K_means_iris_flower_classification_LGMVIP_Intern.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "zvpKegbZav84"
      },
      "source": [
        "from sklearn.cluster import KMeans\n",
        "from sklearn.datasets import load_iris\n",
        "from matplotlib import pyplot as plt"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sWsRk-gta5sN"
      },
      "source": [
        "iris=load_iris()\n",
        "x=iris.data;"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "kmeans = KMeans(n_clusters=3)\n",
        "kmeans.fit(x)\n",
        "kmeans.labels_\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gtH-Xv_FcUQa",
        "outputId": "6408a1db-e7a1-43a6-bf07-6affee8cc9d1"
      },
      "execution_count": 3,
      "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"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([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,\n",
              "       0, 0, 0, 0, 0, 0, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2,\n",
              "       2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 2, 1, 2, 1, 2, 2, 1, 1, 2, 2, 2, 2,\n",
              "       2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 1], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kmeans.predict([[5.9, 3. , 5.1, 1.8]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-q7c4FEDgyLk",
        "outputId": "75769bb5-59f3-4423-fa2d-da282475237e"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kmeans.inertia_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N2rwvN3mNie3",
        "outputId": "26685568-da08-45a7-e7ca-c1a96b8f8b12"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "78.851441426146"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kmeans.cluster_centers_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d48f7YqWdOCi",
        "outputId": "3953363b-e91d-49ed-a1b8-ac742969c04a"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[5.006     , 3.428     , 1.462     , 0.246     ],\n",
              "       [5.9016129 , 2.7483871 , 4.39354839, 1.43387097],\n",
              "       [6.85      , 3.07368421, 5.74210526, 2.07105263]])"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sse = []\n",
        "for k in range(1,10):\n",
        "    km = KMeans(n_clusters=k)\n",
        "    km.fit(x)\n",
        "    sse.append(km.inertia_)\n",
        "\n",
        "plt.xlabel('K')\n",
        "plt.ylabel('Sum of squared error')\n",
        "plt.plot(range(1,10),sse)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 799
        },
        "id": "ZEKIU54WS51E",
        "outputId": "477f9155-3e8a-411d-bf76-0d39ca2edc53"
      },
      "execution_count": 7,
      "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"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f88ec626fb0>]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}