Retail_Market_Customer_Analysis.ipynb

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  "metadata": {
    "colab": {
      "name": "Retail Market Customer Analysis.ipynb",
      "provenance": [],
      "collapsed_sections": []
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      "name": "python3",
      "display_name": "Python 3"
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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wT9mg1048JeW"
      },
      "source": [
        "![](https://miro.medium.com/max/700/1*h0zZjCSgcjZh0Bh2T4kMDg.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dzeJ12Jr8azo"
      },
      "source": [
        "<h1>Introduction</h1>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VZPijLLy8kNo"
      },
      "source": [
        "<h3>Dataset</h3>\n",
        "\n",
        "*  <span><a href=\"https://archive.ics.uci.edu/ml/datasets/online+retail\">Link to the dataset</a></span>\n",
        "*   This is a transnational data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WbK-dQWN9c25"
      },
      "source": [
        "<h3>Aim</h3>\n",
        "\n",
        "The objective is to segment the customers based on recency, frequency and monetary so that the company is able to filter out the target audience."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jkwKib2a-Cle"
      },
      "source": [
        "<h1>Reading and Understanding Data</h1>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "abH82VGA-Tyg"
      },
      "source": [
        "#importing important libraries\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import datetime as dt\n",
        "\n",
        "import sklearn\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.metrics import silhouette_score\n",
        "from sklearn.cluster import AgglomerativeClustering\n",
        "from scipy.cluster.hierarchy import linkage\n",
        "from scipy.cluster.hierarchy import dendrogram\n",
        "from scipy.cluster.hierarchy import cut_tree"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "R_sQn8XEAE2j",
        "outputId": "5fb6f9e6-8775-4497-96f5-cf0cbe592cb9"
      },
      "source": [
        "#Reading data \n",
        "\n",
        "# df = pd.read_csv('OnlineRetail.csv', sep=',', encoding=\"ISO-8859-1\", header=0)\n",
        "data = pd.read_csv('OnlineRetail.csv', sep=',', encoding=\"ISO-8859-1\", header=0)\n",
        "data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>01-12-2010 08:26</td>\n",
              "      <td>2.55</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>536365</td>\n",
              "      <td>71053</td>\n",
              "      <td>WHITE METAL LANTERN</td>\n",
              "      <td>6</td>\n",
              "      <td>01-12-2010 08:26</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>536365</td>\n",
              "      <td>84406B</td>\n",
              "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
              "      <td>8</td>\n",
              "      <td>01-12-2010 08:26</td>\n",
              "      <td>2.75</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>536365</td>\n",
              "      <td>84029G</td>\n",
              "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
              "      <td>6</td>\n",
              "      <td>01-12-2010 08:26</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>536365</td>\n",
              "      <td>84029E</td>\n",
              "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
              "      <td>6</td>\n",
              "      <td>01-12-2010 08:26</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  InvoiceNo StockCode  ... CustomerID         Country\n",
              "0    536365    85123A  ...    17850.0  United Kingdom\n",
              "1    536365     71053  ...    17850.0  United Kingdom\n",
              "2    536365    84406B  ...    17850.0  United Kingdom\n",
              "3    536365    84029G  ...    17850.0  United Kingdom\n",
              "4    536365    84029E  ...    17850.0  United Kingdom\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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        },
        "id": "kaXgxVRhBFsq",
        "outputId": "d8798310-a051-4bb6-d020-22d4eea6907f"
      },
      "source": [
        "#shape of df\n",
        "data.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(541909, 8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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        "id": "iNQUmYz8BLFH",
        "outputId": "67c45a72-8849-473d-ddd4-17400a8ee1c0"
      },
      "source": [
        "data.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 541909 entries, 0 to 541908\n",
            "Data columns (total 8 columns):\n",
            " #   Column       Non-Null Count   Dtype  \n",
            "---  ------       --------------   -----  \n",
            " 0   InvoiceNo    541909 non-null  object \n",
            " 1   StockCode    541909 non-null  object \n",
            " 2   Description  540455 non-null  object \n",
            " 3   Quantity     541909 non-null  int64  \n",
            " 4   InvoiceDate  541909 non-null  object \n",
            " 5   UnitPrice    541909 non-null  float64\n",
            " 6   CustomerID   406829 non-null  float64\n",
            " 7   Country      541909 non-null  object \n",
            "dtypes: float64(2), int64(1), object(5)\n",
            "memory usage: 33.1+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "41uZpSOABMkY",
        "outputId": "69607e84-61d2-4b54-8951-6e07ba0c1e36"
      },
      "source": [
        "data.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Quantity</th>\n",
              "      <th>UnitPrice</th>\n",
              "      <th>CustomerID</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>541909.000000</td>\n",
              "      <td>541909.000000</td>\n",
              "      <td>406829.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>9.552250</td>\n",
              "      <td>4.611114</td>\n",
              "      <td>15287.690570</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>218.081158</td>\n",
              "      <td>96.759853</td>\n",
              "      <td>1713.600303</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-80995.000000</td>\n",
              "      <td>-11062.060000</td>\n",
              "      <td>12346.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.250000</td>\n",
              "      <td>13953.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.080000</td>\n",
