RFMAnalysis.ipynb

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      "name": "RFMAnalysis.ipynb",
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      "cell_type": "markdown",
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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/naenumtou/statisticalModel/blob/main/RFMAnalysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Set auto reload\n",
        "%reload_ext autoreload\n",
        "%autoreload 2"
      ],
      "metadata": {
        "id": "kcsGD75laGOJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Install squarify for treemap plot\n",
        "!pip install squarify"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pSPhRstv-wVB",
        "outputId": "f77e31f4-5e9f-42df-866e-441cf5f19785"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: squarify in /usr/local/lib/python3.7/dist-packages (0.4.3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        "id": "z7KKNVrhCSoO",
        "outputId": "e3dc1ce4-bc0f-4c8a-9e50-e1ad0f4b740f"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/My Drive/Colab Notebooks/onlineRetail\n"
          ]
        }
      ],
      "source": [
        "# Change directory to Google Drive\n",
        "%cd /content/drive/My Drive/Colab Notebooks/onlineRetail"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import datetime\n",
        "import squarify\n",
        "\n",
        "%config InlineBackend.figure_format = 'retina'"
      ],
      "metadata": {
        "id": "YdcXBn8NCe9m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Import data\n",
        "df = pd.read_csv('onlineRetail.csv',\n",
        "                 dtype = {'Invoice': str,\n",
        "                          'StockCode': str,\n",
        "                          'Description': str,\n",
        "                          'Quantity': np.int64,\n",
        "                          'Price': np.float64,\n",
        "                          'Customer ID': str,\n",
        "                          'Country': str},\n",
        "                 parse_dates = ['InvoiceDate'])\n",
        "df.info()"
      ],
      "metadata": {
        "id": "PDhHtMXwE0NC",
        "colab": {
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        },
        "outputId": "61d7c4af-0dbc-4000-b6fc-be20bc96bc02"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1067371 entries, 0 to 1067370\n",
            "Data columns (total 8 columns):\n",
            " #   Column       Non-Null Count    Dtype         \n",
            "---  ------       --------------    -----         \n",
            " 0   Invoice      1067371 non-null  object        \n",
            " 1   StockCode    1067371 non-null  object        \n",
            " 2   Description  1062989 non-null  object        \n",
            " 3   Quantity     1067371 non-null  int64         \n",
            " 4   InvoiceDate  1067371 non-null  datetime64[ns]\n",
            " 5   Price        1067371 non-null  float64       \n",
            " 6   Customer ID  824364 non-null   object        \n",
            " 7   Country      1067371 non-null  object        \n",
            "dtypes: datetime64[ns](1), float64(1), int64(1), object(5)\n",
            "memory usage: 65.1+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Data cleansing\n",
        "# Missing values\n",
        "print(f'Total missing values on each column\\n{df.isnull().sum()}')"
      ],
      "metadata": {
        "id": "JGWkEyxMFEZr",
        "colab": {
          "base_uri": "https://localhost:8080/"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total missing values on each column\n",
            "Invoice             0\n",
            "StockCode           0\n",
            "Description      4382\n",
            "Quantity            0\n",
            "InvoiceDate         0\n",
            "Price               0\n",
            "Customer ID    243007\n",
            "Country             0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove missing 'Customer ID'\n",
        "df = df[df['Customer ID'].notnull()]\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MKxaQmhaIgqD",
        "outputId": "ab51db28-b434-4c83-bf79-b363ede30b66"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 824364 entries, 0 to 1067370\n",
            "Data columns (total 8 columns):\n",
            " #   Column       Non-Null Count   Dtype         \n",
            "---  ------       --------------   -----         \n",
