customer_segmentation.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "bDRpTrOJeBxD",
        "66S3x1ztfej3",
        "_uzzYmXBre0j",
        "5K-cecLfxD7l",
        "wtudKxgXdeiN"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "# imports\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.cluster import KMeans\n",
        "# from sklearn.preprocessing import StandardScaler\n",
        "# from sklearn.metrics import silhouette_score"
      ],
      "metadata": {
        "id": "L_OqukfUdgxr"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Collection and Analysis"
      ],
      "metadata": {
        "id": "bDRpTrOJeBxD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('Mall_Customers.csv')\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "JsMa0-AMeBha",
        "outputId": "38bd752f-f9ad-4fe6-9adb-249ba2e180f6"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)\n",
              "0           1    Male   19                  15                      39\n",
              "1           2    Male   21                  15                      81\n",
              "2           3  Female   20                  16                       6\n",
              "3           4  Female   23                  16                      77\n",
              "4           5  Female   31                  17                      40"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e04c62e3-d891-463b-9b06-dc19d35c15e8\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Annual Income (k$)</th>\n",
              "      <th>Spending Score (1-100)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Male</td>\n",
              "      <td>19</td>\n",
              "      <td>15</td>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Male</td>\n",
              "      <td>21</td>\n",
              "      <td>15</td>\n",
              "      <td>81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Female</td>\n",
              "      <td>20</td>\n",
              "      <td>16</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Female</td>\n",
              "      <td>23</td>\n",
              "      <td>16</td>\n",
              "      <td>77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Female</td>\n",
              "      <td>31</td>\n",
              "      <td>17</td>\n",
              "      <td>40</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-e04c62e3-d891-463b-9b06-dc19d35c15e8')\"\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-e04c62e3-d891-463b-9b06-dc19d35c15e8 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-e04c62e3-d891-463b-9b06-dc19d35c15e8');\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",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": \"CustomerID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 57,\n        \"min\": 1,\n        \"max\": 200,\n        \"num_unique_values\": 200,\n        \"samples\": [\n          96,\n          16,\n          31\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13,\n        \"min\": 18,\n        \"max\": 70,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          55,\n          26\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Annual Income (k$)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26,\n        \"min\": 15,\n        \"max\": 137,\n        \"num_unique_values\": 64,\n        \"samples\": [\n          87,\n          101\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Spending Score (1-100)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25,\n        \"min\": 1,\n        \"max\": 99,\n        \"num_unique_values\": 84,\n        \"samples\": [\n          83,\n          39\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YijiFhwqePRp",
        "outputId": "e02ca5a2-c5d7-41b4-a6af-4b978ccde83d"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(200, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "y0ECW3dreau2",
        "outputId": "951b5539-c3d7-4e44-acd5-f8d700029fb6"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       CustomerID         Age  Annual Income (k$)  Spending Score (1-100)\n",
              "count  200.000000  200.000000          200.000000              200.000000\n",
              "mean   100.500000   38.850000           60.560000               50.200000\n",
              "std     57.879185   13.969007           26.264721               25.823522\n",
              "min      1.000000   18.000000           15.000000                1.000000\n",
              "25%     50.750000   28.750000           41.500000               34.750000\n",
              "50%    100.500000   36.000000           61.500000               50.000000\n",
              "75%    150.250000   49.000000           78.000000               73.000000\n",
              "max    200.000000   70.000000          137.000000               99.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e632f448-3000-4fa7-b549-f3c85e11ee7f\" 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>Age</th>\n",
              "      <th>Annual Income (k$)</th>\n",
              "      <th>Spending Score (1-100)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>200.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>200.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>100.500000</td>\n",
              "      <td>38.850000</td>\n",
              "      <td>60.560000</td>\n",
              "      <td>50.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>57.879185</td>\n",
