Fisher Linear Classifier Implementation

We have successfully implemented the Fisher Linear Classifier functionality and developed MATLAB source code. This classifier performs data classification on datasets and enables straightforward visualization of classification results. The implementation includes key functions such as calculate_scatter_matrices for computing within-class and between-class scatter matrices, and fisher_discriminant for determining the optimal projection direction. We have incorporated various optimization techniques to enhance classifier accuracy and efficiency, including feature selection algorithms and k-fold cross-validation methods. The code structure follows object-oriented programming principles with separate classes for data preprocessing, model training, and result visualization. Detailed documentation explains our implementation approach, mathematical foundations of Fisher's linear discriminant analysis, and parameter optimization strategies. We provide sample datasets for readers to utilize and test the classifier, along with visualization functions that plot decision boundaries and classification performance metrics. This project aims to support machine learning and data science enthusiasts in understanding and applying linear discriminant analysis techniques.