13. PCA_analysis_final.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": [
        "## upload all the necessary files.\n",
        "1.  **numpy :** it is used for numerical operations in Python.\n",
        "2. **pandas** : for data manipulation and analysis.\n",
        "3. **sklearn.decomposition  PCA**: Import the PCA class from scikit-learn for performing Principal Component Analysis.\n",
        "4. **matplotlib**: Import the matplotlib library for creating plots and visualizations.\n",
        "5. **seaborn**: Import the seaborn library for improved data visualization.\n",
        "6. **train_test_split**:  to split the dataset into training and testing sets.\n",
        "7. **DecisionTreeClassifier**: for building a decision tree model.\n",
        "8. **classification_report, accuracy_score, confusion_matrix**: Import metrics for evaluating the classifier's performance."
      ],
      "metadata": {
        "id": "Wgs7HP_kZ9-X"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fLGY2-YMQdRe"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.decomposition import PCA\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import classification_report,accuracy_score,confusion_matrix"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **upload the iris dataset**\n",
        "** Load the Iris dataset into the digits variable.**"
      ],
      "metadata": {
        "id": "0nIWHcNmaLqN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import load_iris\n",
        "digits = load_iris()"
      ],
      "metadata": {
        "id": "dLJIv3JiQrhM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Create a DataFrame named data1 containing the features of the Iris dataset.**"
      ],
      "metadata": {
        "id": "I86f-A8acO5k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data1=pd.DataFrame(digits.data)"
      ],
      "metadata": {
        "id": "cEQW63jkQ3LQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**1. Assign the features to the x variable.**\n",
        "**2. Assign the target labels to the y variable.**"
      ],
      "metadata": {
        "id": "ALZbXlIKcwjd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x=digits.data\n",
        "y=digits.target"
      ],
      "metadata": {
        "id": "JuFGlv8OQ9_0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**size of the IRIS dataset**"
      ],
      "metadata": {
        "id": "NDroSexndrmB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "x.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PJVpHJrMQ_iq",
        "outputId": "2d3e0be8-a9fc-46f3-e47b-c641828d44af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(150, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XUmDLkmaRDFv",
        "outputId": "f051fd7f-e3ab-44ff-d6b2-0f41160824a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(150,)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Standardize the features using 'StandardScaler'.**\n"
      ],
      "metadata": {
        "id": "ABnOjffmd4gx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "# Data Normalization\n",
        "X_std = StandardScaler().fit_transform(x)"
      ],
      "metadata": {
        "id": "3b3n0HlERIdm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**The mean vector and covariance matrix for the standardized features.**\n",
        "**eigenvalues and eigenvectors from the covariance matrix.**"
      ],
      "metadata": {
        "id": "YU2hhkK8eKVQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mean_vec = np.mean(X_std, axis=0)\n",
        "print(mean_vec)\n",
        "cov_mat = np.cov(X_std.T)\n",
        "eig_vals, eig_vecs = np.linalg.eig(cov_mat)"
      ],
      "metadata": {
        "id": "lc2JUCS8RNPh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ba90c8c8-7bb3-4377-9ce4-7d8800004fb4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[-1.69031455e-15 -1.84297022e-15 -1.69864123e-15 -1.40924309e-15]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a list of (eigenvalue, eigenvector) tuples\n",
        "eig_pairs = [ (np.abs(eig_vals[i]),eig_vecs[:,i]) for i in range(len(eig_vals))]"
      ],
      "metadata": {
        "id": "qJQdghtSRSvl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Create a list of eigenvalue-eigenvector pairs and sort them in descending order of eigenvalues.**"
      ],
      "metadata": {
        "id": "Oohb0BVRe1XE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Sort from high to low\n",
        "eig_pairs.sort(key = lambda x: x[0], reverse= True)"
      ],
      "metadata": {
        "id": "FbkTDLqJRX4K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Calculate individual explained variance and cumulative explained variance from eigenvalues.**"
      ],
      "metadata": {
        "id": "VAIbwcYFfBzv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculation of Explained Variance from the eigenvalues\n",
        "tot = sum(eig_vals)\n",
        "var_exp = [(i/tot)*100 for i in sorted(eig_vals, reverse=True)] # Individual explained variance\n",
        "cum_var_exp = np.cumsum(var_exp) # Cumulative explained variance\n",
        "cum_var_exp"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j5zJTUbiRh56",
        "outputId": "032688eb-5afa-46c2-912d-656e9992f1a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 72.96244541,  95.8132072 ,  99.48212909, 100.        ])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Create a bar plot of individual and cumulative explained variance.**"
      ],
      "metadata": {
        "id": "SqfHsTp8fWl6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "plt.figure(figsize=(10, 5))\n",
        "plt.bar(range(len(var_exp)), var_exp, alpha=0.3333, align='center', label='individual explained variance', color = 'g')\n",
        "plt.step(range(len(cum_var_exp)), cum_var_exp, where='mid',label='cumulative explained variance')\n",
        "plt.ylabel('Explained variance ratio')\n",
        "plt.xlabel('Principal components')\n",
        "plt.legend(loc='best')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "pQ2etvZiRkFL",
        "outputId": "5df9896d-6149-466a-82a7-04bb85978ca3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Split the data into training and testing sets using** **train_test_split**."
