StockCode    541909 non-null  object        \n",
            " 2   Description  540455 non-null  object        \n",
            " 3   Quantity     541909 non-null  int64         \n",
            " 4   InvoiceDate  541909 non-null  datetime64[ns]\n",
            " 5   UnitPrice    541909 non-null  float64       \n",
            " 6   CustomerID   406829 non-null  float64       \n",
            " 7   Country      541909 non-null  object        \n",
            "dtypes: datetime64[ns](1), float64(2), int64(1), object(4)\n",
            "memory usage: 33.1+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#RFM->Recency-Frequency-Monetary\n",
        "#Recency--->last purchase\n",
        "#Frequency--->how many purchase\n",
        "#Monetary--->Money\n",
        "\n",
        "data = data[['InvoiceDate','CustomerID','UnitPrice']]\n",
        "data\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "U4UWFs4qkTZu",
        "outputId": "bf821ec7-8bc6-494f-c02f-fcfe41244628"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               InvoiceDate  CustomerID  UnitPrice\n",
              "0      2010-12-01 08:26:00     17850.0       2.55\n",
              "1      2010-12-01 08:26:00     17850.0       3.39\n",
              "2      2010-12-01 08:26:00     17850.0       2.75\n",
              "3      2010-12-01 08:26:00     17850.0       3.39\n",
              "4      2010-12-01 08:26:00     17850.0       3.39\n",
              "...                    ...         ...        ...\n",
              "541904 2011-12-09 12:50:00     12680.0       0.85\n",
              "541905 2011-12-09 12:50:00     12680.0       2.10\n",
              "541906 2011-12-09 12:50:00     12680.0       4.15\n",
              "541907 2011-12-09 12:50:00     12680.0       4.15\n",
              "541908 2011-12-09 12:50:00     12680.0       4.95\n",
              "\n",
              "[541909 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bf503fb0-d25e-4ede-8af6-5f43ff8bb2e1\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>InvoiceDate</th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>UnitPrice</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>2.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>3.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>2.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>3.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>3.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>541904</th>\n",
              "      <td>2011-12-09 12:50:00</td>\n",
              "      <td>12680.0</td>\n",
              "      <td>0.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>541905</th>\n",
              "      <td>2011-12-09 12:50:00</td>\n",
              "      <td>12680.0</td>\n",
              "      <td>2.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>541906</th>\n",
              "      <td>2011-12-09 12:50:00</td>\n",
              "      <td>12680.0</td>\n",
              "      <td>4.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>541907</th>\n",
              "      <td>2011-12-09 12:50:00</td>\n",
              "      <td>12680.0</td>\n",
              "      <td>4.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>541908</th>\n",
              "      <td>2011-12-09 12:50:00</td>\n",
              "      <td>12680.0</td>\n",
              "      <td>4.95</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>541909 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bf503fb0-d25e-4ede-8af6-5f43ff8bb2e1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-bf503fb0-d25e-4ede-8af6-5f43ff8bb2e1 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-bf503fb0-d25e-4ede-8af6-5f43ff8bb2e1');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AOre7wjOk6mJ",
        "outputId": "0789f4f1-d8ba-4bc2-c14f-71f1d7f4a001"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 541909 entries, 0 to 541908\n",
            "Data columns (total 3 columns):\n",
            " #   Column       Non-Null Count   Dtype         \n",
            "---  ------       --------------   -----         \n",
            " 0   InvoiceDate  541909 non-null  datetime64[ns]\n",
            " 1   CustomerID   406829 non-null  float64       \n",
            " 2   UnitPrice    541909 non-null  float64       \n",
            "dtypes: datetime64[ns](1), float64(2)\n",
            "memory usage: 12.4 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Customer frequency with more than 5 transaction\n",
        "frequency_customer = data['CustomerID'].value_counts().rename_axis('CustomerID').to_frame('frequency').reset_index()\n",
        "frequency_customer"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "_ggPePkZk_8-",
        "outputId": "7df22a5c-dd36-461d-fa67-61db8076eaad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      CustomerID  frequency\n",
              "0        17841.0       7983\n",
              "1        14911.0       5903\n",
              "2        14096.0       5128\n",
              "3        12748.0       4642\n",
              "4        14606.0       2782\n",
              "...          ...        ...\n",
              "4367     15070.0          1\n",
              "4368     15753.0          1\n",
              "4369     17065.0          1\n",
              "4370     16881.0          1\n",
              "4371     16995.0          1\n",
              "\n",
              "[4372 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e947ca23-7d5b-4c38-81f6-0d248df2b3e2\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>17841.0</td>\n",
              "      <td>7983</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>14911.0</td>\n",
              "      <td>5903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>14096.0</td>\n",
              "      <td>5128</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12748.0</td>\n",
              "      <td>4642</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>14606.0</td>\n",
              "      <td>2782</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4367</th>\n",
              "      <td>15070.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4368</th>\n",
              "      <td>15753.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4369</th>\n",
              "      <td>17065.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4370</th>\n",
              "      <td>16881.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4371</th>\n",
              "      <td>16995.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4372 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e947ca23-7d5b-4c38-81f6-0d248df2b3e2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e947ca23-7d5b-4c38-81f6-0d248df2b3e2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e947ca23-7d5b-4c38-81f6-0d248df2b3e2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#customer which has more than 5 transactions\n",
        "more_than_5_transaction_customers = list(frequency_customer[frequency_customer['frequency']>5]['CustomerID'])\n",
        "more_than_5_transaction_customers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xo_5vu5omXfK",
        "outputId": "b62957f6-318e-462c-9f83-a294644be23d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[17841.0,\n",
              " 14911.0,\n",
              " 14096.0,\n",
              " 12748.0,\n",
              " 14606.0,\n",
              " 15311.0,\n",
              " 14646.0,\n",
              " 13089.0,\n",
              " 13263.0,\n",
              " 14298.0,\n",
              " 15039.0,\n",
              " 14156.0,\n",
              " 18118.0,\n",
              " 14159.0,\n",
              " 14796.0,\n",
              " 15005.0,\n",
              " 16033.0,\n",
              " 14056.0,\n",
              " 14769.0,\n",
              " 17511.0,\n",
              " 13081.0,\n",
              " 14527.0,\n",
              " 16549.0,\n",
              " 14456.0,\n",
              " 15719.0,\n",
              " 15555.0,\n",
              " 16931.0,\n",
              " 17811.0,\n",
              " 14505.0,\n",
              " 12415.0,\n",
              " 17338.0,\n",
              " 18283.0,\n",
              " 17757.0,\n",
              " 12921.0,\n",
              " 17675.0,\n",
              " 15159.0,\n",
              " 16764.0,\n",
              " 16904.0,\n",
              " 13137.0,\n",
              " 15547.0,\n",
              " 17920.0,\n",
              " 16923.0,\n",
              " 17735.0,\n",
              " 15529.0,\n",
              " 16360.0,\n",
              " 16729.0,\n",
              " 16241.0,\n",
              " 15856.0,\n",
              " 12681.0,\n",
              " 13969.0,\n",
              " 16713.0,\n",
              " 14502.0,\n",
              " 14415.0,\n",
              " 13230.0,\n",
              " 13098.0,\n",
              " 14194.0,\n",
              " 14088.0,\n",
              " 16686.0,\n",
              " 13694.0,\n",
              " 17611.0,\n",
              " 14667.0,\n",
              " 17238.0,\n",
              " 17602.0,\n",
              " 15023.0,\n",
              " 15358.0,\n",
              " 15993.0,\n",
              " 14534.0,\n",
              " 12867.0,\n",
              " 17337.0,\n",
              " 16910.0,\n",
              " 15861.0,\n",
              " 17231.0,\n",
              " 14367.0,\n",
              " 12471.0,\n",
              " 12682.0,\n",
              " 15498.0,\n",
              " 15356.0,\n",
              " 17059.0,\n",
              " 15033.0,\n",
              " 15998.0,\n",
              " 14895.0,\n",
              " 14049.0,\n",
              " 17346.0,\n",
              " 16161.0,\n",
              " 13408.0,\n",
              " 14730.0,\n",
              " 13668.0,\n",
              " 13319.0,\n",
              " 15640.0,\n",
              " 17954.0,\n",
              " 17315.0,\n",
              " 17139.0,\n",
              " 12540.0,\n",
              " 15021.0,\n",
              " 16558.0,\n",
              " 12621.0,\n",
              " 18041.0,\n",
              " 17220.0,\n",
              " 18069.0,\n",
              " 13069.0,\n",
              " 17365.0,\n",
              " 12567.0,\n",
              " 15867.0,\n",
              " 15044.0,\n",
              " 13018.0,\n",
              " 12474.0,\n",
              " 18109.0,\n",
              " 16746.0,\n",
              " 17581.0,\n",
              " 14085.0,\n",
              " 15078.0,\n",
              " 16813.0,\n",
              " 13268.0,\n",
              " 16712.0,\n",
              " 14562.0,\n",
              " 13451.0,\n",
              " 12841.0,\n",
              " 13798.0,\n",
              " 13097.0,\n",
              " 13521.0,\n",
              " 18102.0,\n",
              " 15615.0,\n",
              " 13488.0,\n",
              " 17340.0,\n",
              " 13050.0,\n",
              " 17107.0,\n",
              " 14684.0,\n",
              " 12433.0,\n",
              " 17377.0,\n",
              " 16984.0,\n",
              " 14546.0,\n",
              " 17576.0,\n",
              " 14704.0,\n",
              " 15601.0,\n",
              " 17364.0,\n",
              " 17758.0,\n",
              " 15752.0,\n",
              " 15061.0,\n",
              " 17799.0,\n",
              " 13004.0,\n",
              " 17243.0,\n",
              " 16710.0,\n",
              " 15034.0,\n",
              " 16422.0,\n",
              " 13767.0,\n",
              " 17706.0,\n",
              " 17530.0,\n",
              " 13458.0,\n",
              " 12637.0,\n",
              " 14800.0,\n",
              " 14849.0,\n",
              " 12472.0,\n",
              " 17865.0,\n",
              " 16007.0,\n",
              " 15443.0,\n",
              " 14587.0,\n",
              " 14432.0,\n",
              " 13078.0,\n",
              " 16843.0,\n",
              " 18116.0,\n",
              " 17813.0,\n",
              " 12757.0,\n",
              " 16426.0,\n",
              " 17509.0,\n",
              " 16474.0,\n",
              " 14547.0,\n",
              " 14936.0,\n",
              " 16133.0,\n",
              " 18122.0,\n",
              " 14903.0,\n",
              " 18055.0,\n",
              " 12683.0,\n",
              " 14688.0,\n",
              " 12720.0,\n",
              " 14711.0,\n",
              " 16393.0,\n",
              " 15249.0,\n",
              " 16745.0,\n",
              " 12451.0,\n",
              " 16110.0,\n",
              " 14395.0,\n",
              " 13985.0,\n",
              " 17450.0,\n",
              " 12484.0,\n",
              " 14701.0,\n",
              " 14702.0,\n",
              " 12705.0,\n",
              " 13148.0,\n",
              " 13709.0,\n",
              " 15150.0,\n",
              " 16779.0,\n",
              " 17428.0,\n",
              " 13755.0,\n",
              " 13001.0,\n",
              " 13534.0,\n",
              " 14713.0,\n",
              " 13571.0,\n",
              " 13975.0,\n",
              " 13871.0,\n",
              " 14071.0,\n",
              " 14535.0,\n",
              " 14583.0,\n",
              " 15570.0,\n",
              " 15436.0,\n",
              " 13209.0,\n",
              " 17965.0,\n",
              " 14178.0,\n",
              " 16919.0,\n",
              " 15194.0,\n",
              " 16814.0,\n",
              " 13742.0,\n",
              " 16791.0,\n",
              " 17049.0,\n",
              " 17677.0,\n",
              " 14680.0,\n",
              " 16326.0,\n",
              " 13269.0,\n",
              " 16327.0,\n",
              " 17613.0,\n",
              " 14961.0,\n",
              " 12856.0,\n",
              " 17050.0,\n",
              " 14506.0,\n",
              " 12839.0,\n",
              " 17625.0,\n",
              " 13174.0,\n",
              " 13418.0,\n",
              " 15513.0,\n",
              " 17769.0,\n",
              " 17850.0,\n",
              " 16049.0,\n",
              " 16700.0,\n",
              " 12766.0,\n",
