FSM_L2_ML_Task3.ipynb

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    "language_info": {
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **Unsupervised Learning**"
      ],
      "metadata": {
        "id": "5pmXOvyQho6X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Importing libraries\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import random"
      ],
      "metadata": {
        "id": "YL5SargJhjj_"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder"
      ],
      "metadata": {
        "id": "h7CF2n1jHlY0"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Dataset**"
      ],
      "metadata": {
        "id": "q2x7n8OzhibD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating DataFrame \n",
        "iris_df = pd.read_csv('Iris Dataset.csv')"
      ],
      "metadata": {
        "id": "eimasEsYhggE"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "iris_df.head()"
      ],
      "metadata": {
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          "height": 206
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        "id": "2Sr_kZ8Sj9PP",
        "outputId": "9d3fd583-169e-479e-b9b2-56287023eeea"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
              "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
              "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
              "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
              "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
              "4   5            5.0           3.6            1.4           0.2  Iris-setosa"
            ],
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              "\n",
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              "      <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
              "      <th>Species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **K-means Clustering**"
      ],
      "metadata": {
        "id": "QLiPXah5-LNx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class KMeans:\n",
        "    def __init__(self, k=3, max_iterations=100):\n",
        "        self.k = k\n",
        "        self.max_iterations = max_iterations\n",
        "        self.centroids = []\n",
        "    \n",
        "    def fit(self, X):\n",
        "        # Initialize centroids randomly\n",
        "        self.centroids = random.sample(list(X), self.k)\n",
        "        \n",
        "        for _ in range(self.max_iterations):\n",
        "            # Assign each sample to the nearest centroid\n",
        "            clusters = self.assign_clusters(X)\n",
        "            \n",
        "            # Update centroids\n",
        "            prev_centroids = self.centroids\n",
        "            self.centroids = self.update_centroids(clusters)\n",
        "            \n",
        "            # Check convergence\n",
        "            if self.has_converged(prev_centroids, self.centroids):\n",
        "                break\n",
        "    \n",
        "    def assign_clusters(self, X):\n",
        "        clusters = {}\n",
        "        for sample in X:\n",
        "            distances = [np.linalg.norm(sample - centroid) for centroid in self.centroids]\n",
        "            cluster_index = np.argmin(distances)\n",
        "            \n",
        "            if cluster_index not in clusters:\n",
        "                clusters[cluster_index] = []\n",
        "            \n",
        "            clusters[cluster_index].append(sample)\n",
        "        \n",
        "        return clusters\n",
        "    \n",
        "    def update_centroids(self, clusters):\n",
        "        centroids = []\n",
        "        for cluster_index, samples in clusters.items():\n",
        "            centroid = np.mean(samples, axis=0)\n",
        "            centroids.append(centroid)\n",
        "        \n",
        "        return centroids\n",
        "    \n",
        "    def has_converged(self, prev_centroids, new_centroids, tol=1e-4):\n",
        "        return np.sum(np.abs(np.array(prev_centroids) - np.array(new_centroids))) < tol\n",
        "    \n",
        "    def predict(self, X):\n",
        "        clusters = self.assign_clusters(X)\n",
        "        labels = np.zeros(len(X), dtype=int)\n",
        "        \n",
        "        for cluster_index, samples in clusters.items():\n",
        "            labels[np.array(samples)] = cluster_index\n",
        "        \n",
        "        return labels"
      ],
      "metadata": {
        "id": "AZTtsG4-gOKu"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Preprocess the data\n",
        "X = iris_df[['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm']].values\n",
        "y = iris_df['Species'].values"
      ],
      "metadata": {
        "id": "Tvl1kNozj6Kc"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create and fit the K-means clustering model\n",
        "model = KMeans(k=4)\n",
        "model.fit(X)"
      ],
      "metadata": {
        "id": "jBBKY2_MlWjf"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Make predictions\n",
        "predictions = model.predict(X)\n",
        "print('Predicted Labels:', predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "id": "Dz0-PunpkVYt",
        "outputId": "7bff4976-0b2d-443f-94ac-602236d4360a"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "error",
          "ename": "IndexError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-10-76d5d869c28b>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Make predictions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Predicted Labels:'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredictions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-5-82c7547897a2>\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mcluster_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msamples\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mclusters\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 52\u001b[0;31m             \u001b[0mlabels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msamples\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcluster_index\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     53\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mIndexError\u001b[0m: arrays used as indices must be of integer (or boolean) type"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **PCA Algorithm**"
      ],
      "metadata": {
        "id": "jYrbJ9L-ZABi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class PCA:\n",
        "    def __init__(self, n_components):\n",
        "        self.n_components = n_components\n",
        "        self.components = None\n",
        "        self.mean = None\n",
        "    \n",
        "    def fit(self, X):\n",
        "        # Compute mean of the data\n",
        "        self.mean = np.mean(X, axis=0)\n",
        "        \n",
        "        # Center the data\n",
        "        X_centered = X - self.mean\n",
        "        \n",
        "        # Compute covariance matrix\n",
        "        covariance_matrix = np.cov(X_centered.T)\n",
        "        \n",
        "        # Compute eigenvectors and eigenvalues\n",
        "        eigenvalues, eigenvectors = np.linalg.eig(covariance_matrix)\n",
        "        \n",
        "        # Sort eigenvectors based on eigenvalues\n",
        "        indices = np.argsort(eigenvalues)[::-1]\n",
        "        sorted_eigenvectors = eigenvectors[:, indices]\n",
        "        \n",
        "        # Select the top-k eigenvectors (components)\n",
        "        self.components = sorted_eigenvectors[:, :self.n_components]\n",
        "    \n",
        "    def transform(self, X):\n",
        "        # Center the data\n",
        "        X_centered = X - self.mean\n",
        "        \n",
        "        # Project the data onto the components\n",
        "        return np.dot(X_centered, self.components)\n",
        "    \n",
        "    def fit_transform(self, X):\n",
        "        self.fit(X)\n",
        "        return self.transform(X)"
      ],
      "metadata": {
        "id": "lnzeG2xog_o8"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create and fit the PCA model\n",
        "model = PCA(n_components=2)\n",
        "X_transformed = model.fit_transform(X)"
      ],
      "metadata": {
        "id": "KwCSQQ-HlPVs"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the transformed data\n",
        "transformed_df = pd.DataFrame(X_transformed, columns=['PC1', 'PC2'])\n",
        "transformed_df['species'] = y\n",
        "print(transformed_df.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WSXA3AZ0lax6",
        "outputId": "5018727b-3aee-47ec-b6aa-18874fb410ff"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        PC1       PC2      species\n",
            "0 -2.684207 -0.326607  Iris-setosa\n",
            "1 -2.715391  0.169557  Iris-setosa\n",
            "2 -2.889820  0.137346  Iris-setosa\n",
            "3 -2.746437  0.311124  Iris-setosa\n",
            "4 -2.728593 -0.333925  Iris-setosa\n"
          ]
        }
      ]
    }
  ]
}