            " 0   Invoice      824364 non-null  object        \n",
            " 1   StockCode    824364 non-null  object        \n",
            " 2   Description  824364 non-null  object        \n",
            " 3   Quantity     824364 non-null  int64         \n",
            " 4   InvoiceDate  824364 non-null  datetime64[ns]\n",
            " 5   Price        824364 non-null  float64       \n",
            " 6   Customer ID  824364 non-null  object        \n",
            " 7   Country      824364 non-null  object        \n",
            "dtypes: datetime64[ns](1), float64(1), int64(1), object(5)\n",
            "memory usage: 56.6+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Negative 'Quantity' and 'Price' (less than 0)\n",
        "print(f\"Total 'Quantity' column less than 0: {np.sum(df['Quantity'] <= 0)} row\")\n",
        "print(f\"Total 'Price' column less than 0: {np.sum(df['Price'] <= 0)} row\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AW7UIeC7JAYe",
        "outputId": "5435b205-5a71-4df8-cf45-292328aa2639"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total 'Quantity' column less than 0: 18744 row\n",
            "Total 'Price' column less than 0: 71 row\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove negative 'Quantity' and 'Price'\n",
        "df = df[df['Quantity'] > 0]\n",
        "df = df[df['Price'] > 0]\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TwNeNsbzJW9k",
        "outputId": "30b9f622-c2c8-4326-bc9c-85f487ac9a62"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 805549 entries, 0 to 1067370\n",
            "Data columns (total 8 columns):\n",
            " #   Column       Non-Null Count   Dtype         \n",
            "---  ------       --------------   -----         \n",
            " 0   Invoice      805549 non-null  object        \n",
            " 1   StockCode    805549 non-null  object        \n",
            " 2   Description  805549 non-null  object        \n",
            " 3   Quantity     805549 non-null  int64         \n",
            " 4   InvoiceDate  805549 non-null  datetime64[ns]\n",
            " 5   Price        805549 non-null  float64       \n",
            " 6   Customer ID  805549 non-null  object        \n",
            " 7   Country      805549 non-null  object        \n",
            "dtypes: datetime64[ns](1), float64(1), int64(1), object(5)\n",
            "memory usage: 55.3+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Describe numeric columns\n",
        "df.describe().T"
      ],
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          "height": 112
        },
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      "execution_count": null,
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          "data": {
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              "      <td>805549.0</td>\n",
              "      <td>13.290522</td>\n",
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              "             count       mean         std    min   25%   50%    75%      max\n",
              "Quantity  805549.0  13.290522  143.634088  1.000  2.00  5.00  12.00  80995.0\n",
              "Price     805549.0   3.206561   29.199173  0.001  1.25  1.95   3.75  10953.5"
            ]
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        }
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    {
      "cell_type": "code",
      "source": [
        "# The maximum and minimum of 'Quantity' and 'Price' are too far\n",
        "# Replace with quartile method\n",
        "for col in ['Quantity', 'Price']:\n",
        "  series = sorted(df[col])\n",
        "  Q1, Q3 = np.quantile(series, [0.01, 0.99])\n",
        "  IQR = Q3 - Q1\n",
        "  lowerLimit = Q1 - (1.5 * IQR)\n",
        "  upperLimit = Q3 + (1.5 * IQR)\n",
        "  df[col] = np.where(df[col] < lowerLimit, lowerLimit, df[col])\n",
        "  df[col] = np.where(df[col] > upperLimit, upperLimit, df[col])"
      ],
      "metadata": {
        "id": "uVy-zpkbNpQP"
      },
      "execution_count": null,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "# Describe numeric columns\n",
        "df.describe().T"
      ],
      "metadata": {
        "colab": {
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          "height": 112
        },
        "id": "pXr5rmTSPLe5",
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      "execution_count": null,
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              "    <tr>\n",