              " 13102.0,\n",
              " 12477.0,\n",
              " 12971.0,\n",
              " 12626.0,\n",
              " 15805.0,\n",
              " 13869.0,\n",
              " 15427.0,\n",
              " 15382.0,\n",
              " 13468.0,\n",
              " 16940.0,\n",
              " 15727.0,\n",
              " 12428.0,\n",
              " 15426.0,\n",
              " 17571.0,\n",
              " 18223.0,\n",
              " 15493.0,\n",
              " 14525.0,\n",
              " 16839.0,\n",
              " 16523.0,\n",
              " 16187.0,\n",
              " 15708.0,\n",
              " 17068.0,\n",
              " 17634.0,\n",
              " 17975.0,\n",
              " 15750.0,\n",
              " 16242.0,\n",
              " 17284.0,\n",
              " 14446.0,\n",
              " 14390.0,\n",
              " 14057.0,\n",
              " 16873.0,\n",
              " 16794.0,\n",
              " 14410.0,\n",
              " 17686.0,\n",
              " 18225.0,\n",
              " 15453.0,\n",
              " 12517.0,\n",
              " 14286.0,\n",
              " 16115.0,\n",
              " 16705.0,\n",
              " 16942.0,\n",
              " 16440.0,\n",
              " 16684.0,\n",
              " 14472.0,\n",
              " 16609.0,\n",
              " 13113.0,\n",
              " 15786.0,\n",
              " 17870.0,\n",
              " 17449.0,\n",
              " 15152.0,\n",
              " 17719.0,\n",
              " 14723.0,\n",
              " 14189.0,\n",
              " 12731.0,\n",
              " 15834.0,\n",
              " 15058.0,\n",
              " 15984.0,\n",
              " 15271.0,\n",
              " 12362.0,\n",
              " 16029.0,\n",
              " 12539.0,\n",
              " 17218.0,\n",
              " 15804.0,\n",
              " 16255.0,\n",
              " 12536.0,\n",
              " 14572.0,\n",
              " 17426.0,\n",
              " 17416.0,\n",
              " 12700.0,\n",
              " 17652.0,\n",
              " 17596.0,\n",
              " 14329.0,\n",
              " 16728.0,\n",
              " 17017.0,\n",
              " 13334.0,\n",
              " 15910.0,\n",
              " 13178.0,\n",
              " 12476.0,\n",
              " 14426.0,\n",
              " 13599.0,\n",
              " 15532.0,\n",
              " 14191.0,\n",
              " 17126.0,\n",
              " 16670.0,\n",
              " 16550.0,\n",
              " 16655.0,\n",
              " 15544.0,\n",
              " 17528.0,\n",
              " 13471.0,\n",
              " 16265.0,\n",
              " 12714.0,\n",
              " 14659.0,\n",
              " 14031.0,\n",
              " 14449.0,\n",
              " 17690.0,\n",
              " 12709.0,\n",
              " 14808.0,\n",
              " 16771.0,\n",
              " 14810.0,\n",
              " 17716.0,\n",
              " 12359.0,\n",
              " 17372.0,\n",
              " 17589.0,\n",
              " 15502.0,\n",
              " 14099.0,\n",
              " 12583.0,\n",
              " 16945.0,\n",
              " 17961.0,\n",
              " 15907.0,\n",
              " 14180.0,\n",
              " 15301.0,\n",
              " 14462.0,\n",
              " 12490.0,\n",
              " 18226.0,\n",
              " 14146.0,\n",
              " 12957.0,\n",
              " 14030.0,\n",
              " 16744.0,\n",
              " 16938.0,\n",
              " 14573.0,\n",
              " 14040.0,\n",
              " 12431.0,\n",
              " 13988.0,\n",
              " 17651.0,\n",
              " 15059.0,\n",
              " 14719.0,\n",
              " 17609.0,\n",
              " 15644.0,\n",
              " 14291.0,\n",
              " 14653.0,\n",
              " 16145.0,\n",
              " 16016.0,\n",
              " 15701.0,\n",
              " 17191.0,\n",
              " 14755.0,\n",
              " 16464.0,\n",
              " 16717.0,\n",
              " 16348.0,\n",
              " 13124.0,\n",
              " 17019.0,\n",
              " 15351.0,\n",
              " 16409.0,\n",
              " 15189.0,\n",
              " 12662.0,\n",
              " 12749.0,\n",
              " 12753.0,\n",
              " 12627.0,\n",
              " 17160.0,\n",
              " 13198.0,\n",
              " 12744.0,\n",
              " 14529.0,\n",
              " 15531.0,\n",
              " 16722.0,\n",
              " 16370.0,\n",
              " 17673.0,\n",
              " 13610.0,\n",
              " 14277.0,\n",
              " 13505.0,\n",
              " 16081.0,\n",
              " 18077.0,\n",
              " 17389.0,\n",
              " 13267.0,\n",
              " 13527.0,\n",
              " 17861.0,\n",
              " 13013.0,\n",
              " 16592.0,\n",
              " 16767.0,\n",
              " 14422.0,\n",
              " 18145.0,\n",
              " 15518.0,\n",
              " 17730.0,\n",
              " 17827.0,\n",
              " 16186.0,\n",
              " 17463.0,\n",
              " 13777.0,\n",
              " 12378.0,\n",
              " 12949.0,\n",
              " 15444.0,\n",
              " 12481.0,\n",
              " 16005.0,\n",
              " 15144.0,\n",
              " 15514.0,\n",
              " 14092.0,\n",
              " 17015.0,\n",
              " 14530.0,\n",
              " 14978.0,\n",
              " 14554.0,\n",
              " 14733.0,\n",
              " 15676.0,\n",
              " 13680.0,\n",
              " 14051.0,\n",
              " 17593.0,\n",
              " 17348.0,\n",
              " 15572.0,\n",
              " 17585.0,\n",
              " 15707.0,\n",
              " 15622.0,\n",
              " 12597.0,\n",
              " 17097.0,\n",
              " 16928.0,\n",
              " 17400.0,\n",
              " 17211.0,\n",
              " 18096.0,\n",
              " 16121.0,\n",
              " 13999.0,\n",
              " 16011.0,\n",
              " 15290.0,\n",
              " 14698.0,\n",
              " 16395.0,\n",
              " 15808.0,\n",
              " 14044.0,\n",
              " 16905.0,\n",
              " 17228.0,\n",
              " 16657.0,\n",
              " 17419.0,\n",
              " 13735.0,\n",
              " 13764.0,\n",
              " 13232.0,\n",
              " 15046.0,\n",
              " 16556.0,\n",
              " 13842.0,\n",
              " 15787.0,\n",
              " 14419.0,\n",
              " 14735.0,\n",
              " 17323.0,\n",
              " 14060.0,\n",
              " 16719.0,\n",
              " 16891.0,\n",
              " 14524.0,\n",
              " 18204.0,\n",
              " 16458.0,\n",
              " 13266.0,\n",
              " 12607.0,\n",
              " 13614.0,\n",
              " 12437.0,\n",
              " 17404.0,\n",
              " 17590.0,\n",
              " 14132.0,\n",
              " 15296.0,\n",
              " 16525.0,\n",
              " 15521.0,\n",
              " 14649.0,\n",
              " 17858.0,\n",
              " 13448.0,\n",
              " 17091.0,\n",
              " 16401.0,\n",
              " 17725.0,\n",
              " 14963.0,\n",
              " 12417.0,\n",
              " 16782.0,\n",
              " 15114.0,\n",
              " 13700.0,\n",
              " 14709.0,\n",
              " 14591.0,\n",
              " 18172.0,\n",
              " 18125.0,\n",
              " 15379.0,\n",
              " 13047.0,\n",
              " 12553.0,\n",
              " 15298.0,\n",
              " 14499.0,\n",
              " 14221.0,\n",
              " 17777.0,\n",
              " 13736.0,\n",
              " 17750.0,\n",
              " 17894.0,\n",
              " 15416.0,\n",
              " 14498.0,\n",
              " 16743.0,\n",
              " 15756.0,\n",
              " 16818.0,\n",
              " 12569.0,\n",
              " 12449.0,\n",
              " 15858.0,\n",
              " 14388.0,\n",
              " 18022.0,\n",
              " 17790.0,\n",
              " 15484.0,\n",
              " 17085.0,\n",
              " 13634.0,\n",
              " 15764.0,\n",
              " 13285.0,\n",
              " 15955.0,\n",
              " 16726.0,\n",
              " 16015.0,\n",
              " 16626.0,\n",
              " 14227.0,\n",
              " 13184.0,\n",
              " 12955.0,\n",
              " 17341.0,\n",
              " 14401.0,\n",
              " 15009.0,\n",
              " 16469.0,\n",
              " 12347.0,\n",
              " 14083.0,\n",
              " 17472.0,\n",
              " 16533.0,\n",
              " 17997.0,\n",
              " 14307.0,\n",
              " 15525.0,\n",
              " 14461.0,\n",
              " 13881.0,\n",
              " 13588.0,\n",
              " 16059.0,\n",
              " 17974.0,\n",
              " 13536.0,\n",
              " 12473.0,\n",
              " 16200.0,\n",
              " 16880.0,\n",
              " 13523.0,\n",
              " 15696.0,\n",
              " 13772.0,\n",
              " 18245.0,\n",
              " 17567.0,\n",
              " 12444.0,\n",
              " 15632.0,\n",
              " 15974.0,\n",
              " 13815.0,\n",
              " 13566.0,\n",
              " 16755.0,\n",
              " 16180.0,\n",
              " 15738.0,\n",
              " 13015.0,\n",
              " 12836.0,\n",
              " 13558.0,\n",
              " 14639.0,\n",
              " 16442.0,\n",
              " 17073.0,\n",
              " 13838.0,\n",
              " 12708.0,\n",
              " 16607.0,\n",
              " 13139.0,\n",
              " 13381.0,\n",
              " 14112.0,\n",
              " 14944.0,\n",
              " 16407.0,\n",
              " 17837.0,\n",
              " 14004.0,\n",
              " 12688.0,\n",
              " 16470.0,\n",
              " 16678.0,\n",
              " 14669.0,\n",
              " 13093.0,\n",
              " 18272.0,\n",
              " 16511.0,\n",
              " 15838.0,\n",
              " 17001.0,\n",
              " 17728.0,\n",
              " 13362.0,\n",
              " 17644.0,\n",
              " 15574.0,\n",
              " 14662.0,\n",
              " 12668.0,\n",
              " 14907.0,\n",
              " 15687.0,\n",
              " 12562.0,\n",
              " 16283.0,\n",
              " 16085.0,\n",
              " 17591.0,\n",
              " 12678.0,\n",
              " 12370.0,\n",
              " 14125.0,\n",
              " 13437.0,\n",
              " 16303.0,\n",
              " 15527.0,\n",
              " 16837.0,\n",
              " 13596.0,\n",
              " 13654.0,\n",
              " 18198.0,\n",
              " 15218.0,\n",
              " 14179.0,\n",
              " 13880.0,\n",
              " 16019.0,\n",
              " 14503.0,\n",
              " 14175.0,\n",
              " 13394.0,\n",
              " 15506.0,\n",
              " 17213.0,\n",
              " 18229.0,\n",
              " 16800.0,\n",
              " 12721.0,\n",
              " 14592.0,\n",
              " 16898.0,\n",
              " 14334.0,\n",
              " 12613.0,\n",
              " 18139.0,\n",
              " 14299.0,\n",
              " 17084.0,\n",
              " 13507.0,\n",
              " 14016.0,\n",
              " 17659.0,\n",
              " 16669.0,\n",
              " 14121.0,\n",
              " 13090.0,\n",
              " 15511.0,\n",
              " 14971.0,\n",
              " 12501.0,\n",
              " 13949.0,\n",
              " 15916.0,\n",
              " 13426.0,\n",
              " 12395.0,\n",
              " 14234.0,\n",
              " 12779.0,\n",
              " 16877.0,\n",
              " 15065.0,\n",
              " 13971.0,\n",
              " 15004.0,\n",
              " 13373.0,\n",
              " 14842.0,\n",
              " 16775.0,\n",
              " 15464.0,\n",
              " 14110.0,\n",
              " 17774.0,\n",
              " 16520.0,\n",
              " 15628.0,\n",
              " 12685.0,\n",
              " 16316.0,\n",
              " 14081.0,\n",
              " 14282.0,\n",
              " 13994.0,\n",
              " 15491.0,\n",
              " 13883.0,\n",
              " 13384.0,\n",
              " 17027.0,\n",
              " 12520.0,\n",
              " 16788.0,\n",
              " 15024.0,\n",
              " 15673.0,\n",
              " 17069.0,\n",
              " 12480.0,\n",
              " 16332.0,\n",
              " 16701.0,\n",
              " 12877.0,\n",
              " 16340.0,\n",
              " 14629.0,\n",
              " 18075.0,\n",
              " 15407.0,\n",
              " 16633.0,\n",
              " 14032.0,\n",
              " 17203.0,\n",
              " 17566.0,\n",
              " 16725.0,\n",
              " 13956.0,\n",
              " 14593.0,\n",
              " 15906.0,\n",
              " 16907.0,\n",
              " 15365.0,\n",
              " 16191.0,\n",
              " 12970.0,\n",
              " 16013.0,\n",
              " 15581.0,\n",
              " 13208.0,\n",
              " 15281.0,\n",
              " 16809.0,\n",
              " 14952.0,\n",
              " 13901.0,\n",
              " 14732.0,\n",
              " 16983.0,\n",
              " 15755.0,\n",
              " 17071.0,\n",
              " 16466.0,\n",
              " 14243.0,\n",
              " 14428.0,\n",
              " 17863.0,\n",
              " 12577.0,\n",
              " 13644.0,\n",
              " 16014.0,\n",
              " 13911.0,\n",
              " 13632.0,\n",
              " 15769.0,\n",
              " 17802.0,\n",
              " 12502.0,\n",
              " 17979.0,\n",
              " 14868.0,\n",
              " 15862.0,\n",
              " 13110.0,\n",
              " 16477.0,\n",
              " 17700.0,\n",
              " 16770.0,\n",
              " 17883.0,\n",
              " 15089.0,\n",
              " 17164.0,\n",
              " 17921.0,\n",
              " 14487.0,\n",