              "      <td>13.969007</td>\n",
              "      <td>26.264721</td>\n",
              "      <td>25.823522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>50.750000</td>\n",
              "      <td>28.750000</td>\n",
              "      <td>41.500000</td>\n",
              "      <td>34.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>100.500000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>61.500000</td>\n",
              "      <td>50.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>150.250000</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>78.000000</td>\n",
              "      <td>73.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>200.000000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>137.000000</td>\n",
              "      <td>99.000000</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-e632f448-3000-4fa7-b549-f3c85e11ee7f')\"\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-e632f448-3000-4fa7-b549-f3c85e11ee7f 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-e632f448-3000-4fa7-b549-f3c85e11ee7f');\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",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"CustomerID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 71.77644232399086,\n        \"min\": 1.0,\n        \"max\": 200.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          200.0,\n          100.5,\n          150.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 60.50135224289181,\n        \"min\": 13.969007331558883,\n        \"max\": 200.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          38.85,\n          36.0,\n          200.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Annual Income (k$)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 62.0103834127095,\n        \"min\": 15.0,\n        \"max\": 200.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          60.56,\n          61.5,\n          200.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Spending Score (1-100)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 61.42496609345541,\n        \"min\": 1.0,\n        \"max\": 200.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          50.2,\n          50.0,\n          200.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dy8wvXgxfBti",
        "outputId": "51e9f1a9-fa47-46aa-b933-79d8f6338090"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 200 entries, 0 to 199\n",
            "Data columns (total 5 columns):\n",
            " #   Column                  Non-Null Count  Dtype \n",
            "---  ------                  --------------  ----- \n",
            " 0   CustomerID              200 non-null    int64 \n",
            " 1   Gender                  200 non-null    object\n",
            " 2   Age                     200 non-null    int64 \n",
            " 3   Annual Income (k$)      200 non-null    int64 \n",
            " 4   Spending Score (1-100)  200 non-null    int64 \n",
            "dtypes: int64(4), object(1)\n",
            "memory usage: 7.9+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "0M1nPMvOfJxj",
        "outputId": "51695f0b-c06a-46ae-b21a-eb1ba43bb663"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CustomerID                0\n",
              "Gender                    0\n",
              "Age                       0\n",
              "Annual Income (k$)        0\n",
              "Spending Score (1-100)    0\n",
              "dtype: int64"
            ],
            "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>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>CustomerID</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gender</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Annual Income (k$)</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Spending Score (1-100)</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Extracting Relevant Features"
      ],
      "metadata": {
        "id": "66S3x1ztfej3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.iloc[:, [3, 4]].values\n",
        "\n",
        "# used to specify the columns you want by splicing the data. in this case it splices of index 3 (Annual Income (k$)) and index 4 (Annual Income (k$)) which are what we need"
      ],
      "metadata": {
        "id": "KcJJ4wwkfRLA"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "p0ZnMORYqIlV",
        "outputId": "26699d78-09df-47b8-e87d-e45b2c95f106"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 15  39]\n",
            " [ 15  81]\n",
            " [ 16   6]\n",
            " [ 16  77]\n",
            " [ 17  40]\n",
            " [ 17  76]\n",
            " [ 18   6]\n",
            " [ 18  94]\n",
            " [ 19   3]\n",
            " [ 19  72]\n",
            " [ 19  14]\n",
            " [ 19  99]\n",
            " [ 20  15]\n",
            " [ 20  77]\n",
            " [ 20  13]\n",
            " [ 20  79]\n",
            " [ 21  35]\n",
            " [ 21  66]\n",
            " [ 23  29]\n",
            " [ 23  98]\n",
            " [ 24  35]\n",
            " [ 24  73]\n",
            " [ 25   5]\n",
            " [ 25  73]\n",
            " [ 28  14]\n",
            " [ 28  82]\n",
            " [ 28  32]\n",
            " [ 28  61]\n",
            " [ 29  31]\n",
            " [ 29  87]\n",
            " [ 30   4]\n",
            " [ 30  73]\n",