              "      <td>15152.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>10.000000</td>\n",
              "      <td>4.130000</td>\n",
              "      <td>16791.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>80995.000000</td>\n",
              "      <td>38970.000000</td>\n",
              "      <td>18287.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Quantity      UnitPrice     CustomerID\n",
              "count  541909.000000  541909.000000  406829.000000\n",
              "mean        9.552250       4.611114   15287.690570\n",
              "std       218.081158      96.759853    1713.600303\n",
              "min    -80995.000000  -11062.060000   12346.000000\n",
              "25%         1.000000       1.250000   13953.000000\n",
              "50%         3.000000       2.080000   15152.000000\n",
              "75%        10.000000       4.130000   16791.000000\n",
              "max     80995.000000   38970.000000   18287.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "laUw1rPNBbbi"
      },
      "source": [
        "<h1>Data Cleansing</h1>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ICZtll9IBgnT",
        "outputId": "d0687ec1-e751-4f35-915e-b4860b307e3c"
      },
      "source": [
        "#percent contribution of missing values\n",
        "\n",
        "data_null = round(100*(data.isnull().sum())/len(data), 2)\n",
        "data_null"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "InvoiceNo       0.00\n",
              "StockCode       0.00\n",
              "Description     0.27\n",
              "Quantity        0.00\n",
              "InvoiceDate     0.00\n",
              "UnitPrice       0.00\n",
              "CustomerID     24.93\n",
              "Country         0.00\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IS37tuV3CClo",
        "outputId": "6a4eba94-a527-4823-b9d4-e016caa5920c"
      },
      "source": [
        "data['CustomerID'].nunique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4372"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M0K47D7sDUfg",
        "outputId": "60e6b65c-5a03-464b-ffaa-d486cb468ef7"
      },
      "source": [
        "data['CustomerID'].mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "15287.690570239585"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ABwRYjMyDf--",
        "outputId": "2e288d1e-7f83-4c5a-e479-7acd7e47cee3"
      },
      "source": [
        "data['CustomerID'].median()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "15152.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7pExoGLgDj-E",
        "outputId": "32735f8e-3139-4fbc-bf8d-5ff1724453e3"
      },
      "source": [
        "data['CustomerID'].mode()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    17841.0\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gqmQ6mzeDoNr",
        "outputId": "91eaa59b-ef02-41c7-8860-6032febcca8a"
      },
      "source": [
        "#Dropping null values\n",
        "\n",
        "data = data.dropna()\n",
        "data.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(406829, 8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L8_d71KrKYEu"
      },
      "source": [
        "data['CustomerID'] = data['CustomerID'].astype(str)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9BSY2wFZKiMX"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1PLdGuk5LUKa"
      },
      "source": [
        "<h1>Data Preparation</h1>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uYo0fcrhLbxr"
      },
      "source": [
        "Analysis is going to be based on three factors:\n",
        "\n",
        "\n",
        "*   R(Recency): last purchased date\n",
        "*   F(Frequency): Number of transactions\n",
        "*   Tr(Total Revenue): Total revenue contributed\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "RErzjy_CLcTW",
        "outputId": "7efe0987-28ff-4525-d6ab-79eee53274ff"
      },
      "source": [
        "#Introducing new attribute Total revenue\n",
        "\n",
        "data['TotalRevenue'] = data['Quantity']*data['UnitPrice']\n",
        "data_m = data.groupby('CustomerID')['TotalRevenue'].sum()\n",
        "data_m = data_m.reset_index()\n",
        "data_m.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>TotalRevenue</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12346.0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12347.0</td>\n",
              "      <td>4310.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12348.0</td>\n",
              "      <td>1797.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12349.0</td>\n",
              "      <td>1757.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12350.0</td>\n",
              "      <td>334.40</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  CustomerID  TotalRevenue\n",
              "0    12346.0          0.00\n",
              "1    12347.0       4310.00\n",
              "2    12348.0       1797.24\n",
              "3    12349.0       1757.55\n",
              "4    12350.0        334.40"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
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        "id": "iSYcYNxNLX6c",
        "outputId": "55aaa4dd-d61b-4dca-91c7-09fcf6f5b0d0"
      },
      "source": [
        "data['Country'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['United Kingdom', 'France', 'Australia', 'Netherlands', 'Germany',\n",
              "       'Norway', 'EIRE', 'Switzerland', 'Spain', 'Poland', 'Portugal',\n",
              "       'Italy', 'Belgium', 'Lithuania', 'Japan', 'Iceland',\n",
              "       'Channel Islands', 'Denmark', 'Cyprus', 'Sweden', 'Austria',\n",
              "       'Israel', 'Finland', 'Greece', 'Singapore', 'Lebanon',\n",
              "       'United Arab Emirates', 'Saudi Arabia', 'Czech Republic', 'Canada',\n",
              "       'Unspecified', 'Brazil', 'USA', 'European Community', 'Bahrain',\n",
              "       'Malta', 'RSA'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "8W8l7GYENAMh",
        "outputId": "0ca84ca5-03ab-47a7-c313-f7d1a1a7b5a2"
      },
      "source": [
        "#Introducing new attribute frequency\n",
        "\n",
        "data_f = data.groupby('CustomerID')['InvoiceNo'].count()\n",
        "data_f = data_f.reset_index();\n",
        "data_f.columns = ['CustomerID', 'Frequency']\n",
        "data_f.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Frequency</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12346.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12347.0</td>\n",