              "      <th>Quantity</th>\n",
              "      <td>805549.0</td>\n",
              "      <td>11.841087</td>\n",
              "      <td>26.828279</td>\n",
              "      <td>1.000</td>\n",
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              "      <td>12.00</td>\n",
              "      <td>318.50</td>\n",
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              "      <th>Price</th>\n",
              "      <td>805549.0</td>\n",
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              "\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-109d10f9-e15f-40c5-9699-f3ff266b04d2 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-109d10f9-e15f-40c5-9699-f3ff266b04d2');\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",
              "  "
            ],
            "text/plain": [
              "             count       mean        std    min   25%   50%    75%     max\n",
              "Quantity  805549.0  11.841087  26.828279  1.000  2.00  5.00  12.00  318.50\n",
              "Price     805549.0   2.950138   3.238483  0.001  1.25  1.95   3.75   36.94"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate total price per invoice\n",
        "df = df.eval(\"TotalPrice = Quantity * Price\")\n",
        "df.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "0aQc9pwGQC9d",
        "outputId": "ec47448d-bb50-4554-f37c-3207cdfd1462"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-4e41d068-c8e7-484f-b5e0-2b125aab1a22\">\n",
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              "\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>Invoice</th>\n",
              "      <th>StockCode</th>\n",
              "      <th>Description</th>\n",
              "      <th>Quantity</th>\n",
              "      <th>InvoiceDate</th>\n",
              "      <th>Price</th>\n",
              "      <th>Customer ID</th>\n",
              "      <th>Country</th>\n",
              "      <th>TotalPrice</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>489434</td>\n",
              "      <td>85048</td>\n",
              "      <td>15CM CHRISTMAS GLASS BALL 20 LIGHTS</td>\n",
              "      <td>12.0</td>\n",
              "      <td>2009-12-01 07:45:00</td>\n",
              "      <td>6.95</td>\n",
              "      <td>13085</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>83.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>489434</td>\n",
              "      <td>79323P</td>\n",
              "      <td>PINK CHERRY LIGHTS</td>\n",
              "      <td>12.0</td>\n",
              "      <td>2009-12-01 07:45:00</td>\n",
              "      <td>6.75</td>\n",
              "      <td>13085</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>81.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>489434</td>\n",
              "      <td>79323W</td>\n",
              "      <td>WHITE CHERRY LIGHTS</td>\n",
              "      <td>12.0</td>\n",
              "      <td>2009-12-01 07:45:00</td>\n",
              "      <td>6.75</td>\n",
              "      <td>13085</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>81.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>489434</td>\n",
              "      <td>22041</td>\n",
              "      <td>RECORD FRAME 7\" SINGLE SIZE</td>\n",
              "      <td>48.0</td>\n",
              "      <td>2009-12-01 07:45:00</td>\n",
              "      <td>2.10</td>\n",
              "      <td>13085</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>100.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>489434</td>\n",
              "      <td>21232</td>\n",
              "      <td>STRAWBERRY CERAMIC TRINKET BOX</td>\n",
              "      <td>24.0</td>\n",
              "      <td>2009-12-01 07:45:00</td>\n",
              "      <td>1.25</td>\n",
              "      <td>13085</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>30.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4e41d068-c8e7-484f-b5e0-2b125aab1a22')\"\n",
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              "  </svg>\n",
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              "\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-4e41d068-c8e7-484f-b5e0-2b125aab1a22 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-4e41d068-c8e7-484f-b5e0-2b125aab1a22');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "  Invoice StockCode  ...         Country  TotalPrice\n",