            " [ 33   4]\n",
            " [ 33  92]\n",
            " [ 33  14]\n",
            " [ 33  81]\n",
            " [ 34  17]\n",
            " [ 34  73]\n",
            " [ 37  26]\n",
            " [ 37  75]\n",
            " [ 38  35]\n",
            " [ 38  92]\n",
            " [ 39  36]\n",
            " [ 39  61]\n",
            " [ 39  28]\n",
            " [ 39  65]\n",
            " [ 40  55]\n",
            " [ 40  47]\n",
            " [ 40  42]\n",
            " [ 40  42]\n",
            " [ 42  52]\n",
            " [ 42  60]\n",
            " [ 43  54]\n",
            " [ 43  60]\n",
            " [ 43  45]\n",
            " [ 43  41]\n",
            " [ 44  50]\n",
            " [ 44  46]\n",
            " [ 46  51]\n",
            " [ 46  46]\n",
            " [ 46  56]\n",
            " [ 46  55]\n",
            " [ 47  52]\n",
            " [ 47  59]\n",
            " [ 48  51]\n",
            " [ 48  59]\n",
            " [ 48  50]\n",
            " [ 48  48]\n",
            " [ 48  59]\n",
            " [ 48  47]\n",
            " [ 49  55]\n",
            " [ 49  42]\n",
            " [ 50  49]\n",
            " [ 50  56]\n",
            " [ 54  47]\n",
            " [ 54  54]\n",
            " [ 54  53]\n",
            " [ 54  48]\n",
            " [ 54  52]\n",
            " [ 54  42]\n",
            " [ 54  51]\n",
            " [ 54  55]\n",
            " [ 54  41]\n",
            " [ 54  44]\n",
            " [ 54  57]\n",
            " [ 54  46]\n",
            " [ 57  58]\n",
            " [ 57  55]\n",
            " [ 58  60]\n",
            " [ 58  46]\n",
            " [ 59  55]\n",
            " [ 59  41]\n",
            " [ 60  49]\n",
            " [ 60  40]\n",
            " [ 60  42]\n",
            " [ 60  52]\n",
            " [ 60  47]\n",
            " [ 60  50]\n",
            " [ 61  42]\n",
            " [ 61  49]\n",
            " [ 62  41]\n",
            " [ 62  48]\n",
            " [ 62  59]\n",
            " [ 62  55]\n",
            " [ 62  56]\n",
            " [ 62  42]\n",
            " [ 63  50]\n",
            " [ 63  46]\n",
            " [ 63  43]\n",
            " [ 63  48]\n",
            " [ 63  52]\n",
            " [ 63  54]\n",
            " [ 64  42]\n",
            " [ 64  46]\n",
            " [ 65  48]\n",
            " [ 65  50]\n",
            " [ 65  43]\n",
            " [ 65  59]\n",
            " [ 67  43]\n",
            " [ 67  57]\n",
            " [ 67  56]\n",
            " [ 67  40]\n",
            " [ 69  58]\n",
            " [ 69  91]\n",
            " [ 70  29]\n",
            " [ 70  77]\n",
            " [ 71  35]\n",
            " [ 71  95]\n",
            " [ 71  11]\n",
            " [ 71  75]\n",
            " [ 71   9]\n",
            " [ 71  75]\n",
            " [ 72  34]\n",
            " [ 72  71]\n",
            " [ 73   5]\n",
            " [ 73  88]\n",
            " [ 73   7]\n",
            " [ 73  73]\n",
            " [ 74  10]\n",
            " [ 74  72]\n",
            " [ 75   5]\n",
            " [ 75  93]\n",
            " [ 76  40]\n",
            " [ 76  87]\n",
            " [ 77  12]\n",
            " [ 77  97]\n",
            " [ 77  36]\n",
            " [ 77  74]\n",
            " [ 78  22]\n",
            " [ 78  90]\n",
            " [ 78  17]\n",
            " [ 78  88]\n",
            " [ 78  20]\n",
            " [ 78  76]\n",
            " [ 78  16]\n",
            " [ 78  89]\n",
            " [ 78   1]\n",
            " [ 78  78]\n",
            " [ 78   1]\n",
            " [ 78  73]\n",
            " [ 79  35]\n",
            " [ 79  83]\n",
            " [ 81   5]\n",
            " [ 81  93]\n",
            " [ 85  26]\n",
            " [ 85  75]\n",
            " [ 86  20]\n",
            " [ 86  95]\n",
            " [ 87  27]\n",
            " [ 87  63]\n",
            " [ 87  13]\n",
            " [ 87  75]\n",
            " [ 87  10]\n",
            " [ 87  92]\n",
            " [ 88  13]\n",
            " [ 88  86]\n",
            " [ 88  15]\n",
            " [ 88  69]\n",
            " [ 93  14]\n",
            " [ 93  90]\n",
            " [ 97  32]\n",
            " [ 97  86]\n",
            " [ 98  15]\n",
            " [ 98  88]\n",
            " [ 99  39]\n",
            " [ 99  97]\n",
            " [101  24]\n",
            " [101  68]\n",
            " [103  17]\n",
            " [103  85]\n",
            " [103  23]\n",
            " [103  69]\n",
            " [113   8]\n",
            " [113  91]\n",
            " [120  16]\n",
            " [120  79]\n",
            " [126  28]\n",
            " [126  74]\n",
            " [137  18]\n",
            " [137  83]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Choosing Optimum Number of Clusters"
      ],
      "metadata": {
        "id": "_uzzYmXBre0j"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "now we need to figure out the optimum number of clusters for the dataset. The number of clusters we can use varies depending on the dataset.\n",
        "\n",
        "we'll choose the correct number of clusters by using a parameter called WCSS\n",
        "\n",
        "WCSS --> Within Clusters Sum of Squares\n",
        "\n",
        "Basically when it groups data points it creates centroids which serve as the center for each cluster (so 3 clusters means 3 centroids). so it then measures the distance between each data point and the centroids.\n",