              "      <td>182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12348.0</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12349.0</td>\n",
              "      <td>73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12350.0</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  CustomerID  Frequency\n",
              "0    12346.0          2\n",
              "1    12347.0        182\n",
              "2    12348.0         31\n",
              "3    12349.0         73\n",
              "4    12350.0         17"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "uLi5HzohNu9f",
        "outputId": "717f47da-0761-4916-8d0e-90b0a24362dd"
      },
      "source": [
        "#Merging the two dataframes\n",
        "RFTr = pd.merge(data_f, data_m, on='CustomerID', how='inner')\n",
        "RFTr.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Frequency</th>\n",
              "      <th>TotalRevenue</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12346.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12347.0</td>\n",
              "      <td>182</td>\n",
              "      <td>4310.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12348.0</td>\n",
              "      <td>31</td>\n",
              "      <td>1797.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12349.0</td>\n",
              "      <td>73</td>\n",
              "      <td>1757.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12350.0</td>\n",
              "      <td>17</td>\n",
              "      <td>334.40</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  CustomerID  Frequency  TotalRevenue\n",
              "0    12346.0          2          0.00\n",
              "1    12347.0        182       4310.00\n",
              "2    12348.0         31       1797.24\n",
              "3    12349.0         73       1757.55\n",
              "4    12350.0         17        334.40"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RQJCmJDJOdgP"
      },
      "source": [
        "#Introducing new attribute recency\n",
        "#Convert Invoice Date to proper datetime format\n",
        "data['InvoiceDate'] = pd.to_datetime(data['InvoiceDate'], format='%d-%m-%Y %H:%M')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y_VE945JPB--",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "05904c94-a27a-4638-d766-c5d64914fdcb"
      },
      "source": [
        "#maximum date\n",
        "max_date = max(data['InvoiceDate'])\n",
        "#Computing difference \n",
        "data['Diff'] = max_date-data['InvoiceDate']\n",
        "data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>InvoiceNo</th>\n",
              "      <th>StockCode</th>\n",
              "      <th>Description</th>\n",
              "      <th>Quantity</th>\n",
              "      <th>InvoiceDate</th>\n",
              "      <th>UnitPrice</th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Country</th>\n",
              "      <th>TotalRevenue</th>\n",
              "      <th>Diff</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>536365</td>\n",
              "      <td>85123A</td>\n",
              "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
              "      <td>6</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>2.55</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>15.30</td>\n",
              "      <td>373 days 04:24:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>536365</td>\n",
              "      <td>71053</td>\n",
              "      <td>WHITE METAL LANTERN</td>\n",
              "      <td>6</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>20.34</td>\n",
              "      <td>373 days 04:24:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>536365</td>\n",
              "      <td>84406B</td>\n",
              "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
              "      <td>8</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>2.75</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>22.00</td>\n",
              "      <td>373 days 04:24:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>536365</td>\n",
              "      <td>84029G</td>\n",
              "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
              "      <td>6</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>20.34</td>\n",
              "      <td>373 days 04:24:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>536365</td>\n",
              "      <td>84029E</td>\n",
              "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
              "      <td>6</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>20.34</td>\n",
              "      <td>373 days 04:24:00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  InvoiceNo StockCode  ... TotalRevenue              Diff\n",
              "0    536365    85123A  ...        15.30 373 days 04:24:00\n",
              "1    536365     71053  ...        20.34 373 days 04:24:00\n",
              "2    536365    84406B  ...        22.00 373 days 04:24:00\n",
              "3    536365    84029G  ...        20.34 373 days 04:24:00\n",
              "4    536365    84029E  ...        20.34 373 days 04:24:00\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "TzZtzFTVCC20",
        "outputId": "60151a4c-062a-4c89-f89c-9fa31cf4d191"
      },
      "source": [
        "#Compute last transaction date to get recency\n",
        "\n",
        "RFTr_r = data.groupby('CustomerID')['Diff'].min()\n",
        "RFTr_r = RFTr_r.reset_index()\n",
        "RFTr_r.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Diff</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12346.0</td>\n",
              "      <td>325 days 02:33:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12347.0</td>\n",
              "      <td>1 days 20:58:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12348.0</td>\n",
              "      <td>74 days 23:37:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12349.0</td>\n",
              "      <td>18 days 02:59:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12350.0</td>\n",
              "      <td>309 days 20:49:00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  CustomerID              Diff\n",
              "0    12346.0 325 days 02:33:00\n",
              "1    12347.0   1 days 20:58:00\n",
              "2    12348.0  74 days 23:37:00\n",
              "3    12349.0  18 days 02:59:00\n",