              "0  489434     85048  ...  United Kingdom        83.4\n",
              "1  489434    79323P  ...  United Kingdom        81.0\n",
              "2  489434    79323W  ...  United Kingdom        81.0\n",
              "3  489434     22041  ...  United Kingdom       100.8\n",
              "4  489434     21232  ...  United Kingdom        30.0\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fix date to recent period\n",
        "df['InvoiceDate'] = df['InvoiceDate'].apply(lambda x: x.replace(year = x.year + 9))\n",
        "df.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "MlsOfhNhhZp3",
        "outputId": "38dde845-b4ec-45b5-84b1-c62f043a7181"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-6e5b8f81-4632-4413-ad99-517a8f3f7bf6\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Invoice</th>\n",
              "      <th>StockCode</th>\n",
              "      <th>Description</th>\n",
              "      <th>Quantity</th>\n",
              "      <th>InvoiceDate</th>\n",
              "      <th>Price</th>\n",
              "      <th>Customer ID</th>\n",
              "      <th>Country</th>\n",
              "      <th>TotalPrice</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>489434</td>\n",
              "      <td>85048</td>\n",
              "      <td>15CM CHRISTMAS GLASS BALL 20 LIGHTS</td>\n",
              "      <td>12.0</td>\n",
              "      <td>2018-12-01 07:45:00</td>\n",
              "      <td>6.95</td>\n",
              "      <td>13085</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>83.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>489434</td>\n",
              "      <td>79323P</td>\n",
              "      <td>PINK CHERRY LIGHTS</td>\n",
              "      <td>12.0</td>\n",
              "      <td>2018-12-01 07:45:00</td>\n",
              "      <td>6.75</td>\n",
              "      <td>13085</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>81.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>489434</td>\n",
              "      <td>79323W</td>\n",
              "      <td>WHITE CHERRY LIGHTS</td>\n",
              "      <td>12.0</td>\n",
              "      <td>2018-12-01 07:45:00</td>\n",
              "      <td>6.75</td>\n",
              "      <td>13085</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>81.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>489434</td>\n",
              "      <td>22041</td>\n",
              "      <td>RECORD FRAME 7\" SINGLE SIZE</td>\n",
              "      <td>48.0</td>\n",
              "      <td>2018-12-01 07:45:00</td>\n",
              "      <td>2.10</td>\n",
              "      <td>13085</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>100.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>489434</td>\n",
              "      <td>21232</td>\n",
              "      <td>STRAWBERRY CERAMIC TRINKET BOX</td>\n",
              "      <td>24.0</td>\n",
              "      <td>2018-12-01 07:45:00</td>\n",
              "      <td>1.25</td>\n",
              "      <td>13085</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>30.0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      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",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6e5b8f81-4632-4413-ad99-517a8f3f7bf6 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6e5b8f81-4632-4413-ad99-517a8f3f7bf6');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "  Invoice StockCode  ...         Country  TotalPrice\n",
              "0  489434     85048  ...  United Kingdom        83.4\n",
              "1  489434    79323P  ...  United Kingdom        81.0\n",
              "2  489434    79323W  ...  United Kingdom        81.0\n",
              "3  489434     22041  ...  United Kingdom       100.8\n",
              "4  489434     21232  ...  United Kingdom        30.0\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Histogram plot\n",
        "with plt.style.context('seaborn-deep'):\n",
        "  fig, axs = plt.subplots(2, 2, figsize = (15, 8))\n",
        "  fig.suptitle('Numeric vairables visualization')\n",
        "  axs = axs.ravel()\n",
        "  for i, var in enumerate(df.select_dtypes(np.number).columns):\n",
        "    sns.histplot(x = var,\n",
        "              data = df,\n",
        "              bins = 50,\n",
        "              element = 'step',\n",
        "              ax = axs[i])\n",
        "    axs[i].set_title(f'{var}')\n",