              " 17545.0,\n",
              " 15006.0,\n",
              " 12524.0,\n",
              " 14482.0,\n",
              " 15051.0,\n",
              " 13756.0,\n",
              " 16916.0,\n",
              " 14262.0,\n",
              " 13517.0,\n",
              " 15919.0,\n",
              " 14852.0,\n",
              " 15235.0,\n",
              " 13769.0,\n",
              " 13212.0,\n",
              " 13246.0,\n",
              " 13126.0,\n",
              " 17288.0,\n",
              " 13862.0,\n",
              " 12712.0,\n",
              " 17612.0,\n",
              " 15860.0,\n",
              " 15203.0,\n",
              " 16240.0,\n",
              " 17812.0,\n",
              " 12727.0,\n",
              " 18196.0,\n",
              " 14387.0,\n",
              " 18260.0,\n",
              " 17442.0,\n",
              " 15845.0,\n",
              " 15987.0,\n",
              " 17504.0,\n",
              " 15125.0,\n",
              " 15854.0,\n",
              " 18168.0,\n",
              " 18156.0,\n",
              " 13136.0,\n",
              " 15811.0,\n",
              " 14710.0,\n",
              " 16871.0,\n",
              " 16156.0,\n",
              " 18065.0,\n",
              " 17434.0,\n",
              " 13952.0,\n",
              " 14841.0,\n",
              " 17838.0,\n",
              " 15874.0,\n",
              " 14766.0,\n",
              " 15373.0,\n",
              " 13804.0,\n",
              " 13048.0,\n",
              " 16222.0,\n",
              " 17744.0,\n",
              " 13924.0,\n",
              " 17516.0,\n",
              " 13141.0,\n",
              " 17314.0,\n",
              " 16150.0,\n",
              " 13555.0,\n",
              " 16497.0,\n",
              " 14623.0,\n",
              " 16258.0,\n",
              " 16460.0,\n",
              " 15022.0,\n",
              " 13317.0,\n",
              " 17422.0,\n",
              " 13704.0,\n",
              " 17293.0,\n",
              " 17114.0,\n",
              " 16889.0,\n",
              " 17444.0,\n",
              " 15796.0,\n",
              " 15674.0,\n",
              " 16271.0,\n",
              " 13021.0,\n",
              " 12743.0,\n",
              " 13340.0,\n",
              " 17787.0,\n",
              " 17131.0,\n",
              " 16057.0,\n",
              " 14776.0,\n",
              " 16225.0,\n",
              " 14135.0,\n",
              " 15827.0,\n",
              " 18210.0,\n",
              " 15157.0,\n",
              " 16570.0,\n",
              " 12598.0,\n",
              " 15038.0,\n",
              " 13168.0,\n",
              " 14696.0,\n",
              " 14670.0,\n",
              " 16438.0,\n",
              " 13115.0,\n",
              " 13365.0,\n",
              " 17360.0,\n",
              " 16076.0,\n",
              " 15877.0,\n",
              " 15996.0,\n",
              " 14382.0,\n",
              " 14854.0,\n",
              " 12357.0,\n",
              " 17180.0,\n",
              " 14341.0,\n",
              " 17685.0,\n",
              " 13576.0,\n",
              " 14844.0,\n",
              " 14465.0,\n",
              " 12656.0,\n",
              " 12935.0,\n",
              " 16367.0,\n",
              " 16918.0,\n",
              " 13593.0,\n",
              " 12456.0,\n",
              " 16987.0,\n",
              " 12360.0,\n",
              " 14565.0,\n",
              " 16869.0,\n",
              " 17368.0,\n",
              " 12647.0,\n",
              " 15720.0,\n",
              " 15872.0,\n",
              " 16735.0,\n",
              " 15012.0,\n",
              " 13422.0,\n",
              " 18044.0,\n",
              " 14930.0,\n",
              " 16376.0,\n",
              " 16170.0,\n",
              " 15482.0,\n",
              " 17265.0,\n",
              " 14657.0,\n",
              " 18079.0,\n",
              " 15036.0,\n",
              " 12397.0,\n",
              " 12423.0,\n",
              " 13908.0,\n",
              " 17696.0,\n",
              " 18231.0,\n",
              " 14626.0,\n",
              " 15228.0,\n",
              " 12584.0,\n",
              " 16899.0,\n",
              " 15494.0,\n",
              " 13993.0,\n",
              " 15584.0,\n",
              " 12901.0,\n",
              " 17092.0,\n",
              " 17287.0,\n",
              " 16253.0,\n",
              " 14107.0,\n",
              " 12726.0,\n",
              " 17629.0,\n",
              " 17259.0,\n",
              " 14418.0,\n",
              " 16261.0,\n",
              " 13548.0,\n",
              " 14878.0,\n",
              " 13014.0,\n",
              " 12518.0,\n",
              " 18257.0,\n",
              " 12838.0,\n",
              " 16652.0,\n",
              " 14309.0,\n",
              " 15819.0,\n",
              " 18016.0,\n",
              " 14577.0,\n",
              " 16210.0,\n",
              " 14493.0,\n",
              " 14321.0,\n",
              " 16875.0,\n",
              " 16706.0,\n",
              " 15410.0,\n",
              " 13159.0,\n",
              " 15965.0,\n",
              " 15251.0,\n",
              " 12556.0,\n",
              " 13552.0,\n",
              " 15060.0,\n",
              " 14825.0,\n",
              " 16985.0,\n",
              " 16801.0,\n",
              " 17109.0,\n",
              " 18043.0,\n",
              " 12578.0,\n",
              " 12937.0,\n",
              " 17722.0,\n",
              " 16293.0,\n",
              " 13199.0,\n",
              " 13771.0,\n",
              " 16628.0,\n",
              " 15800.0,\n",
              " 13344.0,\n",
              " 15220.0,\n",
              " 13895.0,\n",
              " 14898.0,\n",
              " 15641.0,\n",
              " 15932.0,\n",
              " 18180.0,\n",
              " 12594.0,\n",
              " 17682.0,\n",
              " 16324.0,\n",
              " 12948.0,\n",
              " 16143.0,\n",
              " 14232.0,\n",
              " 14360.0,\n",
              " 15716.0,\n",
              " 16369.0,\n",
              " 14224.0,\n",
              " 13725.0,\n",
              " 13870.0,\n",
              " 14211.0,\n",
              " 16841.0,\n",
              " 15704.0,\n",
              " 13510.0,\n",
              " 17884.0,\n",
              " 16126.0,\n",
              " 15376.0,\n",
              " 15563.0,\n",
              " 14665.0,\n",
              " 17667.0,\n",
              " 16672.0,\n",
              " 13758.0,\n",
              " 17406.0,\n",
              " 17272.0,\n",
              " 15549.0,\n",
              " 14239.0,\n",
              " 15901.0,\n",
              " 15665.0,\n",
              " 12840.0,\n",
              " 13782.0,\n",
              " 17088.0,\n",
              " 15002.0,\n",
              " 13982.0,\n",
              " 16201.0,\n",
              " 17897.0,\n",
              " 17672.0,\n",
              " 18219.0,\n",
              " 12854.0,\n",
              " 14700.0,\n",
              " 13854.0,\n",
              " 15054.0,\n",
              " 12409.0,\n",
              " 14544.0,\n",
              " 14607.0,\n",
              " 13890.0,\n",
              " 15983.0,\n",
              " 16515.0,\n",
              " 12408.0,\n",
              " 14640.0,\n",
              " 14553.0,\n",
              " 15810.0,\n",
              " 17289.0,\n",
              " 14258.0,\n",
              " 14245.0,\n",
              " 13983.0,\n",
              " 17254.0,\n",
              " 13077.0,\n",
              " 18034.0,\n",
              " 15093.0,\n",
              " 13658.0,\n",
              " 13481.0,\n",
              " 14250.0,\n",
              " 17461.0,\n",
              " 16456.0,\n",
              " 15031.0,\n",
              " 12610.0,\n",
              " 12560.0,\n",
              " 15241.0,\n",
              " 17797.0,\n",
              " 14541.0,\n",
              " 14062.0,\n",
              " 17459.0,\n",
              " 15128.0,\n",
              " 16374.0,\n",
              " 13320.0,\n",
              " 17381.0,\n",
              " 15214.0,\n",
              " 14837.0,\n",
              " 14305.0,\n",
              " 17491.0,\n",
              " 13196.0,\n",
              " 16358.0,\n",
              " ...]"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#merge with the real data\n",
        "data = data.query(\"CustomerID in @more_than_5_transaction_customers\").reset_index(drop=True)\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "ySzEUr_bnZ04",
        "outputId": "ef58d8cd-81b4-473a-b15d-ac47310056df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               InvoiceDate  CustomerID  UnitPrice\n",
              "0      2010-12-01 08:26:00     17850.0       2.55\n",
              "1      2010-12-01 08:26:00     17850.0       3.39\n",
              "2      2010-12-01 08:26:00     17850.0       2.75\n",
              "3      2010-12-01 08:26:00     17850.0       3.39\n",
              "4      2010-12-01 08:26:00     17850.0       3.39\n",
              "...                    ...         ...        ...\n",
              "405943 2011-12-09 12:50:00     12680.0       0.85\n",
              "405944 2011-12-09 12:50:00     12680.0       2.10\n",
              "405945 2011-12-09 12:50:00     12680.0       4.15\n",
              "405946 2011-12-09 12:50:00     12680.0       4.15\n",
              "405947 2011-12-09 12:50:00     12680.0       4.95\n",
              "\n",
              "[405948 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3a462c3d-9fcc-4e2a-a0be-5cf7f1f55d2f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>InvoiceDate</th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>UnitPrice</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>2.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>3.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>2.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>3.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>3.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>405943</th>\n",
              "      <td>2011-12-09 12:50:00</td>\n",
              "      <td>12680.0</td>\n",
              "      <td>0.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>405944</th>\n",
              "      <td>2011-12-09 12:50:00</td>\n",
              "      <td>12680.0</td>\n",
              "      <td>2.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>405945</th>\n",
              "      <td>2011-12-09 12:50:00</td>\n",
              "      <td>12680.0</td>\n",
              "      <td>4.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>405946</th>\n",
              "      <td>2011-12-09 12:50:00</td>\n",
              "      <td>12680.0</td>\n",
              "      <td>4.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>405947</th>\n",
              "      <td>2011-12-09 12:50:00</td>\n",
              "      <td>12680.0</td>\n",
              "      <td>4.95</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>405948 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3a462c3d-9fcc-4e2a-a0be-5cf7f1f55d2f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3a462c3d-9fcc-4e2a-a0be-5cf7f1f55d2f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3a462c3d-9fcc-4e2a-a0be-5cf7f1f55d2f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.query(\"CustomerID == '17846.0'\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "id": "gXKngXDNn4Xv",
        "outputId": "542ad5a2-5b79-4e35-a811-3e819b4a9024"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [InvoiceDate, CustomerID, UnitPrice]\n",
              "Index: []"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ef0aaeb1-8415-4ace-8a9f-79eba88f6546\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>InvoiceDate</th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>UnitPrice</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ef0aaeb1-8415-4ace-8a9f-79eba88f6546')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ef0aaeb1-8415-4ace-8a9f-79eba88f6546 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ef0aaeb1-8415-4ace-8a9f-79eba88f6546');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['InvoiceDate'] = pd.to_datetime(data['InvoiceDate'],format='%d/%m/%y')\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "7X8aMsA3oGG2",
        "outputId": "c14de23f-cab5-43de-cee1-c71883e8dfa2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               InvoiceDate  CustomerID  UnitPrice\n",
              "0      2010-12-01 08:26:00     17850.0       2.55\n",
              "1      2010-12-01 08:26:00     17850.0       3.39\n",
              "2      2010-12-01 08:26:00     17850.0       2.75\n",
              "3      2010-12-01 08:26:00     17850.0       3.39\n",
              "4      2010-12-01 08:26:00     17850.0       3.39\n",
              "...                    ...         ...        ...\n",
              "405943 2011-12-09 12:50:00     12680.0       0.85\n",
              "405944 2011-12-09 12:50:00     12680.0       2.10\n",
              "405945 2011-12-09 12:50:00     12680.0       4.15\n",
              "405946 2011-12-09 12:50:00     12680.0       4.15\n",
              "405947 2011-12-09 12:50:00     12680.0       4.95\n",
              "\n",
              "[405948 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8f7374e3-a766-4e34-abea-1af98a2047f3\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>InvoiceDate</th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>UnitPrice</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>2.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>3.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>2.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>3.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>3.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>405943</th>\n",
              "      <td>2011-12-09 12:50:00</td>\n",
              "      <td>12680.0</td>\n",
              "      <td>0.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>405944</th>\n",