              "4    12350.0 309 days 20:49:00"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "Ex-F9ZtoCrzp",
        "outputId": "8ddd4282-c9e4-4a51-92b2-8c02a6020233"
      },
      "source": [
        "#Extracting the number of days from difference\n",
        "\n",
        "RFTr_r['Diff'] = RFTr_r['Diff'].dt.days\n",
        "RFTr_r.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Diff</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12346.0</td>\n",
              "      <td>325</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12347.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12348.0</td>\n",
              "      <td>74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12349.0</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12350.0</td>\n",
              "      <td>309</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  CustomerID  Diff\n",
              "0    12346.0   325\n",
              "1    12347.0     1\n",
              "2    12348.0    74\n",
              "3    12349.0    18\n",
              "4    12350.0   309"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "WowGwcp3C_5A",
        "outputId": "a445fc67-0ad1-47fa-c390-e74279cf0b2d"
      },
      "source": [
        "#Merging the dataframes to get final RFTr dataframe\n",
        "RFTr = pd.merge(RFTr, RFTr_r, on='CustomerID', how='inner')\n",
        "RFTr.columns = ['CustomerID', 'Frequency', 'TotalRevenue', 'Recency']\n",
        "RFTr.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Frequency</th>\n",
              "      <th>TotalRevenue</th>\n",
              "      <th>Recency</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12346.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.00</td>\n",
              "      <td>325</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12347.0</td>\n",
              "      <td>182</td>\n",
              "      <td>4310.00</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12348.0</td>\n",
              "      <td>31</td>\n",
              "      <td>1797.24</td>\n",
              "      <td>74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12349.0</td>\n",
              "      <td>73</td>\n",
              "      <td>1757.55</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12350.0</td>\n",
              "      <td>17</td>\n",
              "      <td>334.40</td>\n",
              "      <td>309</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  CustomerID  Frequency  TotalRevenue  Recency\n",
              "0    12346.0          2          0.00      325\n",
              "1    12347.0        182       4310.00        1\n",
              "2    12348.0         31       1797.24       74\n",
              "3    12349.0         73       1757.55       18\n",
              "4    12350.0         17        334.40      309"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531
        },
        "id": "zL4ugwIEDYu0",
        "outputId": "cbff876f-54dd-436a-8d47-f69915f1a0f4"
      },
      "source": [
        "#Outlier Analysis for TotalRevenue, Frequency and Recency\n",
        "attributes = ['Frequency','TotalRevenue','Recency']\n",
        "plt.rcParams['figure.figsize'] = [10,8]\n",
        "sns.boxplot(data = RFTr[attributes], orient=\"v\", palette=\"Set2\" ,whis=1.5,saturation=1, width=0.7)\n",
        "plt.title(\"Outliers Variable Distribution\", fontsize = 14, fontweight = 'bold')\n",
        "plt.ylabel(\"Range\", fontweight = 'bold')\n",
        "plt.xlabel(\"Attributes\", fontweight = 'bold')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Attributes')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AwhF9UkkHRw7"
      },
      "source": [
        "# Removing (statistical) outliers for Total Revenue\n",
        "Q1 = RFTr.TotalRevenue.quantile(0.05)\n",
        "Q3 = RFTr.TotalRevenue.quantile(0.95)\n",
        "IQR = Q3 - Q1\n",
        "RFTr = RFTr[(RFTr.TotalRevenue >= Q1 - 1.5*IQR) & (RFTr.TotalRevenue <= Q3 + 1.5*IQR)]\n",
        "\n",
        "# Removing (statistical) outliers for Recency\n",
        "Q1 = RFTr.Recency.quantile(0.05)\n",
        "Q3 = RFTr.Recency.quantile(0.95)\n",
        "IQR = Q3 - Q1\n",
        "RFTr = RFTr[(RFTr.Recency >= Q1 - 1.5*IQR) & (RFTr.Recency <= Q3 + 1.5*IQR)]\n",
        "\n",
        "# Removing (statistical) outliers for Frequency\n",
        "Q1 = RFTr.Frequency.quantile(0.05)\n",
        "Q3 = RFTr.Frequency.quantile(0.95)\n",
        "IQR = Q3 - Q1\n",
        "RFTr = RFTr[(RFTr.Frequency >= Q1 - 1.5*IQR) & (RFTr.Frequency <= Q3 + 1.5*IQR)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531
        },
        "id": "KG1xRpshITwW",
        "outputId": "e7b7f19e-b804-4a15-b659-745ee16258d2"
      },
      "source": [
        "# Outlier Analysis of TotalRevenue Frequency and Recency\n",
        "\n",
        "attributes = ['Frequency','TotalRevenue','Recency']\n",
        "plt.rcParams['figure.figsize'] = [10,8]\n",
        "sns.boxplot(data = RFTr[attributes], orient=\"v\", palette=\"Set2\" ,whis=1.5,saturation=1, width=0.7)\n",
        "plt.title(\"Outliers Variable Distribution\", fontsize = 14, fontweight = 'bold')\n",
        "plt.ylabel(\"Range\", fontweight = 'bold')\n",
        "plt.xlabel(\"Attributes\", fontweight = 'bold')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Attributes')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2d22qNCiIpWf"
      },
      "source": [
        "<h1>Data Scaling</h1>\n",
        "Using Standardisation Scaling\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rqhTeP6GIkVc",
        "outputId": "c6c98342-a86c-4955-98bf-62b3010905ae"
      },
      "source": [
        "#Rescaling the attributes\n",
        "\n",
        "RFTr_df = RFTr[['Frequency', 'TotalRevenue', 'Recency']]\n",
        "sc = StandardScaler()\n",
        "RFTr_df_scaled = sc.fit_transform(RFTr_df)\n",
        "RFTr_df_scaled.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4293, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "mcAEwNXJKsKT",
        "outputId": "474c8fe2-3ceb-44d2-8fe2-78e8921aaf68"
      },
      "source": [
        "RFTr_df_scaled = pd.DataFrame(RFTr_df_scaled)\n",
        "RFTr_df_scaled.columns = ['Frequency', 'TotalRevenue', 'Recency']\n",
        "RFTr_df_scaled.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Frequency</th>\n",
              "      <th>TotalRevenue</th>\n",
              "      <th>Recency</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.752888</td>\n",
              "      <td>-0.723738</td>\n",