        "    axs[i].set(xlabel = '', ylabel = '')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        },
        "id": "p74JhKNwygdO",
        "outputId": "bf66ed04-3149-4730-9ac0-ac74cbfef6eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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0M59qa3MqsFdp8/OIuAL4c+AAYCVwSGa+OMoUSpIkSZIkSZIkSZNGLUlEYFvgoLa6LUoBuBc4srEiMy+KiF2Ao4H9gHWBu6gSfl8qV/i1yMwzI2JZ6edDVFdR3gock5n/OtygMnNhRNxCdeXh3wEvAouBL2bm94fZPiPifVS3NT0EOBxYDVwLnJiZi4Zp82xE/BXwWeB9wBHAU8BFwHGZeetwY5MkSZIkSZIkSZImq1qSiJl5PHB8l22uB/62yzaXAJd02eYc4Jwutn8eOK2UsbZZCRxbiiRJkiRJkiRJkjSlDeKZiJIkSZIkSZIkSZImMZOIkiRJkiRJkiRJklqYRJQkSZIkSZIkSZLUwiSiJEmSJEmSJEmSpBYmESVJkiRJkiRJkiS1MIkoSZIkSZIkSZIkqYVJREmSJEmSJEmSJEktTCJKkiRJkiRJkiRJamESUZIkSZIkSZIkSVILk4iSJEmSJEmSJEmSWphElCRJkiRJkiRJktTCJKIkSZIkSZIkSZKkFiYRJUmSJEmSJEmSJLUwiShJkiRJkiRJkiSphUlESZIkSZIkSZIkSS1MIkqSJEmSJEmSJElqYRJRkiRJkiRJkiRJUguTiJIkSZIkSZIkSZJamESUJEmSJEmSJEmS1MIkoiRJkiRJkiRJkqQWJhElSZIkSZIkSZIktTCJKEmSJEmSJEmSJKmFSURJkiRJkiRJkiRJLUwiSpIkSZIkSZIkSWphElGSJEmSJEmSJElSC5OIkiRJkiRJkiRJklqYRJQkSZIkSZIkSZLUwiSiJEmSJEmSJEmSpBYmESVJkiRJkiRJkiS1WHPQA9DM8a6FF3fdZodtNuW4Q3fswWgkSZIkSZIkSZLUiVciquc2WG+tcbe9YcnDNY5EkiRJkiRJkiRJY+GViOq5oc3njKvdLUsfrXkkkiRJkiRJkiRJGguvRJQkSZIkSZIkSZLUwiSiJEmSJEmSJEmSpBYmEWsSEXMj4uyIeCAino2IZRFxekRsOOixSZIkSZImrq7jvojYqLRbVvp5oPQ7t1djlyRJkqRu+UzEGkTElsAiYBPgYuA24K3Ap4A9I2KnzHxsgEOc0t618OKu2+ywzaYcd+iOPRiNJEmSpJmoruO+iNi49DMPuBI4D9gaOBh4Z0S8PTPv7s1eSJIkSdLYeSViPb5CdSD5yczcJzM/m5m7AacBWwEnDXR0U9QG66017rY3LHm4xpFIkiRJUm3Hff9ElUA8NTN3L/3sQ5WM3KS8jiRJkiQNnFciTlD5NuoewDLgy22rjwP+DvhgRCzMzBV9Ht6UNrT5nHG1u2XpozWPRJIkSdJMVtdxX0SsD3wQWAEc37b6LOAzwF9HxBZejShJkiRp0EwiTtyCsrw8M19sXpGZT0fE9VQHmzsCV/R7cDPZeG6DOp15i1dJkiRp3Oo67tsReEXp5+m2fl6MiB9SJSQXACYRJUmSJA2UScSJ26os7+iw/k6qg8l5jJJEjIgbO6x685IlS9h+++3HN8KaLL1/OQDXDXQUGq/rgNOOHvQoJEmSBm/Lua8a6OsvWbIEYGigg1C36jruG0s/lH5GNdmPISVJkiTVY1DHkSYRJ65xz80nO6xv1E/kTMULq1atenLx4sXLJtDHhM1aY81tiVmzNtho7iCHMa09/fj9ADjHveMc955z3HvOce85x73nHPeec9zZ4sWLF9fY3dZleVsXbYaAp2ocg3qvruO+fhw/AsxatWrVC4sXL/71BPuRhjOe9z1prIwv9ZLxpV4yvtRrbwbW7/eLmkScRDJzUn9NtPEt1+UPL53U45zKnOPec457zznuPee495zj3nOOe8857o/GPE/2z/KanjrFnXGpXjK+1EvGl3rJ+FIvGV/qtRHuQtJTswbxotNM45uiczqsb9Qv78NYJEmSJEn1q+u4z+NHSZIkSVOGScSJu70sOz2z4nVl2emZF5IkSZKkya2u4z6PHyVJkiRNGSYRJ+6qstwjIlrmMyI2AHYCVgI/6/fAJEmSJEm1qOu472fAKmCn0q65n1nAHm2vJ0mSJEkDYxJxgjJzKXA5MAR8om31CcBs4FuZuaLPQ5MkSZIk1WA8x30RsXVEbN3WzzPAt8r2x7f1c1jp/4eZeXeNw5ckSZKkcVlz0AOYJj4OLAK+FBG7A0uAtwELqG5Dc/QAxyZJkiRJmrhuj/uWlGW01R8F7Ap8JiK2BX4BbAPsDTzCy5OUkiRJkjQQkZmDHsO0EBF/Bnwe2BPYGHgQuBA4ITOfGOTYJEmSJEkT181xX0QkQGa2JxGJiI2A44B9gM2Bx4AfAMdm5v293AdJkiRJGiuTiJIkSZIkSZIkSZJa+ExESZIkSZIkSZIkSS1MIkqSJEmSJEmSJElqYRJRkiRJkiRJkiRJUguTiJIkSZIkSZIkSZJamESUJEmSJEmSJEmS1MIkoiRJkiRJkiRJkqQWJhE1qoiYGxFnR8QDEfFsRCyLiNMjYsNBj22yKXOTHcpDHdrMj4hLI+LxiFgVETdHxKcjYo0RXmeviLg6Ip6MiGci4ucRcVDv9qz/ImL/iDgzIq6LiKfKHJ47Spu+zGVEHBQRvyjbP1na7zXefR2UbuY4IoZGiO2MiPNGeJ2u5isi1oiII8rvb1X5fV4aEfPr2O9+iYiNI+LQiLgwIu4q+/JkRPwkIj4SEcP+DTaOx67bOTaOxyci/ndEXBERv23al19FxHERsXGHNsZxF7qZY+O4PhHxgaZ5O7TDNj2Py+k+z6pH1HRMFhEblXbLSj8PlH7n9mrsmvwmGl8RMTsi3h8R/x4Rt0XEioh4OiJuiIiFEbF2r/dBk1dd719tfe4cES+Uv+En1jleTT11xlhEbFfey+4vfT0cEddExId6MXZNfjV+BvvLiLi4tF8dEfeVz/x79mrsmtxiHOe+R+irp/mbyMw6+tE0FRFbAouATYCLgduAtwILgNuBnTLzscGNcHKJiGXAq4DTh1n9TGae3Lb93sD3gNXA+cDjwLuArYALMvOAYV7jMOBM4LHS5jlgf2AucEpmHlnX/gxSRNwEvBl4Brgf2Br4t8z8QIft+zKXEXEysLCM6QJgbeBAYCPg8Mw8a/x73V/dzHFEDAH3AL8GLhqmu99k5gXDtOtqviIigO9Q/R5uBy4p274XWBfYLzMv7n5v+y8iPgb8M/AgcBVwH7ApsC8whypeD8imP8TGcXe6nWPjeHwi4jlgMXAr8AgwG9gR2AF4ANgxM3/btL1x3KVu5tg4rkdE/BlwC7AGsD7w0cz8ets2PY/L6T7Pqkddx2RRfSlhETAPuBL4JdXnv72p3nvenpl392IfNHnVEV/lBOgPqP7mXwXcBWwIvBvYrPS/e2au7tFuaJLqxTmliNgAuBn4E6q/4Sdl5jF1jltTR50xVj77nQE8AfwH8Duqz2VvAO7PzANr3wFNajV+BvsH4CvACuBCqmOFuVTnLtYDjsnMk3qxD5q8uj33PUI/vc/fZKbF0rEAPwSS6qRHc/2ppf6rgx7jZCrAMmDZGLd9JdXB+rPADk3165b/+Akc2NZmiOqk7GPAUFP9hlQHakl18D/wuahhLhcArwMC2LXs27mDnEtgfqm/C9iwra/HSn9DE9nvSTzHQ2X9OV303/V8Ae8rba4H1m2qf0v5/T4CbDDouRvj/u9GlTiZ1Va/GVWyK6lODhvH/Ztj43h887xuh/qTyn5+xTju6xwbxxOf7wB+DCwFvlj289BBxOV0nmdLfYWajsmAfynbn9JW/8lSf9mg99XS/1JHfAHbAu8H1m6r3wC4sfSzcND7aul/qev9q63t2VQJ66NKHycOej8tgys1/o3cA3ix9Peyz17AWoPeV0v/S01/I9cClgOrgK3a1m1TjhFWAusMen8t/S10cV52lH56nr8Z+GRZJm8BtiyBdg8vP0G7AVWWfAUwe9BjnSyF7pKIh5T5/ddh1u1W1l3TVv/5Un9CN/1N9TLaG2m/5hL4Zqk/eJg2HfubCmUMczxE9yetu54v4NpSv6Cb/qZa4aUD3jNHi7uyzjiuZ46N43rn+M1lX340WtyVdcZxPXNsHE98Xj9FdZJoZ+B4hk8i9iUup/M8W+op1HRMRnW1zsqy/QZt62ZRHccksMWg99nSv1JXfI3yGv+tvMYlg95fS39LL+KL6srpBD4AfBiTiDO61BljVHf5WAFsPOj9skyOUuNnsE1LP7