              "      <td>2011-12-09 12:50:00</td>\n",
              "      <td>12680.0</td>\n",
              "      <td>2.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>405945</th>\n",
              "      <td>2011-12-09 12:50:00</td>\n",
              "      <td>12680.0</td>\n",
              "      <td>4.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>405946</th>\n",
              "      <td>2011-12-09 12:50:00</td>\n",
              "      <td>12680.0</td>\n",
              "      <td>4.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>405947</th>\n",
              "      <td>2011-12-09 12:50:00</td>\n",
              "      <td>12680.0</td>\n",
              "      <td>4.95</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>405948 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8f7374e3-a766-4e34-abea-1af98a2047f3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8f7374e3-a766-4e34-abea-1af98a2047f3 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8f7374e3-a766-4e34-abea-1af98a2047f3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#RFM ------------------->Mean -> Min -> Max -> Std.dev\n",
        "#Recency\n",
        "\n",
        "current_date = max(data['InvoiceDate']) + datetime.timedelta(days=1)\n",
        "\n",
        "recency_merchant = data.pivot_table(index='CustomerID', aggfunc={'InvoiceDate': 'max'}).reset_index().rename(columns={'InvoiceDate': 'max_date'})\n",
        "recency_merchant['recency'] = recency_merchant.apply(lambda row: (current_date - row['max_date']).total_seconds()/60/60, axis=1)\n",
        "recency_merchant"
      ],
      "metadata": {
        "id": "EmhWgWUOopzp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "b7c4acaa-2a17-4e79-963c-7b31141840dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      CustomerID            max_date      recency\n",
              "0        12347.0 2011-12-07 15:52:00    68.966667\n",
              "1        12348.0 2011-09-25 13:13:00  1823.616667\n",
              "2        12349.0 2011-11-21 09:51:00   458.983333\n",
              "3        12350.0 2011-02-02 16:01:00  7460.816667\n",
              "4        12352.0 2011-11-03 14:37:00   886.216667\n",
              "...          ...                 ...          ...\n",
              "4060     18280.0 2011-03-07 09:52:00  6674.966667\n",
              "4061     18281.0 2011-06-12 10:53:00  4345.950000\n",
              "4062     18282.0 2011-12-02 11:43:00   193.116667\n",
              "4063     18283.0 2011-12-06 12:02:00    96.800000\n",
              "4064     18287.0 2011-10-28 09:29:00  1035.350000\n",
              "\n",
              "[4065 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-97477dfa-1c18-4625-88ce-5846ee1f5ebf\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>max_date</th>\n",
              "      <th>recency</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12347.0</td>\n",
              "      <td>2011-12-07 15:52:00</td>\n",
              "      <td>68.966667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12348.0</td>\n",
              "      <td>2011-09-25 13:13:00</td>\n",
              "      <td>1823.616667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12349.0</td>\n",
              "      <td>2011-11-21 09:51:00</td>\n",
              "      <td>458.983333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12350.0</td>\n",
              "      <td>2011-02-02 16:01:00</td>\n",
              "      <td>7460.816667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12352.0</td>\n",
              "      <td>2011-11-03 14:37:00</td>\n",
              "      <td>886.216667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4060</th>\n",
              "      <td>18280.0</td>\n",
              "      <td>2011-03-07 09:52:00</td>\n",
              "      <td>6674.966667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4061</th>\n",
              "      <td>18281.0</td>\n",
              "      <td>2011-06-12 10:53:00</td>\n",
              "      <td>4345.950000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4062</th>\n",
              "      <td>18282.0</td>\n",
              "      <td>2011-12-02 11:43:00</td>\n",
              "      <td>193.116667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4063</th>\n",
              "      <td>18283.0</td>\n",
              "      <td>2011-12-06 12:02:00</td>\n",
              "      <td>96.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4064</th>\n",
              "      <td>18287.0</td>\n",
              "      <td>2011-10-28 09:29:00</td>\n",
              "      <td>1035.350000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4065 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-97477dfa-1c18-4625-88ce-5846ee1f5ebf')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-97477dfa-1c18-4625-88ce-5846ee1f5ebf button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-97477dfa-1c18-4625-88ce-5846ee1f5ebf');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#frequency\n",
        "data['Created_Time_prev'] = data.sort_values(['InvoiceDate', 'CustomerID'],ascending=True).groupby('CustomerID')['InvoiceDate'].shift(1)\n",
        "data['hour_diff'] = data.apply(lambda row: (row['InvoiceDate'] - row['Created_Time_prev']).total_seconds()/60/60, axis=1)\n",
        "data['hour_diff' + '_mean'], data['hour_diff' + '_std'], data['hour_diff' + '_count'] = data['hour_diff'], data['hour_diff'], data['hour_diff']\n",
        "frequency_merchant = data.pivot_table(index='CustomerID', aggfunc={'hour_diff' + '_mean': 'mean','hour_diff'+'_std':'std', 'hour_diff'+'_count':'count'}).reset_index().rename(columns={\"hour_diff_mean\": 'frequency'})\n",
        "frequency_merchant"
      ],
      "metadata": {
        "id": "qaHbKwfbuwnk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "938ab337-fb21-4806-9612-aa8318d07122"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      CustomerID  hour_diff_count   frequency  hour_diff_std\n",
              "0        12347.0              181   48.402855     272.267560\n",
              "1        12348.0               30  226.202222     819.007397\n",
              "2        12349.0               72    0.000000       0.000000\n",
              "3        12350.0               16    0.000000       0.000000\n",
              "4        12352.0               94   66.404965     460.189061\n",
              "...          ...              ...         ...            ...\n",
              "4060     18280.0                9    0.000000       0.000000\n",
              "4061     18281.0                6    0.000000       0.000000\n",
              "4062     18282.0               12  237.844444     793.682479\n",
              "4063     18283.0              755   10.614305      96.276584\n",
              "4064     18287.0               69   55.287440     415.023864\n",
              "\n",
              "[4065 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-03e26e7d-1919-4d3f-affd-997655d8e6ba\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>hour_diff_count</th>\n",
              "      <th>frequency</th>\n",
              "      <th>hour_diff_std</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12347.0</td>\n",
              "      <td>181</td>\n",
              "      <td>48.402855</td>\n",
              "      <td>272.267560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12348.0</td>\n",
              "      <td>30</td>\n",
              "      <td>226.202222</td>\n",
              "      <td>819.007397</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12349.0</td>\n",
              "      <td>72</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12350.0</td>\n",
              "      <td>16</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12352.0</td>\n",
              "      <td>94</td>\n",
              "      <td>66.404965</td>\n",
              "      <td>460.189061</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>4060</th>\n",
              "      <td>18280.0</td>\n",
              "      <td>9</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4061</th>\n",
              "      <td>18281.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4062</th>\n",
              "      <td>18282.0</td>\n",
              "      <td>12</td>\n",
              "      <td>237.844444</td>\n",
              "      <td>793.682479</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4063</th>\n",
              "      <td>18283.0</td>\n",
              "      <td>755</td>\n",
              "      <td>10.614305</td>\n",
              "      <td>96.276584</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4064</th>\n",
              "      <td>18287.0</td>\n",
              "      <td>69</td>\n",
              "      <td>55.287440</td>\n",
              "      <td>415.023864</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4065 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-03e26e7d-1919-4d3f-affd-997655d8e6ba')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-03e26e7d-1919-4d3f-affd-997655d8e6ba button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-03e26e7d-1919-4d3f-affd-997655d8e6ba');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#monetary Median\n",
        "monetary_merchant = data.pivot_table(index='CustomerID', aggfunc={'UnitPrice': 'median'}).reset_index().rename(columns={'UnitPrice': 'monetary'})\n",
        "monetary_merchant"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "-EHFFD9O7CVw",
        "outputId": "cb59833e-771f-4f17-8aef-c0cf01046ba5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      CustomerID  monetary\n",
              "0        12347.0     2.015\n",
              "1        12348.0     0.550\n",
              "2        12349.0     2.550\n",
              "3        12350.0     1.650\n",
              "4        12352.0     3.950\n",
              "...          ...       ...\n",
              "4060     18280.0     3.750\n",
              "4061     18281.0     1.650\n",
              "4062     18282.0     2.950\n",
              "4063     18283.0     1.650\n",
              "4064     18287.0     1.250\n",
              "\n",
              "[4065 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1434c19f-99a9-4174-b31d-1f0e03fb79d2\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12347.0</td>\n",
              "      <td>2.015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12348.0</td>\n",
              "      <td>0.550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12349.0</td>\n",
              "      <td>2.550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12350.0</td>\n",
              "      <td>1.650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12352.0</td>\n",
              "      <td>3.950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4060</th>\n",
              "      <td>18280.0</td>\n",
              "      <td>3.750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4061</th>\n",
              "      <td>18281.0</td>\n",
              "      <td>1.650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4062</th>\n",
              "      <td>18282.0</td>\n",
              "      <td>2.950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4063</th>\n",
              "      <td>18283.0</td>\n",
              "      <td>1.650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4064</th>\n",
              "      <td>18287.0</td>\n",
              "      <td>1.250</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4065 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1434c19f-99a9-4174-b31d-1f0e03fb79d2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1434c19f-99a9-4174-b31d-1f0e03fb79d2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1434c19f-99a9-4174-b31d-1f0e03fb79d2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#mean\n",
        "monetary_merchant_mean = data.pivot_table(index='CustomerID', aggfunc={'UnitPrice': 'mean'}).reset_index().rename(columns={'UnitPrice': 'monetary_mean'})\n",
        "monetary_merchant_mean"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "opMRdqysDE7z",
        "outputId": "fde17f67-8c87-4e03-b113-b51d13dc22e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      CustomerID  monetary_mean\n",
              "0        12347.0       2.644011\n",
              "1        12348.0       5.764839\n",
              "2        12349.0       8.289041\n",
              "3        12350.0       3.841176\n",
              "4        12352.0      23.274737\n",
              "...          ...            ...\n",
              "4060     18280.0       4.765000\n",
              "4061     18281.0       5.622857\n",
              "4062     18282.0       4.821538\n",
              "4063     18283.0       1.614987\n",
              "4064     18287.0       1.493571\n",
              "\n",
              "[4065 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1b6c324b-f553-4490-be36-774161db091f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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_mean</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12347.0</td>\n",
              "      <td>2.644011</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12348.0</td>\n",
              "      <td>5.764839</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12349.0</td>\n",
              "      <td>8.289041</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12350.0</td>\n",
              "      <td>3.841176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12352.0</td>\n",
              "      <td>23.274737</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4060</th>\n",
              "      <td>18280.0</td>\n",
              "      <td>4.765000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4061</th>\n",
              "      <td>18281.0</td>\n",
              "      <td>5.622857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4062</th>\n",
              "      <td>18282.0</td>\n",