              "      <td>2.301611</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.042467</td>\n",
              "      <td>1.731617</td>\n",
              "      <td>-0.906466</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.463636</td>\n",
              "      <td>0.300128</td>\n",
              "      <td>-0.183658</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.044720</td>\n",
              "      <td>0.277517</td>\n",
              "      <td>-0.738141</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.603275</td>\n",
              "      <td>-0.533235</td>\n",
              "      <td>2.143188</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Frequency  TotalRevenue   Recency\n",
              "0  -0.752888     -0.723738  2.301611\n",
              "1   1.042467      1.731617 -0.906466\n",
              "2  -0.463636      0.300128 -0.183658\n",
              "3  -0.044720      0.277517 -0.738141\n",
              "4  -0.603275     -0.533235  2.143188"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ttb8KbZVLSTt"
      },
      "source": [
        "<h1>ML Model</h1>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "REn4ElY1aXAa"
      },
      "source": [
        "<h2>K-Means Clustering</h2>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7B2q80Rpaf4c"
      },
      "source": [
        "<h3>Elbow Curve</h3>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "IOKsog3PLNHb",
        "outputId": "1b01117d-cc8d-4d07-975f-cff5a94009fc"
      },
      "source": [
        "# Elbow-curve/SSD\n",
        "\n",
        "ssd = []\n",
        "range_n_clusters = [2, 3, 4, 5, 6, 7, 8]\n",
        "for num_clusters in range_n_clusters:\n",
        "    kmeans = KMeans(n_clusters=num_clusters, max_iter=50)\n",
        "    kmeans.fit(RFTr_df_scaled)\n",
        "    \n",
        "    ssd.append(kmeans.inertia_)\n",
        "    \n",
        "# plot the SSDs for each n_clusters\n",
        "plt.plot(ssd)\n",
        "plt.title('Elbow Graph')\n",
        "plt.xlabel('Number of clusters')\n",
        "plt.ylabel('WCSS')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "odpifAU5LzQ3",
        "outputId": "e417c57b-3e07-4dae-855b-6b36c495ba44"
      },
      "source": [
        "# Final Model with k=5\n",
        "\n",
        "kmeans = KMeans(n_clusters=5, max_iter=50)\n",
        "kmeans.fit(RFTr_df_scaled)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=50,\n",
              "       n_clusters=5, n_init=10, n_jobs=None, precompute_distances='auto',\n",
              "       random_state=None, tol=0.0001, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4W7oSm1uPJvz",
        "outputId": "67b42946-78d9-47f1-a699-d7a7faac03ca"
      },
      "source": [
        "kmeans.labels_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 0, 1, ..., 2, 1, 1], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MmBr91op03-m",
        "outputId": "fc914a0c-987a-4c6d-96b1-c85ce06e2368"
      },
      "source": [
        "kmeans.cluster_centers_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.51798519, -0.50219519,  1.58017996],\n",
              "       [ 3.04183373,  1.4372936 , -0.7230863 ],\n",
              "       [-0.38069813, -0.36364467, -0.42258795],\n",
              "       [ 2.21222813,  4.46067117, -0.72395482],\n",
              "       [ 0.65415302,  0.68837801, -0.61643958]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ezTJ7t1JPMyM"
      },
      "source": [
        "cluster_data= RFTr_df.copy()\n",
        "cluster_data['cluster_pred'] = kmeans.fit_predict(RFTr_df_scaled)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "uG9ed3ilPUNV",
        "outputId": "1a184cb5-9162-4e4e-ee1b-153c30bd4154"
      },
      "source": [
        "cluster_data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Frequency</th>\n",
              "      <th>TotalRevenue</th>\n",
              "      <th>Recency</th>\n",
              "      <th>cluster_pred</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>0.00</td>\n",
              "      <td>325</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>182</td>\n",
              "      <td>4310.00</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>31</td>\n",
              "      <td>1797.24</td>\n",
              "      <td>74</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>73</td>\n",
              "      <td>1757.55</td>\n",
              "      <td>18</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>17</td>\n",
              "      <td>334.40</td>\n",
              "      <td>309</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Frequency  TotalRevenue  Recency  cluster_pred\n",
              "0          2          0.00      325             0\n",
              "1        182       4310.00        1             4\n",
              "2         31       1797.24       74             2\n",
              "3         73       1757.55       18             2\n",
              "4         17        334.40      309             0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "vlsWt-iQPX0i",
        "outputId": "ff807175-7ad7-413d-b73a-33c1cffd013f"
      },
      "source": [
        "cluster_data.loc[cluster_data['cluster_pred']==0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Frequency</th>\n",
              "      <th>TotalRevenue</th>\n",
              "      <th>Recency</th>\n",
              "      <th>cluster_pred</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>182</td>\n",
              "      <td>4310.00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>131</td>\n",
              "      <td>6207.67</td>\n",
              "      <td>32</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>254</td>\n",
              "      <td>6245.53</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>274</td>\n",
              "      <td>5154.58</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>167</td>\n",
              "      <td>3545.69</td>\n",
              "      <td>50</td>\n",
              "      <td>0</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>4324</th>\n",
              "      <td>299</td>\n",
              "      <td>6315.23</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4326</th>\n",
              "      <td>286</td>\n",
              "      <td>5361.02</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4327</th>\n",
              "      <td>245</td>\n",
              "      <td>5192.10</td>\n",
              "      <td>38</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4330</th>\n",