      ],
      "metadata": {
        "id": "KdLiDD9efb_v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "x_train,x_test,y_train,y_test=train_test_split(X_std,y)"
      ],
      "metadata": {
        "id": "4UFTlQkLRq1S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Create a PCA object with n_components set to 3 and transform the training and testing data using PCA.**"
      ],
      "metadata": {
        "id": "RjNKVPZJfwis"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Initiate PCA\n",
        "pca = PCA(n_components=3)\n",
        "pca_x_train = pca.fit_transform(x_train)\n",
        "pca_x_test = pca.transform(x_test)"
      ],
      "metadata": {
        "id": "M7H2pLJCR4dv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Create a Decision Tree classifier (rf) and fit it to the PCA-transformed training data.**"
      ],
      "metadata": {
        "id": "EKFKZwCnf3F-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf=DecisionTreeClassifier().fit(pca_x_train,y_train)"
      ],
      "metadata": {
        "id": "ErH5SO1wR9xo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Predict the labels using the classifier on the PCA-transformed testing data.**"
      ],
      "metadata": {
        "id": "D6eyqjjhgCeo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predicted=rf.predict(pca_x_test)"
      ],
      "metadata": {
        "id": "O4ETCt4oR_Lv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**The accuracy score of the predicted labels.**"
      ],
      "metadata": {
        "id": "wHhyl5oAgKNM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_score(predicted,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iyoygPrRSGjK",
        "outputId": "c3c7e915-03d1-4618-c029-a16a24c72218"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9736842105263158"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Set the expected variable to the true labels of the testing data.**"
      ],
      "metadata": {
        "id": "xxLtTQoJgak3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "expected=y_test"
      ],
      "metadata": {
        "id": "rqlvduBbSIwa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Create a figure for plotting with specified dimensions and adjustments**\n",
        "**Create a scatter plot to visualize the predicted and actual classes using PCA components**\n"
      ],
      "metadata": {
        "id": "7HKFrknjg37k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the prediction\n",
        "fig = plt.figure(figsize=(6, 6))  # figure size in inches\n",
        "fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)\n",
        "\n",
        "# Plot the prediction\n",
        "plt.figure(figsize=(8, 6))\n",
        "\n",
        "# Plot points colored by predicted class\n",
        "for target, target_name in enumerate(digits.target_names):\n",
        "    plt.scatter(pca_x_test[predicted == target, 0], pca_x_test[predicted == target, 1],\n",
        "                label=f'Predicted: {target_name}')\n",
        "\n",
        "# Plot actual classes as markers\n",
        "for target, target_name in enumerate(digits.target_names):\n",
        "    plt.scatter(pca_x_test[expected == target, 0], pca_x_test[expected == target, 1],\n",
        "                marker='x', s=100, label=f'Actual: {target_name}')\n",
        "\n",
        "plt.xlabel('Principal Component 1')\n",
        "plt.ylabel('Principal Component 2')\n",
        "plt.title('Predicted vs Actual Classes')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        },
        "id": "ZTNBABnbTLGC",
        "outputId": "804224de-949f-41ba-9952-7ab977c2ebff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}