Project Overview
A comprehensive Java implementation of fundamental machine learning algorithms achieving 98.35% accuracy on handwritten digit recognition, demonstrating deep understanding of ML theory and software engineering principles. This project showcases my ability to implement complex machine learning algorithms from scratch without relying on external ML libraries. I developed 12+ different classifiers ranging from simple k-NN to advanced ensemble methods, all implemented in pure Java with a focus on clean, modular architecture.
The Challenge
As part of my CST 3170 Machine Learning coursework, I was tasked with implementing and comparing various ML algorithms for digit recognition. Rather than using existing libraries, the challenge was to build each algorithm from the ground up, demonstrating both theoretical understanding and practical coding skills.
No External Libraries
Pure implementation without ML frameworks
Deep Understanding Required
Must grasp mathematical foundations
Performance Goals
Achieve competitive accuracy rates
Technical Highlights
Key Algorithms Implemented
Instance-Based Learning
- 1-NN (Nearest Neighbor)
- Weighted k-NN with configurable k values
Neural Networks
- Multi-Layer Perceptron with backpropagation
- ReLU activation functions
- Softmax output layer
Support Vector Machines
- Linear SVM with one-vs-all multiclass strategy
- Enhanced with centroid features
Tree-Based Methods
- Decision Trees with entropy splitting
- Random Forest with bootstrap aggregating
- Gradient Boosted Trees with softmax
Ensemble Methods
- Voting classifier combining multiple models
- Hybrid classifier with intelligent switching
Technical Architecture
Interface-Based Design
Clean interface-based design pattern for extensibility
Modular Structure
Easy addition of new classifiers through common interfaces
Optimized Operations
Efficient matrix operations for neural network computations
Gradient Descent
Custom implementation of optimization algorithms
Cross-Validation
Framework for robust evaluation and testing
Results & Achievements
Performance Metrics
(Voting Ensemble)
Efficiently
Robust Results
Optimized Speed
Key Accomplishments
- 12+ ML algorithms implemented completely from scratch
- Performance comparable to industry-standard libraries
- Automated experiment runner with detailed logging capabilities
- Visualization tools for performance analysis and comparison
- Extensible architecture for easy algorithm additions
Technical Skills Demonstrated
Machine Learning
- Deep understanding of classification algorithms
- Feature engineering (centroid distance features)
- Hyperparameter tuning and optimization
- Ensemble methods and model combination
- Cross-validation and performance evaluation
Software Engineering
- Object-oriented design with clean interfaces
- Modular, maintainable code structure
- Comprehensive documentation
- Automated testing and experimentation
- Cross-platform compatibility (Windows/Linux/Mac)
Data Processing
- Efficient data loading and preprocessing
- Matrix operations and linear algebra
- Statistical analysis and metrics calculation
- Result visualization and reporting
What Sets This Project Apart
Pure Implementation
Every algorithm coded from scratch, demonstrating deep understanding rather than library usage
Comprehensive Scope
Covers major ML paradigms - instance-based, neural, SVM, tree-based, and ensemble methods
Production Quality
Clean code, proper documentation, automated scripts, and professional presentation
Educational Value
Serves as a reference implementation for understanding ML algorithms
Extensibility
Designed to easily add new algorithms or datasets
Real-World Applications
Embedded Systems
Custom ML solutions for systems with limited dependencies
Educational Platforms
Teaching ML concepts through clear implementations
Research Projects
Modified algorithm implementations for experiments
Performance-Critical Apps
Optimized code for high-speed requirements
Code Quality & Development Process
Code Quality Indicators
- Well-structured Java code following best practices
- Comprehensive comments explaining algorithm logic
- Modular design with clear separation of concerns
- Efficient implementations with optimized operations
- Robust error handling and edge case management
Development Process
Research on each algorithm's theory
Incremental implementation with testing
Performance optimization through profiling
Comprehensive evaluation framework
User-friendly execution scripts
Project Impact
Learning Outcomes
- Mastered fundamental ML algorithms at implementation level
- Gained deep insight into algorithm strengths and trade-offs
- Developed skills in performance optimization
- Enhanced ability to debug and improve ML systems
Portfolio Value
- Demonstrates both theoretical knowledge and practical skills
- Shows ability to tackle complex projects independently
- Highlights clean coding and documentation practices
- Proves capability to deliver end-to-end solutions
Future Enhancements
Deep Learning
CNN implementations for better accuracy
Web Interface
Interactive demonstrations online
GPU Acceleration
Neural network training optimization
More Datasets
MNIST, CIFAR-10 support
Real-time Recognition
Live digit recognition application
Explore the Code
View the complete source code, documentation, and results on GitHub. Try running the experiments yourself with the provided automated scripts. The modular architecture makes it easy to add your own classifiers or datasets to compare against these implementations.