unsupervised.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Unsupervised Learning Tutorial: Drug Type Clustering"
      ],
      "metadata": {
        "id": "VyfauJ6XQM1V"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This tutorial covers two common unsupervised learning methods:\n",
        "\n",
        "- **k-Means Clustering**\n",
        "- **Gaussian Mixture Models (GMM)**\n",
        "\n",
        "We'll use the provided dataset with patient features to cluster individuals based on their characteristics."
      ],
      "metadata": {
        "id": "SXRSkTYiQRO3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Step 1: Load and Inspect the Data**"
      ],
      "metadata": {
        "id": "8auPu3rFQT61"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Load the data\n",
        "# Replace with your file path if needed\n",
        "file_path = 'drug200.csv'\n",
        "data = pd.read_csv(file_path)\n",
        "\n",
        "# Inspect the first few rows\n",
        "data.head()"
      ],
      "metadata": {
        "id": "bzSLtdEVQVy0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "6b00ea22-51a0-4346-e94f-9b3960f5cf0a"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Age Sex      BP Cholesterol  Na_to_K   Drug\n",
              "0   23   F    HIGH        HIGH   25.355  drugY\n",
              "1   47   M     LOW        HIGH   13.093  drugC\n",
              "2   47   M     LOW        HIGH   10.114  drugC\n",
              "3   28   F  NORMAL        HIGH    7.798  drugX\n",
              "4   61   F     LOW        HIGH   18.043  drugY"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4cbbbcad-9fea-4f3c-82c7-41b6f33a38ae\" 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>Age</th>\n",
              "      <th>Sex</th>\n",
              "      <th>BP</th>\n",
              "      <th>Cholesterol</th>\n",
              "      <th>Na_to_K</th>\n",
              "      <th>Drug</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>23</td>\n",
              "      <td>F</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>25.355</td>\n",
              "      <td>drugY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>47</td>\n",
              "      <td>M</td>\n",
              "      <td>LOW</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>13.093</td>\n",
              "      <td>drugC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>47</td>\n",
              "      <td>M</td>\n",
              "      <td>LOW</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>10.114</td>\n",
              "      <td>drugC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>28</td>\n",
              "      <td>F</td>\n",
              "      <td>NORMAL</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>7.798</td>\n",
              "      <td>drugX</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>61</td>\n",
              "      <td>F</td>\n",
              "      <td>LOW</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>18.043</td>\n",
              "      <td>drugY</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-4cbbbcad-9fea-4f3c-82c7-41b6f33a38ae')\"\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-4cbbbcad-9fea-4f3c-82c7-41b6f33a38ae 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-4cbbbcad-9fea-4f3c-82c7-41b6f33a38ae');\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",
              "\n",
              "<div id=\"df-2dbf3fbb-e038-4614-87d5-b4f85a56891e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2dbf3fbb-e038-4614-87d5-b4f85a56891e')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2dbf3fbb-e038-4614-87d5-b4f85a56891e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16,\n        \"min\": 15,\n        \"max\": 74,\n        \"num_unique_values\": 57,\n        \"samples\": [\n          23,\n          49,\n          37\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"M\",\n          \"F\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BP\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"HIGH\",\n          \"LOW\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cholesterol\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"NORMAL\",\n          \"HIGH\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Na_to_K\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.223955528459517,\n        \"min\": 6.269,\n        \"max\": 38.247,\n        \"num_unique_values\": 198,\n        \"samples\": [\n          27.05,\n          9.281\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Drug\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"drugC\",\n          \"drugB\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Age: The age of the individual in years.\n",
        "- Sex: The sex or gender of the individual, where \"F\" stands for Female and \"M\" stands for Male.\n",
        "- BP: Blood pressure category of the individual.\n",
        "- Cholesterol: Cholesterol level category of the individual.\n",
        "- Na_to_K: A numerical value representing the ratio of sodium (Na) to potassium (K) in the individual's blood or dietary intake.\n",
        "- Drug: The type of drug prescribed or recommended for the individual.\n"
      ],
      "metadata": {
        "id": "eW0XqmXs2jYG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Step 2: Data Preprocessing**"
      ],
      "metadata": {
        "id": "R0RrBiggQXY0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
        "\n",
        "# NOTE: this is only an example to get your hands dirty on the data\n",
        "\n",
        "# Encode categorical features\n",
        "le_sex = LabelEncoder()\n",
        "le_bp = LabelEncoder()\n",
        "le_chol = LabelEncoder()\n",
        "\n",
        "data['Sex'] = le_sex.fit_transform(data['Sex'])\n",
        "data['BP'] = le_bp.fit_transform(data['BP'])\n",
        "data['Cholesterol'] = le_chol.fit_transform(data['Cholesterol'])\n",
        "\n",
        "# Select features\n",
        "X = data.drop('Drug', axis=1)\n",
        "\n",
        "# Standardize features\n",
        "scaler = StandardScaler()\n",
        "X_scaled = scaler.fit_transform(X)"
      ],
      "metadata": {
        "id": "kJXxQ5fpQaJw"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# NOTE: we have split the train and test data; please use the 'train' data to train the model\n",
