Clustering with K-Means.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "jRHUUq5-MCi2"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.cluster import KMeans\n",
        "%matplotlib inline\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# load data\n",
        "customer = pd.read_csv('Customer(fix).csv', delimiter=';')\n",
        "transaction = pd.read_csv('Transaction(fix).csv', delimiter=';')"
      ],
      "metadata": {
        "id": "h97nKHX9MevH"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "customer.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CfY9TTpqMxsV",
        "outputId": "a4b2580d-e4de-4e48-c61c-3c5a8bf31a91"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 447 entries, 0 to 446\n",
            "Data columns (total 5 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   CustomerID      447 non-null    int64  \n",
            " 1   Age             447 non-null    int64  \n",
            " 2   Gender          447 non-null    object \n",
            " 3   Marital Status  447 non-null    object \n",
            " 4   Income          447 non-null    float64\n",
            "dtypes: float64(1), int64(2), object(2)\n",
            "memory usage: 17.6+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "transaction.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vg6hHuKxNCLU",
        "outputId": "0591264f-8844-41db-c9cc-a361f5b06676"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5020 entries, 0 to 5019\n",
            "Data columns (total 8 columns):\n",
            " #   Column         Non-Null Count  Dtype \n",
            "---  ------         --------------  ----- \n",
            " 0   TransactionID  5020 non-null   object\n",
            " 1   CustomerID     5020 non-null   int64 \n",
            " 2   Date           5020 non-null   object\n",
            " 3   ProductID      5020 non-null   object\n",
            " 4   Price          5020 non-null   int64 \n",
            " 5   Qty            5020 non-null   int64 \n",
            " 6   TotalAmount    5020 non-null   int64 \n",
            " 7   StoreID        5020 non-null   int64 \n",
            "dtypes: int64(5), object(3)\n",
            "memory usage: 313.9+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# convert Date to datetime\n",
        "transaction['Date'] = pd.to_datetime(transaction['Date'])\n",
        "\n"
      ],
      "metadata": {
        "id": "VfHlBWmYNmh_"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# merge df_transaction and df_customer\n",
        "merged_df = pd.merge(transaction, customer, on='CustomerID', how='left')\n",
        "merged_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        },
        "id": "bbMGDyAJNUTn",
        "outputId": "38be6def-063e-4ab5-825e-299824eef7cc"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  TransactionID  CustomerID       Date ProductID  Price  Qty  TotalAmount  \\\n",
              "0       TR11369         328 2022-01-01        P3   7500    4        30000   \n",
              "1       TR16356         165 2022-01-01        P9  10000    7        70000   \n",
              "2        TR1984         183 2022-01-01        P1   8800    4        35200   \n",
              "3       TR35256         160 2022-01-01        P1   8800    7        61600   \n",
              "4       TR41231         386 2022-01-01        P9  10000    1        10000   \n",
              "\n",
              "   StoreID  Age  Gender Marital Status  Income  \n",
              "0       12   36  Female        Married   10.53  \n",
              "1        1   44    Male        Married   14.58  \n",
              "2        4   27    Male         Single    0.18  \n",
              "3        4   48    Male        Married   12.57  \n",
              "4        4   33  Female        Married    6.95  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-676594e5-437d-4b0c-86c5-7b29970ab190\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TransactionID</th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Date</th>\n",
              "      <th>ProductID</th>\n",
              "      <th>Price</th>\n",
              "      <th>Qty</th>\n",
              "      <th>TotalAmount</th>\n",
              "      <th>StoreID</th>\n",
              "      <th>Age</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Marital Status</th>\n",
              "      <th>Income</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>TR11369</td>\n",
              "      <td>328</td>\n",
              "      <td>2022-01-01</td>\n",
              "      <td>P3</td>\n",
              "      <td>7500</td>\n",
              "      <td>4</td>\n",
              "      <td>30000</td>\n",
              "      <td>12</td>\n",
              "      <td>36</td>\n",
              "      <td>Female</td>\n",
              "      <td>Married</td>\n",
              "      <td>10.53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>TR16356</td>\n",
              "      <td>165</td>\n",
              "      <td>2022-01-01</td>\n",
              "      <td>P9</td>\n",
              "      <td>10000</td>\n",
              "      <td>7</td>\n",
              "      <td>70000</td>\n",
              "      <td>1</td>\n",
