Package Overview

This package provides implementations of key machine learning and deep learning techniques, including PCA, LDA, GMM, and CNN. The package is designed for classification tasks using a custom dataset of 25 categories and a set of 10 selfies.

Package Structure

PCA: Principal Component Analysis implementation for dimensionality reduction and visualization. LDA: Linear Discriminant Analysis for feature extraction and classification. GMM: Gaussian Mixture Model for clustering tasks on both raw and reduced-dimension data. CNN: Convolutional Neural Network for image classification, with examples of training, evaluation, and visualization. data: Contains 25 categories of data selected for the task. selfie: Contains 10 selfies for additional testing and analysis.

Install Environments

First, create and activate a conda virtual environment to manage dependencies:

conda create -n ml_env python=3.9
conda activate ml_env

Then install the required packages:

pip install opencv-python
pip install scikit-learn
pip install matplotlib
pip install tensorflow

How to run the codes

1.Open the Jupyter Notebook: Each algorithm (PCA, LDA, GMM, CNN) is implemented as a standalone Python notebook. To run a specific notebook, navigate to its folder and open it using Jupyter Notebook:

jupyter notebook

2.Execute the Code Blocks: The code is organized into blocks. Simply click the Run button for each block sequentially. Ensure the data and selfie folders are in the correct directory structure.