        "# Directly load the train and test data\n",
        "data_train = pd.read_csv('train.csv')\n",
        "data_test = pd.read_csv('test.csv')\n",
        "\n",
        "X_train = data_train.drop('Unnamed: 5', axis=1)\n",
        "X_test = data_test.drop('Drug', axis=1)\n",
        "y_test = data_test['Drug']"
      ],
      "metadata": {
        "id": "nQHFKjBT5XQ_"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Encode categorical features\n",
        "le_sex = LabelEncoder()\n",
        "le_bp = LabelEncoder()\n",
        "le_chol = LabelEncoder()\n",
        "\n",
        "# Go through the preprocessing again\n",
        "X_train['Sex'] = le_sex.fit_transform(X_train['Sex'])\n",
        "X_train['BP'] = le_bp.fit_transform(X_train['BP'])\n",
        "X_train['Cholesterol'] = le_chol.fit_transform(X_train['Cholesterol'])\n",
        "\n",
        "# Standardize features\n",
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)"
      ],
      "metadata": {
        "id": "Te038zeC8Njx"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Encode categorical features\n",
        "le_sex = LabelEncoder()\n",
        "le_bp = LabelEncoder()\n",
        "le_chol = LabelEncoder()\n",
        "\n",
        "# Go through the preprocessing again\n",
        "X_test['Sex'] = le_sex.fit_transform(X_test['Sex'])\n",
        "X_test['BP'] = le_bp.fit_transform(X_test['BP'])\n",
        "X_test['Cholesterol'] = le_chol.fit_transform(X_test['Cholesterol'])\n",
        "\n",
        "# Standardize features\n",
        "scaler = StandardScaler()\n",
        "X_test = scaler.fit_transform(X_test)"
      ],
      "metadata": {
        "id": "Hu4aZsPC8O4G"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_test"
      ],
      "metadata": {
        "id": "Yh2X4joY9GEA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "44baf821-a277-44c8-a849-f314b2f9aceb"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3, 0, 4, 3, 3, 0, 4, 3, 3, 4, 2, 2, 4, 3, 4, 3, 2, 4, 3, 1, 1, 0,\n",
              "       3, 0, 0, 4, 4, 2, 4, 4, 4, 4, 3, 4, 0, 4, 4, 1, 3, 4])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "le_drug = LabelEncoder()\n",
        "y_test = le_drug.fit_transform(y_test)"
      ],
      "metadata": {
        "id": "uHycwolp8ZBU"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Step 3: k-Means Clustering**"
      ],
      "metadata": {
        "id": "aUGNK30BQcDY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import KMeans\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Train k-Means model\n",
        "kmeans = KMeans(n_clusters=5, random_state=42)\n",
        "kmeans.fit(X_train)\n",
        "\n",
        "# Predict clusters on the test data\n",
        "test_labels = kmeans.predict(X_test)"
      ],
      "metadata": {
        "id": "Zba3RxNaQdrN"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.decomposition import PCA\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Apply PCA to reduce dimensions to 2\n",
        "pca = PCA(n_components=2)\n",
        "X_test_pca = pca.fit_transform(X_test)"
      ],
      "metadata": {
        "id": "8vTr2KVx9smQ"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize clusters on the test data\n",
        "plt.scatter(X_test_pca[:, 0], X_test_pca[:, 1], c=test_labels, cmap='viridis')\n",
        "plt.title('k-Means Clustering on Test Data')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "2ZOD0f2gZZMn",
        "outputId": "919a4374-72c4-484f-99df-1f296738082d"
      },
      "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": "code",
      "source": [
        "from sklearn.metrics import f1_score\n",
        "import numpy as np\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "# Match predicted labels with true labels using Hungarian Algorithm\n",
        "from sklearn.metrics import fowlkes_mallows_score\n",
        "\n",
        "# Evaluate using F1-Score\n",
        "f1 = f1_score(y_test, test_labels, average='weighted')\n",
        "print(f'F1-Score (Weighted): {f1:.4f}')\n",
        "\n",
        "# Detailed Report\n",
        "print(\"Classification Report:\")\n",
        "print(classification_report(y_test, test_labels))\n"
      ],
      "metadata": {
        "id": "wYErJY3x90f1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Changeable Parameters:**\n",
        "- `n_clusters`: Number of clusters (try different values).\n",
        "- `random_state`: Set for reproducibility."
      ],
      "metadata": {
        "id": "_GJdMnmBQfCJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Step 4: Gaussian Mixture Models (GMM)**"
      ],
      "metadata": {
        "id": "ChwwyTL5Qgpz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.mixture import GaussianMixture\n",
        "\n",
        "# Train Gaussian Mixture model\n",
        "gmm = GaussianMixture(n_components=3, random_state=42)\n",
        "gmm.fit(X_train)\n",
        "\n",
        "# Predict cluster labels\n",
        "gmm_labels = gmm.predict(X_train)\n",
        "X_train['Cluster_gmm'] = gmm_labels"
      ],
      "metadata": {
        "id": "4by1jA12QgBg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Predict cluster labels for test data\n",
        "gmm_test_labels = gmm.predict(X_test)\n",
        "\n",
        "# Add predicted labels to test data if needed\n",
        "X_test['Cluster_gmm'] = gmm_test_labels\n",
        "\n",
        "# TODO: Visualize clusters on test data\n",
        "plt.scatter(X_test, y_test, c=gmm_test_labels, cmap='viridis')\n",
        "plt.title('Gaussian Mixture Model Clustering on Test Data')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "3WqjTdKg-m3B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Changeable Parameters:**\n",
        "- `n_components`: Number of clusters.\n",
        "- `covariance_type`: Try 'full', 'tied', 'diag', or 'spherical'."
      ],
      "metadata": {
        "id": "JuKDGVpUQkES"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Next Steps:**\n",
        "- Evaluate clusters using metrics like silhouette scores.\n",
        "- Experiment with different feature combinations.\n",
        "- Add more visualizations.\n",
        "\n",
        "Happy coding!"
      ],
      "metadata": {
        "id": "NAEHV0_KQl86"
      }
    }
  ]
}