              "      <td>44</td>\n",
              "      <td>Male</td>\n",
              "      <td>Married</td>\n",
              "      <td>14.58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>TR1984</td>\n",
              "      <td>183</td>\n",
              "      <td>2022-01-01</td>\n",
              "      <td>P1</td>\n",
              "      <td>8800</td>\n",
              "      <td>4</td>\n",
              "      <td>35200</td>\n",
              "      <td>4</td>\n",
              "      <td>27</td>\n",
              "      <td>Male</td>\n",
              "      <td>Single</td>\n",
              "      <td>0.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>TR35256</td>\n",
              "      <td>160</td>\n",
              "      <td>2022-01-01</td>\n",
              "      <td>P1</td>\n",
              "      <td>8800</td>\n",
              "      <td>7</td>\n",
              "      <td>61600</td>\n",
              "      <td>4</td>\n",
              "      <td>48</td>\n",
              "      <td>Male</td>\n",
              "      <td>Married</td>\n",
              "      <td>12.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>TR41231</td>\n",
              "      <td>386</td>\n",
              "      <td>2022-01-01</td>\n",
              "      <td>P9</td>\n",
              "      <td>10000</td>\n",
              "      <td>1</td>\n",
              "      <td>10000</td>\n",
              "      <td>4</td>\n",
              "      <td>33</td>\n",
              "      <td>Female</td>\n",
              "      <td>Married</td>\n",
              "      <td>6.95</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-676594e5-437d-4b0c-86c5-7b29970ab190')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-676594e5-437d-4b0c-86c5-7b29970ab190 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-676594e5-437d-4b0c-86c5-7b29970ab190');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-843b2a6f-0d3a-4a1e-8b7e-e78e11173249\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-843b2a6f-0d3a-4a1e-8b7e-e78e11173249')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-843b2a6f-0d3a-4a1e-8b7e-e78e11173249 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "merged_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hwVRT6QLNgQ9",
        "outputId": "c516a582-bc9b-4515-8da3-7ce6d93f45ff"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 5020 entries, 0 to 5019\n",
            "Data columns (total 12 columns):\n",
            " #   Column          Non-Null Count  Dtype         \n",
            "---  ------          --------------  -----         \n",
            " 0   TransactionID   5020 non-null   object        \n",
            " 1   CustomerID      5020 non-null   int64         \n",
            " 2   Date            5020 non-null   datetime64[ns]\n",
            " 3   ProductID       5020 non-null   object        \n",
            " 4   Price           5020 non-null   int64         \n",
            " 5   Qty             5020 non-null   int64         \n",
            " 6   TotalAmount     5020 non-null   int64         \n",
            " 7   StoreID         5020 non-null   int64         \n",
            " 8   Age             5020 non-null   int64         \n",
            " 9   Gender          5020 non-null   object        \n",
            " 10  Marital Status  5020 non-null   object        \n",
            " 11  Income          5020 non-null   float64       \n",
            "dtypes: datetime64[ns](1), float64(1), int64(6), object(4)\n",
            "memory usage: 509.8+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# aggregate data\n",
        "agg = {\n",
        "    'TransactionID': 'count',\n",
        "    'Qty': 'sum',\n",
        "    'TotalAmount': 'sum'\n",
        "}\n",
        "cluster_df = merged_df.groupby('CustomerID').aggregate(agg).reset_index()\n",
        "cluster_df.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "CgkE_mk-NyiL",
        "outputId": "1932335b-8622-4c85-c50e-cdb1bf101378"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   CustomerID  TransactionID  Qty  TotalAmount\n",
              "0           1             17   60       623300\n",
              "1           2             13   57       392300\n",
              "2           3             15   56       446200\n",
              "3           4             10   46       302500\n",
              "4           5              7   27       268600"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-be4b42de-c037-4c33-a91c-e93bda3e05b5\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>TransactionID</th>\n",
              "      <th>Qty</th>\n",
              "      <th>TotalAmount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>17</td>\n",
              "      <td>60</td>\n",
              "      <td>623300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>13</td>\n",
              "      <td>57</td>\n",
              "      <td>392300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>15</td>\n",
              "      <td>56</td>\n",
              "      <td>446200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>10</td>\n",
              "      <td>46</td>\n",