              "      <td>164</td>\n",
              "      <td>7276.90</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4361</th>\n",
              "      <td>170</td>\n",
              "      <td>3064.78</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>499 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Frequency  TotalRevenue  Recency  cluster_pred\n",
              "1           182       4310.00        1             0\n",
              "10          131       6207.67       32             0\n",
              "12          254       6245.53        7             0\n",
              "15          274       5154.58        2             0\n",
              "20          167       3545.69       50             0\n",
              "...         ...           ...      ...           ...\n",
              "4324        299       6315.23        1             0\n",
              "4326        286       5361.02        2             0\n",
              "4327        245       5192.10       38             0\n",
              "4330        164       7276.90       11             0\n",
              "4361        170       3064.78        2             0\n",
              "\n",
              "[499 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 179
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MUxBf2ecRDmk"
      },
      "source": [
        "<h1>Agglomerative Clustering</h1>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W1jmlzuuPbJL"
      },
      "source": [
        "from scipy.cluster.hierarchy import dendrogram, linkage"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SX9ZjJpjZBuQ"
      },
      "source": [
        "link = linkage(RFTr_df_scaled, method = 'complete')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "id": "949EZtmRZGgz",
        "outputId": "8835e80e-01a6-4dd2-8a31-becec7d89cf9"
      },
      "source": [
        "fig, ax = plt.subplots(figsize = (10,8))\n",
        "ax = dendrogram(link)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iQ6LT8kSZLYb"
      },
      "source": [
        "from sklearn.cluster import AgglomerativeClustering"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "51KlEnYFZ4Hh"
      },
      "source": [
        "ag_cluster = AgglomerativeClustering(n_clusters = 2, affinity = 'euclidean')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i2IeR4eoZ7k4",
        "outputId": "685c898e-acca-4f23-91c6-818a943648eb"
      },
      "source": [
        "ag_cluster.fit(RFTr_df_scaled)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AgglomerativeClustering(affinity='euclidean', compute_full_tree='auto',\n",
              "                        connectivity=None, distance_threshold=None,\n",
              "                        linkage='ward', memory=None, n_clusters=2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fncYvKUTaCGO"
      },
      "source": [
        "cluster_data= RFTr_df.copy()\n",
        "cluster_data['cluster_pred'] = ag_cluster.fit_predict(RFTr_df_scaled)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "W0pPuMycaKjk",
        "outputId": "4d34b124-4468-417d-d14d-c0a16f4409b9"
      },
      "source": [
        "cluster_data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Frequency</th>\n",
              "      <th>TotalRevenue</th>\n",
              "      <th>Recency</th>\n",
              "      <th>cluster_pred</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>0.00</td>\n",
              "      <td>325</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>182</td>\n",
              "      <td>4310.00</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>31</td>\n",
              "      <td>1797.24</td>\n",
              "      <td>74</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>73</td>\n",
              "      <td>1757.55</td>\n",
              "      <td>18</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>17</td>\n",
              "      <td>334.40</td>\n",
              "      <td>309</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Frequency  TotalRevenue  Recency  cluster_pred\n",
              "0          2          0.00      325             0\n",
              "1        182       4310.00        1             1\n",
              "2         31       1797.24       74             0\n",
              "3         73       1757.55       18             0\n",
              "4         17        334.40      309             0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "klC3GQj96_op"
      },
      "source": [
        "**Getting Optimal Number of Clusters**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AxbrBy0galFC"
      },
      "source": [
        "<h3>Silhouette Analysis</h3>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8j8t_USgLh_Q",
        "outputId": "1d318f4f-2ea3-4eb1-da4b-606119292098"
      },
      "source": [
        "# Silhouette analysis\n",
        "range_n_clusters = [2, 3, 4, 5, 6, 7, 8]\n",
        "sa = []\n",
        "for num_clusters in range_n_clusters:\n",
        "    \n",
        "    # intialise kmeans\n",
        "    kmeans = KMeans(n_clusters=num_clusters, max_iter=50)\n",
        "    kmeans.fit(RFTr_df_scaled)\n",
        "\n",
        "    cluster_labels = kmeans.labels_\n",
        "    \n",
        "    # silhouette score\n",
        "    silhouette_avg = silhouette_score(RFTr_df_scaled, cluster_labels)\n",
        "    sa.append(silhouette_avg)\n",
        "    print(\"For n_clusters={0}, the silhouette score is {1}\".format(num_clusters, silhouette_avg))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "For n_clusters=2, the silhouette score is 0.5415858652525395\n",
            "For n_clusters=3, the silhouette score is 0.5084896296141937\n",
            "For n_clusters=4, the silhouette score is 0.48079109657291913\n",
            "For n_clusters=5, the silhouette score is 0.46481058150500315\n",
            "For n_clusters=6, the silhouette score is 0.4169897163641209\n",
            "For n_clusters=7, the silhouette score is 0.415966062047432\n",
            "For n_clusters=8, the silhouette score is 0.39439948764516547\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "hhea15QCDP8z",
        "outputId": "f7c97598-45e4-4b6e-9731-f886a43e3f6b"
      },
      "source": [
        "# plot the Sas for each n_clusters\n",
        "plt.plot(sa)\n",
        "plt.title('Silhouette Graph')\n",
        "plt.xlabel('Number of clusters')\n",