              "      <td>4.821538</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4063</th>\n",
              "      <td>18283.0</td>\n",
              "      <td>1.614987</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4064</th>\n",
              "      <td>18287.0</td>\n",
              "      <td>1.493571</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4065 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1b6c324b-f553-4490-be36-774161db091f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1b6c324b-f553-4490-be36-774161db091f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1b6c324b-f553-4490-be36-774161db091f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#min\n",
        "monetary_merchant_min = data.pivot_table(index='CustomerID', aggfunc={'UnitPrice': 'min'}).reset_index().rename(columns={'UnitPrice': 'monetary_min'})\n",
        "monetary_merchant_min"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "kvR0QyVLD77P",
        "outputId": "e4cf0656-3422-41b1-f075-71517ff28cb0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      CustomerID  monetary_min\n",
              "0        12347.0          0.25\n",
              "1        12348.0          0.29\n",
              "2        12349.0          0.42\n",
              "3        12350.0          0.85\n",
              "4        12352.0          0.65\n",
              "...          ...           ...\n",
              "4060     18280.0          2.55\n",
              "4061     18281.0          0.42\n",
              "4062     18282.0          0.29\n",
              "4063     18283.0          0.29\n",
              "4064     18287.0          0.29\n",
              "\n",
              "[4065 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a54430b2-ca5e-4f8c-a7ee-913f26d2ff16\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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_min</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12347.0</td>\n",
              "      <td>0.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12348.0</td>\n",
              "      <td>0.29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12349.0</td>\n",
              "      <td>0.42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12350.0</td>\n",
              "      <td>0.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12352.0</td>\n",
              "      <td>0.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4060</th>\n",
              "      <td>18280.0</td>\n",
              "      <td>2.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4061</th>\n",
              "      <td>18281.0</td>\n",
              "      <td>0.42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4062</th>\n",
              "      <td>18282.0</td>\n",
              "      <td>0.29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4063</th>\n",
              "      <td>18283.0</td>\n",
              "      <td>0.29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4064</th>\n",
              "      <td>18287.0</td>\n",
              "      <td>0.29</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4065 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a54430b2-ca5e-4f8c-a7ee-913f26d2ff16')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a54430b2-ca5e-4f8c-a7ee-913f26d2ff16 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a54430b2-ca5e-4f8c-a7ee-913f26d2ff16');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#max\n",
        "monetary_merchant_max = data.pivot_table(index='CustomerID', aggfunc={'UnitPrice': 'max'}).reset_index().rename(columns={'UnitPrice': 'monetary_max'})\n",
        "monetary_merchant_max"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "OnMM_qiLEtln",
        "outputId": "0251be44-3616-47d7-81bc-9eb54be07e66"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      CustomerID  monetary_max\n",
              "0        12347.0         12.75\n",
              "1        12348.0         40.00\n",
              "2        12349.0        300.00\n",
              "3        12350.0         40.00\n",
              "4        12352.0        376.50\n",
              "...          ...           ...\n",
              "4060     18280.0          9.95\n",
              "4061     18281.0         16.95\n",
              "4062     18282.0         12.75\n",
              "4063     18283.0         15.95\n",
              "4064     18287.0          8.50\n",
              "\n",
              "[4065 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b7d7fb3c-29bf-4dec-821f-71d4704be5e8\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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_max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12347.0</td>\n",
              "      <td>12.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12348.0</td>\n",
              "      <td>40.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12349.0</td>\n",
              "      <td>300.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12350.0</td>\n",
              "      <td>40.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12352.0</td>\n",
              "      <td>376.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4060</th>\n",
              "      <td>18280.0</td>\n",
              "      <td>9.95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4061</th>\n",
              "      <td>18281.0</td>\n",
              "      <td>16.95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4062</th>\n",
              "      <td>18282.0</td>\n",
              "      <td>12.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4063</th>\n",
              "      <td>18283.0</td>\n",
              "      <td>15.95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4064</th>\n",
              "      <td>18287.0</td>\n",
              "      <td>8.50</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4065 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b7d7fb3c-29bf-4dec-821f-71d4704be5e8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b7d7fb3c-29bf-4dec-821f-71d4704be5e8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b7d7fb3c-29bf-4dec-821f-71d4704be5e8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#std\n",
        "monetary_merchant_std = data.pivot_table(index = 'CustomerID', aggfunc={'UnitPrice':'std'}).reset_index().rename(columns={'UnitPrice':'monetary_std'})\n",
        "monetary_merchant_std"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "P7lWtAcyFWtN",
        "outputId": "d0300497-7478-455b-bb2c-1f345299f13e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      CustomerID  monetary_std\n",
              "0        12347.0      2.255381\n",
              "1        12348.0     13.400323\n",
              "2        12349.0     35.028021\n",
              "3        12350.0      9.334751\n",
              "4        12352.0     70.438671\n",
              "...          ...           ...\n",
              "4060     18280.0      2.472072\n",
              "4061     18281.0      7.778641\n",
              "4062     18282.0      4.862801\n",
              "4063     18283.0      1.096060\n",
              "4064     18287.0      1.227115\n",
              "\n",
              "[4065 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-21fe3667-ac98-47d1-9834-d5a87891f48f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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_std</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12347.0</td>\n",
              "      <td>2.255381</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12348.0</td>\n",
              "      <td>13.400323</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12349.0</td>\n",
              "      <td>35.028021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12350.0</td>\n",
              "      <td>9.334751</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12352.0</td>\n",
              "      <td>70.438671</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4060</th>\n",
              "      <td>18280.0</td>\n",
              "      <td>2.472072</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4061</th>\n",
              "      <td>18281.0</td>\n",
              "      <td>7.778641</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4062</th>\n",
              "      <td>18282.0</td>\n",
              "      <td>4.862801</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4063</th>\n",
              "      <td>18283.0</td>\n",
              "      <td>1.096060</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4064</th>\n",
              "      <td>18287.0</td>\n",
              "      <td>1.227115</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4065 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-21fe3667-ac98-47d1-9834-d5a87891f48f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-21fe3667-ac98-47d1-9834-d5a87891f48f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-21fe3667-ac98-47d1-9834-d5a87891f48f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#concatenate\n",
        "\n",
        "rfm = pd.concat([frequency_merchant[\"frequency\"],monetary_merchant,monetary_merchant_mean[\"monetary_mean\"],monetary_merchant_min[\"monetary_min\"],monetary_merchant_max[\"monetary_max\"],monetary_merchant_std[\"monetary_std\"],recency_merchant['recency']],axis=1)\n",
        "rfm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "4LAAkP0vF1h7",
        "outputId": "07c080bb-fa32-415e-c4f1-7b0d1ee4000c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       frequency  CustomerID  monetary  monetary_mean  monetary_min  monetary_max  monetary_std      recency\n",
              "0      48.402855     12347.0     2.015       2.644011          0.25         12.75      2.255381    68.966667\n",
              "1     226.202222     12348.0     0.550       5.764839          0.29         40.00     13.400323  1823.616667\n",
              "2       0.000000     12349.0     2.550       8.289041          0.42        300.00     35.028021   458.983333\n",
              "3       0.000000     12350.0     1.650       3.841176          0.85         40.00      9.334751  7460.816667\n",
              "4      66.404965     12352.0     3.950      23.274737          0.65        376.50     70.438671   886.216667\n",
              "...          ...         ...       ...            ...           ...           ...           ...          ...\n",
              "4060    0.000000     18280.0     3.750       4.765000          2.55          9.95      2.472072  6674.966667\n",
              "4061    0.000000     18281.0     1.650       5.622857          0.42         16.95      7.778641  4345.950000\n",
              "4062  237.844444     18282.0     2.950       4.821538          0.29         12.75      4.862801   193.116667\n",
              "4063   10.614305     18283.0     1.650       1.614987          0.29         15.95      1.096060    96.800000\n",
              "4064   55.287440     18287.0     1.250       1.493571          0.29          8.50      1.227115  1035.350000\n",
              "\n",
              "[4065 rows x 8 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9a096811-db25-4559-bbad-010c2be6a680\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>frequency</th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>monetary</th>\n",
              "      <th>monetary_mean</th>\n",
              "      <th>monetary_min</th>\n",
              "      <th>monetary_max</th>\n",
              "      <th>monetary_std</th>\n",
              "      <th>recency</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>48.402855</td>\n",
              "      <td>12347.0</td>\n",
              "      <td>2.015</td>\n",
              "      <td>2.644011</td>\n",
              "      <td>0.25</td>\n",
              "      <td>12.75</td>\n",
              "      <td>2.255381</td>\n",
              "      <td>68.966667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>226.202222</td>\n",
              "      <td>12348.0</td>\n",
              "      <td>0.550</td>\n",
              "      <td>5.764839</td>\n",
              "      <td>0.29</td>\n",
              "      <td>40.00</td>\n",
              "      <td>13.400323</td>\n",
              "      <td>1823.616667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>12349.0</td>\n",
              "      <td>2.550</td>\n",
              "      <td>8.289041</td>\n",
              "      <td>0.42</td>\n",
              "      <td>300.00</td>\n",
              "      <td>35.028021</td>\n",
              "      <td>458.983333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>12350.0</td>\n",
              "      <td>1.650</td>\n",
              "      <td>3.841176</td>\n",
              "      <td>0.85</td>\n",
              "      <td>40.00</td>\n",
              "      <td>9.334751</td>\n",
              "      <td>7460.816667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>66.404965</td>\n",
              "      <td>12352.0</td>\n",
              "      <td>3.950</td>\n",
              "      <td>23.274737</td>\n",
              "      <td>0.65</td>\n",
              "      <td>376.50</td>\n",
              "      <td>70.438671</td>\n",
              "      <td>886.216667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4060</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>18280.0</td>\n",
              "      <td>3.750</td>\n",
              "      <td>4.765000</td>\n",
              "      <td>2.55</td>\n",
              "      <td>9.95</td>\n",
              "      <td>2.472072</td>\n",
              "      <td>6674.966667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4061</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>18281.0</td>\n",
              "      <td>1.650</td>\n",
              "      <td>5.622857</td>\n",
              "      <td>0.42</td>\n",
              "      <td>16.95</td>\n",
              "      <td>7.778641</td>\n",