              "      <td>302500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>27</td>\n",
              "      <td>268600</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-be4b42de-c037-4c33-a91c-e93bda3e05b5')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-be4b42de-c037-4c33-a91c-e93bda3e05b5 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-be4b42de-c037-4c33-a91c-e93bda3e05b5');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-f80cf3d1-a35f-4710-91f8-7d394436a706\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f80cf3d1-a35f-4710-91f8-7d394436a706')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f80cf3d1-a35f-4710-91f8-7d394436a706 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Scaling data\n",
        "scaler = StandardScaler()\n",
        "scaled_df = scaler.fit_transform(cluster_df[['TransactionID', 'Qty', 'TotalAmount']])\n",
        "scaled_df = pd.DataFrame(scaled_df, columns=['TransactionID', 'Qty', 'TotalAmount'])\n",
        "scaled_df.head()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ZjkwUKTMOLji",
        "outputId": "ab4b2525-dd51-442e-a2e5-a13ab5193107"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   TransactionID       Qty  TotalAmount\n",
              "0       1.779816  1.496527     2.094768\n",
              "1       0.545884  1.261093     0.239269\n",
              "2       1.162850  1.182615     0.672218\n",
              "3      -0.379565  0.397833    -0.482047\n",
              "4      -1.305014 -1.093251    -0.754347"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d832ae5c-e558-4334-a327-77faa175a014\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TransactionID</th>\n",
              "      <th>Qty</th>\n",
              "      <th>TotalAmount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.779816</td>\n",
              "      <td>1.496527</td>\n",
              "      <td>2.094768</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.545884</td>\n",
              "      <td>1.261093</td>\n",
              "      <td>0.239269</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.162850</td>\n",
              "      <td>1.182615</td>\n",
              "      <td>0.672218</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.379565</td>\n",
              "      <td>0.397833</td>\n",
              "      <td>-0.482047</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-1.305014</td>\n",
              "      <td>-1.093251</td>\n",
              "      <td>-0.754347</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d832ae5c-e558-4334-a327-77faa175a014')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d832ae5c-e558-4334-a327-77faa175a014 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d832ae5c-e558-4334-a327-77faa175a014');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-45152e54-3676-4197-a80b-8d040a666e8c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-45152e54-3676-4197-a80b-8d040a666e8c')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-45152e54-3676-4197-a80b-8d040a666e8c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Scaling data for clustering serves to ensure that the features used for clustering have similar scales. This is essential to prevent certain features from dominating the clustering process due to their larger scale, ensuring that all features contribute equally to the analysis. Scaling helps create a level playing field for the clustering algorithm, making it more effective and accurate in identifying meaningful patterns in the data."
      ],
      "metadata": {
        "id": "eiYip9ZaPTXv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Inertia Method"
      ],
      "metadata": {
        "id": "zkMTx8vsSifv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# finding optimal number of clusters\n",
        "inertia = []\n",
        "max_clusters = 11\n",
        "for n_cluster in range(1, max_clusters):\n",
        "    kmeans = KMeans(n_clusters=n_cluster, random_state=123, n_init=n_cluster)\n",
        "    kmeans.fit(cluster_df.drop('CustomerID', axis=1))\n",
        "    inertia.append(kmeans.inertia_)"
      ],
      "metadata": {
        "id": "sb8GjHGsSkwJ"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Elbow Curve\n",
        "plt.figure(figsize=(8,5))\n",
        "plt.plot(np.arange(1, max_clusters), inertia, marker='o')\n",
        "plt.xlabel('Number of cluster')\n",
        "plt.ylabel('Inertia')\n",
        "plt.xticks(np.arange(1, max_clusters))\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "Zn8hwFsGSmbo",
        "outputId": "1b24ee6b-55ee-493d-86d7-a78a03060ee9"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In the elbow curve, the point of the 'bend' formed on the plot is generally considered an indicator of the appropriate number of clusters. However, the 'optimal' value of 'k' obtained from the elbow curve method often tends to be somewhat \"ambiguous\" or not definitively conclusive in determining the optimal number of clusters ('k'). In the graph above, the choice of 'k' is set to 3 or 4 because the generated graph exhibits a bend and a subsequent leveling off. To ensure the chosen 'k' is indeed optimal, further analysis will be conducted using silhouette analysis."
