{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Perform a practical on K-means clustering."
      ],
      "metadata": {
        "id": "xIhtgIJLzVIU"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "5a8a065e",
        "outputId": "6de20612-6c71-4c5e-e176-acc9ba48ad4a"
      },
      "source": [
        "from sklearn import datasets\n",
        "import pandas as pd\n",
        "\n",
        "# Load the Iris dataset\n",
        "iris = datasets.load_iris()\n",
        "X = iris.data\n",
        "y = iris.target\n",
        "\n",
        "# Create a pandas DataFrame for easier handling\n",
        "df = pd.DataFrame(X, columns=iris.feature_names)\n",
        "df['target'] = y\n",
        "\n",
        "display(df.head())"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
              "0                5.1               3.5                1.4               0.2   \n",
              "1                4.9               3.0                1.4               0.2   \n",
              "2                4.7               3.2                1.3               0.2   \n",
              "3                4.6               3.1                1.5               0.2   \n",
              "4                5.0               3.6                1.4               0.2   \n",
              "\n",
              "   target  \n",
              "0       0  \n",
              "1       0  \n",
              "2       0  \n",
              "3       0  \n",
              "4       0  "
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal length (cm)</th>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <th>petal length (cm)</th>\n",
              "      <th>petal width (cm)</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
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              "      <th>2</th>\n",
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              "      <th>3</th>\n",
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              "        async function quickchart(key) {\n",
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"sepal length (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2073644135332772,\n        \"min\": 4.6,\n        \"max\": 5.1,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          4.9,\n          5.0,\n          4.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal width (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2588435821108957,\n        \"min\": 3.0,\n        \"max\": 3.6,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3.0,\n          3.6,\n          3.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal length (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07071067811865474,\n        \"min\": 1.3,\n        \"max\": 1.5,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1.4,\n          1.3,\n          1.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal width (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.2,\n        \"max\": 0.2,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "c583f7a0",
        "outputId": "fe2e75e3-bc7d-4e5d-d19c-63d087298663"
      },
      "source": [
        "from sklearn.cluster import KMeans\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Determine the optimal number of clusters using the elbow method\n",
        "sse = []\n",
        "k_range = range(1, 11)\n",
        "for k in k_range:\n",
        "    kmeans = KMeans(n_clusters=k, random_state=42, n_init=10) # Added n_init to suppress warning\n",
        "    kmeans.fit(X)\n",
        "    sse.append(kmeans.inertia_)\n",
        "\n",
        "# Plot the elbow method graph\n",
        "plt.plot(k_range, sse)\n",
        "plt.xlabel('Number of clusters (k)')\n",
        "plt.ylabel('Sum of squared errors (SSE)')\n",
        "plt.title('Elbow Method')\n",
        "plt.show()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}