              "      <td>4345.950000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4062</th>\n",
              "      <td>237.844444</td>\n",
              "      <td>18282.0</td>\n",
              "      <td>2.950</td>\n",
              "      <td>4.821538</td>\n",
              "      <td>0.29</td>\n",
              "      <td>12.75</td>\n",
              "      <td>4.862801</td>\n",
              "      <td>193.116667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4063</th>\n",
              "      <td>10.614305</td>\n",
              "      <td>18283.0</td>\n",
              "      <td>1.650</td>\n",
              "      <td>1.614987</td>\n",
              "      <td>0.29</td>\n",
              "      <td>15.95</td>\n",
              "      <td>1.096060</td>\n",
              "      <td>96.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4064</th>\n",
              "      <td>55.287440</td>\n",
              "      <td>18287.0</td>\n",
              "      <td>1.250</td>\n",
              "      <td>1.493571</td>\n",
              "      <td>0.29</td>\n",
              "      <td>8.50</td>\n",
              "      <td>1.227115</td>\n",
              "      <td>1035.350000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4065 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9a096811-db25-4559-bbad-010c2be6a680')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9a096811-db25-4559-bbad-010c2be6a680 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9a096811-db25-4559-bbad-010c2be6a680');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_GMM = rfm.copy()\n",
        "data_GMM"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "qBZPqSMJHTUq",
        "outputId": "f8311da1-edea-41f6-b214-d66a57816b30"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       frequency  CustomerID  monetary  monetary_mean  monetary_min  monetary_max  monetary_std      recency\n",
              "0      48.402855     12347.0     2.015       2.644011          0.25         12.75      2.255381    68.966667\n",
              "1     226.202222     12348.0     0.550       5.764839          0.29         40.00     13.400323  1823.616667\n",
              "2       0.000000     12349.0     2.550       8.289041          0.42        300.00     35.028021   458.983333\n",
              "3       0.000000     12350.0     1.650       3.841176          0.85         40.00      9.334751  7460.816667\n",
              "4      66.404965     12352.0     3.950      23.274737          0.65        376.50     70.438671   886.216667\n",
              "...          ...         ...       ...            ...           ...           ...           ...          ...\n",
              "4060    0.000000     18280.0     3.750       4.765000          2.55          9.95      2.472072  6674.966667\n",
              "4061    0.000000     18281.0     1.650       5.622857          0.42         16.95      7.778641  4345.950000\n",
              "4062  237.844444     18282.0     2.950       4.821538          0.29         12.75      4.862801   193.116667\n",
              "4063   10.614305     18283.0     1.650       1.614987          0.29         15.95      1.096060    96.800000\n",
              "4064   55.287440     18287.0     1.250       1.493571          0.29          8.50      1.227115  1035.350000\n",
              "\n",
              "[4065 rows x 8 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0e8395c9-b5ee-4a07-b6a0-d5b240a6c7bd\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>frequency</th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>monetary</th>\n",
              "      <th>monetary_mean</th>\n",
              "      <th>monetary_min</th>\n",
              "      <th>monetary_max</th>\n",
              "      <th>monetary_std</th>\n",
              "      <th>recency</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>48.402855</td>\n",
              "      <td>12347.0</td>\n",
              "      <td>2.015</td>\n",
              "      <td>2.644011</td>\n",
              "      <td>0.25</td>\n",
              "      <td>12.75</td>\n",
              "      <td>2.255381</td>\n",
              "      <td>68.966667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>226.202222</td>\n",
              "      <td>12348.0</td>\n",
              "      <td>0.550</td>\n",
              "      <td>5.764839</td>\n",
              "      <td>0.29</td>\n",
              "      <td>40.00</td>\n",
              "      <td>13.400323</td>\n",
              "      <td>1823.616667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>12349.0</td>\n",
              "      <td>2.550</td>\n",
              "      <td>8.289041</td>\n",
              "      <td>0.42</td>\n",
              "      <td>300.00</td>\n",
              "      <td>35.028021</td>\n",
              "      <td>458.983333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>12350.0</td>\n",
              "      <td>1.650</td>\n",
              "      <td>3.841176</td>\n",
              "      <td>0.85</td>\n",
              "      <td>40.00</td>\n",
              "      <td>9.334751</td>\n",
              "      <td>7460.816667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>66.404965</td>\n",
              "      <td>12352.0</td>\n",
              "      <td>3.950</td>\n",
              "      <td>23.274737</td>\n",
              "      <td>0.65</td>\n",
              "      <td>376.50</td>\n",
              "      <td>70.438671</td>\n",
              "      <td>886.216667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4060</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>18280.0</td>\n",
              "      <td>3.750</td>\n",
              "      <td>4.765000</td>\n",
              "      <td>2.55</td>\n",
              "      <td>9.95</td>\n",
              "      <td>2.472072</td>\n",
              "      <td>6674.966667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4061</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>18281.0</td>\n",
              "      <td>1.650</td>\n",
              "      <td>5.622857</td>\n",
              "      <td>0.42</td>\n",
              "      <td>16.95</td>\n",
              "      <td>7.778641</td>\n",
              "      <td>4345.950000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4062</th>\n",
              "      <td>237.844444</td>\n",
              "      <td>18282.0</td>\n",
              "      <td>2.950</td>\n",
              "      <td>4.821538</td>\n",
              "      <td>0.29</td>\n",
              "      <td>12.75</td>\n",
              "      <td>4.862801</td>\n",
              "      <td>193.116667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4063</th>\n",
              "      <td>10.614305</td>\n",
              "      <td>18283.0</td>\n",
              "      <td>1.650</td>\n",
              "      <td>1.614987</td>\n",
              "      <td>0.29</td>\n",
              "      <td>15.95</td>\n",
              "      <td>1.096060</td>\n",
              "      <td>96.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4064</th>\n",
              "      <td>55.287440</td>\n",
              "      <td>18287.0</td>\n",
              "      <td>1.250</td>\n",
              "      <td>1.493571</td>\n",
              "      <td>0.29</td>\n",
              "      <td>8.50</td>\n",
              "      <td>1.227115</td>\n",
              "      <td>1035.350000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4065 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0e8395c9-b5ee-4a07-b6a0-d5b240a6c7bd')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0e8395c9-b5ee-4a07-b6a0-d5b240a6c7bd button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0e8395c9-b5ee-4a07-b6a0-d5b240a6c7bd');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#data_table,\n",
        "\n",
        "#import library\n",
        "import pandas as pd\n",
        "from sklearn.mixture import GaussianMixture\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "#make it array\n",
        "\n",
        "X = data_GMM[['recency','frequency','monetary','monetary_mean','monetary_min','monetary_max','monetary_std']].values\n",
        "\n",
        "#Gaussian Mixture\n",
        "\n",
        "gmm = GaussianMixture(n_components=5,covariance_type='spherical').fit(X)\n",
        "labels = gmm.predict(X)\n",
        "score = gmm.predict_proba(X)\n",
        "data_GMM['labels'] = labels\n",
        "data_GMM"
      ],
      "metadata": {
        "id": "JeFxd_7xHbDx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "ea82a8f0-fb48-4bff-f58a-529fa0b99f7b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       frequency  CustomerID  monetary  monetary_mean  monetary_min  monetary_max  monetary_std      recency  labels\n",
              "0      48.402855     12347.0     2.015       2.644011          0.25         12.75      2.255381    68.966667       4\n",
              "1     226.202222     12348.0     0.550       5.764839          0.29         40.00     13.400323  1823.616667       3\n",
              "2       0.000000     12349.0     2.550       8.289041          0.42        300.00     35.028021   458.983333       0\n",
              "3       0.000000     12350.0     1.650       3.841176          0.85         40.00      9.334751  7460.816667       1\n",
              "4      66.404965     12352.0     3.950      23.274737          0.65        376.50     70.438671   886.216667       3\n",
              "...          ...         ...       ...            ...           ...           ...           ...          ...     ...\n",
              "4060    0.000000     18280.0     3.750       4.765000          2.55          9.95      2.472072  6674.966667       1\n",
              "4061    0.000000     18281.0     1.650       5.622857          0.42         16.95      7.778641  4345.950000       2\n",
              "4062  237.844444     18282.0     2.950       4.821538          0.29         12.75      4.862801   193.116667       4\n",
              "4063   10.614305     18283.0     1.650       1.614987          0.29         15.95      1.096060    96.800000       4\n",
              "4064   55.287440     18287.0     1.250       1.493571          0.29          8.50      1.227115  1035.350000       3\n",
              "\n",
              "[4065 rows x 9 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-370586b9-8046-485f-a279-14d7f24c2d22\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>frequency</th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>monetary</th>\n",
              "      <th>monetary_mean</th>\n",
              "      <th>monetary_min</th>\n",
              "      <th>monetary_max</th>\n",
              "      <th>monetary_std</th>\n",
              "      <th>recency</th>\n",
              "      <th>labels</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>48.402855</td>\n",
              "      <td>12347.0</td>\n",
              "      <td>2.015</td>\n",
              "      <td>2.644011</td>\n",
              "      <td>0.25</td>\n",
              "      <td>12.75</td>\n",
              "      <td>2.255381</td>\n",
              "      <td>68.966667</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>226.202222</td>\n",
              "      <td>12348.0</td>\n",
              "      <td>0.550</td>\n",
              "      <td>5.764839</td>\n",
              "      <td>0.29</td>\n",
              "      <td>40.00</td>\n",
              "      <td>13.400323</td>\n",
              "      <td>1823.616667</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>12349.0</td>\n",
              "      <td>2.550</td>\n",
              "      <td>8.289041</td>\n",
              "      <td>0.42</td>\n",
              "      <td>300.00</td>\n",
              "      <td>35.028021</td>\n",
              "      <td>458.983333</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>12350.0</td>\n",
              "      <td>1.650</td>\n",
              "      <td>3.841176</td>\n",
              "      <td>0.85</td>\n",
              "      <td>40.00</td>\n",
              "      <td>9.334751</td>\n",
              "      <td>7460.816667</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>66.404965</td>\n",
              "      <td>12352.0</td>\n",
              "      <td>3.950</td>\n",
              "      <td>23.274737</td>\n",
              "      <td>0.65</td>\n",
              "      <td>376.50</td>\n",
              "      <td>70.438671</td>\n",
              "      <td>886.216667</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4060</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>18280.0</td>\n",
              "      <td>3.750</td>\n",
              "      <td>4.765000</td>\n",
              "      <td>2.55</td>\n",
              "      <td>9.95</td>\n",
              "      <td>2.472072</td>\n",
              "      <td>6674.966667</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4061</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>18281.0</td>\n",
              "      <td>1.650</td>\n",
              "      <td>5.622857</td>\n",
              "      <td>0.42</td>\n",
              "      <td>16.95</td>\n",
              "      <td>7.778641</td>\n",
              "      <td>4345.950000</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4062</th>\n",
              "      <td>237.844444</td>\n",
              "      <td>18282.0</td>\n",
              "      <td>2.950</td>\n",
              "      <td>4.821538</td>\n",
              "      <td>0.29</td>\n",
              "      <td>12.75</td>\n",
              "      <td>4.862801</td>\n",
              "      <td>193.116667</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4063</th>\n",
              "      <td>10.614305</td>\n",
              "      <td>18283.0</td>\n",
              "      <td>1.650</td>\n",
              "      <td>1.614987</td>\n",
              "      <td>0.29</td>\n",
              "      <td>15.95</td>\n",
              "      <td>1.096060</td>\n",