      ],
      "metadata": {
        "id": "mTP6bV81QhyP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Silhouette Analysis Method"
      ],
      "metadata": {
        "id": "n9BC8_81Q4HZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import silhouette_score\n",
        "\n",
        "# Silhouette Analysis\n",
        "range_n_clusters = [2, 3, 4, 5, 6, 7, 8]\n",
        "for num_clusters in range_n_clusters:\n",
        "\n",
        "    # Initialise kmeans\n",
        "    kmeans = KMeans(n_clusters=num_clusters, max_iter=500)\n",
        "    kmeans.fit(scaled_df)\n",
        "    cluster_labels = kmeans.labels_\n",
        "\n",
        "    # Silhouette Score\n",
        "    silhouette_avg = silhouette_score(scaled_df, cluster_labels)\n",
        "    print(\"For n_clusters={0}, the silhouette score is {1}\".format(num_clusters, silhouette_avg))\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mlBRf588Qf04",
        "outputId": "93324f46-1933-4d67-e229-ece147df7652"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For n_clusters=2, the silhouette score is 0.48530485981509097\n",
            "For n_clusters=3, the silhouette score is 0.4286478086768933\n",
            "For n_clusters=4, the silhouette score is 0.3772419295085858\n",
            "For n_clusters=5, the silhouette score is 0.34207742281768294\n",
            "For n_clusters=6, the silhouette score is 0.3002434408412865\n",
            "For n_clusters=7, the silhouette score is 0.28136022927297194\n",
            "For n_clusters=8, the silhouette score is 0.2917554906658225\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Among the Silhouette Scores, n_clusters=2 had the highest score, indicating strong separation. However, the ambiguous elbow point led us to compare n_clusters=3 and n_clusters=4. n_clusters=3 had a respectable Silhouette Score, the highest after n_clusters=2, making it the optimal choice. Moreover, n_clusters=3 provides more diverse segmentation than n_clusters=2, allowing for a finer analysis of different data patterns and characteristics."
      ],
      "metadata": {
        "id": "y-xa8vnGSKcB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# create cluster\n",
        "n_cluster = 3\n",
        "kmeans = KMeans(n_clusters=n_cluster, random_state=123, n_init=n_cluster)\n",
        "kmeans.fit(cluster_df.drop('CustomerID', axis=1))\n",
        "cluster_df['Cluster'] = kmeans.labels_\n",
        "\n",
        "# plot cluster with a legend\n",
        "plt.figure(figsize=(8, 6))\n",
        "scatter = plt.scatter(cluster_df['Qty'], cluster_df['TotalAmount'], c=cluster_df['Cluster'], cmap='viridis')\n",
        "plt.xlabel('Qty')\n",
        "plt.ylabel('TotalAmount')\n",
        "\n",
        "# Add a legend\n",
        "legend_labels = [f'Cluster {i+1}' for i in range(n_cluster)]\n",
        "plt.legend(handles=scatter.legend_elements()[0], labels=legend_labels, title='Clusters')\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "D1H7io3VRsEn",
        "outputId": "e677e40d-048b-4463-ca63-39643dfa70f9"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "pmcNf_YDOLXi",
        "outputId": "a44f3b76-0152-48bb-d962-ec5a039e6c37"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-16-4d31c13ed879>\"\u001b[0;36m, line \u001b[0;32m18\u001b[0m\n\u001b[0;31m    totalamount_anova = f_oneway(*[cluster_df['TotalAmount'][cluster_df\u001b[0m\n\u001b[0m                                                                       ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m incomplete input\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kqnec_F4VV1j"
      },
      "execution_count": 13,
      "outputs": []
    }
  ]
}