ch27_k_means_clustering.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ThatNinjaGuy/Machine-learning-A-Z-Course/blob/develop/ch27_k_means_clustering.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i_paGd_yLbgH"
      },
      "source": [
        "# K-Means Clustering"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nAuqPwTnLipr"
      },
      "source": [
        "## Importing the libraries"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "o0IJ2xoeSeLU"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5LciKOr8Lo5O"
      },
      "source": [
        "## Importing the dataset"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = pd.read_csv('Mall_Customers.csv')\n",
        "X = dataset.iloc[:, [3,4]].values"
      ],
      "metadata": {
        "id": "_tRn09FCSj1W"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nWC2EWp2Lx5G"
      },
      "source": [
        "## Using the elbow method to find the optimal number of clusters"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import KMeans\n",
        "wcss = []\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_)\n",
        "\n",
        "plt.plot(range(1, 11), wcss)\n",
        "plt.title('The Elbow Method')\n",
        "plt.xlabel('Number of clusters')\n",
        "plt.ylabel('WCSS')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 839
        },
        "id": "GpYdV_-LT--6",
        "outputId": "c3d75976-8455-46f7-b9df-ab0bf057e3df"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VgT0mANLL4Nz"
      },
      "source": [
        "## Training the K-Means model on the dataset"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kmeans = KMeans(n_clusters=5, init='k-means++', random_state=42)\n",
        "y_kmeans = kmeans.fit_predict(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WrCbq-M4TRle",
        "outputId": "0402eaa0-bcde-4f06-8b93-d062e0caeb43"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(y_kmeans)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OlAZoiXTX3Kb",
        "outputId": "9bacc746-9bfb-4961-fafa-b3c8180dad2b"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[2 3 2 3 2 3 2 3 2 3 2 3 2 3 2 3 2 3 2 3 2 3 2 3 2 3 2 3 2 3 2 3 2 3 2 3 2\n",
            " 3 2 3 2 3 2 0 2 3 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 4 1 4 0 4 1 4 1 4 0 4 1 4 1 4 1 4 1 4 0 4 1 4 1 4\n",
            " 1 4 1 4 1 4 1 4 1 4 1 4 1 4 1 4 1 4 1 4 1 4 1 4 1 4 1 4 1 4 1 4 1 4 1 4 1\n",
            " 4 1 4 1 4 1 4 1 4 1 4 1 4 1 4]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.set_printoptions(precision=2)\n",
        "print(np.concatenate((X.reshape(len(X),2), y_kmeans.reshape(len(y_kmeans),1)),1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S1_ygvQ6YMcJ",
        "outputId": "655ab51f-82ad-48cc-aa2d-51302aa4651b"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 15  39   2]\n",
            " [ 15  81   3]\n",
            " [ 16   6   2]\n",
            " [ 16  77   3]\n",
            " [ 17  40   2]\n",
            " [ 17  76   3]\n",
            " [ 18   6   2]\n",
            " [ 18  94   3]\n",
            " [ 19   3   2]\n",
            " [ 19  72   3]\n",
            " [ 19  14   2]\n",
            " [ 19  99   3]\n",
            " [ 20  15   2]\n",
            " [ 20  77   3]\n",
            " [ 20  13   2]\n",
            " [ 20  79   3]\n",
            " [ 21  35   2]\n",
            " [ 21  66   3]\n",
            " [ 23  29   2]\n",
            " [ 23  98   3]\n",
            " [ 24  35   2]\n",
            " [ 24  73   3]\n",
            " [ 25   5   2]\n",
            " [ 25  73   3]\n",
            " [ 28  14   2]\n",
            " [ 28  82   3]\n",
            " [ 28  32   2]\n",
            " [ 28  61   3]\n",
            " [ 29  31   2]\n",
            " [ 29  87   3]\n",
            " [ 30   4   2]\n",
            " [ 30  73   3]\n",
            " [ 33   4   2]\n",
            " [ 33  92   3]\n",
            " [ 33  14   2]\n",
            " [ 33  81   3]\n",
            " [ 34  17   2]\n",
            " [ 34  73   3]\n",
            " [ 37  26   2]\n",
            " [ 37  75   3]\n",
            " [ 38  35   2]\n",
            " [ 38  92   3]\n",
            " [ 39  36   2]\n",
            " [ 39  61   0]\n",
            " [ 39  28   2]\n",
            " [ 39  65   3]\n",
            " [ 40  55   0]\n",
            " [ 40  47   0]\n",
            " [ 40  42   0]\n",
            " [ 40  42   0]\n",
            " [ 42  52   0]\n",
            " [ 42  60   0]\n",
            " [ 43  54   0]\n",
            " [ 43  60   0]\n",
            " [ 43  45   0]\n",
            " [ 43  41   0]\n",
            " [ 44  50   0]\n",
            " [ 44  46   0]\n",
            " [ 46  51   0]\n",
            " [ 46  46   0]\n",
            " [ 46  56   0]\n",
            " [ 46  55   0]\n",
            " [ 47  52   0]\n",
            " [ 47  59   0]\n",
            " [ 48  51   0]\n",
            " [ 48  59   0]\n",
            " [ 48  50   0]\n",
            " [ 48  48   0]\n",
            " [ 48  59   0]\n",
            " [ 48  47   0]\n",
            " [ 49  55   0]\n",
            " [ 49  42   0]\n",
            " [ 50  49   0]\n",
            " [ 50  56   0]\n",
            " [ 54  47   0]\n",