              "      <td>96.800000</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4064</th>\n",
              "      <td>55.287440</td>\n",
              "      <td>18287.0</td>\n",
              "      <td>1.250</td>\n",
              "      <td>1.493571</td>\n",
              "      <td>0.29</td>\n",
              "      <td>8.50</td>\n",
              "      <td>1.227115</td>\n",
              "      <td>1035.350000</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4065 rows × 9 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-370586b9-8046-485f-a279-14d7f24c2d22')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-370586b9-8046-485f-a279-14d7f24c2d22 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-370586b9-8046-485f-a279-14d7f24c2d22');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9o_4EYzQQvDB",
        "outputId": "b92457da-7859-4885-cb42-c82800a04328"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[6.89666667e+01, 4.84028545e+01, 2.01500000e+00, ...,\n",
              "        2.50000000e-01, 1.27500000e+01, 2.25538101e+00],\n",
              "       [1.82361667e+03, 2.26202222e+02, 5.50000000e-01, ...,\n",
              "        2.90000000e-01, 4.00000000e+01, 1.34003233e+01],\n",
              "       [4.58983333e+02, 0.00000000e+00, 2.55000000e+00, ...,\n",
              "        4.20000000e-01, 3.00000000e+02, 3.50280212e+01],\n",
              "       ...,\n",
              "       [1.93116667e+02, 2.37844444e+02, 2.95000000e+00, ...,\n",
              "        2.90000000e-01, 1.27500000e+01, 4.86280071e+00],\n",
              "       [9.68000000e+01, 1.06143046e+01, 1.65000000e+00, ...,\n",
              "        2.90000000e-01, 1.59500000e+01, 1.09605959e+00],\n",
              "       [1.03535000e+03, 5.52874396e+01, 1.25000000e+00, ...,\n",
              "        2.90000000e-01, 8.50000000e+00, 1.22711519e+00]])"
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k1AWFTwKQz_n",
        "outputId": "9f8c2d56-4724-4fe9-cb72-a35c7be8c7b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([4, 3, 0, ..., 4, 4, 3])"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eCp3XIMXQ2Nq",
        "outputId": "bbd6c352-114a-454b-d2d4-001cca51019e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[4.00680646e-15, 6.84925971e-41, 8.09985581e-42, 7.51318545e-15,\n",
              "        1.00000000e+00],\n",
              "       [1.49650673e-73, 4.71030720e-21, 3.16437971e-10, 1.00000000e+00,\n",
              "        0.00000000e+00],\n",
              "       [9.99904174e-01, 1.32973937e-31, 6.00511135e-29, 9.58263568e-05,\n",
              "        4.61110185e-15],\n",
              "       ...,\n",
              "       [1.09496157e-07, 1.79416874e-36, 3.63899064e-36, 6.13048157e-10,\n",
              "        9.99999890e-01],\n",
              "       [4.43825495e-14, 6.86031921e-41, 1.55135684e-41, 9.52260105e-15,\n",
              "        1.00000000e+00],\n",
              "       [4.44966027e-06, 3.54750998e-27, 6.04728107e-20, 9.99995550e-01,\n",
              "        1.83474976e-88]])"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def three_d_plot(data1,data2,data3,data4):\n",
        "  import matplotlib.pyplot as plt\n",
        "  from mpl_toolkits.mplot3d import Axes3D\n",
        "  import matplotlib\n",
        "  #fig = plt.figure()\n",
        "  fig = plt.figure(figsize=(18,16),dpi=80,facecolor='w',edgecolor='k')\n",
        "  ax = fig.add_subplot(111, projection='3d')\n",
        "  colors = ['aqua', 'darkgray', 'blue', 'red', 'black', 'yellow','green']\n",
        "  #ax.scatter(data1,data2,data3,color=\"black\")\n",
        "  #ax.scatter(data4,data5,data6, color=\"red\",s=150)\n",
        "  #ax.scatter(data1,data2,data3, s=100, edgecolor=\"r\", facecolor=\"gold\")\n",
        "  ax.scatter(data1,data2,data3, c=data4, cmap=matplotlib.colors.ListedColormap(colors))\n",
        "  #plt.legend(loc=2)\n",
        "  plt.title(\"GMM 3d plot\")\n",
        "  ax.set_xlabel('Recency')\n",
        "  ax.set_ylabel('Frequency')\n",
        "  ax.set_zlabel('Monetary')\n",
        "  plt.show()\n",
        "three_d_plot(data_GMM[\"recency\"],data_GMM[\"frequency\"],data_GMM[\"monetary\"],data_GMM[\"labels\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 976
        },
        "id": "S7NRAnL4J0SF",
        "outputId": "4d4041b9-7090-4504-dee1-e4eb5570fef7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x1280 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Score adding\n",
        "scores = pd.DataFrame(score)\n",
        "scores.rename(columns={0:'label_zero',1:'label_one',2:'label_two',3:'label_three'},inplace=True)\n",
        "\n",
        "#Reset Index\n",
        "data_GMM = data_GMM.reset_index(drop=True)\n",
        "scores = scores.reset_index(drop=True)\n",
        "#merge two dataframe\n",
        "gmm_score = pd.concat([data_GMM,scores],axis=1)\n",
        "#gmm_score\n",
        "del data_GMM, scores"
      ],
      "metadata": {
        "id": "_HEeF6G4QNdH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#label_zero\n",
        "label_zero = gmm_score.query(\"labels==0\").sort_values(\"label_zero\").reset_index(drop=True)\n",
        "label_zero = label_zero.drop(columns=['label_one','label_two','label_three'])\n",
        "label_zero = label_zero.rename(columns={'label_zero':'score'})\n",
        "\n",
        "#label_one\n",
        "label_one = gmm_score.query(\"labels==1\").sort_values(\"label_one\").reset_index(drop=True)\n",
        "label_one = label_one.drop(columns=['label_zero','label_two','label_three'])\n",
        "label_one = label_one.rename(columns = {'label_one':'score'})\n",
        "\n",
        "#label_two\n",
        "label_two = gmm_score.query(\"labels==2\").sort_values(\"label_three\").reset_index(drop=True)\n",
        "label_two = label_two.drop(columns=['label_zero','label_one','label_three'])\n",
        "label_two = label_two.rename(columns = {'label_two': 'score'})\n",
        "\n",
        "#label_three\n",
        "label_three = gmm_score.query(\"labels==3\").sort_values(\"label_three\").reset_index(drop=True)\n",
        "label_three = label_three.drop(columns = ['label_zero','label_one','label_two'])\n",
        "label_three = label_three.rename(columns = {'label_three':'score'})\n",
        "\n",
        "#merging labels\n",
        "frames = [label_zero,label_one,label_two,label_three]\n",
        "vertical_stack = pd.concat(frames, axis=0).reset_index(drop=True)\n"
      ],
      "metadata": {
        "id": "eKYSA3s_RHHi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vertical_stack"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 661
        },
        "id": "ZU8OPvICaSlk",
        "outputId": "0c116de8-c3be-46f2-911d-d0f9effe7221"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       frequency  CustomerID  monetary  monetary_mean  monetary_min  monetary_max  monetary_std      recency  labels     score              4\n",
              "0      85.833152     17999.0     1.950       2.822903          0.19         16.95      2.630040   908.900000       0  0.518606   3.121391e-63\n",
              "1     173.411290     15651.0     1.720       2.158437          0.29          4.95      1.656754   892.150000       0  0.525283   1.048337e-61\n",
              "2     454.634375     16660.0     1.650       2.576471          0.83          5.95      1.848604   405.950000       0  0.653050   1.064112e-16\n",
              "3      26.314657     15521.0     1.650       4.004600          0.39        125.00     12.692566   886.966667       0  0.677368   1.539517e-60\n",
              "4       7.383533     16729.0     1.650       2.333982          0.12        125.00      5.103011   881.650000       0  0.737586   8.293948e-60\n",
              "...          ...         ...       ...            ...           ...           ...           ...          ...     ...       ...            ...\n",
              "3168  163.711905     14273.0     2.080       2.484828          0.79          5.95      1.217133  1247.500000       3  1.000000  2.849504e-141\n",
              "3169    0.000000     16826.0     3.200       3.985000          0.82         12.75      3.312027  1246.750000       3  1.000000  5.899024e-140\n",
              "3170    0.000000     15740.0     2.890       2.472222          0.29          4.15      1.675394  1198.350000       3  1.000000  2.383972e-127\n",
              "3171    0.000000     16848.0     1.550       1.710000          0.55          2.95      0.793464  1276.133333       3  1.000000  6.366582e-148\n",
              "3172    0.000000     14967.0     2.925       2.925000          1.25          4.95      1.601796  1201.733333       3  1.000000  3.273131e-128\n",
              "\n",
              "[3173 rows x 11 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-254c93ff-62e8-4cb5-be86-4a21a3206335\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>frequency</th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>monetary</th>\n",
              "      <th>monetary_mean</th>\n",
              "      <th>monetary_min</th>\n",
              "      <th>monetary_max</th>\n",
              "      <th>monetary_std</th>\n",
              "      <th>recency</th>\n",
              "      <th>labels</th>\n",
              "      <th>score</th>\n",
              "      <th>4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>85.833152</td>\n",
              "      <td>17999.0</td>\n",
              "      <td>1.950</td>\n",
              "      <td>2.822903</td>\n",
              "      <td>0.19</td>\n",
              "      <td>16.95</td>\n",
              "      <td>2.630040</td>\n",
              "      <td>908.900000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.518606</td>\n",
              "      <td>3.121391e-63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>173.411290</td>\n",
              "      <td>15651.0</td>\n",
              "      <td>1.720</td>\n",
              "      <td>2.158437</td>\n",
              "      <td>0.29</td>\n",
              "      <td>4.95</td>\n",
              "      <td>1.656754</td>\n",
              "      <td>892.150000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.525283</td>\n",
              "      <td>1.048337e-61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>454.634375</td>\n",
              "      <td>16660.0</td>\n",
              "      <td>1.650</td>\n",
              "      <td>2.576471</td>\n",
              "      <td>0.83</td>\n",
              "      <td>5.95</td>\n",
              "      <td>1.848604</td>\n",
              "      <td>405.950000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.653050</td>\n",
              "      <td>1.064112e-16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>26.314657</td>\n",
              "      <td>15521.0</td>\n",
              "      <td>1.650</td>\n",
              "      <td>4.004600</td>\n",
              "      <td>0.39</td>\n",
              "      <td>125.00</td>\n",
              "      <td>12.692566</td>\n",
              "      <td>886.966667</td>\n",
              "      <td>0</td>\n",
              "      <td>0.677368</td>\n",
              "      <td>1.539517e-60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.383533</td>\n",
              "      <td>16729.0</td>\n",
              "      <td>1.650</td>\n",
              "      <td>2.333982</td>\n",
              "      <td>0.12</td>\n",
              "      <td>125.00</td>\n",
              "      <td>5.103011</td>\n",
              "      <td>881.650000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.737586</td>\n",
              "      <td>8.293948e-60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3168</th>\n",
              "      <td>163.711905</td>\n",
              "      <td>14273.0</td>\n",
              "      <td>2.080</td>\n",
              "      <td>2.484828</td>\n",
              "      <td>0.79</td>\n",
              "      <td>5.95</td>\n",
              "      <td>1.217133</td>\n",
              "      <td>1247.500000</td>\n",
              "      <td>3</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.849504e-141</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3169</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>16826.0</td>\n",
              "      <td>3.200</td>\n",
              "      <td>3.985000</td>\n",
              "      <td>0.82</td>\n",
              "      <td>12.75</td>\n",
              "      <td>3.312027</td>\n",
              "      <td>1246.750000</td>\n",
              "      <td>3</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>5.899024e-140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3170</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>15740.0</td>\n",
              "      <td>2.890</td>\n",
              "      <td>2.472222</td>\n",
              "      <td>0.29</td>\n",
              "      <td>4.15</td>\n",
              "      <td>1.675394</td>\n",
              "      <td>1198.350000</td>\n",
              "      <td>3</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.383972e-127</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3171</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>16848.0</td>\n",
              "      <td>1.550</td>\n",
              "      <td>1.710000</td>\n",
              "      <td>0.55</td>\n",
              "      <td>2.95</td>\n",
              "      <td>0.793464</td>\n",
              "      <td>1276.133333</td>\n",
              "      <td>3</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.366582e-148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3172</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>14967.0</td>\n",
              "      <td>2.925</td>\n",
              "      <td>2.925000</td>\n",
              "      <td>1.25</td>\n",
              "      <td>4.95</td>\n",
              "      <td>1.601796</td>\n",
              "      <td>1201.733333</td>\n",