/usP7mst7Ym8GFcSYR+/FZLjN9JqJGtKAsL8/MF5tXZObTVN+YXo/qdlt6yTpRPWPnqIj4VEQsiOGfAbVbWV42zLprqQ7y50fEOmNs84O2bWaSfs2l8w+vjoi/L/H99xHxphG27Wq+ImJdqqs4VgLXjaXNFPaHsny+qc44rtdwc9xgHNfjXWV5c1OdcVyv4ea4wTgeh4jYBvgCcEZmXjvCpj2Py+k8z6pVXcdkOwKvAK4v7Zr7aVx50fx6mhn6ccw/0mcyTW+1xldEbAJ8DbgoM8f1zChNO7XEWES8AXgTcDnweDmPd2RUz3TdPSI8fz4z1fUe9gjwe2BeRLyueUVEzKO6Eu2m9HFhGp++5G/WnEhjTXtbleUdHdbfSXW5/zzgir6MaGrYDPhWW909EXFwZl7TVNdxfjPz+Yi4B3g9sAWwZAxtHoyIFcDciFgvM1dOZCemmJ7PZUTMBl5D9WzLB4cZw51lOW8C+zEV/FUpfxQRVwMHZeZ9TXXjma8tqZ5NdXdmDneSYVrMcUSsCTQeyt58otk4rskIc9xgHI9DRBxJdSXLHKpn9f0lVXLrC02bGccTMMY5bjCOu1TeG75Fdbvjo0bZvB9xOS3nWbWr65hsLP2A8TbT9OOY/5CyHO4zmaa3uuPra1RXTn9s4kPTNFFXjL2lLB8Brqa6W0WzWyJi38y8a5zj1NRUS3xlZkbEJ4BzgRsj4kKq596/BngP8P+onqEujUdf8jd+k0IjmVOWT3ZY36h/VR/GMlV8A9idKpE4G3gj1bNHhoAfRMSbm7Ydz/yOtc2cDuu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            "text/plain": [
              "<Figure size 1080x576 with 4 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "width": 904,
              "height": 523
            },
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Export cleaned data\n",
        "df.to_csv('onlineRetailClean.csv', index = 0)"
      ],
      "metadata": {
        "id": "nj2Sy9MRhIxm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Data for model\n",
        "fixDate = '30/06/2021'\n",
        "fixDate = pd.Timestamp(fixDate) #Convert string to timestamp\n",
        "\n",
        "dfRFM = df.groupby(['Customer ID']).agg(\n",
        "    {\n",
        "        'InvoiceDate': lambda date: (fixDate - date.max()).days,\n",
        "        'Invoice': lambda num: num.nunique(),\n",
        "        'TotalPrice': lambda price: price.sum()\n",
        "    }\n",
        ")\n",
        "\n",
        "dfRFM.columns = ['Recency', 'Frequency', 'Monetary']\n",
        "dfRFM.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "MF_V-56lNBfl",
        "outputId": "9330acd7-68f7-4e8b-f0f0-23f487870809"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    }\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",
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              "\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-339b2f89-c6f5-417a-abdc-9bf1b748b6a7 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-339b2f89-c6f5-417a-abdc-9bf1b748b6a7');\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",
              "  "
            ],
            "text/plain": [
              "             Recency  Frequency  Monetary\n",
              "Customer ID                              \n",
              "12346            528         12    704.10\n",
              "12347            204          8   5633.32\n",
              "12348            277          5   1991.86\n",
              "12349            220          4   3786.50\n",
              "12350            513          1    331.34"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Customer segmentation by score\n",
        "![RFM Analysis](https://www.everydaymarketing.co/wp-content/uploads/2021/04/Screen-Shot-2564-04-08-at-13.23.50.png)"
      ],
      "metadata": {
        "id": "BXTVFWGUFJ3w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create binning for 5 bins\n",
        "dfRFM['RecencyScore'] = pd.qcut(dfRFM['Recency'], 5, labels = [5, 4, 3, 2, 1])\n",
        "dfRFM['FrequencyScore'] = pd.qcut(dfRFM['Frequency'].rank(method = 'first'),\n",
        "                                  5, labels = [1, 2, 3, 4, 5])\n",
        "dfRFM['MonetaryScore'] = pd.qcut(dfRFM['Monetary'], 5, labels = [5, 4, 3, 2, 1])\n",
        "dfRFM.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "nhmwqcLzBpim",
        "outputId": "b43969ed-b9a1-4bac-e05a-87bba87d8985"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "      <th>Recency</th>\n",
              "      <th>Frequency</th>\n",
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              "      <th>FrequencyScore</th>\n",
              "      <th>MonetaryScore</th>\n",
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              "    <tr>\n",
              "      <th>Customer ID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>12346</th>\n",
              "      <td>528</td>\n",
              "      <td>12</td>\n",
              "      <td>704.10</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
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              "      <td>5</td>\n",
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              "    <tr>\n",
