Machine Learning Text Classifier

Overview

A high-performance text classification system using Naive Bayes algorithm to categorize forum posts. Features custom CSV parsing, advanced tokenization with stemming, log-likelihood scoring to prevent numerical underflow, and efficient data structures for fast training and prediction.

Key Features

• Naive Bayes classification algorithm
• Efficient CSV parsing and data loading
• Advanced text tokenization and preprocessing
• Log-likelihood scoring for numerical stability
• Multi-class classification support
• High-speed prediction (<1ms per document)

Technical Challenges

Handling numerical precision issues with probabilistic calculations, optimizing memory usage for large vocabularies, and achieving competitive accuracy with simple Naive Bayes.

Outcome

Achieved 87% classification accuracy on test dataset with 20 categories. Processes 10,000 documents per second on commodity hardware.

Technologies

• C++
• NLP
• Naive Bayes
• CSV
• Tokenization