              "      <td>3</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.273131e-128</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3173 rows × 11 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-254c93ff-62e8-4cb5-be86-4a21a3206335')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-254c93ff-62e8-4cb5-be86-4a21a3206335 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-254c93ff-62e8-4cb5-be86-4a21a3206335');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#MinMax Scaling for scoring\n",
        "\n",
        "vertical_stack_scaling = vertical_stack[['score']]\n",
        "vertical_stack_scaling"
      ],
      "metadata": {
        "id": "SoadntXQboEE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "b1f876df-b53e-4776-9050-d24ec1306d28"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         score\n",
              "0     0.518606\n",
              "1     0.525283\n",
              "2     0.653050\n",
              "3     0.677368\n",
              "4     0.737586\n",
              "...        ...\n",
              "3168  1.000000\n",
              "3169  1.000000\n",
              "3170  1.000000\n",
              "3171  1.000000\n",
              "3172  1.000000\n",
              "\n",
              "[3173 rows x 1 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4b871839-bba4-4c2b-a466-ff8cd6a10640\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.518606</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.525283</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.653050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.677368</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.737586</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3168</th>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3169</th>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3170</th>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3171</th>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3172</th>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3173 rows × 1 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4b871839-bba4-4c2b-a466-ff8cd6a10640')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4b871839-bba4-4c2b-a466-ff8cd6a10640 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4b871839-bba4-4c2b-a466-ff8cd6a10640');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Normalization\n",
        "\n",
        "scaler = MinMaxScaler(feature_range=(0, 1))\n",
        "scaled_scores = scaler.fit_transform(vertical_stack_scaling)\n",
        "dataset_scaled_scores = pd.DataFrame(scaled_scores)\n",
        "dataset_scaled_scores"
      ],
      "metadata": {
        "id": "D7X7qU_7euoZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "17d59b8c-0a26-4dde-f16c-7220dbf2e6a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             0\n",
              "0     0.034769\n",
              "1     0.048156\n",
              "2     0.304339\n",
              "3     0.353100\n",
              "4     0.473841\n",
              "...        ...\n",
              "3168  1.000000\n",
              "3169  1.000000\n",
              "3170  1.000000\n",
              "3171  1.000000\n",
              "3172  1.000000\n",
              "\n",
              "[3173 rows x 1 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-895e006f-3a6e-40e2-b13e-bdab28bfa806\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.034769</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.048156</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.304339</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.353100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.473841</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3168</th>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3169</th>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3170</th>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3171</th>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3172</th>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3173 rows × 1 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-895e006f-3a6e-40e2-b13e-bdab28bfa806')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-895e006f-3a6e-40e2-b13e-bdab28bfa806 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-895e006f-3a6e-40e2-b13e-bdab28bfa806');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_scaled_scores.rename(columns={0:'score'}, inplace=True)\n",
        "#optional\n",
        "dataset_scaled_scores['score'] = 1 - dataset_scaled_scores['score']\n",
        "#Drop unnecessary columns\n",
        "drop_elements = ['score']\n",
        "vertical_stack = vertical_stack.drop(drop_elements, axis=1)\n"
      ],
      "metadata": {
        "id": "CcBp0HGMG1ba"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Merge\n",
        "result = pd.concat([dataset_scaled_scores, vertical_stack],axis=1)\n",
        "\n",
        "#Reorder data\n",
        "del dataset_scaled_scores,vertical_stack\n",
        "result = result[['CustomerID', 'recency','frequency', 'monetary','monetary_mean','monetary_min','monetary_max','monetary_std','labels','score']]"
      ],
      "metadata": {
        "id": "wDXj9JtXHgW5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "result"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "mVChntVfJA1c",
        "outputId": "494a4f0c-82b4-4965-d984-9c26d431f57c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      CustomerID      recency   frequency  monetary  monetary_mean  monetary_min  monetary_max  monetary_std  labels     score\n",
              "0        17999.0   908.900000   85.833152     1.950       2.822903          0.19         16.95      2.630040       0  0.965231\n",
              "1        15651.0   892.150000  173.411290     1.720       2.158437          0.29          4.95      1.656754       0  0.951844\n",
              "2        16660.0   405.950000  454.634375     1.650       2.576471          0.83          5.95      1.848604       0  0.695661\n",
              "3        15521.0   886.966667   26.314657     1.650       4.004600          0.39        125.00     12.692566       0  0.646900\n",
              "4        16729.0   881.650000    7.383533     1.650       2.333982          0.12        125.00      5.103011       0  0.526159\n",
              "...          ...          ...         ...       ...            ...           ...           ...           ...     ...       ...\n",
              "3168     14273.0  1247.500000  163.711905     2.080       2.484828          0.79          5.95      1.217133       3  0.000000\n",
              "3169     16826.0  1246.750000    0.000000     3.200       3.985000          0.82         12.75      3.312027       3  0.000000\n",
              "3170     15740.0  1198.350000    0.000000     2.890       2.472222          0.29          4.15      1.675394       3  0.000000\n",
              "3171     16848.0  1276.133333    0.000000     1.550       1.710000          0.55          2.95      0.793464       3  0.000000\n",
              "3172     14967.0  1201.733333    0.000000     2.925       2.925000          1.25          4.95      1.601796       3  0.000000\n",
              "\n",
              "[3173 rows x 10 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-80bf8983-be88-468c-9e54-0e0ffd627515\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>frequency</th>\n",
              "      <th>monetary</th>\n",
              "      <th>monetary_mean</th>\n",
              "      <th>monetary_min</th>\n",
              "      <th>monetary_max</th>\n",
              "      <th>monetary_std</th>\n",
              "      <th>labels</th>\n",
              "      <th>score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>17999.0</td>\n",
              "      <td>908.900000</td>\n",
              "      <td>85.833152</td>\n",
              "      <td>1.950</td>\n",
              "      <td>2.822903</td>\n",
              "      <td>0.19</td>\n",
              "      <td>16.95</td>\n",
              "      <td>2.630040</td>\n",
              "      <td>0</td>\n",
              "      <td>0.965231</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>15651.0</td>\n",
              "      <td>892.150000</td>\n",
              "      <td>173.411290</td>\n",
              "      <td>1.720</td>\n",
              "      <td>2.158437</td>\n",
              "      <td>0.29</td>\n",
              "      <td>4.95</td>\n",
              "      <td>1.656754</td>\n",
              "      <td>0</td>\n",
              "      <td>0.951844</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>16660.0</td>\n",
              "      <td>405.950000</td>\n",
              "      <td>454.634375</td>\n",
              "      <td>1.650</td>\n",
              "      <td>2.576471</td>\n",
              "      <td>0.83</td>\n",
              "      <td>5.95</td>\n",
              "      <td>1.848604</td>\n",
              "      <td>0</td>\n",
              "      <td>0.695661</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>15521.0</td>\n",
              "      <td>886.966667</td>\n",
              "      <td>26.314657</td>\n",
              "      <td>1.650</td>\n",
              "      <td>4.004600</td>\n",
              "      <td>0.39</td>\n",
              "      <td>125.00</td>\n",
              "      <td>12.692566</td>\n",
              "      <td>0</td>\n",
              "      <td>0.646900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>16729.0</td>\n",
              "      <td>881.650000</td>\n",
              "      <td>7.383533</td>\n",
              "      <td>1.650</td>\n",
              "      <td>2.333982</td>\n",
              "      <td>0.12</td>\n",
              "      <td>125.00</td>\n",
              "      <td>5.103011</td>\n",
              "      <td>0</td>\n",
              "      <td>0.526159</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3168</th>\n",
              "      <td>14273.0</td>\n",
              "      <td>1247.500000</td>\n",
              "      <td>163.711905</td>\n",
              "      <td>2.080</td>\n",
              "      <td>2.484828</td>\n",
              "      <td>0.79</td>\n",
              "      <td>5.95</td>\n",
              "      <td>1.217133</td>\n",
              "      <td>3</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3169</th>\n",
              "      <td>16826.0</td>\n",
              "      <td>1246.750000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.200</td>\n",
              "      <td>3.985000</td>\n",
              "      <td>0.82</td>\n",
              "      <td>12.75</td>\n",
              "      <td>3.312027</td>\n",
              "      <td>3</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3170</th>\n",
              "      <td>15740.0</td>\n",
              "      <td>1198.350000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.890</td>\n",
              "      <td>2.472222</td>\n",
              "      <td>0.29</td>\n",
              "      <td>4.15</td>\n",
              "      <td>1.675394</td>\n",
              "      <td>3</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3171</th>\n",
              "      <td>16848.0</td>\n",
              "      <td>1276.133333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.550</td>\n",
              "      <td>1.710000</td>\n",
              "      <td>0.55</td>\n",
              "      <td>2.95</td>\n",
              "      <td>0.793464</td>\n",
              "      <td>3</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3172</th>\n",
              "      <td>14967.0</td>\n",
              "      <td>1201.733333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.925</td>\n",
              "      <td>2.925000</td>\n",
              "      <td>1.25</td>\n",
              "      <td>4.95</td>\n",
              "      <td>1.601796</td>\n",
              "      <td>3</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3173 rows × 10 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-80bf8983-be88-468c-9e54-0e0ffd627515')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-80bf8983-be88-468c-9e54-0e0ffd627515 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-80bf8983-be88-468c-9e54-0e0ffd627515');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Add Scoring Groups to Plot\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "plt.figure(figsize=(16,8))\n",
        "plt.hist(result['score'],bins=20,edgecolor='white',color='#317DC2',linewidth=1.2)\n",
        "plt.xlim(0,1)\n",
        "plt.title('Scoring Groups', fontweight=\"bold\", fontsize=14)\n",
        "plt.xlabel('Score')\n",
        "plt.ylabel('Count')\n",
        "\n",
        "#Percentile Lines\n",
        "\n",
        "plt.axvline(np.percentile(result['score'],25), color='red', linestyle='dashed', linewidth=2, alpha=0.6)\n",
        "plt.axvline(np.percentile(result['score'],50), color='orange', linestyle='dashed', linewidth=2, alpha=0.6)\n",
        "plt.axvline(np.percentile(result['score'],75), color='green', linestyle='dashed',linewidth=2, alpha=0.6)\n",
        "\n",
        "#Text\n",
        "plt.text(0.10, 200, 'Excellent', color='red', fontweight='bold', style='italic', fontsize=12)\n",
        "plt.text(0.50, 200, 'Fair', color='green', fontweight='bold', style='italic', fontsize=12)\n",
        "plt.text(0.9, 200, 'Outlier', color='blue', fontweight='bold', style='italic', fontsize=12)\n",
        "\n",
        "#Shading between Percentiles\n",
        "plt.axvspan(0, 0.40, alpha=0.1, color='blue')\n",
        "plt.axvspan(0.40, 0.70, alpha=0.1, color='yellow')\n",
        "plt.axvspan(0.70, 1, alpha=0.1, color='red')\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "id": "U5LigpYlJFLM",
        "outputId": "310682b9-e3c2-445e-f9d6-f1f961fdaba3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.patches.Polygon at 0x7fd3c5bec130>"
            ]
          },
          "metadata": {},
          "execution_count": 116
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}