              "      <th>12348</th>\n",
              "      <td>277</td>\n",
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              "      <td>1991.86</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
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              "    <tr>\n",
              "      <th>12349</th>\n",
              "      <td>220</td>\n",
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              "    <tr>\n",
              "      <th>12350</th>\n",
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              "      <td>1</td>\n",
              "      <td>331.34</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
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              "  </tbody>\n",
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              "          const element = document.querySelector('#df-61a14b02-bb29-41d9-9033-af1e641c759e');\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",
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              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "             Recency  Frequency  ...  FrequencyScore MonetaryScore\n",
              "Customer ID                      ...                              \n",
              "12346            528         12  ...               5             3\n",
              "12347            204          8  ...               4             1\n",
              "12348            277          5  ...               4             2\n",
              "12349            220          4  ...               3             1\n",
              "12350            513          1  ...               1             4\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# RFM Score\n",
        "dfRFM['RFMScore'] = dfRFM['RecencyScore'].astype(str) + dfRFM['FrequencyScore'].astype(str)\n",
        "dfRFM.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "xc18JWu6FDdk",
        "outputId": "792f5f36-4a10-425c-bc87-91941f80493c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "      <th></th>\n",
              "      <th>Recency</th>\n",
              "      <th>Frequency</th>\n",
              "      <th>Monetary</th>\n",
              "      <th>RecencyScore</th>\n",
              "      <th>FrequencyScore</th>\n",
              "      <th>MonetaryScore</th>\n",
              "      <th>RFMScore</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Customer ID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
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              "      <td>277</td>\n",
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              "      <td>4</td>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
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              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
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              "      background-color: #E2EBFA;\n",
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              "      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-65702d61-b71c-477f-b6d7-4e16117086a3 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-65702d61-b71c-477f-b6d7-4e16117086a3');\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",
              "  "
            ],
            "text/plain": [
              "             Recency  Frequency  Monetary  ... FrequencyScore MonetaryScore RFMScore\n",
              "Customer ID                                ...                                      \n",
              "12346            528         12    704.10  ...              5             3       25\n",
              "12347            204          8   5633.32  ...              4             1       54\n",
              "12348            277          5   1991.86  ...              4             2       34\n",
              "12349            220          4   3786.50  ...              3             1       53\n",
              "12350            513          1    331.34  ...              1             4       21\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##RFM Score mapping table\n",
        "![RFM Score](https://www.everydaymarketing.co/wp-content/uploads/2021/04/Screen-Shot-2564-04-08-at-13.45.27-1.png)"
      ],
      "metadata": {
        "id": "BT6ePZTrEJy7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert RFM Score to segment label\n",
        "RFMLabel = {\n",
        "    r'55': \"Champion\",\n",
        "    r'[3-5][4-5]': \"Loyal\",\n",
