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/Mehshida/Mehshida/blob/main/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": "code",
      "source": [
        "import  numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n"
      ],
      "metadata": {
        "id": "jex5a2GN723c"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aqHTg9bxWT_u"
      },
      "source": [
        "dataset = pd.read_csv('Mall_Customers.csv')\n",
        "X = dataset.iloc[:, [3,4]].values # The iloc function is used to select rows and columns by their integer index. we use x and y because x is y is outtup we have to find"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "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",
        "plt.plot( range(1, 11), wcss)\n",
        "plt.title(\"the elbow method\")\n",
        "plt.xlabel(\"no of clusters\")\n",
        "plt.xlabel(\"wcss\")\n",
        "plt.show ()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 819
        },
        "id": "cAJ6u701zxy9",
        "outputId": "d3e41e2d-5b6b-40aa-c9e9-1fdfc4a70d1a"
      },
      "execution_count": 11,
      "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": "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": "fN6m8PeDFEpV",
        "outputId": "d11d106a-a26a-4db7-edc1-4fe03aa82a34"
      },
      "execution_count": 17,
      "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": "M1bri2BSGwv9",
        "outputId": "295dbffa-6f8f-4fcf-8f1f-652180c963b0"
      },
      "execution_count": 18,
      "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": [
        "plt.scatter(X[y_kmeans == 0, 0] , X[y_kmeans == 0, 1],s = 100, c ='red', label='cluster1')\n",
        "plt.scatter(X[y_kmeans == 1, 0] , X[y_kmeans == 1, 1],s = 100, c ='blue', label='cluster1')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "QMUZp8BIH5lF",
        "outputId": "1bdab5d5-9703-491d-e96a-0ec1790f89f6"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x79a07815e680>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}