Machine Learning
StackOverflow Tag Prediction
Multi-label ML system for recommending relevant tags from question content.
PythonScikit-learnNLPMulti-label Classification
Overview
Built at the University of Wisconsin-Madison, this project focused on developing a machine learning-based tag recommendation system for Stack Overflow questions.
Problem
- Stack Overflow questions often need multiple relevant tags for discoverability and routing.
- Manual tagging is inconsistent, time-consuming, and may miss key topics in the question body.
Solution
- Developed a robust multi-label classification pipeline to assign multiple tags simultaneously from question content.
- Engineered text preprocessing and feature representations tailored for technical Q&A language.
- Evaluated model quality iteratively and improved performance across frequent and long-tail tags.
Architecture
- Question title/body preprocessing and normalization
- Feature extraction for text representation
- Multi-label classification training and validation
- Inference pipeline for simultaneous tag recommendation
Metrics
- Validated model behavior using precision, recall, and F1 scores.
- Improved consistency of tag recommendations compared to manual-only workflows.
- Enabled fast, scalable inference for multi-tag prediction.
Highlights
- Associated with University of Wisconsin-Madison.
- Spearheaded end-to-end model development and evaluation.
- Focused on practical recommendation quality for real Q&A content.