        "plt.ylabel('Silhouette Average')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QBTDjHc9pUdT",
        "outputId": "8253eec6-90a7-4335-d4cd-fd3b30479cea"
      },
      "source": [
        "# Final model with k=2\n",
        "kmeans = KMeans(n_clusters=2, max_iter=50)\n",
        "kmeans.fit(RFTr_df_scaled)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=50,\n",
              "       n_clusters=2, n_init=10, n_jobs=None, precompute_distances='auto',\n",
              "       random_state=None, tol=0.0001, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JmVqhOeIpaZg",
        "outputId": "1425bcb1-edca-43db-9606-24b9d9e69e28"
      },
      "source": [
        "kmeans.labels_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 0, ..., 0, 0, 0], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "bZFwJbC7a6mP",
        "outputId": "9295d48c-5eaf-499a-aedd-17b2a44262a8"
      },
      "source": [
        "# assign the label\n",
        "RFTr['Cluster_Id'] = kmeans.labels_\n",
        "RFTr.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Frequency</th>\n",
              "      <th>TotalRevenue</th>\n",
              "      <th>Recency</th>\n",
              "      <th>Cluster_Id</th>\n",
              "      <th>Cluster_Labels</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12346.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.00</td>\n",
              "      <td>325</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12347.0</td>\n",
              "      <td>182</td>\n",
              "      <td>4310.00</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12348.0</td>\n",
              "      <td>31</td>\n",
              "      <td>1797.24</td>\n",
              "      <td>74</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12349.0</td>\n",
              "      <td>73</td>\n",
              "      <td>1757.55</td>\n",
              "      <td>18</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12350.0</td>\n",
              "      <td>17</td>\n",
              "      <td>334.40</td>\n",
              "      <td>309</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  CustomerID  Frequency  TotalRevenue  Recency  Cluster_Id  Cluster_Labels\n",
              "0    12346.0          2          0.00      325           0               0\n",
              "1    12347.0        182       4310.00        1           1               0\n",
              "2    12348.0         31       1797.24       74           0               0\n",
              "3    12349.0         73       1757.55       18           0               0\n",
              "4    12350.0         17        334.40      309           0               0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 515
        },
        "id": "zuIdwGxNbBVG",
        "outputId": "21b5a957-c2df-47a5-ab42-dfc8faaead30"
      },
      "source": [
        "# Box plot to visualize Cluster Id vs Total Revenue\n",
        "\n",
        "sns.boxplot(x='Cluster_Id', y='TotalRevenue', data=RFTr)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f71c9ab2110>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 515
        },
        "id": "eyKNDjMB7HuT",
        "outputId": "f56cbcb1-00a3-4f74-b53b-11cea640ed8a"
      },
      "source": [
        "# Box plot to visualize Cluster Id vs Frequency\n",
        "\n",
        "sns.boxplot(x='Cluster_Id', y='Frequency', data=RFTr)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f71c9ab0890>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 515
        },
        "id": "RvlQCZle7f3w",
        "outputId": "77da5d10-7be6-4f3c-a3fe-5cd70e086e24"
      },
      "source": [
        "# Box plot to visualize Cluster Id vs Recency\n",
        "\n",
        "sns.boxplot(x='Cluster_Id', y='Recency', data=RFTr)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f71c9c4f790>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kzr9FKnQpCnl",
        "outputId": "0cf04f87-875a-4a47-96d8-19ba17da992f"
      },
      "source": [
        "RFTr['Cluster_Id'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aklzcFo5757_"
      },
      "source": [
        "<h1>Hierarchical Clustering</h1>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yT3AnKJl8ALv"
      },
      "source": [
        "**Single Linkage**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "id": "MTzdP6iWpEGU",
        "outputId": "a7089c70-6846-4e1d-aebe-d1df619a8fa1"
      },
      "source": [
        "# Single linkage: \n",
        "\n",
        "mergings = linkage(RFTr_df_scaled, method=\"single\", metric='euclidean')\n",
        "dendrogram(mergings)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h0fE4rSj8qey"
      },
      "source": [
        "**Complete Linkage**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "id": "YqhdOY5U8JOf",
        "outputId": "af2f1854-b917-4322-d75b-05a8cb05968c"
      },
      "source": [
        "# Complete linkage\n",
        "\n",
        "mergings = linkage(RFTr_df_scaled, method=\"complete\", metric='euclidean')\n",
        "dendrogram(mergings)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UAB2C9im9qF1"
      },
      "source": [
        "**Average Linkage**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "id": "hZnMnCas9osn",
        "outputId": "243e3eb8-3b55-4060-8138-3556b1d4f4fc"
      },
      "source": [
        "# Average linkage\n",
        "\n",
        "mergings = linkage(RFTr_df_scaled, method=\"average\", metric='euclidean')\n",
        "dendrogram(mergings)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iMw4VkfECTFq"
      },
      "source": [
        "Cutting Dendrogram by k"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JTzbg8sc-aUJ",
        "outputId": "52b9e10b-12be-4aba-e0d0-2644793b24aa"
      },
      "source": [
        "# 2 clusters\n",
        "cluster_labels = cut_tree(mergings, n_clusters=2).reshape(-1, )\n",
        "cluster_labels\n",
        "\n",
        "ag_cluster = AgglomerativeClustering(n_clusters = 2, affinity = 'euclidean')\n",
        "ag_cluster.fit(RFTr_df_scaled)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AgglomerativeClustering(affinity='euclidean', compute_full_tree='auto',\n",
              "                        connectivity=None, distance_threshold=None,\n",
              "                        linkage='ward', memory=None, n_clusters=2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XRGa_ket_39S"
      },
      "source": [
        "# Assign cluster labels\n",
        "\n",
        "RFTr['Cluster_Labels'] = cluster_labels\n",
        "RFTr.head()\n",