        "\n",
        "I'm assuming that's what determines which cluster that data point will belong to.\n",
        "\n",
        "It's very important to have good partitioned clusters. meaning, each data points should be as close as possible to a particluar data point so that the clusters are easily distinguishable"
      ],
      "metadata": {
        "id": "zEOxAPkTrmSj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We're going to find the minimum WCSS value using the 'elbow method'"
      ],
      "metadata": {
        "id": "Aogq1ECPtH9E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Finding the WCSS value for different number of clusters\n",
        "\n",
        "WCSS =[]\n",
        "\n",
        "for i in range(1, 11):\n",
        "  kmeans = KMeans(n_clusters=i, init='k-means++', random_state=42)\n",
        "  kmeans.fit(X)\n",
        "  WCSS.append(kmeans.inertia_) # this will give us the WCSS value the '.inertia_' method lies in the KMeans algorithm\n",
        "\n"
      ],
      "metadata": {
        "id": "20UIQ6JBqIg9"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# plot an elbow graph\n",
        "\n",
        "sns.set()\n",
        "plt.plot(range(1, 11), WCSS)\n",
        "plt.title('The Elbow Point Graph')\n",
        "plt.xlabel('Number of clusters')\n",
        "plt.ylabel('WCSS')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "f6e-tSkqvrAT",
        "outputId": "c6e59d36-54a8-48e8-c25c-13afd0f2be9b"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The elbow point graph can also be called 'Cut-off Point Graph'.\n",
        "\n",
        "There are 2 cut-off points on the graph, at point 3 and point 5 (number of clusters).\n",
        "\n",
        "So notice that after the drop at point 5 there was no other sharp significant drop. so we'll use 5 as the optimum number of clusters"
      ],
      "metadata": {
        "id": "51-lLQLEwWJB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training the K-Means Clustering Model"
      ],
      "metadata": {
        "id": "5K-cecLfxD7l"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "this is a very good example of an unsupervised learning model"
      ],
      "metadata": {
        "id": "-KRl5TgtxIXJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# k = 5\n",
        "\n",
        "kmeans = KMeans(n_clusters=5, init='k-means++', random_state=42) # returns a label for each data point based on their cluster\n",
        "y = kmeans.fit_predict(X)\n",
        "print(y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZOVmcnOqqIcL",
        "outputId": "ae2d488d-6997-466b-d97e-bdcea2351b98"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[4 2 4 2 4 2 4 2 4 2 4 2 4 2 4 2 4 2 4 2 4 2 4 2 4 2 4 2 4 2 4 2 4 2 4 2 4\n",
            " 2 4 2 4 2 4 0 4 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 1 3 1 0 1 3 1 3 1 0 1 3 1 3 1 3 1 3 1 0 1 3 1 3 1\n",
            " 3 1 3 1 3 1 3 1 3 1 3 1 3 1 3 1 3 1 3 1 3 1 3 1 3 1 3 1 3 1 3 1 3 1 3 1 3\n",
            " 1 3 1 3 1 3 1 3 1 3 1 3 1 3 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Visualizing all the Clusters"
      ],
      "metadata": {
        "id": "dnUEkM8xyulX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "plotting the clusters and their data points"
      ],
      "metadata": {
        "id": "qAs3IGQ49uHk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "5 Clusters - 0, 1, 2, 3, 4, 5"
      ],
      "metadata": {
        "id": "tZUXYj2j-498"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8,8))\n",
        "plt.scatter(X[y==0,0], X[y==0,1], s=50, c='green', label='Cluster 1')\n",
        "plt.scatter(X[y==1,0], X[y==1,1], s=50, c='blue', label='Cluster 2')\n",
        "plt.scatter(X[y==2,0], X[y==2,1], s=50, c='red', label='Cluster 3')\n",
        "plt.scatter(X[y==3,0], X[y==3,1], s=50, c='purple', label='Cluster 4')\n",
        "plt.scatter(X[y==4,0], X[y==4,1], s=50, c='orange', label='Cluster 5')\n",
        "\n",
        "# using this as an example - plt.scatter(X[y==0,0], X[y==0,1], s=50, c='green', label='Cluster 1'):\n",
        "# the X[y==0,0] part represents x-axis while X[y==0,1] represents y-axis\n",
        "# the last 0 in X[y==0,0] represents the annual income (since it's on the 0th index in the array), while the last 1 in X[y==0,1] represents spending score (since it's on the 1st index in the array)\n",
        "# the first 0 in X[y==0,0] and X[y==0,1] represents the cluster (in this case, cluster 1 but since puthon starts counting at 0, that's why we have 0 there instead of 1)\n",
        "\n",
        "# figsize=(8,8) is the size of the plotted graph (8 inches by 8 inches), s=50 is the size of each point, c='green' for color, label for the cluster labels.\n",
        "\n",
        "\n",
        "\n",
        "# plot the centroids\n",
        "plt.scatter(kmeans.cluster_centers_[:,0], kmeans.cluster_centers_[:,1], s=100, c='yellow', label='Centroids')\n",
        "\n",
        "plt.title('Customer Groups')\n",
        "plt.xlabel('Annual Income')\n",
        "plt.ylabel('Spending Score')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "xvUsZwJqqIXg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 727
        },
        "outputId": "aabc2978-f15d-489c-9e9e-88061498852c"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_i9_kC4OCNrB"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "UDFnRjlWAPCx"
      }
    }
  ]
}