            " [ 54  54   0]\n",
            " [ 54  53   0]\n",
            " [ 54  48   0]\n",
            " [ 54  52   0]\n",
            " [ 54  42   0]\n",
            " [ 54  51   0]\n",
            " [ 54  55   0]\n",
            " [ 54  41   0]\n",
            " [ 54  44   0]\n",
            " [ 54  57   0]\n",
            " [ 54  46   0]\n",
            " [ 57  58   0]\n",
            " [ 57  55   0]\n",
            " [ 58  60   0]\n",
            " [ 58  46   0]\n",
            " [ 59  55   0]\n",
            " [ 59  41   0]\n",
            " [ 60  49   0]\n",
            " [ 60  40   0]\n",
            " [ 60  42   0]\n",
            " [ 60  52   0]\n",
            " [ 60  47   0]\n",
            " [ 60  50   0]\n",
            " [ 61  42   0]\n",
            " [ 61  49   0]\n",
            " [ 62  41   0]\n",
            " [ 62  48   0]\n",
            " [ 62  59   0]\n",
            " [ 62  55   0]\n",
            " [ 62  56   0]\n",
            " [ 62  42   0]\n",
            " [ 63  50   0]\n",
            " [ 63  46   0]\n",
            " [ 63  43   0]\n",
            " [ 63  48   0]\n",
            " [ 63  52   0]\n",
            " [ 63  54   0]\n",
            " [ 64  42   0]\n",
            " [ 64  46   0]\n",
            " [ 65  48   0]\n",
            " [ 65  50   0]\n",
            " [ 65  43   0]\n",
            " [ 65  59   0]\n",
            " [ 67  43   0]\n",
            " [ 67  57   0]\n",
            " [ 67  56   0]\n",
            " [ 67  40   0]\n",
            " [ 69  58   0]\n",
            " [ 69  91   4]\n",
            " [ 70  29   1]\n",
            " [ 70  77   4]\n",
            " [ 71  35   0]\n",
            " [ 71  95   4]\n",
            " [ 71  11   1]\n",
            " [ 71  75   4]\n",
            " [ 71   9   1]\n",
            " [ 71  75   4]\n",
            " [ 72  34   0]\n",
            " [ 72  71   4]\n",
            " [ 73   5   1]\n",
            " [ 73  88   4]\n",
            " [ 73   7   1]\n",
            " [ 73  73   4]\n",
            " [ 74  10   1]\n",
            " [ 74  72   4]\n",
            " [ 75   5   1]\n",
            " [ 75  93   4]\n",
            " [ 76  40   0]\n",
            " [ 76  87   4]\n",
            " [ 77  12   1]\n",
            " [ 77  97   4]\n",
            " [ 77  36   1]\n",
            " [ 77  74   4]\n",
            " [ 78  22   1]\n",
            " [ 78  90   4]\n",
            " [ 78  17   1]\n",
            " [ 78  88   4]\n",
            " [ 78  20   1]\n",
            " [ 78  76   4]\n",
            " [ 78  16   1]\n",
            " [ 78  89   4]\n",
            " [ 78   1   1]\n",
            " [ 78  78   4]\n",
            " [ 78   1   1]\n",
            " [ 78  73   4]\n",
            " [ 79  35   1]\n",
            " [ 79  83   4]\n",
            " [ 81   5   1]\n",
            " [ 81  93   4]\n",
            " [ 85  26   1]\n",
            " [ 85  75   4]\n",
            " [ 86  20   1]\n",
            " [ 86  95   4]\n",
            " [ 87  27   1]\n",
            " [ 87  63   4]\n",
            " [ 87  13   1]\n",
            " [ 87  75   4]\n",
            " [ 87  10   1]\n",
            " [ 87  92   4]\n",
            " [ 88  13   1]\n",
            " [ 88  86   4]\n",
            " [ 88  15   1]\n",
            " [ 88  69   4]\n",
            " [ 93  14   1]\n",
            " [ 93  90   4]\n",
            " [ 97  32   1]\n",
            " [ 97  86   4]\n",
            " [ 98  15   1]\n",
            " [ 98  88   4]\n",
            " [ 99  39   1]\n",
            " [ 99  97   4]\n",
            " [101  24   1]\n",
            " [101  68   4]\n",
            " [103  17   1]\n",
            " [103  85   4]\n",
            " [103  23   1]\n",
            " [103  69   4]\n",
            " [113   8   1]\n",
            " [113  91   4]\n",
            " [120  16   1]\n",
            " [120  79   4]\n",
            " [126  28   1]\n",
            " [126  74   4]\n",
            " [137  18   1]\n",
            " [137  83   4]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e7YrS1JAMFnm"
      },
      "source": [
        "## Visualising the clusters"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(X[y_kmeans==0, 0], X[y_kmeans==0, 1], s = 100, c = 'red', label = 'Cluster 1')\n",
        "plt.scatter(X[y_kmeans==1, 0], X[y_kmeans==1, 1], s = 100, c = 'blue', label = 'Cluster 2')\n",
        "plt.scatter(X[y_kmeans==2, 0], X[y_kmeans==2, 1], s = 100, c = 'green', label = 'Cluster 3')\n",
        "plt.scatter(X[y_kmeans==3, 0], X[y_kmeans==3, 1], s = 100, c = 'cyan', label = 'Cluster 4')\n",
        "plt.scatter(X[y_kmeans==4, 0], X[y_kmeans==4, 1], s = 100, c = 'magenta', label = 'Cluster 5')\n",
        "plt.scatter(kmeans.cluster_centers_[:, [0]], kmeans.cluster_centers_[:, [1]], s=200, c='yellow', label = 'Centroids')\n",
        "plt.title(label='Clusters of customers')\n",
        "plt.xlabel(xlabel='Annual Income (k$)')\n",
        "plt.ylabel(ylabel='Spending Score (1-100)')\n",
        "plt.legend(loc='upper right')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "I_-4Vq4QUAJW",
        "outputId": "5183e4ae-1d10-4e2c-80fd-4ec0c8e867e3"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}