        "    r'[4-5][2-3]': \"Promising\",\n",
        "    r'51': \"New Customers\",\n",
        "    r'41': \"Warm Leads\",\n",
        "    r'31': \"Cold Leads\",\n",
        "    r'[2-3][2-3]': \"Need Attention\",\n",
        "    r'[1-2][5]': \"Shouldn't Lost\",\n",
        "    r'[1-2][3-4]': 'Sleepers',\n",
        "    r'[1-2][1-2]': \"Lost\"\n",
        "}\n",
        "\n",
        "dfRFM['Segment'] = dfRFM['RFMScore'].replace(RFMLabel, regex = True)\n",
        "dfRFM.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "w8mKx-jnF3im",
        "outputId": "d4d6a4de-56a5-4928-8cbc-f3e9255770d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Recency</th>\n",
              "      <th>Frequency</th>\n",
              "      <th>Monetary</th>\n",
              "      <th>RecencyScore</th>\n",
              "      <th>FrequencyScore</th>\n",
              "      <th>MonetaryScore</th>\n",
              "      <th>RFMScore</th>\n",
              "      <th>Segment</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Customer ID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
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              "      <th>12346</th>\n",
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              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>25</td>\n",
              "      <td>Shouldn't Lost</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12347</th>\n",
              "      <td>204</td>\n",
              "      <td>8</td>\n",
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              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>54</td>\n",
              "      <td>Loyal</td>\n",
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              "    <tr>\n",
              "      <th>12348</th>\n",
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              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>34</td>\n",
              "      <td>Loyal</td>\n",
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              "      <th>12349</th>\n",
              "      <td>220</td>\n",
              "      <td>4</td>\n",
              "      <td>3786.50</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>53</td>\n",
              "      <td>Promising</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12350</th>\n",
              "      <td>513</td>\n",
              "      <td>1</td>\n",
              "      <td>331.34</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>21</td>\n",
              "      <td>Lost</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "                                                     [key], {});\n",
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              "          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",
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              "  "
            ],
            "text/plain": [
              "             Recency  Frequency  ...  RFMScore         Segment\n",
              "Customer ID                      ...                          \n",
              "12346            528         12  ...        25  Shouldn't Lost\n",
              "12347            204          8  ...        54           Loyal\n",
              "12348            277          5  ...        34           Loyal\n",
              "12349            220          4  ...        53       Promising\n",
              "12350            513          1  ...        21            Lost\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# RFM Result\n",
        "result = dfRFM.groupby(['Segment'])['Segment'].count()\n",
        "values = list(result)\n",
        "labels = result.index\n",
        "\n",
        "# Plot\n",
        "colors = [list(np.random.uniform(size = 4)) for i in range(len(values))]\n",
        "\n",
        "plt.figure(figsize = (18, 11))\n",
        "with plt.style.context('seaborn-deep'):\n",
        "  squarify.plot(sizes = values, color = colors, label = labels)\n",
        "  plt.axis('off')\n",
        "  plt.title('Customer segmentation')\n",
        "  plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 644
        },
        "id": "2FxoUTaGJdwS",
        "outputId": "3589a8c2-ad02-4286-df1a-2047e57ab9fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 1296x792 with 1 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "width": 1018,
              "height": 627
            },
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}