        "\n",
        "cluster_data= RFTr.copy()\n",
        "cluster_data['cluster_pred'] = ag_cluster.fit_predict(RFTr_df_scaled)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xjICuDEOACgO",
        "outputId": "c0a24031-68d6-4519-bbcf-e91836e505ae"
      },
      "source": [
        "ag_cluster.labels_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 0, ..., 0, 0, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "zavf8QvjAH_T",
        "outputId": "8863b36c-d088-4528-8fa5-ba600f5fdf96"
      },
      "source": [
        "cluster_data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Frequency</th>\n",
              "      <th>TotalRevenue</th>\n",
              "      <th>Recency</th>\n",
              "      <th>Cluster_Id</th>\n",
              "      <th>Cluster_Labels</th>\n",
              "      <th>cluster_pred</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12346.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.00</td>\n",
              "      <td>325</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12347.0</td>\n",
              "      <td>182</td>\n",
              "      <td>4310.00</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12348.0</td>\n",
              "      <td>31</td>\n",
              "      <td>1797.24</td>\n",
              "      <td>74</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12349.0</td>\n",
              "      <td>73</td>\n",
              "      <td>1757.55</td>\n",
              "      <td>18</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12350.0</td>\n",
              "      <td>17</td>\n",
              "      <td>334.40</td>\n",
              "      <td>309</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4366</th>\n",
              "      <td>18278.0</td>\n",
              "      <td>9</td>\n",
              "      <td>173.90</td>\n",
              "      <td>73</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4367</th>\n",
              "      <td>18280.0</td>\n",
              "      <td>10</td>\n",
              "      <td>180.60</td>\n",
              "      <td>277</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4368</th>\n",
              "      <td>18281.0</td>\n",
              "      <td>7</td>\n",
              "      <td>80.82</td>\n",
              "      <td>180</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4369</th>\n",
              "      <td>18282.0</td>\n",
              "      <td>13</td>\n",
              "      <td>176.60</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4371</th>\n",
              "      <td>18287.0</td>\n",
              "      <td>70</td>\n",
              "      <td>1837.28</td>\n",
              "      <td>42</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4293 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     CustomerID  Frequency  ...  Cluster_Labels  cluster_pred\n",
              "0       12346.0          2  ...               0             0\n",
              "1       12347.0        182  ...               0             1\n",
              "2       12348.0         31  ...               0             0\n",
              "3       12349.0         73  ...               0             0\n",
              "4       12350.0         17  ...               0             0\n",
              "...         ...        ...  ...             ...           ...\n",
              "4366    18278.0          9  ...               0             0\n",
              "4367    18280.0         10  ...               0             0\n",
              "4368    18281.0          7  ...               0             0\n",
              "4369    18282.0         13  ...               0             0\n",
              "4371    18287.0         70  ...               0             0\n",
              "\n",
              "[4293 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 515
        },
        "id": "XjiwpK6yAKl0",
        "outputId": "23c73b6d-cae9-456b-8497-c192008b0fc9"
      },
      "source": [
        "# Plot Cluster Id vs TotalRevenue\n",
        "\n",
        "sns.boxplot(x='Cluster_Labels', y='TotalRevenue', data=RFTr)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f71c98d2710>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        },
        "id": "vur4DC0VAasi",
        "outputId": "a8e0171c-1113-48f5-97cc-f2047e405e09"
      },
      "source": [
        "plt.scatter(cluster_data['TotalRevenue'], cluster_data['cluster_pred'], cmap='rainbow')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f71cf6978d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 515
        },
        "id": "_rV12v-yAkId",
        "outputId": "02b5cc2f-14c6-42e8-c31e-40641a4610f5"
      },
      "source": [
        "# Plot Cluster Id vs Frequency\n",
        "\n",
        "sns.boxplot(x='Cluster_Labels', y='Frequency', data=RFTr)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f71ce666190>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 515
        },
        "id": "DufPo0Q3F0o5",
        "outputId": "369fce52-334b-482d-bcc3-2c8ad6eb0110"
      },
      "source": [
        "# Plot Cluster Id vs Recency\n",
        "\n",
        "sns.boxplot(x='Cluster_Labels', y='Recency', data=RFTr)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f71c98a9490>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AIF_aAjkGAEX"
      },
      "source": [
        "<h1>Inference</h1>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mJrRnRz6GI_x"
      },
      "source": [
        "K-Means Clustering with 2 Cluster Ids\n",
        "- Customers with Cluster Id 1 are the customers with high amount of transactions as compared to other customers.\n",
        "- Customers with Cluster Id 1 are frequent buyers.\n",
        "- Customers with Cluster Id 1 are recent buyers and hence of most importance from business point of view."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_swzbxL1IDI6"
      },
      "source": [
        "Hierarchical Clustering with 2 Cluster Ids\n",
        "- Customers with Cluster Id 1 are the customers with high amount of transactions as compared to other customers.\n",
        "- Customers with Cluster Id 1 are frequent buyers.\n",
        "- Customers with Cluster Id 1 are recent buyers and hence of most importance from business point of view."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BxtaCuGhILmI"
      },
      "source": [
        "**Customers with cluster id 1 are therefore customers of most importance.**"
      ]
    }
  ]
}